Text Generation
Transformers
Safetensors
GGUF
English
phi3
biology
materials science
code
scientific AI
biological materials
bioinspiration
machine learning
generative
conversational
custom_code
text-generation-inference
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
- Transformers
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
Download config.json from lamm-mit/Bioinspired-Phi-3-mini-4k: direct link, hf CLI and curl.
- Browser
- Download file 989 Bytes
-
https://huggingface.co/lamm-mit/Bioinspired-Phi-3-mini-4k/resolve/main/config.json
- Command line
-
hf download hf://lamm-mit/Bioinspired-Phi-3-mini-4k/config.json
-
curl -L -o config.json https://huggingface.co/lamm-mit/Bioinspired-Phi-3-mini-4k/resolve/main/config.json
989 Bytes
| { | |
| "_name_or_path": "lamm-mit/Bioinspired-Phi-3-mini-4k", | |
| "architectures": [ | |
| "Phi3ForCausalLM" | |
| ], | |
| "attention_dropout": 0.0, | |
| "auto_map": { | |
| "AutoConfig": "microsoft/Phi-3-mini-4k-instruct--configuration_phi3.Phi3Config", | |
| "AutoModelForCausalLM": "microsoft/Phi-3-mini-4k-instruct--modeling_phi3.Phi3ForCausalLM" | |
| }, | |
| "bos_token_id": 1, | |
| "embd_pdrop": 0.0, | |
| "eos_token_id": 32000, | |
| "hidden_act": "silu", | |
| "hidden_size": 3072, | |
| "initializer_range": 0.02, | |
| "intermediate_size": 8192, | |
| "max_position_embeddings": 4096, | |
| "model_type": "phi3", | |
| "num_attention_heads": 32, | |
| "num_hidden_layers": 32, | |
| "num_key_value_heads": 32, | |
| "original_max_position_embeddings": 4096, | |
| "pad_token_id": 32000, | |
| "resid_pdrop": 0.0, | |
| "rms_norm_eps": 1e-05, | |
| "rope_scaling": null, | |
| "rope_theta": 10000.0, | |
| "sliding_window": 2047, | |
| "tie_word_embeddings": false, | |
| "torch_dtype": "bfloat16", | |
| "transformers_version": "4.41.0.dev0", | |
| "use_cache": true, | |
| "vocab_size": 32064 | |
| } | |