Publish PrismQuant model card, loading guide and upstream notices

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README.md

@@ -1,45 +1,79 @@
 ---
 license: apache-2.0
-tags: [quantization, w4a4, gptq, rotation, research-artifact]
+base_model:
+- Qwen/Qwen3-0.6B-Base
+- Qwen/Qwen3-1.7B-Base
+- Qwen/Qwen3-4B-Base
+- Qwen/Qwen3-8B-Base
+pipeline_tag: text-generation
+tags:
+- prismquant
+- quantization
+- gptq
+- w4a4kv4
+- custom-code
+- research
 ---
 
-# NAR W4A4KV4 GPTQ checkpoints (qwen3_0.6b_base, qwen3_1.7b_base, qwen3_4b_base, qwen3_8b_base)
-
-Private research artifact from [nar-offline-validation](https://github.com/ForeverBlue816/nar-offline-validation), report section "E22 — the Qwen3 Base family under one protocol".
-Base model(s): [`Qwen/Qwen3-0.6B-Base`](https://huggingface.co/Qwen/Qwen3-0.6B-Base), [`Qwen/Qwen3-1.7B-Base`](https://huggingface.co/Qwen/Qwen3-1.7B-Base), [`Qwen/Qwen3-4B-Base`](https://huggingface.co/Qwen/Qwen3-4B-Base), [`Qwen/Qwen3-8B-Base`](https://huggingface.co/Qwen/Qwen3-8B-Base).
-
-## What the files are
-
-`checkpoints/<model>/<checkpoint>/layer_XX.pt` holds one decoder layer's weights after the rotation
-(Hadamard or NAR) has been folded into the base model and GPTQ has quantized them. The values are the
-**dequantized** INT4 weights stored in floating point (fake quantization), not a packed INT4 format, so
-`transformers` cannot load them on its own. `DONE.json` records the fold audit, GPTQ settings and
-provenance (hostnames and GPU ids redacted). `gptq_audit.csv` has per-module GPTQ statistics.
-
-At inference the code also quantizes activations per group of 128 and the KV cache (KIVI), and applies
-the online rotations (R2 on V, R4 at the down-projection input). Those need the calibrated factors in
-`rotations/<model>/`.
-
-## Loading
-
-Clone the code repository, download this repo so that `checkpoints/<model>/` sits at
-`<workdir>/artifacts/e22/<model>/` and `rotations/<model>/` at
-`<workdir>/activations/<model>/`, then use `nar/e14_w4a4kv4.py::load_quantized_model` (as the
-experiment's `evaluate` command does).
-
-## Checkpoints
-
-| model | checkpoint | rotation | seed | weight protocol | size |
-|---|---|---|---:|---|---:|
-| `qwen3_0.6b_base` | `gptq_hadamard_seed0_g128_asym` | hadamard | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 1.8 GB |
-| `qwen3_0.6b_base` | `gptq_nar_k8_seed0_g128_asym` | nar_k8 | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 1.8 GB |
-| `qwen3_0.6b_base` | `gptq_nar_kmax_seed0_g128_asym` | nar_kmax | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 1.8 GB |
-| `qwen3_1.7b_base` | `gptq_hadamard_seed0_g128_asym` | hadamard | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 5.6 GB |
-| `qwen3_1.7b_base` | `gptq_nar_k8_seed0_g128_asym` | nar_k8 | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 5.6 GB |
-| `qwen3_1.7b_base` | `gptq_nar_kmax_seed0_g128_asym` | nar_kmax | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 5.6 GB |
-| `qwen3_4b_base` | `gptq_hadamard_seed0_g128_asym` | hadamard | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 14.5 GB |
-| `qwen3_4b_base` | `gptq_nar_k8_seed0_g128_asym` | nar_k8 | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 14.5 GB |
-| `qwen3_4b_base` | `gptq_nar_kmax_seed0_g128_asym` | nar_kmax | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 14.5 GB |
-| `qwen3_8b_base` | `gptq_hadamard_seed0_g128_asym` | hadamard | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 27.8 GB |
-| `qwen3_8b_base` | `gptq_nar_k8_seed0_g128_asym` | nar_k8 | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 27.8 GB |
-| `qwen3_8b_base` | `gptq_nar_kmax_seed0_g128_asym` | nar_kmax | 0 | GPTQ group-128 asymmetric INT4 weights (4.156 bits) | 27.8 GB |
+# Qwen3 Base — PrismQuant
+
+Released checkpoints for [PrismQuant](https://github.com/ForeverBlue816/PrismQuant): quantizer-aware rotations for low-bit language models.
