Instructions to use lamm-mit/Bioinspired-Phi-3-mini-4k with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lamm-mit/Bioinspired-Phi-3-mini-4k with Transformers:

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

pipe = pipeline("text-generation", model="lamm-mit/Bioinspired-Phi-3-mini-4k", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("lamm-mit/Bioinspired-Phi-3-mini-4k", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("lamm-mit/Bioinspired-Phi-3-mini-4k", trust_remote_code=True, 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lamm-mit/Bioinspired-Phi-3-mini-4k 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/Bioinspired-Phi-3-mini-4k:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_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/Bioinspired-Phi-3-mini-4k:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M

Use Docker

docker model run hf.co/lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M

LM Studio
Jan

vLLM

How to use lamm-mit/Bioinspired-Phi-3-mini-4k with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lamm-mit/Bioinspired-Phi-3-mini-4k"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lamm-mit/Bioinspired-Phi-3-mini-4k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M

SGLang

How to use lamm-mit/Bioinspired-Phi-3-mini-4k 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/Bioinspired-Phi-3-mini-4k" \
    --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": "lamm-mit/Bioinspired-Phi-3-mini-4k",
		"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 "lamm-mit/Bioinspired-Phi-3-mini-4k" \
        --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": "lamm-mit/Bioinspired-Phi-3-mini-4k",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use lamm-mit/Bioinspired-Phi-3-mini-4k with Ollama:
```
ollama run hf.co/lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M
```
Unsloth Desktop
Docker Model Runner
How to use lamm-mit/Bioinspired-Phi-3-mini-4k with Docker Model Runner:
```
docker model run hf.co/lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M
```

Lemonade

How to use lamm-mit/Bioinspired-Phi-3-mini-4k with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lamm-mit/Bioinspired-Phi-3-mini-4k:Q5_K_M

Run and chat with the model

lemonade run user.Bioinspired-Phi-3-mini-4k-Q5_K_M

List all available models

lemonade list

Atomic Chat

mjbuehler commited on Apr 28, 2024

Commit

5fca28c

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1 Parent(s): e18632a

Update README.md

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@@ -1,11 +1,29 @@
 ---
 library_name: transformers
-tags: []
 ---
 # Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->

 ---
 library_name: transformers
+language:
+- en
+tags:
+- biology
+- materials science
+- code
+- scientific AI
+- biological materials
+- bioinspiration
+- machine learning
+- generative
 ---
+# BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
+Reference: R. Luu and M.J. Buehler, "BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials," Adv. Science, 2023, DOI: https://doi.org/10.1002/advs.202306724
+Abstract: The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/623ce1c6b66fedf374859fe7/Xdp_nCYiF2IAPamG5ffIC.png)
 # Model Card for Model ID
+Fine-tuned LLM with domain knowledge in biological materials, mechanics of materials, modeling and simulation, and related fields.