Instructions to use ynanxiu/minicpm5-coffee-lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ynanxiu/minicpm5-coffee-lora with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B")
model = PeftModel.from_pretrained(base_model, "ynanxiu/minicpm5-coffee-lora")

Transformers

How to use ynanxiu/minicpm5-coffee-lora with Transformers:

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

pipe = pipeline("text-generation", model="ynanxiu/minicpm5-coffee-lora")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("ynanxiu/minicpm5-coffee-lora", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ynanxiu/minicpm5-coffee-lora with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ynanxiu/minicpm5-coffee-lora"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ynanxiu/minicpm5-coffee-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ynanxiu/minicpm5-coffee-lora

SGLang

How to use ynanxiu/minicpm5-coffee-lora 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 "ynanxiu/minicpm5-coffee-lora" \
    --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": "ynanxiu/minicpm5-coffee-lora",
		"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 "ynanxiu/minicpm5-coffee-lora" \
        --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": "ynanxiu/minicpm5-coffee-lora",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ynanxiu/minicpm5-coffee-lora with Docker Model Runner:
```
docker model run hf.co/ynanxiu/minicpm5-coffee-lora
```

MiniCPM5-1B Coffee LoRA

Base model: openbmb/MiniCPM5-1B

This LoRA adapter fine-tunes MiniCPM5-1B for specialty coffee knowledge Q&A. Trained on ynanxiu/coffee-sft-dataset.

Training

Method: LoRA (r=16, alpha=32), BF16
Data: coffee-sft-dataset (80/10/10 train/val/test split)
Epochs: 3
Hardware: RTX 4060 8GB
Framework: TRL SFTTrainer + PEFT

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base = AutoModelForCausalLM.from_pretrained(
    "openbmb/MiniCPM5-1B",
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True
)
model = PeftModel.from_pretrained(base, "ynanxiu/minicpm5-coffee-lora")
tokenizer = AutoTokenizer.from_pretrained("openbmb/MiniCPM5-1B", trust_remote_code=True)

messages = [{"role": "user", "content": "阿拉比卡和罗布斯塔的区别是什么？"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Framework versions

PEFT 0.19.1
TRL 0.24.0
Transformers 5.5.0
PyTorch 2.6.0+cu124

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