Model save

README.md

@@ -1,306 +1,90 @@
 ---
-language: en
-license: apache-2.0
+library_name: transformers
 tags:
-- causal-lm
-- research
-- fp8
-- attention
-- normalization
-- neollm
-- pace
-datasets:
-- HuggingFaceFW/fineweb-edu
+- generated_from_trainer
+model-index:
+- name: NeoLLM
+  results: []
 ---
 
-# NeoLLM
-
-NeoLLM is a **135 M parameter** decoder-only language model trained from scratch on
-[FineWeb-Edu](https://huggingface.co/datasets/HuggingFaceFW/fineweb-edu) in **FP8**
-precision, completing training in approximately **6 hours** on a single NVIDIA RTX 5090.
-It integrates a collection of recently published attention and normalization techniques
-into a single architecture, with the goal of studying how they interact during
-pretraining. The model is actively being developed and the current checkpoint represents
-an intermediate training state.
-
-> **Author / contact:** [@Kyokopom](https://x.com/Kyokopom) on X
-> **Repository:** [KitsuVp/NeoLLM](https://huggingface.co/KitsuVp/NeoLLM)
-
----
-
-## Architecture
-
-NeoLLM is a decoder-only transformer with the following configuration:
-
-| Parameter | Value |
-|---|---|
-| Hidden size | 512 |
-| Layers | 12 |
-| Attention heads | 8 |
-| KV heads (GQA) | 4 |
-| Head dim | 64 |
-| Intermediate size | 1536 |
-| Vocabulary | Qwen3 tokenizer (64,402 tokens) |
-| Context length | 512 tokens |
-
-### Parameter breakdown
-
-| Parameter bucket | Count |
-|---|---|
-| **Total parameters** | 116.22M (116,216,184) |
-| **Embedding parameters** (tied) | 32.97M (32,973,824) |
-| **Non-embedding parameters** | 83.24M (83,242,360) |
-| **Effective trainable parameters** | 116.22M (116,216,184) |
-
-> Weight tying is **enabled**: the input embedding matrix and the language-model head
-> share the same parameters, so the effective trainable budget is
-> `total − embed = 83.24M`.
-
-### Integrated techniques
-
-NeoLLM combines architecture modules, optional auxiliary objectives, and
-training-time optimizer/stability components from the following papers.
-
-**Embedding and token representation**
-
-- **Learnable Multipliers** ([arXiv:2601.04890](https://arxiv.org/abs/2601.04890)) — Adds
-  per-row and per-column learnable scalar parameters to selected matrix layers and, when
-  enabled, embeddings.
-- **Leviathan** ([arXiv:2601.22040](https://arxiv.org/abs/2601.22040)) — Optional
-  continuous token embedding generator that can replace the discrete input lookup table.
-- **KHRONOS** ([arXiv:2505.13315](https://arxiv.org/abs/2505.13315)) — Kernel/basis
-  reference used by the Leviathan continuous token generator implementation.
-- **JTok / JTok-M** ([arXiv:2602.00800](https://arxiv.org/abs/2602.00800)) — Optional
-  token-indexed self-modulation surfaces over Leviathan coordinates.
-- **Spelling Bee Embeddings** ([arXiv:2601.18030](https://arxiv.org/abs/2601.18030)) —
-  Augments token embeddings with character-level spelling information.
-- **Token Embedding Manifold analysis** ([arXiv:2504.01002](https://arxiv.org/abs/2504.01002)) —
-  Reference motivation for treating token embeddings as structured objects rather than
-  unconstrained lookup rows.
-
-**Attention, positions, and output projection**
-
-- **FAN** ([arXiv:2502.21309](https://arxiv.org/abs/2502.21309)) — Fourier Analysis Networks.
-  A portion of the projection channels are dedicated to periodic cosine/sine features.
-- **MEA** ([arXiv:2601.19611](https://arxiv.org/abs/2601.19611)) — Explicit Multi-head
-  Attention. Adds small learnable interaction matrices between attention heads for K and V.
-- **LUCID** ([arXiv:2602.10410](https://arxiv.org/abs/2602.10410)) — Applies a learned
-  lower-triangular preconditioner to V before attention, decorrelating value representations
-  across positions.
