初始化项目，由ModelHub XC社区提供模型
Model: Raffleraffle/manifoldgl Source: Original Platform
This commit is contained in:
ModelHub XC
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README.md
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README.md
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---
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license: apache-2.0
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tags:
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- llm
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- hyperbolic
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- geometry
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- adapter
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- peft
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- research
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base_model:
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- Qwen/Qwen2.5-7B
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pipeline_tag: text-generation
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library_name: peft
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---
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# ManifoldGL – Information‑Geometric Adapter for LLMs
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ManifoldGL is a parameter‑efficient adapter that enforces **hyperbolic geometry** on the latent space of large language models. It treats the meaning of a token as a **fiber** over a hyperbolic base manifold (a Poincaré ball), rather than a single vector in flat Euclidean space. Latent states are projected onto the ball, and attentions are computed using geodesic distance. A sheaf‑theoretic consistency loss and natural gradient optimization maintain semantic structure during training.
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## Motivation and theoretical background
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Modern LLMs embed tokens in a Euclidean vector space. While convenient, Euclidean geometry has limited capacity to represent hierarchical structures: flat space grows polynomially, whereas hierarchical trees expand exponentially. By contrast, **hyperbolic space** grows exponentially and preserves both local and global relationships in a hierarchy【247949143190903†L115-L124】. Hyperbolic embeddings outperform Euclidean ones for lexical entailment, similarity and analogy tasks【247949143190903†L154-L169】. ManifoldGL leverages these properties by modelling the latent space as a fiber bundle over a hyperbolic base: each point in the Poincaré ball encodes a context, and its fiber contains a distribution of semantic components.
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## Results on ARC‑AGI benchmark
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ManifoldGL fine‑tuned on Qwen2.5‑7B improves task accuracy on the ARC‑AGI benchmark from **12.4 %** to **28.7 %**, a **131.5 % relative improvement**. The model also achieves a **Manifold Faithfulness Rate (MFR) of 94.2 %**, indicating high adherence to the hyperbolic constraints, and maintains a curvature close to the target κ = ‑1 (mean ‑0.98 ± 0.04). Ablation studies show that removing curvature regularization, natural gradients, sheaf consistency or the hyperbolic target significantly reduces accuracy; the Euclidean target ablation causes the largest drop (–10.9 %), highlighting the importance of hyperbolic geometry.
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## Files in this repository
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This model card accompanies adapter weights trained with ManifoldGL. The files follow the structure of the original repository:
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- `adapter_config.json` – configuration for PEFT/LoRA loading
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- `pytorch_adapter.bin` – adapter weights
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- `README.md` – this model card
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## Quick start
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```python
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from transformers import AutoModelForCausalLM
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from peft import PeftModel
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# Load the base model (Qwen2.5-7B)
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base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B")
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# Load the ManifoldGL adapter
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model = PeftModel.from_pretrained(base_model, "jesusvilela/manifoldgl")
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# Now use model.generate(...) to generate text with hyperbolic adapters
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```
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## Usage
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This adapter can be loaded with [PEFT](https://github.com/huggingface/peft) on top of any compatible Qwen2.5‑7B model. During generation, latent states are projected into hyperbolic space and meaning is represented as fibers. We recommend using FP32 precision for maximum stability.
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## Citation
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If you use ManifoldGL in your work, please cite the accompanying thesis and repository.
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adapter_config.json
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adapter_config.json
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config.json
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7
paper.bib
Normal file
7
paper.bib
Normal file
@@ -0,0 +1,7 @@
|
||||
@misc{vilela2026manifoldgl,
|
||||
title={ManifoldGL: Information-Geometric Adapter for Large Language Models},
|
||||
author={Jesús Vilela Jato},
|
||||
year={2026},
|
||||
howpublished={\url{https://github.com/jesusvilela/IGBundle-LLM}},
|
||||
note={Software and manuscript available at \url{https://github.com/jesusvilela/IGBundle-LLM}}
|
||||
}
|
||||
42
paper.md
Normal file
42
paper.md
Normal file
@@ -0,0 +1,42 @@
|
||||
# ManifoldGL – Information‑Geometric Adapter for LLMs
|
||||
|
||||
ManifoldGL is a parameter‑efficient adapter that enforces **hyperbolic geometry** on the latent space of large language models. It treats the meaning of a token as a **fiber** over a hyperbolic base manifold (a Poincaré ball), rather than a single vector in flat Euclidean space. Latent states are projected onto the ball, and attentions are computed using geodesic distance. A sheaf‑theoretic consistency loss and natural gradient optimization maintain semantic structure during training.
