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Model: NEU-HAI/Llama-2-7b-alpaca-cleaned Source: Original Platform
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---
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license: cc-by-nc-4.0
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datasets:
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- yahma/alpaca-cleaned
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- llama-2
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- alpaca
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---
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# Model Card for Llama-2-7b-alpaca-cleaned
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<!-- Provide a quick summary of what the model is/does. -->
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This model checkpoint is the Llama-2-7b fine-tuned on [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) with the original Alpaca fine-tuning hyper-parameters.
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## Model Details
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### Model Description
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This model checkpoint is the Llama-2-7b fine-tuned on [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) with the original Alpaca fine-tuning hyper-parameters. \
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The original Alpaca model is fine-tuned on Llama with the alpaca dataset by researchers from Stanford University
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- **Developed by:** NEU Human-centered AI Lab
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- **Shared by [optional]:** NEU Human-centered AI Lab
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- **Model type:** Text-generation
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- **Language(s) (NLP):** English
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- **License:** cc-by-nc-4.0 (comply with the alpaca-cleaned dataset)
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- **Finetuned from model [optional]:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b)
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### Model Sources
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<!-- Provide the basic links for the model. -->
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- **Repository:** https://huggingface.co/meta-llama/Llama-2-7b
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## Uses
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Direct Use
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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The model is intended to be used for research purposes only in English, complying with [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca). \
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The model has been fine-tuned on the [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned) for assistant-like chat and general natural language generation tasks. \
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The use of this model should also comply with the restrictions from [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b).
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### Out-of-Scope Use
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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The out-of-Scope use of this model should also comply with [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca) and [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b).
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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{{ bias_risks_limitations | default("[More Information Needed]", true)}}
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```
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# Load model directly
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("NEU-HAI/Llama-2-7b-alpaca-cleaned")
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model = AutoModelForCausalLM.from_pretrained("NEU-HAI/Llama-2-7b-alpaca-cleaned")
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```
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## Training Details
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### Training Data
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<!-- This should link to a Data 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. -->
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We use the [alpaca-cleaned dataset](https://huggingface.co/datasets/yahma/alpaca-cleaned), which is the cleaned version of the original [alpaca dataset](https://huggingface.co/datasets/tatsu-lab/alpaca) created by researchers from Stanford University.
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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We follow the same training procedure and mostly same hyper-parameters to fine-tune the original Alpaca model on Llama. The procedure can be found in [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca).
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#### Training Hyperparameters
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```
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--bf16 True \
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--num_train_epochs 3 \
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--per_device_train_batch_size 4 \
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--per_device_eval_batch_size 4 \
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--gradient_accumulation_steps 8 \
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--evaluation_strategy "no" \
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--save_strategy "steps" \
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--save_steps 2000 \
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--save_total_limit 1 \
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--learning_rate 2e-5 \
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--weight_decay 0. \
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--warmup_ratio 0.03 \
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--lr_scheduler_type "cosine" \
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--logging_steps 1 \
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--fsdp "full_shard auto_wrap" \
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--fsdp_transformer_layer_cls_to_wrap 'LlamaDecoderLayer' \
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--tf32 True
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```
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Data Card if possible. -->
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N/A
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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N/A
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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N/A
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### Results
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N/A
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#### Summary
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N/A
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<!--
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## Environmental Impact
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Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly
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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).
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|
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- **Hardware Type:** {{ hardware | default("[More Information Needed]", true)}}
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||||
- **Hours used:** {{ hours_used | default("[More Information Needed]", true)}}
|
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- **Cloud Provider:** {{ cloud_provider | default("[More Information Needed]", true)}}
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- **Compute Region:** {{ cloud_region | default("[More Information Needed]", true)}}
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- **Carbon Emitted:** {{ co2_emitted | default("[More Information Needed]", true)}}
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-->
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## Citation
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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Please cite the [stanford_alpaca project](https://github.com/tatsu-lab/stanford_alpaca)
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```
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@misc{alpaca,
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author = {Rohan Taori and Ishaan Gulrajani and Tianyi Zhang and Yann Dubois and Xuechen Li and Carlos Guestrin and Percy Liang and Tatsunori B. Hashimoto },
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title = {Stanford Alpaca: An Instruction-following LLaMA model},
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year = {2023},
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publisher = {GitHub},
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journal = {GitHub repository},
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howpublished = {\url{https://github.com/tatsu-lab/stanford_alpaca}},
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}
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```
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## Model Card Authors
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Northeastern Human-centered AI Lab
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## Model Card Contact
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"vocab_size": 32001
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||||
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||||
24
special_tokens_map.json
Normal file
24
special_tokens_map.json
Normal file
@@ -0,0 +1,24 @@
|
||||
{
|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
35
tokenizer_config.json
Normal file
35
tokenizer_config.json
Normal file
@@ -0,0 +1,35 @@
|
||||
{
|
||||
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|
||||
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|
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
7333
trainer_state.json
Normal file
7333
trainer_state.json
Normal file
File diff suppressed because it is too large
Load Diff
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
||||
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||||
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||||
size 3899
|
||||
Reference in New Issue
Block a user