commit 32ce48fdd9c9b7954aff00cebe0b0d3ad57f0a64 Author: ModelHub XC Date: Thu Oct 29 18:44:26 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: gabriellarson/jupyter-agent-qwen3-4b-thinking-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..0948a80 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,56 @@ +*.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 diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs -text +*.pb filter=lfs diff=lfs merge=lfs -text +*.pickle filter=lfs diff=lfs merge=lfs -text +*.pkl filter=lfs diff=lfs merge=lfs -text +*.pt filter=lfs diff=lfs merge=lfs -text +*.pth filter=lfs diff=lfs merge=lfs -text +*.rar filter=lfs diff=lfs merge=lfs -text +*.safetensors filter=lfs diff=lfs merge=lfs -text +saved_model/**/* filter=lfs diff=lfs merge=lfs -text +*.tar.* filter=lfs diff=lfs merge=lfs -text +*.tar filter=lfs diff=lfs merge=lfs -text +*.tflite filter=lfs diff=lfs merge=lfs -text +*.tgz filter=lfs diff=lfs merge=lfs -text +*.wasm filter=lfs diff=lfs merge=lfs -text +*.xz filter=lfs diff=lfs merge=lfs -text +*.zip filter=lfs diff=lfs merge=lfs -text +*.zst filter=lfs diff=lfs merge=lfs -text +*tfevents* filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4B-thinking-F16.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q5_0.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q5_K_M.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q6_K.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ2_M.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ2_S.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ2_XXS.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ3_M.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ3_S.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ3_XXS.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ4_NL.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-IQ4_XS.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q2_K.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q3_K_L.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q3_K_M.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q3_K_S.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q4_0.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q4_K_S.gguf filter=lfs diff=lfs merge=lfs -text +jupyter-agent-qwen3-4b-thinking-Q5_K_S.gguf filter=lfs diff=lfs merge=lfs -text diff --git a/README.md b/README.md new file mode 100644 index 0000000..c0d5f39 --- /dev/null +++ b/README.md @@ -0,0 +1,253 @@ +--- +license: apache-2.0 +library_name: transformers +tags: +- code +- jupyter +- agent +- data-science +- qwen +- thinking +base_model: +- jupyter-agent/jupyter-agent-qwen3-4b-thinking +datasets: +- jupyter-agent/jupyter-agent-dataset +language: +- en +- code +pipeline_tag: text-generation +--- +# Jupyter Agent Qwen3-4B Thinking + +![image/png](https://cdn-uploads.huggingface.co/production/uploads/650ed7adf141bc34f91a12ae/ZyF9foqe5SLECwkq0dOpT.png) + +**Jupyter Agent Qwen3-4B Thinking** is a fine-tuned version of [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) specifically optimized for **data science agentic tasks** in Jupyter notebook environments. This model can execute Python code, analyze datasets, and provide step-by-step reasoning with intermediate computations to solve realistic data analysis problems. + +- **Model type:** Causal Language Model (Thinking) +- **Language(s):** English, Python +- **License:** Apache 2.0 +- **Finetuned from:** [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) + +## Key Features + +- **Jupyter-native agent** that lives inside notebook environments +- **Code execution** with pandas, numpy, matplotlib, and other data science libraries +- **Step-by-step reasoning** with intermediate computations and thinking traces +- **Dataset-grounded analysis** trained on real Kaggle notebook workflows +- **Tool calling** for structured code execution and final answer generation + +## Performance + +On the [DABStep benchmark](https://huggingface.co/spaces/adyen/DABstep) for data science tasks: + +| Model | Easy Tasks | Hard Tasks | +|-------|------------|------------| +| Qwen3-4B-Thinking-2507 (Base) | 44.0% | 2.1% | +| **Jupyter Agent Qwen3-4B Thinking** | **70.8%** | **3.4%** | + +**State-of-the-art performance** for small models on realistic data analysis tasks. + +## Model Sources + +- **Repository:** [jupyter-agent](https://github.com/huggingface/jupyter-agent) +- **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset) +- **Blog post:** [Jupyter Agents: training LLMs to reason with notebooks](https://huggingface.co/blog/jupyter-agent-2) +- **Demo:** [Jupyter Agent 2](https://huggingface.co/spaces/lvwerra/jupyter-agent-2) + +## Usage + +### Basic Usage + +```python +from transformers import AutoModelForCausalLM, AutoTokenizer +model_name = "jupyter-agent/jupyter-agent-qwen3-4b-thinking" +# Load model and tokenizer +tokenizer = AutoTokenizer.from_pretrained(model_name) +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype="auto", + device_map="auto" +) +# Prepare input +prompt = "Analyze this sales dataset and find the top 3 performing products by revenue." +messages = [ + {"role": "user", "content": prompt} +] +text = tokenizer.apply_chat_template( + messages, + tokenize=False, + add_generation_prompt=True +) +model_inputs = tokenizer([text], return_tensors="pt").to(model.device) +# Generate response +generated_ids = model.generate( + **model_inputs, + max_new_tokens=16384 +) +output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist() +``` + +### Decoding Thinking and Content + +For thinking models, you can extract both the reasoning and final response: + +```python +try: + # Find the end of thinking section () + index = len(output_ids) - output_ids[::-1].index(151668) +except ValueError: + index = 0 +thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n") +content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n") +print("Thinking:", thinking_content) +print("Response:", content) +``` + +### Agentic Usage with Tool Calling + +The model works best with proper scaffolding for tool calling: + +```python +tools = [ + { + "type": "function", + "function": { + "name": "execute_code", + "description": "Execute Python code in a Jupyter environment", + "parameters": { + "type": "object", + "properties": { + "code": { + "type": "string", + "description": "Python code to execute" + } + }, + "required": ["code"] + } + } + }, + { + "type": "function", + "function": { + "name": "final_answer", + "description": "Provide the final answer to the question", + "parameters": { + "type": "object", + "properties": { + "answer": { + "type": "string", + "description": "The final answer" + } + }, + "required": ["answer"] + } + } + } +] +# Include tools in the conversation +messages = [ + { + "role": "system", + "content": "You are a data science assistant. Use the available tools to analyze data and provide insights." + }, + {"role": "user", "content": prompt} +] +``` + +## Training Details + +### Training Data + +The model was fine-tuned on the [Jupyter Agent Dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset), which contains: + +- **51,389 synthetic notebooks** (~0.2B tokens, total 1B tokens) +- **Dataset-grounded QA pairs** from real Kaggle notebooks +- **Executable reasoning traces** with intermediate computations +- **High-quality educational content** filtered and scored by LLMs + +### Training Procedure + +- **Base Model:** Qwen3-4B-Thinking-2507 +- **Training Method:** Full-parameter fine-tuning (not PEFT) +- **Optimizer:** AdamW with cosine learning rate scheduling +- **Learning Rate:** 5e-6 +- **Epochs:** 5 (optimal based on ablation study) +- **Context Length:** 32,768 tokens +- **Batch Size:** Distributed across multiple GPUs +- **Loss:** Assistant-only loss (`assistant_loss_only=True`) +- **Regularization:** NEFTune noise (α=7) for full-parameter training + +### Training Infrastructure + +- **Framework:** [TRL](https://github.com/huggingface/trl) with [Transformers](https://github.com/huggingface/transformers) +- **Distributed Training:** DeepSpeed ZeRO-2 across multiple nodes +- **Hardware:** Multi-GPU setup with SLURM orchestration + +## Evaluation + +### Benchmark: DABStep + +The model was evaluated on [DABStep](https://huggingface.co/spaces/adyen/DABstep), a benchmark for data science agents with realistic tasks involving: + +- **Dataset analysis** with pandas and numpy +- **Visualization** with matplotlib/seaborn +- **Statistical analysis** and business insights +- **Multi-step reasoning** with intermediate computations + +The model achieves **26.8% improvement** over the base model and **11.1% improvement** over scaffolding alone. + +DABstep Easy Score + +We can also see, that the hard score can increase too even though our dataset is focused on easier questions. + +DABstep Hard Score + +## Limitations and Bias + +### Technical Limitations + +- **Context window:** Limited to 32K tokens, may struggle with very large notebooks +- **Tool calling format:** Requires specific scaffolding for optimal performance +- **Dataset domains:** Primarily trained on Kaggle-style data science tasks +- **Code execution:** Requires proper sandboxing for safe execution + +### Potential Biases + +- **Domain bias:** Trained primarily on Kaggle notebooks, may not generalize to all data science workflows +- **Language bias:** Optimized for English and Python, limited multilingual support +- **Task bias:** Focused on structured data analysis, may underperform on unstructured data tasks + +### Recommendations + +- Use in **sandboxed environments** like [E2B](https://e2b.dev/) for safe code execution +- **Validate outputs** before using in production systems +- **Review generated code** for security and correctness +- Consider **domain adaptation** for specialized use cases + +## Ethical Considerations + +- **Code Safety:** Always execute generated code in secure, isolated environments +- **Data Privacy:** Be cautious when analyzing sensitive datasets +- **Verification:** Validate all analytical conclusions and insights +- **Attribution:** Acknowledge model assistance in data analysis workflows + +## Citation + +```bibtex +@misc{jupyteragentqwen3thinking, + title={Jupyter Agent Qwen3-4B Thinking}, + author={Baptiste Colle and Hanna Yukhymenko and Leandro von Werra}, + year={2025}, + publisher={Hugging Face}, + url={https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-thinking} +} +``` + +## Related Work + +- **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset) +- **Non-thinking version:** [jupyter-agent-qwen3-4b-instruct](https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-instruct) +- **Base model:** [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507) +- 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