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Model: jupyter-agent/jupyter-agent-qwen3-4b-thinking Source: Original Platform
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---
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license: apache-2.0
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library_name: transformers
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tags:
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- code
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- jupyter
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- agent
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- data-science
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- qwen
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- thinking
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base_model: Qwen/Qwen3-4B-Thinking-2507
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datasets:
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- jupyter-agent/jupyter-agent-dataset
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language:
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- en
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- code
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pipeline_tag: text-generation
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---
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# Jupyter Agent Qwen3-4B Thinking
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**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.
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- **Model type:** Causal Language Model (Thinking)
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- **Language(s):** English, Python
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- **License:** Apache 2.0
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- **Finetuned from:** [Qwen/Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
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## Key Features
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- **Jupyter-native agent** that lives inside notebook environments
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- **Code execution** with pandas, numpy, matplotlib, and other data science libraries
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- **Step-by-step reasoning** with intermediate computations and thinking traces
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- **Dataset-grounded analysis** trained on real Kaggle notebook workflows
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- **Tool calling** for structured code execution and final answer generation
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## Performance
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On the [DABStep benchmark](https://huggingface.co/spaces/adyen/DABstep) for data science tasks:
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| Model | Easy Tasks | Hard Tasks |
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|-------|------------|------------|
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| Qwen3-4B-Thinking-2507 (Base) | 44.0% | 2.1% |
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| **Jupyter Agent Qwen3-4B Thinking** | **70.8%** | **3.4%** |
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**State-of-the-art performance** for small models on realistic data analysis tasks.
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## Model Sources
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- **Repository:** [jupyter-agent](https://github.com/huggingface/jupyter-agent)
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- **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
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- **Blog post:** [Jupyter Agents: training LLMs to reason with notebooks](https://huggingface.co/blog/jupyter-agent-2)
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- **Demo:** [Jupyter Agent 2](https://huggingface.co/spaces/lvwerra/jupyter-agent-2)
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## Usage
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### Basic Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "jupyter-agent/jupyter-agent-qwen3-4b-thinking"
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# Load model and tokenizer
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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# Prepare input
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prompt = "Analyze this sales dataset and find the top 3 performing products by revenue."
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messages = [
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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# Generate response
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=16384
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)
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output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
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```
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### Decoding Thinking and Content
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For thinking models, you can extract both the reasoning and final response:
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```python
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try:
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# Find the end of thinking section (</think>)
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index = len(output_ids) - output_ids[::-1].index(151668)
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except ValueError:
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index = 0
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thinking_content = tokenizer.decode(output_ids[:index], skip_special_tokens=True).strip("\n")
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content = tokenizer.decode(output_ids[index:], skip_special_tokens=True).strip("\n")
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print("Thinking:", thinking_content)
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print("Response:", content)
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```
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### Agentic Usage with Tool Calling
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The model works best with proper scaffolding for tool calling:
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```python
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tools = [
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{
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"type": "function",
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"function": {
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"name": "execute_code",
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"description": "Execute Python code in a Jupyter environment",
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"parameters": {
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"type": "object",
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"properties": {
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"code": {
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"type": "string",
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"description": "Python code to execute"
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}
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},
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"required": ["code"]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "final_answer",
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"description": "Provide the final answer to the question",
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"parameters": {
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"type": "object",
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"properties": {
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"answer": {
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"type": "string",
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"description": "The final answer"
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}
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},
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"required": ["answer"]
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}
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}
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}
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]
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# Include tools in the conversation
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messages = [
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{
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"role": "system",
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"content": "You are a data science assistant. Use the available tools to analyze data and provide insights."
