Upload 24 files

.gitattributes

@@ -0,0 +1,4 @@
+model-00001-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00002-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text
+model-00003-of-00013.safetensors filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,117 @@
+{% macro render_extra_keys(json_dict, handled_keys) %}
+    {%- if json_dict is mapping %}
+        {%- for json_key in json_dict if json_key not in handled_keys %}
+            {%- if json_dict[json_key] is mapping or (json_dict[json_key] is sequence and json_dict[json_key] is not string) %}
+                {{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
+            {%- else %}
+                {{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
+            {%- endif %}
+        {%- endfor %}
+    {%- endif %}
+{% endmacro %}
+
+{%- if messages[0]["role"] == "system" %}
+    {%- set system_message = messages[0]["content"] %}
+    {%- set loop_messages = messages[1:] %}
+{%- else %}
+    {%- set loop_messages = messages %}
+{%- endif %}
+
+{%- if not tools is defined %}
+    {%- set tools = [] %}
+{%- endif %}
+
+{%- if system_message is defined %}
+    {{- "<|im_start|>system\n" + system_message }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- "<|im_start|>system\nYou are Qwen, a helpful AI assistant that can interact with a computer to solve tasks." }}
+    {%- endif %}
+{%- endif %}
+{%- if tools is iterable and tools | length > 0 %}
+    {{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }}
+    {{- "<tools>" }}
+    {%- for tool in tools %}
+        {%- if tool.function is defined %}
+            {%- set tool = tool.function %}
+        {%- endif %}
+        {{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
+        {%- if tool.description is defined %}
+            {{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
+        {%- endif %}
+        {{- '\n<parameters>' }}
+        {%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
+            {%- for param_name, param_fields in tool.parameters.properties|items %}
+                {{- '\n<parameter>' }}
+                {{- '\n<name>' ~ param_name ~ '</name>' }}
+                {%- if param_fields.type is defined %}
+                    {{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
+                {%- endif %}
+                {%- if param_fields.description is defined %}
+                    {{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
+                {%- endif %}
+                {%- set handled_keys = ['name', 'type', 'description'] %}
+                {{- render_extra_keys(param_fields, handled_keys) }}
+                {{- '\n</parameter>' }}
+            {%- endfor %}
+        {%- endif %}
+        {% set handled_keys = ['type', 'properties'] %}
+        {{- render_extra_keys(tool.parameters, handled_keys) }}
+        {{- '\n</parameters>' }}
+        {%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
+        {{- render_extra_keys(tool, handled_keys) }}
+        {{- '\n</function>' }}
+    {%- endfor %}
+    {{- "\n</tools>" }}
+    {{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
+{%- endif %}
+{%- if system_message is defined %}
+    {{- '<|im_end|>\n' }}
+{%- else %}
+    {%- if tools is iterable and tools | length > 0 %}
+        {{- '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in loop_messages %}
+    {%- if message.role == "assistant" and message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
+            {{- '\n' + message.content | trim + '\n' }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
+            {%- if tool_call.arguments is defined %}
+                {%- for args_name, args_value in tool_call.arguments|items %}
+                    {{- '<parameter=' + args_name + '>\n' }}
+                    {%- set args_value = args_value | tojson | safe if args_value is mapping or (args_value is sequence and args_value is not string) else args_value | string %}
+                    {{- args_value }}
+                    {{- '\n</parameter>\n' }}
+                {%- endfor %}
+            {%- endif %}
+            {{- '</function>\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "user" or message.role == "system" or message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.previtem and loop.previtem.role != "tool" %}
+            {{- '<|im_start|>user\n' }}
+        {%- endif %}
+        {{- '<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>\n' }}
+        {%- if not loop.last and loop.nextitem.role != "tool" %}
+            {{- '<|im_end|>\n' }}
+        {%- elif loop.last %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- else %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,41 @@
+{
+  "architectures": [
+    "Qwen3MoeForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "decoder_sparse_step": 1,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 5472,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 28,
+  "mlp_only_layers": [],
+  "model_type": "qwen3_moe",
+  "moe_intermediate_size": 768,
+  "norm_topk_prob": true,
+  "num_attention_heads": 32,
+  "num_experts": 128,
+  "num_experts_per_tok": 8,
+  "num_hidden_layers": 48,
+  "num_key_value_heads": 4,
+  "output_router_logits": false,
+  "pad_token_id": 151643,
+  "qkv_bias": false,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 10000000,
+  "router_aux_loss_coef": 0.0,
+  "shared_expert_intermediate_size": 0,
+  "sliding_window": null,
+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": false,
+  "use_qk_norm": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "repetition_penalty": 1.05,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.57.3"
+}

latest

@@ -0,0 +1 @@
+global_step167

model-00001-of-00013.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:dfeb779b03567122132f68a1e7e4e7dd75db25ea07f9a693ddf565195bbcf35f
+size 4997184968

model-00002-of-00013.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:e8f43a95d7dcf6e973d1e03a9228bcc04815e49952602eb47637e8bd05dec4ec
+size 4997741608

model-00003-of-00013.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3a54dd35e7a7cbb03d5f316e72f202fd62d8be7de4f7487c4fdd84d16291dabc
+size 4997742208

special_tokens_map.json

@@ -0,0 +1,31 @@
+{
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "eos_token": {
+    "content": "<|im_end|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<|endoftext|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:aeb13307a71acd8fe81861d94ad54ab689df773318809eed3cbe794b4492dae4
+size 11422654

tokenizer_config.json

@@ -0,0 +1,241 @@
+{
+  "add_bos_token": false,
+  "add_prefix_space": false,
+  "added_tokens_decoder": {
+    "151643": {
+      "content": "<|endoftext|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151644": {
+      "content": "<|im_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151645": {
+      "content": "<|im_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151646": {
+      "content": "<|object_ref_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151647": {
+      "content": "<|object_ref_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151648": {
+      "content": "<|box_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151649": {
+      "content": "<|box_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151650": {
+      "content": "<|quad_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151651": {
+      "content": "<|quad_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151652": {
+      "content": "<|vision_start|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151653": {
+      "content": "<|vision_end|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151654": {
+      "content": "<|vision_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151655": {
+      "content": "<|image_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151656": {
+      "content": "<|video_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "151657": {
+      "content": "<tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151658": {
+      "content": "</tool_call>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151659": {
+      "content": "<|fim_prefix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151660": {
+      "content": "<|fim_middle|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151661": {
+      "content": "<|fim_suffix|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151662": {
+      "content": "<|fim_pad|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151663": {
+      "content": "<|repo_name|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151664": {
+      "content": "<|file_sep|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151665": {
+      "content": "<tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
+      "content": "</tool_response>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151667": {
+      "content": "<think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151668": {
+      "content": "</think>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 1048576,
+  "pad_token": "<|endoftext|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "truncation_side": "left",
+  "unk_token": null
+}

