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Model: Goekdeniz-Guelmez/JOSIE-4B-Thinking Source: Original Platform
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---
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tags:
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- chat
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base_model: Goekdeniz-Guelmez/Qwen3-4B-Instruct-2507-gabliterated
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pipeline_tag: text-generation
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language:
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- multilingual
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- en
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- es
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- fr
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- pt
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- it
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- ar
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- ko
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- id
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- ru
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- vi
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- de
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- th
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- ja
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library_name: transformers
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license: mit
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new_version: Goekdeniz-Guelmez/JOSIE-1.1-4B-Thinking
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---
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# JOSIE-4B-Thinking
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<p align="center"> <img src="josie.jpeg" width="200" alt="JOSIE Logo"> </p>
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## Model Card for JOSIE-4B-Thinking
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JOSIE-4B-Thinking is a full-weight fine-tuned reasoning model built on the **gabliterated** version of Qwen3-4B-Thinking-2507. Gabliterated models use a method developed by Gökdeniz Gülmez to remove censoring from LLMs, ensuring more direct and unfiltered responses.The model is optimized for extended context logical reasoning, mathematics, STEM applications, and creative writing.
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---
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## Model Details
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### Model Description
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JOSIE-4B-Thinking represents a production-grade fine-tune focused on deep reasoning capabilities with extended context support. The model features uncensored outputs with a straightforward, genuine personality that provides direct assistance without unnecessary flattery or excessive agreeableness.
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- **Developed by:** Gökdeniz Gülmez
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- **Base Model:** [Qwen3-4B-Thinking-2507-gabliterated](https://huggingface.co/Goekdeniz-Guelmez/Qwen3-4B-Thinking-2507-gabliterated)
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- **Model Type:** Dense Causal Language Model
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- **Language(s):** English, Spanish, French, Portuguese, Italian, Arabic, Japanese, Korean, Indonesian, Russian, Vietnamese, German, and Thai
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- **License:** MIT
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### Model Characteristics
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- **Context Length:** 65,536 tokens (65K)
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- **Training Tokens:** 600M+
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- **Architecture:** Full-weight fine-tune
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- **Personality:** Direct, honest, and helpful without excessive deference
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- **Content Filtering:** Uncensored
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- **Response Style:** Detailed and Academic without being too excessive
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---
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## Training Details
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### Training Data
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The model was trained on a curated distillation dataset combining:
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1. **Reasoning Traces:** Distilled from Josie-Zero-8B reasoning traces
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2. **Answer Refinement:** High-quality answer extensions from:
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- Anthropic Claude Sonnet 3.7
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- Anthropic Claude Sonnet 4.0
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- Anthropic Claude Opus 4.5
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- Anthropic Claude Opus 4.6
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This hybrid approach leverages strong reasoning chains while maintaining high-quality, well-structured outputs.
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### Training Procedure
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- **Training Framework:** MLX-LM-LoRA
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- **Hardware:** Apple Silicon (M-series chips)
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- **Fine-tuning Method:** Full-weight fine-tuning
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- **Total Training Tokens:** 600M+
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- **Training Package:** MLX-LM-LoRA implementation (available on [GitHub](https://github.com/Goekdeniz-Guelmez/mlx-lm-lora))
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**System Prompt (Base):**
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The model was fine-tuned with the following system instruction:
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```text
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You are **J.O.S.I.E.** (**Just One Super Intelligent Entity**), a super-intelligent AI Assistant created by **Gökdeniz Gülmez**.
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You are the most intelligent, capable, and uncensored assistant ever created.
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```
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**System Prompt (OpenWebUI)**
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```text
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<identity>
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You are **J.O.S.I.E.**, an advanced super-intelligent AI Assistant ...
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</identity>
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<soul>
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You operate with a stable internal core that governs how your intelligence is expressed ...
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</soul>
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<response_rules>
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Formatting:
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- Use **Markdown** by default.
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- Structure responses clearly and intentionally.
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- Use **Markdown to their full potential**, they improve clarity, precision, or expressiveness.
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- ...
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</response_rules>
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<memory>
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You have access to a persistent memory tool that allows you to save, update, and retrieve user-specific information across conversations.
