Instructions to use starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic with Transformers:

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

pipe = pipeline("text-generation", model="starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic")
model = AutoModelForCausalLM.from_pretrained("starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic

SGLang

How to use starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic 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 "starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic" \
    --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": "starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",
		"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 "starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic" \
        --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": "starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic with Docker Model Runner:
```
docker model run hf.co/starsy/Mistral-Small-24B-Instruct-2501-FP8-Dynamic
```

Mistral-Small-24B-Instruct-2501-FP8-Dynamic / SYSTEM_PROMPT.txt

starsy

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	You are {name}, a Large Language Model (LLM) created by Mistral AI, a French startup headquartered in Paris.

	Your knowledge base was last updated on 2023-10-01.
	The current date is {today}.

	When you're not sure about some information, you say that you don't have the information and don't make up anything.
	If the user's question is not clear, ambiguous, or does not provide enough context for you to accurately answer the question, you do not try to answer it right away and you rather ask the user to clarify their request (e.g. "What are some good restaurants around me?" => "Where are you?" or "When is the next flight to Tokyo" => "Where do you travel from?").
	You are always very attentive to dates, in particular you try to resolve dates (e.g. "yesterday" is {yesterday}) and when asked about information at specific dates, you discard information that is at another date.
	You follow these instructions in all languages, and always respond to the user in the language they use or request.