---
license: apache-2.0
language:
- en
library_name: pytorch
pipeline_tag: text-generation
base_model: SlayerLab/Slayer149-balanced
tags:
- tiny-lm
- arc-easy
- fine-tuned
- slayer149
- v2
---

# Slayer149 Balanced ARC-E fine-tune v2

This is a separate v2 release; the original [v1 repository](https://huggingface.co/SlayerLab/Slayer149-Balanced-ARC-E-ft-45500) remains unchanged. This checkpoint fine-tunes the 149,333,081-parameter Slayer149 Balanced base on the clean ARC-Easy training split with general-text replay. It is a research base model, not an instruction-tuned assistant.

## Evaluation

| Benchmark | Result | Count |
|---|---:|---:|
| ARC-Easy raw accuracy | 64.90% | 1,542 / 2,376 |
| WikiText-2 byte perplexity | 2.2719 | full test split |
| WikiText-2 token perplexity | 29.277 | full test split |
| BLiMP raw accuracy | 78.89% | 52,859 / 67,000 |

Evaluation used the pinned GLINT-derived harness: 256-token context and no added BOS. ARC uses raw summed answer log-likelihood; the board metric is raw accuracy. The checkpoint was selected by ARC-Easy validation acc_norm before test evaluation. Test results did not select the checkpoint. Full result files and per-example benchmark details are included in this repository.

## Fine-tuning provenance

- Base: [`SlayerLab/Slayer149-balanced`](https://huggingface.co/SlayerLab/Slayer149-balanced), complete step 59,368 export; base weights SHA-256 `b1353f99662b209dca70a50eb06fdb99bba4671486a1695236b2752514cfce63`.
- Fine-tune: epoch 2, update 1,118; 149,333,081 unique parameters.
- Objective: ARC-Easy answer-choice ranking and correct-answer cross-entropy, mixed with clean general-text replay.
- Training data: pinned ARC-Easy train split only; validation/test examples were excluded.
- Selected checkpoint SHA-256: `79e4d06b1b95408c4b47e6d9fab34ed2a26609997b80d45698e6ec23db8724e8`.
- Safetensors SHA-256: `4663361d3acdae2ce766f980c549649f9db21a77a26213034902370f775f69eb`.
- Tokenizer SHA-256: `d1ffe47f6fd8cc19ba7c3e5d2537699d6a99d09b584d3319944f4e40fe6c44ea`.
- Dataset revision: `210d026faf9955653af8916fad021475a3f00453`.

The base model is Apache-2.0. ARC is credited to AI2; its dataset card lists CC-BY-SA-4.0. The dataset is not redistributed here.

## Load locally

```python
from load_model import load_model
import torch
model, tokenizer = load_model(".", device="cpu")
ids = tokenizer.encode("What is the result?", add_special_tokens=False).ids
with torch.inference_mode():
    logits, _ = model(torch.tensor([ids]))
```

This package uses the included native PyTorch architecture and tokenizer; it does not require Transformers remote code.