Instructions to use z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat with Transformers:

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

pipe = pipeline("text-generation", model="z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat", trust_remote_code=True)
model = AutoModel.from_pretrained("z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat", trust_remote_code=True, device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat

SGLang

How to use z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat 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 "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat" \
    --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": "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat",
		"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 "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat" \
        --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": "z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat with Docker Model Runner:
```
docker model run hf.co/z-lab/LLaMA3.1-8B-Instruct-DFlash-UltraChat
```

LLaMA3.1-8B-Instruct-DFlash-UltraChat / model_index.json

kashif HF Staff

Add diffusers model_index.json and scheduler config for DFlashPipeline.from_pretrained

12aadeb verified 28 days ago

Raw

History Blame

219 Bytes

	{
	"_class_name": "DFlashPipeline",
	"_diffusers_version": "0.40.0.dev0",
	"_target_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
	"scheduler": [
	"diffusers",
	"DFlashTokenDiffusionScheduler"
	]
	}