Instructions to use YeungNLP/firefly-llama2-13b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use YeungNLP/firefly-llama2-13b with Transformers:

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

pipe = pipeline("text-generation", model="YeungNLP/firefly-llama2-13b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("YeungNLP/firefly-llama2-13b")
model = AutoModelForCausalLM.from_pretrained("YeungNLP/firefly-llama2-13b", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use YeungNLP/firefly-llama2-13b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "YeungNLP/firefly-llama2-13b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "YeungNLP/firefly-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/YeungNLP/firefly-llama2-13b

SGLang

How to use YeungNLP/firefly-llama2-13b 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 "YeungNLP/firefly-llama2-13b" \
    --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": "YeungNLP/firefly-llama2-13b",
		"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 "YeungNLP/firefly-llama2-13b" \
        --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": "YeungNLP/firefly-llama2-13b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use YeungNLP/firefly-llama2-13b with Docker Model Runner:
```
docker model run hf.co/YeungNLP/firefly-llama2-13b
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

firefly-llama2-13b模型，目前在Open LLM排行榜上，以62分的成绩，在所有13B模型中排名第三，且仅比榜首略低0.5分。

该模型是个英文模型，仅使用英文数据训练，未针对中文扩充词表

值得注意的是，我们采用了qlora技术，比其他排名前列的模型，需要更少的训练资源，24G的显卡即可训练百亿模型。

训练代码以及更多细节，欢迎关注我们的开源中文大模型项目Firefly，以及公众号【YeungNLP】

Downloads last month: 45

YeungNLP
/

firefly-llama2-13b

Spaces using YeungNLP/firefly-llama2-13b 30