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

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
```

Adding Evaluation Results

by bedio - opened Oct 10, 2024

base: refs/heads/main

←

from: refs/pr/2

Discussion Files changed

+14

-0

Files changed (1) hide show

README.md +14 -0

README.md CHANGED Viewed

@@ -314,3 +314,17 @@ Detailed results can be found [here](https://huggingface.co/datasets/open-llm-le
 |MuSR (0-shot)      | 1.09|
 |MMLU-PRO (5-shot)  | 8.31|

 |MuSR (0-shot)      | 1.09|
 |MMLU-PRO (5-shot)  | 8.31|
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_DeepAutoAI__Explore_Llama-3.2-1B-Inst)
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |13.58|
+|IFEval (0-Shot)    |57.68|
+|BBH (3-Shot)       | 8.31|
+|MATH Lvl 5 (4-Shot)| 4.53|
+|GPQA (0-shot)      | 1.57|
+|MuSR (0-shot)      | 1.09|
+|MMLU-PRO (5-shot)  | 8.31|