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},
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{"role": "user", "content": prompt}
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]
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```
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## Training Details
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### Training Data
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The model was fine-tuned on the [Jupyter Agent Dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset), which contains:
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- **51,389 synthetic notebooks** (~0.2B tokens, total 1B tokens)
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- **Dataset-grounded QA pairs** from real Kaggle notebooks
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- **Executable reasoning traces** with intermediate computations
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- **High-quality educational content** filtered and scored by LLMs
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### Training Procedure
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- **Base Model:** Qwen3-4B-Thinking-2507
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- **Training Method:** Full-parameter fine-tuning (not PEFT)
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- **Optimizer:** AdamW with cosine learning rate scheduling
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- **Learning Rate:** 5e-6
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- **Epochs:** 5 (optimal based on ablation study)
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- **Context Length:** 32,768 tokens
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- **Batch Size:** Distributed across multiple GPUs
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- **Loss:** Assistant-only loss (`assistant_loss_only=True`)
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- **Regularization:** NEFTune noise (α=7) for full-parameter training
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### Training Infrastructure
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- **Framework:** [TRL](https://github.com/huggingface/trl) with [Transformers](https://github.com/huggingface/transformers)
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- **Distributed Training:** DeepSpeed ZeRO-2 across multiple nodes
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- **Hardware:** Multi-GPU setup with SLURM orchestration
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## Evaluation
|
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### Benchmark: DABStep
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The model was evaluated on [DABStep](https://huggingface.co/spaces/adyen/DABstep), a benchmark for data science agents with realistic tasks involving:
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- **Dataset analysis** with pandas and numpy
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- **Visualization** with matplotlib/seaborn
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- **Statistical analysis** and business insights
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- **Multi-step reasoning** with intermediate computations
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The model achieves **26.8% improvement** over the base model and **11.1% improvement** over scaffolding alone.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/jupyter-agent-2/training_dabstep_easy.png" alt="DABstep Easy Score"/>
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We can also see, that the hard score can increase too even though our dataset is focused on easier questions.
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<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/jupyter-agent-2/training_dabstep_hard.png" alt="DABstep Hard Score"/>
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## Limitations and Bias
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### Technical Limitations
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- **Context window:** Limited to 32K tokens, may struggle with very large notebooks
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- **Tool calling format:** Requires specific scaffolding for optimal performance
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- **Dataset domains:** Primarily trained on Kaggle-style data science tasks
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- **Code execution:** Requires proper sandboxing for safe execution
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### Potential Biases
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- **Domain bias:** Trained primarily on Kaggle notebooks, may not generalize to all data science workflows
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- **Language bias:** Optimized for English and Python, limited multilingual support
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- **Task bias:** Focused on structured data analysis, may underperform on unstructured data tasks
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### Recommendations
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- Use in **sandboxed environments** like [E2B](https://e2b.dev/) for safe code execution
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- **Validate outputs** before using in production systems
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- **Review generated code** for security and correctness
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- Consider **domain adaptation** for specialized use cases
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## Ethical Considerations
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- **Code Safety:** Always execute generated code in secure, isolated environments
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- **Data Privacy:** Be cautious when analyzing sensitive datasets
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- **Verification:** Validate all analytical conclusions and insights
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- **Attribution:** Acknowledge model assistance in data analysis workflows
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## Citation
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```bibtex
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@misc{jupyteragentqwen3thinking,
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title={Jupyter Agent Qwen3-4B Thinking},
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author={Baptiste Colle and Hanna Yukhymenko and Leandro von Werra},
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year={2025},
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publisher={Hugging Face},
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url={https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-thinking}
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}
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```
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## Related Work
|
||||
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- **Dataset:** [jupyter-agent-dataset](https://huggingface.co/datasets/jupyter-agent/jupyter-agent-dataset)
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- **Non-thinking version:** [jupyter-agent-qwen3-4b-instruct](https://huggingface.co/jupyter-agent/jupyter-agent-qwen3-4b-instruct)
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- **Base model:** [Qwen3-4B-Thinking-2507](https://huggingface.co/Qwen/Qwen3-4B-Thinking-2507)
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- **Benchmark:** [DABStep](https://huggingface.co/spaces/adyen/DABstep)
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*For more details, see our [blog post](https://huggingface.co/blog/jupyter-agent-2) and [GitHub repository](https://github.com/huggingface/jupyter-agent).*
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added_tokens.json
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{
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"</think>": 151668,
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"</tool_call>": 151658,
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"</tool_response>": 151666,
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"<think>": 151667,
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"<tool_call>": 151657,
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"<tool_response>": 151665,
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"<|box_end|>": 151649,
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"<|box_start|>": 151648,
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"<|endoftext|>": 151643,
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"<|file_sep|>": 151664,
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"<|fim_middle|>": 151660,
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"<|fim_pad|>": 151662,
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"<|fim_prefix|>": 151659,