-- **Affine-Scaled Attention** ([arXiv:2602.23057](https://arxiv.org/abs/2602.23057)) — Adds
-  two learnable per-head scalars (α and β) to the softmax weights:
-  `[α·softmax(QKᵀ) + β]·V`.
-- **XSA** ([arXiv:2603.09078](https://arxiv.org/abs/2603.09078)) — Exclusive Self Attention.
-  After computing attention, removes the component of the output aligned with the token's
-  own value vector.
-- **Directional Routing** ([arXiv:2603.14923](https://arxiv.org/abs/2603.14923)) — Each head
-  learns K=4 directions in the output space; a learned router suppresses the attention output
-  along each direction per input.
-- **Gated Attention** ([arXiv:2505.06708](https://arxiv.org/abs/2505.06708)) — A sigmoid gate
-  is applied to the attention output before the output projection, introducing non-linearity
-  and preventing attention sinks.
-- **Momentum Attention** ([arXiv:2411.03884](https://arxiv.org/abs/2411.03884)) — Modifies Q
-  and K by subtracting a fraction of the previous position's Q and K values (causal
-  first-difference).
-- **Interleaved Head Attention / IHA** ([arXiv:2602.21371](https://arxiv.org/abs/2602.21371)) —
-  Builds pseudo-heads from learned cross-head mixtures to create multiple attention patterns
-  per original head.
-- **REPO** ([arXiv:2512.14391](https://arxiv.org/abs/2512.14391)) — Context re-positioning
-  module that learns contextual position coordinates above a configurable start layer.
-- **GRAPE** ([arXiv:2512.07805](https://arxiv.org/abs/2512.07805)) — Group representational
-  position encoding used by the REPO-GRAPE positional path.
-- **GOAT priors** ([arXiv:2601.15380](https://arxiv.org/abs/2601.15380)) — Optional
-  factorized attention log-prior channels inspired by trainable attention priors.
-- **Hadamard output projection** ([arXiv:2603.08343](https://arxiv.org/abs/2603.08343)) —
-  Replaces dense attention output projection with a structured Hadamard transform plus
-  lightweight scaling.
-
-**Normalization, residual flow, and MLP**
-
-- **SeeDNorm** ([arXiv:2510.22777](https://arxiv.org/abs/2510.22777)) — Applied to Q and K
-  projections. Dynamically rescales normalization from the input's own statistics.
-- **LayerNorm Scaling / LNS** ([arXiv:2502.05795](https://arxiv.org/abs/2502.05795)) — Each
-  layer's output is scaled by 1/√ℓ where ℓ is the layer index.
-- **GPAS** ([arXiv:2506.22049](https://arxiv.org/abs/2506.22049)) — Gradient-Preserving
-  Activation Scaling for residual junctions.
-- **PolyNorm** ([arXiv:2602.04902](https://arxiv.org/abs/2602.04902)) — Replaces the standard
-  MLP activation with normalized linear, quadratic, and cubic branches.
-- **SimpleGPT** ([arXiv:2602.01212](https://arxiv.org/abs/2602.01212)) — Second-order
-  geometry-inspired normalization strategy applied inside MLP projections.
-- **StackMemory / STACKTRANS** ([NeurIPS 2025](https://openreview.net/forum?id=2bbDg587uh)) —
-  Optional differentiable hidden-state stack between decoder layers.
-- **Attention Residuals / AttnRes** ([arXiv:2603.15031](https://arxiv.org/abs/2603.15031)) —
-  Optional learned depth-wise aggregation over previous layer outputs or block summaries.
-- **LAUREL** ([arXiv:2411.07501](https://arxiv.org/abs/2411.07501)) — Optional learned
-  augmented residual layer with residual-weight and low-rank variants.
-
-**Training objectives and training-time regularizers**
-
-- **TWEO** ([arXiv:2511.23225](https://arxiv.org/abs/2511.23225)) — Optional
-  Transformers Without Extreme Outliers activation regularizer for FP8/low-bit-friendly
-  training.
-- **NITP** ([arXiv:2605.24956](https://arxiv.org/abs/2605.24956)) — Optional Next Implicit
-  Token Prediction auxiliary objective using shallow-layer implicit token targets and a
-  cosine loss.
-- **NextLat** ([arXiv:2511.05963](https://arxiv.org/abs/2511.05963)) — Optional next-latent
-  prediction objective using latent dynamics, Smooth L1 supervision, and frozen-head KL.
