Instructions to use lamm-mit/BioinspiredLLM with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lamm-mit/BioinspiredLLM with Transformers:

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

pipe = pipeline("text-generation", model="lamm-mit/BioinspiredLLM")

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

tokenizer = AutoTokenizer.from_pretrained("lamm-mit/BioinspiredLLM")
model = AutoModelForCausalLM.from_pretrained("lamm-mit/BioinspiredLLM", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lamm-mit/BioinspiredLLM with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lamm-mit/BioinspiredLLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lamm-mit/BioinspiredLLM:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf lamm-mit/BioinspiredLLM:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf lamm-mit/BioinspiredLLM:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf lamm-mit/BioinspiredLLM:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf lamm-mit/BioinspiredLLM:Q4_K_M

Use Docker

docker model run hf.co/lamm-mit/BioinspiredLLM:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use lamm-mit/BioinspiredLLM with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lamm-mit/BioinspiredLLM:Q4_K_M

SGLang

How to use lamm-mit/BioinspiredLLM 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 "lamm-mit/BioinspiredLLM" \
    --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": "lamm-mit/BioinspiredLLM",
		"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 "lamm-mit/BioinspiredLLM" \
        --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": "lamm-mit/BioinspiredLLM",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use lamm-mit/BioinspiredLLM with Ollama:
```
ollama run hf.co/lamm-mit/BioinspiredLLM:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use lamm-mit/BioinspiredLLM with Docker Model Runner:
```
docker model run hf.co/lamm-mit/BioinspiredLLM:Q4_K_M
```

Lemonade

How to use lamm-mit/BioinspiredLLM with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lamm-mit/BioinspiredLLM:Q4_K_M

Run and chat with the model

lemonade run user.BioinspiredLLM-Q4_K_M

List all available models

lemonade list

Atomic Chat

mjbuehler commited on Feb 20, 2024

Commit

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@@ -213,6 +213,11 @@ response = index_doc.as_query_engine(service_context=service_context,
 print(response)
 ```
 ### Notes and licenses
 BioinspiredLLM is a 13b parameter model. It was fine-tuned based on the Orca-2 model (https://huggingface.co/microsoft/Orca-2-13b), using the LLaMA-2 13b base model. Please refer to LLaMA-2 technical report for details on the model architecture. Details see: https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202306724

 print(response)
 ```
+### Reference
+Rachel K. Luu, Markus J. Buehler, BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
+arxiv.org/abs/2309.08788 and Adv. Science, https://doi.org/10.1002/advs.202306724, 2023
 ### Notes and licenses
 BioinspiredLLM is a 13b parameter model. It was fine-tuned based on the Orca-2 model (https://huggingface.co/microsoft/Orca-2-13b), using the LLaMA-2 13b base model. Please refer to LLaMA-2 technical report for details on the model architecture. Details see: https://onlinelibrary.wiley.com/doi/full/10.1002/advs.202306724