Instructions to use Deci/DeciLM-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Deci/DeciLM-7B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Deci/DeciLM-7B", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Deci/DeciLM-7B", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Deci/DeciLM-7B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Deci/DeciLM-7B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deci/DeciLM-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Deci/DeciLM-7B
- SGLang
How to use Deci/DeciLM-7B 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 "Deci/DeciLM-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deci/DeciLM-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "Deci/DeciLM-7B" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Deci/DeciLM-7B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Deci/DeciLM-7B with Docker Model Runner:
docker model run hf.co/Deci/DeciLM-7B
Commit History
-
Adding Evaluation Results
Verified signatureThis is an automated PR created with https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr The purpose of this PR is to add evaluation results from the Open LLM Leaderboard to your model card. If you encounter any issues, please report them to https://huggingface.co/spaces/Weyaxi/open-llm-leaderboard-results-pr/discussionsa4e1c3f
-
Update README.md
Verified signaturec3c9f42 -
Update README.md
Verified signaturebf449a2
-
Update README.md
Verified signaturec3aef1e
-
Update README.md
Added text-generation pipeline documentationcdd1aef
-
added chat_template
8d9cdb8 -
added support for text-generation pipeline
83e1abe -
add model_type to config.json
0be2d64
-
Update README.md
62d1c2f -
Update transformers_v4_35_2__modeling_llama.py
9817f85
-
change model_type to deci
761ba47
-
Fixed unnecessary flash_attn dependency
e790e33 -
Added README.md
89289b0
-
Upload benchmark_hf_model.py
b943e32 -
Upload folder using huggingface_hub
0ae6e17 -
initial commit
d41a24b