---
license: apache-2.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- small-language-model
- pretrained
---

# flame-27m-base

A **27.1M-parameter** English base language model, pretrained on curated educational web +
synthetic textbooks + math. The largest of the ember/spark/flame family of deliberately tiny
models for small-model research, fast experimentation, and as a lightweight decoder backbone.
~5× smaller than SmolLM-135M.

- **Architecture:** Llama-style decoder — hidden 512, 8 layers, 8 heads / 2 KV heads (GQA),
  intermediate 1280, RoPE (θ=1e6), context 2048, vocab 12000 (English BPE).
- **Pretraining:** 300B tokens (~11,070 tokens/param), mixture FineWeb-Edu-dedup 0.44 /
  Cosmopedia-v2 0.24 / ClimbMix 0.16 / FineMath-3+ 0.10 / FineMath-4+ 0.06. Decontaminated
  against the eval benchmarks.

## Usage

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ifx-pse-sys-ml/flame-27m-base", trust_remote_code=True)
tok = AutoTokenizer.from_pretrained("ifx-pse-sys-ml/flame-27m-base")

ids = tok("The moon is", return_tensors="pt").input_ids
out = model.generate(ids, max_new_tokens=40, do_sample=True, temperature=0.8, top_p=0.9)
print(tok.decode(out[0], skip_special_tokens=True))
```

It also accepts `inputs_embeds` (pass exactly one of `input_ids` / `inputs_embeds`), so a
vision projector can inject visual tokens — usable as a small VLM text backbone. The tokenizer
keeps the Qwen2.5 multimodal special tokens (`<|vision_start|>` etc.) intact. A raw PyTorch
checkpoint (`pytorch_model.pth`) is included alongside the safetensors weights.

## Benchmarks

Accuracy (%) via [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
0.4, **same harness and shots for every model**, so columns are directly comparable.

| Benchmark | chance | **flame-27m-base** | SmolLM-135M |
|---|---|---|---|
| hellaswag | 25 | 30.7 | 42.6 |
| arc_easy | 25 | 40.8 | 56.1 |
| arc_challenge | 25 | 24.5 | 28.9 |
| piqa | 50 | 61.6 | 68.4 |
| winogrande | 50 | 51.5 | 53.2 |
| openbookqa | 25 | 29.0 | 34.0 |
| commonsense_qa | 20 | 19.5 | 19.8 |
| mmlu | 25 | 25.8 | 25.2 |
| **average** | — | **35.4** | **41.0** |

flame reaches 35.4 avg at **1/5 the parameters** of SmolLM-135M and edges it on MMLU (25.8 vs
25.2). On the neutral Wikipedia holdout its bits-per-byte (the tokenizer-fair metric) is 1.140,
a 15% reduction over the 13M spark and 24% over the 6.5M ember — the family's scaling curve is
still steep at this size.

## Honest limitations

At **27M parameters** this model is near random chance on the hardest reasoning/knowledge
benchmarks (ARC-Challenge, CommonsenseQA, most of MMLU). Generation is fluent and on-register
but factually unreliable — it models *how* educational text reads, not *what* is true. A
research/prototyping model and a lightweight decoder, not a knowledge model. English only.
Trained with the Nexus codebase.