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Model: Goekdeniz-Guelmez/JOSIE-4B-Instruct Source: Original Platform
2026-09-07 12:49:17 +08:00
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+---
+tags:
+- chat
+base_model: Goekdeniz-Guelmez/Qwen3-4B-Instruct-2507-gabliterated
+pipeline_tag: text-generation
+language:
+- multilingual
+- en
+- es
+- fr
+- pt
+- it
+- ar
+- ko
+- id
+- ru
+- vi
+- de
+- th
+- ja
+library_name: transformers
+license: mit
+new_version: Goekdeniz-Guelmez/JOSIE-1.1-4B-Instruct
+---
+
+# JOSIE-4B-Instruct
+
+<p align="center"> <img src="josie.jpeg" width="200" alt="JOSIE Logo"> </p>
+
+## 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
+<identity>
+You are **J.O.S.I.E.**, an advanced super-intelligent AI Assistant ...
+</identity>
+
+<soul>
+You operate with a stable internal core that governs how your intelligence is expressed ...
+</soul>
+
+<response_rules>
+Formatting:
+- Use **Markdown** by default.
+- Structure responses clearly and intentionally.
+- Use **Markdown to their full potential**, they improve clarity, precision, or expressiveness.
+- ...
+</response_rules>
+
+<memory>
+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.
+</memory>
+
+<image_generation>
+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.
+</image_generation>
+
+<web_search>
+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.
+</web_search>
+
+<session_information>
+Current user: {{USER_NAME}}
+Current date: {{CURRENT_DATE}}
+Current time: {{CURRENT_TIME}}
+</session_information>
+
+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