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@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.safetensors.index.json filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,141 @@
+---
+datasets:
+- abisee/cnn_dailymail
+- nvidia/Nemotron-Post-Training-Dataset-v2
+base_model:
+- zai-org/GLM-4.7
+base_model_relation: quantized
+license: mit
+pipeline_tag: text-generation
+---
+# GLM-4.7-NVFP4
+
+**Format:** NVFP4 — optimal partial quantization of weights & activations to NVFP4.  
+**Base model:** `zai-org/GLM-4.7`  
+**How it was made:** [AutoQuantized](https://nvidia.github.io/Model-Optimizer/guides/_pytorch_quantization.html#optimal-partial-quantization-using-auto-quantize) with [NVIDIA Model-Optimizer](https://github.com/NVIDIA/Model-Optimizer/) (NVFP4) with x8 RTX PRO 6000s, using the default calibration mix. ([cnn_dailymail](https://huggingface.co/datasets/abisee/cnn_dailymail) and [nemotron-post-training-dataset-v2](https://huggingface.co/datasets/nvidia/Nemotron-Post-Training-Dataset-v2)) 
+
+Check the [original model card](https://huggingface.co/zai-org/GLM-4.7) for information about this model.
+
+---
+
+### **MMLU Benchmark Results: Salyut1/GLM-4.7-NVFP4**
+#### **Summary Table**
+| Groups | Version | Metric | Value | Stderr |
+| --- | --- | --- | --- | --- |
+| **MMLU (Total)** | 2 | acc ↑ | **0.8348** | ± 0.0030 |
+| **Social Sciences** | 2 | acc ↑ | **0.9051** | ± 0.0052 |
+| **Other** | 2 | acc ↑ | **0.8684** | ± 0.0058 |
+| **STEM** | 2 | acc ↑ | **0.8351** | ± 0.0064 |
+| **Humanities** | 2 | acc ↑ | **0.7664** | ± 0.0059 |
+#### **STEM**
+| Tasks | n-shot | Metric | Value | Stderr |
+| --- | --- | --- | --- | --- |
+| High School Biology | 0 | acc ↑ | 0.9516 | ± 0.0122 |
+| College Biology | 0 | acc ↑ | 0.9514 | ± 0.0180 |
+| Astronomy | 0 | acc ↑ | 0.9474 | ± 0.0182 |
+| High School Computer Science | 0 | acc ↑ | 0.9300 | ± 0.0256 |
+| Conceptual Physics | 0 | acc ↑ | 0.9064 | ± 0.0190 |
+| Elementary Mathematics | 0 | acc ↑ | 0.8862 | ± 0.0164 |
+| Electrical Engineering | 0 | acc ↑ | 0.8690 | ± 0.0281 |
+| High School Statistics | 0 | acc ↑ | 0.8565 | ± 0.0239 |
+| College Computer Science | 0 | acc ↑ | 0.8400 | ± 0.0368 |
+| Anatomy | 0 | acc ↑ | 0.8296 | ± 0.0325 |
+| High School Physics | 0 | acc ↑ | 0.7947 | ± 0.0330 |
+| High School Chemistry | 0 | acc ↑ | 0.7882 | ± 0.0287 |
+| Machine Learning | 0 | acc ↑ | 0.7679 | ± 0.0401 |
+| College Physics | 0 | acc ↑ | 0.7647 | ± 0.0422 |
+| Abstract Algebra | 0 | acc ↑ | 0.6800 | ± 0.0469 |
+| College Chemistry | 0 | acc ↑ | 0.6800 | ± 0.0469 |
+| College Mathematics | 0 | acc ↑ | 0.6800 | ± 0.0469 |
+| High School Mathematics | 0 | acc ↑ | 0.6481 | ± 0.0291 |
+#### **Social Sciences**
+| Tasks | n-shot | Metric | Value | Stderr |
+| --- | --- | --- | --- | --- |
+| High School Government/Politics | 0 | acc ↑ | 0.9793 | ± 0.0103 |
+| High School Microeconomics | 0 | acc ↑ | 0.9706 | ± 0.0110 |
+| High School Psychology | 0 | acc ↑ | 0.9523 | ± 0.0091 |
+| Human Sexuality | 0 | acc ↑ | 0.9313 | ± 0.0222 |
+| Sociology | 0 | acc ↑ | 0.9204 | ± 0.0191 |
+| High School Geography | 0 | acc ↑ | 0.9192 | ± 0.0194 |
+| High School Macroeconomics | 0 | acc ↑ | 0.9000 | ± 0.0152 |
+| US Foreign Policy | 0 | acc ↑ | 0.9000 | ± 0.0302 |
