Qwen3-4B-Thinking-2507-GLM-4.7-Distilled

Qwen3-4B-Thinking-2507-GLM-4.7-Distilled

Qwen3-4B-Thinking-2507-GLM-4.7-Distilled is a fine-tuned model built upon the GRPO-optimized Jackrong/DASD-4B-Thinking-2507-GRPO-v2 (originally based on Qwen/Qwen3-4B-Thinking-2507). This model was developed using a Supervised Fine-Tuning (SFT) strategy heavily distilled from the GLM-4.7 model series (at a default temperature of 1.0), with a central focus on multi-turn conversational alignment and structured Chain-of-Thought (CoT) execution.

🎯 Core Improvement: The primary objective of this fine-tuning was to transform the model's reasoning pattern for everyday and lightweight tasks. Instead of the typical linear, free-associative, and highly self-correcting ("think-as-you-go") stream of consciousness, this model has learned to adopt a highly confident, "Plan-then-Execute" paradigm. It systematically breaks down tasks into logical outlines and executes modular, report-like responses without unnecessary self-doubt or hesitation.

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🧬 Training Pipeline Overview

This model is the culmination of two sequential training stages targeting mathematical reasoning and conversational CoT tracking:

Qwen/Qwen3-4B-Thinking-2507
         │
         ▼  Stage 0: GRPO (RL on Math & Reasoning)
DASD-4B-Thinking-2507-GRPO-v2
         │
         ▼  Stage 1: SFT with GLM-4.7 Series Distilled Datasets (T=1.0)
Qwen3-4B-Thinking-2507-GLM-4.7-Distilled  ← (this model)

🧠 Chain of Thought (CoT) Evolution: Base vs. Distilled

A significant shift in the model's reasoning style is observed after distillation from the GLM-4.7 series data. The model transitions from a spontaneous thinker into a structured planner:

🌟 Key Takeaway: Through the GLM-4.7 dataset distillation, the model successfully learned the modular thinking paradigm. Instead of continuously questioning itself, it now breaks down tasks, creates a clear outline, and systematically executes each step like writing a formal report.

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📚 Stage Details

Stage 0 — GRPO Reinforcement Learning: DASD-4B-Thinking-2507-GRPO-v2

Starting from the base model Qwen/Qwen3-4B-Thinking-2507, Group Relative Policy Optimization (GRPO) was applied. This stage consisted of:

• Cold Start: Fine-tuning on the unsloth/OpenMathReasoning-mini dataset.
• Reinforcement Learning: Applying GRPO via the open-r1/DAPO-Math-17k-Processed dataset.

This stage significantly improved the model's:

• Correctness on math problem solving
• Step-by-step logical reasoning
• Reward signal alignment for verifiable tasks

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Stage 1 — SFT GLM-4.7 Distillation (T=1.0): Qwen3-4B-Thinking-2507-GLM-4.7-Distilled (this model)

Building on the reasoning foundation of DASD-4B-Thinking-2507-GRPO-v2, Stage 1 SFT was performed using a mixed dataset heavily utilizing GLM-4.7 synthetic data generated at a default temperature of 1.0, along with multi-turn alignments.

Higher-temperature data introduces greater lexical diversity, broader mode coverage, and more formatted/structured chain-of-thought traces, enabling the model to generalize better across diverse conversational reasoning patterns and problem domains. It helps the model handle multi-turn conversations effectively while protecting its internal structure of <think>...</think> tracking.

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🗂️ All Datasets Used

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🏃 Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Jackrong/Qwen3-4B-Thinking-2507-GLM-4.7-Distilled"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype="auto", device_map="auto")

messages = [
    {"role": "user", "content": "Solve: find all real solutions to x^3 - 6x^2 + 11x - 6 = 0."}
]

text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer([text], return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=4096)
response = tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True)
print(response)

Tip: This model naturally generates <think>...</think> reasoning traces before the final answer. You can parse these to inspect the chain-of-thought.

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📋 Model Details

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⚠️ Limitations & Intended Use

• This model is intended for research and educational purposes related to reasoning and mathematical problem-solving.
• While mathematical and logical reasoning capabilities have been enhanced, the model may still produce incorrect answers or hallucinations — always verify outputs on critical tasks.
• The model inherits the capabilities and limitations of the underlying Qwen3-4B-Thinking-2507 architecture.
• Not intended for deployment in high-stakes applications without additional safety evaluation.

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📎 Related Models

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🙏 Acknowledgements

• Zhipu AI for the GLM-4.7 model series capability
• Alibaba Cloud Apsara Lab for reasoning datasets
• Open-R1 for the DAPO Math dataset
• Unsloth for efficient fine-tuning infrastructure
• Qwen Team for the excellent base model