Instructions to use mrfakename/WizardChatML-7B-v0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mrfakename/WizardChatML-7B-v0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mrfakename/WizardChatML-7B-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("mrfakename/WizardChatML-7B-v0")
model = AutoModelForCausalLM.from_pretrained("mrfakename/WizardChatML-7B-v0")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mrfakename/WizardChatML-7B-v0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mrfakename/WizardChatML-7B-v0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrfakename/WizardChatML-7B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mrfakename/WizardChatML-7B-v0

SGLang

How to use mrfakename/WizardChatML-7B-v0 with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mrfakename/WizardChatML-7B-v0" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrfakename/WizardChatML-7B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mrfakename/WizardChatML-7B-v0" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mrfakename/WizardChatML-7B-v0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use mrfakename/WizardChatML-7B-v0 with Docker Model Runner:
```
docker model run hf.co/mrfakename/WizardChatML-7B-v0
```

WizardChatML 7B v0

GGUF Quants

I personally think ChatML is the best prompt format. It allows:

Easier templating for generation
Lower risk of inadvertently generating role tokens
Better long-context performance and higher quality on quantized models

This model is an experiment attempting to extend WizardLM 2 7B to ChatML. It was trained on a small ChatML dataset, it probably isn't as good as WizardLM 2 Base, but it's an attempt.

Aside from using the ChatML prompt format, this model supports system prompts. In fact, it adheres very well to these prompts.

If you want to use this model for task-specific purposes, you should probably fine-tune it.

Capabilities & Challenges

Seems ok-ish at writing
Pretty good at math
Sometimes calls itself ChatGPT/OpenAI

Risks

It has not been trained on guardrail data and may generate offensive content if prompted.

License

If you use this model, you must include the Apache 2.0 license AND the following notice:

I'm releasing this model under the Apache 2.0 license, with the added restriction that it cannot be used to compete with OpenAI (due to the nature of the training data). Additionally, this model was finetuned from the WizardLM 2 7B model, which was recently removed by Microsoft (it was Apache licensed, but may have been trained on NC-licensed data). You are responsible for the usage of this model. You are responsible for checking that your usage of this model is legal in your jurisdiction. Commercial use is not advised, as this model is finetuned from a model that may have been trained on NC-licensed data. Make sure to consult a lawyer before using in production or commercially.

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Safetensors

Model size

7B params

Tensor type

BF16