Instructions to use CultriX/MistralTrix-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use CultriX/MistralTrix-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="CultriX/MistralTrix-v1")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("CultriX/MistralTrix-v1") model = AutoModelForCausalLM.from_pretrained("CultriX/MistralTrix-v1") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use CultriX/MistralTrix-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "CultriX/MistralTrix-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "CultriX/MistralTrix-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/CultriX/MistralTrix-v1
- SGLang
How to use CultriX/MistralTrix-v1 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 "CultriX/MistralTrix-v1" \ --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": "CultriX/MistralTrix-v1", "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 "CultriX/MistralTrix-v1" \ --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": "CultriX/MistralTrix-v1", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use CultriX/MistralTrix-v1 with Docker Model Runner:
docker model run hf.co/CultriX/MistralTrix-v1
This is not a 7B. It's a ~9B. Please label appropriately.
Like several of the top '7B' models on the leaderboard, this is actually a 9B, downstream of https://huggingface.co/zyh3826/GML-Mistral-merged-v1, a merge that combined the first 32 layers (ie all of them) of one Mistral-7B finetune with the last 8 layers of another Mistral finetune, creating a model that is about 9B parameters.
It is helpful to label model sizes appropriately. Better would be if Huggingface labeled models based on their file size and bpw, instead of allowing for these sorts of mistakes to occur and proliferate, as one mislabeled model begets others derived from it.
Some responses from this model appear better considered than those of some other 7B models, but this model employs 25% more layers to achieve its winning performance. I agree that this model best be labeled as a 9B model.