Instructions to use GPUburnout/GPUburnout-1B-160K 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-1B-160K 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-1B-160K:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GPUburnout/GPUburnout-1B-160K: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-1B-160K:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf GPUburnout/GPUburnout-1B-160K: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-1B-160K:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf GPUburnout/GPUburnout-1B-160K: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-1B-160K:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GPUburnout/GPUburnout-1B-160K:Q4_K_M

Use Docker

docker model run hf.co/GPUburnout/GPUburnout-1B-160K:Q4_K_M

LM Studio
Jan

vLLM

How to use GPUburnout/GPUburnout-1B-160K with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GPUburnout/GPUburnout-1B-160K"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "GPUburnout/GPUburnout-1B-160K",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GPUburnout/GPUburnout-1B-160K:Q4_K_M

Ollama
How to use GPUburnout/GPUburnout-1B-160K with Ollama:
```
ollama run hf.co/GPUburnout/GPUburnout-1B-160K:Q4_K_M
```

Unsloth Studio

How to use GPUburnout/GPUburnout-1B-160K 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-1B-160K 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-1B-160K 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-1B-160K to start chatting

Atomic Chat new
Docker Model Runner
How to use GPUburnout/GPUburnout-1B-160K with Docker Model Runner:
```
docker model run hf.co/GPUburnout/GPUburnout-1B-160K:Q4_K_M
```

Lemonade

How to use GPUburnout/GPUburnout-1B-160K with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GPUburnout/GPUburnout-1B-160K:Q4_K_M

Run and chat with the model

lemonade run user.GPUburnout-1B-160K-Q4_K_M

List all available models

lemonade list

GPUburnout-1B-160K

A 1.04 billion parameter Llama-style language model trained from scratch to Chinchilla-optimal on 20.97B tokens.

This is the 160K step (Chinchilla-optimal) checkpoint. For the earlier 90K step checkpoint, see GPUburnout-1B.

Model Details

Architecture: Llama-style decoder-only transformer
Parameters: 1.04B
Hidden dim: 2048
Layers: 16
Attention: GQA (32 query heads, 8 KV heads)
FFN: SwiGLU (intermediate 8192)
Position encoding: RoPE (theta=500000)
Context length: 2048 tokens
Vocabulary: 32,005 tokens (BPE + 5 special tokens)
Weight tying: Yes (embedding + LM head)

Training

Data: 20.97B tokens (FineWeb-Edu 85%, Python-Edu 4.2%, FineMath 10.8%)
Hardware: A100 SXM 80GB on RunPod
Steps: 160,000 (Chinchilla-optimal: 20x params in tokens)
Final loss: 2.446
Throughput: ~30,500 tokens/sec

Training Phases

Phase	Steps	Loss	Cost
Phase 1 (smoke test)	200	~6-7	~$0.50
Phase 2 (proof of life)	10K	2.93	~$22
Phase 3	60K	2.57	~$94
Phase 4	90K	2.494	~$61
Phase 5 (spot)	120K	2.530	~$34
Phase 6 (spot, Chinchilla)	160K	2.446	—

Tokenizer

Includes ChatML special tokens for SFT:

<|im_start|> (32000), <|im_end|> (32001)
<|system|> (32002), <|user|> (32003), <|assistant|> (32004)

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("GPUburnout/GPUburnout-1B-160K", torch_dtype="float16")
tokenizer = AutoTokenizer.from_pretrained("GPUburnout/GPUburnout-1B-160K")

inputs = tokenizer("The capital of France is", return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Blog

Full training journey documented at gpuburnout.com

Author

Jun Park (@GPUburnout)

Downloads last month: 100

Safetensors

Model size

1B params

Tensor type

F16

GPUburnout
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GPUburnout-1B-160K