Add TwIL-LM3: formal-logic model (LoRA SFT -> fusion -> WiSE-FT -> MGPO)

.gitattributes

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 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

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README.md

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+---
+language:
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+base_model: HuggingFaceTB/SmolLM3-3B
+license: other
+license_name: webai-non-commercial-license-ver.-1.0
+license_link: https://huggingface.co/webAI-Official/webAI-ColVec1-4b/blob/main/LICENSE.md
+tags:
+  - formal-logic
+  - reasoning
+  - lora
+  - model-merging
+  - wise-ft
+  - reinforcement-learning
+  - grpo
+  - smollm3
+  - twil-lm
+---
+
+# TwIL-LM3
+
+A 3B reasoning model for **formal logic** tasks, built from
+[`HuggingFaceTB/SmolLM3-3B`](https://huggingface.co/HuggingFaceTB/SmolLM3-3B) through LoRA
+supervised fine-tuning, checkpoint fusion, WiSE-FT weight interpolation, and entropy-weighted
+GRPO reinforcement learning.
+
+It improves in-domain formal-logic performance by **+26% relative** over its base model
+(macro gate 0.336 → 0.422) **and improves held-out benchmark performance at the same time**
+(+0.022 core average). It is the only arm in this project that gains on both tracks, which is
+why it is the recommended release of the pair.
+
+## Results
+
+### Track A — in-domain formal logic
+
+The **macro gate** is the mean of five objective scores: entailment labelling, multiple-choice
+answering, procedural reasoning, Lean proof critique, and rule induction (scored by its
+continuous derivation score). MCQ and procedural are credited as `max(exact_match, loose_match)`.
+`n = 200` prompts per objective, greedy decoding, 2048 max new tokens.
+
+| objective | SmolLM3-3B | TwIL-LM3 | Δ |
+|---|---:|---:|---:|
+| rule_induction | 0.103 | **0.319** | +0.216 |
+| entailment_label | 0.335 | **0.575** | +0.240 |
+| lean_critic | 0.630 | **0.660** | +0.030 |
+| procedural | 0.105 | 0.110 | +0.005 |
+| mcq_answer | 0.505 | 0.445 | −0.060 |
+| **macro gate** | **0.3356** | **0.4218** | **+0.0862** |
+
+Four of five objectives improve. MCQ answering regressed by six points, and that loss is
+averaged into the macro above rather than excluded.
+
+### Track B — held-out benchmarks
+
+Nothing in this suite was trained on. Scores are re-derived from saved generations with
+delimiter-aware answer extractors rather than read from harness metrics.
+
+| | SmolLM3-3B | TwIL-LM3 | Δ |
+|---|---:|---:|---:|
+| core average | 0.790 | **0.812** | **+0.022** |
+| suite average (14 datasets) | 0.661 | **0.669** | **+0.008** |
+
+This model **passes the per-capability floor**: no core or held-out transfer metric drops by
+more than the 0.02 tolerance against its base.
+
+Per-dataset, largest moves in each direction:
+
+| dataset | base | TwIL-LM3 | Δ |
+|---|---:|---:|---:|
+| LogicBench BQA | 0.647 | 0.717 | +0.070 |
+| DROP | 0.700 | 0.747 | +0.047 |
+| CommonsenseQA | 0.707 | 0.737 | +0.030 |
+| StrategyQA | 0.633 | 0.650 | +0.017 |
+| MMLU-Redux | 0.663 | 0.667 | +0.003 |
+| GSM8K | 0.883 | 0.873 | −0.010 |
+| MATH-500 | 0.700 | 0.690 | −0.010 |
+| IFEval (strict) | 0.677 | 0.643 | −0.033 |
+
+Every regression is within 0.033, and the gains on logical-reasoning transfer tasks
+(LogicBench +0.070, DROP +0.047) are larger than any loss. IFEval is the one place worth
+noting — instruction-following degrades slightly, which is a common cost of verifier-driven RL.
+
+## Usage
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "webAI-Official/TwIL-LM3/TwIL-LM3"
+tok = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id, torch_dtype=torch.bfloat16, device_map="auto"
+)
+
+messages = [{"role": "user", "content":
+             "Does 'All dogs are mammals. Rex is a dog.' entail 'Rex is a mammal'? "
+             "Answer entailment, contradiction, or neutral."}]
+inputs = tok.apply_chat_template(
+    messages, add_generation_prompt=True,
+    return_tensors="pt", return_dict=True,
+).to(model.device)
+
+out = model.generate(**inputs, max_new_tokens=2048, do_sample=False)
+print(tok.decode(out[0][inputs["input_ids"].shape[-1]:], skip_special_tokens=True))