trainer_state.json

@@ -0,0 +1,546 @@
+{
+  "best_global_step": null,
+  "best_metric": null,
+  "best_model_checkpoint": null,
+  "epoch": 0.20048019207683074,
+  "eval_steps": 500,
+  "global_step": 167,
+  "is_hyper_param_search": false,
+  "is_local_process_zero": true,
+  "is_world_process_zero": true,
+  "log_history": [
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 0.0,
+      "completions/clipped_ratio": 0.01041666679084301,
+      "completions/max_length": 2827.7,
+      "completions/max_terminated_length": 2365.2,
+      "completions/mean_length": 630.6958435058593,
+      "completions/mean_terminated_length": 594.2115203857422,
+      "completions/min_length": 132.0,
+      "completions/min_terminated_length": 132.0,
+      "entropy": 0.11724737156182527,
+      "epoch": 0.012004801920768308,
+      "frac_reward_zero_std": 0.13333333507180214,
+      "grad_norm": 0.038755763322114944,
+      "learning_rate": 5.389221556886228e-07,
+      "loss": 0.0113,
+      "num_tokens": 799206.0,
+      "reward": -0.18518302096053957,
+      "reward_std": 0.20015475898981094,
+      "rewards/grpo_reward_function/mean": -0.18518302938900888,
+      "rewards/grpo_reward_function/std": 0.6885311886668205,
+      "sampling/importance_sampling_ratio/max": 2.1990586280822755,
+      "sampling/importance_sampling_ratio/mean": 0.4647279143333435,
+      "sampling/importance_sampling_ratio/min": 0.0005068443759228102,
+      "sampling/sampling_logp_difference/max": 2.5390082478523253,
+      "sampling/sampling_logp_difference/mean": 0.013516949955374002,
+      "step": 10,
+      "step_time": 569.2043406252749
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 0.0,
+      "completions/clipped_ratio": 0.010416666977107525,
+      "completions/max_length": 2742.3,
+      "completions/max_terminated_length": 2382.7,
+      "completions/mean_length": 646.3521057128906,
+      "completions/mean_terminated_length": 610.6208740234375,
+      "completions/min_length": 131.6,
+      "completions/min_terminated_length": 131.6,
+      "entropy": 0.1267015876248479,
+      "epoch": 0.024009603841536616,
+      "frac_reward_zero_std": 0.1083333358168602,
+      "grad_norm": 0.10266362875699997,
+      "learning_rate": 1.1377245508982037e-06,
+      "loss": -0.0225,
+      "num_tokens": 1617099.0,
+      "reward": 0.01770310625433922,
+      "reward_std": 0.23654931634664536,
+      "rewards/grpo_reward_function/mean": 0.0177031047642231,
+      "rewards/grpo_reward_function/std": 0.8463600814342499,
+      "sampling/importance_sampling_ratio/max": 1.9834328293800354,
+      "sampling/importance_sampling_ratio/mean": 0.40317725837230683,
+      "sampling/importance_sampling_ratio/min": 0.0032052009667828283,
+      "sampling/sampling_logp_difference/max": 2.1022926926612855,
+      "sampling/sampling_logp_difference/mean": 0.01352061601355672,
+      "step": 20,
+      "step_time": 548.7977518392727
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 0.0,
+      "completions/clipped_ratio": 0.014583333767950535,
+      "completions/max_length": 3068.7,
+      "completions/max_terminated_length": 2151.3,
+      "completions/mean_length": 682.6271087646485,
+      "completions/mean_terminated_length": 632.6335662841797,
+      "completions/min_length": 188.7,
+      "completions/min_terminated_length": 188.7,
+      "entropy": 0.125905223749578,
+      "epoch": 0.03601440576230492,
+      "frac_reward_zero_std": 0.11666666939854622,
+      "grad_norm": 0.06394433230161667,
+      "learning_rate": 1.7365269461077847e-06,
+      "loss": 0.0229,
+      "num_tokens": 2465988.0,
+      "reward": -0.18962360136210918,
+      "reward_std": 0.19849726594984532,
+      "rewards/grpo_reward_function/mean": -0.18962358720600606,
+      "rewards/grpo_reward_function/std": 0.6894359931349754,
+      "sampling/importance_sampling_ratio/max": 2.460964298248291,
+      "sampling/importance_sampling_ratio/mean": 0.4253284126520157,
+      "sampling/importance_sampling_ratio/min": 1.070212653598215e-05,
+      "sampling/sampling_logp_difference/max": 2.8448187589645384,
+      "sampling/sampling_logp_difference/mean": 0.013953791093081236,
+      "step": 30,
+      "step_time": 554.4699578347615
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 0.0,
+      "completions/clipped_ratio": 0.01458333358168602,
+      "completions/max_length": 2789.3,
+      "completions/max_terminated_length": 1984.1,
+      "completions/mean_length": 639.958349609375,
+      "completions/mean_terminated_length": 589.7686553955078,
+      "completions/min_length": 163.7,
+      "completions/min_terminated_length": 163.7,
+      "entropy": 0.11666738856583833,
+      "epoch": 0.04801920768307323,
+      "frac_reward_zero_std": 0.1166666679084301,
+      "grad_norm": 0.08781701326370239,
+      "learning_rate": 2.3353293413173654e-06,
+      "loss": -0.0064,
+      "num_tokens": 3297428.0,
+      "reward": -0.03914917185902596,
+      "reward_std": 0.21894535794854164,
+      "rewards/grpo_reward_function/mean": -0.03914917148649692,
+      "rewards/grpo_reward_function/std": 0.8605277180671692,
+      "sampling/importance_sampling_ratio/max": 2.0170334696769716,
+      "sampling/importance_sampling_ratio/mean": 0.4818507760763168,
+      "sampling/importance_sampling_ratio/min": 0.0015486635098906688,
+      "sampling/sampling_logp_difference/max": 2.52269823551178,
+      "sampling/sampling_logp_difference/mean": 0.012881174683570862,
+      "step": 40,
+      "step_time": 541.3490906376392
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 4.2163060425082224e-05,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 4.2163060425082224e-05,
+      "completions/clipped_ratio": 0.016666667349636555,
+      "completions/max_length": 2619.1,
+      "completions/max_terminated_length": 2081.8,
+      "completions/mean_length": 660.1000122070312,
+      "completions/mean_terminated_length": 602.3141662597657,
+      "completions/min_length": 205.5,
+      "completions/min_terminated_length": 205.5,
+      "entropy": 0.12803181819617748,
+      "epoch": 0.060024009603841535,
+      "frac_reward_zero_std": 0.1416666701436043,
+      "grad_norm": 0.03966222703456879,
+      "learning_rate": 2.9341317365269463e-06,
+      "loss": 0.0112,
+      "num_tokens": 4129824.0,
+      "reward": -0.11274411627091467,
+      "reward_std": 0.2275936236605048,
+      "rewards/grpo_reward_function/mean": -0.1127441140357405,
+      "rewards/grpo_reward_function/std": 0.8841595828533173,
+      "sampling/importance_sampling_ratio/max": 2.008124852180481,
+      "sampling/importance_sampling_ratio/mean": 0.46366433799266815,
+      "sampling/importance_sampling_ratio/min": 0.0006394427657710367,
+      "sampling/sampling_logp_difference/max": 2.58479106426239,
+      "sampling/sampling_logp_difference/mean": 0.013615725003182888,
+      "step": 50,
+      "step_time": 545.8774313618429
+    },
+    {
+      "clip_ratio/high_max": 4.673766961786896e-05,
+      "clip_ratio/high_mean": 7.7896114817122e-06,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 7.7896114817122e-06,
+      "completions/clipped_ratio": 0.01250000037252903,
+      "completions/max_length": 2304.9,
+      "completions/max_terminated_length": 1602.8,
+      "completions/mean_length": 596.5750213623047,
+      "completions/mean_terminated_length": 552.8821624755859,
+      "completions/min_length": 150.0,
+      "completions/min_terminated_length": 150.0,
+      "entropy": 0.12017892487347126,
+      "epoch": 0.07202881152460984,
+      "frac_reward_zero_std": 0.14166666865348815,
+      "grad_norm": 0.09313877671957016,
+      "learning_rate": 3.5329341317365273e-06,
+      "loss": -0.0307,
+      "num_tokens": 4936176.0,
+      "reward": -0.03057028874754906,
