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--- |
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license: apache-2.0 |
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base_model: |
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- Qwen/Qwen3-4B-Instruct-2507 |
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language: |
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- en |
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pipeline_tag: text-generation |
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library_name: transformers |
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tags: |
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- text-generation-inference |
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- trl |
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- event-driven |
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- abliterated |
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- smoothing |
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--- |
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# **Kepler-186f-Qwen3-Instruct-4B** |
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> **Kepler-186f-Qwen3-Instruct-4B** is a reasoning-focused model fine-tuned on **Qwen** for **Abliterated Reasoning** and **polished token probabilities**, enhancing balanced **multilingual generation** across mathematics and general-purpose reasoning. |
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> It specializes in **event-driven logic**, **structured analysis**, and precise probabilistic modeling—making it an ideal tool for researchers, educators, and developers working with uncertainty and structured reasoning. |
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> [!note] |
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> GGUF: [https://huggingface.co/prithivMLmods/Kepler-186f-Qwen3-Instruct-4B-GGUF](https://huggingface.co/prithivMLmods/Kepler-186f-Qwen3-Instruct-4B-GGUF) |
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--- |
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## **Key Features** |
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1. **Abliterated Reasoning** |
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Enhanced reasoning precision through polished token probability distributions in Qwen and similar models, ensuring balanced and context-aware outputs. |
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2. **Event Simulation & Logical Analysis** |
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Models random events, probability-driven reasoning, and logical decision-making with strong consistency. |
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3. **Multilingual Mathematical & General-Purpose Problem Solving** |
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Delivers robust performance in **math**, **probability**, and **structured multilingual tasks**, enabling wide applicability in global research and education. |
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4. **Hybrid Symbolic-Probabilistic Thinking** |
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Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks. |
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5. **Structured Output Mastery** |
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Generates well-structured outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, supporting technical workflows and data-driven research. |
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6. **Optimized Lightweight Footprint** |
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Large **4B parameter size**, deployable on **mid-range GPUs**, **offline clusters**, and **edge devices**, while maintaining reasoning quality. |
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--- |
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## **Quickstart with Transformers** |
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```python |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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model_name = "prithivMLmods/Kepler-186f-Qwen3-Instruct-4B" |
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model = AutoModelForCausalLM.from_pretrained( |
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model_name, |
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torch_dtype="auto", |
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device_map="auto" |
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) |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning." |
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messages = [ |
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{"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."}, |
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{"role": "user", "content": prompt} |
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] |
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text = tokenizer.apply_chat_template( |
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messages, |
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tokenize=False, |
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add_generation_prompt=True |
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) |
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device) |
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generated_ids = model.generate( |
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**model_inputs, |
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max_new_tokens=512 |
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) |
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generated_ids = [ |
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids) |
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] |
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0] |
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print(response) |
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``` |
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--- |
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## **Intended Use** |
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* Balanced multilingual reasoning and probability modeling |
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* Event simulation, uncertainty analysis, and structured problem solving |
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* Educational and research-focused reasoning tasks |
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* Deployment on mid-resource environments with efficient reasoning |
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* Technical content and structured data generation |
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--- |
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## **Limitations** |
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* Focused on reasoning and mathematics—less suited for creative writing |
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* Despite 4B size, very complex multi-hop tasks may still challenge the model |
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* Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone |
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* May produce inconsistent outputs when handling **very long contexts** or cross-domain multi-document inputs |
