111 lines
4.4 KiB
Markdown
111 lines
4.4 KiB
Markdown
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---
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base_model:
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- LiquidAI/LFM2.5-350M
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library_name: transformers
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license: other
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license_name: lfm1.0
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license_link: LICENSE
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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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- synth
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- synthlabs
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- lfm
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- lfm2
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- reasoning
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- text-generation
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datasets:
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- PleIAs/SYNTH
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- mkurman/medical-reasoning-synthlabs-I
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- mkurman/gsm8k-SynthLabs-reasoning
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---
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# LFM2.5-350M-SYNTH
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A 350M-parameter **reasoning** model fine-tuned from [LiquidAI/LFM2.5-350M](https://huggingface.co/LiquidAI/LFM2.5-350M) on
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synthetic reasoning data. It produces explicit chain-of-thought (`<think> … </think>`) before answering, and is small enough
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to run on CPU or modest GPUs while retaining the base model's 128k context window.
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## Highlights
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- **Reasoning-first**: trained on SynthLabs-style traces for math, medical, and general reasoning.
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- **Compact & fast**: 350M params on the hybrid **LFM2** architecture (convolution + sparse full-attention blocks), bf16.
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- **Long context**: inherits the base model's 128k `max_position_embeddings`.
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- **Chat + tools**: ChatML format with native tool-calling special tokens.
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## Model details
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```
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│ │ │ │ │
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────────────────┼──────────────────────────────────────────────────┼──────────┼─────────┼────────┼────
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Base model │ LiquidAI/LFM2.5-350M │ │ │ │
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Architecture │ `Lfm2ForCausalLM` (hybrid conv / full-attention) │ │ │ │
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Parameters │ ~350M │ │ │ │
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Hidden size │ 1024 │ │ │ │
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Layers │ 16 (12 conv + 4 full-attention) │ │ │ │
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Attention heads │ 16 (8 KV heads, GQA) │ │ │ │
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Vocab size │ 65,536 │ │ │ │
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Context length │ 128,000 │ │ │ │
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Precision │ bfloat16 │ │ │ │
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Chat format │ ChatML (`< │ im_start │ >` / `< │ im_end │ >`)
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```
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## Training
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Fully fine-tuned for 4,000 steps on a single A100:
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- Sequence length: 2,048
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- Effective batch size: 64
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- Optimizer: AdamW, LR 5e-5, warmup ratio 0.03
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- Precision: bf16
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Datasets
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- PleIAs/SYNTH (https://huggingface.co/datasets/PleIAs/SYNTH)
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- mkurman/medical-reasoning-synthlabs-I (https://huggingface.co/datasets/mkurman/medical-reasoning-synthlabs-I)
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- mkurman/gsm8k-SynthLabs-reasoning (https://huggingface.co/datasets/mkurman/gsm8k-SynthLabs-reasoning)
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "mkurman/LiquidAI-LFM2.5-350M-SYNTH"
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tok = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id, dtype=torch.bfloat16, device_map="auto")
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messages = [{"role": "user", "content": "Natalia sold 48 clips in April and half as many in May. How many in total?"}]
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inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
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out = model.generate(inputs, max_new_tokens=512, do_sample=True, temperature=0.6, top_p=0.95)
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print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))
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```
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The model emits its reasoning inside <think> … </think> followed by the final answer.
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## Evaluation
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TODO
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## Intended use & limitations
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Research and experimentation with small reasoning models. As a 350M model trained largely on synthetic data, it can
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hallucinate and make reasoning errors. Not intended for medical, legal, or other high-stakes decisions - outputs must be
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independently verified.
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## License
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Released under the LFM Open License (lfm1.0); see LICENSE (LICENSE).
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## Citation
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```bibtex
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@misc{kurman2025lfm25synth,
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title = {LFM2.5-350M-SYNTH},
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author = {Kurman, Mariusz},
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year = {2026},
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url = {https://huggingface.co/mkurman/LiquidAI-LFM2.5-350M-SYNTH}
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}
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```
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