Instructions to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
PEFT
How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

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

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

Atomic Chat

MaterialsAnalyst-AI-7B

File size: 1,214 Bytes

c67333b

from llama_cpp import Llama

# INSTRUCTIONS: Replace the JSON below with your material's properties
# Common data sources: materialsproject.org, DFT calculations, experimental databases

JSON_INPUT = """
{
  "material_id": "mp-8062",
  "formula": "SiC",
  "elements": [
    "Si",
    "C"
  ],
  "spacegroup": "P63mc",
  "band_gap": 3.26,
  "formation_energy_per_atom": -0.73,
  "density": 3.21,
  "volume": 41.2,
  "nsites": 8,
  "is_stable": true,
  "elastic_modulus": 448,
  "bulk_modulus": 220,
  "thermal_expansion": 4.2e-06,
  "electron_affinity": 4.0,
  "ionization_energy": 6.7,
  "crystal_system": "Hexagonal",
  "magnetic_property": "Non-magnetic",
  "thermal_conductivity": 490,
  "specific_heat": 0.69,
  "is_superconductor": false,
  "band_gap_type": "Indirect"
}
"""

model_path = "./" # Path to the directory containing your model weight files

llm = Llama(
    model_path=model_path,
    n_gpu_layers=29, 
    n_ctx=10000,
    n_threads=4
)

topic = JSON_INPUT.strip()
prompt = f"USER: {topic}\nASSISTANT:"


output = llm(
    prompt,
    max_tokens=3000,
    temperature=0.7,
    top_p=0.9,
    repeat_penalty=1.1
)

result = output.get("choices", [{}])[0].get("text", "").strip()

print(result)