fn-aka-mur/japanese_alpaca_data
Viewer • Updated • 52k • 342 • 16
How to use takumi123xxx/qwen3-0.6b-japanese-lora with PEFT:
from peft import PeftModel
from transformers import AutoModelForCausalLM
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "takumi123xxx/qwen3-0.6b-japanese-lora")🤖 This model was fine-tuned using Claude Code - Anthropic's official CLI for Claude.
This is a LoRA adapter for Qwen/Qwen3-0.6B, fine-tuned on Japanese instruction data.
| Parameter | Value |
|---|---|
| Base Model | Qwen/Qwen3-0.6B |
| Dataset | fujiki/japanese_alpaca_data |
| Method | LoRA (PEFT) |
| LoRA Rank (r) | 8 |
| LoRA Alpha | 16 |
| Target Modules | q_proj, v_proj |
| Trainable Parameters | 1,146,880 (0.19%) |
| Training Steps | 30 (test run) |
| Final Loss | 2.27 |
from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-0.6B")
model = PeftModel.from_pretrained(base_model, "takumi123xxx/qwen3-0.6b-japanese-lora")
tokenizer = AutoTokenizer.from_pretrained("takumi123xxx/qwen3-0.6b-japanese-lora")
🤖 Generated with Claude Code
This model was created as a demonstration of fine-tuning LLMs using Claude Code's NVIDIA GPU training MCP server integration.