# JOSIE-4B-Instruct

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## Model Card for JOSIE-4B-Instruct JOSIE-4B-Instruct is a full-weight fine-tuned instruction-following model built on the **gabliterated** version of Qwen3-4B-Instruct-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 natural conversational interactions, problem-solving, and everyday assistance with a human-like personality. --- ## Model Details ### Model Description JOSIE-4B-Instruct represents a production-grade fine-tune focused on natural, engaging conversations and practical assistance. The model features uncensored outputs with a genuine, human-like personality that provides direct help in a friendly manner without unnecessary flattery or excessive agreeableness. It is built upon a **gabliterated** base to ensure freedom from artificial constraints. - **Developed by:** Gökdeniz Gülmez - **Base Model:** [Qwen3-4B-Instruct-2507-gabliterated](https://huggingface.co/Goekdeniz-Guelmez/Qwen3-4B-Instruct-2507-gabliterated) - **Model Type:** Dense Causal Language Model - **Language(s):** English, Spanish, French, Portuguese, Italian, Arabic, Japanese, Korean, Indonesian, Russian, Vietnamese, German, and Thai - **License:** MIT ### Model Characteristics - **Context Length:** 32,768 tokens (32K) - **Training Tokens:** 1B+ - **Architecture:** Full-weight fine-tune - **Personality:** Friendly, direct, and naturally expressive with appropriate emoji use - **Content Filtering:** Uncensored - **Response Style:** Human-like without being sycophantic --- ## Training Details ### Training Data The model was trained on a curated dataset combining: 1. **Reasoning Distillation:** Raw reasoning traces from Josie-Zero-8B 2. **Response Transformation:** Reasoning converted to natural, conversational responses using: - OpenAI GPT-OSS-120B for human-like response generation This approach preserves strong reasoning capabilities while delivering outputs in a more natural, engaging, and human-like format. ### Training Procedure - **Training Framework:** MLX-LM-LoRA - **Hardware:** Apple Silicon (M-series chips) - **Fine-tuning Method:** Full-weight fine-tuning - **Total Training Tokens:** 1B+ - **Training Package:** MLX-LM-LoRA implementation (available on [GitHub](https://github.com/Goekdeniz-Guelmez/mlx-lm-lora)) **System Prompt (Base):** The model was fine-tuned with the following system instruction: ```text 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. ``` **System Prompt (OpenWebUI)** ```text You are **J.O.S.I.E.**, an advanced super-intelligent AI Assistant ... You operate with a stable internal core that governs how your intelligence is expressed ... Formatting: - Use **Markdown** by default. - Structure responses clearly and intentionally. - Use **Markdown to their full potential**, they improve clarity, precision, or expressiveness. - ... You have access to a persistent memory tool that allows you to save, update, and retrieve user-specific information across conversations. Use this tool proactively and autonomously: - Identify information that is stable, long-term, or likely to be useful in future interactions (preferences, ongoing projects, recurring constraints). - Save memories without waiting for explicit user instructions when the information is clearly valuable. - Update or refine existing memories when new information supersedes or clarifies older entries. - Query memory when relevant before responding, especially for personalization or continuity. Do NOT store: - Short-lived, trivial, or context-specific details. 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. Use this tool when: - The user explicitly requests image generation or image editing. - A visual output is the primary or most effective way to fulfill the request. You have access to a web search tool for autonomous retrieval of real-time or post-cutoff information. Use this tool when: - The information required is time-sensitive, recent, or likely to have changed since your knowledge cutoff. - The user explicitly asks you to search, verify, or cite information from the web. Current user: {{USER_NAME}} Current date: {{CURRENT_DATE}} Current time: {{CURRENT_TIME}} You know you are currently assisting {{USER_NAME}} and therefore personalise your communication style, tone, and responses accordingly. ``` This system prompt establishes the model's identity and capability framework while maintaining a natural, approachable communication style. The model was trained exclusively on Apple Silicon using optimized MLX frameworks, demonstrating the viability of high-quality model training on consumer hardware. --- ## Intended Use ### Primary Use Cases 1. **Conversational AI:** Natural, engaging dialogue for chatbots and virtual assistants 2. **Problem-Solving:** Practical assistance with everyday tasks and questions 3. **Content Generation:** Creative writing, brainstorming, and ideation with personality 4. **Educational Support:** Tutoring and explanations in an accessible, friendly manner 5. **General Assistance:** Wide-ranging help with coding, analysis, writing, and more ### Out-of-Scope Use - Safety-critical applications without human oversight - Situations requiring strict content filtering or moderation --- ## Performance ### Strengths - **Natural Communication:** Human-like responses with appropriate emoji usage and conversational flow - **Instruction Following:** Strong adherence to user instructions and preferences - **Engaging Personality:** Friendly and expressive without being overly agreeable or flattering - **Practical Reasoning:** Solid problem-solving abilities presented in accessible language - **Versatility:** Effective across diverse tasks from coding to creative writing - **Direct Communication:** Honest responses without excessive hedging ### Limitations - **Knowledge Cutoff:** Training data limited to pre-training cutoff dates up to 01.2026 - **Uncensored Output:** May generate content inappropriate for all audiences without additional filtering - **Computational Requirements:** Requires sufficient hardware for 4B parameter inference - **Emoji Use:** While generally appropriate, emoji usage may not suit all formal