---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
license: apache-2.0
language:
- en
- fr
tags:
- granite
- fine-tuned
- conversational
- distillation
- thinking
- reasoning
datasets:
- TeichAI/glm-4.7-2000x
pipeline_tag: text-generation
---

# Qwen3-0.6B-DISTILL-glm-4.7-think

This model is a fine-tuned version of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B) trained on high-reasoning conversational data from GLM 4.7 by Z.ai.

## Model Details

- **Base Model:** Qwen/Qwen3-0.6B
- **Fine-tuning Dataset:** TeichAI/glm-4.7-2000x
- **Context Length:** 1048576 tokens
- **Special Feature:** Thinking/Reasoning with `<think>` tags

## Usage

### Transformers

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("glogwa68/Qwen3-0.6B-DISTILL-glm-4.7-think")
tokenizer = AutoTokenizer.from_pretrained("glogwa68/Qwen3-0.6B-DISTILL-glm-4.7-think")

messages = [{"role": "user", "content": "Hello, how are you?"}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

## Training Details

- **Epochs:** 2
- **Learning Rate:** 2e-5
- **Batch Size:** 8 (with gradient accumulation)
- **Precision:** FP16
- **Hardware:** Multi-GPU with DeepSpeed ZeRO-3

## License

Apache 2.0
