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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+TheBeagle.png filter=lfs diff=lfs merge=lfs -text

README.md

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+---
+license: cc-by-nc-nd-4.0
+tags:
+- generated_from_trainer
+model-index:
+- name: UNA-TheBeagle-7b-v1
+  results: []
+datasets:
+- jondurbin/bagel-v0.3
+library_name: transformers
+---
+     -- In the Love Memory of my "LoLa" --
+     
+# UNA-TheBeagle-7b-v1
+TheBeagle, a model of 7B parameters trained on The Bagel dataset. DPO & UNA applied over a set of curated DPO Pairs.
+
+- Scored #1 on the HF Leaderboard, dramatic scores!!! 73 ARC, and very well balanced!
+
+The dataset was generated using the original bagel code, including the decontamination step.
+
+As base model, we used the latest Intel's neural-chat model.
+
+It performs very good in many tasks, but its always better that you play with it by yourself.
+
+![TheBeagle](https://huggingface.co/fblgit/UNA-TheBeagle-7b-v1/resolve/main/TheBeagle.png)
+
+## Evaluations
+
+Ran with VLLM so expect them to dont be exactly as the one's shown in the board, but not too far :)
+
+```
+vllm (pretrained=fblgit/UNA-TheBeagle-7b-v1,dtype=auto,tensor_parallel_size=1,gpu_memory_utilization=0.8,data_parallel_size=8,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 32
+|    Tasks     |Version|  Filter  |n-shot|  Metric   |Value |   |Stderr|
+|--------------|-------|----------|-----:|-----------|-----:|---|-----:|
+|arc_challenge |Yaml   |none      |    25|acc        |0.7090|±  |0.0133|
+|              |       |none      |    25|acc_norm   |0.7329|±  |0.0129|
+|gsm8k         |Yaml   |get-answer|     5|exact_match|0.7210|±  |0.0124|
+|hellaswag     |Yaml   |none      |    10|acc        |0.7202|±  |0.0045|
+|              |       |none      |    10|acc_norm   |0.8792|±  |0.0033|
+|truthfulqa_mc2|Yaml   |none      |     0|acc        |0.7062|±  |0.0151|
+|winogrande    |Yaml   |none      |     5|acc        |0.8366|±  |0.0104|
+```
+
+## UNA Details
+
+For this release, we only applied UNA thru the perceptrons. It was done at a 3.5e-7 speed, and the training loop code is also the original one of the bagel and transformers-4.35.2-UNA
+
+## Prompt
+
+Im not entirely sure of it, as we used the vanilla version of the bagel training code. But a good model should be able to generalize with different prompt formats, so feel free to give it a shot.
+
+## Citations
+
+Remember if you use UNA's models, cite it in your model card.
+
+## Limitations
+Not for commercial use, and only for academic & research purposes.

config.json

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+{
+  "architectures": [
+    "MistralForCausalLM"
+  ],
+  "attention_dropout": 0.0,
+  "bos_token_id": 1,
+  "eos_token_id": 2,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 32768,
+  "model_type": "mistral",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-05,
+  "rope_theta": 10000.0,
+  "sliding_window": 4096,
+  "tie_word_embeddings": false,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.36.2",
+  "use_cache": false,
+  "vocab_size": 32000
+}

model.safetensors.index.json

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