---
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B-Instruct
tags:
- text-generation-inference
- transformers
- qwen
- knowledge-distillation
- hidden-state-distillation
language:
- en
metrics:
- perplexity
pipeline_tag: text-generation
---

# Qwen2.5-0.5B-HiddenDistilled

This repository contains the **fully merged base + adapter weights** for Qwen2.5-0.5B-Instruct distilled from the teacher model **Qwen2.5-3B-Instruct**. The distillation pipeline optimizes a composite objective combining supervised learning cross-entropy, logit-level KL divergence, and MSE alignment of projected hidden states.

* **Base Student Model:** [Qwen/Qwen2.5-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct)
* **Teacher Model:** [Qwen/Qwen2.5-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-3B-Instruct)
* **LoRA Adapter Repo:** [sarimahsan101/Qwen2.5-0.5B-HiddenDistilled-LoRA](https://huggingface.co/sarimahsan101/Qwen2.5-0.5B-HiddenDistilled-LoRA)
* **Training Code (GitHub):** [sarimahsan101/distillation-hiddenstates](https://github.com/sarimahsan/distillation-hiddenstates-code)


## 📊 Trial Run Evaluation Metrics

Evaluation metrics compiled on a single **NVIDIA Tesla T4 (16GB)** GPU for **1 epoch** on a subset of Dolly, Alpaca SFT, and Ultrachat:

| Metric | Before Distillation | After Distillation | Change | Status |
| :--- | :---: | :---: | :---: | :---: |
| **Validation Perplexity** | 5.0924 | 5.2620 | +0.1696 | ✗ |
| **Teacher-Student KL Divergence** | 2.7913 | 1.9637 | -0.8276 | ✓ |
| **Hidden State Cosine Similarity** | 0.0075 | 0.0054 | -0.0021 | ✗ |

## 🚀 How to Use (Merged Model)

You can load this model directly using standard Hugging Face Transformers:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "sarimahsan101/Qwen2.5-0.5B-HiddenDistilled"

# Load tokenizer and merged model
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)
model.eval()

# Inference example
messages = [{"role": "user", "content": "Explain gravity in one sentence."}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)

with torch.no_grad():
    outputs = model.generate(**inputs, max_new_tokens=50)

print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
```

## 🛠️ Training Configurations & Details
* **Framework:** PyTorch & Hugging Face Transformers / Trainer
* **Quantization:** 4-bit NF4 double quantization (`bitsandbytes`) for training, adapter weights were merged with FP16 base student.
* **Loss Weights:** Cross-Entropy: 0.3, KL Divergence: 0.4, Hidden MSE: 0.3