Instructions to use gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect with Transformers:

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

pipe = pipeline("text-generation", model="gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect")
model = AutoModelForCausalLM.from_pretrained("gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect

SGLang

How to use gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect 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 "gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect" \
    --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": "gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect",
		"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 "gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect" \
        --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": "gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect with Docker Model Runner:
```
docker model run hf.co/gokul-pv/Llama-3.2-1B-Instruct-16bit-CodeArchitect
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Uploaded model

This model is finetuned using Code Architect dataset to recommend optimisation in system architecture by provinding diagram as code.

Developed by: gokul-pv
License: apache-2.0
Finetuned from model : unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit

This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 18

Safetensors

Model size

1B params

Tensor type

F16

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Quantizations

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/

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Uploaded model

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