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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

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{%- 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 %}
{%- 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" %}
{{- '<|im_start|>' + message.role + '\n' + content }}
{%- 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' }}
{%- endif %}

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"151649": {
"content": "<|box_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151650": {
"content": "<|quad_start|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"single_word": false,
"special": true
},
"151651": {
"content": "<|quad_end|>",
"lstrip": false,
"normalized": false,
"rstrip": false,
"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|>",
"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{%- 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 {{- '<|im_start|>' + message.role + '\\n' + content }}\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' }}\n{%- endif %}"
}

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vocab.json Normal file
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