Instructions to use arcee-ai/Llama-3-SEC-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use arcee-ai/Llama-3-SEC-Base with Transformers:

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

pipe = pipeline("text-generation", model="arcee-ai/Llama-3-SEC-Base")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("arcee-ai/Llama-3-SEC-Base")
model = AutoModelForCausalLM.from_pretrained("arcee-ai/Llama-3-SEC-Base", device_map="auto")

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use arcee-ai/Llama-3-SEC-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "arcee-ai/Llama-3-SEC-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "arcee-ai/Llama-3-SEC-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/arcee-ai/Llama-3-SEC-Base

SGLang

How to use arcee-ai/Llama-3-SEC-Base 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 "arcee-ai/Llama-3-SEC-Base" \
    --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": "arcee-ai/Llama-3-SEC-Base",
		"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 "arcee-ai/Llama-3-SEC-Base" \
        --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": "arcee-ai/Llama-3-SEC-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use arcee-ai/Llama-3-SEC-Base with Docker Model Runner:
```
docker model run hf.co/arcee-ai/Llama-3-SEC-Base
```

Crystalcareai

todenthal commited on Jun 12, 2024

Commit

3ff7ef4

verified ·

1 Parent(s): ec74fce

Code Fix for Inference Example (#1)

Browse files

- Code Fix for Inference Example (1c64bc5611cca49ab7261c86d3a5bed3e9abf469)

Co-authored-by: Tyler Odenthal <todenthal@users.noreply.huggingface.co>

Files changed (1) hide show

README.md +2 -2

README.md CHANGED Viewed

@@ -68,11 +68,11 @@ device = "cuda"
 model_name = "arcee-ai/Llama-3-SEC"
 model = AutoModelForCausalLM.from_pretrained(
-    "model_name",
     torch_dtype="auto",
     device_map="auto"
 )
-tokenizer = AutoTokenizer.from_pretrained("model_name")
 prompt = "What are the key regulatory considerations for a company planning to conduct an initial public offering (IPO) in the United States?"
 messages = [

 model_name = "arcee-ai/Llama-3-SEC"
 model = AutoModelForCausalLM.from_pretrained(
+    model_name,
     torch_dtype="auto",
     device_map="auto"
 )
+tokenizer = AutoTokenizer.from_pretrained(model_name)
 prompt = "What are the key regulatory considerations for a company planning to conduct an initial public offering (IPO) in the United States?"
 messages = [