Instructions to use CarperAI/diff-codegen-6b-v2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CarperAI/diff-codegen-6b-v2 with Transformers:

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

pipe = pipeline("text-generation", model="CarperAI/diff-codegen-6b-v2")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("CarperAI/diff-codegen-6b-v2")
model = AutoModelForCausalLM.from_pretrained("CarperAI/diff-codegen-6b-v2")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use CarperAI/diff-codegen-6b-v2 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CarperAI/diff-codegen-6b-v2"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CarperAI/diff-codegen-6b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/CarperAI/diff-codegen-6b-v2

SGLang

How to use CarperAI/diff-codegen-6b-v2 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 "CarperAI/diff-codegen-6b-v2" \
    --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": "CarperAI/diff-codegen-6b-v2",
		"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 "CarperAI/diff-codegen-6b-v2" \
        --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": "CarperAI/diff-codegen-6b-v2",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use CarperAI/diff-codegen-6b-v2 with Docker Model Runner:
```
docker model run hf.co/CarperAI/diff-codegen-6b-v2
```

HyperionHF commited on Jan 28, 2023

Commit

a73f849

1 Parent(s): 633ea75

Add colab link

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README.md +2 -0

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@@ -25,6 +25,8 @@ This model is a fine-tune of [codegen-6B-mono](https://huggingface.co/Salesforce
 diff-codegen-6b-v2 is an experimental research artifact and should be treated as such. We are releasing these results and this model in the hopes that it may be useful to the greater research community, especially those interested in LMs for code.
 ## Training Data
 This model is a fine-tune of [codegen-6B-mono](https://huggingface.co/Salesforce/codegen-6B-mono) by Salesforce. This language model was first pre-trained on The Pile, an 800Gb dataset composed of varied web corpora. The datasheet and paper for the Pile can be found [here](https://arxiv.org/abs/2201.07311) and [here](https://arxiv.org/abs/2101.00027) respectively. The model was then fine-tuned on a large corpus of code data in multiple languages, before finally being fine-tuned on a Python code dataset. The Codegen paper with full details of these datasets can be found [here](https://arxiv.org/abs/2203.13474).

 diff-codegen-6b-v2 is an experimental research artifact and should be treated as such. We are releasing these results and this model in the hopes that it may be useful to the greater research community, especially those interested in LMs for code.
+An example Colab notebook with a brief example of prompting the model is [here](https://colab.research.google.com/drive/1ySm6HYvALerDiGmk6g3pDz68V7fAtrQH#scrollTo=thvzNpmahNNx).
 ## Training Data
 This model is a fine-tune of [codegen-6B-mono](https://huggingface.co/Salesforce/codegen-6B-mono) by Salesforce. This language model was first pre-trained on The Pile, an 800Gb dataset composed of varied web corpora. The datasheet and paper for the Pile can be found [here](https://arxiv.org/abs/2201.07311) and [here](https://arxiv.org/abs/2101.00027) respectively. The model was then fine-tuned on a large corpus of code data in multiple languages, before finally being fine-tuned on a Python code dataset. The Codegen paper with full details of these datasets can be found [here](https://arxiv.org/abs/2203.13474).