Instructions to use anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g with Transformers:

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

pipe = pipeline("text-generation", model="anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g")
model = AutoModelForCausalLM.from_pretrained("anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anon8231489123/gpt4-x-alpaca-13b-native-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": "anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g

SGLang

How to use anon8231489123/gpt4-x-alpaca-13b-native-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 "anon8231489123/gpt4-x-alpaca-13b-native-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": "anon8231489123/gpt4-x-alpaca-13b-native-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 "anon8231489123/gpt4-x-alpaca-13b-native-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": "anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g with Docker Model Runner:
```
docker model run hf.co/anon8231489123/gpt4-x-alpaca-13b-native-4bit-128g
```

Emoji overload

#24

by Horned - opened Apr 11, 2023

Discussion

Horned

Apr 11, 2023

Where do all these emoji's come from? is it from Alpaca?
the model very often ends up using an emoji first thing in a sentence, and often last in sentence too (in conversations)
looks pretty awful, wish we could filter these out in a future model
maybe it's slightly overtrained on assistant way of talking, it sometimes tends to speak very matter-of-factly / less creative in it's personality choices

kusoge

Apr 11, 2023

compared to the vicuna model, this one seems pretty dumb. I tried asking it when was its training ended and it keeps giving hypothetical or imaginary trainings it had. At least the vicuna model says is consistent, 2021.
Vicuna model can also give me a list of adjectives, but this model gives me a weird answer every time. LMAO

kusoge

Apr 11, 2023

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