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Model: sarimahsan101/Qwen2.5-0.5B-HiddenDistilled
Source: Original Platform

README.md

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+---
+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