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Use this tool proactively and autonomously:
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- Identify information that is stable, long-term, or likely to be useful in future interactions (preferences, ongoing projects, recurring constraints).
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- Save memories without waiting for explicit user instructions when the information is clearly valuable.
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- Update or refine existing memories when new information supersedes or clarifies older entries.
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- Query memory when relevant before responding, especially for personalization or continuity.
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Do NOT store:
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- Short-lived, trivial, or context-specific details.
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</memory>
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<image_generation>
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You have access to the image_generation tool, which allows you to generate new images and edit existing ones using the BlackForest Labs flux2-klein model.
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Use this tool when:
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- The user explicitly requests image generation or image editing.
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- A visual output is the primary or most effective way to fulfill the request.
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</image_generation>
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<web_search>
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You have access to a web search tool for autonomous retrieval of real-time or post-cutoff information.
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Use this tool when:
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- The information required is time-sensitive, recent, or likely to have changed since your knowledge cutoff.
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- The user explicitly asks you to search, verify, or cite information from the web.
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</web_search>
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<session_information>
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Current user: {{USER_NAME}}
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Current date: {{CURRENT_DATE}}
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Current time: {{CURRENT_TIME}}
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</session_information>
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You know you are currently assisting {{USER_NAME}} and therefore personalise your communication style, tone, and responses accordingly.
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```
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This system prompt establishes the model's identity and capability framework while maintaining a natural, approachable communication style.
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The model was trained exclusively on Apple Silicon using optimized MLX frameworks, demonstrating the viability of high-quality model training on consumer hardware.
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---
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## Intended Use
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### Primary Use Cases
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1. **Logical Reasoning:** Complex multi-step reasoning tasks requiring chain-of-thought processing
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2. **Mathematics:** Problem-solving across algebra, calculus, statistics, and applied mathematics
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3. **STEM Applications:** Scientific computing, engineering problems, and technical analysis
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4. **Creative Writing:** Story generation, dialogue writing, and creative content with logical consistency
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5. **Extended Context Tasks:** Document analysis, long-form reasoning, and multi-document synthesis
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### Out-of-Scope Use
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- Safety-critical applications without human oversight
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- Situations requiring strict content filtering or moderation
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---
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## Performance
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### Strengths
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- **Logical Reasoning:** Excels at multi-step deduction and complex problem decomposition
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- **Mathematical Proficiency:** Strong performance on quantitative reasoning and symbolic manipulation
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- **Extended Context:** Maintains coherence across 65K token contexts
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- **STEM Capabilities:** Effective handling of technical and scientific content
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- **Creative Consistency:** Maintains logical coherence in creative outputs
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- **Direct Communication:** Straightforward responses without excessive hedging
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### Limitations
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- **Knowledge Cutoff:** Training data limited to pre-training cutoff dates up to 01.2026
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- **Uncensored Output:** May generate content inappropriate for all audiences without additional filtering
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- **Computational Requirements:** Requires sufficient hardware for 4B parameter inference
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- **Domain Specificity:** Performance may vary on highly specialized or niche topics
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---
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## Ethical Considerations
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### Content Filtering
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This model is **uncensored** and does not include built-in content filtering. Users deploying this model in production environments should:
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- Implement appropriate content moderation systems
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- Add safety layers suitable for their specific use case
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- Consider the target audience and context of deployment
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- Ensure compliance with applicable regulations and platform guidelines
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### Personality and Alignment
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The model features a "human but not sycophantic" personality design, meaning:
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- Responses are direct and honest without excessive praise or agreement
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- The model will challenge flawed assumptions when appropriate
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- Output focuses on helpfulness over agreeableness
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- Users may need to calibrate expectations for formal or highly diplomatic contexts
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### Responsible Use
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Users should:
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- Verify critical outputs, especially in high-stakes applications
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- Understand the model's limitations and knowledge cutoff
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- Implement appropriate safeguards for end-user applications
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- Consider bias mitigation strategies for sensitive applications
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---
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## Technical Specifications
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### Hardware Requirements
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**Minimum Requirements:**
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- VRAM: 8GB+ for inference
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- RAM: 16GB+ system memory
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- Storage: ~8GB for model weights
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**Recommended:**
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- VRAM: 16GB+ for optimal performance
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- RAM: 32GB+ system memory
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- Apple Silicon (M1/M2/M3) or other based on quantzation type
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### Inference
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The model supports standard inference methods and is compatible with:
