Instructions to use shailja/fine-tuned-codegen-2B-Verilog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shailja/fine-tuned-codegen-2B-Verilog with Transformers:

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

pipe = pipeline("text-generation", model="shailja/fine-tuned-codegen-2B-Verilog")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog")
model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-2B-Verilog")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use shailja/fine-tuned-codegen-2B-Verilog with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "shailja/fine-tuned-codegen-2B-Verilog"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "shailja/fine-tuned-codegen-2B-Verilog",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shailja/fine-tuned-codegen-2B-Verilog

SGLang

How to use shailja/fine-tuned-codegen-2B-Verilog 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 "shailja/fine-tuned-codegen-2B-Verilog" \
    --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": "shailja/fine-tuned-codegen-2B-Verilog",
		"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 "shailja/fine-tuned-codegen-2B-Verilog" \
        --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": "shailja/fine-tuned-codegen-2B-Verilog",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shailja/fine-tuned-codegen-2B-Verilog with Docker Model Runner:
```
docker model run hf.co/shailja/fine-tuned-codegen-2B-Verilog
```

shailja commited on Aug 30, 2023

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@@ -50,7 +50,7 @@ extra_gated_fields:
 ## Model Summary
-The CodeGen models are 15.5B parameter models trained on 80+ programming languages from [The Stack (v1.2)](https://huggingface.co/datasets/bigcode/the-stack), with opt-out requests excluded. The model uses [Multi Query Attention](https://arxiv.org/abs/1911.02150), [a context window of 8192 tokens](https://arxiv.org/abs/2205.14135),  and was trained using the [Fill-in-the-Middle objective](https://arxiv.org/abs/2207.14255) on 1 trillion tokens.
 - **Repository:** [shailja-thakur/VGen](https://github.com/shailja-thakur/VGen)
 - **Baseline LLM** [SalesForce/CodeGen](https://github.com/salesforce/CodeGen)

 ## Model Summary
+The VeriGen model is 2B parameter model is a fine-tuned version of [CodeGen-multi-2B](https://github.com/salesforce/codegen), trained on [Verilog Dataset](https://huggingface.co/datasets/shailja/Verilog_GitHub), with a context length of 2048.
 - **Repository:** [shailja-thakur/VGen](https://github.com/shailja-thakur/VGen)
 - **Baseline LLM** [SalesForce/CodeGen](https://github.com/salesforce/CodeGen)