| --- |
| base_model: OpenOneRec/OneReason-0.8B-pretrain-competition |
| library_name: peft |
| pipeline_tag: text-generation |
| tags: |
| - recommendation |
| - lora |
| - generative-recommendation |
| - itemic-token |
| - qwen3 |
| - llama-factory |
| - transformers |
| license: apache-2.0 |
| --- |
| |
| # OneReason-0.8B-LoRA-ExpA |
|
|
| LoRA adapter fine-tuned from [OpenOneRec/OneReason-0.8B-pretrain-competition](https://huggingface.co/OpenOneRec/OneReason-0.8B-pretrain-competition) on the Kuaishou Explorer LLM-Rec Challenge dataset. |
|
|
| ## Model Details |
|
|
| - **Base Model:** OpenOneRec/OneReason-0.8B-pretrain-competition (Qwen3-0.8B) |
| - **Fine-tuning Method:** LoRA (rank=16, alpha=32) |
| - **Target Modules:** q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj |
| - **Training Data:** Kuaishou LLM-Rec Challenge competition dataset (32K samples) |
| - **Training Steps:** 4,660 / 11,571 (~40% of 3 epochs) |
| - **Hardware:** NVIDIA RTX 4090 (47GB VRAM) |
| - **Training Time:** ~2h 41min |
| |
| ## Usage |
| |
| ```python |
| from transformers import AutoModelForCausalLM, AutoTokenizer |
| from peft import PeftModel |
| |
| base_model = "OpenOneRec/OneReason-0.8B-pretrain-competition" |
| model = AutoModelForCausalLM.from_pretrained( |
| base_model, |
| trust_remote_code=True, |
| torch_dtype="auto", |
| device_map="auto" |
| ) |
| tokenizer = AutoTokenizer.from_pretrained(base_model, trust_remote_code=True) |
| |
| # Load LoRA adapter |
| model = PeftModel.from_pretrained(model, "dfdu233/OneReason-0.8B-lora-expA") |
| |
| # Example inference |
| prompt = "<|prod_begin|><s_a_1183><s_b_746><s_c_5290>，这个商品卖的是什么？ /no_think" |
| messages = [{"role": "user", "content": prompt}] |
| 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=512, do_sample=True, top_p=0.95, temperature=0.7) |
| print(tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)) |
| ``` |
| |
| ## Training Details |
| |
| - **Loss:** 3.023 → 1.337 (56% reduction over 4660 steps) |
| - **Validation Loss:** 1.373 (decreasing, no overfitting) |
| - **Cutoff Length:** 8192 |
| - **Batch Size:** 1 (gradient accumulation 8 = effective batch 8) |
| - **Learning Rate:** 3e-5 (cosine schedule) |
| - **Optimizer:** paged_adamw_8bit |
| - **Context Length:** 8192 tokens |
| - **Flash Attention:** fa2 |
|
|
| ## Training Data Distribution |
|
|
| | Task | Samples | |
| |------|--------:| |
| | Recommendation (懂推荐) | 21,885 | |
| | Item Understanding (懂物料) | 5,807 | |
| | User Prediction (懂用户) | 4,788 | |
| | **Total** | **32,480** | |
|
|
| ## Framework versions |
|
|
| - PEFT 0.18.1 |
| - PyTorch 2.4.0 |
| - Transformers 5.6.0 |
| - Flash-Attn 2.6.3 |
