Instructions to use jaydeepb/llama-3.2-1b-code-run2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jaydeepb/llama-3.2-1b-code-run2 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-run2")

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

tokenizer = AutoTokenizer.from_pretrained("jaydeepb/llama-3.2-1b-code-run2")
model = AutoModelForCausalLM.from_pretrained("jaydeepb/llama-3.2-1b-code-run2", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jaydeepb/llama-3.2-1b-code-run2 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-run2"
# 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-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jaydeepb/llama-3.2-1b-code-run2

SGLang

How to use jaydeepb/llama-3.2-1b-code-run2 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-run2" \
    --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-run2",
		"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-run2" \
        --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-run2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jaydeepb/llama-3.2-1b-code-run2 with Docker Model Runner:
```
docker model run hf.co/jaydeepb/llama-3.2-1b-code-run2
```

jaydeepb commited on 9 days ago

Commit

397439f

verified ·

1 Parent(s): 76f32ec

Upload folder using huggingface_hub

Browse files

Files changed (6) hide show

.gitattributes +1 -0
config.json +36 -0
generation_config.json +9 -0
model.safetensors +3 -0
tokenizer.json +3 -0
tokenizer_config.json +15 -0

.gitattributes CHANGED Viewed

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 *.zst filter=lfs diff=lfs merge=lfs -text
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config.json ADDED Viewed

	@@ -0,0 +1,36 @@

+{
+  "architectures": [
+    "LlamaForCausalLM"
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+  "mlp_bias": false,
+  "model_type": "llama",
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+    "low_freq_factor": 1.0,
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+    "rope_theta": 500000.0,
+    "rope_type": "llama3"
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+  "tie_word_embeddings": true,
+  "transformers_version": "5.14.1",
+  "use_cache": false,
+  "vocab_size": 128256
+}

generation_config.json ADDED Viewed

	@@ -0,0 +1,9 @@

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+  "eos_token_id": 128001,
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+}