Instructions to use fbellame/mistral-7b-json-quizz-fine-tuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fbellame/mistral-7b-json-quizz-fine-tuned with Transformers:

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

pipe = pipeline("text-generation", model="fbellame/mistral-7b-json-quizz-fine-tuned")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fbellame/mistral-7b-json-quizz-fine-tuned")
model = AutoModelForCausalLM.from_pretrained("fbellame/mistral-7b-json-quizz-fine-tuned")
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 fbellame/mistral-7b-json-quizz-fine-tuned with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/fbellame/mistral-7b-json-quizz-fine-tuned

SGLang

How to use fbellame/mistral-7b-json-quizz-fine-tuned 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 "fbellame/mistral-7b-json-quizz-fine-tuned" \
    --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": "fbellame/mistral-7b-json-quizz-fine-tuned",
		"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 "fbellame/mistral-7b-json-quizz-fine-tuned" \
        --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": "fbellame/mistral-7b-json-quizz-fine-tuned",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use fbellame/mistral-7b-json-quizz-fine-tuned with Docker Model Runner:
```
docker model run hf.co/fbellame/mistral-7b-json-quizz-fine-tuned
```

mistral-7b-json-quizz-fine-tuned

File size: 1,256 Bytes

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from transformers import TextGenerationPipeline
from transformers.pipelines.text_generation import ReturnType

STYLE = "{instruction}"


class H2OTextGenerationPipeline(TextGenerationPipeline):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self.prompt = STYLE

    def preprocess(
        self, prompt_text, prefix="", handle_long_generation=None, **generate_kwargs
    ):
        prompt_text = self.prompt.format(instruction=prompt_text)
        return super().preprocess(
            prompt_text,
            prefix=prefix,
            handle_long_generation=handle_long_generation,
            **generate_kwargs,
        )

    def postprocess(
        self,
        model_outputs,
        return_type=ReturnType.FULL_TEXT,
        clean_up_tokenization_spaces=True,
    ):
        records = super().postprocess(
            model_outputs,
            return_type=return_type,
            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
        )
        for rec in records:
            rec["generated_text"] = (
                rec["generated_text"]
                .split("")[1]
                .strip()
                .split("")[0]
                .strip()
            )
        return records