108 lines
3.9 KiB
Markdown
108 lines
3.9 KiB
Markdown
---
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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 |