Instructions to use OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k with Transformers:

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

pipe = pipeline("text-generation", model="OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k")
model = AutoModelForCausalLM.from_pretrained("OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k

SGLang

How to use OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k 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 "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k" \
    --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": "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k",
		"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 "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k" \
        --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": "OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Desktop
Docker Model Runner
How to use OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k with Docker Model Runner:
```
docker model run hf.co/OliverSundaram/Llama-3.2-1B-MathCodeInstruct-20k
```

Llama-3.2-1B-MathCodeInstruct-20k / assets /speed_results.json

OliverSundaram

Upload folder using huggingface_hub

79bbbbf verified 17 days ago

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	{
	"config": {
	"prompt": "Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May? Solve step by step.",
	"tokens_per_run": 256,
	"rounds": 3,
	"interleaved": true,
	"dtype": "bfloat16",
	"greedy": true,
	"device": "NVIDIA GeForce RTX 4060",
	"torch": "2.11.0+cu128"
	},
	"models": {
	"base": {
	"model_path": "unsloth/Llama-3.2-1B",
	"decode_tok_s": 40.59,
	"decode_tok_s_min": 39.28,
	"decode_tok_s_max": 40.62,
	"end_to_end_tok_s": 40.56,
	"prefill_ms": 29.3,
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"rounds": [
	{
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"prefill_s": 0.0321,
	"total_s": 6.5236,
	"decode_tok_s": 39.28,
	"end_to_end_tok_s": 39.24,
	"gpu": "51C / 2775MHz"
	},
	{
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"prefill_s": 0.0293,
	"total_s": 6.307,
	"decode_tok_s": 40.62,
	"end_to_end_tok_s": 40.59,
	"gpu": "58C / 2775MHz"
	},
	{
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"prefill_s": 0.0293,
	"total_s": 6.3119,
	"decode_tok_s": 40.59,
	"end_to_end_tok_s": 40.56,
	"gpu": "51C / 2775MHz"
	}
	]
	},
	"20k": {
	"model_path": "Llama-3.2-1B-MathCodeInstruct-20k/outputs/llama-3.2-1b-20k",
	"decode_tok_s": 40.63,
	"decode_tok_s_min": 40.61,
	"decode_tok_s_max": 40.67,
	"end_to_end_tok_s": 40.6,
	"prefill_ms": 29.7,
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"rounds": [
	{
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"prefill_s": 0.0295,
	"total_s": 6.3088,
	"decode_tok_s": 40.61,
	"end_to_end_tok_s": 40.58,
	"gpu": "57C / 2775MHz"
	},
	{
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"prefill_s": 0.0308,
	"total_s": 6.3005,
	"decode_tok_s": 40.67,
	"end_to_end_tok_s": 40.63,
	"gpu": "51C / 2775MHz"
	},
	{
	"prompt_tokens": 44,
	"generated_tokens": 256,
	"prefill_s": 0.0297,
	"total_s": 6.3062,
	"decode_tok_s": 40.63,
	"end_to_end_tok_s": 40.6,
	"gpu": "52C / 2775MHz"
	}
	]
	}
	}
	}