114 lines
3.6 KiB
Markdown
114 lines
3.6 KiB
Markdown
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---
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library_name: transformers
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tags:
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- text-generation-inference
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- code
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- llama-3.2
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- math
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- general-purpose
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license: llama3.2
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language:
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- en
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base_model:
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- meta-llama/Llama-3.2-1B
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pipeline_tag: text-generation
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---
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# **Oganesson-TinyLlama-1.2B**
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> **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.
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> \[!note]
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> GGUF: [https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF](https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF)
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---
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## **Key Features**
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1. **LLaMA 3.2 1.2B Core**
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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.
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2. **Modular Fine-Tuning**
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Trained on a handcrafted modular dataset covering general-purpose reasoning, programming problems, and mathematical challenges.
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3. **Mathematical Competence**
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Solves equations, explains concepts, and performs symbolic logic in algebra, geometry, and calculus—ideal for lightweight tutoring use cases.
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4. **Code Understanding & Generation**
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Produces clean, interpretable code in Python, JavaScript, and more. Useful for micro-agents, code assistants, and embedded development tools.
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5. **Versatile Output Formats**
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Handles JSON, Markdown, LaTeX, and structured data output, enabling integration into tools and platforms needing formatted results.
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6. **Edge-Optimized**
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At only 1.2B parameters, this model is built for **local inference**, **on-device usage**, and **battery-efficient environments**.
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---
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## **Quickstart with Transformers**
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "prithivMLmods/Oganesson-TinyLlama-1.2B"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Write a Python function to compute the Fibonacci sequence."
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messages = [
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{"role": "system", "content": "You are a helpful coding and math assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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---
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## **Intended Use**
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* Lightweight reasoning for embedded and edge AI
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* Basic math tutoring and symbolic computation
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* Code generation and explanation for small apps
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* Technical content in Markdown, JSON, and LaTeX
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* Educational tools, personal agents, and low-power deployments
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---
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## **Limitations**
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* Smaller context window than 7B+ models
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* Less suitable for abstract reasoning or creative writing
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* May require prompt engineering for complex technical queries
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* Knowledge is limited to pretraining and fine-tuning datasets
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---
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## **References**
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1. [LLaMA 3 Technical Report (Meta)](https://ai.meta.com/llama/)
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2. [YaRN: Efficient Context Window Extension of Large Language Models](https://arxiv.org/pdf/2309.00071)
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