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Model: PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think Source: Original Platform
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README.md
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---
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license: apache-2.0
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base_model: Qwen/Qwen1.5-0.5B
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library_name: transformers
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pipeline_tag: text-generation
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language:
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- en
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tags:
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- reasoning
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- chain-of-thought
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- math
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- rlvr
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- qwen2
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---
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# Argonne-Qwen1.5-0.5B-think
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A **0.46B-parameter chain-of-thought reasoner**: the base model **Qwen1.5-0.5B** fine-tuned with a fully
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open five-stage reasoning recipe — general SFT → DPO → CoT-SFT → **STaR** self-improvement → **GRPO/RLVR**
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(verifiable reward). It produces an explicit `<think> … </think>` reasoning trace followed by a final
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`\boxed{}` answer on math word problems.
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The point of this model is to show how much math-reasoning ability a **well-pretrained sub-1B base** can be
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given by a light, fully open post-training recipe. It is a compact research reasoner — useful for studying
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test-time compute on small models — **not** a production-grade or competition-math solver.
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## Benchmarks (honest, held-out)
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Evaluated on **contamination-free** elementary math-word-problem benchmarks — SVAMP, ASDiv, MAWPS (clean)
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and GSM-Plus (semi-clean, adversarial) — that appear in **none** of the training stages. `n=500/set`,
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with-think decoding. Metrics: **greedy / +budget-forcing / self-consistency@32 / pass@32** (%).
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| set | greedy | +budget | self-cons@32 | pass@32 |
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|---|:---:|:---:|:---:|:---:|
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| SVAMP | 33.4 | 33.4 | 44.4 | 83.6 |
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| ASDiv | 49.2 | 48.6 | 58.4 | 83.8 |
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| MAWPS | 39.8 | 39.6 | 47.4 | 78.4 |
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| GSM-Plus (adversarial) | 16.8 | 16.8 | 21.2 | 60.2 |
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| **mean (SVAMP/ASDiv/MAWPS)** | **40.8** | 40.5 | **50.1** | **81.9** |
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Read plainly: single-shot greedy is ~41% on the clean sets, self-consistency lifts it to ~50%, and the
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model **can** solve far more than it reliably answers in one pass — **pass@32 ≈ 82%**. These are
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absolute-modest numbers for a 0.46B model; it is a small reasoner, and the greedy→pass@K headroom is the
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interesting part.
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> **Note on GSM8K:** the CoT/STaR/GRPO training overlaps GSM8K, so GSM8K is **not** a valid held-out
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> benchmark for this model. Use the clean SVAMP / ASDiv / MAWPS numbers above as the honest signal.
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## How it was trained
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Base **Qwen1.5-0.5B** → five stages (base-agnostic harness `reasoning/reason_control/` + RLVR in the
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[training repo](https://github.com/PursuitOfDataScience/ArgonneAI)):
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1. **General SFT** on [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) (LR 2e-5).
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2. **DPO** preference alignment on [argilla/dpo-mix-7k](https://huggingface.co/datasets/argilla/dpo-mix-7k) (LR 5e-6, β 0.1).
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3. **CoT-SFT** on a `<think>…</think>` + `\boxed{}` math-reasoning mix (LR 1e-5, 1 epoch).
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4. **STaR** — self-distillation on the model's own verified-correct, concise (terminating) traces on
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GSM8K-train + MATH-L1-3 (two rounds; fixes the non-termination that caps greedy).
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5. **GRPO / RLVR** — group-relative policy optimization with a verifiable reward (correct `\boxed` on
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GSM8K-train), early-stopped to avoid over-optimization.
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Cumulatively, greedy accuracy on the clean sets rose from ~24% (after stage 3) to ~41% (after stage 5) —
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STaR and RLVR mostly **convert** the large latent pass@K ceiling into single-shot answers rather than
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raising the ceiling itself.
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## Inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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mid = "PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think"
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tok = AutoTokenizer.from_pretrained(mid)
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model = AutoModelForCausalLM.from_pretrained(mid, dtype=torch.bfloat16).to("cuda").eval()
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msgs = [{"role": "user", "content": "Ana has 3 boxes with 12 pencils each. She gives away 8. How many are left?"}]
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ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to("cuda")
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out = model.generate(ids, max_new_tokens=512, do_sample=False)
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print(tok.decode(out[0][ids.shape[1]:], skip_special_tokens=True))
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# -> <think> 3 * 12 = 36 ... 36 - 8 = 28 ... </think> The answer is $\boxed{28}$.
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```
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Uses the standard ChatML format (`<|im_start|>` / `<|im_end|>`). For the best accuracy, sample `K≈16–32`
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traces (`temperature 0.8, top_p 0.95`) and majority-vote the `\boxed{}` answers (self-consistency ≈ 50%).
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## Limitations
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- **0.46B parameters** — a small reasoner. Absolute accuracy on clean grade-school math is ~41% greedy /
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~50% self-consistency; expect errors, especially on multi-step or adversarial problems (GSM-Plus greedy
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~17%).
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- **Scope:** trained and evaluated on English elementary math word problems + general chat. Not a
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general-purpose or competition-math model.
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- **GSM8K is not a valid benchmark here** (train-mix overlap) — use the clean numbers above.
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## Base model & license
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Fine-tuned from [Qwen/Qwen1.5-0.5B](https://huggingface.co/Qwen/Qwen1.5-0.5B) (Apache-2.0) and released under
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Apache-2.0; please also observe the base model's terms.
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## Provenance
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Training + evaluation code: [PursuitOfDataScience/ArgonneAI](https://github.com/PursuitOfDataScience/ArgonneAI)
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(`reasoning/reason_control/` recipe, `reasoning/grpo.py` RLVR, `reasoning/clean_eval.py` honest evaluator).
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## Citation
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```bibtex
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@misc{argonne_qwen05b_think_2026,
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title = {Argonne-Qwen1.5-0.5B-think: a compact chain-of-thought reasoner (SFT->DPO->CoT->STaR->RLVR)},
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author = {PursuitOfDataScience},
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year = {2026},
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url = {https://huggingface.co/PursuitOfDataScience/Argonne-Qwen1.5-0.5B-think}
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}
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```
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chat_template.jinja
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{% for m in messages %}{{ '<|im_start|>' + m['role'] + '
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' + (m['content'] | trim) + '<|im_end|>' + '
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' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant
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' }}{% endif %}
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config.json
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{
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"dtype": "bfloat16",
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1024,
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"initializer_range": 0.02,
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"intermediate_size": 2816,
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"layer_types": [
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention",
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"full_attention"
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],
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"max_position_embeddings": 32768,
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"max_window_layers": 21,
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"model_type": "qwen2",
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"num_attention_heads": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pad_token_id": null,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 1000000.0,
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"rope_type": "default"
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},
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"sliding_window": null,
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"tie_word_embeddings": true,
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"transformers_version": "5.6.2",
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"use_cache": false,
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"use_sliding_window": false,
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"vocab_size": 151936,
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"torch_dtype": "bfloat16"
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}
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generation_config.json
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"transformers_version": "5.6.2"
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}
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"add_prefix_space": false,
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|endoftext|>",
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"errors": "replace",
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"unk_token": null,
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"chat_template": "{% for m in messages %}{{ '<|im_start|>' + m['role'] + '\n' + (m['content'] | trim) + '<|im_end|>' + '\n' }}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}"
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}
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