+      "reward_std": 0.2686158835887909,
+      "rewards/grpo_reward_function/mean": -0.03057028613984585,
+      "rewards/grpo_reward_function/std": 0.8661522060632706,
+      "sampling/importance_sampling_ratio/max": 2.2043559432029722,
+      "sampling/importance_sampling_ratio/mean": 0.4847503274679184,
+      "sampling/importance_sampling_ratio/min": 7.13271651690217e-05,
+      "sampling/sampling_logp_difference/max": 2.4851160287857055,
+      "sampling/sampling_logp_difference/mean": 0.013510057888925075,
+      "step": 60,
+      "step_time": 546.4311281181872
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 6.860105058876797e-05,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 6.860105058876797e-05,
+      "completions/clipped_ratio": 0.018750000558793545,
+      "completions/max_length": 3205.2,
+      "completions/max_terminated_length": 2270.1,
+      "completions/mean_length": 702.3604431152344,
+      "completions/mean_terminated_length": 636.497216796875,
+      "completions/min_length": 131.8,
+      "completions/min_terminated_length": 131.8,
+      "entropy": 0.11768119670450687,
+      "epoch": 0.08403361344537816,
+      "frac_reward_zero_std": 0.1083333358168602,
+      "grad_norm": 0.0433771014213562,
+      "learning_rate": 4.131736526946108e-06,
+      "loss": 0.0553,
+      "num_tokens": 5841149.0,
+      "reward": -0.0784481130540371,
+      "reward_std": 0.23132488708943127,
+      "rewards/grpo_reward_function/mean": -0.07844811640679836,
+      "rewards/grpo_reward_function/std": 0.8492624998092652,
+      "sampling/importance_sampling_ratio/max": 2.233333742618561,
+      "sampling/importance_sampling_ratio/mean": 0.4808308959007263,
+      "sampling/importance_sampling_ratio/min": 0.0008302704439188347,
+      "sampling/sampling_logp_difference/max": 2.9827078700065615,
+      "sampling/sampling_logp_difference/mean": 0.012630783580243587,
+      "step": 70,
+      "step_time": 561.5568902881816
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 8.41788569232449e-05,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 8.41788569232449e-05,
+      "completions/clipped_ratio": 0.01250000037252903,
+      "completions/max_length": 2541.9,
+      "completions/max_terminated_length": 2033.2,
+      "completions/mean_length": 612.5875213623046,
+      "completions/mean_terminated_length": 569.556509399414,
+      "completions/min_length": 149.1,
+      "completions/min_terminated_length": 149.1,
+      "entropy": 0.13153507560491562,
+      "epoch": 0.09603841536614646,
+      "frac_reward_zero_std": 0.10833333656191826,
+      "grad_norm": 0.08665835857391357,
+      "learning_rate": 4.730538922155689e-06,
+      "loss": 0.0701,
+      "num_tokens": 6606395.0,
+      "reward": -0.011540251970291137,
+      "reward_std": 0.19073452726006507,
+      "rewards/grpo_reward_function/mean": -0.011540257930755615,
+      "rewards/grpo_reward_function/std": 0.784630474448204,
+      "sampling/importance_sampling_ratio/max": 2.1984647274017335,
+      "sampling/importance_sampling_ratio/mean": 0.5050391256809235,
+      "sampling/importance_sampling_ratio/min": 0.00014755414913452113,
+      "sampling/sampling_logp_difference/max": 1.8997669577598573,
+      "sampling/sampling_logp_difference/mean": 0.013426258694380522,
+      "step": 80,
+      "step_time": 551.2647462010384
+    },
+    {
+      "clip_ratio/high_max": 2.2563176753465086e-05,
+      "clip_ratio/high_mean": 3.760529580176808e-06,
+      "clip_ratio/low_mean": 1.3224284339230508e-05,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 1.6984813919407314e-05,
+      "completions/clipped_ratio": 0.01041666679084301,
+      "completions/max_length": 2347.6,
+      "completions/max_terminated_length": 2078.4,
+      "completions/mean_length": 621.4354370117187,
+      "completions/mean_terminated_length": 586.118701171875,
+      "completions/min_length": 163.2,
+      "completions/min_terminated_length": 163.2,
+      "entropy": 0.1206895818002522,
+      "epoch": 0.10804321728691477,
+      "frac_reward_zero_std": 0.1083333358168602,
+      "grad_norm": 0.034579165279865265,
+      "learning_rate": 5.32934131736527e-06,
+      "loss": 0.0011,
+      "num_tokens": 7424828.0,
+      "reward": 0.02708094713743776,
+      "reward_std": 0.23181376457214356,
+      "rewards/grpo_reward_function/mean": 0.027080959058366716,
+      "rewards/grpo_reward_function/std": 0.8183064997196198,
+      "sampling/importance_sampling_ratio/max": 2.499999237060547,
+      "sampling/importance_sampling_ratio/mean": 0.486982923746109,
+      "sampling/importance_sampling_ratio/min": 0.000999147113179788,
+      "sampling/sampling_logp_difference/max": 2.079863798618317,
+      "sampling/sampling_logp_difference/mean": 0.012986462097615004,
+      "step": 90,
+      "step_time": 550.7672496054322
+    },
+    {
+      "clip_ratio/high_max": 0.00031043787457747386,
+      "clip_ratio/high_mean": 5.173964618734317e-05,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 5.173964618734317e-05,
+      "completions/clipped_ratio": 0.01666666716337204,
+      "completions/max_length": 2765.9,
+      "completions/max_terminated_length": 1842.8,
+      "completions/mean_length": 675.4458435058593,
+      "completions/mean_terminated_length": 618.1135345458985,
+      "completions/min_length": 178.2,
+      "completions/min_terminated_length": 178.2,
+      "entropy": 0.149181258212775,
+      "epoch": 0.12004801920768307,
+      "frac_reward_zero_std": 0.1416666701436043,
+      "grad_norm": 0.13291294872760773,
+      "learning_rate": 5.928143712574851e-06,
+      "loss": 0.0212,
+      "num_tokens": 8278282.0,
+      "reward": 0.0703774506226182,
+      "reward_std": 0.2336222641170025,
+      "rewards/grpo_reward_function/mean": 0.07037745183333755,
+      "rewards/grpo_reward_function/std": 0.8314530551433563,
+      "sampling/importance_sampling_ratio/max": 2.2870466232299806,
+      "sampling/importance_sampling_ratio/mean": 0.4643064886331558,
+      "sampling/importance_sampling_ratio/min": 2.921815394074656e-05,
+      "sampling/sampling_logp_difference/max": 1.9242668151855469,
+      "sampling/sampling_logp_difference/mean": 0.014198462665081023,
+      "step": 100,
+      "step_time": 547.0480061549694
+    },
+    {
+      "clip_ratio/high_max": 0.0003238706885895226,
+      "clip_ratio/high_mean": 5.397844997787615e-05,
+      "clip_ratio/low_mean": 7.069677012623287e-05,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 0.00012467522010410902,
+      "completions/clipped_ratio": 0.020833334513008596,
+      "completions/max_length": 2925.3,
+      "completions/max_terminated_length": 2297.9,
+      "completions/mean_length": 680.045849609375,
+      "completions/mean_terminated_length": 605.8596954345703,
+      "completions/min_length": 186.9,
+      "completions/min_terminated_length": 186.9,
+      "entropy": 0.1511568833142519,
+      "epoch": 0.13205282112845138,
+      "frac_reward_zero_std": 0.08333333507180214,
+      "grad_norm": 0.0421764962375164,
+      "learning_rate": 6.526946107784432e-06,
+      "loss": -0.0031,
+      "num_tokens": 9165800.0,
+      "reward": 0.04289367534220219,
+      "reward_std": 0.24053554534912108,
+      "rewards/grpo_reward_function/mean": 0.042893677949905396,
+      "rewards/grpo_reward_function/std": 0.835248938202858,
+      "sampling/importance_sampling_ratio/max": 2.332168984413147,
+      "sampling/importance_sampling_ratio/mean": 0.4403663039207458,
+      "sampling/importance_sampling_ratio/min": 0.0001706225667930994,
+      "sampling/sampling_logp_difference/max": 2.4483426809310913,
+      "sampling/sampling_logp_difference/mean": 0.014532316662371158,
+      "step": 110,
+      "step_time": 548.8019280240871
+    },
