Instructions to use h2oai/h2ogpt-16k-aquilachat2-34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use h2oai/h2ogpt-16k-aquilachat2-34b with Transformers:

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

pipe = pipeline("text-generation", model="h2oai/h2ogpt-16k-aquilachat2-34b", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("h2oai/h2ogpt-16k-aquilachat2-34b", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use h2oai/h2ogpt-16k-aquilachat2-34b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "h2oai/h2ogpt-16k-aquilachat2-34b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "h2oai/h2ogpt-16k-aquilachat2-34b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/h2oai/h2ogpt-16k-aquilachat2-34b

SGLang

How to use h2oai/h2ogpt-16k-aquilachat2-34b 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 "h2oai/h2ogpt-16k-aquilachat2-34b" \
    --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": "h2oai/h2ogpt-16k-aquilachat2-34b",
		"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 "h2oai/h2ogpt-16k-aquilachat2-34b" \
        --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": "h2oai/h2ogpt-16k-aquilachat2-34b",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use h2oai/h2ogpt-16k-aquilachat2-34b with Docker Model Runner:
```
docker model run hf.co/h2oai/h2ogpt-16k-aquilachat2-34b
```

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

AquilaChat2 long-text chat model AquilaChat2-34B-16k.

Inference

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
device = torch.device("cuda:0")
model_info = "h2oai/h2ogpt-16k-aquilachat2-34b"
tokenizer = AutoTokenizer.from_pretrained(model_info, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_info, trust_remote_code=True, torch_dtype=torch.bfloat16)
model.eval()
model.to(device)
text = "Who are you?"
from predict import predict
out = predict(model, text, tokenizer=tokenizer, max_gen_len=200, top_p=0.95,
              seed=1234, topk=100, temperature=0.9, sft=True, device=device,
              model_name="h2oai/h2ogpt-16k-aquilachat2-34b")
print(out)

License Aquila2 series open-source model is licensed under BAAI Aquila Model Licence Agreement

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