Instructions to use sizzlebop/tinybrainbot-350mV3-base-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use sizzlebop/tinybrainbot-350mV3-base-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="sizzlebop/tinybrainbot-350mV3-base-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("sizzlebop/tinybrainbot-350mV3-base-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use sizzlebop/tinybrainbot-350mV3-base-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 sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sizzlebop/tinybrainbot-350mV3-base-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 sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf sizzlebop/tinybrainbot-350mV3-base-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 sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf sizzlebop/tinybrainbot-350mV3-base-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 sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M

Use Docker

docker model run hf.co/sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use sizzlebop/tinybrainbot-350mV3-base-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "sizzlebop/tinybrainbot-350mV3-base-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": "sizzlebop/tinybrainbot-350mV3-base-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M

SGLang

How to use sizzlebop/tinybrainbot-350mV3-base-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "sizzlebop/tinybrainbot-350mV3-base-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sizzlebop/tinybrainbot-350mV3-base-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "sizzlebop/tinybrainbot-350mV3-base-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "sizzlebop/tinybrainbot-350mV3-base-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use sizzlebop/tinybrainbot-350mV3-base-GGUF with Ollama:
```
ollama run hf.co/sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use sizzlebop/tinybrainbot-350mV3-base-GGUF with Docker Model Runner:
```
docker model run hf.co/sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M
```

Lemonade

How to use sizzlebop/tinybrainbot-350mV3-base-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull sizzlebop/tinybrainbot-350mV3-base-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.tinybrainbot-350mV3-base-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

TinyBrainBot 350M V3 Base GGUF

This repository provides GGUF quantizations for nkthebass/tinybrainbot-350mV3-base.

TinyBrainBot 350M V3 Base is a 348M-parameter decoder-only causal language model pretrained from scratch on 22.7B tokens. It features a modern transformer architecture with pre-normalization, RMSNorm, SwiGLU MLP, RoPE, and Grouped-Query Attention (18 query heads, 6 key-value heads). This is the base pretrained model designed for text completion, domain adaptation, and downstream fine-tuning.

All GGUF files were converted from the original safetensors weights using llama.cpp at native F16 precision, followed by standard k-quant quantization.

Available Files and Quantizations

File	Quant Type	Size	Description / Recommendation
`tinybrainbot-350mV3-base-F16.gguf`	F16	665.25 MB	Unquantized full precision base conversion. Highest fidelity reference weights.
`tinybrainbot-350mV3-base-Q8_0.gguf`	Q8_0	353.85 MB	Near-lossless 8-bit quantization. Recommended for best quality evaluation.
`tinybrainbot-350mV3-base-Q6_K.gguf`	Q6_K	335.87 MB	High quality retention with minimal degradation. Excellent accuracy/memory balance.
`tinybrainbot-350mV3-base-Q5_K_M.gguf`	Q5_K_M	262.20 MB	Good balance between memory consumption and text generation fidelity.
`tinybrainbot-350mV3-base-Q4_K_M.gguf`	Q4_K_M	243.48 MB	Fast, lightweight 4-bit quant. Recommended default for edge devices and mobile setups.
`tinybrainbot-350mV3-base-Q3_K_M.gguf`	Q3_K_M	210.46 MB	Compact footprint when memory headroom is strictly limited.
`tinybrainbot-350mV3-base-Q2_K.gguf`	Q2_K	195.49 MB	Maximum compression for ultra-constrained low-RAM environments.

Model Architecture Details

Parameters: ~348M (348,342,912)
Architecture: Decoder-only transformer (LlamaForCausalLM)
Layers: 22
Hidden Size: 1152
Attention Heads: 18 query heads / 6 KV heads (GQA), head dimension 64
Intermediate Size: 3072
Context Length: 2048 tokens
Vocabulary Size: 32,000 (tbb-32k-v2, tied word embeddings)
RoPE Theta: 10,000.0

Prompt Format and Usage

Because this is a base completion model (not instruction tuned), it expects raw text prompts for completion:

Once upon a time in a small village,

Quickstart Guide

1. llama.cpp

Run text completion with llama-cli:

llama-cli -m ./tinybrainbot-350mV3-base-Q4_K_M.gguf \
    -p "The future of artificial intelligence on edge devices relies on" \
    -n 256 \
    --temp 0.7 \
    --repeat-penalty 1.1

Run local inference server with llama-server:

llama-server -m ./tinybrainbot-350mV3-base-Q4_K_M.gguf \
    --port 8080 \
    -c 2048

2. Ollama

Create a Modelfile:

FROM ./tinybrainbot-350mV3-base-Q4_K_M.gguf
PARAMETER stop "<|end_of_text|>"
PARAMETER temperature 0.7

Build and run:

ollama create tinybrainbot-base -f Modelfile
ollama run tinybrainbot-base "Artificial intelligence is"

3. LM Studio

Copy the .gguf file to your LM Studio models directory (e.g. ~/.cache/lm-studio/models/nkthebass/tinybrainbot-350mV3-base-GGUF/).
Select tinybrainbot-350mV3-base-Q4_K_M.gguf in the model selector.
Set prompt template to raw text completion.

Downloads last month: 303

GGUF

Model size

0.3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for sizzlebop/tinybrainbot-350mV3-base-GGUF

Base model

nkthebass/tinybrainbot-350mV3-base

Quantized

(2)

this model