Instructions to use Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf with Transformers:

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

pipe = pipeline("text-generation", model="Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf")
model = AutoModelForCausalLM.from_pretrained("Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf

SGLang

How to use Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf 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/oasst-rlhf-2-llama-30b-7k-steps-hf" \
    --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/oasst-rlhf-2-llama-30b-7k-steps-hf",
		"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/oasst-rlhf-2-llama-30b-7k-steps-hf" \
        --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/oasst-rlhf-2-llama-30b-7k-steps-hf",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf with Docker Model Runner:
```
docker model run hf.co/Yhyu13/oasst-rlhf-2-llama-30b-7k-steps-hf
```

This is the merged hf tr version of llama 30B and OA's rlhf 30B xor weights:

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

deb33dd4ffc3d2baddcce275a00b7c1b  ./tokenizer.json
ed59bfee4e87b9193fea5897d610ab24  ./tokenizer_config.json
704373f0c0d62be75e5f7d41d39a7e57  ./special_tokens_map.json
4c5941b4ee12dc0d8e6b5ca3f6819f4d  ./pytorch_model-00004-of-00007.bin
13a3641423840eb89f9a86507a90b2bf  ./pytorch_model.bin.index.json
d08594778f00abe70b93899628e41246  ./pytorch_model-00007-of-00007.bin
9a4d2468ecf85bf07420b200faefb4af  ./config.json
2c92d306969c427275f34b4ebf66f087  ./pytorch_model-00006-of-00007.bin
148bfd184af630a7633b4de2f41bfc49  ./generation_config.json
b6e90377103e9270cbe46b13aed288ec  ./pytorch_model-00005-of-00007.bin
27b0dc092f99aa2efaf467b2d8026c3f  ./added_tokens.json
ed991042b2a449123824f689bb94b29e  ./pytorch_model-00002-of-00007.bin
f11acc069334434d68c45a80ee899fe5  ./pytorch_model-00003-of-00007.bin
9f41bd4d5720d28567b3e7820b4a8023  ./pytorch_model-00001-of-00007.bin
eeec4125e9c7560836b4873b6f8e3025  ./tokenizer.model

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