Instructions to use LegionIntel/yurts-python-code-gen-30-sparse with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use LegionIntel/yurts-python-code-gen-30-sparse with Transformers:

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

pipe = pipeline("text-generation", model="LegionIntel/yurts-python-code-gen-30-sparse")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("LegionIntel/yurts-python-code-gen-30-sparse")
model = AutoModelForCausalLM.from_pretrained("LegionIntel/yurts-python-code-gen-30-sparse")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use LegionIntel/yurts-python-code-gen-30-sparse with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "LegionIntel/yurts-python-code-gen-30-sparse"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "LegionIntel/yurts-python-code-gen-30-sparse",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/LegionIntel/yurts-python-code-gen-30-sparse

SGLang

How to use LegionIntel/yurts-python-code-gen-30-sparse 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 "LegionIntel/yurts-python-code-gen-30-sparse" \
    --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": "LegionIntel/yurts-python-code-gen-30-sparse",
		"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 "LegionIntel/yurts-python-code-gen-30-sparse" \
        --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": "LegionIntel/yurts-python-code-gen-30-sparse",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use LegionIntel/yurts-python-code-gen-30-sparse with Docker Model Runner:
```
docker model run hf.co/LegionIntel/yurts-python-code-gen-30-sparse
```

yurts-python-code-gen-30-sparse

Commit History

Adding `safetensors` variant of this model

dab9f4f

SFconvertbot commited on Mar 16, 2023

Adds license to the readme

c79e430

William Du commited on Oct 27, 2022

Updates copy on readme

1b870da

William Du commited on Oct 27, 2022

Update readme with model description

2ce311d

William Du commited on Oct 27, 2022

Init sparsified 30% version of python-tuned code gen model

cdd067e

William Du commited on Oct 25, 2022

initial commit

ba2de98

William Du commited on Oct 24, 2022

Commit History

Adding `safetensors` variant of this model dab9f4f

Adds license to the readme c79e430

Updates copy on readme 1b870da

Update readme with model description 2ce311d

Init sparsified 30% version of python-tuned code gen model cdd067e

initial commit ba2de98

Adding `safetensors` variant of this model

dab9f4f

Adds license to the readme

c79e430

Updates copy on readme

1b870da

Update readme with model description

2ce311d

Init sparsified 30% version of python-tuned code gen model

cdd067e

initial commit

ba2de98