Super-squash branch 'main' using huggingface_hub

Co-authored-by: amazingvince <amazingvince@users.noreply.huggingface.co>

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README.md

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+---
+library_name: transformers
+language:
+- en
+license: apache-2.0
+base_model: BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L1
+tags:
+- gqa
+- t5
+- instruct
+datasets:
+- pszemraj/infinity-instruct-7m-T2T_en
+pipeline_tag: text2text-generation
+---
+
+
+# tFINE-680m-e32-d16-infinity_instruct-L2
+
+this is an instruction-tuned version of a pretrained t5 with GQA.
+
+## Model description
+
+This model is a fine-tuned version of [BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L1](https://huggingface.co/BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L1) on the pszemraj/infinity-instruct-7m-T2T_en dataset (config `deduped-L2`).
+
+It achieves the following results on the evaluation set:
+- Loss: 1.3139
+- Num Input Tokens Seen: 361724696
+
+## usage
+
+prerequisite: you need to have [t5-gqa fork of transformers installed](https://huggingface.co/BEE-spoke-data/tFINE-680m-e32-d16-gqa-flan#testing), and accelerate.
+
+```py
+from transformers import pipeline
+
+pipe = pipeline(
+    "text2text-generation",
+    model="BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2",
+    device_map="auto",
+)
+prompt = "Write me a python fn that demonstrates an advanced sorting algorithm"
+res = pipe(
+    prompt, max_new_tokens=384, num_beams=4, early_stopping=True, repetition_penalty=1.1
+)
+print(res[0]["generated_text"])
+```
+
+## Quick eval
+
+Quick eval for:	`BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2`
+
+
+hf (pretrained=BEE-spoke-data/tFINE-680m-e32-d16-infinity_instruct-L2,trust_remote_code=True,dtype=bfloat16,trust_remote_code=True), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
+|    Tasks    |Version|Filter|n-shot| Metric |   |Value |   |Stderr|
+|-------------|------:|------|-----:|--------|---|-----:|---|------|
+|boolq        |      2|none  |     0|acc     |↑  |0.6364|±  |0.0084|
+|openbookqa   |      1|none  |     0|acc     |↑  |0.1480|±  |0.0159|
+|             |       |none  |     0|acc_norm|↑  |0.2860|±  |0.0202|
+|piqa         |      1|none  |     0|acc     |↑  |0.6083|±  |0.0114|
+|             |       |none  |     0|acc_norm|↑  |0.6132|±  |0.0114|
+|social_iqa   |      0|none  |     0|acc     |↑  |0.3854|±  |0.0110|
+|tinyArc      |      0|none  |    25|acc_norm|↑  |0.3122|±  |   N/A|
+|tinyHellaswag|      0|none  |    10|acc_norm|↑  |0.3356|±  |   N/A|
+|tinyMMLU     |      0|none  |     0|acc_norm|↑  |0.2793|±  |   N/A|
+|winogrande   |      1|none  |     0|acc     |↑  |0.5201|±  |0.0140|
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 2.5e-05
+- train_batch_size: 4
+- eval_batch_size: 4
+- seed: 17868
+- distributed_type: multi-GPU
+- num_devices: 2
+- gradient_accumulation_steps: 32
+- total_train_batch_size: 256
+- total_eval_batch_size: 8
+- optimizer: Use paged_ademamix_32bit and the args are:
+No additional optimizer arguments
+- lr_scheduler_type: constant_with_warmup
+- lr_scheduler_warmup_ratio: 0.02
+- num_epochs: 1.0
+
+### Training results
+
+| Training Loss | Epoch  | Step | Validation Loss | Input Tokens Seen |
+|:-------------:|:------:|:----:|:---------------:|:-----------------:|
+| 1.4008        | 0.2534 | 1000 | 1.4020          | 91375832          |
+| 1.3456        | 0.5068 | 2000 | 1.3669          | 182939052         |
+| 1.3437        | 0.7602 | 3000 | 1.3378          | 274855796         |

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all_results.json

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config.json

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+  "layer_norm_epsilon": 1e-06,
+  "model_type": "t5",
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+  "transformers_version": "4.46.0.dev0",
+  "use_cache": true,
+  "use_gqa": true,
+  "vocab_size": 28800
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train_results.json

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trainer_state.json

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training_args.bin

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