| ---
|
| language:
|
| - zh
|
| - en
|
| license: apache-2.0
|
| base_model: Qwen/Qwen3.5-4B
|
| tags:
|
| - lora
|
| - qlora
|
| - roleplay
|
| - character-ai
|
| - taiwanese-mandarin
|
| - llama-factory
|
| - gguf
|
| datasets:
|
| - RX5950XTP/silicon-girlfriend-dataset
|
| ---
|
| # Silicon-Based-Girlfriend — QLoRA Adapter
|
|
|
| ---
|
|
|
| 基於 **Qwen3.5-4B** 的 QLoRA 微調 Adapter，訓練目標為沉浸式繁體中文角色扮演。本倉庫包含 LoRA Adapter 權重與 GGUF 格式模型。
|
|
|
| ---
|
|
|
| ## Model Details / 模型資訊
|
|
|
| | 項目 | 內容 |
|
| | ------------------ | ------------------------------------------------------ |
|
| | Base Model | [Qwen/Qwen3.5-4B](https://huggingface.co/Qwen/Qwen3.5-4B) |
|
| | Fine-tuning Method | QLoRA (4-bit NF4) |
|
| | LoRA Rank | 32 |
|
| | LoRA Alpha | 64 |
|
| | LoRA Dropout | 0.05 |
|
| | LoRA Target | All linear layers |
|
| | Training Epochs | 5 |
|
| | Context Length | 8192 tokens |
|
| | Learning Rate | 1e-4 |
|
| | LR Scheduler | Cosine |
|
| | Optimizer | paged_adamw_8bit |
|
| | Training Samples | 985 |
|
| | Train Loss | 1.108 |
|
| | Eval Loss | 1.434 |
|
| | Hardware | NVIDIA RTX A6000 (48GB VRAM) |
|
| | Training Time | ~19 hours |
|
| | Framework | [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory) |
|
| | Chat Template | `qwen3_5_nothink` (non-thinking mode) |
|
|
|
| ---
|
|
|
| ## Files / 檔案說明
|
|
|
| | 檔案 | 說明 |
|
| | ------------------------------- | ---------------------------------------------------- |
|
| | `adapter_config.json` | LoRA 設定檔 |
|
| | `adapter_model.safetensors` | LoRA 權重（248 MB） |
|
| | `tokenizer_config.json` | Tokenizer 設定（含 nothink chat template） |
|
| | `tokenizer.json` | Tokenizer |
|
| | `vocab.json` / `merges.txt` | Vocabulary |
|
| | `silicon-gf-q8_0.gguf` | Q8_0 量化 GGUF（4.2 GB，適用 llama.cpp / LM Studio） |
|
| | `training_loss.png` | 訓練 Loss 曲線 |
|
| | `training_eval_loss.png` | 評估 Loss 曲線 |
|
|
|
| ---
|
|
|
| ## Usage / 使用方式
|
|
|
| ### Option 1: GGUF (Recommended / 推薦)
|
|
|
| 直接在 **LM Studio** 或 **llama.cpp** 載入 `silicon-gf-q8_0.gguf`，無需額外安裝。
|
|
|
| ```bash
|
| # llama.cpp
|
| ./llama-cli -m silicon-gf-q8_0.gguf -c 8192 --temp 0.8
|
| ```
|
|
|
| ### Option 2: LoRA Adapter with transformers + PEFT
|
|
|
| ```python
|
| from peft import PeftModel
|
| from transformers import AutoModelForCausalLM, AutoTokenizer
|
|
|
| base_model = "Qwen/Qwen3.5-4B"
|
| adapter = "RX5950XTP/silicon-based-girlfriend"
|
|
|
| tokenizer = AutoTokenizer.from_pretrained(adapter)
|
| model = AutoModelForCausalLM.from_pretrained(base_model, device_map="auto")
|
| model = PeftModel.from_pretrained(model, adapter)
|
|
|
| messages = [
|
| {"role": "user", "content": "嘿，你在幹嘛？"}
|
| ]
|
| 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, temperature=0.8, do_sample=True)
|
| print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
|
| ```
|
|
|
| ### Option 3: LLaMA-Factory inference
|
|
|
| ```bash
|
| llamafactory-cli chat \
|
| --model_name_or_path Qwen/Qwen3.5-4B \
|
| --adapter_name_or_path RX5950XTP/silicon-based-girlfriend \
|
| --template qwen3_5_nothink \
|
| --finetuning_type lora
|
| ```
|
|
|
| ---
|
|
|
| ## Training Curves / 訓練曲線
|
|
|
| 
|
| 
|
|
|
| ---
|
|
|
| ## Dataset / 訓練資料集
|
|
|
| - **倉庫**：[RX5950XTP/silicon-girlfriend-dataset](https://huggingface.co/datasets/RX5950XTP/silicon-girlfriend-dataset)
|
| - **格式**：ShareGPT（`system` + `conversations` with `from`/`value`）
|
| - **筆數**：985 筆多輪對話
|
| - **語言**：繁體中文（臺灣用語）
|
| - **生成方式**：由 Kimi K2.5 根據角色設定生成
|
|
|
| ---
|
|
|
| ## Notes / 注意事項
|
|
|
| - 本模型使用 `qwen3_5_nothink` chat template，**預設不啟用思考模式**，回覆會直接輸出角色對話。
|
| - 角色設定包含不良用語與成人主題，請自行評估使用場景。
|
| - 模型以 QLoRA 訓練，推理時需搭配 base model（Qwen3.5-4B）一同載入，或直接使用 GGUF。
|
|
|
| ---
|
|
|
| ## License
|
|
|
| Apache 2.0（遵循 Qwen3.5-4B 原授權）
|
|
|
