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---
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
---
# **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) |