Instructions to use prithivMLmods/Oganesson-TinyLlama-1.2B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use prithivMLmods/Oganesson-TinyLlama-1.2B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="prithivMLmods/Oganesson-TinyLlama-1.2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("prithivMLmods/Oganesson-TinyLlama-1.2B")
model = AutoModelForCausalLM.from_pretrained("prithivMLmods/Oganesson-TinyLlama-1.2B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use prithivMLmods/Oganesson-TinyLlama-1.2B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "prithivMLmods/Oganesson-TinyLlama-1.2B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Oganesson-TinyLlama-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/prithivMLmods/Oganesson-TinyLlama-1.2B

SGLang

How to use prithivMLmods/Oganesson-TinyLlama-1.2B with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "prithivMLmods/Oganesson-TinyLlama-1.2B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Oganesson-TinyLlama-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "prithivMLmods/Oganesson-TinyLlama-1.2B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "prithivMLmods/Oganesson-TinyLlama-1.2B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use prithivMLmods/Oganesson-TinyLlama-1.2B with Docker Model Runner:
```
docker model run hf.co/prithivMLmods/Oganesson-TinyLlama-1.2B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

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.

GGUF: https://huggingface.co/prithivMLmods/Oganesson-TinyLlama-1.2B-GGUF

Key Features

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.
Modular Fine-Tuning Trained on a handcrafted modular dataset covering general-purpose reasoning, programming problems, and mathematical challenges.
Mathematical Competence Solves equations, explains concepts, and performs symbolic logic in algebra, geometry, and calculus—ideal for lightweight tutoring use cases.
Code Understanding & Generation Produces clean, interpretable code in Python, JavaScript, and more. Useful for micro-agents, code assistants, and embedded development tools.
Versatile Output Formats Handles JSON, Markdown, LaTeX, and structured data output, enabling integration into tools and platforms needing formatted results.
Edge-Optimized At only 1.2B parameters, this model is built for local inference, on-device usage, and battery-efficient environments.

Quickstart with Transformers

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)