Instructions to use TechxGenus/Mistral-7B-v0.2-hf-AWQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TechxGenus/Mistral-7B-v0.2-hf-AWQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TechxGenus/Mistral-7B-v0.2-hf-AWQ")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TechxGenus/Mistral-7B-v0.2-hf-AWQ") model = AutoModelForCausalLM.from_pretrained("TechxGenus/Mistral-7B-v0.2-hf-AWQ") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TechxGenus/Mistral-7B-v0.2-hf-AWQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TechxGenus/Mistral-7B-v0.2-hf-AWQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TechxGenus/Mistral-7B-v0.2-hf-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TechxGenus/Mistral-7B-v0.2-hf-AWQ
- SGLang
How to use TechxGenus/Mistral-7B-v0.2-hf-AWQ 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 "TechxGenus/Mistral-7B-v0.2-hf-AWQ" \ --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": "TechxGenus/Mistral-7B-v0.2-hf-AWQ", "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 "TechxGenus/Mistral-7B-v0.2-hf-AWQ" \ --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": "TechxGenus/Mistral-7B-v0.2-hf-AWQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TechxGenus/Mistral-7B-v0.2-hf-AWQ with Docker Model Runner:
docker model run hf.co/TechxGenus/Mistral-7B-v0.2-hf-AWQ
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Check out the documentation for more information.
AWQ quantized version of Mistral-7B-v0.2-hf model.
Mistral 7b v0.2 with attention_dropout=0.6, for training purposes
Conversion process:
Download original weights from https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar
You may need to copy the tokenizer.model from Mistral-7B-Instruct-v0.2 repo.
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