Instructions to use DeepAuto-AI/Explore_Llama-3.2-1B-Inst with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeepAuto-AI/Explore_Llama-3.2-1B-Inst with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("DeepAuto-AI/Explore_Llama-3.2-1B-Inst")
model = AutoModelForCausalLM.from_pretrained("DeepAuto-AI/Explore_Llama-3.2-1B-Inst")
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 DeepAuto-AI/Explore_Llama-3.2-1B-Inst with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DeepAuto-AI/Explore_Llama-3.2-1B-Inst

SGLang

How to use DeepAuto-AI/Explore_Llama-3.2-1B-Inst 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 "DeepAuto-AI/Explore_Llama-3.2-1B-Inst" \
    --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": "DeepAuto-AI/Explore_Llama-3.2-1B-Inst",
		"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 "DeepAuto-AI/Explore_Llama-3.2-1B-Inst" \
        --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": "DeepAuto-AI/Explore_Llama-3.2-1B-Inst",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use DeepAuto-AI/Explore_Llama-3.2-1B-Inst with Docker Model Runner:
```
docker model run hf.co/DeepAuto-AI/Explore_Llama-3.2-1B-Inst
```

bedio commited on Oct 9, 2024

Commit

2f17c5f

verified ·

1 Parent(s): 46c4c6e

Update README.md

Browse files

Files changed (1) hide show

README.md +2 -1

README.md CHANGED Viewed

@@ -133,7 +133,8 @@ We generate task specific weights on winogrande and arc_challenge then transfer
 - **Shared by [optional]:** DeepAuto.ai
 - **Model type:** llama-3.2-1B
 - **Language(s) (NLP):** English
-- **License:** NA
 - **Finetuned from model [optional]:** No fine-tuning
 ### Model Sources [optional]

 - **Shared by [optional]:** DeepAuto.ai
 - **Model type:** llama-3.2-1B
 - **Language(s) (NLP):** English
+- **License:** Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in
+- compliance with the License. You may obtain a copy of the License at http://www.apache.org/licenses/LICENSE-2.0
 - **Finetuned from model [optional]:** No fine-tuning
 ### Model Sources [optional]