Release Mossez-100M-Base

LICENSE

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NOTICE.md

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+Mossez-100M-Base
+Copyright 2026 Mossez Systems
+
+This product includes model weights and accompanying original materials
+developed by Mossez Systems.
+
+TRAINING DATA ATTRIBUTION
+
+The model was trained on processed subsets derived in part from the
+following third-party data sources. The source datasets and underlying
+documents are not redistributed in this model repository.
+
+1. FineWeb2
+
+   Project: HuggingFaceFW/fineweb-2
+   Source: https://huggingface.co/datasets/HuggingFaceFW/fineweb-2
+   License: Open Data Commons Attribution License (ODC-By) v1.0
+   Additional terms: Common Crawl Terms of Use may apply.
+
+   FineWeb2 was created by the Hugging Face FineWeb team and contributors.
+   See the dataset card for authorship, methodology, citations, limitations,
+   opt-out information, and the complete licensing notice.
+
+2. FineWeb / FineWeb-Edu-derived English data
+
+   Project: HuggingFaceFW/fineweb
+   Source: https://huggingface.co/datasets/HuggingFaceFW/fineweb
+   License: Open Data Commons Attribution License (ODC-By) v1.0
+   Additional terms: Common Crawl Terms of Use may apply.
+
+   FineWeb was created by the Hugging Face FineWeb team and contributors.
+   See the dataset card for authorship, methodology, citations, limitations,
+   opt-out information, and the complete licensing notice.
+
+3. Wikimedia Wikipedia
+
+   Project: wikimedia/wikipedia
+   Source: https://huggingface.co/datasets/wikimedia/wikipedia
+   Configurations used: 20231101.ru and 20231101.en
+   Dataset revision used:
+   b04c8d1ceb2f5cd4588862100d08de323dccfbaa
+
+   Wikipedia text is made available under the applicable Wikimedia
+   licensing terms, including the Creative Commons Attribution-ShareAlike
+   license and the GNU Free Documentation License. Individual media and
+   other materials may use different licenses. Consult the source article
+   and Wikimedia licensing pages for details.
+
+4. Common Crawl
+
+   FineWeb and FineWeb2 are derived from Common Crawl snapshots.
+   Source: https://commoncrawl.org/
+   Use of Common Crawl data is subject to Common Crawl's terms and the
+   rights associated with the original source documents.
+
+The licenses and notices above apply to the respective third-party
+datasets and source materials. They are separate from the Apache License
+2.0 applied by Mossez Systems to the model weights and original repository
+materials.
+
+No dataset creator, source-site operator, Wikimedia contributor, Common
+Crawl, Hugging Face, PyTorch, or Transformers contributor endorses
+Mossez Systems or Mossez-100M-Base merely because their work or services
+were used during model development.
+
+"Mossez" and related product names may be trademarks of Mossez Systems.
+Third-party names and trademarks belong to their respective owners.
+
+This NOTICE file is informational and does not modify the terms of the
+Apache License 2.0 or any third-party license.

