Leporis-Qwen3-Radiation-1.7B/README.md

---
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