-
-**Optimizer and training stability**
-
-- **Conda** ([arXiv:2509.24218](https://arxiv.org/abs/2509.24218)) —
-  Column-Normalized Adam optimizer path used by the training script.
-- **Cautious Weight Decay** ([arXiv:2510.12402](https://arxiv.org/abs/2510.12402)) —
-  Sign-selective weight decay variant used by the custom optimizer logic.
-- **Correction of Decoupled Weight Decay** ([arXiv:2512.08217](https://arxiv.org/abs/2512.08217)) —
-  Adapts decoupled weight decay during learning-rate decay.
-- **AdamHD** ([arXiv:2511.14721](https://arxiv.org/abs/2511.14721)) —
-  Decoupled Huber decay regularization reference used by the optimizer.
-- **GradientStabilizer** ([arXiv:2502.17055](https://arxiv.org/abs/2502.17055)) —
-  Optional threshold-free gradient magnitude stabilizer.
-- **PACE** ([arXiv:2606.25086](https://arxiv.org/abs/2606.25086)) —
-  Optional iterate-average controller that trains for the EMA model returned at evaluation
-  and final serialization. The Conda-basis adaptation and its difference from AdamW are
-  documented below.
-
----
-
-### PACE integration and AdamW-reference differences
-
-PACE follows Au and Block's returned-model objective: the live weights are pulled toward a
-power-law EMA with a clipped per-coordinate gain, and evaluation/final serialization use that
-EMA estimator.
-
-- **Reference AdamW rule:** the gain uses AdamW's original-coordinate diagonal
-  second moment, `eta * c * (1+t)^(-kappa) / (sqrt(v_hat) + eps)`.
-- **NeoLLM Conda rule (`mode=conda`):** for projected 2-D tensors, both the EMA
-  displacement and `v_hat` are represented in Conda's cached SVD basis. The control is
-  projected back after applying the diagonal gain. This is a deliberate change from AdamW
-  required to avoid mixing incompatible coordinate systems.
-- **Optional exact AdamW pullback geometry (`mode=adamw`):** an additional
-  original-coordinate second moment is maintained for projected matrices. The live optimizer
-  step remains Conda.
-- **Conda scale:** in `mode=conda`, Conda's matrix-update scale multiplies the unsaturated
-  gain automatically because it is part of the effective Conda preconditioner. AdamW has no
-  corresponding scale.
-- **Fixed algorithm internals:** the EMA is stored in FP32, the gain is clipped at `1`, and PACE
-  reuses each Conda group’s numerical epsilon. These are not exposed as independent switches.
-- **Minimal modes:** `use_pace=False` is plain Conda; `use_pace=True, c=0` is Conda+EMA;
-  `use_pace=True, c>0` is complete PACE.
-- **Ordering:** PACE runs only after Conda, CWD/CHD, and weight-decay correction have fully
-  updated the live weights.
-- **Disabled guarantee:** with `use_pace=False`, no PACE state is allocated and no existing
-  Conda arithmetic or parameter update is changed.
-- **Checkpoint policy:** resumable internal checkpoints retain live weights and complete optimizer
-  state, while evaluation and the final returned/Hub model always use the EMA when PACE is active.
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
 
-Current run: **disabled; no EMA state, auxiliary moment, or pullback is allocated**.
-
----
-
-## Training
-
-| Setting | Value |
-|---|---|
-| Dataset | FineWeb-Edu (sample-10BT) |
-| Tokens seen | ~0.51B (15,625 steps × batch 64 × length 512) |
-| Precision | FP8 native (E4M3 weights/activations, E5M2 gradients) + BF16 fallback |
-| Optimizer | Conda (PACE disabled) |
-| PACE | disabled; no EMA state, auxiliary moment, or pullback is allocated |
-| Learning rate | 6e-04 with linear warmup (10 % of steps) |
-| Weight decay | 0.1 |
-| Training time | ~1h 23m |
-| Hardware | NVIDIA RTX 5090 (single GPU) |
-
-### Training curve
-
-| Step | Train Loss | Val Loss |
-|---|---|---|
-| 5,000 | 4.167 | 4.168 |
-| 10,000 | 3.874 | 3.844 |
-| 15,000 | 3.699 | 3.660 |
-| 15,625 | — | 3.645 |
-
----
-
-## Limitations
-
-- **Token budget** — ~1.5 B tokens seen; below estimated optimum. Knowledge-intensive tasks
-  will improve with more training.
