README.md

---
library_name: transformers
tags:
- text-generation-inference
- meta
license: fair-noncommercial-research-license
language:
- en
base_model:
- facebook/MobileLLM-R1-950M
pipeline_tag: text-generation
---

![1.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/XigVVq2bin3YAFrrHuqiy.png)

# **MobileLLM-R1-950M-Radiation**

> **MobileLLM-R1-950M-Radiation** is a reasoning-focused model based on **facebook/MobileLLM-R1-950M**, fine-tuned 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 ideal for research, education, and lightweight deployment in constrained environments.

---

## **Key Features**

1. **Abliterated Reasoning**
   Enhanced reasoning precision through polished token probability distributions, ensuring accurate and context-aware outputs.

2. **Event Simulation & Logical Analysis**
   Models random events, probability-driven reasoning, and logical decision-making reliably.

3. **Multilingual Mathematical & General-Purpose Problem Solving**
   Delivers strong performance in **math**, **probability**, and **structured multilingual tasks**, supporting global research and educational use.

4. **Hybrid Symbolic-Probabilistic Thinking**
   Combines structured logic, probabilistic inference, and chain-of-thought reasoning for robust uncertainty-driven problem-solving.

5. **Structured Output Mastery**
   Generates well-structured outputs in **LaTeX**, **Markdown**, **JSON**, **CSV**, and **YAML**, suitable for technical workflows and research documentation.

6. **Optimized Lightweight Footprint**
   Compact **950M parameter size**, deployable on **edge devices**, **offline clusters**, and **mid-range GPUs**, balancing reasoning capability with efficiency.

---

## **Quickstart with Transformers🤗**

```python
!pip install transformers gradio
```

```python
import gradio as gr
from transformers import pipeline
import torch

# Initialize the model pipeline
model_id = "prithivMLmods/MobileLLM-R1-950M-Radiation"
pipe = pipeline(
    "text-generation",
    model=model_id,
    torch_dtype=torch.float16,
    device_map="auto",
)

def respond(message, history):
    # Build prompt from history
    prompt = ""
    for user_msg, assistant_msg in history:
        if user_msg:
            prompt += f"User: {user_msg}\n"
        if assistant_msg:
            prompt += f"Assistant: {assistant_msg}\n"

    # Add current message
    prompt += f"User: {message}\nAssistant: "

    # Generate response with streaming
    streamer = pipe.tokenizer.decode

    # Generate tokens
    inputs = pipe.tokenizer(prompt, return_tensors="pt").to(pipe.model.device)

    with torch.no_grad():
        outputs = pipe.model.generate(
            **inputs,
            max_new_tokens=10000,
            temperature=0.7,
            do_sample=True,
            pad_token_id=pipe.tokenizer.eos_token_id,
        )

    # Decode the generated tokens, skipping the input tokens
    generated_tokens = outputs[0][inputs['input_ids'].shape[-1]:]

    # Stream the output token by token
    response_text = ""
    for i in range(len(generated_tokens)):
        token = generated_tokens[i:i+1]
        token_text = pipe.tokenizer.decode(token, skip_special_tokens=True)
        response_text += token_text
        yield response_text

# Create the chat interface
demo = gr.ChatInterface(
    fn=respond,
    examples=[
        "Compute: 1-2+3-4+5- ... +99-100.",
    ],
)

if __name__ == "__main__":
    demo.launch(share=True)
```

---

## **Intended Use**

* Balanced multilingual reasoning and probability modeling
* Event simulation, uncertainty analysis, and structured problem solving
* Educational and research-focused reasoning tasks
* Lightweight deployment on edge devices and offline clusters
* Technical content and structured data generation

---

## **Limitations**

* Focused on reasoning and mathematics—less suited for creative writing
* limit performance on very complex multi-step reasoning tasks
* Prioritizes structured reasoning and probabilistic accuracy over conversational or emotional tone
* May produce inconsistent outputs for **very long contexts** or cross-domain multi-document tasks