Upload model

README.md

@@ -1,134 +1,21 @@
 ---
-language: 
-- en
-- sp
-- ja
-- pe
-- hi
-- fr
-- ch
-- be
-- gu
-- ge
-- te
-- it
-- ar
-- po
-- ta
-- ma
-- ma
-- or
-- pa
-- po
-- ur
-- ga
-- he
-- ko
-- ca
-- th
-- du
-- in
-- vi
-- bu
-- fi
-- ce
-- la
-- tu
-- ru
-- cr
-- sw
-- yo
-- ku
-- bu
-- ma
-- cz
-- fi
-- so
-- ta
-- sw
-- si
-- ka
-- zh
-- ig
-- xh
-- ro
-- ha
-- es
-- sl
-- li
-- gr
-- ne
-- as
-- no
-
-widget:
-- text: "Translate to German:  My name is Arthur"
-  example_title: "Translation"
-- text: "Please answer to the following question. Who is going to be the next Ballon d'or?"
-  example_title: "Question Answering"
-- text: "Q: Can Geoffrey Hinton have a conversation with George Washington? Give the rationale before answering."
-  example_title: "Logical reasoning"
-- text: "Please answer the following question. What is the boiling point of Nitrogen?"
-  example_title: "Scientific knowledge"
-- text: "Answer the following yes/no question. Can you write a whole Haiku in a single tweet?"
-  example_title: "Yes/no question"
-- text: "Answer the following yes/no question by reasoning step-by-step. Can you write a whole Haiku in a single tweet?"
-  example_title: "Reasoning task"
-- text: "Q: ( False or not False or False ) is? A: Let's think step by step"
-  example_title: "Boolean Expressions"
-- text: "The square root of x is the cube root of y. What is y to the power of 2, if x = 4?"
-  example_title: "Math reasoning"
-- text: "Premise:  At my age you will probably have learnt one lesson. Hypothesis:  It's not certain how many lessons you'll learn by your thirties. Does the premise entail the hypothesis?"
-  example_title: "Premise and hypothesis"
-
-tags:
-- text2text-generation
-
-datasets:
-- svakulenk0/qrecc
-- taskmaster2
-- djaym7/wiki_dialog
-- deepmind/code_contests
-- lambada
-- gsm8k
-- aqua_rat
-- esnli
-- quasc
-- qed
-- financial_phrasebank
-
-
-license: apache-2.0
+library_name: peft
 ---
+## Training procedure
 
-# Model Card for LoRA-FLAN-T5 large
-
-![model image](https://s3.amazonaws.com/moonup/production/uploads/1666363435475-62441d1d9fdefb55a0b7d12c.png)
-
-This repository contains the LoRA (Low Rank Adapters) of `flan-t5-large` that has been fine-tuned on [`financial_phrasebank`](https://huggingface.co/datasets/financial_phrasebank) dataset.
-
-## Usage
-
-Use this adapter with `peft` library
-
-```python
-# pip install peft transformers
-import torch
-from peft import PeftModel, PeftConfig
-from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
-
-peft_model_id = "ybelkada/flan-t5-large-financial-phrasebank-lora"
-config = PeftConfig.from_pretrained(peft_model_id)
 
-model = AutoModelForSeq2SeqLM.from_pretrained(
-    config.base_model_name_or_path, 
-    torch_dtype='auto', 
-    device_map='auto'
-)
-tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+The following `bitsandbytes` quantization config was used during training:
+- quant_method: bitsandbytes
+- load_in_8bit: True
+- load_in_4bit: False
+- llm_int8_threshold: 6.0
+- llm_int8_skip_modules: None
+- llm_int8_enable_fp32_cpu_offload: False
+- llm_int8_has_fp16_weight: False
+- bnb_4bit_quant_type: fp4
+- bnb_4bit_use_double_quant: False
+- bnb_4bit_compute_dtype: float32
+### Framework versions
 
-# Load the Lora model
-model = PeftModel.from_pretrained(model, peft_model_id)
-```
 
-Enjoy!
+- PEFT 0.6.0.dev0

adapter_config.json

@@ -1,15 +1,20 @@
 {
+  "alpha_pattern": {},
+  "auto_mapping": null,
   "base_model_name_or_path": "google/flan-t5-large",
   "bias": "none",
-  "enable_lora": null,
   "fan_in_fan_out": false,
   "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
   "lora_alpha": 32,
   "lora_dropout": 0.05,
-  "merge_weights": false,
   "modules_to_save": null,
   "peft_type": "LORA",
   "r": 16,
+  "rank_pattern": {},
+  "revision": null,
   "target_modules": [
     "q",
     "v"

adapter_model.bin

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+oid sha256:d7a8f09c82273e681b599f6e41598e8b044747e1d73988a83c9b571f0fe4c0f0
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