+```
+
+`return_dict=True` matters on transformers 5.x, where `apply_chat_template` returns a
+`BatchEncoding` rather than a bare tensor; the above works on both 4.x and 5.x.
+
+The reported numbers use **greedy decoding** (`do_sample=False`) and a **2048-token** generation
+budget. Note that the shipped `generation_config.json` inherits SmolLM3's sampling defaults
+(`do_sample=true`, `temperature=0.6`, `top_p=0.95`), so `do_sample=False` must be passed
+explicitly to reproduce the evaluation. The model opens a `<think>...</think>` reasoning block
+before answering, so a short generation 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 = 0.25** — i.e. only a quarter of the fine-tuned delta is retained. λ was chosen by
+   constrained optimisation: maximise in-domain score subject to minimal degradation on held-out
+   benchmarks. This conservative λ is the direct reason held-out capability survives.
+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 2071**.
+
+A sibling arm that skipped stage 3's conservative interpolation scores considerably higher
+in-domain (macro gate 0.515) but gives back roughly twelve points of held-out capability. This
+release is the balanced point of that trade; the other was not published.
+
+## Limitations and caveats
+
+**Truncation.** At a 2048-token budget, 4.4% of Track A generations hit the cap — better than
+the base's 17.4%, but still above the 2% threshold our protocol requires to mark a comparison
+`rankable`. The Track A macro gate should therefore be read as indicative rather than exact.
+Because a truncated response scores zero regardless of reasoning quality, both numbers are
+pessimistic, and the base substantially more so — meaning the true Track A gap is probably
+narrower than +0.086.
+
+**Scope.** Tuned for formal logic. The Track B suite does not cover code generation or tool use
+(HumanEval, LiveCodeBench and BFCL were not run for this model or its base), so this release
+makes no claim about those.
+
+**Not a chat model.** It was optimised against automatic verifiers on logic tasks. It has had no
+safety tuning beyond whatever the base model carries, and no instruction-following alignment
+work — IFEval regressed slightly.
+
+**Failed consolidation stage.** A post-RL self-distillation round (SDFT) was attempted and made
+both tracks worse at every budget tried (−18% Track A at one epoch on this family). It is not
+part of this model. See the accompanying `SDFT_RESULT.md` in the project repository.
+
+## Evaluation protocol
+
+- Track A: `n = 200` per objective, greedy (`temperature = 0`), `max_new_tokens = 2048`, one
+  retry at 4096 for truncated rows, `max_seq_len = 8192`, seed 42.
+- Track B: 300 examples per task, greedy, `max_gen_toks = 4096`, `max_model_len = 8192`,
+  `repetition_penalty = 1.0`, chat template applied, vLLM backend.
+- Both tracks use the same protocol for the model and its base, in a paired run over identical
+  sampled rows.
+
+`repetition_penalty = 1.0` is load-bearing. A 1.1 penalty produced apparent 20-point swings on
+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 TwIL-LM
+
+[`webAI-Official/TwIL-LM`](https://huggingface.co/webAI-Official/TwIL-LM) is the 1.7B member of
+this family, built from SmolLM2 by the same pipeline. It reaches a higher in-domain score
+relative to its own base but gives back held-out capability; this model is the one that improves
+both. Unlike TwIL-LM's `main` branch, which ships a PEFT LoRA adapter, this repository ships a
+full merged model loaded directly with `AutoModelForCausalLM`.
+
+## License and attribution
+
+Released under the **webAI Non-Commercial License ver. 1.0** — see `LICENSE.md` in this
+repository.
+
+The base model, [`HuggingFaceTB/SmolLM3-3B`](https://huggingface.co/HuggingFaceTB/SmolLM3-3B),
+is Apache 2.0; its licence text is retained as `apache-2.0-LICENSE.txt` and all credit for the
+base model goes to the HuggingFaceTB team. Apache 2.0 permits distributing derivative works
+under different terms provided attribution is preserved, which is what the pair of licence files
+in this repository does.

