Instructions to use FlagAlpha/Llama3-Chinese-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FlagAlpha/Llama3-Chinese-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="FlagAlpha/Llama3-Chinese-8B-Instruct", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("FlagAlpha/Llama3-Chinese-8B-Instruct", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("FlagAlpha/Llama3-Chinese-8B-Instruct", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FlagAlpha/Llama3-Chinese-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FlagAlpha/Llama3-Chinese-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FlagAlpha/Llama3-Chinese-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/FlagAlpha/Llama3-Chinese-8B-Instruct

SGLang

How to use FlagAlpha/Llama3-Chinese-8B-Instruct 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 "FlagAlpha/Llama3-Chinese-8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FlagAlpha/Llama3-Chinese-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "FlagAlpha/Llama3-Chinese-8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FlagAlpha/Llama3-Chinese-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use FlagAlpha/Llama3-Chinese-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/FlagAlpha/Llama3-Chinese-8B-Instruct
```

wuyongyu commited on Apr 24, 2024

Commit

d76c4a5

verified ·

1 Parent(s): 8d84de9

Update README.md

Browse files

Files changed (1) hide show

README.md +35 -2

README.md CHANGED Viewed

@@ -16,11 +16,44 @@ Llama3-Chinese-8B-Instruct基于Llama3-8B中文微调对话模型，由Llama中
 ```
 import transformers
 import torch
 model_id = "FlagAlpha/Llama3-Chinese-8B-Instruct"
 pipeline = transformers.pipeline(
-    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.float16}, device_map="auto"
 )
-print(pipeline("介绍一下机器学习?"))
 ```

 ```
 import transformers
 import torch
 model_id = "FlagAlpha/Llama3-Chinese-8B-Instruct"
 pipeline = transformers.pipeline(
+    "text-generation",
+    model=model_id,
+    model_kwargs={"torch_dtype": torch.float16},
+    device="cuda",
 )
+messages = [{"role": "system", "content": ""}]
+messages.append(
+                {"role": "user", "content": "介绍一下机器学习"}
+            )
+prompt = pipeline.tokenizer.apply_chat_template(
+        messages,
+        tokenize=False,
+        add_generation_prompt=True
+    )
+terminators = [
+        pipeline.tokenizer.eos_token_id,
+        pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    ]
+outputs = pipeline(
+    prompt,
+    max_new_tokens=512,
+    eos_token_id=terminators,
+    do_sample=True,
+    temperature=0.6,
+    top_p=0.9
+)
+content = outputs[0]["generated_text"][len(prompt):]
+print(content)
 ```