-- **Gradient spike at step 40k** — Reorganized the attention pattern in layer 9 that
-  previously captured long-range token correlations. A checkpoint from ~step 38k is expected
-  to have better aggregate benchmark scores.
-- **PolyNorm exclusivity** — The quadratic branch has become partially redundant with the
-  linear branch. Will be corrected in the next training run.
-- **Base model only** — Not instruction-tuned or aligned; purely a next-token-prediction
-  base model.
-
----
-
-## References
-
-All papers whose techniques are integrated into NeoLLM's architecture,
-training objective, or training stack:
-
-| Area | Technique | Paper title | Reference |
-|---|---|---|---|
-| Embeddings | Learnable Multipliers | Freeing the Scale of Language Model Matrix Layers | [arXiv:2601.04890](https://arxiv.org/abs/2601.04890) |
-| Embeddings | Leviathan | A Separable Architecture for Continuous Token Representation in Language Models | [arXiv:2601.22040](https://arxiv.org/abs/2601.22040) |
-| Embeddings | KHRONOS | KHRONOS: a Kernel-Based Neural Architecture for Rapid, Resource-Efficient Scientific Computation | [arXiv:2505.13315](https://arxiv.org/abs/2505.13315) |
-| Embeddings | JTok / JTok-M | JTok: On Token Embedding as Another Axis of Scaling Law via Joint Token Self-Modulation | [arXiv:2602.00800](https://arxiv.org/abs/2602.00800) |
-| Embeddings | Spelling Bee | Spelling Bee Embeddings for Language Modeling | [arXiv:2601.18030](https://arxiv.org/abs/2601.18030) |
-| Embeddings | Token embedding analysis | Token Embeddings Violate the Manifold Hypothesis | [arXiv:2504.01002](https://arxiv.org/abs/2504.01002) |
-| Attention / positions | FAN | Fourier Analysis Networks | [arXiv:2502.21309](https://arxiv.org/abs/2502.21309) |
-| Attention / positions | MEA | Explicit Multi-head Attention for Inter-head Interaction in Large Language Models | [arXiv:2601.19611](https://arxiv.org/abs/2601.19611) |
-| Attention / positions | LUCID | Attention with Preconditioned Representations | [arXiv:2602.10410](https://arxiv.org/abs/2602.10410) |
-| Attention / positions | Affine-Scaled Attention | Affine-Scaled Attention: Towards Flexible and Stable Transformer Attention | [arXiv:2602.23057](https://arxiv.org/abs/2602.23057) |
-| Attention / positions | XSA | Exclusive Self Attention | [arXiv:2603.09078](https://arxiv.org/abs/2603.09078) |
-| Attention / positions | Directional Routing | Directional Routing in Transformers | [arXiv:2603.14923](https://arxiv.org/abs/2603.14923) |
-| Attention / positions | Gated Attention | Gated Attention for Large Language Models: Non-linearity, Sparsity, and Attention-Sink-Free | [arXiv:2505.06708](https://arxiv.org/abs/2505.06708) |
-| Attention / positions | Momentum Attention | Momentum Attention | [arXiv:2411.03884](https://arxiv.org/abs/2411.03884) |
-| Attention / positions | IHA | Interleaved Head Attention | [arXiv:2602.21371](https://arxiv.org/abs/2602.21371) |
-| Attention / positions | REPO | Language Models with Context Re-Positioning | [arXiv:2512.14391](https://arxiv.org/abs/2512.14391) |
-| Attention / positions | GRAPE | Group Representational Position Encoding | [arXiv:2512.07805](https://arxiv.org/abs/2512.07805) |
-| Attention / positions | GOAT priors | You Need Better Attention Priors | [arXiv:2601.15380](https://arxiv.org/abs/2601.15380) |
-| Attention / positions | Hadamard o_proj | Rethinking Attention Output Projection: Structured Hadamard Transforms for Efficient Transformers | [arXiv:2603.08343](https://arxiv.org/abs/2603.08343) |
-| Residual / normalization | SeeDNorm | Self-Rescaled Dynamic Normalization | [arXiv:2510.22777](https://arxiv.org/abs/2510.22777) |
-| Residual / normalization | LNS | The Curse of Depth in LLMs | [arXiv:2502.05795](https://arxiv.org/abs/2502.05795) |
-| Residual / normalization | GPAS | Gradient-Preserving Activation Scaling | [arXiv:2506.22049](https://arxiv.org/abs/2506.22049) |
-| Residual / normalization | PolyNorm | PolyNorm / PolyCom | [arXiv:2602.04902](https://arxiv.org/abs/2602.04902) |
