Instructions to use China-NCTIEDA/ChipExpert-8B-Instruct with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use China-NCTIEDA/ChipExpert-8B-Instruct with Transformers:

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

pipe = pipeline("text-generation", model="China-NCTIEDA/ChipExpert-8B-Instruct")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("China-NCTIEDA/ChipExpert-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("China-NCTIEDA/ChipExpert-8B-Instruct", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use China-NCTIEDA/ChipExpert-8B-Instruct with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "China-NCTIEDA/ChipExpert-8B-Instruct"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "China-NCTIEDA/ChipExpert-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/China-NCTIEDA/ChipExpert-8B-Instruct

SGLang

How to use China-NCTIEDA/ChipExpert-8B-Instruct 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 "China-NCTIEDA/ChipExpert-8B-Instruct" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "China-NCTIEDA/ChipExpert-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "China-NCTIEDA/ChipExpert-8B-Instruct" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "China-NCTIEDA/ChipExpert-8B-Instruct",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use China-NCTIEDA/ChipExpert-8B-Instruct with Docker Model Runner:
```
docker model run hf.co/China-NCTIEDA/ChipExpert-8B-Instruct
```

Kiiiiii commited on Jul 17, 2024

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@@ -10,14 +10,15 @@ tags:
 <div style="text-align:center">
    <p><strong><span style="font-size: 24px;">ChipExpert-8B-Instruct</span></strong></p>
 </div>
-<p align="center">
-💻<a href="https://github.com/NCTIE/ChipExpert" target="_blank">Github</a>
-</p>
 <!-- Provide a quick summary of what the model is/does. -->
 <div style="text-align:center">
    <p><strong><span style="font-size: 24px;">The First Open-Source Integrated-Circuit-Design-Specific Large Language Model．</span></strong></p>
 </div>
 ## Introduction:
 ChipExpert is the first open-source, instructional LLM dedicated to the Integrated-Circuit-Design industry, covering knowledge across multiple sub-domains, including analog circuits, digital circuits, radio frequency (RF), semiconductor devices, electronic design automation (EDA), system-on-chip (SoC), computing-in-memory, and more. This model aims to provide teaching assistant services for students in the field of IC to learn fundamental knowledge, engineers to inquire about technical details, and researchers to investigate cutting-edge papers and research topics. The ultimate goal of this model is to help the integrated circuit industry reduce the learning barrier and lower the training costs.

 <div style="text-align:center">
    <p><strong><span style="font-size: 24px;">ChipExpert-8B-Instruct</span></strong></p>
 </div>
 <!-- Provide a quick summary of what the model is/does. -->
 <div style="text-align:center">
    <p><strong><span style="font-size: 24px;">The First Open-Source Integrated-Circuit-Design-Specific Large Language Model．</span></strong></p>
 </div>
+<p align="center">
+💻<a href="https://github.com/NCTIE/ChipExpert" target="_blank">Github</a>
+</p>
 ## Introduction:
 ChipExpert is the first open-source, instructional LLM dedicated to the Integrated-Circuit-Design industry, covering knowledge across multiple sub-domains, including analog circuits, digital circuits, radio frequency (RF), semiconductor devices, electronic design automation (EDA), system-on-chip (SoC), computing-in-memory, and more. This model aims to provide teaching assistant services for students in the field of IC to learn fundamental knowledge, engineers to inquire about technical details, and researchers to investigate cutting-edge papers and research topics. The ultimate goal of this model is to help the integrated circuit industry reduce the learning barrier and lower the training costs.