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.
```

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

llama.cpp

How to use Raymond-dev-546730/MaterialsAnalyst-AI-7B with llama.cpp:

Install from brew

brew install llama.cpp
# 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

Install from WinGet (Windows)

winget install llama.cpp
# 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

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 new

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

MaterialsAnalyst-AI-7B / Scripts /Inference_llama.cpp.py

Raymond-dev-546730

Upload 2 files

c67333b verified 12 months ago

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	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)