Instructions to use TaylorAI/Flash-Llama-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TaylorAI/Flash-Llama-3B with Transformers:

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

pipe = pipeline("text-generation", model="TaylorAI/Flash-Llama-3B", trust_remote_code=True)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TaylorAI/Flash-Llama-3B", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("TaylorAI/Flash-Llama-3B", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TaylorAI/Flash-Llama-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/TaylorAI/Flash-Llama-3B

SGLang

How to use TaylorAI/Flash-Llama-3B 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 "TaylorAI/Flash-Llama-3B" \
    --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": "TaylorAI/Flash-Llama-3B",
		"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 "TaylorAI/Flash-Llama-3B" \
        --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": "TaylorAI/Flash-Llama-3B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TaylorAI/Flash-Llama-3B with Docker Model Runner:
```
docker model run hf.co/TaylorAI/Flash-Llama-3B
```

Flash-Llama-3B

Commit History

Sep 6, 2023

14:39
Adding `safetensors` variant of this model⁠

SFconvertbot

ad997fe

Aug 29, 2023

23:44
Upload modeling_flash_llama.py

andersonbcdefg

263ce5b

Aug 26, 2023

01:13
Upload modeling_flash_llama.py

andersonbcdefg

a640dac

Aug 25, 2023

04:49
Update config.json

andersonbcdefg

b4c7bb4
04:48
Upload modeling_flash_llama.py

andersonbcdefg

87cede6

Aug 13, 2023

08:43
Upload LlamaForCausalLM

andersonbcdefg

22143dc
05:08
Upload config

andersonbcdefg

51c454d
05:02
Upload tokenizer

andersonbcdefg

830ec8c
04:59
Upload modeling_flash_llama.py

andersonbcdefg

638ffb6
04:58
Update config.json

andersonbcdefg

92d26a4
04:58
Upload config

andersonbcdefg

a946cb3
04:49
initial commit

andersonbcdefg

b90f76d

Commit History

Sep 6, 2023

Adding `safetensors` variant of this model⁠

Aug 29, 2023

Upload modeling_flash_llama.py

Aug 26, 2023

Upload modeling_flash_llama.py

Aug 25, 2023

Update config.json

Upload modeling_flash_llama.py

Aug 13, 2023

Upload LlamaForCausalLM

Upload config

Upload tokenizer

Upload modeling_flash_llama.py

Update config.json

Upload config

initial commit