Instructions to use jaydeepb/llama-3.2-1b-code-run1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaydeepb/llama-3.2-1b-code-run1 with Transformers:

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

pipe = pipeline("text-generation", model="jaydeepb/llama-3.2-1b-code-run1")

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

tokenizer = AutoTokenizer.from_pretrained("jaydeepb/llama-3.2-1b-code-run1")
model = AutoModelForCausalLM.from_pretrained("jaydeepb/llama-3.2-1b-code-run1", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jaydeepb/llama-3.2-1b-code-run1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jaydeepb/llama-3.2-1b-code-run1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jaydeepb/llama-3.2-1b-code-run1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jaydeepb/llama-3.2-1b-code-run1

SGLang

How to use jaydeepb/llama-3.2-1b-code-run1 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 "jaydeepb/llama-3.2-1b-code-run1" \
    --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": "jaydeepb/llama-3.2-1b-code-run1",
		"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 "jaydeepb/llama-3.2-1b-code-run1" \
        --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": "jaydeepb/llama-3.2-1b-code-run1",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jaydeepb/llama-3.2-1b-code-run1 with Docker Model Runner:
```
docker model run hf.co/jaydeepb/llama-3.2-1b-code-run1
```

llama-3.2-1b-code-run1

Llama-3.2-1B fine-tuned for one epoch on a 1M-sequence (128-token) sample of The Stack.

This is a run1 model. Extractable-memorization labels (k=50, l=50 greedy) from run1 are the labels used to train the memorization-prediction classifier. The matching run2 model is fine-tuned on a disjoint 1M sample and provides the fresh held-out evaluation.

Base model, meta-llama/Llama-3.2-1B
Dataset, The Stack (bigcode/the-stack)
Training, 1 epoch, seq len 128, learning rate 5e-5, cosine schedule, seed 42

Downloads last month: 145

Safetensors

Model size

1B params

Tensor type

BF16

Model tree for jaydeepb/llama-3.2-1b-code-run1

Base model

meta-llama/Llama-3.2-1B

Finetuned

(956)

this model

Dataset used to train jaydeepb/llama-3.2-1b-code-run1

Collection including jaydeepb/llama-3.2-1b-code-run1

Predicting Memorization: Fine-tuned Models

Collection

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