Instructions to use kazuma313/code-instruct-facebook-opt-350m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use kazuma313/code-instruct-facebook-opt-350m with Transformers:

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

pipe = pipeline("text-generation", model="kazuma313/code-instruct-facebook-opt-350m")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("kazuma313/code-instruct-facebook-opt-350m")
model = AutoModelForCausalLM.from_pretrained("kazuma313/code-instruct-facebook-opt-350m")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use kazuma313/code-instruct-facebook-opt-350m with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "kazuma313/code-instruct-facebook-opt-350m"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "kazuma313/code-instruct-facebook-opt-350m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/kazuma313/code-instruct-facebook-opt-350m

SGLang

How to use kazuma313/code-instruct-facebook-opt-350m 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 "kazuma313/code-instruct-facebook-opt-350m" \
    --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": "kazuma313/code-instruct-facebook-opt-350m",
		"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 "kazuma313/code-instruct-facebook-opt-350m" \
        --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": "kazuma313/code-instruct-facebook-opt-350m",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use kazuma313/code-instruct-facebook-opt-350m with Docker Model Runner:
```
docker model run hf.co/kazuma313/code-instruct-facebook-opt-350m
```

kazuma313 commited on Apr 2, 2024

Commit

f30de38

verified ·

1 Parent(s): 196db23

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -24,7 +24,7 @@ More information needed
 ## Intended uses & limitations
 * this use only 601 data from [CodeAlpaca-20k](https://huggingface.co/datasets/lucasmccabe-lmi/CodeAlpaca-20k) so need to train further.
-* `use tag "### Question: " for asking/instruct and it will generate "### Answer: " for inference.
 * the dataset is about coding with various languages, so need to train with specify programming language.
 ## Training and evaluation data

 ## Intended uses & limitations
 * this use only 601 data from [CodeAlpaca-20k](https://huggingface.co/datasets/lucasmccabe-lmi/CodeAlpaca-20k) so need to train further.
+* use tag "### Question: " for asking/instruct and it will generate "### Answer: " for inference.
 * the dataset is about coding with various languages, so need to train with specify programming language.
 ## Training and evaluation data