Instructions to use Yhyu13/llama-30B-hf-openassitant with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yhyu13/llama-30B-hf-openassitant with Transformers:

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

pipe = pipeline("text-generation", model="Yhyu13/llama-30B-hf-openassitant")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yhyu13/llama-30B-hf-openassitant")
model = AutoModelForCausalLM.from_pretrained("Yhyu13/llama-30B-hf-openassitant", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yhyu13/llama-30B-hf-openassitant with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yhyu13/llama-30B-hf-openassitant"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yhyu13/llama-30B-hf-openassitant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Yhyu13/llama-30B-hf-openassitant

SGLang

How to use Yhyu13/llama-30B-hf-openassitant 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 "Yhyu13/llama-30B-hf-openassitant" \
    --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": "Yhyu13/llama-30B-hf-openassitant",
		"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 "Yhyu13/llama-30B-hf-openassitant" \
        --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": "Yhyu13/llama-30B-hf-openassitant",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yhyu13/llama-30B-hf-openassitant with Docker Model Runner:
```
docker model run hf.co/Yhyu13/llama-30B-hf-openassitant
```

This is the hf tr version of llama 30B converted specifically as open assistant's 30B model required:

https://huggingface.co/OpenAssistant/oasst-rlhf-2-llama-30b-7k-steps-xor

This the md5 checksum that I get locally, which matchs the original repo suggests

fdb311c39b8659a5d5c1991339bafc09  ./tokenizer.json
edd1a5897748864768b1fab645b31491  ./tokenizer_config.json
6b2e0a735969660e720c27061ef3f3d3  ./special_tokens_map.json
3eddc6fc02c0172d38727e5826181adb  ./pytorch_model-00004-of-00007.bin
fecfda4fba7bfd911e187a85db5fa2ef  ./pytorch_model.bin.index.json
462a2d07f65776f27c0facfa2affb9f9  ./pytorch_model-00007-of-00007.bin
598538f18fed1877b41f77de034c0c8a  ./config.json
99762d59efa6b96599e863893cf2da02  ./pytorch_model-00006-of-00007.bin
aee09e21813368c49baaece120125ae3  ./generation_config.json
92754d6c6f291819ffc3dfcaf470f541  ./pytorch_model-00005-of-00007.bin
5cfcb78b908ffa02e681cce69dbe4303  ./pytorch_model-00002-of-00007.bin
e1dc8c48a65279fb1fbccff14562e6a3  ./pytorch_model-00003-of-00007.bin
9cffb1aeba11b16da84b56abb773d099  ./pytorch_model-00001-of-00007.bin
eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model

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