Revert to v0.1-alpha baseline and withdraw v0.5-alpha

README.md

@@ -17,111 +17,28 @@ pipeline_tag: text-generation
 
 **India's first sovereign SSM-based language model.**
 
-Non-transformer architecture. No attention mechanism. Constitutional training via Gurukul. 7 patents filed at IP India.
-
----
-
-## What's in this repo
-
-Three model tiers are available, each built on the same 2.7B parameter base:
-
-| Tier | File | Use this when… |
-|---|---|---|
-| **Base** | `base/Anvaya-Rabbit-2.7B-0.5-alpha-base.pt` | You want raw pretrained weights for your own fine-tuning |
-| **Instruct** | `instruct/Anvaya-Rabbit-2.7B-0.5-alpha-instruct.pt` | You want a general-purpose assistant that follows instructions |
-| **Imprint** | `imprint/Anvaya-Rabbit-2.7B-0.5-alpha-imprint.pt` | You want the full Rabbit persona — opinionated, constitutional, identity-aware |
-
-If you're not sure which to use, start with **Instruct**.
-
 ---
 
-## Quickstart
-
-```bash
-pip install rtaforge transformers
-```
-
-```python
-from transformers import AutoModelForCausalLM, AutoTokenizer
+## Status Update (2026-05-19)
 
-tokenizer = AutoTokenizer.from_pretrained("EleutherAI/gpt-neox-20b")
-tokenizer.add_special_tokens({"additional_special_tokens": ["<|im_start|>", "<|im_end|>"]})
-
-model = AutoModelForCausalLM.from_pretrained(
-    "RtaForge/Anvaya-Rabbit-2.7B",
-    trust_remote_code=True,
-    torch_dtype="bfloat16",
-    device_map="auto",
-)
-
-# v0.5-alpha uses raw completion format
-prompt = "Rabbit is a helpful and honest assistant.\n\nUser: Who are you?\nRabbit:"
-inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
-outputs = model.generate(**inputs, max_new_tokens=60, repetition_penalty=1.3)
-print(tokenizer.decode(outputs[0], skip_special_tokens=True))
-```
-
-> *v0.5-alpha uses raw completion format. Chat template support (ChatML) coming in v0.9.*
-
-> The `rtaforge` runtime package provides the compiled architecture. Source is not distributed.
+**v0.5-alpha weights have been withdrawn.** 
+A regression was identified in the Guru governance layer during the v0.5 SFT phase, leading to sub-optimal weights. 
+We are restarting the SFT process from the v0.1 baseline with a fixed governance harness.
 
 ---
 
-## Why SSM?
+## Available Tiers (v0.1-alpha)
 
-> Transformers scale quadratically with context length because every token attends to every other token. SSMs replace attention with a fixed-size recurrent state: inference cost stays **constant per token** regardless of context length, VRAM footprint shrinks dramatically, and long-document throughput improves by orders of magnitude — all at the same parameter count.
+| Tier | File |
+|---|---|
+| **Base** | `base/Anvaya-Rabbit-2.7B-0.1-alpha-base.pt` |
+| **Imprint** | `imprint/Anvaya-Rabbit-2.7B-0.1-alpha-imprint.pt` |
 
 ---
 
 ## Architecture
 
-Rabbit is built on **RtaSSM v7.2.2-FU "Fortress Unbroken"**, a custom state-space model developed at RtaForge:
-
-- **No attention mechanism** — purely recurrent SSM layers with learned state dynamics
-- **64 layers, 2560 hidden dimensions**, 2.7B parameters, bfloat16
-- **Constitutional training** — Gurukul curriculum with wiki pretraining → instruct SFT → persona imprint
-- **Vocabulary** 50,280 tokens (GPT-NeoX tokenizer)
-
----
-
-## Training
-
-| Stage | Data | Notes |
-|---|---|---|
-| Wiki pretraining | Wikipedia (en) | 732 constitutional proposals via Gurukul |
-| Instruct SFT | ChatML instruction pairs | `gate_only` trainable strategy |
-| Persona imprint | Rabbit constitutional corpus | Identity and value alignment |
-
----
-
-## Evaluation Access
-
-Weights are publicly available. Runtime package is live:
-
-```bash
-pip install rtaforge
-```
-
-To evaluate Rabbit or discuss deployment:
-📧 guha@rtaforge.in
-🌐 rtaforge.in
-
-Runtime documentation coming soon.
-
----
-
-## Maturity and Roadmap
-
-**v0.5-alpha is a proof of concept.** It demonstrates that the RtaSSM architecture trains end-to-end, the Gurukul constitutional pipeline works, and the weights are real.
-
-Usable conversational behaviour is targeted at **v0.8–v0.9**, currently in training.
-
-- Evaluating for deployment? Wait for v0.9.
-- Evaluating the architecture or training methodology? v0.5-alpha is exactly what you need.
-
-## Limitations
-
-v0.5-alpha has not been evaluated on standard benchmarks. She is small, she is new, and she is learning. Feedback welcome at guha@rtaforge.in.
+Rabbit is built on **RtaSSM v7.2.2-FU "Fortress Unbroken"**, a custom state-space model developed at RtaForge.
 
 ---
 
@@ -135,7 +52,3 @@ v0.5-alpha has not been evaluated on standard benchmarks. She is small, she is n
   url    = {https://huggingface.co/RtaForge/Anvaya-Rabbit-2.7B}
 }
 ```
-
----
-
-*Anvaya (अन्वय) — logical connection, coherence. Rabbit — the fast runner.*