Update README.md

README.md

@@ -5,16 +5,16 @@ language:
 library_name: adapter-transformers
 ---
 # Wizardlm Alpaca Dolly Orca Open_LLaMa_13b
-An Open_LLaMA-13B model trained on custom explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying [Orca Research Paper](https://arxiv.org/abs/2306.02707) dataset construction approaches.
+An Open_LLaMA-13B model trained on custom explain tuned datasets, created using Instructions and Input from WizardLM, Alpaca & Dolly-V2 datasets and applying Orca Research Paper dataset construction approaches.
 
 
 # Dataset
 
-We trained [OpenLLaMa-3B model](https://github.com/openlm-research/open_llama) on custom explain tuned [Alpaca dataset](https://crfm.stanford.edu/2023/03/13/alpaca.html) (~52K) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). 
+We trained [OpenLLaMa-13B model](https://github.com/openlm-research/open_llama) on custom explain tuned [WizardLM ~70K](https://github.com/nlpxucan/WizardLM), [Alpaca dataset ~52K](https://crfm.stanford.edu/2023/03/13/alpaca.html)  & [Dolly-V2 ~15K](https://github.com/databrickslabs/dolly) created using approaches from [Orca Research Paper](https://arxiv.org/abs/2306.02707). 
 
-We leverage all of the 15 system instructions provided in [Orca Research Paper](https://arxiv.org/abs/2306.02707) to generate custom Alpaca dataset, in contrast to vanilla instruction tuning approaches used by original [Alpaca research paper](https://crfm.stanford.edu/2023/03/13/alpaca.html).
+We leverage all of the 15 system instructions provided in Orca Research Paper. to generate custom datasets, in contrast to vanilla instruction tuning approaches used by original datasets.
 
-This helps student model aka [alpaca_orca_open_llama_3b](psmathur/alpaca_orca_open_llama_3b) to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
+This helps student model aka [wizardlm_alpaca_dolly_orca_open_llama_13b](https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_13b) to learn ***thought*** process from teacher model, which is ChatGPT (gpt-3.5-turbo-0301 version).
 
 Please see below example usage how the **System** prompt is added before each *instruction*.
 
@@ -22,7 +22,7 @@ Please see below example usage how the **System** prompt is added before each *i
 
 The training configurations are provided in the table below.
 
-The training takes on 4x A600(50G) GPUs and lasts for around 20 Hours for cost of $66 using [Lambda Labs](https://lambdalabs.com)
+The training takes on 8x A100(80G) GPUs and lasts for around 15 Hours for cost of $180 using [Lambda Labs](https://lambdalabs.com)
 
 We used DeepSpeed with Zero-3 approaches for parallel gpu training by writing our own fine tunning scripts plus leveraging some of the model training code provided by amazing [OpenAlpaca repo](https://github.com/yxuansu/OpenAlpaca)
 
@@ -32,7 +32,7 @@ Here are some of params used during training:
 |:-------------:|:-------------:|
 |*batch_size*|16|
 |*train_micro_batch_size_per_gpu*|2|
-|*gradient_accumulation_steps*|2|
+|*gradient_accumulation_steps*|1|
 |*Learning rate*|2e-5|
 |*Max length*|1024|
 |*Epochs*|3|
@@ -41,14 +41,14 @@ Here are some of params used during training:
 
 # Example Usage
 
-Below shows an example on how to use [alpaca_orca_open_llama_3b](psmathur/alpaca_orca_open_llama_3b)
+Below shows an example on how to use this model
 
 ```python
 import torch
 from transformers import LlamaForCausalLM, LlamaTokenizer
 
-# change model_path between 3b,7b or 13b
-model_path = 'psmathur/alpaca_orca_open_llama_3b'
+# Hugging Face model_path
+model_path = 'psmathur/wizardlm_alpaca_dolly_orca_open_llama_13b'
 tokenizer = LlamaTokenizer.from_pretrained(model_path)
 model = LlamaForCausalLM.from_pretrained(
     model_path, torch_dtype=torch.float16, device_map='auto',
@@ -94,24 +94,23 @@ generate_text(system, instruction, input)
 **P.S. I am #opentowork and #collaboration, if you can help, please reach out to me at [email protected]**
 
 Next Goals:
-1) Try more data, Dolly V2, WizardLM, & Others (we are open for suggestions)
-2) Try bigger OpenLLaMA models 7B and 13B
-3) Try better GPU for training, couldn't get 8xA100 (40GB), I guess they are in hot demand now.
-4) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui)
-6) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here)
+1) Try more data like actually using FLAN-v2, just like Orka Research Paper (I am open for suggestions)
+2) Try smaller OpenLLaMA models 7B and 3B
+3) Provide more options for Text generation UI. (may be https://github.com/oobabooga/text-generation-webui)
+4) Provide 4bit GGML/GPTQ quantized model (may be [TheBloke](https://huggingface.co/TheBloke) can help here)
 
 
 Reference:
-If you found [alpaca_orca_open_llama_3b](psmathur/alpaca_orca_open_llama_3b) useful in your research or applications, please kindly cite using the following BibTeX:
+If you found wizardlm_alpaca_dolly_orca_open_llama_13b useful in your research or applications, please kindly cite using the following BibTeX:
 
 ```
-@misc{alpaca_orca_open_llama_3b,
+@misc{wizardlm_alpaca_dolly_orca_open_llama_13b,
   author = {Pankaj Mathur},
-  title = {alpaca_orca_open_llama_3b: A custom explain tuned Alpaca Model Based On OpenLLaMA},
+  title = {wizardlm_alpaca_dolly_orca_open_llama_13b: An explain tuned OpenLLaMA-13b model on custom wizardlm, alpaca, & dolly datasets},
   year = {2023},
   publisher = {GitHub, HuggingFace},
   journal = {GitHub repository, HuggingFace repository},
-  howpublished = {\url{https://github.com/pankajarm/alpaca_orca_open_llama_3b}, \url{https://https://huggingface.co/psmathur/alpaca_orca_open_llama_3b}},
+  howpublished = {\url{https://github.com/pankajarm/wizardlm_alpaca_dolly_orca_open_llama_13b}, \url{https://https://huggingface.co/psmathur/wizardlm_alpaca_dolly_orca_open_llama_13b}},
 }
 ```
 ```