Instructions to use ContaAI/ContaLLM-Beauty-8B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ContaAI/ContaLLM-Beauty-8B-Instruct-4bit with Transformers:

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

pipe = pipeline("text-generation", model="ContaAI/ContaLLM-Beauty-8B-Instruct-4bit")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ContaAI/ContaLLM-Beauty-8B-Instruct-4bit")
model = AutoModelForCausalLM.from_pretrained("ContaAI/ContaLLM-Beauty-8B-Instruct-4bit")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ContaAI/ContaLLM-Beauty-8B-Instruct-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ContaAI/ContaLLM-Beauty-8B-Instruct-4bit

SGLang

How to use ContaAI/ContaLLM-Beauty-8B-Instruct-4bit 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 "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit" \
    --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": "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit",
		"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 "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit" \
        --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": "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ContaAI/ContaLLM-Beauty-8B-Instruct-4bit with Docker Model Runner:
```
docker model run hf.co/ContaAI/ContaLLM-Beauty-8B-Instruct-4bit
```

Tonya66 commited on Dec 4, 2024

Commit

ef883bc

verified ·

1 Parent(s): 789a3e3

update readme.txt

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README.md +3 -3

README.md CHANGED Viewed

@@ -46,7 +46,7 @@ To load the model with HuggingFace, use the following snippet:
 ```
 from transformers import AutoModelForCausalLM
-model = AutoModelForCausalLM.from_pretrained("ContaAI/ContaLLM-Beauty-8B-Instruct")
 ```
@@ -80,8 +80,8 @@ user_prompt = '营销需求：美白水乳推荐，推广HBN原白水乳。\n关
 ```
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
-model_name = "ContaAI/ContaLLM-Beauty-8B-Instruct"
-model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 system_prompt = '请根据用户提供的营销需求和其他信息写一篇美妆护肤行业的营销推文。'

 ```
 from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained("ContaAI/ContaLLM-Beauty-8B-Instruct-4bit")
 ```
 ```
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
+model_name = "ContaAI/ContaLLM-Beauty-8B-Instruct-4bit"
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
 tokenizer = AutoTokenizer.from_pretrained(model_name)
 system_prompt = '请根据用户提供的营销需求和其他信息写一篇美妆护肤行业的营销推文。'