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chat_template.jinja

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+{# ───── defaults ───── #}
+{%- if enable_thinking is not defined -%}
+{%- set enable_thinking = true -%}
+{%- endif -%}
+
+{# ───── reasoning mode ───── #}
+{%- if enable_thinking -%}
+  {%- set reasoning_mode = "/think" -%}
+{%- else -%}
+  {%- set reasoning_mode = "/no_think" -%}
+{%- endif -%}
+
+{# ───── header (system message) ───── #}
+{{- "<|im_start|>system\n" -}}
+
+{%- if messages[0].role == "system" -%}
+  {%- set system_message = messages[0].content -%}
+  {%- if "/no_think" in system_message -%}
+    {%- set reasoning_mode = "/no_think" -%}
+  {%- elif "/think" in system_message -%}
+    {%- set reasoning_mode = "/think" -%}
+  {%- endif -%}
+  {%- set custom_instructions = system_message.replace("/no_think", "").replace("/think", "").rstrip() -%}
+{%- endif -%}
+
+{%- if "/system_override" in system_message -%}
+  {{- custom_instructions.replace("/system_override", "").rstrip() -}}
+  {{- "<|im_end|>\n" -}}
+{%- else -%}
+  {{- "## Metadata\n\n" -}}
+  {{- "Knowledge Cutoff Date: June 2025\n" -}}
+  {%- set today = strftime_now("%d %B %Y") -%}
+  {{- "Today Date: " ~ today ~ "\n" -}}
+  {{- "Reasoning Mode: " + reasoning_mode + "\n\n" -}}
+  
+  {{- "## Custom Instructions\n\n" -}}
+  {%- if custom_instructions -%}
+    {{- custom_instructions + "\n\n" -}}
+  {%- elif reasoning_mode == "/think" -%}
+    {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracking, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format: <think> Thought section </think> Solution section. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion.\n\n" -}}
+  {%- else -%}
+    {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
+  {%- endif -%}
+
+  {%- if xml_tools or python_tools or tools -%}
+    {{- "### Tools\n\n" -}}
+    {%- if xml_tools or tools -%}
+      {%- if tools -%}
+        {%- set xml_tools = tools -%}
+      {%- endif -%}
+      {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
+      {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
+        {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}
+      {%- endfor -%}
+      {%- set xml_tool_string = ns.xml_tool_string + "</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call>" -%}
+      {{- xml_tool_string -}}
+    {%- endif -%}
+    {%- if python_tools -%}
+      {%- set ns = namespace(python_tool_string="When you send a message containing Python code between '<code>' and '</code>' tags, it will be executed in a stateful Jupyter notebook environment, and you will then be given the output to continued reasoning in an agentic loop.\n\nYou can use the following tools in your python code like regular functions:\n<tools>\n") -%}
+      {%- for tool in python_tools[:] -%} {# The slicing makes sure that python_tools is a list #}
+        {%- set ns.python_tool_string = ns.python_tool_string ~ (tool | string) ~ "\n" -%}
+      {%- endfor -%}
+      {%- set python_tool_string = ns.python_tool_string + "</tools>\n\nThe state persists between code executions: so variables that you define in one step are still available thereafter." -%}
+      {{- python_tool_string -}}
+    {%- endif -%}
+    {{- "\n\n" -}}
+    {{- "<|im_end|>\n" -}}
+  {%- endif -%}
+{%- endif -%}
+{# ───── main loop ───── #}
+{%- for message in messages -%}
+    {%- set content = message.content if message.content is string else "" -%}
+    {%- if message.role == "user" -%}
+        {{ "<|im_start|>" + message.role + "\n"  + content + "<|im_end|>\n" }}
+    {%- elif message.role == "assistant" -%}
+        {% generation %}
+        {%- if reasoning_mode == "/think" -%}
+            {{ "<|im_start|>assistant\n" + content.lstrip("\n") + "<|im_end|>\n" }}
+        {%- else -%}
+            {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n" + content.lstrip("\n") + "<|im_end|>\n" }}
+        {%- endif -%}
+        {% endgeneration %}
+    {%- elif message.role == "tool" -%}
+    {{ "<|im_start|>" + "user\n"  + content + "<|im_end|>\n" }}
+    {%- endif -%}
+{%- endfor -%}
+{# ───── generation prompt ───── #}
+{%- if add_generation_prompt -%}
+    {%- if reasoning_mode == "/think" -%}
+        {{ "<|im_start|>assistant\n" }}
+    {%- else -%}
+        {{ "<|im_start|>assistant\n" + "<think>\n\n</think>\n"  }}
+    {%- endif -%}
+{%- endif -%}

config.json

@@ -0,0 +1,113 @@
+{
+  "architectures": [
+    "SmolLM3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "dtype": "bfloat16",
+  "eos_token_id": 128012,
+  "hidden_act": "silu",
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 11008,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 65536,
+  "max_window_layers": 28,
+  "mlp_bias": false,
+  "model_type": "smollm3",
+  "no_rope_layer_interval": 4,
+  "no_rope_layers": [
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0,
+    1,
+    1,
+    1,
+    0
+  ],
+  "num_attention_heads": 16,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 4,
+  "pad_token_id": 128004,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-06,
+  "rope_parameters": {
+    "rope_theta": 5000000.0,
+    "rope_type": "default"
+  },
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "5.5.0",
+  "use_cache": false,
+  "use_sliding_window": false,
+  "vocab_size": 128256,
+  "rope_theta": 5000000.0,
+  "torch_dtype": "bfloat16"
+}

generation_config.json

@@ -0,0 +1,9 @@
+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": 128012,
+  "pad_token_id": 128004,
+  "temperature": 0.6,
+  "top_p": 0.95,
+  "transformers_version": "5.5.0"
+}

model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:85a87a416ace45bf71a44db090108fef10f08a77a9381ba2471df5e8cca2d16e
+size 6150235096

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7b6a500b662a34eb3f0374db856ba4ad7de4c81040571d78dc0d357238930005
+size 17208819

tokenizer_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token": null,
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|im_end|>",
+  "fast": false,
+  "is_local": true,
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|im_end|>"
+}