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Model: wimpSquad/Llama-3.2-1B-glyph-translator-v8
Source: Original Platform

README.md

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+---
+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.