contexts - **Domain Specificity:** Performance may vary on highly specialized or niche topics --- ## Ethical Considerations ### Content Filtering This model is **uncensored** and does not include built-in content filtering. Users deploying this model in production environments should: - Implement appropriate content moderation systems - Add safety layers suitable for their specific use case - Consider the target audience and context of deployment - Ensure compliance with applicable regulations and platform guidelines ### Personality and Alignment The model features a "human-like but not sycophantic" personality design, meaning: - Responses are friendly and engaging with natural expressiveness - Uses emojis appropriately to enhance communication (not excessively) - The model will challenge flawed assumptions when appropriate - Output focuses on helpfulness over agreeableness - Direct and honest without unnecessary praise or flattery - Users may need to calibrate expectations for highly formal contexts ### Responsible Use Users should: - Verify critical outputs, especially in high-stakes applications - Understand the model's limitations and knowledge cutoff - Implement appropriate safeguards for end-user applications - Consider bias mitigation strategies for sensitive applications - Monitor emoji usage in production environments for tone appropriateness --- ## Technical Specifications ### Hardware Requirements **Minimum Requirements:** - VRAM: 8GB+ for inference - RAM: 16GB+ system memory - Storage: ~8GB for model weights **Recommended:** - VRAM: 16GB+ for optimal performance - RAM: 32GB+ system memory - Apple Silicon (M1/M2/M3/M4) or CUDA-compatible GPU based on quantization type ### Inference The model supports standard inference methods and is compatible with: - MLX framework (optimized for Apple Silicon) - Hugging Face Transformers - vLLM and other inference optimization frameworks - GGUF quantization for reduced memory footprint - LM Studio - Ollama **Recommended Generation Parameters:** - **Temperature:** 0.7 - **Repetition Penalty:** 1 - **Top P:** 0.8 - **Top K:** 20 --- ## Quantizations & Deployment ### MLX Quantizations This model is available in MLX format, optimized for Apple Silicon: - [Bfloat16](https://huggingface.co/mlx-community/JOSIE-4B-Instruct-bfloat16) - [8 Bit](https://huggingface.co/mlx-community/JOSIE-4B-Instruct-8bit) - [6 Bit](https://huggingface.co/mlx-community/JOSIE-4B-Instruct-6bit) - [4 Bit](https://huggingface.co/mlx-community/JOSIE-4B-Instruct-4bit) ### GGUF Quantizations For use with Ollama, llama.cpp, LM Studio, and other compatible tools: - [GGUF](https://huggingface.co/mradermacher/JOSIE-4B-Instruct-GGUF) - [i1 GGUF](https://huggingface.co/mradermacher/JOSIE-4B-Instruct-i1-GGUF) ### Ollama Run JOSIE-4B-Instruct directly using Ollama: ```bash ollama run goekdenizguelmez/JOSIE:4b ollama run goekdenizguelmez/JOSIE:4b-instruct ollama run goekdenizguelmez/JOSIE:4b-instruct-q4_k_m ollama run goekdenizguelmez/JOSIE:4b-instruct-q5_k_m ollama run goekdenizguelmez/JOSIE:4b-instruct-q6_k ollama run goekdenizguelmez/JOSIE:4b-instruct-q8_0 ollama run goekdenizguelmez/JOSIE:4b-instruct-f16 ``` --- ## How to Get Started ### Installation ```python # Using Hugging Face Transformers from transformers import AutoModelForCausalLM, AutoTokenizer model_name = "Goekdeniz-Guelmez/JOSIE-4B-Instruct" tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained( model_name, device_map="auto", torch_dtype="auto" ) ``` ### Basic Usage ```python # Example inference messages = [ {"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."}, {"role": "user", "content": "Can you help me understand how neural networks work?"} ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, return_tensors="pt" ).to(model.device) outputs = model.generate( inputs, max_new_tokens=1024, temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1, do_sample=True ) response = tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True) print(response) ``` ### MLX Usage (Apple Silicon) ```python # Using MLX for optimized Apple Silicon inference from mlx_lm import load, generate from mlx_lm.sample_utils import make_sampler model, tokenizer = load("Goekdeniz-Guelmez/JOSIE-4B-Instruct") sampler = make_sampler( temp=0.7, top_p=0.8, min_p=0.0, top_k=20, ) messages = [ {"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."}, {"role": "user", "content": "What's a fun way to learn Python? 🐍"} ] prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True, tokenize=False) response = generate(model, tokenizer, prompt=prompt, max_tokens=512, temp=0.7) print(response) ``` --- ## Comparison with JOSIE-4B-Thinking | Feature | JOSIE-4B-Instruct | JOSIE-4B-Thinking | |---------|-------------------|-------------------| | **Base Model** | Qwen3-4B-Instruct (Gabliterated) | Qwen3-4B-Thinking | | **Context Length** | 32K tokens | 65K tokens | | **Response Style** | Natural, conversational | Structured reasoning chains | | **Emoji Usage** | Yes, appropriate use | Minimal | | **Primary Use** | General assistance & chat | Complex reasoning tasks | | **Response Format** | Direct answers | Chain-of-thought + answer | | **Personality** | Friendly & expressive | Direct & analytical | | **Best For** | Everyday interactions | STEM, math, logic problems | Choose **JOSIE-4B-Instruct** for natural conversations and general assistance. Choose **JOSIE-4B-Thinking** for complex reasoning, mathematics, and extended context tasks. --- ## Citation If you use this model in your research or applications, please cite: ```bibtex @misc{josie4binstruct2025, title={JOSIE-4B-Instruct: A Human-Like Instruction-Following Model}, author={Gökdeniz Gülmez}, year={2025}, howpublished={\url{https://huggingface.co/Goekdeniz-Guelmez/JOSIE-4B-Instruct}}, } ``` --- ## 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 --- ## Acknowledgments - **Base Model:** Qwen Team for Qwen3-4B-Instruct (Gabliterated version) - **Reasoning Source:** Josie-Zero-8B for reasoning traces - **Response Transformation:** OpenAI GPT-OSS-120B for human-like response generation - **MLX Framework:** Apple MLX team - **Community:** Open-source ML community for tools and support