81 lines
2.6 KiB
Markdown
81 lines
2.6 KiB
Markdown
---
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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_v5
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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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- lora
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- merged
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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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# Qwen2.5-7B-Instruct-SDFT-2ep-fp16
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This repository provides a fine-tuned model based on **Qwen/Qwen2.5-7B-Instruct**.
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The model was initially trained using **LoRA + Unsloth** and has been **merged with the base model**.
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The weights in this repository are saved in **fp16** format, so you can load and use it directly without needing to load the base model and adapter separately.
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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: LoRA (merged into base model)
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- Precision: fp16
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- **Experimental Methods:** SDFT & Epiplexity *(Note: Implementation is still a work in progress)*
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- Max sequence length: 4096
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- Epochs: 2
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- Learning rate: 2e-06
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- LoRA: r=64, alpha=128
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## Experimental Features
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This version incorporates experimental training techniques, specifically **SDFT** and **Epiplexity**.
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However, the integration of these methods is not yet fully completed. We are still evaluating their impact on the model's reasoning capabilities and plan to refine them in future updates.
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## Usage
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You can load this model directly using `AutoModelForCausalLM`.
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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 = "aolans/Qwen2.5-7B-Instruct-SDFT-2ep-fp16"
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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.float16,
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device_map="auto",
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)
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```
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## References
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The experimental training methods (SDFT and Epiplexity) applied in this model are based on the following research:
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* [Self-Distillation Enables Continual Learning](https://arxiv.org/abs/2601.19897)
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* [From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence](https://arxiv.org/abs/2601.03220)
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## Sources & Terms (IMPORTANT)
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Training data: u-10bei/sft_alfworld_trajectory_dataset_v5, u-10bei/dbbench_sft_dataset_react_v4
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Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
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Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.
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