---
base_model: nkthebass/tinybrainbot-350mV3-base
library_name: transformers
pipeline_tag: text-generation
license: apache-2.0
language:
- en
tags:
- gguf
- llama-cpp
- text-generation
- tinybrainbot
- small-language-model
- from-scratch
- gqa
---

# TinyBrainBot 350M V3 Base GGUF

This repository provides GGUF quantizations for [nkthebass/tinybrainbot-350mV3-base](https://huggingface.co/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:

```text
Once upon a time in a small village,
```

---

## Quickstart Guide

### 1. llama.cpp

Run text completion with `llama-cli`:

```bash
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`:

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

### 2. Ollama

Create a `Modelfile`:

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

Build and run:

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

### 3. LM Studio

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