Instructions to use Monero/oasst-alpaca13b-4epoch-4bit-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Monero/oasst-alpaca13b-4epoch-4bit-128g with Transformers:

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

pipe = pipeline("text-generation", model="Monero/oasst-alpaca13b-4epoch-4bit-128g")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Monero/oasst-alpaca13b-4epoch-4bit-128g")
model = AutoModelForCausalLM.from_pretrained("Monero/oasst-alpaca13b-4epoch-4bit-128g", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Monero/oasst-alpaca13b-4epoch-4bit-128g with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Monero/oasst-alpaca13b-4epoch-4bit-128g"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Monero/oasst-alpaca13b-4epoch-4bit-128g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Monero/oasst-alpaca13b-4epoch-4bit-128g

SGLang

How to use Monero/oasst-alpaca13b-4epoch-4bit-128g 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 "Monero/oasst-alpaca13b-4epoch-4bit-128g" \
    --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": "Monero/oasst-alpaca13b-4epoch-4bit-128g",
		"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 "Monero/oasst-alpaca13b-4epoch-4bit-128g" \
        --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": "Monero/oasst-alpaca13b-4epoch-4bit-128g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Monero/oasst-alpaca13b-4epoch-4bit-128g with Docker Model Runner:
```
docker model run hf.co/Monero/oasst-alpaca13b-4epoch-4bit-128g
```

oasst-alpaca13b-4epoch-4bit-128g

File size: 986 Bytes

7bb4e46

---
datasets:
- OpenAssistant/oasst1
---

<p><strong><font size="5">Information</font></strong></p>
OpenAssistant-Alpaca-13B-4-bit working with GPTQ versions used in Oobabooga's Text Generation Webui and KoboldAI.
<br>This was made using https://huggingface.co/chavinlo/alpaca-13b and <a href="https://huggingface.co/serpdotai/llama-oasst-lora-13B">Serpdotai's Open Assistant 13b LoRa trained for 4 epochs</a> using Open Assistant's dataset.</p>

    python llama.py /Models/alpaca13b-oaast4ep-lora c4 --wbits 4 --true-sequential --groupsize 128 --save_safetensors oasst-alpaca13b-4ep-lora-4bit-128g.safetensors

<p><strong><font size="5">Benchmarks</font></strong></p>

<p><strong><font size="4">--true-sequential --groupsize 128</font></strong></p>

<strong>Wikitext2</strong>: 6.854333400726318

<strong>Ptb-New</strong>: 12.411578178405762

<strong>C4-New</strong>: 9.355494499206543

<strong>Note</strong>: This version uses <i>--groupsize 128</i>, resulting in better evaluations.