---
library_name: transformers
tags:
- math
- code
- text-generation-inference
- llama3.2
license: apache-2.0
language:
- en
base_model:
- meta-llama/Llama-3.2-3B-Instruct
pipeline_tag: text-generation
---
![6.png](https://cdn-uploads.huggingface.co/production/uploads/65bb837dbfb878f46c77de4c/ro8JJK0IQgseIvg2SgMb3.png)

# **Flerovium-Llama-3B**

> **Flerovium-Llama-3B** is a compact, general-purpose language model based on the powerful **llama 3.2** (llama) architecture. It is fine-tuned for a broad range of tasks including **mathematical reasoning**, **code generation**, and **natural language understanding**, making it a versatile choice for developers, students, and researchers seeking reliable performance in a lightweight model.

> \[!note]
> GGUF: [https://huggingface.co/prithivMLmods/Flerovium-Llama-3B-GGUF](https://huggingface.co/prithivMLmods/Flerovium-Llama-3B-GGUF)

---

## **Key Features**

1. **LLaMA 3.2 Backbone**
   Built on **Meta’s LLaMA 3.2 (3B)** architecture, offering state-of-the-art performance in a compact footprint with better instruction-following and multilingual support.

2. **Multi-Task Fine-Tuning**
   Finetuned on a modular and diverse dataset combining math, code, and general-purpose tasks—enabling clear explanations, problem solving, and practical utility.

3. **Strong Mathematical Reasoning**
   Handles algebra, calculus, logic, and numerical problems with step-by-step clarity. Ideal for tutoring and academic use cases.

4. **Coding Capabilities**
   Understands and generates clean, bug-free code in Python, JavaScript, C++, and more. Also excels at debugging, documentation, and logic explanations.

5. **General-Purpose Utility**
   Performs well across everyday reasoning tasks—summarization, Q\&A, content drafting, and structured generation (Markdown, LaTeX, JSON).

6. **Efficient & Deployable**
   With only 3 billion parameters, Flerovium-Llama-3B is resource-efficient and suitable for local deployment, offline tools, and edge AI setups.

---

## **Quickstart with Transformers**

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "prithivMLmods/Flerovium-Llama-3B"

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

prompt = "Explain how to solve a quadratic equation step-by-step."

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

* General-purpose text and reasoning
* Math tutoring and problem-solving
* Code generation, review, and debugging
* Content drafting in Markdown, LaTeX, and JSON
* Lightweight deployment in educational and developer environments

---

## **Limitations**

* Limited context length compared to large models (>7B)
* May require prompt refinement for very complex code/math problems
* Not ideal for long-form creative writing or deep conversational tasks
* Knowledge is limited to training data (no real-time web search)

---

## **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)