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Model: aolans/Qwen2.5-7B-Instruct-SDFT-fp16
Source: Original Platform

README.md

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+---
+base_model: Qwen/Qwen2.5-7B-Instruct
+datasets:
+- u-10bei/sft_alfworld_trajectory_dataset_v5
+- u-10bei/dbbench_sft_dataset_react_v4
+language:
+- en
+license: apache-2.0
+pipeline_tag: text-generation
+tags:
+- lora
+- merged
+- agent
+- tool-use
+- alfworld
+- dbbench
+---
+
+# Qwen2.5-7B-Instruct-SDFT-fp16
+
+This repository provides a fine-tuned model based on **Qwen/Qwen2.5-7B-Instruct**. 
+The model was initially trained using **LoRA + Unsloth** and has been **merged with the base model**. 
+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.
+
+## Training Objective
+
+This model is trained to improve **multi-turn agent task performance**
+on ALFWorld (household tasks) and DBBench (database operations).
+
+Loss is applied to **all assistant turns** in the multi-turn trajectory,
+enabling the model to learn environment observation, action selection,
+tool use, and recovery from errors.
+
+## Training Configuration
+
+- Base model: Qwen/Qwen2.5-7B-Instruct
+- Method: LoRA (merged into base model)
+- Precision: fp16
+- **Experimental Methods:** SDFT & Epiplexity *(Note: Implementation is still a work in progress)*
+- Max sequence length: 4096
+- Epochs: 2
+- Learning rate: 2e-06
+- LoRA: r=64, alpha=128
+
+## Experimental Features
+
+This version incorporates experimental training techniques, specifically **SDFT** and **Epiplexity**. 
+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.
+
+## Usage
+
+You can load this model directly using `AutoModelForCausalLM`.
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "aolans/Qwen2.5-7B-Instruct-SDFT-2ep-fp16"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+)
+```
+
+## References
+
+The experimental training methods (SDFT and Epiplexity) applied in this model are based on the following research:
+
+* [Self-Distillation Enables Continual Learning](https://arxiv.org/abs/2601.19897)
+* [From Entropy to Epiplexity: Rethinking Information for Computationally Bounded Intelligence](https://arxiv.org/abs/2601.03220)
+
+## Sources & Terms (IMPORTANT)
+
+Training data: u-10bei/sft_alfworld_trajectory_dataset_v5, u-10bei/dbbench_sft_dataset_react_v4
+
+Dataset License: MIT License. This dataset is used and distributed under the terms of the MIT License.
+Compliance: Users must comply with the MIT license (including copyright notice) and the base model's original terms of use.