Instructions to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B with Transformers:

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

pipe = pipeline("text-generation", model="WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B")
model = AutoModelForCausalLM.from_pretrained("WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B

SGLang

How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B 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 "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B with Docker Model Runner:
```
docker model run hf.co/WithinUsAI/Llama3.2-Octo.Thinker.iNano-1B
```

Llama3.2-Octo.Thinker.iNano-1B

3.01 GB

Ctrl+K

1 contributor

History: 9 commits

Guy DuGan II

## Source Model Ingredients This merge combines three Llama 3.2-based components: - [NeuraLakeAi/iSA-02-Nano-Llama-3.2-1B](https://huggingface.co/NeuraLakeAi/iSA-02-Nano-Llama-3.2-1B) A Llama-3.2-1B-based base model optimized for reasoning, with an advertised 1,048,576-token context window. - [OctoThinker/OctoThinker-1B-Hybrid-Base](https://huggingface.co/OctoThinker/OctoThinker-1B-Hybrid-Base) A 1B Llama-family hybrid base model designed with mid-training insights to support reinforcement-learning-friendly behavior. - [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) Meta’s instruction-tuned 1B multilingual Llama 3.2 model, optimized for dialogue, summarization, and agentic retrieval tasks.

f493622 verified 3 months ago