File size: 1,626 Bytes
fcb44cc | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 | ---
library_name: peft
model_name: minicpm5-coffee-lora
tags:
- peft
- lora
- sft
- coffee
- transformers
- trl
license: apache-2.0
base_model: openbmb/MiniCPM5-1B
pipeline_tag: text-generation
---
# MiniCPM5-1B Coffee LoRA
Base model: [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B)
This LoRA adapter fine-tunes MiniCPM5-1B for specialty coffee knowledge Q&A.
Trained on [ynanxiu/coffee-sft-dataset](https://huggingface.co/datasets/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
```python
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
|