+    {
+      "clip_ratio/high_max": 0.00012998266611248256,
+      "clip_ratio/high_mean": 2.16637781704776e-05,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 2.16637781704776e-05,
+      "completions/clipped_ratio": 0.002083333395421505,
+      "completions/max_length": 1812.6,
+      "completions/max_terminated_length": 1592.9,
+      "completions/mean_length": 602.1333526611328,
+      "completions/mean_terminated_length": 594.61220703125,
+      "completions/min_length": 179.7,
+      "completions/min_terminated_length": 179.7,
+      "entropy": 0.16710406728088856,
+      "epoch": 0.14405762304921968,
+      "frac_reward_zero_std": 0.0833333358168602,
+      "grad_norm": 0.07664494961500168,
+      "learning_rate": 7.125748502994012e-06,
+      "loss": -0.0309,
+      "num_tokens": 9975204.0,
+      "reward": 0.0826782912015915,
+      "reward_std": 0.23934805542230606,
+      "rewards/grpo_reward_function/mean": 0.0826782874763012,
+      "rewards/grpo_reward_function/std": 0.8862796187400818,
+      "sampling/importance_sampling_ratio/max": 2.2503564238548277,
+      "sampling/importance_sampling_ratio/mean": 0.4635925680398941,
+      "sampling/importance_sampling_ratio/min": 0.0009416027547558823,
+      "sampling/sampling_logp_difference/max": 2.073215699195862,
+      "sampling/sampling_logp_difference/mean": 0.01480921907350421,
+      "step": 120,
+      "step_time": 539.0031213279814
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 1.9831826648442074e-06,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 1.9831826648442074e-06,
+      "completions/clipped_ratio": 0.006250000186264515,
+      "completions/max_length": 2290.7,
+      "completions/max_terminated_length": 1880.6,
+      "completions/mean_length": 599.5916900634766,
+      "completions/mean_terminated_length": 578.0104217529297,
+      "completions/min_length": 168.6,
+      "completions/min_terminated_length": 168.6,
+      "entropy": 0.16012020353227854,
+      "epoch": 0.15606242496998798,
+      "frac_reward_zero_std": 0.11666667014360428,
+      "grad_norm": 0.05220530927181244,
+      "learning_rate": 7.724550898203594e-06,
+      "loss": -0.0377,
+      "num_tokens": 10768324.0,
+      "reward": -0.0507307555526495,
+      "reward_std": 0.18351687043905257,
+      "rewards/grpo_reward_function/mean": -0.05073075201362372,
+      "rewards/grpo_reward_function/std": 0.7544578343629837,
+      "sampling/importance_sampling_ratio/max": 2.375898337364197,
+      "sampling/importance_sampling_ratio/mean": 0.524286350607872,
+      "sampling/importance_sampling_ratio/min": 0.00018855740054277704,
+      "sampling/sampling_logp_difference/max": 2.009746181964874,
+      "sampling/sampling_logp_difference/mean": 0.01376222250983119,
+      "step": 130,
+      "step_time": 548.6409472068772
+    },
+    {
+      "clip_ratio/high_max": 0.00017211703816428782,
+      "clip_ratio/high_mean": 2.86861730273813e-05,
+      "clip_ratio/low_mean": 0.0,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 2.86861730273813e-05,
+      "completions/clipped_ratio": 0.00833333358168602,
+      "completions/max_length": 3136.9,
+      "completions/max_terminated_length": 2221.0,
+      "completions/mean_length": 684.8500244140625,
+      "completions/mean_terminated_length": 655.5515625,
+      "completions/min_length": 168.7,
+      "completions/min_terminated_length": 168.7,
+      "entropy": 0.12020768839865922,
+      "epoch": 0.16806722689075632,
+      "frac_reward_zero_std": 0.09166666939854622,
+      "grad_norm": 0.058197326958179474,
+      "learning_rate": 8.323353293413174e-06,
+      "loss": -0.0342,
+      "num_tokens": 11642436.0,
+      "reward": 0.04102597634773701,
+      "reward_std": 0.2864942252635956,
+      "rewards/grpo_reward_function/mean": 0.041025977826211604,
+      "rewards/grpo_reward_function/std": 0.8844284832477569,
+      "sampling/importance_sampling_ratio/max": 2.37525737285614,
+      "sampling/importance_sampling_ratio/mean": 0.46323378682136535,
+      "sampling/importance_sampling_ratio/min": 2.6157076149502247e-08,
+      "sampling/sampling_logp_difference/max": 2.5657184720039368,
+      "sampling/sampling_logp_difference/mean": 0.012760929018259048,
+      "step": 140,
+      "step_time": 550.6772611703724
+    },
+    {
+      "clip_ratio/high_max": 0.00029233113455120476,
+      "clip_ratio/high_mean": 4.872185563726816e-05,
+      "clip_ratio/low_mean": 4.4254150270717216e-05,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 9.297600590798538e-05,
+      "completions/clipped_ratio": 0.01041666679084301,
+      "completions/max_length": 2211.6,
+      "completions/max_terminated_length": 1961.7,
+      "completions/mean_length": 601.2291839599609,
+      "completions/mean_terminated_length": 565.5705291748047,
+      "completions/min_length": 143.6,
+      "completions/min_terminated_length": 143.6,
+      "entropy": 0.10819828314706684,
+      "epoch": 0.18007202881152462,
+      "frac_reward_zero_std": 0.10833333656191826,
+      "grad_norm": 0.04749957472085953,
+      "learning_rate": 8.922155688622756e-06,
+      "loss": -0.0236,
+      "num_tokens": 12486318.0,
+      "reward": 0.03778684511780739,
+      "reward_std": 0.25178585574030876,
+      "rewards/grpo_reward_function/mean": 0.03778683394193649,
+      "rewards/grpo_reward_function/std": 0.7447861909866333,
+      "sampling/importance_sampling_ratio/max": 2.481464517116547,
+      "sampling/importance_sampling_ratio/mean": 0.5163449585437775,
+      "sampling/importance_sampling_ratio/min": 3.668112331070006e-05,
+      "sampling/sampling_logp_difference/max": 2.379316544532776,
+      "sampling/sampling_logp_difference/mean": 0.012185737490653992,
+      "step": 150,
+      "step_time": 551.4488250606694
+    },
+    {
+      "clip_ratio/high_max": 0.0,
+      "clip_ratio/high_mean": 0.0,
+      "clip_ratio/low_mean": 5.082125426270068e-06,
+      "clip_ratio/low_min": 0.0,
+      "clip_ratio/region_mean": 5.082125426270068e-06,
+      "completions/clipped_ratio": 0.002083333395421505,
+      "completions/max_length": 2301.5,
+      "completions/max_terminated_length": 2174.7,
+      "completions/mean_length": 625.8979309082031,
+      "completions/mean_terminated_length": 618.7978820800781,
+      "completions/min_length": 170.2,
+      "completions/min_terminated_length": 170.2,
+      "entropy": 0.10213978644460439,
+      "epoch": 0.19207683073229292,
+      "frac_reward_zero_std": 0.08333333507180214,
+      "grad_norm": 0.05615560710430145,
+      "learning_rate": 9.520958083832336e-06,
+      "loss": 0.0043,
+      "num_tokens": 13325121.0,
+      "reward": 0.06630225274711847,
+      "reward_std": 0.18489644899964333,
+      "rewards/grpo_reward_function/mean": 0.0663022572407499,
+      "rewards/grpo_reward_function/std": 0.7666326016187668,
+      "sampling/importance_sampling_ratio/max": 2.1301008343696592,
+      "sampling/importance_sampling_ratio/mean": 0.44916791915893556,
+      "sampling/importance_sampling_ratio/min": 7.07070047610614e-05,
+      "sampling/sampling_logp_difference/max": 2.524372959136963,
+      "sampling/sampling_logp_difference/mean": 0.013301923777908087,
+      "step": 160,
+      "step_time": 538.0738848904148
+    }
+  ],
+  "logging_steps": 10,
+  "max_steps": 833,
+  "num_input_tokens_seen": 13935881,
+  "num_train_epochs": 1,
+  "save_steps": 167,
+  "stateful_callbacks": {
+    "TrainerControl": {
+      "args": {
+        "should_epoch_stop": false,
+        "should_evaluate": false,
+        "should_log": false,
+        "should_save": true,
+        "should_training_stop": false
+      },
+      "attributes": {}
+    }
+  },
+  "total_flos": 0.0,
+  "train_batch_size": 2,
+  "trial_name": null,
+  "trial_params": null
+}

