Instructions to use flytech/devchat-llama-7b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use flytech/devchat-llama-7b with Transformers:

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

pipe = pipeline("text-generation", model="flytech/devchat-llama-7b")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("flytech/devchat-llama-7b")
model = AutoModelForMultimodalLM.from_pretrained("flytech/devchat-llama-7b")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use flytech/devchat-llama-7b with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/flytech/devchat-llama-7b

SGLang

How to use flytech/devchat-llama-7b 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 "flytech/devchat-llama-7b" \
    --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": "flytech/devchat-llama-7b",
		"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 "flytech/devchat-llama-7b" \
        --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": "flytech/devchat-llama-7b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use flytech/devchat-llama-7b with Docker Model Runner:
```
docker model run hf.co/flytech/devchat-llama-7b
```

devchat-llama-7b / events.out.tfevents.1694220084.ef7f542c87ff.575017.0

flytech

Training in progress, step 100

4f5f066 almost 3 years ago

download

history blame contribute delete

4.5 kB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: f77dc7667beb840ba64b73b9556f97e3c94088dc7c9b45e307e1c10bade4b5ae
Size of remote file:: 4.5 kB
SHA256:: 3a0c9317a91de5aa9531ceb311b8155e7f1e9a22ec5860296b8ff81994f6776e

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.