---
license: apache-2.0
datasets:
- amphora/QwQ-LongCoT-130K-2
- PowerInfer/QWQ-LONGCOT-500K
- PowerInfer/LONGCOT-Refine-500K
language:
- en
metrics:
- perplexity
base_model:
- Qwen/Qwen2.5-0.5B-Instruct
library_name: transformers
---
## Model Details:

- **Base Model:** Qwen/Qwen2-0.5B-Instruct  
- **Teacher Model:** Qwen/QwQ-32B-Preview  
- **Distillation Framework:** Instruction Tuning  
- **Task Type:** Conversational AI / Causal Language Modeling  
- **Parameters:** 0.5B  
- **Special Features:**
  - Integrated gradient checkpointing for efficient training
  - Step-by-step reasoning capabilities for better problem-solving

---

## Training:

QwQ-0.5B-Distilled was trained using the **amphora/QwQ-LongCoT-130K-2**, **PowerInfer/QWQ-LONGCOT-500K**, and **PowerInfer/LONGCOT-Refine-500K** with supervised finetuning. This model can be used as a competitive reasoning model on edge devices as well as a draft model for Qwen/QwQ-32B-Preview.
### Training Progress:
[▓▓▓▓▓▓▓▓▓▓] 100%

## Example Usage:

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
# Model name
model_name = "kz919/QwQ-0.5B-Distilled-SFT"
# Load the model
print(f"Starting to load the model {model_name} into memory")
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map={"": 0}
)
# Load the tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Define the prompt
prompt = "How many r in strawberry."
messages = [
    {"role": "system", "content": "You are a helpful and harmless assistant. You are Qwen developed by Alibaba. You should think step-by-step."},
    {"role": "user", "content": prompt}
]
# Tokenize the input
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate a response
generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=4096
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
# Decode the response
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)
```

---

## Applications:

1. **Conversational Assistants:**  
   Suitable for AI chatbots that require reasoning and long-context understanding.  

2. **Educational Tools:**  
   Provides step-by-step explanations, making it ideal for learning environments.  

3. **Creative Writing:**  
   Assists in generating coherent, contextually aware long-form content.  

4. **Technical Support:**  
   Handles complex customer queries with precision and clarity.  

---

## Draft model for Qwen/QwQ-32B-Preview:

This model can be used as a draft model for [Qwen/QwQ-32B-Preview](https://huggingface.co/Qwen/QwQ-32B-Preview) in sepculative decoding. We observe out of 5 tokens it generates, on average 3 tokens are accepted for math queries and 2.3 tokens are accepted for general reasoning queries.

---

## Limitations:

- While distilled for efficiency, performance on highly complex reasoning tasks may slightly trail the teacher model.  
- This model could still be under trained, merely a proof of concept. Don't yell at me if it's outputing nonesense.
---

## Citation:

If you use this model in your research or applications, please cite it as:

```bibtex
@model{qwq_0.5B_distilled,
  author = {Kaizhao Liang},
  title = {Mini-QwQ: A Reasoning Model for Edge Devices},
  year = {2024},
  publisher = {Hugging Face},
  version = {1.0}
}
```

---

This model is an example of how efficient fine-tuning and distillation methods can deliver robust conversational AI capabilities in a smaller, more manageable footprint.