Instructions to use maicomputer/gpt4-x-alpaca with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use maicomputer/gpt4-x-alpaca with Transformers:

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

pipe = pipeline("text-generation", model="maicomputer/gpt4-x-alpaca")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("maicomputer/gpt4-x-alpaca")
model = AutoModelForCausalLM.from_pretrained("maicomputer/gpt4-x-alpaca")

Inference
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use maicomputer/gpt4-x-alpaca with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "maicomputer/gpt4-x-alpaca"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "maicomputer/gpt4-x-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/maicomputer/gpt4-x-alpaca

SGLang

How to use maicomputer/gpt4-x-alpaca 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 "maicomputer/gpt4-x-alpaca" \
    --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": "maicomputer/gpt4-x-alpaca",
		"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 "maicomputer/gpt4-x-alpaca" \
        --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": "maicomputer/gpt4-x-alpaca",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use maicomputer/gpt4-x-alpaca with Docker Model Runner:
```
docker model run hf.co/maicomputer/gpt4-x-alpaca
```

maicomputer commited on Dec 6, 2025

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@@ -1,14 +1,10 @@
 # GPT4 x Alpaca
-As a base model we used: https://huggingface.co/chavinlo/alpaca-13b
 Finetuned on GPT4's responses, for 3 epochs.
-NO LORA
-Please do note that the configurations files maybe messed up, this is because of the trainer I used. I WILL NOT EDIT THEM because there are repos hat automatically fix this, changing it might break it. Generally you just need to change anything that's under the name of "LLaMa" to "Llama" NOTE THE UPPER AND LOWER CASE!!!!
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
-Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_chavinlo__gpt4-x-alpaca)
 | Metric                | Value                     |
 |-----------------------|---------------------------|

 # GPT4 x Alpaca
+As a base model we used: alpaca-13b
 Finetuned on GPT4's responses, for 3 epochs.
 # [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
 | Metric                | Value                     |
 |-----------------------|---------------------------|