Instructions to use Salesforce/codegen25-7b-instruct_P with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Salesforce/codegen25-7b-instruct_P with Transformers:

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

pipe = pipeline("text-generation", model="Salesforce/codegen25-7b-instruct_P")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Salesforce/codegen25-7b-instruct_P")
model = AutoModelForCausalLM.from_pretrained("Salesforce/codegen25-7b-instruct_P")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Salesforce/codegen25-7b-instruct_P with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Salesforce/codegen25-7b-instruct_P"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Salesforce/codegen25-7b-instruct_P",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Salesforce/codegen25-7b-instruct_P

SGLang

How to use Salesforce/codegen25-7b-instruct_P 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 "Salesforce/codegen25-7b-instruct_P" \
    --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": "Salesforce/codegen25-7b-instruct_P",
		"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 "Salesforce/codegen25-7b-instruct_P" \
        --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": "Salesforce/codegen25-7b-instruct_P",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Salesforce/codegen25-7b-instruct_P with Docker Model Runner:
```
docker model run hf.co/Salesforce/codegen25-7b-instruct_P
```

rooa commited on Jul 11, 2023

Commit

a68f571

1 Parent(s): 3a46065

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@@ -102,6 +102,13 @@ Please refer to the [blog](https://blog.salesforceairesearch.com/codegen25) for
 As an autoregressive language model, CodeGen2.5 is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
 However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
 ## BibTeX entry and citation info
 Please cite CodeGen2 paper:

 As an autoregressive language model, CodeGen2.5 is capable of extracting features from given natural language and programming language texts, and calculating the likelihood of them.
 However, the model is intended for and best at **program synthesis**, that is, generating executable code given English prompts, where the prompts should be in the form of a comment string. The model can complete partially-generated code as well.
+## Attribution & Other Requirements
+The pretraining dataset of the model was filtered for permissive licenses only.
+Nevertheless, the model can generate source code verbatim from the dataset.
+The code's license might require attribution and/or other specific requirements that must be respected.
+The data provider BigCode provides a [search index](https://huggingface.co/spaces/bigcode/starcoder-search) that lets you search through the pretraining data to identify where generated code came from and apply the proper attribution to your code.
 ## BibTeX entry and citation info
 Please cite CodeGen2 paper: