Qwen3-0.6B-DISTILL-glm-4.7-think/README.md

base_model, library_name, license, language, tags, datasets, pipeline_tag

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

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

This model is a fine-tuned version of 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

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