-| Residual / normalization | SimpleGPT | SimpleGPT | [arXiv:2602.01212](https://arxiv.org/abs/2602.01212) |
-| Residual / normalization | StackMemory / STACKTRANS | Recursive Transformer: Boosting Reasoning Ability with State Stack | [NeurIPS 2025](https://openreview.net/forum?id=2bbDg587uh) |
-| Residual / normalization | Attention Residuals | Attention Residuals | [arXiv:2603.15031](https://arxiv.org/abs/2603.15031) |
-| Residual / normalization | LAUREL | LAUREL: Learned Augmented Residual Layer | [arXiv:2411.07501](https://arxiv.org/abs/2411.07501) |
-| Objectives | TWEO | Transformers Without Extreme Outliers Enables FP8 Training And Quantization For Dummies | [arXiv:2511.23225](https://arxiv.org/abs/2511.23225) |
-| Objectives | NITP | Next Implicit Token Prediction for LLM Pre-training | [arXiv:2605.24956](https://arxiv.org/abs/2605.24956) |
-| Objectives | NextLat | Next-Latent Prediction Transformers Learn Compact World Models | [arXiv:2511.05963](https://arxiv.org/abs/2511.05963) |
-| Optimizer / training | Conda | Column-Normalized Adam for Training Large Language Models Faster | [arXiv:2509.24218](https://arxiv.org/abs/2509.24218) |
-| Optimizer / training | CWD | Cautious Weight Decay | [arXiv:2510.12402](https://arxiv.org/abs/2510.12402) |
-| Optimizer / training | WD correction | Correction of Decoupled Weight Decay | [arXiv:2512.08217](https://arxiv.org/abs/2512.08217) |
-| Optimizer / training | AdamHD | AdamHD: Decoupled Huber Decay Regularization for Language Model Pre-Training | [arXiv:2511.14721](https://arxiv.org/abs/2511.14721) |
-| Optimizer / training | GradientStabilizer | GradientStabilizer | [arXiv:2502.17055](https://arxiv.org/abs/2502.17055) |
-| Optimizer / training | PACE | Training for the Model You Return: Improving Optimization for Iterate-Averaged Language Models | [arXiv:2606.25086](https://arxiv.org/abs/2606.25086) |
-
----
-
-## Citation
-
-```bibtex
-@misc{neollm2026,
-  title  = {NeoLLM: A Research Language Model Integrating Recent Attention and Normalization Techniques},
-  author = {KitsuVp},
-  year   = {2026},
-  url    = {https://huggingface.co/KitsuVp/NeoLLM}
-}
-```
-
----
-
-## Author
-
-[@Kyokopom](https://x.com/Kyokopom) on X
-
----
-
-## License
+# NeoLLM
 
-Apache 2.0
+This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
+It achieves the following results on the evaluation set:
+- Loss: 3.6218
+- Ntp Loss: 3.3629
+- Tweo Loss: 0.0027
+- Nitp Loss: 0.1235
+- Total Model Loss: 3.4865
+- Optimizer Step: 15625.0
+- Optimizer Metrics Due: 0.0
+- Pace Step: 0.0
+- Pace Update Due: 0.0
+- Pace Previous Iterate Active: 1.0
+- Conda Adaptive Scale Active: 1.0
+- Conda Scale Smoothed Metric Exact: 1.0
+- Conda Scale Beta: 0.0080
+- Conda Scale Energy Attenuation: 0.0053
+- Conda Scale Candidate Ratio Mean: 1.0006
+- Conda Scale Smoothed Ratio Mean: 0.9927
+- Conda Scale Applied Ratio Mean: 0.9927
+- Conda Scale Gain Mean: 0.1366
+- Conda Scale Gain Clip Fraction: 0.0
+- Conda Scale Ratio Min: 0.9214
+- Conda Scale Ratio Max: 1.0000
+- Conda Scale Low Limit Fraction: 0.0
+- Conda Scale High Limit Fraction: 0.0
+- Conda Scale Amplified Fraction: 0.0
+- Conda Scale Attenuated Fraction: 1.0
+- Conda Scale Energy Weighted Amplification: 0.0
+- Conda Scale Energy Weighted Attenuation Applied: 0.0073
+- Conda Scale Risk Ema Mean: 0.0111
+- Conda Scale Risk Drop Mean: 0.0058
+- Conda Scale Amplification Bonus Mean: 0.0058
+- Conda Scale Monitored Matrices: 146.0
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0006
+- train_batch_size: 64
+- eval_batch_size: 64
+- seed: 42
+- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
+- lr_scheduler_type: linear
+- lr_scheduler_warmup_steps: 0.1
