JOSIE-1.1-4B-Instruct

JOSIE-1.1-4B-Instruct

Model Card for JOSIE-1.1-4B-Instruct

JOSIE-1.1-4B-Instruct is a full-weight fine-tuned instruction-following model built on Qwen3-4B-Instruct, optimized for natural conversational interactions, problem-solving, and everyday assistance with a human-like personality.

<p align="center"> <img src="josie.png" width="100%" alt="JOSIE Logo"> </p>

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Model Details

Model Description

JOSIE-1.1-4B-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.

• Developed by: Gökdeniz Gülmez
• Base Model: Qwen3-4B-Instruct
• Model Type: Dense Causal Language Model
• Language(s): English, Spanish, French, Portuguese, Italian, Arabic, Chinese, 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

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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-o3-mini-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 Repository: Custom MLX-LM-LoRA implementation (available on GitHub)

System Prompt:

The model was fine-tuned with the following system instruction:

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.

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.

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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

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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
• 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

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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

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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

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How to Get Started

Installation

# Using Hugging Face Transformers
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "Goekdeniz-Guelmez/JOSIE-1.1-4B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype="auto"
)

Basic Usage

# Example inference
messages = [
    {"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)

# 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-1.1-4B-Instruct")

sampler = make_sampler(
    temp=0.7,
    top_p=0.8,
    min_p=0.0,
    top_k=20,
)

messages = [
    {"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)

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Comparison with JOSIE-1.1-4B-Thinking

Choose JOSIE-1.1-4B-Instruct for natural conversations and general assistance. Choose JOSIE-1.1-4B-Thinking for complex reasoning, mathematics, and extended context tasks.

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Citation

If you use this model in your research or applications, please cite:

@misc{josie4binstruct2025,
  title={JOSIE-1.1-4B-Instruct: A Human-Like Instruction-Following Model},
  author={Gökdeniz Gülmez},
  year={2025},
  howpublished={\url{https://huggingface.co/Goekdeniz-Guelmez/JOSIE-1.1-4B-Instruct}},
}

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Model Card Contact

For questions, issues, or feedback regarding this model:

• GitHub: Profile
• Hugging Face: Profile
• Email: [email protected]

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Acknowledgments

• Base Model: Qwen Team for Qwen3-4B-Instruct
• Reasoning Source: Josie-Zero-8B for reasoning traces
• Response Transformation: OpenAI GPT-o3-mini-120B for human-like response generation
• Training Framework: Apple MLX team
• Community: Open-source ML community for tools and support