Instructions to use tocsa/llama2-7b-dapt-chipdesign with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tocsa/llama2-7b-dapt-chipdesign with Transformers:

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

pipe = pipeline("text-generation", model="tocsa/llama2-7b-dapt-chipdesign")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("tocsa/llama2-7b-dapt-chipdesign")
model = AutoModelForCausalLM.from_pretrained("tocsa/llama2-7b-dapt-chipdesign", device_map="auto")

NeMo
How to use tocsa/llama2-7b-dapt-chipdesign with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use tocsa/llama2-7b-dapt-chipdesign with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tocsa/llama2-7b-dapt-chipdesign"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tocsa/llama2-7b-dapt-chipdesign",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/tocsa/llama2-7b-dapt-chipdesign

SGLang

How to use tocsa/llama2-7b-dapt-chipdesign 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 "tocsa/llama2-7b-dapt-chipdesign" \
    --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": "tocsa/llama2-7b-dapt-chipdesign",
		"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 "tocsa/llama2-7b-dapt-chipdesign" \
        --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": "tocsa/llama2-7b-dapt-chipdesign",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use tocsa/llama2-7b-dapt-chipdesign with Docker Model Runner:
```
docker model run hf.co/tocsa/llama2-7b-dapt-chipdesign
```

Llama-2-7B + domain-adaptive pretraining (chip-design corpus: Wikipedia/GitHub/arXiv, see 01_data_curation and 03_domain_adaptive_pretraining). Base checkpoint: meta-llama/Llama-2-7b-hf. Not chained with the SFT repo below.

Downloads last month: -

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for tocsa/llama2-7b-dapt-chipdesign

Base model

meta-llama/Llama-2-7b-hf

Finetuned

(989)

this model