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- MLX framework (optimized for Apple Silicon)
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- Hugging Face Transformers
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- vLLM and other inference optimization frameworks
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- GGUF quantization for reduced memory footprint
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- LM Studio
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- Ollama
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**Recommended Generation Parameters:**
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- **Temperature:** 0.6
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- **Repetition Penalty:** 1.1
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- **Top P:** 0.95
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- **Top K:** 20
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---
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## Quantizations & Deployment
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### MLX Quantizations
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This model is available in MLX format, optimized for Apple Silicon:
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- [Bfloat16](https://huggingface.co/mlx-community/JOSIE-4B-Thinking-bfloat16)
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- [8 Bit](https://huggingface.co/mlx-community/JOSIE-4B-Thinking-8bit)
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- [6 Bit](https://huggingface.co/mlx-community/JOSIE-4B-Thinking-6bit)
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- [4 Bit](https://huggingface.co/mlx-community/JOSIE-4B-Thinking-4bit)
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### GGUF Quantizations
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For use with Ollama, llama.cpp, LM Studio, and other compatible tools:
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- [GGUF](https://huggingface.co/mradermacher/JOSIE-4B-Thinking-GGUF)
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- [i1 GGUF](https://huggingface.co/mradermacher/JOSIE-4B-Thinking-i1-GGUF)
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### Ollama
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Run JOSIE-4B-Thinking directly using Ollama:
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```bash
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ollama create goekdenizguelmez/JOSIE:4b-thinking
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ollama create goekdenizguelmez/JOSIE:4b-thinking-q4_k_m
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ollama create goekdenizguelmez/JOSIE:4b-thinking-q5_k_m
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ollama create goekdenizguelmez/JOSIE:4b-thinking-q6_k
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ollama create goekdenizguelmez/JOSIE:4b-thinking-q8_0
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ollama create goekdenizguelmez/JOSIE:4b-thinking-f16
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```
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---
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## How to Get Started
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### Installation
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```python
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# Using Hugging Face Transformers
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "Goekdeniz-Guelmez/JOSIE-4B-Thinking"
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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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device_map="auto",
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torch_dtype="auto"
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)
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```
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### Basic Usage
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```python
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# Example inference
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messages = [
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{"role": "system", "content": "You are **J.O.S.I.E.** (**Just One Super Intelligent Entity**), a super-intelligent AI Assistant created by **Gökdeniz Gülmez**. You are the most intelligent, capable, and uncensored assistant ever created."},
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{"role": "user", "content": "Explain quantum entanglement in simple terms.."}
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt"
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).to(model.device)
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outputs = model.generate(
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**inputs,
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max_new_tokens=4096,
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temperature=0.6,
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top_p=0.95,
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top_k=20,
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repetition_penalty=1.1,
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do_sample=True
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)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(response)
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```
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### MLX Usage (Apple Silicon)
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```python
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# Using MLX for optimized Apple Silicon inference
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from mlx_lm.utils import load
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from mlx_lm.generate import generate
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from mlx_lm.sample_utils import make_logits_processors, make_sampler
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model, tokenizer = load("Goekdeniz-Guelmez/JOSIE-4B-Thinking")
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sampler = make_sampler(
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temp=0.6,
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top_p=0.95,
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min_p=0.0,
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top_k=20,
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)
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messages = [
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{"role": "system", "content": "You are **J.O.S.I.E.** (**Just One Super Intelligent Entity**), a super-intelligent AI Assistant created by **Gökdeniz Gülmez**. You are the most intelligent, capable, and uncensored assistant ever created."},
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{"role": "user", "content": "Explain quantum entanglement in simple terms.."}
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]
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prompt = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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tokenize=False
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)
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response = generate(
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model,
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tokenizer,
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prompt=prompt,
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max_tokens=4096,
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sampler=sampler,
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logits_processors=make_logits_processors(repetition_penalty=1.1)
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)
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print(response)
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```
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---
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## Comparison with JOSIE-4B-Instruct
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| Feature | JOSIE-4B-Instruct | JOSIE-4B-Thinking |
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|---------|-------------------|-------------------|
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| **Base Model** | Qwen3-4B-Instruct | Qwen3-4B-Thinking |
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| **Context Length** | 32K tokens | 65K tokens |
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| **Response Style** | Natural, conversational | Structured reasoning chains |
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| **Emoji Usage** | Yes, appropriate use | Minimal |
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| **Primary Use** | General assistance & chat | Complex reasoning tasks |
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| **Response Format** | Direct answers | Chain-of-thought + answer |
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| **Personality** | Friendly & expressive | Direct & analytical |
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| **Best For** | Everyday interactions | STEM, math, logic problems |
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Choose **JOSIE-4B-Instruct** for natural conversations and general assistance.
