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tatasauce4life
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Model Details
Model Provider
tatasauce4life
Model Tree
unsloth/Qwen3-4B-Base-unsloth-bnb-4bit
Input Modalities
Output Modalities
Supported Functionality
unsloth/Qwen3-4B-Base-unsloth-bnb-4bittatasauce4life/qwen3-4b-en2zh-lorajson
{ "prompt": "英文原文", "chosen": "人工譯文（高品質）", "rejected": "機器翻譯（低品質）"}text
### English{english_text}### Chinese{chinese_translation}| 參數 | 值 |
|---|---|
| ORPO beta | 0.05 |
| Learning Rate | 5e-05 |
| Epochs | 3 |
| Batch Size | 1 |
| Gradient Accumulation | 16 |
| Max Sequence Length | 1024 |
| LoRA r | 16 |
| LoRA alpha | 32 |
| Epoch | Training Loss | Validation Loss | Runtime | Samples Per Second | Steps Per Second | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen | Nll Loss | Log Odds Ratio | Log Odds Chosen |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 1.832741 | 1.839185 | 89.071000 | 4.087000 | 4.087000 | -0.066546 | -0.058645 | 0.329670 | -0.007902 | -1.172890 | -1.330927 | 7.650677 | 7.531346 | 1.794205 | -0.899610 | -0.191442 |
| 2 | 1.647559 | 1.800575 | 87.179100 | 4.175000 | 4.175000 | -0.065111 | -0.066198 | 0.423077 | 0.001087 | -1.323966 | -1.302224 | 6.627131 | 6.495617 | 1.760711 | -0.797270 | 0.070304 |
| 3 | 1.594447 | 1.808474 | 88.543700 | 4.111000 | 4.111000 | -0.065936 | -0.070564 | 0.450549 | 0.004629 | -1.411288 | -1.318715 | 6.002413 | 5.855793 | 1.770063 | -0.768214 | 0.175277 |
此模型是 LoRA adapter，包含 SFT + ORPO 的累積更新。
python
from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name="tatasauce4life/qwen3-4b-en2zh-orpo_beta0.3", max_seq_length=1024, load_in_4bit=True,)FastLanguageModel.for_inference(model)python
from peft import AutoPeftModelForCausalLMfrom transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained( "tatasauce4life/qwen3-4b-en2zh-orpo_beta0.3", load_in_4bit=True,)tokenizer = AutoTokenizer.from_pretrained("tatasauce4life/qwen3-4b-en2zh-orpo_beta0.3")注意: 此 adapter 的 base model 為
unsloth/Qwen3-4B-Base-unsloth-bnb-4bit。 adapter 包含 SFT 階段與 ORPO 階段的累積權重更新。
python
prompt = "### English\n{your_english_text}\n### Chinese\n"inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)result = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(result){ "prompt": "英文原文", "chosen": "人工譯文（高品質）", "rejected": "機器翻譯（低品質）"}### English{english_text}### Chinese{chinese_translation}from unsloth import FastLanguageModel model, tokenizer = FastLanguageModel.from_pretrained( model_name="tatasauce4life/qwen3-4b-en2zh-orpo_beta0.3", max_seq_length=1024, load_in_4bit=True,)FastLanguageModel.for_inference(model)from peft import AutoPeftModelForCausalLMfrom transformers import AutoTokenizer model = AutoPeftModelForCausalLM.from_pretrained( "tatasauce4life/qwen3-4b-en2zh-orpo_beta0.3", load_in_4bit=True,)tokenizer = AutoTokenizer.from_pretrained("tatasauce4life/qwen3-4b-en2zh-orpo_beta0.3")prompt = "### English\n{your_english_text}\n### Chinese\n"inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False)result = tokenizer.decode(outputs[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)print(result)