Instructions to use scrapegoat/Scrapegoat-Tiny-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use scrapegoat/Scrapegoat-Tiny-Coder with Transformers:

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

pipe = pipeline("text-generation", model="scrapegoat/Scrapegoat-Tiny-Coder")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("scrapegoat/Scrapegoat-Tiny-Coder", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use scrapegoat/Scrapegoat-Tiny-Coder with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/scrapegoat/Scrapegoat-Tiny-Coder

SGLang

How to use scrapegoat/Scrapegoat-Tiny-Coder 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 "scrapegoat/Scrapegoat-Tiny-Coder" \
    --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": "scrapegoat/Scrapegoat-Tiny-Coder",
		"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 "scrapegoat/Scrapegoat-Tiny-Coder" \
        --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": "scrapegoat/Scrapegoat-Tiny-Coder",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use scrapegoat/Scrapegoat-Tiny-Coder with Docker Model Runner:
```
docker model run hf.co/scrapegoat/Scrapegoat-Tiny-Coder
```

Suggestion: Upload Model Weights, Benchmarks

by jd-vijay - opened 3 days ago

Discussion

jd-vijay

3 days ago

Please upload the model weights, benchmark results, and a few usage examples directly in the repository as well.

Right now, the model weights are stored in the bucket ("hf://buckets/Nathan9/dump/scrapegoat-fp8"), but most normal users won't understand or know how to access that. If you publish them in a way that's directly visible on the repository, people will be able to browse, understand, and use them much more easily.

I think it would make the project much more user-friendly and accessible.

Nathan9

spacelabs org 3 days ago

Hello from Space-labs.pro,
we will roll out an official release with Benchmarks/Metrics and DSpark inference. The Fp8 weights you found isn't properly initialised, it won't be useful at this time for you (: Kindly wait and Please, watch this space.

Thank You 🤗🐐

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