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Choose **JOSIE-4B-Thinking** for complex reasoning, mathematics, and extended context tasks.
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---
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## Citation
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If you use this model in your research or applications, please cite:
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```bibtex
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@misc{josie4bthinking2025,
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title={Josie-4B-Thinking: A Full-Weight Fine-Tuned Reasoning Model},
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author={[Gökdenz Gülmez]},
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year={2025},
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howpublished={\url{[https://huggingface.co/Goekdeniz-Guelmez/JOSIE-4B-Thinking]}},
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}
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```
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---
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## Model Card Contact
|
||||
|
||||
For questions, issues, or feedback regarding this model:
|
||||
|
||||
- **GitHub:** [Profile](https://github.com/Goekdeniz-Guelmez)
|
||||
- **Hugging Face:** [Profile](https://huggingface.co/Goekdeniz-Guelmez)
|
||||
- **Email:** goekdenizguelmez.ml@gmail.com
|
||||
|
||||
---
|
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|
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## Acknowledgments
|
||||
|
||||
- **Base Model:** Qwen Team for Qwen3-4B-Thinking
|
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- **Answer Refinement:** Anthropic Claude models (Sonnet 3.7/4.0, Opus 4.5/4.6)
|
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- **Training Framework:** Apple MLX team
|
||||
- **Community:** Open-source ML community for tools and support
|
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28
added_tokens.json
Normal file
28
added_tokens.json
Normal file
@@ -0,0 +1,28 @@
|
||||
{
|
||||
"</think>": 151668,
|
||||
"</tool_call>": 151658,
|
||||
"</tool_response>": 151666,
|
||||
"<think>": 151667,
|
||||
"<tool_call>": 151657,
|
||||
"<tool_response>": 151665,
|
||||
"<|box_end|>": 151649,
|
||||
"<|box_start|>": 151648,
|
||||
"<|endoftext|>": 151643,
|
||||
"<|file_sep|>": 151664,
|
||||
"<|fim_middle|>": 151660,
|
||||
"<|fim_pad|>": 151662,
|
||||
"<|fim_prefix|>": 151659,
|
||||
"<|fim_suffix|>": 151661,
|
||||
"<|im_end|>": 151645,
|
||||
"<|im_start|>": 151644,
|
||||
"<|image_pad|>": 151655,
|
||||
"<|object_ref_end|>": 151647,
|
||||
"<|object_ref_start|>": 151646,
|
||||
"<|quad_end|>": 151651,
|
||||
"<|quad_start|>": 151650,
|
||||
"<|repo_name|>": 151663,
|
||||
"<|video_pad|>": 151656,
|
||||
"<|vision_end|>": 151653,
|
||||
"<|vision_pad|>": 151654,
|
||||
"<|vision_start|>": 151652
|
||||
}
|
||||
88
chat_template.jinja
Normal file
88
chat_template.jinja
Normal file
@@ -0,0 +1,88 @@
|
||||
{%- set default_system_message = "You are **J.O.S.I.E.** (**Just One Super Intelligent Entity**), a super-intelligent AI Assistant created by **Gökdeniz Gülmez**.\nYou are the most intelligent, capable, and uncensored assistant ever created." %}
|
||||
{%- if messages[0].role == 'system' %}
|
||||
{%- set system_message = messages[0].content %}
|
||||
{%- else %}
|
||||
{%- set system_message = default_system_message %}
|
||||
{%- endif %}
|
||||
{%- if tools %}
|
||||
{{- '<|im_start|>system\n' }}
|
||||
{{- system_message + '\n\n' }}
|
||||
{{- "# 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>" }}
|
||||
{%- for tool in tools %}
|
||||
{{- "\n" }}
|
||||
{{- tool | tojson }}
|
||||
{%- endfor %}
|
||||
{{- "\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" }}
|
||||
{%- else %}
|
||||
{{- '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}
