Instructions to use timothywong731/tim-360m-instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use timothywong731/tim-360m-instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="timothywong731/tim-360m-instruct-GGUF",
	filename="tim-360m-instruct.F16.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 timothywong731/tim-360m-instruct-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 timothywong731/tim-360m-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf timothywong731/tim-360m-instruct-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 timothywong731/tim-360m-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf timothywong731/tim-360m-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 timothywong731/tim-360m-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf timothywong731/tim-360m-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 timothywong731/tim-360m-instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf timothywong731/tim-360m-instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/timothywong731/tim-360m-instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use timothywong731/tim-360m-instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "timothywong731/tim-360m-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": "timothywong731/tim-360m-instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/timothywong731/tim-360m-instruct-GGUF:Q4_K_M

Ollama
How to use timothywong731/tim-360m-instruct-GGUF with Ollama:
```
ollama run hf.co/timothywong731/tim-360m-instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use timothywong731/tim-360m-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 timothywong731/tim-360m-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 timothywong731/tim-360m-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 timothywong731/tim-360m-instruct-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use timothywong731/tim-360m-instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/timothywong731/tim-360m-instruct-GGUF:Q4_K_M
```

Lemonade

How to use timothywong731/tim-360m-instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull timothywong731/tim-360m-instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tim-360m-instruct-GGUF-Q4_K_M

List all available models

lemonade list

TIM-360M-instruct — GGUF

GGUF quantizations of TIM-360M-instruct for llama.cpp, Ollama, and LM Studio. This is the instruction-tuned (SFT+DPO) chat model — the one to use for conversation.

Arch qwen3, byte-level BPE (Llama-3-family pre-tokenizer), ChatML chat template, generation stops on <|im_end|>.

Files

File	Quant	Size	Use when
`tim-360m-instruct.F16.gguf`	F16	692 MB	Reference / lossless
`tim-360m-instruct.Q8_0.gguf`	Q8_0	369 MB	Near-lossless
`tim-360m-instruct.Q4_K_M.gguf`	Q4_K_M	258 MB	Smallest; best size/quality for CPU

Usage

Ollama:

ollama run hf.co/timothywong731/tim-360m-instruct-GGUF:Q4_K_M "Explain what a transformer is in two sentences."

llama.cpp (conversation mode applies the ChatML template automatically):

llama-cli -m tim-360m-instruct.Q4_K_M.gguf -cnv -p "You are a helpful assistant."

Limitations

360M parameters, ~30B pretraining tokens + light post-training. Basic instruction-following — short, sometimes shallow answers, weak arithmetic and multi-step reasoning, confident hallucination. English-only. No safety tuning — filter before exposing to end users.

Provenance & license

Apache-2.0. See the source model card for architecture, training data, and evaluation.

Downloads last month: -

GGUF

Model size

0.4B params

Architecture

qwen3

Hardware compatibility

4-bit

8-bit

16-bit

Model tree for timothywong731/tim-360m-instruct-GGUF

Base model

timothywong731/tim-360m-base

Finetuned

timothywong731/tim-360m-instruct

Quantized

(1)

this model