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Model: tawer12/qwen3-4b-recursive-sft-v3.2
Source: Original Platform

README.md

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+---
+language:
+- en
+license: apache-2.0
+library_name: transformers
+pipeline_tag: text-generation
+base_model: Qwen/Qwen3-4B-Base
+base_model_relation: finetune
+tags:
+- math
+- recursive-reasoning
+- supervised-fine-tuning
+- qwen3
+datasets:
+- openai/gsm8k
+---
+
+# Qwen3-4B Recursive SFT V3.2
+
+This is the **original-tag recursive V3.2 balanced SFT checkpoint**. It is not
+V3.1, the JSON variant, the flattened baseline, or an RL-finetuned model.
+The weights and tokenizer are unchanged from the final saved checkpoint named
+`qwen3-4b-recursive-sft-v3.2-balanced`.
+
+The model was trained from Qwen3-4B-Base with additional recursive protocol
+tokens. It was not initialized from the already SFT-trained V3.1 model.
+It is a research math-solving model, not a general-purpose chat assistant.
+
+## Download
+
+No ZIP extraction or login is required:
+
+```python
+from huggingface_hub import snapshot_download
+
+snapshot_download(
+    "tawer12/qwen3-4b-recursive-sft-v3.2",
+    local_dir="qwen3-4b-recursive-sft-v3.2",
+)
+```
+
+The two safetensors shards, tokenizer files, and configuration files must be
+kept together. `release_manifest.json` lists file sizes and SHA256 checksums.
+The download is approximately 8.06 GB. There are no optimizer states, pickled
+training arguments, intermediate checkpoints, training examples, or evaluation
+rollouts in this repository.
+
+## Training
+
+V3.2 downsamples only direct-solve examples from the expanded V3.1 call-level
+training corpus, retaining the decomposition and aggregation examples unchanged.
+
+| Setting | Value |
+| --- | --- |
+| Decomposition examples | 4,707 |
+| Direct-solve examples | 4,707: 530 root, 4,177 child |
+| Aggregation examples | 4,712 |
+| Total component-level examples | 14,126 |
+| Unique source problems represented | 5,241 |
+| Configured epochs | 6 |
+| Optimizer updates | 660 |
+| Global batch | 128 component calls, not 128 source problems |
+| GPUs / per-device batch / accumulation | 8 / 1 / 16 |
+| Maximum sequence length | 2,048 tokens |
+| Learning rate / weight decay | 1e-5 / 0.01 |
+| Warmup / scheduler | 10% / cosine |
+| Precision / seed | BF16 / 1 |
+| Objective | Output-token-only causal language modeling |
+
+The six epochs approximately match the previous four-epoch V3.1 run's total
+example exposure; this is not an exactly token- or update-matched experiment.
+The corpus contains independent component calls, not necessarily complete trees
+after downsampling. No syntax repair was applied: 387 targets retain known
+defects under the newer strict `Final answer:` requirement. The training summary
+is included separately from the raw training state.
+
+## Recursive Inference
+
+Inputs are **raw recursive prompt strings**, not Qwen chat-template conversations.
+Use `prompts/recursive_sft_prefix_prompt.txt`. Preserve protocol special tokens
+when decoding (`skip_special_tokens=False`). A single generation returns one
+action, not necessarily the entire solution:
+
+- `SOLVE_DIRECTLY`: solve the supplied problem.
+- `DECOMPOSE`: propose at least two child paths for the engine to execute.
+- `AGGREGATE`: combine child answers when the engine requests aggregation.
+
+The bundled `recursive_engine.py` provides a standalone, unconstrained baseline
+engine. This example executes a complete tree:
+
+```bash
+pip install 'transformers==4.51.1' torch accelerate huggingface_hub
+```
+
+```python
+import sys
+from pathlib import Path
+from huggingface_hub import snapshot_download
+
+snapshot = Path(snapshot_download("tawer12/qwen3-4b-recursive-sft-v3.2"))
+sys.path.insert(0, str(snapshot))
+from recursive_engine import RecursiveEngine, load_model_and_tokenizer
+
+model, tokenizer = load_model_and_tokenizer(str(snapshot), dtype="bf16", device_map="auto")
+engine = RecursiveEngine(
+    model=model,
+    tokenizer=tokenizer,
+    prefix_template=(snapshot / "prompts/recursive_sft_prefix_prompt.txt").read_text(),
+    max_new_tokens=2048,
+    temperature=0.0,
+    top_p=1.0,
+    max_depth=3,
+    max_calls=10,
+)
+result = engine.rollout("What is the smallest multiple of 5 greater than -32?")
+print(result.full_trace)
+print(result.final_answer, result.parsed_ok, result.parse_errors)
+```
+
+For this bundled legacy engine, `max_calls=10` is a **whole-tree model-call limit**,
+including the root and aggregations, not ten children per node. The newer
+constrained GRPO/evaluation implementation in the
+[math-recursive repository](https://github.com/0315jaewon/math-recursive)
+has separate per-decomposition and whole-tree limits. Do not conflate them.
+The example helper enables the generation cache even though the saved training
+configuration has `use_cache=false`.
+
+## Limitations And Attribution
+
+Format validity and answer correctness are distinct. A malformed downstream call
+can invalidate a tree, and well-formed child answers can be mathematically wrong.
+Decomposition paths may be dependent or underspecified. Aggregation receives
+child answer summaries rather than their full reasoning. The standalone engine
+executes children sequentially; critical-path token counts are not measured
+parallel wall-clock speedups. Grammar constraints are external decoding support,
+not an intrinsic guarantee supplied by this checkpoint.
+
+The historical helper's answer extraction is not an authoritative benchmark
+grader; use explicit root-answer grading and the standard evaluation protocol
+for comparisons. No new benchmark accuracy claims are made in this release.
+
+This is a modified, fine-tuned version of the Qwen team's Apache-2.0
+Qwen3-4B-Base model. The upstream license is included in `LICENSE`. GSM8K supplies
+the curated training questions. The model card and publishing recipe were added
+for this V3.2 release on 2026-09-30.