Instructions to use zai-org/LongCite-llama3.1-8b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zai-org/LongCite-llama3.1-8b with Transformers:

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

pipe = pipeline("text-generation", model="zai-org/LongCite-llama3.1-8b", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("zai-org/LongCite-llama3.1-8b", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("zai-org/LongCite-llama3.1-8b", trust_remote_code=True)

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use zai-org/LongCite-llama3.1-8b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zai-org/LongCite-llama3.1-8b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zai-org/LongCite-llama3.1-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zai-org/LongCite-llama3.1-8b

SGLang

How to use zai-org/LongCite-llama3.1-8b 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 "zai-org/LongCite-llama3.1-8b" \
    --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": "zai-org/LongCite-llama3.1-8b",
		"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 "zai-org/LongCite-llama3.1-8b" \
        --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": "zai-org/LongCite-llama3.1-8b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zai-org/LongCite-llama3.1-8b with Docker Model Runner:
```
docker model run hf.co/zai-org/LongCite-llama3.1-8b
```

NeoZ123 commited on Dec 16, 2024

Commit

4dcf5f0

verified ·

1 Parent(s): c30dc0f

Update modeling_llama.py

Browse files

Files changed (1) hide show

modeling_llama.py +1 -1

modeling_llama.py CHANGED Viewed

@@ -1261,7 +1261,7 @@ class LlamaForCausalLM(LlamaPreTrainedModel):
         def text_split_by_punctuation(original_text, return_dict=False):
             # text = re.sub(r'([a-z])\.([A-Z])', r'\1. \2', original_text)  # separate period without space
             text = original_text
-            custom_sent_tokenizer = PunktSentenceTokenizer(text)
             punctuations = r"([。；！？])"  # For Chinese support
             separated = custom_sent_tokenizer.tokenize(text)

         def text_split_by_punctuation(original_text, return_dict=False):
             # text = re.sub(r'([a-z])\.([A-Z])', r'\1. \2', original_text)  # separate period without space
             text = original_text
+            custom_sent_tokenizer = PunktSentenceTokenizer()
             punctuations = r"([。；！？])"  # For Chinese support
             separated = custom_sent_tokenizer.tokenize(text)