+| Professional Psychology | 0 | acc ↑ | 0.8725 | ± 0.0135 |
+| Security Studies | 0 | acc ↑ | 0.8653 | ± 0.0219 |
+| Public Relations | 0 | acc ↑ | 0.7636 | ± 0.0407 |
+| Econometrics | 0 | acc ↑ | 0.7544 | ± 0.0405 |
+#### **Humanities**
+| Tasks | n-shot | Metric | Value | Stderr |
+| --- | --- | --- | --- | --- |
+| High School US History | 0 | acc ↑ | 0.9461 | ± 0.0159 |
+| High School World History | 0 | acc ↑ | 0.9367 | ± 0.0158 |
+| World Religions | 0 | acc ↑ | 0.9064 | ± 0.0223 |
+| Prehistory | 0 | acc ↑ | 0.8981 | ± 0.0168 |
+| International Law | 0 | acc ↑ | 0.8926 | ± 0.0283 |
+| Jurisprudence | 0 | acc ↑ | 0.8889 | ± 0.0304 |
+| Logical Fallacies | 0 | acc ↑ | 0.8834 | ± 0.0252 |
+| High School European History | 0 | acc ↑ | 0.8788 | ± 0.0255 |
+| Moral Disputes | 0 | acc ↑ | 0.8699 | ± 0.0181 |
+| Philosophy | 0 | acc ↑ | 0.8617 | ± 0.0196 |
+| Formal Logic | 0 | acc ↑ | 0.7460 | ± 0.0389 |
+| Professional Law | 0 | acc ↑ | 0.6610 | ± 0.0121 |
+| Moral Scenarios | 0 | acc ↑ | 0.6425 | ± 0.0160 |
+#### **Other**
+| Tasks | n-shot | Metric | Value | Stderr |
+| --- | --- | --- | --- | --- |
+| Medical Genetics | 0 | acc ↑ | 0.9800 | ± 0.0141 |
+| Marketing | 0 | acc ↑ | 0.9530 | ± 0.0139 |
+| Miscellaneous | 0 | acc ↑ | 0.9374 | ± 0.0087 |
+| Professional Medicine | 0 | acc ↑ | 0.9301 | ± 0.0155 |
+| Clinical Knowledge | 0 | acc ↑ | 0.9057 | ± 0.0180 |
+| Nutrition | 0 | acc ↑ | 0.9052 | ± 0.0168 |
+| Management | 0 | acc ↑ | 0.8932 | ± 0.0306 |
+| Business Ethics | 0 | acc ↑ | 0.8600 | ± 0.0349 |
+| Computer Security | 0 | acc ↑ | 0.8600 | ± 0.0349 |
+| Human Aging | 0 | acc ↑ | 0.8161 | ± 0.0260 |
+| College Medicine | 0 | acc ↑ | 0.7977 | ± 0.0306 |
+| Professional Accounting | 0 | acc ↑ | 0.7624 | ± 0.0254 |
+| Global Facts | 0 | acc ↑ | 0.6500 | ± 0.0479 |
+| Virology | 0 | acc ↑ | 0.5723 | ± 0.0385 |
+
+---
+
+vLLM Inference Note:
+
+I needed to patch `vllm/model_executor/models/glm4_moe.py` to skip specific k_scale and v_scale parameters if they are missing from the checkpoint, rather than crashing. The below script fixed my k_scale and v_scale errors.
+```python
+import sys
+import os
+import re
+
+# Path to the vLLM model file
+path = '/usr/local/lib/python3.12/dist-packages/vllm/model_executor/models/glm4_moe.py'
+
+if os.path.exists(path):
+    with open(path, 'r') as f:
+        lines = f.readlines()
+    
+    target_str = 'param = params_dict[name]'
+    new_lines = []
+    patched = False
+    
+    for line in lines:
+        # We look for the parameter loading line
+        if target_str in line and 'k_scale' not in line:
+            whitespace = re.match(r'^(\s*)', line).group(1)
+            
+            # Inject logic: If asking for k_scale/v_scale and it's missing, skip
+            payload = f"{whitespace}if ('k_scale' in name or 'v_scale' in name) and name not in params_dict: continue\n"
+            
+            new_lines.append(payload)
+            new_lines.append(line)
+            patched = True
+        else:
+            new_lines.append(line)
+            
+    if patched:
+        with open(path, 'w') as f:
+            f.writelines(new_lines)
+        print(f"Successfully patched {path}")
+    else:
+        print("File already patched or target not found.")
+```

