---
language:
  - en
license: apache-2.0
tags:
  - mamba
  - ssm
  - causal-lm
  - neuralai
  - base-model
  - sft-queued
pipeline_tag: text-generation
base_model: state-spaces/mamba-2.8b
---

# 🧠 NeuralAI · Mamba K3

NeuralAI's **2.8B Mamba SSM** — the third and largest model in the NeuralAI line (K1 130M → K2 793M → **K3 2.8B**), fine-tuned from `state-spaces/mamba-2.8b` for assistant-style chat with worked reasoning, code, math, writing, and safety behaviors.

## Model Details

- **Architecture:** Mamba SSM (`state-spaces/mamba-2.8b`)
- **Parameters:** 2.8B
- **Status:** SFT queued — weights will land here when training completes
- **Fine-tune:** LoRA r=16, α=32 on Mamba `in_proj`, bf16, cosine LR 2e-4
- **Dataset:** NeuralAI curated set — 3,068 examples (computed-answer math, reasoning puzzles, code, safety, writing, factual, multi-turn)
- **Chat template:** NeuralAI `[INST]` (same as Mamba K1)
- **Creator:** De'Andrew Preston Harris, NeuralAI

## Status

- This page is **created ahead of the SFT run** (per the model-line order in [github.com/Subject-Emu-5259/NeuralAI](https://github.com/Subject-Emu-5259/NeuralAI)).
- Training is **paused pending GPU allocation** on the Colab runtime; the dataset and training script are ready to launch the moment a GPU is available.
- When the SFT run completes, the merged weights + tokenizer + config are pushed here automatically.

## Training Command (when GPU is available)

```bash
BASE_MODEL=state-spaces/mamba-2.8b HF_REPO=Subject-Emu-5259/NeuralAI-Mamba-K3 \
  python training/finetune_run.py
```

## Prompt Format

Same as Mamba K1 — the NeuralAI `[INST]` chat template:

```text
<|endoftext|>[INST] {user message} [/INST]
```

## License

Apache 2.0