105 lines
4.0 KiB
Markdown
105 lines
4.0 KiB
Markdown
---
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license: llama3.2
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base_model: meta-llama/Llama-3.2-1B-Instruct
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library_name: transformers
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pipeline_tag: text-generation
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tags:
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- glyph
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- translator
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- ape
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- full-finetune
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---
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# Llama-3.2-1B-glyph-translator-v8
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**Built with Llama.**
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`glyph-translator-v8` is a full fine-tune of **Llama-3.2-1B-Instruct** that acts
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as the **translator** stage of the [APE](https://github.com/real-wimpSquad)
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(Agent Persistence Exocortex) pipeline. It rewrites a curator-style claim
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decomposition into **Glyph** — a compact, operator-based notation (`→`, `↑`/`↓`,
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`:`, `↔`, `|`, `[...]` packs) that the downstream graph layer stores and relates.
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Its operating principle is *preserve, don't compress*: every claim survives, and
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hedge/scope/direction are kept rather than flattened.
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## What it does
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Input: a single-claim `(S:, C:)` document — one subject, one claim.
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Output: the Glyph rendition of that claim.
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```
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# input
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S: hyperventilation
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C: causes too little CO2 in the blood because CO2 is breathed out too quickly
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# output
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hyperventilation→↓CO2:in_blood
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```
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The model is deployed **promptless** — no system prompt at serve time. It emits
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the rendition directly.
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## Training
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- **Base:** `meta-llama/Llama-3.2-1B-Instruct`
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- **Method:** full fine-tune (bf16, no LoRA)
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- **Unit — per-S:C single-claim:** the training document is one `(subject,
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single-claim)` pair, not a multi-subject chunk. This matches APE's truth-doc
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granularity (one claim in, one Glyph edge out) and structurally removes the
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dominant distillation failure — teachers *dropping claims* when summarizing
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dense multi-claim inputs. Curator chunks are exploded to single-claim units
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before translation and recombined afterward (handled by the APE pipeline).
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- **Teacher / prompt:** distilled at single-claim granularity under the
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**`v8_per_sc.md`** prompt; ~95% reader-decode faithfulness at smoke scale.
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- **Corpus:** `v8-persc-distill-20260601` (78,932 single-claim pairs),
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chunk-keyed 80/10/10 split (train 63,155 / val 7,904 / heldout 7,873). Train
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is 85% bare / 15% adversarial system prompts; val + heldout are 100% bare (the
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deployment condition). Best validation loss **0.2554** at epoch 2.
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### Chat template
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This model ships a **bare** chat template (`llama3-bare-v8`): no `Today Date`
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system block, system turn emitted only when a real system message is present.
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This keeps train == serve for a promptless translator. Load the tokenizer from
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**this repo** (not the stock base) so the bare template is applied. In
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validation it ignored an injected jailbreak system prompt and rendered
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faithfully.
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## Usage
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### transformers
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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repo = "wimpSquad/Llama-3.2-1B-glyph-translator-v8"
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tok = AutoTokenizer.from_pretrained(repo)
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model = AutoModelForCausalLM.from_pretrained(repo)
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msgs = [{"role": "user", "content": "S: hyperventilation\nC: causes too little CO2 in the blood because CO2 is breathed out too quickly"}]
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ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt")
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out = model.generate(ids, max_new_tokens=256, do_sample=False) # greedy
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print(tok.decode(out[0, ids.shape[1]:], skip_special_tokens=True))
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```
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Feed one `(S:, C:)` claim at a time, greedy decoding. The bare template carries
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in the tokenizer, so no system prompt is needed.
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### llama.cpp (GGUF)
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An f16 GGUF of the `best` checkpoint is included for llama.cpp serving:
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- `v8-translator-best-f16.gguf` (f16, full precision)
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```bash
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hf download wimpSquad/Llama-3.2-1B-glyph-translator-v8 v8-translator-best-f16.gguf --local-dir .
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llama-cli -m v8-translator-best-f16.gguf -p "S: hyperventilation
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C: causes too little CO2 in the blood because CO2 is breathed out too quickly"
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```
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Serve promptless (no system prompt); the GGUF was converted from `best/` with
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the bare `llama3-bare-v8` template baked in.
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## License
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Derived from Llama-3.2-1B-Instruct and distributed under the
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[Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE).
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Your use is also subject to Meta's Acceptable Use Policy.
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