Instructions to use TheBloke/falcon-40b-instruct-GPTQ with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TheBloke/falcon-40b-instruct-GPTQ with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TheBloke/falcon-40b-instruct-GPTQ", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("TheBloke/falcon-40b-instruct-GPTQ", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use TheBloke/falcon-40b-instruct-GPTQ with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TheBloke/falcon-40b-instruct-GPTQ" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TheBloke/falcon-40b-instruct-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TheBloke/falcon-40b-instruct-GPTQ
- SGLang
How to use TheBloke/falcon-40b-instruct-GPTQ 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 "TheBloke/falcon-40b-instruct-GPTQ" \ --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": "TheBloke/falcon-40b-instruct-GPTQ", "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 "TheBloke/falcon-40b-instruct-GPTQ" \ --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": "TheBloke/falcon-40b-instruct-GPTQ", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TheBloke/falcon-40b-instruct-GPTQ with Docker Model Runner:
docker model run hf.co/TheBloke/falcon-40b-instruct-GPTQ
GGML?
is it possible to quantize it for llama.cpp?
Not yet - there's no GGML support yet.
As soon as there is I will upload GGMLs.
Any update on ggml versions?
No not yet I'm afraid. No-one has started work on it, to my knowledge.
You can track the discussions here: https://github.com/ggerganov/llama.cpp/issues/1602
Can't wait for GGML version of wizard-falcon 40b. This is gonna be big.
Looks like they have got quantised GGML working for falcon in a branch: https://github.com/ggerganov/llama.cpp/issues/1602#issuecomment-1580330824
Someone posted quantized versions of the 7B Falcon model: https://huggingface.co/RachidAR/falcon-7B-ggml/tree/main