Instructions to use Mathildeholst/Good-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Mathildeholst/Good-generator with Transformers:

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

pipe = pipeline("text-generation", model="Mathildeholst/Good-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Mathildeholst/Good-generator")
model = AutoModelForCausalLM.from_pretrained("Mathildeholst/Good-generator")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Mathildeholst/Good-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Mathildeholst/Good-generator

SGLang

How to use Mathildeholst/Good-generator 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 "Mathildeholst/Good-generator" \
    --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": "Mathildeholst/Good-generator",
		"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 "Mathildeholst/Good-generator" \
        --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": "Mathildeholst/Good-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Mathildeholst/Good-generator with Docker Model Runner:
```
docker model run hf.co/Mathildeholst/Good-generator
```

Good-generator / adapter_model.safetensors

Mathildeholst

Training in progress, step 939

3b26be1 verified 8 months ago

download

history blame contribute delete

19.6 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 4a6dbcb993de61cc87ba3a8745fcc627b7d90579bf500182c1d85f0265a61423
Size of remote file:: 19.6 MB
SHA256:: 6f1117e7b5db07c235f64f28bb3d9c2212054e038dd6228670fd047558567afd

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.