Create README.md

README.md

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+---
+language:
+- en
+license: mit
+library_name: transformers
+base_model:
+- Qwen/Qwen2.5-32B-Instruct
+datasets:
+- Magpie-Align/Magpie-Pro-300K-Filtered
+model-index:
+- name: TheBeagle-v2beta-32B-MGS
+  results:
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: IFEval (0-Shot)
+      type: HuggingFaceH4/ifeval
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: inst_level_strict_acc and prompt_level_strict_acc
+      value: 45.03
+      name: strict accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: BBH (3-Shot)
+      type: BBH
+      args:
+        num_few_shot: 3
+    metrics:
+    - type: acc_norm
+      value: 58.07
+      name: normalized accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MATH Lvl 5 (4-Shot)
+      type: hendrycks/competition_math
+      args:
+        num_few_shot: 4
+    metrics:
+    - type: exact_match
+      value: 39.43
+      name: exact match
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: GPQA (0-shot)
+      type: Idavidrein/gpqa
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 20.13
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MuSR (0-shot)
+      type: TAUR-Lab/MuSR
+      args:
+        num_few_shot: 0
+    metrics:
+    - type: acc_norm
+      value: 24.5
+      name: acc_norm
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
+      name: Open LLM Leaderboard
+  - task:
+      type: text-generation
+      name: Text Generation
+    dataset:
+      name: MMLU-PRO (5-shot)
+      type: TIGER-Lab/MMLU-Pro
+      config: main
+      split: test
+      args:
+        num_few_shot: 5
+    metrics:
+    - type: acc
+      value: 54.57
+      name: accuracy
+    source:
+      url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=fblgit/TheBeagle-v2beta-32B-MGS
+      name: Open LLM Leaderboard
+---
+
+# TheBeagle-v2beta-32B-MGS
+This model is an experimental version of our latest innovation: `MGS`. Its up to you to figure out what does it means, but its very explicit.
+We didn't applied our known `UNA` algorithm to the forward pass, but they are entirely compatible and operates in different parts of the neural network and in different ways, tho they both can be seen as a regularization technique.
+
+
+## MGS
+MGS stands for... Many-Geeks-Searching... and thats it. Hint: `1+1 is 2, and 1+1 is not 3`
+
+We still believe on 1-Epoch should be enough, so we just did 1 Epoch only.
+
+## Dataset
+Used here the first decent (corpora & size) dataset on the hub: `Magpie-Align/Magpie-Pro-300K-Filtered`
+Kudos to the Magpie team to contribute with some decent stuff that I personally think is very good to ablate.
+
+It achieves the following results on the evaluation set:
+- Loss: 0.5378 (1 Epoch), outperforming the baseline model.
+## Quants
+
+[All versions available](https://huggingface.co/fblgit/TheBeagle-v2beta-MGS-GGUF/tree/main)
+
+... being uploaded ...
+
+  
+## Licensing terms:
+
+*Quants versions of this model must ONLY be distributed from the author repository, submit a commit/PR and be credited for it*
+
+## Training
+[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 8e-05
+- train_batch_size: 2
+- eval_batch_size: 2
+- seed: 42
+- distributed_type: multi-GPU
+- num_devices: 8
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 64
+- total_eval_batch_size: 16
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: cosine
+- lr_scheduler_warmup_steps: 25
+- num_epochs: 1
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss |
+|:-------------:|:------:|:----:|:---------------:|
+| 9.8642        | 0.0012 | 1    | 0.7195          |
+| 2.077         | 0.0507 | 42   | 0.6161          |
+| 1.0325        | 0.1014 | 84   | 0.6093          |
+| 0.8945        | 0.1520 | 126  | 0.5962          |
+| 0.8532        | 0.2027 | 168  | 0.5869          |
+| 0.8185        | 0.2534 | 210  | 0.5805          |
+| 0.81          | 0.3041 | 252  | 0.5719          |
+| 0.7901        | 0.3548 | 294  | 0.5663          |
+| 0.7766        | 0.4054 | 336  | 0.5618          |
+| 0.7687        | 0.4561 | 378  | 0.5590          |
+| 0.7443        | 0.5068 | 420  | 0.5564          |
+| 0.7494        | 0.5575 | 462  | 0.5525          |
+| 0.7787        | 0.6081 | 504  | 0.5485          |
+| 0.7381        | 0.6588 | 546  | 0.5466          |
+| 0.7359        | 0.7095 | 588  | 0.5444          |
+| 0.7447        | 0.7602 | 630  | 0.5435          |
+| 0.7378        | 0.8109 | 672  | 0.5415          |
+| 0.7302        | 0.8615 | 714  | 0.5398          |
+| 0.7476        | 0.9122 | 756  | 0.5391          |
+| 0.715         | 0.9629 | 798  | 0.5378          |
+
+
+# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
+Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__TheBeagle-v2beta-32B-MGS)
+
+|      Metric       |Value|
+|-------------------|----:|
+|Avg.               |40.29|
+|IFEval (0-Shot)    |45.03|
+|BBH (3-Shot)       |58.07|
+|MATH Lvl 5 (4-Shot)|39.43|
+|GPQA (0-shot)      |20.13|
+|MuSR (0-shot)      |24.50|
+|MMLU-PRO (5-shot)  |54.57|
+
+## Thanks
+- Qwen Team for their outstanding model
+- MagPie Team for contributing plenty of datasets
+- Cybertron Cloud Compute