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"<|fim_suffix|>": 151661,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644,
|
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"<|image_pad|>": 151655,
|
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"<|object_ref_end|>": 151647,
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"<|object_ref_start|>": 151646,
|
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"<|quad_end|>": 151651,
|
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"<|quad_start|>": 151650,
|
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"<|repo_name|>": 151663,
|
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"<|video_pad|>": 151656,
|
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"<|vision_end|>": 151653,
|
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"<|vision_pad|>": 151654,
|
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"<|vision_start|>": 151652
|
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}
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chat_template.jinja
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{%- if tools %}
|
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{{- '<|im_start|>system\n' }}
|
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{%- if messages[0].role == 'system' %}
|
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{{- messages[0].content + '\n\n' }}
|
||||
{%- endif %}
|
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{{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
|
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{%- for tool in tools %}
|
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{{- "\n" }}
|
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{{- tool | tojson }}
|
||||
{%- endfor %}
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{{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
|
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{%- else %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- endif %}
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{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
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{%- for message in messages[::-1] %}
|
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{%- set index = (messages|length - 1) - loop.index0 %}
|
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{%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
|
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{%- set ns.multi_step_tool = false %}
|
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{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
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{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
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{%- set content = '' %}
|
||||
{%- endif %}
|
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{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
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{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
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{%- elif message.role == "assistant" %}
|
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{% generation %}
|
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{%- set reasoning_content = '' %}
|
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{%- if message.reasoning_content is string %}
|
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{%- set reasoning_content = message.reasoning_content %}
|
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{%- else %}
|
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{%- if '</think>' in content %}
|
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{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
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{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
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{%- endif %}
|
||||
{%- endif %}
|
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{%- if loop.index0 > ns.last_query_index %}
|
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{%- if loop.last or (not loop.last and reasoning_content) %}
|
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{{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content }}
|
||||
{%- endif %}
|
||||
{%- if message.tool_calls %}
|
||||
{%- for tool_call in message.tool_calls %}
|
||||
{%- if (loop.first and content) or (not loop.first) %}
|
||||
{{- '\n' }}
|
||||
{%- endif %}
|
||||
{%- if tool_call.function %}
|
||||
{%- set tool_call = tool_call.function %}
|
||||
{%- endif %}
|
||||
{{- '<tool_call>\n{"name": "' }}
|
||||
{{- tool_call.name }}
|
||||
{{- '", "arguments": ' }}
|
||||
{%- if tool_call.arguments is string %}
|
||||
{{- tool_call.arguments }}
|
||||
{%- else %}
|
||||
{{- tool_call.arguments | tojson }}
|
||||
{%- endif %}
|
||||
{{- '}\n</tool_call>' }}
|
||||
{%- endfor %}
|
||||
{%- endif %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{% endgeneration %}
|
||||
{%- elif message.role == "tool" %}
|
||||
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
|
||||
{{- '<|im_start|>user' }}
|
||||
{%- endif %}
|
||||
{{- '\n<tool_response>\n' }}
|
||||
{{- content }}
|
||||
{{- '\n</tool_response>' }}
|
||||
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
|
||||
{{- '<|im_end|>\n' }}
|
||||
{%- endif %}
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||||
{%- endif %}
|
||||
{%- endfor %}
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||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n' }}
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{%- if enable_thinking is defined and enable_thinking is false %}
|
||||
{{- '<think>\n\n</think>\n\n' }}
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||||
68
config.json
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68
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Normal file
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generation_config.json
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generation_config.json
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||||
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||||
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|
||||
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||||
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||||
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|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
@@ -0,0 +1,31 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"eos_token": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
BIN
tokenizer.json
(Stored with Git LFS)
Normal file
Binary file not shown.
239
tokenizer_config.json
Normal file
239
tokenizer_config.json
Normal file
@@ -0,0 +1,239 @@
|
||||
{
|
||||
"add_bos_token": false,
|
||||
"add_prefix_space": false,
|
||||
"added_tokens_decoder": {
|
||||
"151643": {
|
||||
"content": "<|endoftext|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151644": {
|
||||
"content": "<|im_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151645": {
|
||||
"content": "<|im_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151646": {
|
||||
"content": "<|object_ref_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151647": {
|
||||
"content": "<|object_ref_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151648": {
|
||||
"content": "<|box_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151649": {
|
||||
"content": "<|box_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151650": {
|
||||
"content": "<|quad_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151651": {
|
||||
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|
||||
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|
||||
"normalized": false,
|
||||
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|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151652": {
|
||||
"content": "<|vision_start|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151653": {
|
||||
"content": "<|vision_end|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151654": {
|
||||
"content": "<|vision_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151655": {
|
||||
"content": "<|image_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151656": {
|
||||
"content": "<|video_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": true
|
||||
},
|
||||
"151657": {
|
||||
"content": "<tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151658": {
|
||||
"content": "</tool_call>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151665": {
|
||||
"content": "<tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151666": {
|
||||
"content": "</tool_response>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151667": {
|
||||
"content": "<think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151668": {
|
||||
"content": "</think>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|im_end|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 262144,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user