README.md

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+---
+license: apache-2.0
+language:
+  - ru
+  - en
+library_name: transformers
+pipeline_tag: text-generation
+datasets:
+  - HuggingFaceFW/fineweb-2
+  - HuggingFaceFW/fineweb
+  - wikimedia/wikipedia
+tags:
+  - causal-lm
+  - base-model
+  - llama
+  - gqa
+  - russian
+  - english
+  - research
+  - mossez
+---
+
+# Mossez-100M-Base
+
+**Mossez-100M-Base** is a compact decoder-only causal language model developed by Mossez Systems for research, local inference, and experimentation with Russian–English language modeling.
+
+The released weights include the model's initial web pretraining followed by a quality-focused continued-pretraining stage. The continued-pretraining stage is part of the model's training history; the public model name remains **Mossez-100M-Base**.
+
+> This is a base model, not a chat or instruction-following model.
+
+## Model details
+
+| Property | Value |
+|---|---:|
+| Parameters | 100,092,672 |
+| Architecture | Llama-compatible decoder-only Transformer |
+| Layers | 12 |
+| Hidden size | 768 |
+| Query attention heads | 12 |
+| Key/value heads | 4 |
+| Intermediate size | 2,048 |
+| Context length | 1,024 tokens |
+| Vocabulary | 32,000 |
+| Tokenizer | Byte-level BPE |
+| Activation | SwiGLU / SiLU |
+| Normalization | RMSNorm |
+| Position encoding | RoPE |
+| Embeddings | Tied input/output embeddings |
+| Primary languages | Russian, English |
+| Weight format | Safetensors, FP32 |
+
+## Intended use
+
+The model is intended for:
+
+- research on compact causal language models;
+- local text completion;
+- tokenizer, inference, and post-training experiments;
+- use as a base checkpoint for continued pretraining or supervised fine-tuning;
+- educational and reproducibility work.
+
+The model is **not** intended to be used as a factual authority, production assistant, safety-critical system, or autonomous decision-maker.
+
+## Training
+
+### Initial pretraining
+
+The initial model was trained on a Russian–English web corpus:
+
+- approximately 85% Russian FineWeb2-HQ data;
+- approximately 15% English FineWeb-Edu data;
+- 2,000,027,648 training-token exposure;
+- 30,518 optimizer steps;
+- 65,536 effective tokens per optimizer step;
+- sequence length 1,024.
+
+### Quality continued pretraining
+
+The final public weights received an additional 100,007,936-token quality-focused continued-pretraining stage:
+
+| Source | Training tokens |
+|---|---:|
+| Russian Wikipedia | 60,004,352 |
+| English Wikipedia | 25,001,984 |
+| Replay from the original corpus | 15,001,600 |
+| **Total** | **100,007,936** |
+
+Wikipedia source configurations:
+
+- `wikimedia/wikipedia`, `20231101.ru`;
+- `wikimedia/wikipedia`, `20231101.en`;
+- pinned dataset revision: `b04c8d1ceb2f5cd4588862100d08de323dccfbaa`.
+
+The Wikipedia corpus was normalized, filtered, exactly deduplicated, near-deduplicated, and split at source-article level to prevent chunks from the same article crossing train, validation, and test sets.
+
+Continued-pretraining configuration:
+
+- 1,526 optimizer steps;
+- effective batch: 65,536 tokens;
+- maximum learning rate: `5e-5`;
+- 50 warmup steps;
+- cosine decay;
+- AdamW;
+- gradient checkpointing;
+- final selected checkpoint: step 1,526.
+
+### Total training exposure
+
+The combined training exposure was:
+
+**2,100,035,584 tokens**
+
+This number is token exposure, not a claim of unique corpus size.
+
+## Evaluation
+
+Perplexity was measured with sequence length 1,024. Lower is better.
+
+| Evaluation set | Before quality CPT | Mossez-100M-Base | Relative PPL improvement |
+|---|---:|---:|---:|
+| Wikipedia validation | 19.5363 | 13.6782 | 29.99% |
+| Independent Russian test | 18.0996 | 12.8478 | 29.02% |
+| Independent English test | 22.4920 | 16.0280 | 28.74% |
+| Independent combined test | 19.3179 | 13.7286 | 28.93% |
+| Original web-domain validation | 20.0903 | 20.7899 | -3.48% |
+
+The quality-focused stage substantially improved performance on held-out encyclopedic text while causing a modest regression on the original web-domain validation set.
+
+The independent combined test contained 2,048 blocks, or 2,097,152 tokens:
+
+- Russian: 1,434 blocks;
+- English: 614 blocks.
+
+## Usage
+
+Install a recent version of `transformers`, `torch`, and `safetensors`.
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "mossez-systems/Mossez-100M-Base"
+
+device = "cuda" if torch.cuda.is_available() else "cpu"
+dtype = torch.float16 if device == "cuda" else torch.float32
