Instructions to use inference-optimization/Llama-3.2-0.5B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use inference-optimization/Llama-3.2-0.5B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="inference-optimization/Llama-3.2-0.5B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use inference-optimization/Llama-3.2-0.5B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "inference-optimization/Llama-3.2-0.5B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "inference-optimization/Llama-3.2-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/inference-optimization/Llama-3.2-0.5B-Instruct

SGLang

How to use inference-optimization/Llama-3.2-0.5B-Instruct 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 "inference-optimization/Llama-3.2-0.5B-Instruct" \
    --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": "inference-optimization/Llama-3.2-0.5B-Instruct",
		"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 "inference-optimization/Llama-3.2-0.5B-Instruct" \
        --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": "inference-optimization/Llama-3.2-0.5B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use inference-optimization/Llama-3.2-0.5B-Instruct with Docker Model Runner:
```
docker model run hf.co/inference-optimization/Llama-3.2-0.5B-Instruct
```

Llama-3.2-0.5B-Instruct / README.md

kylesayrs

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	---
	license: mit
	base_model:
	- meta-llama/Llama-3.2-1B-Instruct
	library_name: transformers
	---

	# Llama-3.2-0.5B-Instruct

	This is a tiny version of [meta-llama/Llama-3.2-1B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-1B-Instruct) created for testing and development.

	## Model Details

	- Base Model: meta-llama/Llama-3.2-1B-Instruct
	- Architecture: llama
	- Total Parameters: 0.51B
	- Activated Parameters: 0.51B (non-MoE)

	## Configuration Changes

	The following parameters were reduced from the original model:

	\| Parameter \| Original \| Tiny \|
	\|-----------\|----------\|------\|
	\| num_hidden_layers \| 16 \| 4 \|
	\| hidden_size \| 2048 \| 2048 \|
	\| intermediate_size \| 8192 \| 8192 \|
	\| num_attention_heads \| 32 \| 32 \|
	\| num_key_value_heads \| 8 \| 8 \|

	## Checkpoint Structure

	This model uses a single `model.safetensors` file containing all weights. The checkpoint structure is identical to the original model, with the standard Llama architecture tensors:
	- `model.embed_tokens.weight`
	- `model.layers.*.self_attn.{q,k,v,o}_proj.weight`
	- `model.layers.*.mlp.{gate,up,down}_proj.weight`
	- `model.layers.*.{input,post_attention}_layernorm.weight`
	- `model.norm.weight`

	## Usage

	```python
	from transformers import AutoModelForCausalLM, AutoTokenizer

	model = AutoModelForCausalLM.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct", device_map="auto")
	tokenizer = AutoTokenizer.from_pretrained("inference-optimization/Llama-3.2-0.5B-Instruct")

	input_ids = tokenizer("According to all known laws", return_tensors="pt").input_ids.to(model.device)
	output = model.generate(input_ids, max_new_tokens=20)
	print(tokenizer.decode(output[0]))
	```

	## Validation

	```
	Success: 1.0247299671173096 <= 10.0

	==================================================
	Generating sample text:
	According to all known laws of aviation, there is no way a bee should be able to fly
	==================================================
	```

	## Creation Process

	This model was created using the llm-compressor `create-tiny-model` claude skill:

	1. Inspected the original model configuration to identify key parameters
	2. Created a tiny version by reducing `num_hidden_layers` from 16 to 4
	3. Fine-tuned the model on a toy dataset (famous copypastas) to validate learning capability
	4. Achieved target perplexity of ~1.02 on the validation text
	5. Validated checkpoint structure matches the original model format
	6. Confirmed successful loading and inference

	## Notes

	- This model was fine-tuned on a small corpus of internet copypastas to ensure it can learn effectively
	- The model maintains the same Llama 3.2 architecture (including RoPE parameters) as the base model, just with fewer layers
	- Due to the reduced layer count, this model has approximately 25% of the original model's parameters
	- This is intended for development and testing purposes, not production use