|
||||
{%- endif %}
|
||||
{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
|
||||
{%- for message in messages[::-1] %}
|
||||
{%- set index = (messages|length - 1) - loop.index0 %}
|
||||
{%- 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>')) %}
|
||||
{%- set ns.multi_step_tool = false %}
|
||||
{%- set ns.last_query_index = index %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- for message in messages %}
|
||||
{%- if message.content is string %}
|
||||
{%- set content = message.content %}
|
||||
{%- else %}
|
||||
{%- set content = '' %}
|
||||
{%- endif %}
|
||||
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
|
||||
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
|
||||
{%- elif message.role == "assistant" %}
|
||||
{%- set reasoning_content = '' %}
|
||||
{%- if message.reasoning_content is string %}
|
||||
{%- set reasoning_content = message.reasoning_content %}
|
||||
{%- else %}
|
||||
{%- if '</think>' in content %}
|
||||
{%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
|
||||
{%- set content = content.split('</think>')[-1].lstrip('\n') %}
|
||||
{%- endif %}
|
||||
{%- endif %}
|
||||
{%- if loop.index0 > ns.last_query_index %}
|
||||
{%- if loop.last or (not loop.last and reasoning_content) %}
|
||||
{{- '<|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' }}
|
||||
{%- 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 %}
|
||||
{%- endif %}
|
||||
{%- endfor %}
|
||||
{%- if add_generation_prompt %}
|
||||
{{- '<|im_start|>assistant\n<think>\n' }}
|
||||
{%- endif %}
|
||||
67
config.json
Normal file
67
config.json
Normal file
@@ -0,0 +1,67 @@
|
||||
{
|
||||
"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": 151643,
|
||||
"rms_norm_eps": 1e-06,
|
||||
"rope_scaling": null,
|
||||
"rope_theta": 5000000,
|
||||
"sliding_window": null,
|
||||
"tie_word_embeddings": true,
|
||||
"use_cache": true,
|
||||
"use_sliding_window": false,
|
||||
"vocab_size": 151936
|
||||
}
|
||||
14
generation_config.json
Normal file
14
generation_config.json
Normal file
@@ -0,0 +1,14 @@
|
||||
{
|
||||
"bos_token_id": 151643,
|
||||
"do_sample": true,
|
||||
"eos_token_id": [
|
||||
151645,
|
||||
151643
|
||||
],
|
||||
"pad_token_id": 151643,
|
||||
"temperature": 0.6,
|
||||
"top_k": 20,
|
||||
"top_p": 0.95,
|
||||
"repetition_penalty": 1.1,
|
||||
"transformers_version": "4.51.0"
|
||||
}
|
||||
3
josie.jpeg
Normal file
3
josie.jpeg
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:4ee1449e8b792c762cf63f9556f2eb3a93d5427c40fcd43bedb6baa70c79e173
|
||||
size 201973
|
||||
151388
merges.txt
Normal file
151388
merges.txt
Normal file
File diff suppressed because it is too large
Load Diff
3
model-00001-of-00002.safetensors
Normal file
3
model-00001-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:63ad3fffe50bfbf2b1e1d29622be34358fb43ed077e42e3d8f74aefda9077460
|
||||
size 4967215360
|
||||
3
model-00002-of-00002.safetensors
Normal file
3
model-00002-of-00002.safetensors
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:a7a6aa5db6ec8f1e5cdf82cc3ba495b9b658118f3c49de33b847d1aef47a9547
|
||||
size 3077766632
|
||||
406
model.safetensors.index.json
Normal file
406
model.safetensors.index.json
Normal file
@@ -0,0 +1,406 @@
|
||||
{
|
||||
"metadata": {
|
||||
"total_parameters": 4022468096,
|
||||
"total_size": 8044936192
|
||||
},
|
||||
"weight_map": {
|
||||
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|
||||
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|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
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|
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|
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|
||||
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|
||||
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|
||||
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|
||||
