---
license: llama3.2
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: transformers
pipeline_tag: text-generation
tags:
- glyph
- translator
- ape
- full-finetune
---

# Llama-3.2-1B-glyph-translator-v8

**Built with Llama.**

`glyph-translator-v8` is a full fine-tune of **Llama-3.2-1B-Instruct** that acts
as the **translator** stage of the [APE](https://github.com/real-wimpSquad)
(Agent Persistence Exocortex) pipeline. It rewrites a curator-style claim
decomposition into **Glyph** — a compact, operator-based notation (`→`, `↑`/`↓`,
`:`, `↔`, `|`, `[...]` packs) that the downstream graph layer stores and relates.
Its operating principle is *preserve, don't compress*: every claim survives, and
hedge/scope/direction are kept rather than flattened.

## What it does

Input: a single-claim `(S:, C:)` document — one subject, one claim.
Output: the Glyph rendition of that claim.

```
# input
S: hyperventilation
C: causes too little CO2 in the blood because CO2 is breathed out too quickly

# output
hyperventilation→↓CO2:in_blood
```

The model is deployed **promptless** — no system prompt at serve time. It emits
the rendition directly.

## Training

- **Base:** `meta-llama/Llama-3.2-1B-Instruct`
- **Method:** full fine-tune (bf16, no LoRA)
- **Unit — per-S:C single-claim:** the training document is one `(subject,
  single-claim)` pair, not a multi-subject chunk. This matches APE's truth-doc
  granularity (one claim in, one Glyph edge out) and structurally removes the
  dominant distillation failure — teachers *dropping claims* when summarizing
  dense multi-claim inputs. Curator chunks are exploded to single-claim units
  before translation and recombined afterward (handled by the APE pipeline).
- **Teacher / prompt:** distilled at single-claim granularity under the
  **`v8_per_sc.md`** prompt; ~95% reader-decode faithfulness at smoke scale.
- **Corpus:** `v8-persc-distill-20260601` (78,932 single-claim pairs),
  chunk-keyed 80/10/10 split (train 63,155 / val 7,904 / heldout 7,873). Train
  is 85% bare / 15% adversarial system prompts; val + heldout are 100% bare (the
  deployment condition). Best validation loss **0.2554** at epoch 2.

### Chat template

This model ships a **bare** chat template (`llama3-bare-v8`): no `Today Date`
system block, system turn emitted only when a real system message is present.
This keeps train == serve for a promptless translator. Load the tokenizer from
**this repo** (not the stock base) so the bare template is applied. In
validation it ignored an injected jailbreak system prompt and rendered
faithfully.

## Usage

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

repo = "wimpSquad/Llama-3.2-1B-glyph-translator-v8"
tok = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(repo)

msgs = [{"role": "user", "content": "S: hyperventilation\nC: causes too little CO2 in the blood because CO2 is breathed out too quickly"}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
out = model.generate(ids, max_new_tokens=256, do_sample=False)  # greedy
print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
```

Feed one `(S:, C:)` claim at a time, greedy decoding. The bare template carries
in the tokenizer, so no system prompt is needed.

### llama.cpp (GGUF)
An f16 GGUF of the `best` checkpoint is included for llama.cpp serving:
- `v8-translator-best-f16.gguf` (f16, full precision)

```bash
hf download wimpSquad/Llama-3.2-1B-glyph-translator-v8 v8-translator-best-f16.gguf --local-dir .
llama-cli -m v8-translator-best-f16.gguf -p "S: hyperventilation
C: causes too little CO2 in the blood because CO2 is breathed out too quickly"
```

Serve promptless (no system prompt); the GGUF was converted from `best/` with
the bare `llama3-bare-v8` template baked in.

## License

Derived from Llama-3.2-1B-Instruct and distributed under the
[Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE).
Your use is also subject to Meta's Acceptable Use Policy.