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss | Loss   | Model Loss | Metrics Due | Update Due | Previous Iterate Active | Adaptive Scale Active | Scale Smoothed Metric Exact | Scale Beta | Scale Energy Attenuation | Scale Candidate Ratio Mean | Scale Smoothed Ratio Mean | Scale Applied Ratio Mean | Scale Gain Mean | Scale Gain Clip Fraction | Scale Ratio Min | Scale Ratio Max | Scale Low Limit Fraction | Scale High Limit Fraction | Scale Amplified Fraction | Scale Attenuated Fraction | Scale Energy Weighted Amplification | Scale Energy Weighted Attenuation Applied | Scale Risk Ema Mean | Scale Risk Drop Mean | Scale Amplification Bonus Mean | Scale Monitored Matrices |
+|:-------------:|:-----:|:----:|:---------------:|:------:|:----------:|:-----------:|:----------:|:-----------------------:|:---------------------:|:---------------------------:|:----------:|:------------------------:|:--------------------------:|:-------------------------:|:------------------------:|:---------------:|:------------------------:|:---------------:|:---------------:|:------------------------:|:-------------------------:|:------------------------:|:-------------------------:|:-----------------------------------:|:-----------------------------------------:|:-------------------:|:--------------------:|:------------------------------:|:------------------------:|
+| 4.2787        | 0.32  | 0.0  | 4.1788          | 0.1946 | 4.0601     | 1.0         | 0.0        | 1.0                     | 1.0                   | 1.0                         | 0.0141     | 0.1788                   | 0.8214                     | 0.8242                    | 0.8242                   | 0.3658          | 0.2413                   | 0.0000          | 0.9996          | 0.0274                   | 0.0                       | 0.0                      | 1.0                       | 0.0                                 | 0.1758                                    | 0.1770              | 0.0002               | 0.0002                         | 146.0                    |
+| 3.8661        | 0.64  | 0.0  | 3.8389          | 0.1189 | 3.7143     | 1.0         | 0.0        | 1.0                     | 1.0                   | 1.0                         | 0.0100     | 0.0503                   | 0.9538                     | 0.9471                    | 0.9471                   | 0.2364          | 0.1863                   | 0.4435          | 0.9999          | 0.0                      | 0.0                       | 0.0                      | 1.0                       | 0.0                                 | 0.0529                                    | 0.0545              | 0.0042               | 0.0042                         | 146.0                    |
+| 3.6763        | 0.96  | 0.0  | 3.6376          | 0.1266 | 3.5009     | 1.0         | 0.0        | 1.0                     | 1.0                   | 1.0                         | 0.0082     | 0.0255                   | 0.9746                     | 0.9858                    | 0.9858                   | 0.1701          | 0.1385                   | 0.8801          | 1.0000          | 0.0                      | 0.0                       | 0.0                      | 1.0                       | 0.0                                 | 0.0142                                    | 0.0155              | 0.0001               | 0.0001                         | 146.0                    |
+| 3.6592        | 1.0   | 0.0  | 3.6218          | 0.1235 | 3.4865     | 0.0         | 0.0        | 1.0                     | 1.0                   | 1.0                         | 0.0080     | 0.0053                   | 1.0006                     | 0.9927                    | 0.9927                   | 0.1366          | 0.0                      | 0.9214          | 1.0000          | 0.0                      | 0.0                       | 0.0                      | 1.0                       | 0.0                                 | 0.0073                                    | 0.0111              | 0.0058               | 0.0058                         | 146.0                    |
+
+
+### Framework versions
+
+- Transformers 5.13.0
+- Pytorch 2.13.0+cu132
+- Datasets 5.0.0
+- Tokenizers 0.22.2

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