Instructions to use GPUburnout/GPUburnout-3B-75K-Chat with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use GPUburnout/GPUburnout-3B-75K-Chat 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 GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GPUburnout/GPUburnout-3B-75K-Chat: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 GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GPUburnout/GPUburnout-3B-75K-Chat: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 GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M

Use Docker

docker model run hf.co/GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M

LM Studio
Jan
Ollama
How to use GPUburnout/GPUburnout-3B-75K-Chat with Ollama:
```
ollama run hf.co/GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M
```

Unsloth Studio

How to use GPUburnout/GPUburnout-3B-75K-Chat 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 GPUburnout/GPUburnout-3B-75K-Chat 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 GPUburnout/GPUburnout-3B-75K-Chat to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GPUburnout/GPUburnout-3B-75K-Chat to start chatting

Atomic Chat new
Docker Model Runner
How to use GPUburnout/GPUburnout-3B-75K-Chat with Docker Model Runner:
```
docker model run hf.co/GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M
```

Lemonade

How to use GPUburnout/GPUburnout-3B-75K-Chat with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GPUburnout/GPUburnout-3B-75K-Chat:Q4_K_M

Run and chat with the model

lemonade run user.GPUburnout-3B-75K-Chat-Q4_K_M

List all available models

lemonade list

GPUburnout-3B-75K-Chat / README.md

GPUburnout

Mark as retired - superseded by v2

bf7534f verified about 1 month ago

preview code

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	---
	license: mit
	tags:
	- llama
	- chat
	- sft
	- gpuburnout
	- retired
	---

	# GPUburnout-3B-75K-Chat (RETIRED)

	This model is retired. It was trained in April 2026 with a buggy SFT data formatter
	(plain string concatenation instead of `apply_chat_template`), which caused loop collapse
	at inference and made it underperform the smaller 2B chat model on most benchmarks.

	Use [`GPUburnout/GPUburnout-3B-75K-Chat-v2`](https://huggingface.co/GPUburnout/GPUburnout-3B-75K-Chat-v2) instead.

	Full postmortem: [It Took Me Two Weeks to Read My Own Code](https://gpuburnout.com/posts/s5-ch4-two-weeks-read-my-own-code/)

	This repo is kept as a historical artifact, not for production use.