---
library_name: transformers
tags:
- text-generation-inference
- code
- llama-3.2
- math
- general-purpose
license: llama3.2
language:
- en
base_model:
- meta-llama/Llama-3.2-1B
pipeline_tag: text-generation
---
![8.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/K_bYZlzTOZjl5YJnjEy8j.png)

# **Oganesson-TinyLlama-1.2B**

> **Oganesson-TinyLlama-1.2B** is a lightweight and efficient language model built on the **LLaMA 3.2 1.2B** architecture. Fine-tuned for **general-purpose inference**, **mathematical reasoning**, and **code generation**, it’s ideal for edge devices, personal assistants, and educational applications requiring a compact yet capable model.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF](https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF)

---

## **Key Features**

1. **LLaMA 3.2 1.2B Core**
   Powered by the latest **TinyLLaMA (1.2B)** variant of Meta's LLaMA 3.2, offering modern instruction-following and multilingual capabilities in a very small footprint.

2. **Modular Fine-Tuning**
   Trained on a handcrafted modular dataset covering general-purpose reasoning, programming problems, and mathematical challenges.

3. **Mathematical Competence**
   Solves equations, explains concepts, and performs symbolic logic in algebra, geometry, and calculus—ideal for lightweight tutoring use cases.

4. **Code Understanding & Generation**
   Produces clean, interpretable code in Python, JavaScript, and more. Useful for micro-agents, code assistants, and embedded development tools.

5. **Versatile Output Formats**
   Handles JSON, Markdown, LaTeX, and structured data output, enabling integration into tools and platforms needing formatted results.

6. **Edge-Optimized**
   At only 1.2B parameters, this model is built for **local inference**, **on-device usage**, and **battery-efficient environments**.

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Oganesson-TinyLlama-1.2B"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Write a Python function to compute the Fibonacci sequence."

messages = [
    {"role": "system", "content": "You are a helpful coding and math assistant."},
    {"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**

* Lightweight reasoning for embedded and edge AI
* Basic math tutoring and symbolic computation
* Code generation and explanation for small apps
* Technical content in Markdown, JSON, and LaTeX
* Educational tools, personal agents, and low-power deployments

---

## **Limitations**

* Smaller context window than 7B+ models
* Less suitable for abstract reasoning or creative writing
* May require prompt engineering for complex technical queries
* Knowledge is limited to pretraining and fine-tuning datasets

---

## **References**

1. [LLaMA 3 Technical Report (Meta)](https://ai.meta.com/llama/)
2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)