Instructions to use davidVTR/LFM2.5-1.2B-PACK-Doc with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use davidVTR/LFM2.5-1.2B-PACK-Doc with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="davidVTR/LFM2.5-1.2B-PACK-Doc",
	filename="LFM2.5-1.2B-Instruct.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use davidVTR/LFM2.5-1.2B-PACK-Doc with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0
# Run inference directly in the terminal:
llama-cli -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0
# Run inference directly in the terminal:
llama-cli -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

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 davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

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 davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

Use Docker

docker model run hf.co/davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

LM Studio
Jan
Ollama
How to use davidVTR/LFM2.5-1.2B-PACK-Doc with Ollama:
```
ollama run hf.co/davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0
```

Unsloth Studio new

How to use davidVTR/LFM2.5-1.2B-PACK-Doc 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 davidVTR/LFM2.5-1.2B-PACK-Doc 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 davidVTR/LFM2.5-1.2B-PACK-Doc to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for davidVTR/LFM2.5-1.2B-PACK-Doc to start chatting

Pi new

How to use davidVTR/LFM2.5-1.2B-PACK-Doc with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

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": "davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use davidVTR/LFM2.5-1.2B-PACK-Doc with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

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 davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

Run Hermes

hermes

Docker Model Runner
How to use davidVTR/LFM2.5-1.2B-PACK-Doc with Docker Model Runner:
```
docker model run hf.co/davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0
```

Lemonade

How to use davidVTR/LFM2.5-1.2B-PACK-Doc with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull davidVTR/LFM2.5-1.2B-PACK-Doc:Q8_0

Run and chat with the model

lemonade run user.LFM2.5-1.2B-PACK-Doc-Q8_0

List all available models

lemonade list

LFM2.5-1.2B-PACK-Doc

Part of the PACKMate framework by Veritasr LLC

Model Description

LFM2.5-1.2B-PACK-Doc is a fine-tuned version of LFM2.5-1.2B-Instruct, trained on a custom dataset derived from current Tactical Combat Casualty Care (TCCC) protocols. It is designed for air-gapped edge deployment in high-stress environments where network connectivity is unavailable or operationally unacceptable.

This model will not refuse domain-relevant queries due to language or context. That is a feature, not an oversight.

Intended Use

Combat medics and 68W personnel requiring immediate procedural guidance
Air-gapped edge devices including rugged Android, Raspberry Pi, and similar hardware
Offline deployment with no cloud dependency

Out of Scope

General-purpose assistant tasks
Diagnosis or replacement of professional medical judgment in non-field settings
Any deployment where connectivity to cloud services is acceptable and preferable

Training Details

Base model: LFM2.5-1.2B-Instruct
Quantization: Q8_0 GGUF
Dataset: 2,500+ instruction-completion pairs derived from current TCCC protocols

Evaluation

Refusal rate on domain queries: pending formal evaluation
Average response latency on target hardware: pending formal evaluation
Accuracy on TCCC protocol queries: pending formal evaluation

PACKMate Framework

PACK-Doc is one module in an extensible architecture:

Module	Domain
PACK-Doc	Combat medicine - TCCC protocols
PACK-Tac	Tactical procedures - ROE, CQB, patrol
PACK-Q	Logistics - supply chain, requisitions
PACK-Maint	Equipment maintenance - vehicles, weapons

Same pipeline, different datasets. Additional modules are in development.

License

CC BY-NC 4.0 - Free for personal, research, and non-commercial use. Commercial use requires written permission from Veritasr LLC.

Citation

If you use this model in research or derivative work:

@misc{packdoc2025,
  author = {Veritasr LLC},
  title = {LFM2.5-1.2B-PACK-Doc},
  year = {2026},
  publisher = {HuggingFace},
  url = {https://huggingface.co/veritasr/LFM2.5-1.2B-PACK-Doc}
}

Contact

Veritasr LLC - veritasr.com

Downloads last month: 2

GGUF

Model size

1B params

Architecture

lfm2

Hardware compatibility

8-bit

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Model tree for davidVTR/LFM2.5-1.2B-PACK-Doc

Base model

LiquidAI/LFM2.5-1.2B-Base

Finetuned

LiquidAI/LFM2.5-1.2B-Instruct

Quantized

(49)

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