Instructions to use OpenAssistant/falcon-7b-sft-mix-2000 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OpenAssistant/falcon-7b-sft-mix-2000 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="OpenAssistant/falcon-7b-sft-mix-2000", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OpenAssistant/falcon-7b-sft-mix-2000", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use OpenAssistant/falcon-7b-sft-mix-2000 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OpenAssistant/falcon-7b-sft-mix-2000"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OpenAssistant/falcon-7b-sft-mix-2000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/OpenAssistant/falcon-7b-sft-mix-2000

SGLang

How to use OpenAssistant/falcon-7b-sft-mix-2000 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 "OpenAssistant/falcon-7b-sft-mix-2000" \
    --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": "OpenAssistant/falcon-7b-sft-mix-2000",
		"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 "OpenAssistant/falcon-7b-sft-mix-2000" \
        --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": "OpenAssistant/falcon-7b-sft-mix-2000",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use OpenAssistant/falcon-7b-sft-mix-2000 with Docker Model Runner:
```
docker model run hf.co/OpenAssistant/falcon-7b-sft-mix-2000
```

Fine Tuning

by carlosmtoro - opened Jun 15, 2023

Discussion

carlosmtoro

Jun 15, 2023

Can fine tuning be done on this model, which is a falcon-7b fine tuning? Is there documentation about it? Examples?

cetusian

Jul 3, 2023

Hey @carlosmtoro ! Some helpful resources about fine-tuning Falcon that helped me: https://huggingface.co/blog/falcon and https://github.com/rmihaylov/falcontune.

cmp-nct

Jul 5, 2023

The tunes appear to work very well
Can you explain a bit more how the different variants are to be chosen from ? (sft-mix etc)
Also you have a system prompt but it's not documented, is that just reserved for the future or is it part of the fine tuning ?

neuralworm

Jul 18, 2023

•

edited Jul 18, 2023

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