Instructions to use varox34/7B-Model_Stock with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use varox34/7B-Model_Stock with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="varox34/7B-Model_Stock")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("varox34/7B-Model_Stock") model = AutoModelForCausalLM.from_pretrained("varox34/7B-Model_Stock", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use varox34/7B-Model_Stock with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "varox34/7B-Model_Stock" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "varox34/7B-Model_Stock", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/varox34/7B-Model_Stock
- SGLang
How to use varox34/7B-Model_Stock 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 "varox34/7B-Model_Stock" \ --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": "varox34/7B-Model_Stock", "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 "varox34/7B-Model_Stock" \ --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": "varox34/7B-Model_Stock", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use varox34/7B-Model_Stock with Docker Model Runner:
docker model run hf.co/varox34/7B-Model_Stock
File size: 243 Bytes
4a52f9a | 1 2 3 4 5 6 7 8 | models:
- model: NeverSleep/Noromaid-7B-0.4-DPO
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
- model: Undi95/Toppy-M-7B
- model: Epiculous/Fett-uccine-7B
merge_method: model_stock
base_model: SanjiWatsuki/Kunoichi-DPO-v2-7B
dtype: bfloat16 |