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

Libraries

How to use Infinirc/Llama-3.2-Infinirc-1B-Instruct with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Infinirc/Llama-3.2-Infinirc-1B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Infinirc/Llama-3.2-Infinirc-1B-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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Infinirc/Llama-3.2-Infinirc-1B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Infinirc/Llama-3.2-Infinirc-1B-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": "Infinirc/Llama-3.2-Infinirc-1B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Infinirc/Llama-3.2-Infinirc-1B-Instruct

SGLang

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

Docker Model Runner
How to use Infinirc/Llama-3.2-Infinirc-1B-Instruct with Docker Model Runner:
```
docker model run hf.co/Infinirc/Llama-3.2-Infinirc-1B-Instruct
```

RickyChen-Infinirc commited on Dec 12, 2024

Commit

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@@ -24,7 +24,7 @@ tags:
 Llama-3.2-Infinirc-11B-Vision-Instruct模型是專門為了更好地理解和生成與台灣文化相關的文本而設計和微調的。目標是提供一個能夠捕捉台灣特有文化元素和語言習慣的強大語言模型，適用於文本生成、自動回答等多種應用。
 ## 模型架構
-**基礎模型**：meta-llama/Llama-3.2-11B-Vision-Instruct
 ## llama-vision-gradio-webui
 https://github.com/Infinirc/llama-vision-gradio-webui

 Llama-3.2-Infinirc-11B-Vision-Instruct模型是專門為了更好地理解和生成與台灣文化相關的文本而設計和微調的。目標是提供一個能夠捕捉台灣特有文化元素和語言習慣的強大語言模型，適用於文本生成、自動回答等多種應用。
 ## 模型架構
+**基礎模型**：meta-llama/Llama-3.2-1B-Instruct
 ## llama-vision-gradio-webui
 https://github.com/Infinirc/llama-vision-gradio-webui