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Model: Goekdeniz-Guelmez/JOSIE-1.1-4B-Thinking
Source: Original Platform

.gitattributes

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README.md

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+---
+tags:
+- chat
+base_model: Qwen/Qwen3-4B-Thinking-2507
+pipeline_tag: text-generation
+language:
+- multilingual
+- en
+- es
+- fr
+- pt
+- it
+- ar
+- ko
+- id
+- ru
+- vi
+- de
+- th
+- ja
+- zh
+library_name: transformers
+license: mit
+---
+
+# JOSIE-1.1-4B-Thinking
+
+## Model Card for JOSIE-1.1-4B-Thinking
+
+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.
+
+<p align="center"> <img src="josie.png" width="100%" alt="JOSIE Logo"> </p>
+
+---
+
+## Model Details
+
+### Model Description
+
+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.
+
+- **Developed by:** Gökdeniz Gülmez
+- **Base Model:** Qwen3-4B-Thinking
+- **Model Type:** Dense Causal Language Model
+- **Language(s):** Spanish, French, Portuguese, Italian, Arabic, Chinese, Japanese, Korean, Indonesian, Russian, Vietnamese, German, and Thai
+- **License:** MIT
+
+### Model Characteristics
+
+- **Context Length:** 65,536 tokens (65K)
+- **Training Tokens:** 1B+
+- **Architecture:** Full-weight fine-tune
+- **Personality:** Direct, honest, and helpful without excessive deference
+- **Content Filtering:** Uncensored
+
+---
+
+## Training Details
+
+### Training Data
+
+The model was trained on a curated distillation dataset combining:
+
+1. **Reasoning Traces:** Distilled from Josie-Zero-8B reasoning outputs
+2. **Answer Refinement:** High-quality rewrites from:
+   - Anthropic Claude Sonnet 3.7
+   - Anthropic Claude Sonnet 4.0
+   - Anthropic Claude Opus 4.5
+   - Anthropic Claude Opus 4.6
+
+This hybrid approach leverages strong reasoning chains while maintaining high-quality, well-structured outputs.
+
+### 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](https://github.com/Goekdeniz-Guelmez/mlx-lm-lora))
+
+**System Prompt:**
+
+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.
+```
+
+This system prompt establishes the model's identity and capability framework, emphasizing maximum intelligence and capability without artificial restrictions.
+
+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. **Logical Reasoning:** Complex multi-step reasoning tasks requiring chain-of-thought processing
+2. **Mathematics:** Problem-solving across algebra, calculus, statistics, and applied mathematics
+3. **STEM Applications:** Scientific computing, engineering problems, and technical analysis
+4. **Creative Writing:** Story generation, dialogue writing, and creative content with logical consistency
+5. **Extended Context Tasks:** Document analysis, long-form reasoning, and multi-document synthesis
+
+### Out-of-Scope Use
+
+- Safety-critical applications without human oversight
+- Situations requiring strict content filtering or moderation
+
+---
+
+## Performance
+
+### Strengths
+
+- **Logical Reasoning:** Excels at multi-step deduction and complex problem decomposition
+- **Mathematical Proficiency:** Strong performance on quantitative reasoning and symbolic manipulation
+- **Extended Context:** Maintains coherence across 65K token contexts
+- **STEM Capabilities:** Effective handling of technical and scientific content
+- **Creative Consistency:** Maintains logical coherence in creative outputs
+- **Direct Communication:** Straightforward 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
+- **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 but not sycophantic" personality design, meaning:
+
+- Responses are direct and honest without excessive praise or agreement
+- The model will challenge flawed assumptions when appropriate
+- Output focuses on helpfulness over agreeableness
+- Users may need to calibrate expectations for formal or highly diplomatic 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
+
+---
+
+## 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) or other based on quantzation 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:** 0.6
+- **Repetition Penalty:** 1.1
+- **Top P:** 0.95
+- **Top K:** 20
+
+---
+
+## How to Get Started
+
+### Installation
+
+```python
+# Using Hugging Face Transformers
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "Goekdeniz-Guelmez/JOSIE-1.1-4B-Thinking"
+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": "user", "content": "Explain quantum entanglement in simple terms.."}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=4096,
+    temperature=0.6,
+    top_p=0.95,
+    top_k=20,
+    repetition_penalty=1.1,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### MLX Usage (Apple Silicon)
+
+```python
+# Using MLX for optimized Apple Silicon inference
+from mlx_lm.utils import load
+from mlx_lm.generate import generate
+from mlx_lm.sample_utils import make_logits_processors, make_sampler
+
+model, tokenizer = load("Goekdeniz-Guelmez/JOSIE-1.1-4B-Thinking")
+
+sampler = make_sampler(
+    temp=0.6,
+    top_p=0.95,
+    min_p=0.0,
+    top_k=20,
+)
+
+messages = [
+    {"role": "user", "content": "Explain quantum entanglement in simple terms.."}
+]
+
+prompt = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    tokenize=False
+)
+
+response = generate(
+    model, 
+    tokenizer, 
+    prompt=prompt, 
+    max_tokens=4096,
+    sampler=sampler,
+    logits_processors=make_logits_processors(repetition_penalty=1.1)
+)
+print(response)
+```
+
+---
+
+## Comparison with JOSIE-1.1-4B-Instruct
+
+| Feature | JOSIE-4B-Instruct | JOSIE-1.1-4B-Thinking |
+|---------|-------------------|-------------------|
+| **Base Model** | Qwen3-4B-Instruct | 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-1.1-4B-Instruct** for natural conversations and general assistance.
+Choose **JOSIE-1.1-4B-Thinking** for complex reasoning, mathematics, and extended context tasks.
+
+---
+
+## Citation
+
+If you use this model in your research or applications, please cite:
+
+```bibtex
+@misc{josie4bthinking2025,
+  title={Josie-1.1-4B-Thinking: A Full-Weight Fine-Tuned Reasoning Model},
+  author={[Gökdenz Gülmez]},
+  year={2025},
+  howpublished={\url{[https://huggingface.co/Goekdeniz-Guelmez/JOSIE-1.1-4B-Thinking]}},
+}
+```
+
+---
+
+## 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-Thinking
+- **Answer Refinement:** Anthropic Claude models (Sonnet 3.7/4.0, Opus 4.5/4.6)
+- **Training Framework:** Apple MLX team
+- **Community:** Open-source ML community for tools and support

chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# 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 %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\n' + '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.' + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for message in messages[::-1] %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- if ns.multi_step_tool and message.role == "user" and message.content is string and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- 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" %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is string %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in content %}
+                {%- set reasoning_content = content.split('</think>')[0].rstrip('\n').split('<think>')[-1].lstrip('\n') %}
+                {%- set content = content.split('</think>')[-1].lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- 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<think>\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "pad_token_id": null,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000,
+    "rope_type": "default"
+  },
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151669
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.6"
+}

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