Text Generation
Transformers
Safetensors
mistral
alignment-handbook
trl
sft
Generated from Trainer
text-generation-inference
Instructions to use Minbyul/mistral-7b-wo-kqa_silver_wogold-sft with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Minbyul/mistral-7b-wo-kqa_silver_wogold-sft with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Minbyul/mistral-7b-wo-kqa_silver_wogold-sft")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Minbyul/mistral-7b-wo-kqa_silver_wogold-sft") model = AutoModelForCausalLM.from_pretrained("Minbyul/mistral-7b-wo-kqa_silver_wogold-sft", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Minbyul/mistral-7b-wo-kqa_silver_wogold-sft with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Minbyul/mistral-7b-wo-kqa_silver_wogold-sft" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Minbyul/mistral-7b-wo-kqa_silver_wogold-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Minbyul/mistral-7b-wo-kqa_silver_wogold-sft
- SGLang
How to use Minbyul/mistral-7b-wo-kqa_silver_wogold-sft 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 "Minbyul/mistral-7b-wo-kqa_silver_wogold-sft" \ --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": "Minbyul/mistral-7b-wo-kqa_silver_wogold-sft", "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 "Minbyul/mistral-7b-wo-kqa_silver_wogold-sft" \ --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": "Minbyul/mistral-7b-wo-kqa_silver_wogold-sft", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Minbyul/mistral-7b-wo-kqa_silver_wogold-sft with Docker Model Runner:
docker model run hf.co/Minbyul/mistral-7b-wo-kqa_silver_wogold-sft
Download training_args.bin from Minbyul/mistral-7b-wo-kqa_silver_wogold-sft: direct link, hf CLI and curl.
- Browser
- Download file 6.2 kB
-
https://huggingface.co/Minbyul/mistral-7b-wo-kqa_silver_wogold-sft/resolve/main/training_args.bin
- Command line
-
hf download hf://Minbyul/mistral-7b-wo-kqa_silver_wogold-sft/training_args.bin
-
curl -L -o training_args.bin https://huggingface.co/Minbyul/mistral-7b-wo-kqa_silver_wogold-sft/resolve/main/training_args.bin
6.2 kB
- Xet hash:
- 9ba5c347457b12621ebd924ae2d435af306a353233b73731672fa41226442640
- Size of remote file:
- 6.2 kB
- SHA256:
- 97bdbf7d7e4cdeae67c0eb68582933fc4088def4cccec74c6a8e5c79481a9e39
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.