update

README.md

@@ -1,11 +1,11 @@
 ---
 license: bsd-3-clause
 ---
-# codgen-16B-action
+# codgen-16B-mono-toolbench
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-codgen-16B-action is a 16 billion parameter model used for api based action generation. It is instruction tuned from [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono) on api based action generation datasets.
+codgen-16B-mono-toolbench is a 16 billion parameter model used for api based action generation. It is instruction tuned from [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono) on api based action generation datasets.
 
 ## Model Details
 
@@ -45,7 +45,7 @@ This model is intended for commercial and research use.
 <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
 
 
-codgen-16B-action should NOT be used for purpose other than API based action generation.
+codgen-16B-mono-toolbench should NOT be used for purpose other than API based action generation.
 
 ### Recommendations
 
@@ -67,8 +67,8 @@ Users should be made aware of the risks, biases, limitations, and restrictions o
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/codegen-16b-action")
-model = AutoModelForCausalLM.from_pretrained("sambanovasystems/codegen-16b-action", device_map="auto", torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/codegen-16b-mono-toolbench")
+model = AutoModelForCausalLM.from_pretrained("sambanovasystems/codegen-16b-mono-toolbench", device_map="auto", torch_dtype="auto")
 ```
 
 ### Suggested Inference Parameters
@@ -108,7 +108,7 @@ Input text: 十七岁的风是什么颜色的?
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
-We trained codegen-16b-action on 4 80GB A100 gpu's. We started from [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono). We finetuned it on XXX dataset. 
+We trained codegen-16b-mono-toolbench on 4 80GB A100 gpu's. We started from [codegen-16B-mono](https://huggingface.co/Salesforce/codegen-16B-mono). We finetuned it on XXX dataset. 
 All of the code used to prepare the datasets and the scripts to run training and inference are open-sourced and freely available at [githublink here](dummy link)
 
 
@@ -129,19 +129,7 @@ All of the code used to prepare the datasets and the scripts to run training and
 - Learning Rate Scheduler: Fixed LR
 - Weight decay: 0.1
 
-**Instruction-tuned Training on Dolly 2.0 and Oasst1**
 
-- Hardware: SambaNova Reconfigurable Dataflow Unit (RDU)
-- Optimizer: AdamW
-- Grad accumulation: 1
-- Epochs: 3
-- Global Batch size: 128
-- Batch tokens: 128 * 2048 = 262,144 tokens
-- Learning Rate: 1e-5
-- Learning Rate Scheduler: Cosine Schedule with Warmup
-- Warmup Steps: 0
-- End Learning Ratio: 0.1
-- Weight decay: 0.1
 
 </details>
 
@@ -150,7 +138,7 @@ All of the code used to prepare the datasets and the scripts to run training and
 ## Acknowledgment
 
 
-## Cite codegen-16b-action
+## Cite codegen-16b-mono-toolbench
 ```
 @software{bloomchat,
   title = {{BLOOMChat: a New Open Multilingual Chat LLM}},