zero_to_fp32.py

@@ -0,0 +1,760 @@
+#!/usr/bin/env python
+
+# Copyright (c) Microsoft Corporation.
+# SPDX-License-Identifier: Apache-2.0
+
+# DeepSpeed Team
+
+# This script extracts fp32 consolidated weights from a zero 1, 2 and 3 DeepSpeed checkpoints. It gets
+# copied into the top level checkpoint dir, so the user can easily do the conversion at any point in
+# the future. Once extracted, the weights don't require DeepSpeed and can be used in any
+# application.
+#
+# example:
+#   python zero_to_fp32.py . output_dir/
+#   or
+#   python zero_to_fp32.py . output_dir/ --safe_serialization
+
+import argparse
+import torch
+import glob
+import math
+import os
+import re
+import gc
+import json
+import numpy as np
+from tqdm import tqdm
+from collections import OrderedDict
+from dataclasses import dataclass
+
+# while this script doesn't use deepspeed to recover data, since the checkpoints are pickled with
+# DeepSpeed data structures it has to be available in the current python environment.
+from deepspeed.utils import logger
+from deepspeed.checkpoint.constants import (DS_VERSION, OPTIMIZER_STATE_DICT, SINGLE_PARTITION_OF_FP32_GROUPS,
+                                            FP32_FLAT_GROUPS, ZERO_STAGE, PARTITION_COUNT, PARAM_SHAPES, BUFFER_NAMES,
+                                            FROZEN_PARAM_SHAPES, FROZEN_PARAM_FRAGMENTS)
+
+
+@dataclass
+class zero_model_state:
+    buffers: dict()
+    param_shapes: dict()
+    shared_params: list
+    ds_version: int
+    frozen_param_shapes: dict()
+    frozen_param_fragments: dict()
+
+
+debug = 0
+
+# load to cpu
+device = torch.device('cpu')
+
+
+def atoi(text):
+    return int(text) if text.isdigit() else text
+
+
+def natural_keys(text):
+    '''
+    alist.sort(key=natural_keys) sorts in human order
+    http://nedbatchelder.com/blog/200712/human_sorting.html
+    (See Toothy's implementation in the comments)
+    '''
+    return [atoi(c) for c in re.split(r'(\d+)', text)]
+
+
+def get_model_state_file(checkpoint_dir, zero_stage):
+    if not os.path.isdir(checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{checkpoint_dir}' doesn't exist")
+
+    # there should be only one file
+    if zero_stage <= 2:
+        file = os.path.join(checkpoint_dir, "mp_rank_00_model_states.pt")
+    elif zero_stage == 3:
+        file = os.path.join(checkpoint_dir, "zero_pp_rank_0_mp_rank_00_model_states.pt")
+
+    if not os.path.exists(file):
+        raise FileNotFoundError(f"can't find model states file at '{file}'")
+
+    return file
+
+
+def get_checkpoint_files(checkpoint_dir, glob_pattern):
+    # XXX: need to test that this simple glob rule works for multi-node setup too
+    ckpt_files = sorted(glob.glob(os.path.join(checkpoint_dir, glob_pattern)), key=natural_keys)
+
+    if len(ckpt_files) == 0:
+        raise FileNotFoundError(f"can't find {glob_pattern} files in directory '{checkpoint_dir}'")
+
+    return ckpt_files
+
+
+def get_optim_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_optim_states.pt")
+
+
+def get_model_state_files(checkpoint_dir):
+    return get_checkpoint_files(checkpoint_dir, "*_model_states.pt")
+
+
+def parse_model_states(files):
+    zero_model_states = []
+    for file in files:
+        state_dict = torch.load(file, map_location=device, weights_only=False)
+
+        if BUFFER_NAMES not in state_dict:
+            raise ValueError(f"{file} is not a model state checkpoint")
+        buffer_names = state_dict[BUFFER_NAMES]
+        if debug:
+            print("Found buffers:", buffer_names)
+
+        # recover just the buffers while restoring them to fp32 if they were saved in fp16
+        buffers = {k: v.float() for k, v in state_dict["module"].items() if k in buffer_names}
+        param_shapes = state_dict[PARAM_SHAPES]
+
+        # collect parameters that are included in param_shapes
+        param_names = []
+        for s in param_shapes:
+            for name in s.keys():
+                param_names.append(name)
+
+        # update with frozen parameters
+        frozen_param_shapes = state_dict.get(FROZEN_PARAM_SHAPES, None)
+        if frozen_param_shapes is not None:
+            if debug:
+                print(f"Found frozen_param_shapes: {frozen_param_shapes}")
+            param_names += list(frozen_param_shapes.keys())
+
+        # handle shared params
+        shared_params = [[k, v] for k, v in state_dict["shared_params"].items()]
+
+        ds_version = state_dict.get(DS_VERSION, None)
+
+        frozen_param_fragments = state_dict.get(FROZEN_PARAM_FRAGMENTS, None)
+
+        z_model_state = zero_model_state(buffers=buffers,
+                                         param_shapes=param_shapes,
+                                         shared_params=shared_params,
+                                         ds_version=ds_version,
+                                         frozen_param_shapes=frozen_param_shapes,
+                                         frozen_param_fragments=frozen_param_fragments)
+        zero_model_states.append(z_model_state)
+
+    return zero_model_states
+
+
+def parse_optim_states(files, ds_checkpoint_dir):
+    total_files = len(files)
+    state_dicts = []
+    for f in tqdm(files, desc='Loading checkpoint shards'):
+        state_dict = torch.load(f, map_location=device, mmap=True, weights_only=False)
+        # immediately discard the potentially huge 2 optimizer states as we only care for fp32 master weights
+        # and also handle the case where it was already removed by another helper script
+        state_dict["optimizer_state_dict"].pop("optimizer_state_dict", None)
+        state_dicts.append(state_dict)
+
+    if ZERO_STAGE not in state_dicts[0][OPTIMIZER_STATE_DICT]:
+        raise ValueError(f"{files[0]} is not a zero checkpoint")
+    zero_stage = state_dicts[0][OPTIMIZER_STATE_DICT][ZERO_STAGE]
+    world_size = state_dicts[0][OPTIMIZER_STATE_DICT][PARTITION_COUNT]
+
+    # For ZeRO-2 each param group can have different partition_count as data parallelism for expert
+    # parameters can be different from data parallelism for non-expert parameters. So we can just
+    # use the max of the partition_count to get the dp world_size.
+
+    if type(world_size) is list:
+        world_size = max(world_size)
+
+    if world_size != total_files:
+        raise ValueError(
+            f"Expected {world_size} of '*_optim_states.pt' under '{ds_checkpoint_dir}' but found {total_files} files. "
+            "Possibly due to an overwrite of an old checkpoint, or a checkpoint didn't get saved by one or more processes."
+        )
+
+    # the groups are named differently in each stage
+    if zero_stage <= 2:
+        fp32_groups_key = SINGLE_PARTITION_OF_FP32_GROUPS
+    elif zero_stage == 3:
+        fp32_groups_key = FP32_FLAT_GROUPS
+    else:
+        raise ValueError(f"unknown zero stage {zero_stage}")
+
+    fp32_flat_groups = [state_dicts[i][OPTIMIZER_STATE_DICT][fp32_groups_key] for i in range(len(state_dicts))]
+    return zero_stage, world_size, fp32_flat_groups
+
+
+def _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters):
+    """
+    Returns fp32 state_dict reconstructed from ds checkpoint
+
+    Args:
+        - ``ds_checkpoint_dir``: path to the deepspeed checkpoint folder (where the optimizer files are)
+
+    """
+    print(f"Processing zero checkpoint '{ds_checkpoint_dir}'")
+
+    optim_files = get_optim_files(ds_checkpoint_dir)
+    zero_stage, world_size, fp32_flat_groups = parse_optim_states(optim_files, ds_checkpoint_dir)
+    print(f"Detected checkpoint of type zero stage {zero_stage}, world_size: {world_size}")
+
+    model_files = get_model_state_files(ds_checkpoint_dir)
+
+    zero_model_states = parse_model_states(model_files)
