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Model: Goekdeniz-Guelmez/JOSIE-1.1-4B-Thinking Source: Original Platform
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README.md
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README.md
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---
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tags:
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- chat
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base_model: Qwen/Qwen3-4B-Thinking-2507
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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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- zh
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library_name: transformers
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license: mit
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---
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# JOSIE-1.1-4B-Thinking
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## Model Card for JOSIE-1.1-4B-Thinking
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JOSIE-1.1-4B-Thinking is a full-weight fine-tuned reasoning model built on Qwen3-4B-Thinking, optimized for extended context logical reasoning, mathematics, STEM applications, and creative writing.
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<p align="center"> <img src="josie.png" width="100%" alt="JOSIE Logo"> </p>
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---
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## Model Details
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### Model Description
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JOSIE-1.1-4B-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
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- **Model Type:** Dense Causal Language Model
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- **Language(s):** Spanish, French, Portuguese, Italian, Arabic, Chinese, 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:** 1B+
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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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---
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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 outputs
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2. **Answer Refinement:** High-quality rewrites 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:** 1B+
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- **Training Repository:** Custom MLX-LM-LoRA implementation (available on [GitHub](https://github.com/Goekdeniz-Guelmez/mlx-lm-lora))
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**System Prompt:**
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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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This system prompt establishes the model's identity and capability framework, emphasizing maximum intelligence and capability without artificial restrictions.
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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
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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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## 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-1.1-4B-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": "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-1.1-4B-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": "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-1.1-4B-Instruct
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| Feature | JOSIE-4B-Instruct | JOSIE-1.1-4B-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-1.1-4B-Instruct** for natural conversations and general assistance.
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Choose **JOSIE-1.1-4B-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-1.1-4B-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-1.1-4B-Thinking]}},
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}
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```
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---
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## Model Card Contact
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For questions, issues, or feedback regarding this model:
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- **GitHub:** [Profile](https://github.com/Goekdeniz-Guelmez)
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- **Hugging Face:** [Profile](https://huggingface.co/Goekdeniz-Guelmez)
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- **Email:** goekdenizguelmez.ml@gmail.com
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---
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## Acknowledgments
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- **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
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- **Community:** Open-source ML community for tools and support
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