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Model: KiteFishAI/Minnow-Math-1.5B Source: Original Platform
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global_step1000
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latest
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rng_state_0.pth
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rng_state_1.pth
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trainer_state.json
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training_args.bin
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zero_to_fp32.py
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README.md
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---
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license: mit
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language:
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- en
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tags:
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- causal-lm
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- scientific-language-model
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- mathematics
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- arxiv
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- research
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library_name: transformers
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---
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# Minnow-Math-1.5B
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**Minnow-Math-1.5B** is a ~1.5B parameter decoder-only transformer trained from scratch on raw arXiv LaTeX sources across mathematics, computer science, and theoretical physics.
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📄 **Paper:** https://arxiv.org/abs/2602.17288
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💻 **Github:** https://github.com/kitefishai/Minnow-Math-1.5B
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This is a **base scientific language model** (not instruction-tuned).
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## Overview
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Minnow-Math-1.5B explores what it takes to train a domain-specialized scientific language model directly from structured LaTeX archives.
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**Training Scale**
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- ~52B pretraining tokens
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- ~5B additional post-training tokens
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- ~200GB processed scientific corpus
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- LLaMA-compatible tokenizer (~102k vocab)
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- 2× NVIDIA A100 (80GB) GPUs
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- 24 experimental training runs
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The focus of this project is *scientific language modeling robustness*, not benchmark optimization.
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## Model Architecture
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- 24 Transformer layers
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- Hidden size: 2048
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- FFN size: 5504
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- 16 attention heads
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- Context length: 4096 (trained at 768 tokens)
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- Dense LLaMA-style architecture
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**Optimization**
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- AdamW
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- Learning rate: 2e-4
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- Warmup: 500 steps
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- Weight decay: 0.1
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- Gradient accumulation: 32
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- bf16 mixed precision
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- Gradient checkpointing enabled
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**Validation Perplexity:** ~4.2 (held-out scientific corpus)
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## Intended Use
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Minnow-Math-1.5B is suitable for:
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- Scientific text modeling research
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- Mathematical language modeling experiments
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- Pretraining initialization for domain fine-tuning
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- Tokenization and symbolic modeling research
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- Studying LaTeX structure modeling
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It is **not optimized for:**
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- Instruction following
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- Chat-based applications
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- General conversational AI
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- Benchmark leaderboard performance
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## Performance Notes
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This model was trained under moderate compute constraints and without instruction tuning or alignment stages.
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Observed characteristics:
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- Strong familiarity with scientific writing style
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- Stable LaTeX structural modeling
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- Reasonable symbolic fluency
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- Limited reasoning depth
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- Low downstream benchmark accuracy without fine-tuning
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Performance improves significantly with supervised fine-tuning (SFT), LoRA adaptation, or domain-specific instruction tuning.
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## Limitations
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- Not instruction-tuned
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- No RLHF or preference alignment
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- Trained at 768-token sequence length
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- Domain restricted to selected arXiv categories
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- Not optimized for reasoning benchmarks
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- General NLP benchmark scores may be low
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This release is intended primarily for research and experimentation.
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## Example Usage
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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import torch
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model_id = "KiteFishAI/Minnow-Math-1.5B"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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prompt = "Prove that the sum of two continuous functions is continuous."
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inputs = tokenizer(prompt, return_tensors="pt")
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with torch.no_grad():
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outputs = model.generate(**inputs, max_new_tokens=200)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Citation
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If you use this model in your research, please cite:
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```
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@article{kitefish_a1_2026,
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title={KiteFish-A1: Training a Scientific Language Model from Raw LaTeX Archives},
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author={...},
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year={2026},
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eprint={2602.17288},
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archivePrefix={arXiv}
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}
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 100000,
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"dtype": "bfloat16",
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"eos_token_id": 100001,
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"head_dim": 128,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 5504,
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"max_position_embeddings": 4096,
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"mlp_bias": false,
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"model_type": "llama",
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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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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_parameters": {
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"rope_theta": 10000.0,
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"rope_type": "default"
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},
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"tie_word_embeddings": false,
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"transformers_version": "5.1.0",
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"use_cache": false,
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"vocab_size": 102400
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 100000,
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"eos_token_id": 100001,
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"output_attentions": false,
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"output_hidden_states": false,
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"transformers_version": "5.1.0",
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"use_cache": false
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:2ff0b84be573afae45893dcfab3082b07ebafc3d9faa790230c49b506714fb8e
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size 3267588880
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tokenizer.json
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tokenizer_config.json
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{
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"backend": "tokenizers",
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"bos_token": "<s>",
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"clean_up_tokenization_spaces": false,
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"eos_token": "</s>",
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"is_local": false,
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"legacy": false,
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "</s>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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"tokenizer_class": "TokenizersBackend",
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"truncation_side": "right",
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"unk_token": "<unk>"
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}
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