Instructions to use TULLUS/codeparrot-small-multi-F32 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TULLUS/codeparrot-small-multi-F32 with Transformers:

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

pipe = pipeline("text-generation", model="TULLUS/codeparrot-small-multi-F32")

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

tokenizer = AutoTokenizer.from_pretrained("TULLUS/codeparrot-small-multi-F32")
model = AutoModelForCausalLM.from_pretrained("TULLUS/codeparrot-small-multi-F32", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TULLUS/codeparrot-small-multi-F32 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TULLUS/codeparrot-small-multi-F32"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TULLUS/codeparrot-small-multi-F32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/TULLUS/codeparrot-small-multi-F32

SGLang

How to use TULLUS/codeparrot-small-multi-F32 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 "TULLUS/codeparrot-small-multi-F32" \
    --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": "TULLUS/codeparrot-small-multi-F32",
		"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 "TULLUS/codeparrot-small-multi-F32" \
        --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": "TULLUS/codeparrot-small-multi-F32",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use TULLUS/codeparrot-small-multi-F32 with Docker Model Runner:
```
docker model run hf.co/TULLUS/codeparrot-small-multi-F32
```

TULLUS commited on 2 days ago

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Update README.md

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@@ -68,7 +68,7 @@ File size: 444,048,000 bytes
 SHA256:    9cbfd95c12db0da2a0a475516d16e7f424a0b6d78883b4b663d281fe20ba0ed7
 ```
-A local source-to-artifact audit completed on with the following results:
 - The pinned upstream checkpoint hash matched the expected source hash.
 - All **149 logical state-dictionary tensors** compared exactly between the upstream source and the published TULLUS artifact.

 SHA256:    9cbfd95c12db0da2a0a475516d16e7f424a0b6d78883b4b663d281fe20ba0ed7
 ```
+A local source-to-artifact audit completed with the following results:
 - The pinned upstream checkpoint hash matched the expected source hash.
 - All **149 logical state-dictionary tensors** compared exactly between the upstream source and the published TULLUS artifact.