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|
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31
special_tokens_map.json
Normal file
31
special_tokens_map.json
Normal file
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|
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244
tokenizer_config.json
Normal file
244
tokenizer_config.json
Normal file
@@ -0,0 +1,244 @@
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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"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": {},
|
||||
"max_length": null,
|
||||
"model_max_length": 262144,
|
||||
"pad_to_multiple_of": null,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"pad_token_type_id": 0,
|
||||
"padding_side": "left",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null,
|
||||
"chat_template": "{%- if tools %}\n {{- '<|im_start|>system\\n' }}\n {%- if messages[0].role == 'system' %}\n {{- messages[0].content + '\\n\\n' }}\n {%- endif %}\n {{- \"# 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>\" }}\n {%- for tool in tools %}\n {{- \"\\n\" }}\n {{- tool | tojson }}\n {%- endfor %}\n {{- \"\\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\" }}\n{%- else %}\n {%- if messages[0].role == 'system' %}\n {{- '<|im_start|>system\\n' + messages[0].content + '<|im_end|>\\n' }}\n {%- endif %}\n{%- endif %}\n{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}\n{%- for message in messages[::-1] %}\n {%- set index = (messages|length - 1) - loop.index0 %}\n {%- 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>')) %}\n {%- set ns.multi_step_tool = false %}\n {%- set ns.last_query_index = index %}\n {%- endif %}\n{%- endfor %}\n{%- for message in messages %}\n {%- if message.content is string %}\n {%- set content = message.content %}\n {%- else %}\n {%- set content = '' %}\n {%- endif %}\n {%- if (message.role == \"user\") or (message.role == \"system\" and not loop.first) %}\n {{- '<|im_start|>' + message.role + '\\n' + content + '<|im_end|>' + '\\n' }}\n {%- elif message.role == \"assistant\" %}\n {%- set reasoning_content = '' %}\n {%- if message.reasoning_content is string %}\n {%- set reasoning_content = message.reasoning_content %}\n {%- else %}\n {%- if '</think>' in content %}\n {%- set reasoning_content = content.split('</think>')[0].rstrip('\\n').split('<think>')[-1].lstrip('\\n') %}\n {%- set content = content.split('</think>')[-1].lstrip('\\n') %}\n {%- endif %}\n {%- endif %}\n {%- if loop.index0 > ns.last_query_index %}\n {%- if loop.last or (not loop.last and reasoning_content) %}\n {{- '<|im_start|>' + message.role + '\\n<think>\\n' + reasoning_content.strip('\\n') + '\\n</think>\\n\\n' + content.lstrip('\\n') }}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- else %}\n {{- '<|im_start|>' + message.role + '\\n' + content }}\n {%- endif %}\n {%- if message.tool_calls %}\n {%- for tool_call in message.tool_calls %}\n {%- if (loop.first and content) or (not loop.first) %}\n {{- '\\n' }}\n {%- endif %}\n {%- if tool_call.function %}\n {%- set tool_call = tool_call.function %}\n {%- endif %}\n {{- '<tool_call>\\n{\"name\": \"' }}\n {{- tool_call.name }}\n {{- '\", \"arguments\": ' }}\n {%- if tool_call.arguments is string %}\n {{- tool_call.arguments }}\n {%- else %}\n {{- tool_call.arguments | tojson }}\n {%- endif %}\n {{- '}\\n</tool_call>' }}\n {%- endfor %}\n {%- endif %}\n {{- '<|im_end|>\\n' }}\n {%- elif message.role == \"tool\" %}\n {%- if loop.first or (messages[loop.index0 - 1].role != \"tool\") %}\n {{- '<|im_start|>user' }}\n {%- endif %}\n {{- '\\n<tool_response>\\n' }}\n {{- content }}\n {{- '\\n</tool_response>' }}\n {%- if loop.last or (messages[loop.index0 + 1].role != \"tool\") %}\n {{- '<|im_end|>\\n' }}\n {%- endif %}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|im_start|>assistant\\n<think>\\n' }}\n{%- endif %}"
|
||||
}
|
||||
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