82 lines
2.3 KiB
Markdown
82 lines
2.3 KiB
Markdown
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---
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base_model: Qwen/Qwen2.5-7B-Instruct
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datasets:
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- u-10bei/dbbench_sft_dataset_react
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- u-10bei/dbbench_sft_dataset_react_v2
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- u-10bei/dbbench_sft_dataset_react_v3
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- u-10bei/dbbench_sft_dataset_react_v4
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language:
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- en
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- sft
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- agent
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- tool-use
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- dbbench
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- text-to-sql
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---
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# Qwen2.5-7B DB Bench Combined SFT (v1-v4)
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This repository provides a **merged full-weight model** fine-tuned from
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**Qwen2.5-7B-Instruct** using **LoRA + Unsloth**, then merged to 16bit.
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## Training Objective
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This model is trained to improve **DB Bench (database operation) performance**
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on the AgentBench evaluation benchmark. ALFWorld performance relies entirely
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on the base model's inherent capability (no ALFWorld training data used).
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Loss is applied to **all assistant turns** in the multi-turn trajectory,
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enabling the model to learn SQL generation, action selection, and error recovery.
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## Training Data
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- DB Bench v1 (u-10bei/dbbench_sft_dataset_react): ~750 samples
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- DB Bench v2 (u-10bei/dbbench_sft_dataset_react_v2): ~750 samples
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- DB Bench v3 (u-10bei/dbbench_sft_dataset_react_v3): ~750 samples
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- DB Bench v4 (u-10bei/dbbench_sft_dataset_react_v4): ~750 samples
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- Total: ~3,000 samples
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- **ALFWorld data intentionally excluded** to preserve base model performance
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## Training Configuration
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- Base model: Qwen/Qwen2.5-7B-Instruct
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- Method: LoRA → merged to 16bit
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- Max sequence length: 2048
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- Epochs: 2
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- Learning rate: 2e-6
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- LoRA: r=64, alpha=128
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- Batch size: 2, Gradient accumulation: 4 (effective batch 8)
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- Optimizer: AdamW (cosine scheduler)
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- Framework: Unsloth
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "koguma-ai/dbbench-combined-baseline0301"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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```
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## Sources & Terms
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Training data: u-10bei/dbbench_sft_dataset_react (v1-v4)
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Dataset License: Apache-2.0. Users must comply with the Apache-2.0 license
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and the base model's original terms of use.
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## Limitations
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- Optimized for DB Bench tasks only
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- ALFWorld performance relies on base model capability
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- Weak categories: aggregation-MAX (16.7%), INSERT (33.3%)
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