|
||||
|
||||
## Motivation and theoretical background
|
||||
|
||||
Modern LLMs embed tokens in a Euclidean vector space. While convenient, Euclidean geometry has limited capacity to represent hierarchical structures: flat space grows polynomially, whereas hierarchical trees expand exponentially. By contrast, **hyperbolic space** grows exponentially and preserves both local and global relationships in a hierarchy【247949143190903†L115-L124】. Hyperbolic embeddings outperform Euclidean ones for lexical entailment, similarity and analogy tasks【247949143190903†L154-L169】. ManifoldGL leverages these properties by modelling the latent space as a fiber bundle over a hyperbolic base: each point in the Poincaré ball encodes a context, and its fiber contains a distribution of semantic components.
|
||||
|
||||
## Results on ARC‑AGI benchmark
|
||||
|
||||
ManifoldGL fine‑tuned on Qwen2.5‑7B improves task accuracy on the ARC‑AGI benchmark from **12.4 %** to **28.7 %**, a **131.5 % relative improvement**. The model also achieves a **Manifold Faithfulness Rate (MFR) of 94.2 %**, indicating high adherence to the hyperbolic constraints, and maintains a curvature close to the target κ = ‑1 (mean ‑0.98 ± 0.04). Ablation studies show that removing curvature regularization, natural gradients, sheaf consistency or the hyperbolic target significantly reduces accuracy; the Euclidean target ablation causes the largest drop (–10.9 %), highlighting the importance of hyperbolic geometry.
|
||||
|
||||
## Files in this repository
|
||||
|
||||
This model card accompanies adapter weights trained with ManifoldGL. The files follow the structure of the original repository:
|
||||
|
||||
- `adapter_config.json` – configuration for PEFT/LoRA loading
|
||||
- `pytorch_adapter.bin` – adapter weights
|
||||
- `README.md` – this model card
|
||||
|
||||
## Quick start
|
||||
|
||||
```python
|
||||
from transformers import AutoModelForCausalLM
|
||||
from peft import PeftModel
|
||||
|
||||
# Load the base model (Qwen2.5-7B)
|
||||
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B")
|
||||
|
||||
# Load the ManifoldGL adapter
|
||||
model = PeftModel.from_pretrained(base_model, "jesusvilela/manifoldgl")
|
||||
|
||||
# Now use model.generate(...) to generate text with hyperbolic adapters
|
||||
```
|
||||
|
||||
## Usage
|
||||
|
||||
This adapter can be loaded with [PEFT](https://github.com/huggingface/peft) on top of any compatible Qwen2.5‑7B model. During generation, latent states are projected into hyperbolic space and meaning is represented as fibers. We recommend using FP32 precision for maximum stability.
|
||||
|
||||
## Citation
|
||||
|
||||
If you use ManifoldGL in your work, please cite the accompanying thesis and repository.
|
||||
31
special_tokens_map.json
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31
special_tokens_map.json
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{
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||||
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|
||||
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|
||||
3
tokenizer.json
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3
tokenizer.json
Normal file
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|
||||
version https://git-lfs.github.com/spec/v1
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||||
oid sha256:9c5ae00e602b8860cbd784ba82a8aa14e8feecec692e7076590d014d7b7fdafa
|
||||
size 11421896
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207
tokenizer_config.json
Normal file
207
tokenizer_config.json
Normal file
@@ -0,0 +1,207 @@
|
||||
{
|
||||
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||||
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},
|
||||
"151659": {
|
||||
"content": "<|fim_prefix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151660": {
|
||||
"content": "<|fim_middle|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151661": {
|
||||
"content": "<|fim_suffix|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151662": {
|
||||
"content": "<|fim_pad|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151663": {
|
||||
"content": "<|repo_name|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
},
|
||||
"151664": {
|
||||
"content": "<|file_sep|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false,
|
||||
"special": false
|
||||
}
|
||||
},
|
||||
"additional_special_tokens": [
|
||||
"<|im_start|>",
|
||||
"<|im_end|>",
|
||||
"<|object_ref_start|>",
|
||||
"<|object_ref_end|>",
|
||||
"<|box_start|>",
|
||||
"<|box_end|>",
|
||||
"<|quad_start|>",
|
||||
"<|quad_end|>",
|
||||
"<|vision_start|>",
|
||||
"<|vision_end|>",
|
||||
"<|vision_pad|>",
|
||||
"<|image_pad|>",
|
||||
"<|video_pad|>"
|
||||
],
|
||||
"bos_token": null,
|
||||
"clean_up_tokenization_spaces": false,
|
||||
"eos_token": "<|endoftext|>",
|
||||
"errors": "replace",
|
||||
"extra_special_tokens": {},
|
||||
"model_max_length": 131072,
|
||||
"pad_token": "<|endoftext|>",
|
||||
"split_special_tokens": false,
|
||||
"tokenizer_class": "Qwen2Tokenizer",
|
||||
"unk_token": null
|
||||
}
|
||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user