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Model: PleIAs/baguettotron-600m
Source: Original Platform

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README.md

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+---
+language:
+- en
+- fr
+- it
+- de
+- es
+- pl
+license: apache-2.0
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- transformers
+- reasoning
+- synthetic-data
+datasets:
+- PleIAs/SYNTH
+---
+
+# 🥖 Baguettotron-600M
+
+<div align="center">
+  <img src="https://huggingface.co/PleIAs/Baguettotron/resolve/main/figures/pleias.jpg" width="60%" alt="Pleias" />
+</div>
+
+<p align="center">
+  <a href="https://arxiv.org/abs/2609.37891"><b>Paper (NeurIPS 2026)</b></a> ·
+  <a href="https://huggingface.co/datasets/PleIAs/SYNTH"><b>SYNTH dataset</b></a> ·
+  <a href="https://huggingface.co/PleIAs/Baguettotron"><b>Baguettotron-350M</b></a> ·
+  <a href="https://huggingface.co/PleIAs/baguettotron-moe"><b>Baguettotron-MoE</b></a>
+</p>
+
+**Baguettotron-600M** is a 0.6B-parameter reasoning model trained from scratch on 158B tokens of [SYNTH](https://huggingface.co/datasets/PleIAs/SYNTH), a fully synthetic corpus amplified from 58,698 Wikipedia articles. It is the main reference model of our NeurIPS 2026 paper, [*It's All Training: A Fully Synthetic Single-Stage Recipe for LLMs*](https://arxiv.org/abs/2609.37891).
+
+- **Single training stage:** no separate mid-training, SFT or RLHF. The model follows instructions, reasons in `<think>` traces and recalls facts because SYNTH trains those capabilities directly.
+- **Token-efficient:** within 4.7 points of Qwen3-0.6B on 22 multiple-choice tasks and 0.7 points on 8 open-ended tasks, trained on ~230× fewer tokens.
+- **Factually precise:** highest FActScore in its parameter tier (41.7% macro vs. 31.6% for Qwen3-0.6B).
+
+## Model design
+
+Baguettotron-600M is a standard dense decoder (Llama/Qwen-style) and loads natively in `transformers` and vLLM (`LlamaForCausalLM`, no remote code).
+
+<p align="center">
+  <img width="80%" src="figures/architecture.png" alt="Baguettotron-600M architecture">
+</p>
+
+| | |
+|---|---|
+| Parameters | 608M (incl. 67M tied embeddings) |
+| Layers | 48 |
+| Hidden size | 1024 |
+| Attention | GQA, 16 query / 4 KV heads, head dim 64 |
+| MLP | SwiGLU, intermediate size 2816 |
+| Position encoding | RoPE, θ = 10,000 |
+| Norm | RMSNorm (pre-norm), ε = 1e-6 |
+| Embeddings | Tied input/output |
+| Vocabulary | 65,536 (Pleias tokenizer) |
+| Context length | 2,048 tokens |
+
+## Training
+
+- **Data:** SYNTH, 158.3B tokens (~2 passes over the ~75B-token corpus)
+- **Steps:** 151,000 at global batch 512 × 2,048 tokens (~1.05M tokens/step)
+- **Optimizer:** AdamW, peak LR 1.5e-3, 10k-step warmup, linear decay over the final 16.6% of steps to 0.2% of peak; weight decay 0.01, grad clip 1.0
+- **Hardware:** 16× H100 (4 nodes × 4 GPUs) on MareNostrum 5 (BSC), `torchtitan` with FSDP, ~52.6 h, ~30% MFU
+
+## Evaluation
+
+All scores are from the paper. Each model is prompted in its native format; Baguettotron models get no system prompt and have `<think>\n` seeded after the assistant turn.
+
+<p align="center">
+  <img width="90%" src="figures/benchmarks_pareto.png" alt="Accuracy vs training tokens">
+</p>
+
+| Model | Training tokens | MCQ (22 tasks) | Open-ended (8 tasks) | FActScore (macro) |
+|---|---|---|---|---|
+| **Baguettotron-600M** | 158B | 42.2 | 24.3 | **41.7** |
+| Baguettotron-MoE (13B / 1B active) | 50B | 39.6 | 23.8 | 46.3 |
+| Baguettotron-350M | 200B | 36.3 | 16.0 | 32.4 |
+| Qwen3-0.6B | ~36T | **46.9** | **25.0** | 31.6 |
+| LFM2.5-350M | 28T | 42.3 | 20.8 | 22.0 |
+| SmolLM2-360M-Instruct | 4T | 22.8 | 21.6 | 26.6 |
+| Gemma-3-270M-IT | 2T | 22.5 | 14.9 | 19.4 |
+
+- **Ties or beats Qwen3-0.6B** on TruthfulQA (42.6 vs. 32.7), ESGenius (62.2 vs. 55.1) and FormationEval (62.8 vs. 58.0)
+- **Weak:** benchmarks that reward broad web knowledge (ARC-Challenge, GeoBench), since SYNTH covers only its seed articles
+- **Data, not architecture:** the same architecture, tokenizer and step count trained on FineWiki or FinePDFs-Edu reaches only 26.1 / 25.1 on MCQ, even after SmolTalk post-training
