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

Libraries

How to use ContaAI/ContaLLM-Food-Beverage-8B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ContaAI/ContaLLM-Food-Beverage-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("ContaAI/ContaLLM-Food-Beverage-8B-Instruct")
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 Settings

vLLM

How to use ContaAI/ContaLLM-Food-Beverage-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ContaAI/ContaLLM-Food-Beverage-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": "ContaAI/ContaLLM-Food-Beverage-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ContaAI/ContaLLM-Food-Beverage-8B-Instruct

SGLang

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

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

Tonya77 commited on Dec 19, 2024

Commit

f237656

verified ·

1 Parent(s): 27a1e54

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@@ -73,7 +73,14 @@ Users can enter the required marketing needs according to their own needs, non-r
 Example:
 ```
-user_prompt = '营销需求：美白水乳推荐，推广HBN原白水乳。\n关键词：HBN原白水乳\n话题： 分享护肤 提亮肤色\n标签：爱情、浪漫\n话题： 分享护肤 提亮肤色\n人设：美白水乳推荐，推广HBN原白水乳。\n相关素材：美白水乳推荐，推广HBN原白水乳。\n内容长度：较长\n'
 ```
 ### Use example (with template)

 Example:
 ```
+user_prompt = """营销需求：夏日清凉，日料风味体验
+选品：清新柠檬寿司卷
+选品知识库：1、选用新鲜的三文鱼和牛油果，搭配清爽柠檬汁，口感层次丰富。2、低脂健康，适合健身人士。3、每份仅含200大卡，轻松享受美味。
+关键词：日料、寿司、健康饮食、夏日美食
+主推卖点：清新健康
+主推场景：夏日聚会
+标签：#日料# #寿司# #健康美食
+文章类型：美食推荐"""
 ```
 ### Use example (with template)