Instructions to use HaitameLaframe/Mistral-v3-7b-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HaitameLaframe/Mistral-v3-7b-coder with Transformers:

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

pipe = pipeline("text-generation", model="HaitameLaframe/Mistral-v3-7b-coder")

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

tokenizer = AutoTokenizer.from_pretrained("HaitameLaframe/Mistral-v3-7b-coder")
model = AutoModelForCausalLM.from_pretrained("HaitameLaframe/Mistral-v3-7b-coder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HaitameLaframe/Mistral-v3-7b-coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HaitameLaframe/Mistral-v3-7b-coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HaitameLaframe/Mistral-v3-7b-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HaitameLaframe/Mistral-v3-7b-coder

SGLang

How to use HaitameLaframe/Mistral-v3-7b-coder 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 "HaitameLaframe/Mistral-v3-7b-coder" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HaitameLaframe/Mistral-v3-7b-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "HaitameLaframe/Mistral-v3-7b-coder" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HaitameLaframe/Mistral-v3-7b-coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Unsloth Desktop
Docker Model Runner
How to use HaitameLaframe/Mistral-v3-7b-coder with Docker Model Runner:
```
docker model run hf.co/HaitameLaframe/Mistral-v3-7b-coder
```

Mistral-v3-7b-coder

Commit History

Jan 3, 2025

17:01
Adding `safetensors` variant of this model⁠

SFconvertbot

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17ce5e3

Jan 2, 2025

23:56
Trained with Unsloth

HaitameLaframe

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Jan 1, 2025

17:51
Trained with Unsloth

HaitameLaframe

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17:32
Trained with Unsloth

HaitameLaframe

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15:31
Trained with Unsloth

HaitameLaframe

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15:21
Upload tokenizer

HaitameLaframe

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15:21
Upload README.md with huggingface_hub

HaitameLaframe

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15:21
initial commit

HaitameLaframe

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6c39e9f

Commit History

Jan 3, 2025

Adding `safetensors` variant of this model⁠

Jan 2, 2025

Trained with Unsloth

Jan 1, 2025

Trained with Unsloth

Trained with Unsloth

Trained with Unsloth

Upload tokenizer

Upload README.md with huggingface_hub

initial commit