Upload README.md with huggingface_hub

README.md

@@ -0,0 +1,38 @@
+---
+library_name: transformers
+base_model: Qwen/Qwen3-4B-Instruct-2507
+tags:
+- program-as-weights
+- compiler
+- lora
+- hypernetwork
+pipeline_tag: text-generation
+---
+
+# paw-4b-gpt2 — ProgramAsWeights "Compact" compiler
+
+This is the **Compact** compiler from **ProgramAsWeights (PAW)**. Given a natural-language **spec**, it emits a tiny per-task **program** — a LoRA adapter — that runs locally on a **GPT-2 (124M)** interpreter (small enough to run in the browser).
+
+It is the model invoked by `paw.compile(spec, compiler="paw-4b-gpt2")`.
+
+- Compiler base model: `Qwen/Qwen3-4B-Instruct-2507`
+- Target interpreter: **a custom GPT-2 (124M)** whose positional embeddings are extended from 1024 → 2048 (`n_ctx=2048`); tokenizer is stock GPT-2 BPE.
+- Snapshot: `20260406` (see git tag `20260406`)
+
+## Contents
+
+- `compiler/` — a finetuned **Qwen3-4B-Instruct-2507** causal LM (the compiler).
+- `lora_mapper.pt` — the mapper head (trunk + coefficient head + learnable LoRA basis matrices) that turns the compiler's hidden states into a LoRA program.
+- `meta.json` — `lora_rank=64`, `lora_alpha=16`, `lora_num_bases=64`, `prefix_steps=64`, target modules `[c_attn, c_proj, c_fc]`.
+
+## How it works
+
+1. The 4B compiler generates a short "pseudo-program" (a task description plus a few I/O examples) from the spec.
+2. The text `chat_template(spec) + pseudo-program + 64 prefix tokens` is run through the compiler; the mapper reads the 64 prefix hidden states and emits per-layer LoRA `A`/`B` matrices as a learned mixture of basis matrices.
+3. The resulting LoRA (~5 MB) is the **program**. It loads onto the GPT-2 interpreter and runs locally/offline (including in-browser).
+
+## Status
+
+- Inference/runtime SDK (load + run a compiled program locally): https://github.com/programasweights/programasweights-python (browser SDK: https://github.com/programasweights/programasweights-js)
+- The cleaned compile/runtime code and the arXiv preprint ("Program-as-Weights: A Programming Paradigm for Fuzzy Functions", AIware 2026) will be public by Jul 6, 2026. An uncleaned reference snapshot is at https://anonymous.4open.science/r/programasweights
+- Live demo + program hub: https://programasweights.com