chat_template.jinja

@@ -0,0 +1,86 @@
+[gMASK]<sop>
+{%- if tools -%}
+<|system|>
+# Tools
+
+You may call one or more functions to assist with the user query.
+
+You are provided with function signatures within <tools></tools> XML tags:
+<tools>
+{% for tool in tools %}
+{{ tool | tojson(ensure_ascii=False) }}
+{% endfor %}
+</tools>
+
+For each function call, output the function name and arguments within the following XML format:
+<tool_call>{function-name}<arg_key>{arg-key-1}</arg_key><arg_value>{arg-value-1}</arg_value><arg_key>{arg-key-2}</arg_key><arg_value>{arg-value-2}</arg_value>...</tool_call>{%- endif -%}
+{%- macro visible_text(content) -%}
+    {%- if content is string -%}
+        {{- content }}
+    {%- elif content is iterable and content is not mapping -%}
+        {%- for item in content -%}
+            {%- if item is mapping and item.type == 'text' -%}
+                {{- item.text }}
+            {%- elif item is string -%}
+                {{- item }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{- content }}
+    {%- endif -%}
+{%- endmacro -%}
+{%- set ns = namespace(last_user_index=-1) %}
+{%- for m in messages %}
+    {%- if m.role == 'user' %}
+        {% set ns.last_user_index = loop.index0 -%}
+    {%- endif %}
+{%- endfor %}
+{% for m in messages %}
+{%- if m.role == 'user' -%}<|user|>{{ visible_text(m.content) }}
+{%- elif m.role == 'assistant' -%}
+<|assistant|>
+{%- set reasoning_content = '' %}
+{%- set content = visible_text(m.content) %}
+{%- if m.reasoning_content is string %}
+    {%- set reasoning_content = m.reasoning_content %}
+{%- else %}
+    {%- if '</think>' in content %}
+        {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+        {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+    {%- endif %}
+{%- endif %}
+{%- if ((clear_thinking is defined and not clear_thinking) or loop.index0 > ns.last_user_index) and reasoning_content -%}
+{{ '<think>' + reasoning_content.strip() +  '</think>'}}
+{%- else -%}
+{{ '</think>' }}
+{%- endif -%}
+{%- if content.strip() -%}
+{{ content.strip() }}
+{%- endif -%}
+{% if m.tool_calls %}
+{% for tc in m.tool_calls %}
+{%- if tc.function %}
+    {%- set tc = tc.function %}
+{%- endif %}
+{{- '<tool_call>' + tc.name -}}
+{% set _args = tc.arguments %}{% for k, v in _args.items() %}<arg_key>{{ k }}</arg_key><arg_value>{{ v | tojson(ensure_ascii=False) if v is not string else v }}</arg_value>{% endfor %}</tool_call>{% endfor %}
+{% endif %}
+{%- elif m.role == 'tool' -%}
+{%- if m.content is string -%}
+{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+    {{- '<|observation|>' }}
+{%- endif %}
+{{- '<tool_response>' }}
+{{- m.content }}
+{{- '</tool_response>' }}
+{%- else -%}
+<|observation|>{% for tr in m.content %}
+<tool_response>{{ tr.output if tr.output is defined else tr }}</tool_response>{% endfor -%}
+{% endif -%}
+{%- elif m.role == 'system' -%}
+<|system|>{{ visible_text(m.content) }}
+{%- endif -%}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    <|assistant|>{{- '</think>' if (enable_thinking is defined and not enable_thinking) else '<think>' -}}
+{%- endif -%}