+    print(f'Parsing checkpoint created by deepspeed=={zero_model_states[0].ds_version}')
+
+    if zero_stage <= 2:
+        return _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+    elif zero_stage == 3:
+        return _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                                          exclude_frozen_parameters)
+
+
+def _zero2_merge_frozen_params(state_dict, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+    frozen_param_fragments = zero_model_states[0].frozen_param_fragments
+
+    if debug:
+        num_elem = sum(s.numel() for s in frozen_param_shapes.values())
+        print(f'rank 0: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in frozen_param_fragments.values()])
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        state_dict[name] = frozen_param_fragments[name]
+
+        if debug:
+            print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _has_callable(obj, fn):
+    attr = getattr(obj, fn, None)
+    return callable(attr)
+
+
+def _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+
+    # Reconstruction protocol:
+    #
+    # XXX: document this
+
+    if debug:
+        for i in range(world_size):
+            for j in range(len(fp32_flat_groups[0])):
+                print(f"{FP32_FLAT_GROUPS}[{i}][{j}].shape={fp32_flat_groups[i][j].shape}")
+
+    # XXX: memory usage doubles here (zero2)
+    num_param_groups = len(fp32_flat_groups[0])
+    merged_single_partition_of_fp32_groups = []
+    for i in range(num_param_groups):
+        merged_partitions = [sd[i] for sd in fp32_flat_groups]
+        full_single_fp32_vector = torch.cat(merged_partitions, 0)
+        merged_single_partition_of_fp32_groups.append(full_single_fp32_vector)
+    avail_numel = sum(
+        [full_single_fp32_vector.numel() for full_single_fp32_vector in merged_single_partition_of_fp32_groups])
+
+    if debug:
+        wanted_params = sum([len(shapes) for shapes in param_shapes])
+        wanted_numel = sum([sum(shape.numel() for shape in shapes.values()) for shapes in param_shapes])
+        # not asserting if there is a mismatch due to possible padding
+        print(f"Have {avail_numel} numels to process.")
+        print(f"Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    total_numel = 0
+    total_params = 0
+    for shapes, full_single_fp32_vector in zip(param_shapes, merged_single_partition_of_fp32_groups):
+        offset = 0
+        avail_numel = full_single_fp32_vector.numel()
+        for name, shape in shapes.items():
+
+            unpartitioned_numel = shape.numel() if _has_callable(shape, 'numel') else math.prod(shape)
+            total_numel += unpartitioned_numel
+            total_params += 1
+
+            if debug:
+                print(f"{name} full shape: {shape} unpartitioned numel {unpartitioned_numel} ")
+            state_dict[name] = full_single_fp32_vector.narrow(0, offset, unpartitioned_numel).view(shape)
+            offset += unpartitioned_numel
+
+        # Z2 started to align to 2*world_size to improve nccl performance. Therefore both offset and
+        # avail_numel can differ by anywhere between 0..2*world_size. Due to two unrelated complex
+        # paddings performed in the code it's almost impossible to predict the exact numbers w/o the
+        # live optimizer object, so we are checking that the numbers are within the right range
+        align_to = 2 * world_size
+
+        def zero2_align(x):
+            return align_to * math.ceil(x / align_to)
+
+        if debug:
+            print(f"original offset={offset}, avail_numel={avail_numel}")
+
+        offset = zero2_align(offset)
+        avail_numel = zero2_align(avail_numel)
+
+        if debug:
+            print(f"aligned  offset={offset}, avail_numel={avail_numel}")
+
+        # Sanity check
+        if offset != avail_numel:
+            raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero2_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero2_merge_frozen_params(state_dict, zero_model_states)
+
+    _zero2_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def zero3_partitioned_param_info(unpartitioned_numel, world_size):
+    remainder = unpartitioned_numel % world_size
+    padding_numel = (world_size - remainder) if remainder else 0
+    partitioned_numel = math.ceil(unpartitioned_numel / world_size)
+    return partitioned_numel, padding_numel
+
+
+def _zero3_merge_frozen_params(state_dict, world_size, zero_model_states):
+    if zero_model_states[0].frozen_param_shapes is None or len(zero_model_states[0].frozen_param_shapes) == 0:
+        return
+
+    if debug:
+        for i in range(world_size):
+            num_elem = sum(s.numel() for s in zero_model_states[i].frozen_param_fragments.values())
+            print(f'rank {i}: {FROZEN_PARAM_SHAPES}.numel = {num_elem}')
+
+        frozen_param_shapes = zero_model_states[0].frozen_param_shapes
+        wanted_params = len(frozen_param_shapes)
+        wanted_numel = sum(s.numel() for s in frozen_param_shapes.values())
+        avail_numel = sum([p.numel() for p in zero_model_states[0].frozen_param_fragments.values()]) * world_size
+        print(f'Frozen params: Have {avail_numel} numels to process.')
+        print(f'Frozen params: Need {wanted_numel} numels in {wanted_params} params')
+
+    total_params = 0
+    total_numel = 0
+    for name, shape in zero_model_states[0].frozen_param_shapes.items():
+        total_params += 1
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+
+        param_frags = tuple(model_state.frozen_param_fragments[name] for model_state in zero_model_states)
+        state_dict[name] = torch.cat(param_frags, 0).narrow(0, 0, unpartitioned_numel).view(shape)
+
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Frozen params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+    print(f"Reconstructed Frozen fp32 state dict with {total_params} params {total_numel} elements")
+
+
+class GatheredTensor:
+    """
+    A pseudo tensor that collects partitioned weights.
+    It is more memory efficient when there are multiple groups.
+    """
+
+    def __init__(self, flat_groups, flat_groups_offset, offset, partitioned_numel, shape):
+        self.flat_groups = flat_groups
+        self.flat_groups_offset = flat_groups_offset
+        self.offset = offset
+        self.partitioned_numel = partitioned_numel
+        self.shape = shape
+        self.dtype = self.flat_groups[0][0].dtype
+
+    def contiguous(self):
+        """
+        Merge partitioned weights from flat_groups into a single tensor.
+        """
+        end_idx = self.offset + self.partitioned_numel
+        world_size = len(self.flat_groups)
+        pad_flat_param_chunks = []
+
+        for rank_i in range(world_size):
+            # for each rank, we need to collect weights from related group/groups
+            flat_groups_at_rank_i = self.flat_groups[rank_i]
+            start_group_id = None
+            end_group_id = None
+            for group_id in range(len(self.flat_groups_offset)):
+                if self.flat_groups_offset[group_id] <= self.offset < self.flat_groups_offset[group_id + 1]:
+                    start_group_id = group_id
+                if self.flat_groups_offset[group_id] < end_idx <= self.flat_groups_offset[group_id + 1]:
+                    end_group_id = group_id
+                    break
+            # collect weights from related group/groups
+            for group_id in range(start_group_id, end_group_id + 1):
+                flat_tensor = flat_groups_at_rank_i[group_id]
+                start_offset = self.offset - self.flat_groups_offset[group_id]
