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Model: Raffleraffle/manifoldgl
Source: Original Platform

.gitattributes

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README.md

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+---
+license: apache-2.0
+tags:
+- llm
+- hyperbolic
+- geometry
+- adapter
+- peft
+- research
+base_model:
+- Qwen/Qwen2.5-7B
+pipeline_tag: text-generation
+library_name: peft
+---
+
+# 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.

adapter_config.json

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+{
+    "task_type": "CAUSAL_LM",
+    "base_model_name_or_path": "Qwen/Qwen2.5-7B",
+    "inference_mode": true,
+    "target_modules": ["q_proj", "k_proj", "v_proj", "o_proj"],
+    "r": 8,
+    "lora_alpha": 32,
+    "lora_dropout": 0.0
+}

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chat_template.jinja

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+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- messages[0]['content'] }}
+    {%- else %}
+        {{- 'You are a helpful assistant.' }}
+    {%- endif %}
+    {{- "\n\n# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0]['role'] == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0]['content'] + '<|im_end|>\n' }}
+    {%- else %}
+        {{- '<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) or (message.role == "assistant" and not message.tool_calls) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role }}
+        {%- if message.content %}
+            {{- '\n' + message.content }}
+        {%- endif %}
+        {%- for tool_call in message.tool_calls %}
+            {%- if tool_call.function is defined %}
+                {%- set tool_call = tool_call.function %}
+            {%- endif %}
+            {{- '\n<tool_call>\n{"name": "' }}
+            {{- tool_call.name }}
+            {{- '", "arguments": ' }}
+            {{- tool_call.arguments | tojson }}
+            {{- '}\n</tool_call>' }}
+        {%- endfor %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if (loop.index0 == 0) or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- message.content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

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+{
+  "architectures": [
+    "Qwen2ForCausalLM"
+  ],
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+  "tie_word_embeddings": false,
+  "transformers_version": "4.57.3",
+  "use_cache": true,
+  "use_mrope": false,
+  "use_sliding_window": false,
+  "vocab_size": 152064
+}

generation_config.json

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paper.bib

@@ -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}}
+}

paper.md

@@ -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.

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