Instructions to use INC4AI/falcon-7b-sq-int8-inc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use INC4AI/falcon-7b-sq-int8-inc with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="INC4AI/falcon-7b-sq-int8-inc", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("INC4AI/falcon-7b-sq-int8-inc", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("INC4AI/falcon-7b-sq-int8-inc", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use INC4AI/falcon-7b-sq-int8-inc with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "INC4AI/falcon-7b-sq-int8-inc" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "INC4AI/falcon-7b-sq-int8-inc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/INC4AI/falcon-7b-sq-int8-inc
- SGLang
How to use INC4AI/falcon-7b-sq-int8-inc 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 "INC4AI/falcon-7b-sq-int8-inc" \ --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": "INC4AI/falcon-7b-sq-int8-inc", "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 "INC4AI/falcon-7b-sq-int8-inc" \ --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": "INC4AI/falcon-7b-sq-int8-inc", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use INC4AI/falcon-7b-sq-int8-inc with Docker Model Runner:
docker model run hf.co/INC4AI/falcon-7b-sq-int8-inc
Download pytorch_model.bin from INC4AI/falcon-7b-sq-int8-inc: direct link, hf CLI and curl.
- Browser
- Download file 8.12 GB
-
https://huggingface.co/INC4AI/falcon-7b-sq-int8-inc/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://INC4AI/falcon-7b-sq-int8-inc/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/INC4AI/falcon-7b-sq-int8-inc/resolve/main/pytorch_model.bin
8.12 GB
- Xet hash:
- 7b0f8299d3c4a15dce7fb6182cb253c3b5a4d4789eea09ccfeb25c4e6d162046
- Size of remote file:
- 8.12 GB
- SHA256:
- 0814d5d9ab159425988684c3d2456e037adead3cfac85aa9cd4cf229249d7df1
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