+- **Reasoning traces matter:** retraining without traces costs 9.1 points on TruthfulQA and 10.0 on NuclearQA
+
+## Prompt format
+
+The model was trained on ChatML with a `<think>` block and no system prompt. The bundled chat template opens the assistant turn with `<think>\n`:
+
+```xml
+<|im_start|>user
+What do you know about the Treaty of Westphalia?<|im_end|>
+<|im_start|>assistant
+<think>
+```
+
+The model writes its reasoning, closes it with `</think>`, answers, and ends the turn with `<|im_end|>`.
+
+**RAG.** Pass sources inside the user turn. The answer then cites them with `<ref>[quote]</ref>`:
+
+```xml
+<|im_start|>user
+{question}
+
+<source_1>[…]</source_1>
+<source_2>[…]</source_2><|im_end|>
+<|im_start|>assistant
+<think>
+```
+
+**Reasoning notation.** Traces use SYNTH's compact stenographic style (`→` derivation, `↺` backtracking, `∴` conclusion, …). The confidence markers `●` (high), `◐` (partial) and `○` (low) are informative: on traces dominated by uncertain markers, FActScore drops from 0.44 to 0.40, and the model commits to ~15–20% fewer facts. See the [Baguettotron-350M card](https://huggingface.co/PleIAs/Baguettotron) for the full notation.
+
+## Inference
+
+### vLLM
+
+```bash
+vllm serve PleIAs/baguettotron-600m --reasoning-parser deepseek_r1
+```
+
+`--reasoning-parser deepseek_r1` moves the `<think>` trace into a separate `reasoning` field. Tested with vLLM 0.24.0.
+
+```python
+from openai import OpenAI
+
+client = OpenAI(base_url="http://localhost:8000/v1", api_key="EMPTY")
+resp = client.chat.completions.create(
+    model="PleIAs/baguettotron-600m",
+    messages=[{"role": "user", "content": "What do you know about the Treaty of Westphalia?"}],
+    temperature=0.1,
+    top_p=0.95,
+    presence_penalty=0.1,
+    max_tokens=1536,
+)
+print(resp.choices[0].message.reasoning)  # <think> trace
+print(resp.choices[0].message.content)    # answer
+```
+
+Recommended sampling: `temperature=0.1`, `top_p=0.95`, `presence_penalty=0.1`. Prompt and generation share the 2,048-token context, so keep `max_tokens` below 2,048 minus the prompt length.
+
+### Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+repo = "PleIAs/baguettotron-600m"
+tokenizer = AutoTokenizer.from_pretrained(repo)
+model = AutoModelForCausalLM.from_pretrained(repo, dtype="bfloat16", device_map="auto")
+
+messages = [{"role": "user", "content": "What do you know about the Treaty of Westphalia?"}]
+inputs = tokenizer.apply_chat_template(
+    messages, add_generation_prompt=True, return_tensors="pt"
+).to(model.device)
+out = model.generate(inputs, max_new_tokens=1536, do_sample=True, temperature=0.1, top_p=0.95)
+print(tokenizer.decode(out[0, inputs.shape[1]:]))
+```
+
+## Limitations
+
+- **2,048-token context** shared by prompt, reasoning and answer
+- **Knowledge is bounded by the seed corpus** (~58.7k Wikipedia articles): recall is precise on seed topics and weaker outside them
+- **Reasoning traces are English-only**, even when the prompt and answer are in another language
+- **No preference tuning or safety alignment**; not intended for high-stakes use without further evaluation
+
+## Citation
+
+```bibtex
+@inproceedings{langlais2026itsalltraining,
+  title         = {It's All Training: A Fully Synthetic Single-Stage Recipe for {LLMs}},
+  author        = {Langlais, Pierre-Carl and Delobelle, Pieter and Detrois, Yannick and Chizhov, Pavel and Rosas-Hinostroza, Carlos and Si Smail, Neil and Burtin, Benjamin and Shcharbakova, Hanna and Yamshchikov, Ivan and Stasenko, Anastasia},
+  booktitle     = {Advances in Neural Information Processing Systems},
+  year          = {2026},
+  eprint        = {2609.37891},
+  archivePrefix = {arXiv},
+  primaryClass  = {cs.CL},
+  url           = {https://arxiv.org/abs/2609.37891}
+}
+```
+
+## Acknowledgements
+
+Trained on MareNostrum 5 (BSC) through the EuroHPC Extreme Scale Access call (EHPC-EXT-2025E01-092, *JULIP: Powerful LLMs made in Europe*). Parts of this research received funding from SPRIN-D, the German Federal Agency for Breakthrough Innovation.

