Instructions to use forkjoin-ai/tinyllama-1.1b-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use forkjoin-ai/tinyllama-1.1b-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="forkjoin-ai/tinyllama-1.1b-gguf",
	filename="tinyllama-1.1b-chat-v1.0.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/tinyllama-1.1b-gguf 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 forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf forkjoin-ai/tinyllama-1.1b-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/tinyllama-1.1b-gguf:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf forkjoin-ai/tinyllama-1.1b-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/tinyllama-1.1b-gguf:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M

Use Docker

docker model run hf.co/forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M

LM Studio
Jan

vLLM

How to use forkjoin-ai/tinyllama-1.1b-gguf with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "forkjoin-ai/tinyllama-1.1b-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/tinyllama-1.1b-gguf",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M

Ollama
How to use forkjoin-ai/tinyllama-1.1b-gguf with Ollama:
```
ollama run hf.co/forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M
```

Unsloth Studio

How to use forkjoin-ai/tinyllama-1.1b-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/tinyllama-1.1b-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/tinyllama-1.1b-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/tinyllama-1.1b-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use forkjoin-ai/tinyllama-1.1b-gguf with Docker Model Runner:
```
docker model run hf.co/forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M
```

Lemonade

How to use forkjoin-ai/tinyllama-1.1b-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull forkjoin-ai/tinyllama-1.1b-gguf:Q4_K_M

Run and chat with the model

lemonade run user.tinyllama-1.1b-gguf-Q4_K_M

List all available models

lemonade list

Tinyllama 1.1b (GGUF, Q4_K_M)

Production-ready GGUF quantization of TinyLlama/TinyLlama-1.1B-Chat-v1.0 for distributed text generation and conversation — powered by the Aether edge inference runtime on Edgework.ai.

Model Details

Property	Value
Base model	TinyLlama/TinyLlama-1.1B-Chat-v1.0
Parameters	1.1B
Architecture	LLaMA
Quantization	Q4_K_M
Format	GGUF
Size	~0.64 GB
License	llama3.1

Usage

With llama.cpp

./llama-cli -m tinyllama-1.1b-chat-v1.0.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 tinyllama-1.1b.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/tinyllama-1.1b-gguf tinyllama-1.1b.knot --local-dir ./knots

Full format spec: KNOT_FORMAT.md. Inspect the header with bun run open-source/bitwise/scripts/dump-knot.ts tinyllama-1.1b.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 (2 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

Downloads last month: 207

GGUF

Model size

1B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for forkjoin-ai/tinyllama-1.1b-gguf

Base model

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Quantized

(150)

this model