89 lines
3.0 KiB
Markdown
89 lines
3.0 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/sft_alfworld_trajectory_dataset_v2
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- u-10bei/sft_alfworld_trajectory_dataset_v3
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- u-10bei/sft_alfworld_trajectory_dataset_v4
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- u-10bei/sft_alfworld_trajectory_dataset_v5
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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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library_name: transformers
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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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- alfworld
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- dbbench
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---
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# qwen25-7b-sft-merged-v5v6-a50
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This repository provides a **fully merged model** fine-tuned from
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**Qwen2.5-7B-Instruct** using **QLoRA + Unsloth**.
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Two SFT models (v5 and v6) were trained independently, then combined via
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weight interpolation (alpha=0.5). This is a **complete model** — no adapters
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or additional weights are needed.
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## Training Objective
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This model is trained to improve **multi-turn agent task performance**
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on ALFWorld (household tasks) and DBBench (database operations).
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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 environment observation, action selection,
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tool use, and recovery from errors.
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## Training Configuration
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- Base model: Qwen/Qwen2.5-7B-Instruct
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- Method: QLoRA (4-bit) + Unsloth, merged into base model
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- Max sequence length: 2048
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- Epochs: 2
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- Learning rate: 5e-5
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- LoRA: r=32, alpha=64
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- Post-training: weight interpolation of v5 and v6 (alpha=0.5)
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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 = "plotMaker/qwen25-7b-sft-merged-v5v6-a50"
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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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## References
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- [Model Soups (Wortsman et al., 2022)](https://arxiv.org/abs/2203.05482) — Weight interpolation of fine-tuned
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models
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- [LoRA (Hu et al., 2021)](https://arxiv.org/abs/2106.09685) — Low-Rank Adaptation
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- [NEFTune (Jain et al., 2024)](https://arxiv.org/abs/2310.05914) — Noisy embedding fine-tuning
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- [rsLoRA (Kalajdzievski, 2023)](https://arxiv.org/abs/2312.03732) — Rank-stabilized LoRA scaling
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- [ALFWorld (Shridhar et al., 2021)](https://arxiv.org/abs/2010.03768) — Interactive text-world environments
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- [ReAct (Yao et al., 2023)](https://arxiv.org/abs/2210.03629) — Reasoning and acting in LLMs
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## Sources & Terms (IMPORTANT)
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Training data:
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- u-10bei/sft_alfworld_trajectory_dataset_v2 ~ v5
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- u-10bei/dbbench_sft_dataset_react ~ v4
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Base model: [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
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This repository does NOT redistribute the dataset.
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Users must comply with the dataset license and base model terms.
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