+
+**Base models:** [Qwen/Qwen3-0.6B-Base](https://huggingface.co/Qwen/Qwen3-0.6B-Base), [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base), [Qwen/Qwen3-4B-Base](https://huggingface.co/Qwen/Qwen3-4B-Base), [Qwen/Qwen3-8B-Base](https://huggingface.co/Qwen/Qwen3-8B-Base).
+
+**arXiv:** coming soon.
+
+Model weights retain [Apache-2.0](LICENSE).
+
+## Quick start
+
+```bash
+git clone --depth 1 https://github.com/ForeverBlue816/PrismQuant.git
+cd PrismQuant
+pip install -e .
+prismquant models
+```
+
+```python
+from prismquant import load_model
+
+loaded = load_model("qwen3_0.6b_base")
+print(loaded.generate("The key idea behind quantization is", max_new_tokens=64))
+```
+
+The loader selectively downloads one checkpoint and its required rotation factors, pins its revision, and obtains the matching base model/tokenizer. Defaults use PrismQuant `k=max`, seed 0. Use `prismquant models` to select another exact checkpoint name. See the [loading guide](https://github.com/ForeverBlue816/PrismQuant/blob/main/docs/models.md).
+
+## Format and limitations
+
+These are **dequantized GPTQ INT4 weights stored in floating-point tensors**, not packed INT4 weights or standalone Transformers checkpoints. Do not pass this repository directly to `AutoModelForCausalLM.from_pretrained`. The PrismQuant loader restores unquantized state from the base model, loads the saved decoder linears, and installs the online rotations and activation/KV quantizers.
+
+The reference runtime retains floating-point weight and KV storage. It uses the original bf16 containers for Llama 3B/8B and fp32 for Qwen3/70B. W4A4KV4 names the simulated numerical quantization, not this artifact's disk size or serving memory. Dedicated packed-kernel deployment benchmarks are documented separately in the code repository.
+
+These are base-model completions rather than chat-tuned assistants. The original evaluation protocols, seeds, caveats and negative findings remain in the [research archive](https://github.com/ForeverBlue816/PrismQuant/tree/research-archive-2026-09-25). Quantization does not remove upstream model limitations.
+
+## Contents
+
+- `checkpoints/<model>/<checkpoint>/layer_XX.pt`: the folded, GPTQ-quantized decoder linear tensors.
+- `DONE.json` and `gptq_audit.csv`: quantization settings and provenance.
+- `rotations/<model>/...`: the calibrated factors used by the runtime.
+
+The `nar` strings in filenames are the original internal name of PrismQuant and are retained for compatibility. Existing weight files are unchanged by this public-release update. The two smaller Llama repositories additionally include the previously omitted `k=8` down-projection factors.
+
+## Available checkpoints
+
+Download sizes below cover one variant and its required factors, excluding the separately downloaded base model.
+
+| Model key | Checkpoint | Weight protocol | Download (GB) |
+| --- | --- | --- | --- |
+| `qwen3_0.6b_base` | `gptq_hadamard_seed0_g128_asym` | g128_asym | 1.76 |
+| `qwen3_0.6b_base` | `gptq_nar_k8_seed0_g128_asym` | g128_asym | 1.77 |
+| `qwen3_0.6b_base` | `gptq_nar_kmax_seed0_g128_asym` | g128_asym | 1.77 |
+| `qwen3_1.7b_base` | `gptq_hadamard_seed0_g128_asym` | g128_asym | 5.64 |
+| `qwen3_1.7b_base` | `gptq_nar_k8_seed0_g128_asym` | g128_asym | 5.65 |
+| `qwen3_1.7b_base` | `gptq_nar_kmax_seed0_g128_asym` | g128_asym | 5.67 |
+| `qwen3_4b_base` | `gptq_hadamard_seed0_g128_asym` | g128_asym | 14.53 |
+| `qwen3_4b_base` | `gptq_nar_k8_seed0_g128_asym` | g128_asym | 14.55 |
+| `qwen3_4b_base` | `gptq_nar_kmax_seed0_g128_asym` | g128_asym | 14.65 |
+| `qwen3_8b_base` | `gptq_hadamard_seed0_g128_asym` | g128_asym | 27.78 |
+| `qwen3_8b_base` | `gptq_nar_k8_seed0_g128_asym` | g128_asym | 27.80 |
+| `qwen3_8b_base` | `gptq_nar_kmax_seed0_g128_asym` | g128_asym | 27.96 |