Upload 8 files

LLM.txt

@@ -0,0 +1,35 @@
+(i)
+https://community.deeplearning.ai/c/generative-ai-with-large-language-models/gaia-week-1/350
+
+https://community.deeplearning.ai/t/genai-with-llms-lab-faq/374869
+
+AWS
+sagemaker-user@studio$ aws sts get-caller-identity --query "Account" --output text
+684612602584
+sagemaker-user@studio$ 
+
+https://docs.google.com/forms/d/e/1FAIpQLSfzegoOO0vitgyP4bu1-pf--gnfcSFoX_dGLPTgiciKmkrAnA/viewform
+
+Video
+https://synopsys.zoom.us/rec/share/gS0uOubW4xJ5rqnIAvMYIMtZXj2itgSvamshmU5bvPFKlznC9GyM4lpDwFc8XNAz.mDBk_AnE-Jgu7rB7
+
+https://urldefense.com/v3/__https:/synopsys.zoom.us/rec/share/orOq6IH4vYA3SrCI0z-1NYrdmtUMAjNUdW1OyMQOA-vV4Y7bk8A1Xa1CxIARfq-Z.8VME9CnFDvdhKaEo__;!!A4F2R9G_pg!cwYjaYgjxRMf9ClXBwQcPAVOIWujg5sgQYnP9zh1PEYQc9y9BhE-axUiyFips91XmeJ7s0cciDyhtibhxZuh$
+
+https://synopsys.zoom.us/rec/play/GToaarqsI5fgNmXTkUrjCp3qBy40u84qT2BmoaY6Q-8njFVmmSYj8gpJMO9vtoKpKGqQl6GrZQ8lIESp._fbgzU4gf1vOcX9A?canPlayFromShare=true&from=share_recording_detail&continueMode=true&componentName=rec-play&originRequestUrl=https%3A%2F%2Fsynopsys.zoom.us%2Frec%2Fshare%2FPVfRewZNYYvcjVgZ3K9-ixuWKXtLfQMafWOpMWPGvrW7BCA1JDrwwydh44C8u85C.-9cLYBBTtvFiN-ie
+
+[Friday 1:39 PM] Jingfeng Huang
+git clone https://huggingface.co/google/flan-t5-base
+google/flan-t5-base · Hugging Face
+We’re on a journey to advance and democratize artificial intelligence through open source and open science.
+[Friday 1:40 PM] Jingfeng Huang
+或者直接在网页里下载到disk也可以。
+[Friday 1:40 PM] Jingfeng Huang
+cpu server也可以run起来，慢一点点
+
+https://github.com/Ryota-Kawamura/Generative-AI-with-LLMs/
+
+https://huggingface.co/learn/nlp-course/chapter1/3?fw=pt
+https://colab.research.google.com/github/huggingface/notebooks/blob/master/course/en/chapter1/section3.ipynb?pli=1#scrollTo=7MWCQNutr4kA
+https://www.kaggle.com/code/dschettler8845/transformers-course-chapter-1-tf-torch
+
+https://colab.research.google.com/drive/1oPIwPjAjJIHGs_eBgPfkRT6ZTwxYnZKH#scrollTo=6ORFYRzYgG3o

README.md

@@ -1,201 +1,56 @@
 ---
-library_name: transformers
-tags: []
+language: 
+- en
+thumbnail: https://github.com/karanchahal/distiller/blob/master/distiller.jpg
+tags:
+- question-answering
+license: apache-2.0
+datasets:
+- squad
+metrics:
+- squad
 ---
 
-# Model Card for Model ID
+# DistilBERT with a second step of distillation
 
-<!-- Provide a quick summary of what the model is/does. -->
+## Model description
 
+This model replicates the "DistilBERT (D)" model from Table 2 of the [DistilBERT paper](https://arxiv.org/pdf/1910.01108.pdf). In this approach, a DistilBERT student is fine-tuned on SQuAD v1.1, but with a BERT model (also fine-tuned on SQuAD v1.1) acting as a teacher for a second step of task-specific distillation.
 
+In this version, the following pre-trained models were used:
 
-## Model Details
+* Student: `distilbert-base-uncased`
+* Teacher: `lewtun/bert-base-uncased-finetuned-squad-v1`
 
-### Model Description
+## Training data
 
-<!-- Provide a longer summary of what this model is. -->
+This model was trained on the SQuAD v1.1 dataset which can be obtained from the `datasets` library as follows:
 
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
+```python
+from datasets import load_dataset
+squad = load_dataset('squad')
+```
 
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
+## Training procedure
 
-### Model Sources [optional]
+## Eval results
 
-<!-- Provide the basic links for the model. -->
+|                  | Exact Match | F1   |
+|------------------|-------------|------|
+| DistilBERT paper | 79.1        | 86.9 |
+| Ours             | 78.4        | 86.5 |
 
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
-
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-
-### Direct Use
-
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-
-[More Information Needed]
-
-### Downstream Use [optional]
-
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-
-[More Information Needed]
-
-### Out-of-Scope Use
-
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
-
-## Bias, Risks, and Limitations
-
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
-
-### Recommendations
-
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
-
-[More Information Needed]
-
-## Training Details
-
-### Training Data
-
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-
-[More Information Needed]
-
-### Training Procedure 
-
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-
-#### Preprocessing [optional]
-
-[More Information Needed]
-
-
-#### Training Hyperparameters
-
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-
-#### Speeds, Sizes, Times [optional]
-
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
-
-## Evaluation
-
-<!-- This section describes the evaluation protocols and provides the results. -->
-
-### Testing Data, Factors & Metrics
-
-#### Testing Data
-
-<!-- This should link to a Dataset Card if possible. -->
-
-[More Information Needed]
-
-#### Factors
-
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
-
-#### Metrics
-
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
-
-### Results
-
-[More Information Needed]
-
-#### Summary
-
-
-
-## Model Examination [optional]
-
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
-
-## Environmental Impact
-
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-
-## Technical Specifications [optional]
-
-### Model Architecture and Objective
-
-[More Information Needed]
-
-### Compute Infrastructure
-
-[More Information Needed]
-
-#### Hardware
-
-[More Information Needed]
-
-#### Software
-
-[More Information Needed]
-
-## Citation [optional]
-
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-
-**BibTeX:**
-
-[More Information Needed]
-
-**APA:**
-
-[More Information Needed]
-
-## Glossary [optional]
-
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
-
-## More Information [optional]
-
-[More Information Needed]
-
-## Model Card Authors [optional]
-
-[More Information Needed]
-
-## Model Card Contact
-
-[More Information Needed]
+The scores were calculated using the `squad` metric from `datasets`.
 
+### BibTeX entry and citation info
 
+```bibtex
+@misc{sanh2020distilbert,
+      title={DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter}, 
+      author={Victor Sanh and Lysandre Debut and Julien Chaumond and Thomas Wolf},
+      year={2020},
+      eprint={1910.01108},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```

gitattributes

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+*.bin.* filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tar.gz filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text

pytorch_model.bin

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special_tokens_map.json

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+{"unk_token": "[UNK]", "sep_token": "[SEP]", "pad_token": "[PAD]", "cls_token": "[CLS]", "mask_token": "[MASK]"}

tokenizer_config.json

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training_args.bin

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