Instructions to use DeviceAlchemy/devicealchemy-mira-mini with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use DeviceAlchemy/devicealchemy-mira-mini with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="DeviceAlchemy/devicealchemy-mira-mini",
	filename="mira-mini-q4_k_m.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use DeviceAlchemy/devicealchemy-mira-mini 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 DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DeviceAlchemy/devicealchemy-mira-mini: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 DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DeviceAlchemy/devicealchemy-mira-mini: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 DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Use Docker

docker model run hf.co/DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

LM Studio
Jan

vLLM

How to use DeviceAlchemy/devicealchemy-mira-mini with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Ollama
How to use DeviceAlchemy/devicealchemy-mira-mini with Ollama:
```
ollama run hf.co/DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M
```

Unsloth Studio

How to use DeviceAlchemy/devicealchemy-mira-mini 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 DeviceAlchemy/devicealchemy-mira-mini 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 DeviceAlchemy/devicealchemy-mira-mini to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DeviceAlchemy/devicealchemy-mira-mini to start chatting

How to use DeviceAlchemy/devicealchemy-mira-mini with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use DeviceAlchemy/devicealchemy-mira-mini with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use DeviceAlchemy/devicealchemy-mira-mini with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use DeviceAlchemy/devicealchemy-mira-mini with Docker Model Runner:
```
docker model run hf.co/DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M
```

Lemonade

How to use DeviceAlchemy/devicealchemy-mira-mini with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DeviceAlchemy/devicealchemy-mira-mini:Q4_K_M

Run and chat with the model

lemonade run user.devicealchemy-mira-mini-Q4_K_M

List all available models

lemonade list

devicealchemy-mira-mini / README.md

ssayed122

Update README.md

dc9cab1 verified 26 days ago

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	---
	license: apache-2.0
	base_model: Qwen/Qwen2.5-1.5B-Instruct
	tags:
	- materials-science
	- condensed-matter-physics
	- device-engineering
	- gguf
	- qlora
	- qwen2
	- chat
	language:
	- en
	pipeline_tag: text-generation
	---

	# MIRA-mini v1.0 — Materials Intelligence and Reasoning Agent

	MIRA is a domain-specialized chat model fine-tuned from Qwen2.5-1.5B-Instruct to explain
	material stacks, physical mechanisms, and correlations between material properties and
	phenomena in condensed matter physics, device engineering, and materials science.

	Developed by [DeviceAlchemy.ai](https://devicealchemy.ai).

	This repo hosts the GGUF (Q4_K_M quantized) version for local inference with
	[Ollama](https://ollama.com) or [llama.cpp](https://github.com/ggerganov/llama.cpp).

	Training code: [github.com/DeviceAlchemy/devicealchemy-mira-mini](https://github.com/DeviceAlchemy/devicealchemy-mira-mini)

	## Model details

	- Base model: [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) (Alibaba, Apache 2.0)
	- Fine-tuning method: QLoRA — continued pre-training on raw abstracts, followed by
	instruction tuning on synthetically generated Q&A pairs derived from those abstracts
	- Quantization: Q4_K_M (~940 MB)
	- Training data: Openly licensed (CC BY) scientific text dataset containing 120 million words.
	- Context length: 4096 tokens (inference); trained on 384-token blocks

	## Intended use

	MIRA-mini is designed to:
	- Explain why specific material stacks are predicted to exhibit a given phenomenon
	- Describe physical mechanisms at material interfaces (spin-orbit coupling, exchange
	interactions, band hybridization, etc.)
	- Answer domain questions in condensed matter physics, materials science, and device & circuit
	engineering

	It is not a substitute for primary literature, peer review, or experimental
	validation. Outputs may contain errors, omissions, or hallucinations and should be
	verified against original research.

	## How to use (Ollama)

	```bash
	ollama pull hf.co/DeviceAlchemy/devicealchemy-mira-mini
	ollama run hf.co/DeviceAlchemy/devicealchemy-mira-mini
	```

	Or download the GGUF directly and build a local `Modelfile`:

	```
	FROM ./mira-mini-q4_k_m.gguf

	TEMPLATE """<\|im_start\|>system
	{{ .System }}<\|im_end\|>
	<\|im_start\|>user
	{{ .Prompt }}<\|im_end\|>
	<\|im_start\|>assistant
	"""

	SYSTEM """You are MIRA, Materials Intelligence and Reasoning Agent. You have been trained on a large corpus of scientific abstracts on condensed matter physics, device engineering, and materials science. You explain material stack predictions, physical mechanisms, and correlations between material properties and phenomena. Be precise, scientific, and concise."""

	PARAMETER temperature 0.8
	PARAMETER top_p 0.9
	PARAMETER top_k 40
	PARAMETER repeat_penalty 1.5
	PARAMETER num_ctx 4096
	PARAMETER stop "<\|im_end\|>"
	PARAMETER stop "<\|im_start\|>"
	```

	```bash
	ollama create mira-mini -f Modelfile
	ollama run mira-mini
	```

	## Training pipeline

	Full training code (4-step pipeline: data prep → continued pre-training → chat
	fine-tuning → GGUF export) is available at:
	[github.com/DeviceAlchemy/devicealchemy-mira-mini](https://github.com/DeviceAlchemy/devicealchemy-mira-mini)

	## Limitations

	- This is a 1.5B parameter model — it has narrower general-purpose reasoning ability
	than larger models, and is specifically tuned toward the materials science and device engineering domain.
	- Training data consists of abstracts only (not full papers), so depth on any single
	topic is bounded by what's typically conveyed in an abstract.
	- Q4_K_M quantization trades a small amount of precision for a ~3x smaller file size
	and faster local inference; the full-precision merged model is referenced in the
	GitHub repo for those who want F16 or further quantization options.

	## Disclaimer

	MIRA-mini outputs are for informational and exploratory purposes only and should not
	be considered professional, scientific, medical, legal, or financial advice. Always
	verify critical information against original research papers and conventional
	theoretical/experimental means. DeviceAlchemy.ai is not affiliated with, endorsed by,
	or partnered with any academic publishers.

	## Author

	Shehrin Sayed, Ph.D. — Founder, [DeviceAlchemy.ai](https://devicealchemy.ai)