commit 7ca283b9ff5078228c0025d3529d69930907964e Author: ModelHub XC Date: Tue Sep 22 07:07:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: Ademola265/Qwen3-4B-Thinking-2507-GLM-4.7-Distilled-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..056f2cc --- /dev/null +++ b/.gitattributes @@ -0,0 +1,49 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ckpt filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.mlmodel filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.npy filter=lfs diff=lfs merge=lfs -text +*.npz filter=lfs 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a/README.md b/README.md new file mode 100644 index 0000000..7637e3b --- /dev/null +++ b/README.md @@ -0,0 +1,163 @@ +--- +language: +- en +- zh +license: apache-2.0 +base_model: Jackrong/DASD-4B-Thinking-2507-GRPO-v2 +tags: +- qwen3 +- unsloth +- text-generation +- reasoning +- math +- grpo +- sft +- distillation +- conversational +- glm-4.7 +pipeline_tag: text-generation +--- + +# 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`](https://huggingface.co/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. + +--- + +## 🧬 Training Pipeline Overview + +This model is the culmination of two sequential training stages targeting mathematical reasoning and conversational CoT tracking: + +```text +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**: + +| 🎯 Feature | 🌀 Base Model (Qwen3-4B-Thinking) | ✨ Distilled Model (GLM-4.7-Distilled) | +| :--- | :--- | :--- | +| **Thinking Style** | 🌊 Linear, stream-of-consciousness | 🧱 Modularized, report-like | +| **Execution** | 🏃 Thinks on the fly, writes as it thinks | 📝 "Plan-then-Execute" framework | +| **Structure** | 🔀 Unstructured, organic self-correction mid-thought | 📑 Highly structured with headings & logical phases | +| **Confidence** | 🤔 High self-doubt ("Wait...", "Maybe...", "Should I...") | 🚀 Highly confident, rarely hesitates | +| **Output Tone** | 🗣️ Conversational, exploring multiple paths | 📊 Objective, direct, and systematic | + +**🌟 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. + +--- + +## 📚 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`](https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini) dataset. +- **Reinforcement Learning:** Applying GRPO via the [`open-r1/DAPO-Math-17k-Processed`](https://huggingface.co/datasets/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 + +--- + +### 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 `...` tracking. + +--- + +## 🗂️ All Datasets Used + +| Stage | Dataset | Purpose | +|-------|---------|---------| +| GRPO (Cold Start) | [`unsloth/OpenMathReasoning-mini`](https://huggingface.co/datasets/unsloth/OpenMathReasoning-mini) | Initial foundational mathematical reasoning | +| GRPO (RL) | [`open-r1/DAPO-Math-17k-Processed`](https://huggingface.co/datasets/open-r1/DAPO-Math-17k-Processed) | Math & reasoning RL training via GRPO | +| SFT Distillation | [`Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b`](https://huggingface.co/datasets/Alibaba-Apsara/Superior-Reasoning-SFT-gpt-oss-120b) (Stage 2) | Diverse reasoning structures | +| SFT Distillation | [`Jackrong/glm-4.7-multiturn-CoT`](https://huggingface.co/datasets/Jackrong/glm-4.7-multiturn-CoT) | Multi-turn CoT alignment | +| SFT Distillation | [`Jackrong/glm-4.7-Superior-Reasoning-stage1`](https://huggingface.co/datasets/Jackrong/glm-4.7-Superior-Reasoning-stage1) | Enhanced fundamental reasoning | +| SFT Distillation | [`TeichAI/glm-4.7-2000x`](https://huggingface.co/datasets/TeichAI/glm-4.7-2000x) | Generalization and lexical diversity | +| SFT Distillation | [`Jackrong/MultiReason-ChatAlpaca`](https://huggingface.co/datasets/Jackrong/MultiReason-ChatAlpaca) | Conversational multi-turn tracking | + +--- + +## 🏃 Quickstart + +```python +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 `...` reasoning traces before the final answer. You can parse these to inspect the chain-of-thought. + +--- + +## 📋 Model Details + +| Attribute | Value | +|-----------|-------| +| **Base Model** | `Jackrong/DASD-4B-Thinking-2507-GRPO-v2` | +| **Architecture** | Qwen3 (4B Dense) | +| **License** | Apache 2.0 | +| **Language(s)** | English, Chinese | +| **Training Framework** | [Unsloth](https://github.com/unslothai/unsloth) + Hugging Face TRL | +| **RL Algorithm** | GRPO (Group Relative Policy Optimization) | +| **Fine-tuning Method** | SFT (GLM-4.7 Distillation at T=1.0) | +| **Developed by** | Jackrong | + +--- + +## ⚠️ 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. + +--- + +## 📎 Related Models + +| Model | Description | +|-------|-------------| +| [`Qwen/Qwen3-4B-Thinking-2507`](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) | Base model | +| [`Jackrong/DASD-4B-Thinking-2507-GRPO-v2`](https://huggingface.co/Jackrong/DASD-4B-Thinking-2507-GRPO-v2) | After GRPO RL training | +| [`Jackrong/Qwen3-4B-Thinking-2507-GLM-4.7-Distilled`](https://huggingface.co/Jackrong/Qwen3-4B-Thinking-2507-GLM-4.7-Distilled) | **This model** — GLM-4.7 Distilled | + +--- + +## 🙏 Acknowledgements + +- [Zhipu AI](https://huggingface.co/THUDM) for the GLM-4.7 model series capability +- [Alibaba Cloud Apsara Lab](https://huggingface.co/Alibaba-Apsara) for reasoning datasets +- [Open-R1](https://huggingface.co/open-r1) for the DAPO Math dataset +- [Unsloth](https://github.com/unslothai/unsloth) for efficient fine-tuning infrastructure +- [Qwen Team](https://huggingface.co/Qwen) for the excellent base model diff --git a/config.json b/config.json new file mode 100644 index 0000000..09062c2 --- /dev/null +++ b/config.json @@ -0,0 +1,69 @@ +{ + "architectures": [ + "Qwen3ForCausalLM" + ], + "attention_bias": false, + "attention_dropout": 0.0, + "torch_dtype": "bfloat16", + "eos_token_id": 151645, + "head_dim": 128, + "hidden_act": "silu", + "hidden_size": 2560, + "initializer_range": 0.02, + "intermediate_size": 9728, + "layer_types": [ + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention", + "full_attention" + ], + "max_position_embeddings": 262144, + "max_window_layers": 36, + "model_type": "qwen3", + "num_attention_heads": 32, + "num_hidden_layers": 36, + "num_key_value_heads": 8, + "pad_token_id": 151654, + "rms_norm_eps": 1e-06, + "rope_scaling": null, + "rope_theta": 5000000, + "sliding_window": null, + "tie_word_embeddings": true, + "unsloth_fixed": true, + "unsloth_version": "2026.2.1", + "use_cache": true, + "use_sliding_window": false, + "vocab_size": 151936 +} \ No newline at end of file