Instructions to use shailja/fine-tuned-codegen-16B-Verilog with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use shailja/fine-tuned-codegen-16B-Verilog with Transformers:

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

pipe = pipeline("text-generation", model="shailja/fine-tuned-codegen-16B-Verilog")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-16B-Verilog")
model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-16B-Verilog", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use shailja/fine-tuned-codegen-16B-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-16B-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-16B-Verilog",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/shailja/fine-tuned-codegen-16B-Verilog

SGLang

How to use shailja/fine-tuned-codegen-16B-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-16B-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-16B-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-16B-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-16B-Verilog",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use shailja/fine-tuned-codegen-16B-Verilog with Docker Model Runner:
```
docker model run hf.co/shailja/fine-tuned-codegen-16B-Verilog
```

shailja commited on Aug 30, 2023

Commit

ea3cbb6

1 Parent(s): 2196758

Update README.md

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README.md +3 -3

README.md CHANGED Viewed

@@ -76,9 +76,9 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 prompt = "//module half adder "
 device='cuda'
 # Load model and tokenizer
-model_name = "shailja/CodeGen_16B_Verilog"
-tokenizer = AutoTokenizer.from_pretrained("shailja/fine-tuned-codegen-16B-Verilog")
-model = AutoModelForCausalLM.from_pretrained("shailja/fine-tuned-codegen-16B-Verilog").to(device)
 # Sample
 input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)

 prompt = "//module half adder "
 device='cuda'
 # Load model and tokenizer
+model_name = "shailja/fine-tuned-codegen-16B-Verilog"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to(device)
 # Sample
 input_ids = tokenizer(prompt, return_tensors="pt").input_ids.to(device)