Instructions to use muhtasham/santacoder-finetuned-the-stack-cobol with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use muhtasham/santacoder-finetuned-the-stack-cobol with Transformers:

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

pipe = pipeline("text-generation", model="muhtasham/santacoder-finetuned-the-stack-cobol", trust_remote_code=True)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("muhtasham/santacoder-finetuned-the-stack-cobol", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("muhtasham/santacoder-finetuned-the-stack-cobol", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use muhtasham/santacoder-finetuned-the-stack-cobol with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/muhtasham/santacoder-finetuned-the-stack-cobol

SGLang

How to use muhtasham/santacoder-finetuned-the-stack-cobol 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 "muhtasham/santacoder-finetuned-the-stack-cobol" \
    --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": "muhtasham/santacoder-finetuned-the-stack-cobol",
		"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 "muhtasham/santacoder-finetuned-the-stack-cobol" \
        --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": "muhtasham/santacoder-finetuned-the-stack-cobol",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use muhtasham/santacoder-finetuned-the-stack-cobol with Docker Model Runner:
```
docker model run hf.co/muhtasham/santacoder-finetuned-the-stack-cobol
```

muhtasham commited on Feb 9, 2023

Commit

e238be0

1 Parent(s): 7c14f0c

Update README.md

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@@ -9,10 +9,11 @@ model-index:
 - name: santacoder-finetuned-the-stack-cobol
   results: []
 datasets:
-  - bigcode/the-stack-dedup
 language:
-  - code
 pipeline_tag: text-generation
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
@@ -75,4 +76,4 @@ The following hyperparameters were used during training:
 - Transformers 4.26.0
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
-- Tokenizers 0.13.2

 - name: santacoder-finetuned-the-stack-cobol
   results: []
 datasets:
+- bigcode/the-stack-dedup
 language:
+- code
 pipeline_tag: text-generation
+library_name: transformers
 ---
 <!-- This model card has been generated automatically according to the information the Trainer had access to. You
 - Transformers 4.26.0
 - Pytorch 1.13.1+cu116
 - Datasets 2.9.0
+- Tokenizers 0.13.2