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Model: prithivMLmods/Leporis-Qwen3-Radiation-1.7B
Source: Original Platform

README.md

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+---
+library_name: transformers
+tags:
+- text-generation-inference
+- Abliterated
+- math
+- multilingual
+- polished
+license: apache-2.0
+language:
+- en
+- zh
+base_model:
+- Qwen/Qwen3-1.7B
+pipeline_tag: text-generation
+---
+
+![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/E-pa8TWAfbIvoPH_j1bTR.png)
+
+# **Leporis-Qwen3-Radiation-1.7B**
+
+> **Leporis-Qwen3-Radiation-1.7B** 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.
+> 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.
+
+> \[!note]
+> GGUF: [https://huggingface.co/prithivMLmods/Leporis-Qwen3-Radiation-1.7B-GGUF](https://huggingface.co/prithivMLmods/Leporis-Qwen3-Radiation-1.7B-GGUF)
+
+---
+
+## **Key Features**
+
+1. **Abliterated Reasoning**
+   Enhanced reasoning precision through polished token probability distributions in Qwen and similar models, ensuring balanced and context-aware outputs.
+
+2. **Event Simulation & Logical Analysis**
+   Models random events, probability-driven reasoning, and logical decision-making with strong consistency.
+
+3. **Multilingual Mathematical & General-Purpose Problem Solving**
+   Delivers robust performance in **math**, **probability**, and **structured multilingual tasks**, enabling wide applicability in global research and education.
+
+4. **Hybrid Symbolic-Probabilistic Thinking**
+   Combines structured logic, probabilistic inference, and reasoning fluency, providing accuracy across uncertainty-driven tasks.
+
+5. **Structured Output Mastery**
+   Generates well-structured outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, supporting technical workflows and data-driven research.
+
+6. **Optimized Lightweight Footprint**
+   Compact **1.7B parameter size**, deployable on **edge devices**, **offline clusters**, and **mid-range GPUs**, while maintaining reasoning quality.
+
+---
+
+## **Quickstart with Transformers**
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "prithivMLmods/Leporis-Qwen3-Radiation-1.7B"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_name,
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+
+prompt = "Simulate the probability of rolling two dice and getting a sum greater than 9. Show the reasoning."
+
+messages = [
+    {"role": "system", "content": "You are a reasoning tutor skilled in probability, logic, and multilingual problem-solving."},
+    {"role": "user", "content": prompt}
+]
+
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=512
+)
+generated_ids = [
+    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
+]
+
+response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
+print(response)
+```
+
+---
+
+## **Intended Use**
+
+* Balanced multilingual reasoning and probability modeling
+* Event simulation, uncertainty analysis, and structured problem solving
+* Educational and research-focused reasoning tasks
+* Lightweight deployment in constrained environments
+* Technical content and structured data generation
+
+---
+
+## **Limitations**
+
+* Focused on reasoning and mathematics—less suited for creative writing
+* Smaller size compared to large-scale LLMs may limit performance on complex, multi-hop reasoning tasks
+* Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone
+* May produce inconsistent outputs when dealing with **very long contexts** or cross-domain multi-document inputs