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+  ],
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+  "num_hidden_layers": 48,
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+  "use_cache": true
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special_tokens_map.json

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tokenizer_config.json

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+    "65510": {
+      "content": "?maybe?",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65511": {
+      "content": "●",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65512": {
+      "content": "◐",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65513": {
+      "content": "○",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65514": {
+      "content": "⚠",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65515": {
+      "content": "☐",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65516": {
+      "content": "☑",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65517": {
+      "content": "✓",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65518": {
+      "content": "⟨H≈0.1⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65519": {
+      "content": "⟨H≈0.2⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65520": {
+      "content": "⟨H≈0.3⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65521": {
+      "content": "⟨H≈0.4⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65522": {
+      "content": "⟨H≈0.5⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65523": {
+      "content": "⟨H≈0.6⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65524": {
+      "content": "⟨H≈0.7⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65525": {
+      "content": "⟨H≈0.8⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65526": {
+      "content": "⟨H≈0.9⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65527": {
+      "content": "⟨H≈1.0⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65528": {
+      "content": "⟨H≈1.1⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65529": {
+      "content": "⟨H≈1.2⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65530": {
+      "content": "⟨H≈1.3⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65531": {
+      "content": "⟨H≈1.4⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65532": {
+      "content": "⟨H≈1.5⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65533": {
+      "content": "⟨H≈1.6⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65534": {
+      "content": "⟨H≈1.7⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "65535": {
+      "content": "⟨H≈1.8⟩",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<think>",
+    "</think>",
+    "source_1",
+    "source_2",
+    "source_3",
+    "source_4",
+    "source_5",
+    "source_6",
+    "source_7",
+    "source_8",
+    "source_9",
+    "source_10",
+    "<ref",
+    "</ref>",
+    "→",
+    "↺",
+    "※",
+    "?maybe?",
+    "●",
+    "◐",
+    "○",
+    "⚠",
+    "☐",
+    "☑",
+    "✓",
+    "⟨H≈0.1⟩",
+    "⟨H≈0.2⟩",
+    "⟨H≈0.3⟩",
+    "⟨H≈0.4⟩",
+    "⟨H≈0.5⟩",
+    "⟨H≈0.6⟩",
+    "⟨H≈0.7⟩",
+    "⟨H≈0.8⟩",
+    "⟨H≈0.9⟩",
+    "⟨H≈1.0⟩",
+    "⟨H≈1.1⟩",
+    "⟨H≈1.2⟩",
+    "⟨H≈1.3⟩",
+    "⟨H≈1.4⟩",
+    "⟨H≈1.5⟩",
+    "⟨H≈1.6⟩",
+    "⟨H≈1.7⟩",
+    "⟨H≈1.8⟩"
+  ],
+  "clean_up_tokenization_spaces": true,
+  "extra_special_tokens": {},
+  "model_max_length": 1000000000000000019884624838656,
+  "tokenizer_class": "PreTrainedTokenizer",
+  "chat_template": "{% for m in messages %}<|im_start|>{{ m['role'] }}\n{{ m['content'] }}<|im_end|>\n{% endfor %}{% if add_generation_prompt %}<|im_start|>assistant\n<think>\n{% endif %}"
+}