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    dtype=dtype,
+)
+model.to(device)
+model.eval()
+
+prompt = "Искусственный интеллект — область информатики, которая"
+inputs = tokenizer(
+    prompt,
+    return_tensors="pt",
+    add_special_tokens=False,
+).to(device)
+
+with torch.inference_mode():
+    output = model.generate(
+        **inputs,
+        do_sample=True,
+        temperature=0.8,
+        top_p=0.9,
+        top_k=50,
+        repetition_penalty=1.08,
+        max_new_tokens=96,
+        pad_token_id=tokenizer.eos_token_id,
+    )
+
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+```
+
+### Prompting notes
+
+This is a base completion model. It does not have a chat template and was not trained to follow `system`, `user`, and `assistant` roles. Sentence beginnings and document-style prefixes work better than chat-style prompts.
+
+Greedy decoding is useful for diagnostics but is more likely to collapse into repetition. Sampling does not guarantee factuality.
+
+## Limitations
+
+Mossez-100M-Base is a small research model and has significant limitations:
+
+- it can hallucinate names, dates, places, quotations, and causal relationships;
+- it may confuse entities or continue a prompt as if it referred to another subject;
+- it can repeat words, phrases, headings, or entire semantic patterns;
+- it may generate fluent but false encyclopedic-looking text;
+- it is not instruction tuned and may ignore requests or formatting constraints;
+- its 1,024-token context window is small;
+- Russian performance is generally stronger than English performance;
+- its knowledge is not current and should not be treated as a reliable snapshot of the world;
+- it may reproduce biases, errors, personal information, or undesirable content present in public web data;
+- its outputs have not been comprehensively evaluated for safety.
+
+Do not rely on the model for medical, legal, financial, security, or other high-stakes decisions.
+
+## Training data and attribution
+
+The model was trained on processed subsets derived from:
+
+- [FineWeb2](https://huggingface.co/datasets/HuggingFaceFW/fineweb-2), released under ODC-By 1.0 and subject to Common Crawl terms;
+- [FineWeb](https://huggingface.co/datasets/HuggingFaceFW/fineweb), released under ODC-By 1.0 and subject to Common Crawl terms;
+- [Wikimedia Wikipedia](https://huggingface.co/datasets/wikimedia/wikipedia), whose source text is available under the applicable Wikimedia licensing terms, including CC BY-SA and GFDL.
+
+The original datasets are not redistributed in this model repository. Dataset licenses and source-site terms remain applicable to the underlying data. See `NOTICE.md` for attribution information.
+
+## License
+
+The model weights and original repository materials are released under the Apache License 2.0. Third-party datasets, source text, names, and trademarks remain subject to their own licenses and terms.
+
+See `LICENSE` and `NOTICE.md`.
+
+## Citation
+
+```bibtex
+@software{mossez_100m_base_2026,
+  author       = {{Mossez Systems}},
+  title        = {Mossez-100M-Base},
+  year         = {2026},
+  publisher    = {Hugging Face},
+  url          = {https://huggingface.co/mossez-systems/Mossez-100M-Base}
+}
+```
+
+## Acknowledgements
+
+This work uses resources made available by Hugging Face, Common Crawl, Wikimedia contributors, the PyTorch project, and the Transformers project. Their inclusion here does not imply endorsement of Mossez Systems or this model.

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config.json

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+{
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "dtype": "float32",
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+  "head_dim": 64,
+  "hidden_act": "silu",
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+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "max_position_embeddings": 1024,
+  "mlp_bias": false,
+  "model_type": "llama",
+  "num_attention_heads": 12,
+  "num_hidden_layers": 12,
+  "num_key_value_heads": 4,
+  "pad_token_id": 3,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 10000.0,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.56.2",
+  "use_cache": true,
+  "vocab_size": 32000
+}

generation_config.json

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+  "transformers_version": "4.56.2",
+  "use_cache": false
+}

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special_tokens_map.json

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tokenizer_config.json

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+}