---
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
library_name: transformers
pipeline_tag: text-generation
tags:
  - depth-recurrent
  - latent-reasoning
  - huginn
  - raven
  - test-time-compute
  - recurrent-depth
---

# Recurrent-Llama-3.2-1B

A **depth-recurrent (Huginn / Raven) language model** retrofitted from
[`meta-llama/Llama-3.2-1B`](https://huggingface.co/meta-llama/Llama-3.2-1B) by **model surgery**
followed by a **recurrence-curriculum healing** phase.

Instead of a fixed stack of layers, the model has a small **recurrent core block** that is looped
a controllable number of times at inference. This lets you **spend more compute on harder inputs
without adding parameters** — the "think deeper" knob is `num_steps` (a.k.a. recurrence depth).

Method: [*Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence*](https://arxiv.org/abs/2511.07384)
(McLeish et al., 2025), built on the Huginn/Raven architecture of
[*Scaling up Test-Time Compute with Latent Reasoning*](https://arxiv.org/abs/2502.05171).

## Architecture

```
input → embed → prelude (4 layers) → [ adapter + recurrent core (6 layers) ] × R → coda (4 layers) → norm → lm_head
                                       └──────────── looped R times ───────────┘
```

| | |
|---|---|
| Base model | Llama-3.2-1B (16 layers) |
| Split (prelude / **recurrent core** / coda) | 4 / **6** / 4 (source layers 4–5 dropped) |
| Recurrence at inference | any `num_steps`; trained up to 16 |
| Block / norm / RoPE | Llama pre-norm, RMSNorm, native Llama-3 RoPE (θ=500000) |
| Params | ~1.39B |
| `model_type` | `huginn_raven` |

The adapter re-injects the prelude output at **every** recurrent step, so the latent state cannot
drift away from the input regardless of depth.

## Usage

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

repo = "irafm-llm/Recurrent-Llama-3.2-1B"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo, trust_remote_code=True, torch_dtype=torch.bfloat16).cuda().eval()

ids = tok("The history of mathematics is", return_tensors="pt").input_ids.cuda()
out = model.generate(ids, max_new_tokens=40, do_sample=False,
                     num_steps=32,          # <-- recurrence depth: raise for more test-time compute
                     tokenizer=tok, pad_token_id=tok.eos_token_id)
print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
```

`trust_remote_code=True` is required — the repo bundles its own `raven_modeling_minimal.py`. The
model exposes the full Huginn-0125 step API (`embed_inputs`, `initialize_state`, `iterate_one_step`,
`predict_from_latents`, `forward_with_adaptive_compute`) and is a **drop-in for code written against
Huginn-0125**, including per-sentence selective-recurrence control.

## How it was made

1. **Surgery.** Llama-3.2-1B's layers are split into prelude (0–3), recurrent core (6–11) and coda
   (12–15); layers 4–5 are dropped. Attention/MLP/norm weights are copied verbatim (fused QKV and
   gate-up), embeddings untied. The conversion reproduces the source model's logits **exactly**
   (full-cover check: logits MSE ~1e-11) and is **bit-identical** to the official
   `smcleish/Recurrent-Llama-3.2-untrained` on all non-adapter tensors.
2. **Healing.** ~98M tokens of [FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu),
   sequence length 1024, AdamW lr 5e-5, grad-clip 1.0, bf16. The **mean recurrence is ramped with a
   `1-sqrt` curriculum** up to 16; depth is sampled per-step (log-normal-Poisson) and gradients are
   truncated to the last 8 recurrent passes (truncated BPTT). Eval loss (@rec 16): **14.2 → 2.8**.

## Limitations

- **Demonstration-scale healing.** ~98M tokens vs the paper's ~50B; output is fluent but can be
  repetitive under greedy decoding. Not instruction-tuned.
- Inherits Llama-3.2's knowledge cutoff, biases and the **Llama 3.2 Community License**.
- Recurrence was trained up to depth 16; higher `num_steps` works but is extrapolation.

## Citation

```bibtex
@article{mcleish2025teaching,
  title={Teaching Pretrained Language Models to Think Deeper with Retrofitted Recurrence},
  author={McLeish, Sean and Li, Ang and Kirchenbauer, John and Kalra, Dayal Singh and Bartoldson, Brian R. and Kailkhura, Bhavya and Schwarzschild, Avi and Geiping, Jonas and Goldstein, Tom and Goldblum, Micah},
  journal={arXiv preprint arXiv:2511.07384}, year={2025}
}
```

*Built with Llama. Converted with [huginn_surgery](https://huggingface.co/irafm-llm).*