Update model card for TwIL-LM2 on main

Drop obsolete TwIL-LM2 revision pins and the How it was built section; point the prior release at TwIL-LM1.

README.md

@@ -89,9 +89,9 @@ import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
 model_id = "webAI-Official/TwIL-LM"
-tok = AutoTokenizer.from_pretrained(model_id, revision="TwIL-LM2")
+tok = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
-    model_id, revision="TwIL-LM2", torch_dtype=torch.bfloat16, device_map="auto"
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
 )
 
 messages = [{"role": "user", "content":
@@ -114,22 +114,6 @@ The model was trained and evaluated with **greedy decoding** (`do_sample=False`)
 answering, so give it room — the reported numbers assume at least 2048 new tokens, and a shorter
 budget truncates reasoning and scores far worse.
 
-## How it was built
-
-Four stages on top of the base model:
-
-1. **LoRA supervised fine-tuning** on a synthetic formal-logic corpus covering the Track A
-   objectives (first-order-logic translation, entailment labelling, semantic parsing, Lean
-   formalisation and critique, procedural reasoning, rule induction).
-2. **Checkpoint fusion** — parameter-space averaging of intermediate SFT checkpoints selected
-   by a diversity probe, rather than taking the final checkpoint.
-3. **WiSE-FT interpolation** toward the pretrained base, `W = (1 − λ)·W_base + λ·W_finetuned`
-   with **λ = 0.75**. λ was chosen by constrained optimisation: maximise in-domain score
-   subject to minimal degradation on held-out benchmarks.
-4. **MGPO** — entropy-weighted GRPO reinforcement learning against a programmatic verifier,
-   with partial credit for loose matches and token-F1 so that all-fail prompt groups still
-   produce gradient. Published checkpoint is **step 1680**.
-
 ## Limitations and caveats
 
 **Truncation.** At a 2048-token budget, 6.9% of Track A generations hit the cap (down from
@@ -163,22 +147,24 @@ model. See the accompanying `SDFT_RESULT.md` in the project repository.
 Track B that were pure decoding artefact; the decoding kwargs are hashed into the protocol
 identity so a mismatched runner fails loudly instead of quietly producing a different number.
 
-## Relationship to the `main` branch
+## Relationship to prior releases
+
+`main` holds **TwIL-LM2**: a **full merged model** from later in the pipeline — after fusion,
+WiSE-FT interpolation and MGPO reinforcement learning — so it is loaded directly with
+`AutoModelForCausalLM`, with no adapter and no base checkpoint required.
 
-The `main` branch of this repository holds the original TwIL-LM release: a PEFT **LoRA adapter**
-(plus GGUF builds) for the supervised fine-tuning stage only. This branch holds a **full merged
-model** from a later point in the pipeline — after fusion, WiSE-FT interpolation and MGPO
-reinforcement learning — so it is loaded directly with `AutoModelForCausalLM`, with no adapter
-and no base checkpoint required.
+The original TwIL-LM (v1) release — a PEFT **LoRA adapter** plus GGUF builds for the supervised
+fine-tuning stage only — is archived on the [`TwIL-LM1`](https://huggingface.co/webAI-Official/TwIL-LM/tree/TwIL-LM1)
+branch (and matching tag). Load it with `revision="TwIL-LM1"`.
 
 The two are scored on different protocols and their headline numbers are not directly
-comparable: `main` reports a macro-*primary* average, while this card reports the five-component
+comparable: v1 reports a macro-*primary* average, while this card reports the five-component
 macro *gate* described above.
 
 ## License and attribution
 
 Released under the **webAI Non-Commercial License ver. 1.0** — see `LICENSE.md` in this
-repository. This matches the licensing of the `main` branch.
+repository.
 
 The base model,
 [`HuggingFaceTB/SmolLM2-1.7B-Instruct`](https://huggingface.co/HuggingFaceTB/SmolLM2-1.7B-Instruct),