Libraries PEFT How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with PEFT:
Task type is invalid. llama-cpp-python How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with llama-cpp-python:
# !pip install llama-cpp-python
from llama_cpp import Llama
llm = Llama.from_pretrained(
repo_id="Raymond-dev-546730/MaterialsAnalyst-AI-7B",
filename="Model_Weights/llama.cpp/MaterialsAnalyst-AI-7B-IQ3_XS.gguf",
)
output = llm(
"Once upon a time,",
max_tokens=512,
echo=True
)
print(output) Notebooks Google Colab Kaggle Local Apps Settings llama.cpp How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B 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 Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M Install from WinGet (Windows) winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_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 Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_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 Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M Use Docker docker model run hf.co/Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M LM Studio Jan vLLM How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with vLLM:
Install from pip and serve model # Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Raymond-dev-546730/MaterialsAnalyst-AI-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
-H "Content-Type: application/json" \
--data '{
"model": "Raymond-dev-546730/MaterialsAnalyst-AI-7B",
"prompt": "Once upon a time,",
"max_tokens": 512,
"temperature": 0.5
}' Use Docker docker model run hf.co/Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M Ollama How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with Ollama:
ollama run hf.co/Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M Unsloth Studio How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL) curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Raymond-dev-546730/MaterialsAnalyst-AI-7B to start chatting Install Unsloth Studio (Windows) irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for Raymond-dev-546730/MaterialsAnalyst-AI-7B to start chatting Using HuggingFace Spaces for Unsloth # No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Raymond-dev-546730/MaterialsAnalyst-AI-7B to start chatting Atomic Chat new Docker Model Runner How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with Docker Model Runner:
docker model run hf.co/Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M Lemonade How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with Lemonade:
Pull the model # Download Lemonade from https://lemonade-server.ai/
lemonade pull Raymond-dev-546730/MaterialsAnalyst-AI-7B:Q4_K_M Run and chat with the model lemonade run user.MaterialsAnalyst-AI-7B-Q4_K_M List all available models lemonade list 