+                end_offset = min(end_idx, self.flat_groups_offset[group_id + 1]) - self.flat_groups_offset[group_id]
+                pad_flat_param_chunks.append(flat_tensor[start_offset:end_offset])
+
+        # collect weights from all ranks
+        pad_flat_param = torch.cat(pad_flat_param_chunks, dim=0)
+        param = pad_flat_param[:self.shape.numel()].view(self.shape).contiguous()
+        return param
+
+
+def _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states):
+    param_shapes = zero_model_states[0].param_shapes
+    avail_numel = sum([flat_group.numel() for flat_group in fp32_flat_groups[0]]) * world_size
+
+    # Reconstruction protocol: For zero3 we need to zip the partitions together at boundary of each
+    # param, re-consolidating each param, while dealing with padding if any
+
+    # merge list of dicts, preserving order
+    param_shapes = {k: v for d in param_shapes for k, v in d.items()}
+
+    if debug:
+        for i in range(world_size):
+            print(f"{FP32_FLAT_GROUPS}[{i}].shape={fp32_flat_groups[i].shape}")
+
+        wanted_params = len(param_shapes)
+        wanted_numel = sum(shape.numel() for shape in param_shapes.values())
+        # not asserting if there is a mismatch due to possible padding
+        avail_numel = fp32_flat_groups[0].numel() * world_size
+        print(f"Trainable params: Have {avail_numel} numels to process.")
+        print(f"Trainable params: Need {wanted_numel} numels in {wanted_params} params.")
+
+    # params
+    # XXX: for huge models that can't fit into the host's RAM we will have to recode this to support
+    # out-of-core computing solution
+    offset = 0
+    total_numel = 0
+    total_params = 0
+    flat_groups_offset = [0] + list(np.cumsum([flat_tensor.numel() for flat_tensor in fp32_flat_groups[0]]))
+    for name, shape in tqdm(param_shapes.items(), desc='Gathering sharded weights'):
+        unpartitioned_numel = shape.numel()
+        total_numel += unpartitioned_numel
+        total_params += 1
+        partitioned_numel, partitioned_padding_numel = zero3_partitioned_param_info(unpartitioned_numel, world_size)
+
+        if debug:
+            print(
+                f"Trainable params: {total_params} {name} full shape: {shape} partition0 numel={partitioned_numel} partitioned_padding_numel={partitioned_padding_numel}"
+            )
+
+        # memory efficient tensor
+        tensor = GatheredTensor(fp32_flat_groups, flat_groups_offset, offset, partitioned_numel, shape)
+        state_dict[name] = tensor
+        offset += partitioned_numel
+
+    offset *= world_size
+
+    # Sanity check
+    if offset != avail_numel:
+        raise ValueError(f"consumed {offset} numels out of {avail_numel} - something is wrong")
+
+    print(f"Reconstructed Trainable fp32 state dict with {total_params} params {total_numel} elements")
+
+
+def _get_fp32_state_dict_from_zero3_checkpoint(world_size, fp32_flat_groups, zero_model_states,
+                                               exclude_frozen_parameters):
+    state_dict = OrderedDict()
+
+    # buffers
+    buffers = zero_model_states[0].buffers
+    state_dict.update(buffers)
+    if debug:
+        print(f"added {len(buffers)} buffers")
+
+    if not exclude_frozen_parameters:
+        _zero3_merge_frozen_params(state_dict, world_size, zero_model_states)
+
+    _zero3_merge_trainable_params(state_dict, world_size, fp32_flat_groups, zero_model_states)
+
+    # recover shared parameters
+    for pair in zero_model_states[0].shared_params:
+        if pair[1] in state_dict:
+            state_dict[pair[0]] = state_dict[pair[1]]
+
+    return state_dict
+
+
+def to_torch_tensor(state_dict, return_empty_tensor=False):
+    """
+    Convert state_dict of GatheredTensor to torch tensor
+    """
+    torch_state_dict = {}
+    converted_tensors = {}
+    for name, tensor in state_dict.items():
+        tensor_id = id(tensor)
+        if tensor_id in converted_tensors:  # shared tensors
+            shared_tensor = torch_state_dict[converted_tensors[tensor_id]]
+            torch_state_dict[name] = shared_tensor
+        else:
+            converted_tensors[tensor_id] = name
+            if return_empty_tensor:
+                torch_state_dict[name] = torch.empty(tensor.shape, dtype=tensor.dtype)
+            else:
+                torch_state_dict[name] = tensor.contiguous()
+    return torch_state_dict
+
+
+def get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                             tag=None,
+                                             exclude_frozen_parameters=False,
+                                             lazy_mode=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated state_dict that can be loaded with
+    ``load_state_dict()`` and used for training without DeepSpeed or shared with others, for example
+    via a model hub.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in 'latest' file. e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+        - ``lazy_mode``: get state_dict in lazy mode. It returns a dict of pesduo tensor instead of torch tensor, which is more memory efficient.
+          Convert the pesduo tensor to torch tensor by ``.contiguous()``
+
+    Returns:
+        - pytorch ``state_dict``
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        # do the training and checkpoint saving
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir) # already on cpu
+        model = model.cpu() # move to cpu
+        model.load_state_dict(state_dict)
+        # submit to model hub or save the model to share with others
+
+    In this example the ``model`` will no longer be usable in the deepspeed context of the same
+    application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    If you want it all done for you, use ``load_state_dict_from_zero_checkpoint`` instead.
+
+    Note: the above usage may not work if your application doesn't have sufficient free CPU memory.
+    You may need to use the offline approach using the ``zero_to_fp32.py`` script that is saved with
+    the checkpoint. Or you can load state_dict in lazy mode ::
+
+        from deepspeed.utils.zero_to_fp32 import get_fp32_state_dict_from_zero_checkpoint
+        state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, lazy_mode=True) # not on cpu
+        for name, lazy_tensor in state_dict.item():
+            tensor = lazy_tensor.contiguous()  # to cpu
+            print(name, tensor)
+            # del tensor to release memory if it no longer in use
+    """
+    if tag is None:
+        latest_path = os.path.join(checkpoint_dir, 'latest')
+        if os.path.isfile(latest_path):
+            with open(latest_path, 'r') as fd:
+                tag = fd.read().strip()
+        else:
+            raise ValueError(f"Unable to find 'latest' file at {latest_path}")
+
+    ds_checkpoint_dir = os.path.join(checkpoint_dir, tag)
+
+    if not os.path.isdir(ds_checkpoint_dir):
+        raise FileNotFoundError(f"Directory '{ds_checkpoint_dir}' doesn't exist")
+
+    state_dict = _get_fp32_state_dict_from_zero_checkpoint(ds_checkpoint_dir, exclude_frozen_parameters)
+    if lazy_mode:
+        return state_dict
+    else:
+        return to_torch_tensor(state_dict)
+
+
+def convert_zero_checkpoint_to_fp32_state_dict(checkpoint_dir,
+                                               output_dir,
+                                               max_shard_size="5GB",
+                                               safe_serialization=False,
+                                               tag=None,
+                                               exclude_frozen_parameters=False):
+    """
+    Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict`` file that can be
