Instructions to use forkjoin-ai/qwen2.5-72b-instruct-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="forkjoin-ai/qwen2.5-72b-instruct-gguf",
	filename="Qwen2.5-72B-Instruct-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 forkjoin-ai/qwen2.5-72b-instruct-gguf with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf forkjoin-ai/qwen2.5-72b-instruct-gguf: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 forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf forkjoin-ai/qwen2.5-72b-instruct-gguf: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 forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M

Use Docker

docker model run hf.co/forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "forkjoin-ai/qwen2.5-72b-instruct-gguf"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "forkjoin-ai/qwen2.5-72b-instruct-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M

Ollama
How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with Ollama:
```
ollama run hf.co/forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M
```

Unsloth Studio

How to use forkjoin-ai/qwen2.5-72b-instruct-gguf 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 forkjoin-ai/qwen2.5-72b-instruct-gguf 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 forkjoin-ai/qwen2.5-72b-instruct-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for forkjoin-ai/qwen2.5-72b-instruct-gguf to start chatting

How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf forkjoin-ai/qwen2.5-72b-instruct-gguf: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": "forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf forkjoin-ai/qwen2.5-72b-instruct-gguf: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 forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with Docker Model Runner:
```
docker model run hf.co/forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M
```

Lemonade

How to use forkjoin-ai/qwen2.5-72b-instruct-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull forkjoin-ai/qwen2.5-72b-instruct-gguf:Q4_K_M

Run and chat with the model

lemonade run user.qwen2.5-72b-instruct-gguf-Q4_K_M

List all available models

lemonade list

Qwen2.5 72b Instruct (GGUF, Q4_K_M)

Production-ready GGUF quantization of Qwen/Qwen2.5-72B-Instruct for distributed text generation and conversation — powered by the Aether edge inference runtime on Edgework.ai.

Model Details

Property	Value
Base model	Qwen/Qwen2.5-72B-Instruct
Parameters	72B
Architecture	Qwen2
Quantization	Q4_K_M
Format	GGUF
Size	~43 GB
License	apache-2.0

Usage

With llama.cpp

./llama-cli -m Qwen2.5-72B-Instruct-Q4_K_M.gguf -p "Your prompt here" -n 256

With Aether (Distributed Inference)

This model is deployed across the Aether distributed inference network. Weights are layer-sharded and distributed across multiple edge nodes for parallel inference.

Also available: `.knot` (sovereign format)

This repo ships qwen2.5-72b-instruct.knot — the model weights in the KNOT container that the Aether distributed-inference runtime loads natively (the GGUF, when present, sits right beside it). A KNOT is a single self-describing file with a JSON table-of-contents, so any single tensor is one HTTP Range request — ideal for streaming weights to edge nodes.

	GGUF	KNOT
Container	format-specific header	single file, JSON table-of-contents
Per-tensor fetch	whole-file oriented	one tensor = one Range request
Ecosystem	broad (llama.cpp, …)	Aether / Gnosis runtime

huggingface-cli download forkjoin-ai/qwen2.5-72b-instruct-gguf qwen2.5-72b-instruct.knot --local-dir ./knots

Full format spec: KNOT_FORMAT.md. Inspect the header with bun run open-source/bitwise/scripts/dump-knot.ts qwen2.5-72b-instruct.knot.

Deployment Architecture

This model runs on the Aether distributed inference runtime — a custom engine that shards model layers across multiple nodes for parallel execution:

Coordinator receives requests and manages token generation
Layer nodes each hold a subset of model layers (6 nodes for this model)
Hidden states flow between nodes via gRPC
Zero cold start via warm pool scheduling

Deployed via Edgework.ai — bringing fast, cheap, and private inference as close to the user as possible.

About

Published by AFFECTIVELY · Managed by @buley

We quantize and publish production-ready models for distributed edge inference via the Aether runtime. Every release is tested for correctness and stability before publication.

All models · GitHub · Edgework.ai

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GGUF

Model size

73B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for forkjoin-ai/qwen2.5-72b-instruct-gguf

Base model

Qwen/Qwen2.5-72B

Finetuned

Qwen/Qwen2.5-72B-Instruct

Quantized

(85)

this model