Self-Distillation Enables Continual Learning
Paper • 2601.19897 • Published • 29
Qwen2.5-7B-Instruct SDFT — Tool Use (Step 1011)
This model is a Self-Distillation Fine-Tuned (SDFT) version of Qwen/Qwen2.5-7B-Instruct, trained on the ToolAlpaca tool-use dataset.
SDFT is an on-policy learning method from "Self-Distillation Enables Continual Learning" that acquires new skills while preserving prior capabilities, significantly reducing catastrophic forgetting compared to standard SFT.
Training Details
| Parameter | Value |
|---|---|
| Base model | Qwen/Qwen2.5-7B-Instruct |
| Method | SDFT (On-Policy Self-Distillation) |
| Dataset | ToolAlpaca (4,046 training examples) |
| Training step | 1011 / 1011 |
| Learning rate | 2e-5 (cosine schedule, 10% warmup) |
| Batch size | 32 (gradient accumulation) |
| Epochs | 1 |
| Precision | bf16 |
| Max prompt length | 1024 |
| Max completion length | 1024 |
| EMA alpha | 0.01 |
| Hardware | 1x NVIDIA L40S 48GB |
| Training time | ~42 hours (full run) |
Evaluation Results
Tool-Use Accuracy (ToolAlpaca test set, 68 examples)
| Metric | Base Model | This Model (Step 1011) |
|---|---|---|
| Greedy Accuracy | 54.4% | 57.4% |
| pass@1 | 52.6% | 49.9% |
| pass@5 | 61.5% | 64.6% |
| pass@10 | 64.3% | 70.0% |
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("Ayushnangia/qwen2.5-7b-instruct-sdft-tooluse-step-1011")
tokenizer = AutoTokenizer.from_pretrained("Ayushnangia/qwen2.5-7b-instruct-sdft-tooluse-step-1011")
messages = [{"role": "user", "content": "Your tool-use prompt here"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=1024)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
All Checkpoints
Citation
@article{shenfeld2025selfdistillation,
title={Self-Distillation Enables Continual Learning},
author={Shenfeld, Idan and others},
journal={arXiv preprint arXiv:2601.19897},
year={2025}
}
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