+    loaded with ``torch.load(file)`` + ``load_state_dict()`` and used for training without DeepSpeed.
+
+    Args:
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``output_dir``: directory to the pytorch fp32 state_dict output files
+        - ``max_shard_size``: the maximum size for a checkpoint before being sharded, default value is 5GB
+        - ``safe_serialization``:  whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+        - ``exclude_frozen_parameters``: exclude frozen parameters
+    """
+
+    # Dependency pre-check
+    if safe_serialization:
+        try:
+            from safetensors.torch import save_file
+        except ImportError:
+            print('If you want to use `safe_serialization`, please `pip install safetensors`')
+            raise
+    if max_shard_size is not None:
+        try:
+            from huggingface_hub import split_torch_state_dict_into_shards
+        except ImportError:
+            print('If you want to use `max_shard_size`, please `pip install huggingface_hub`')
+            raise
+
+    # Convert zero checkpoint to state_dict
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir,
+                                                          tag,
+                                                          exclude_frozen_parameters,
+                                                          lazy_mode=True)
+
+    # Shard the model if it is too big.
+    weights_name = "model.safetensors" if safe_serialization else "pytorch_model.bin"
+    if max_shard_size is not None:
+        filename_pattern = weights_name.replace(".bin", "{suffix}.bin").replace(".safetensors", "{suffix}.safetensors")
+        # an memory-efficient approach for sharding
+        empty_state_dict = to_torch_tensor(state_dict, return_empty_tensor=True)
+        state_dict_split = split_torch_state_dict_into_shards(empty_state_dict,
+                                                              filename_pattern=filename_pattern,
+                                                              max_shard_size=max_shard_size)
+    else:
+        from collections import namedtuple
+        StateDictSplit = namedtuple("StateDictSplit", ["is_sharded", "filename_to_tensors"])
+        state_dict_split = StateDictSplit(is_sharded=False,
+                                          filename_to_tensors={weights_name: list(state_dict.keys())})
+
+    # Save the model by shard
+    os.makedirs(output_dir, exist_ok=True)
+    filename_to_tensors = state_dict_split.filename_to_tensors.items()
+    for shard_file, tensors in tqdm(filename_to_tensors, desc="Saving checkpoint shards"):
+        shard_state_dict = {tensor_name: state_dict[tensor_name] for tensor_name in tensors}
+        shard_state_dict = to_torch_tensor(shard_state_dict)
+        output_path = os.path.join(output_dir, shard_file)
+        if safe_serialization:
+            save_file(shard_state_dict, output_path, metadata={"format": "pt"})
+        else:
+            torch.save(shard_state_dict, output_path)
+        # release the memory of current shard
+        for tensor_name in list(shard_state_dict.keys()):
+            del state_dict[tensor_name]
+            del shard_state_dict[tensor_name]
+        del shard_state_dict
+        gc.collect()
+
+    # Save index if sharded
+    if state_dict_split.is_sharded:
+        index = {
+            "metadata": state_dict_split.metadata,
+            "weight_map": state_dict_split.tensor_to_filename,
+        }
+        save_index_file = "model.safetensors.index.json" if safe_serialization else "pytorch_model.bin.index.json"
+        save_index_file = os.path.join(output_dir, save_index_file)
+        with open(save_index_file, "w", encoding="utf-8") as f:
+            content = json.dumps(index, indent=2, sort_keys=True) + "\n"
+            f.write(content)
+
+
+def load_state_dict_from_zero_checkpoint(model, checkpoint_dir, tag=None):
+    """
+    1. Put the provided model to cpu
+    2. Convert ZeRO 2 or 3 checkpoint into a single fp32 consolidated ``state_dict``
+    3. Load it into the provided model
+
+    Args:
+        - ``model``: the model object to update
+        - ``checkpoint_dir``: path to the desired checkpoint folder. (one that contains the tag-folder, like ``global_step14``)
+        - ``tag``: checkpoint tag used as a unique identifier for checkpoint. If not provided will attempt to load tag in the file named ``latest`` in the checkpoint folder, e.g., ``global_step14``
+
+    Returns:
+        - ``model`: modified model
+
+    Make sure you have plenty of CPU memory available before you call this function. If you don't
+    have enough use the ``zero_to_fp32.py`` utility to do the conversion. You will find it
+    conveniently placed for you in the checkpoint folder.
+
+    A typical usage might be ::
+
+        from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
+        model = load_state_dict_from_zero_checkpoint(trainer.model, checkpoint_dir)
+        # submit to model hub or save the model to share with others
+
+    Note, that once this was run, the ``model`` will no longer be usable in the deepspeed context
+    of the same application. i.e. you will need to re-initialize the deepspeed engine, since
+    ``model.load_state_dict(state_dict)`` will remove all the deepspeed magic from it.
+
+    """
+    logger.info("Extracting fp32 weights")
+    state_dict = get_fp32_state_dict_from_zero_checkpoint(checkpoint_dir, tag)
+
+    logger.info("Overwriting model with fp32 weights")
+    model = model.cpu()
+    model.load_state_dict(state_dict, strict=False)
+
+    return model
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser()
+    parser.add_argument("checkpoint_dir",
+                        type=str,
+                        help="path to the desired checkpoint folder, e.g., path/checkpoint-12")
+    parser.add_argument("output_dir",
+                        type=str,
+                        help="directory to the pytorch fp32 state_dict output files"
+                        "(e.g. path/checkpoint-12-output/)")
+    parser.add_argument(
+        "--max_shard_size",
+        type=str,
+        default="5GB",
+        help="The maximum size for a checkpoint before being sharded. Checkpoints shard will then be each of size"
+        "lower than this size. If expressed as a string, needs to be digits followed by a unit (like `5MB`"
+        "We default it to 5GB in order for models to be able to run easily on free-tier google colab instances"
+        "without CPU OOM issues.")
+    parser.add_argument(
+        "--safe_serialization",
+        default=False,
+        action='store_true',
+        help="Whether to save the model using `safetensors` or the traditional PyTorch way (that uses `pickle`).")
+    parser.add_argument("-t",
+                        "--tag",
+                        type=str,
+                        default=None,
+                        help="checkpoint tag used as a unique identifier for checkpoint. e.g., global_step1")
+    parser.add_argument("--exclude_frozen_parameters", action='store_true', help="exclude frozen parameters")
+    parser.add_argument("-d", "--debug", action='store_true', help="enable debug")
+    args = parser.parse_args()
+
+    debug = args.debug
+
+    convert_zero_checkpoint_to_fp32_state_dict(args.checkpoint_dir,
+                                               args.output_dir,
+                                               max_shard_size=args.max_shard_size,
+                                               safe_serialization=args.safe_serialization,
+                                               tag=args.tag,
+                                               exclude_frozen_parameters=args.exclude_frozen_parameters)