| | --- |
| | language: en |
| | license: apache-2.0 |
| | base_model: Nanbeige/Nanbeige4.1-3B |
| | datasets: |
| | - TurkishCodeMan/Nanbeige4.1-3B-Gmail-Tool-Use-Datasets |
| | tags: |
| | - tool-use |
| | - gmail |
| | - function-calling |
| | - sft |
| | - dpo |
| | pipeline_tag: text-generation |
| | --- |
| | |
| | # Nanbeige4.1-3B — Gmail Tool-Use (SFT + DPO) |
| |
|
| | Fine-tuned version of [Nanbeige/Nanbeige4.1-3B](https://huggingface.co/Nanbeige/Nanbeige4.1-3B) |
| | for Gmail tool-calling tasks using a two-stage training pipeline. |
| |
|
| | **Training datasets:** [TurkishCodeMan/Nanbeige4.1-3B-Gmail-Tool-Use-Datasets](https://huggingface.co/datasets/TurkishCodeMan/Nanbeige4.1-3B-Gmail-Tool-Use-Datasets) |
| |
|
| | ## Training Pipeline |
| |
|
| | ### Stage 1 — Supervised Fine-Tuning (SFT) |
| | - **Dataset:** 740 multi-turn Gmail agent traces (`sft/traces_chatml_clean.jsonl`) |
| | - **Format:** ChatML with tool_calls (OpenAI function-calling schema) |
| | - **Method:** LoRA r=16, α=32, 7 target modules |
| | - **Result:** loss 0.8464 → 0.1888 · PPL 2.33 → 1.21 |
| | |
| | ### Stage 2 — Direct Preference Optimization (DPO) |
| | - **Dataset:** 3223 preference pairs (`dpo/dpo_dataset.jsonl`) — 3 rejection strategies: |
| | - `wrong_tool` — incorrect tool selected (~34%) |
| | - `missing_args` — required arguments omitted (~32%) |
| | - `bad_answer` — poor final response (~34%) |
| | - **Method:** DPO β=0.1, sigmoid loss, LoRA r=16, `ref_model=None` (PEFT implicit ref) |
| | - **Result:** val_loss=0.000765 · reward accuracy=100% · normalized margin=+0.52 |
| | |
| | ## Supported Tools |
| | |
| | | Tool | Description | |
| | |---|---| |
| | | `search_emails` | Search Gmail inbox with filters | |
| | | `read_email` | Read full email content by ID | |
| | | `send_email` | Send a new email | |
| | | `draft_email` | Create a draft | |
| | | `modify_email` | Add/remove labels, mark read/unread | |
| | | `download_attachment` | Download email attachment | |
| |
|
| | ## Usage |
| |
|
| | ```python |
| | from transformers import AutoTokenizer, AutoModelForCausalLM |
| | import torch |
| | |
| | model = AutoModelForCausalLM.from_pretrained( |
| | "TurkishCodeMan/Nanbeige4.1-3B-Gmail-Tool-Use", |
| | torch_dtype=torch.bfloat16, |
| | trust_remote_code=True, |
| | ) |
| | tokenizer = AutoTokenizer.from_pretrained( |
| | "TurkishCodeMan/Nanbeige4.1-3B-Gmail-Tool-Use", |
| | trust_remote_code=True, |
| | ) |
| | ``` |
| |
|
| | ## Training Details |
| |
|
| | | Parameter | Value | |
| | |---|---| |
| | | Base model | Nanbeige/Nanbeige4.1-3B | |
| | | SFT LoRA rank | 16 | |
| | | DPO LoRA rank | 16 | |
| | | DPO β | 0.1 | |
| | | Max length | 2682 tokens | |
| | | GPU | 1× RTX 4090 24GB | |
| | | Framework | TRL 0.22 · Transformers 4.57 · PEFT 0.18 | |
| |
|
