Instructions to use Johnohhh1/darren-7b-complete with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Johnohhh1/darren-7b-complete with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Johnohhh1/darren-7b-complete",
	filename="darren-7b-complete.Q6_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Johnohhh1/darren-7b-complete with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Johnohhh1/darren-7b-complete:Q6_K
# Run inference directly in the terminal:
llama-cli -hf Johnohhh1/darren-7b-complete:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Johnohhh1/darren-7b-complete:Q6_K
# Run inference directly in the terminal:
llama-cli -hf Johnohhh1/darren-7b-complete:Q6_K

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 Johnohhh1/darren-7b-complete:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf Johnohhh1/darren-7b-complete:Q6_K

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 Johnohhh1/darren-7b-complete:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Johnohhh1/darren-7b-complete:Q6_K

Use Docker

docker model run hf.co/Johnohhh1/darren-7b-complete:Q6_K

LM Studio
Jan
Ollama
How to use Johnohhh1/darren-7b-complete with Ollama:
```
ollama run hf.co/Johnohhh1/darren-7b-complete:Q6_K
```

Unsloth Studio

How to use Johnohhh1/darren-7b-complete 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 Johnohhh1/darren-7b-complete 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 Johnohhh1/darren-7b-complete to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Johnohhh1/darren-7b-complete to start chatting

Atomic Chat new
Docker Model Runner
How to use Johnohhh1/darren-7b-complete with Docker Model Runner:
```
docker model run hf.co/Johnohhh1/darren-7b-complete:Q6_K
```

Lemonade

How to use Johnohhh1/darren-7b-complete with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Johnohhh1/darren-7b-complete:Q6_K

Run and chat with the model

lemonade run user.darren-7b-complete-Q6_K

List all available models

lemonade list

DARREN-7B-Complete (GGUF)

This is the GGUF quantized version of DARREN-7B, the tactical emergency AI assistant for fireground operations, hazmat, and real-time response.

Trained on:

Full MIPHEROv2 + REETEO frameworks
NIOSH chemical hazard database
Real fireground incident transcripts
Expert tactical validation overlays

Format: GGUF (llama.cpp compatible)
Quant: Q6_K
Prompt Format: [INST]...[/INST] with prepended SYSTEM message

Downloads last month: 1

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

6-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support