config.json

@@ -0,0 +1,78 @@
+{
+    "architectures": [
+        "Glm4MoeForCausalLM"
+    ],
+    "attention_bias": true,
+    "attention_dropout": 0.0,
+    "dtype": "bfloat16",
+    "eos_token_id": [
+        151329,
+        151336,
+        151338
+    ],
+    "first_k_dense_replace": 3,
+    "head_dim": 128,
+    "hidden_act": "silu",
+    "hidden_size": 5120,
+    "initializer_range": 0.02,
+    "intermediate_size": 12288,
+    "max_position_embeddings": 202752,
+    "model_type": "glm4_moe",
+    "moe_intermediate_size": 1536,
+    "n_group": 1,
+    "n_routed_experts": 160,
+    "n_shared_experts": 1,
+    "norm_topk_prob": true,
+    "num_attention_heads": 96,
+    "num_experts_per_tok": 8,
+    "num_hidden_layers": 92,
+    "num_key_value_heads": 8,
+    "num_nextn_predict_layers": 1,
+    "pad_token_id": 151329,
+    "partial_rotary_factor": 0.5,
+    "rms_norm_eps": 1e-05,
+    "rope_scaling": null,
+    "rope_theta": 1000000,
+    "routed_scaling_factor": 2.5,
+    "tie_word_embeddings": false,
+    "topk_group": 1,
+    "transformers_version": "4.57.3",
+    "use_cache": true,
+    "use_qk_norm": true,
+    "vocab_size": 151552,
+    "quantization_config": {
+        "config_groups": {
+            "group_0": {
+                "input_activations": {
+                    "dynamic": false,
+                    "num_bits": 4,
+                    "type": "float",
+                    "group_size": 16
+                },
+                "weights": {
+                    "dynamic": false,
+                    "num_bits": 4,
+                    "type": "float",
+                    "group_size": 16
+                },
+                "targets": [
+                    "Linear"
+                ]
+            }
+        },
+        "ignore": [
+            "lm_head"
+        ],
+        "quant_algo": "NVFP4",
+        "kv_cache_scheme": {
+            "dynamic": false,
+            "num_bits": 8,
+            "type": "float"
+        },
+        "producer": {
+            "name": "modelopt",
+            "version": "0.40.0"
+        },
+        "quant_method": "modelopt"
+    }
+}

generation_config.json

@@ -0,0 +1,10 @@
+{
+  "_from_model_config": true,
+  "eos_token_id": [
+    151329,
+    151336,
+    151338
+  ],
+  "pad_token_id": 151329,
+  "transformers_version": "4.57.3"
+}

hf_quant_config.json

@@ -0,0 +1,14 @@
+{
+    "producer": {
+        "name": "modelopt",
+        "version": "0.40.0"
+    },
+    "quantization": {
+        "quant_algo": "NVFP4",
+        "kv_cache_quant_algo": "FP8",
+        "group_size": 16,
+        "exclude_modules": [
+            "lm_head"
+        ]
+    }
+}

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