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Model: prithivMLmods/Leporis-Qwen3-Radiation-1.7B
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2026-06-03 01:31:14 +08:00
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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

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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" }}
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{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
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{%- for message in messages[::-1] %}
{%- set index = (messages|length - 1) - loop.index0 %}
{%- if ns.multi_step_tool and message.role == "user" and not(message.content.startswith('<tool_response>') and message.content.endswith('</tool_response>')) %}
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{%- endif %}
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{%- 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") %}
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{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
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{%- endif %}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- if enable_thinking is defined and enable_thinking is false %}
{{- '<think>\n\n</think>\n\n' }}
{%- endif %}
{%- endif %}

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{
"architectures": [
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],
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"max_position_embeddings": 40960,
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"model_type": "qwen3",
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"tie_word_embeddings": true,
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"use_cache": true,
"use_sliding_window": false,
"vocab_size": 151936
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}

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