Instructions to use britllm/britllm-3b-v0.1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use britllm/britllm-3b-v0.1 with Transformers:

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

pipe = pipeline("text-generation", model="britllm/britllm-3b-v0.1")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("britllm/britllm-3b-v0.1")
model = AutoModelForCausalLM.from_pretrained("britllm/britllm-3b-v0.1")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use britllm/britllm-3b-v0.1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "britllm/britllm-3b-v0.1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "britllm/britllm-3b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/britllm/britllm-3b-v0.1

SGLang

How to use britllm/britllm-3b-v0.1 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 "britllm/britllm-3b-v0.1" \
    --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": "britllm/britllm-3b-v0.1",
		"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 "britllm/britllm-3b-v0.1" \
        --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": "britllm/britllm-3b-v0.1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use britllm/britllm-3b-v0.1 with Docker Model Runner:
```
docker model run hf.co/britllm/britllm-3b-v0.1
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

This is a raw, pretrained model, which should be further finetuned for most use cases.

Visit our webpage for detailed information: https://llm.org.uk .

Contact
Email: nlp-britllm@cs.ucl.ac.uk

Acknowledgements
We would like to acknowledge the support of DiRAC (Distributed Research using Advanced Computing), Microsoft Research's Accelerate Foundation Models Research Grant, the UCL Centre for Artificial Intelligence, and the Generative Models AI Hub.

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