Update README.md

README.md

@@ -8,19 +8,26 @@ base_model: tiiuae/falcon-40b
 inference: false
 ---
 
-# falcon-40b-openassistant-peft 🦅
+<div align="center">
 
-Falcon-40b-openassistant-peft is a chatbot model for dialogue generation. It was built by fine-tuning [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) on the [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset. This repo only includes the LoRA adapters from fine-tuning with 🤗's [peft](https://github.com/huggingface/peft) package. 
+<img src="./falcon.webp" width="150px">
+
+</div>
+
+
+# Falcon-40B-Chat-v0.1 🦅
+
+Falcon-40B-Chat-v0.1 is a chatbot model for dialogue generation. It was built by fine-tuning [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) on the [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) dataset. This repo only includes the LoRA adapters from fine-tuning with 🤗's [peft](https://github.com/huggingface/peft) package. 
 
 ## Model Summary
 
-- **Model Type:** Causal decoder-only
+- **Model Type:** Decoder-only
 - **Language(s):** English
 - **Base Model:** [Falcon-40B](https://huggingface.co/tiiuae/falcon-40b) (License: [Apache 2.0](https://huggingface.co/tiiuae/falcon-40b#license))
 - **Dataset:** [OpenAssistant/oasst1](https://huggingface.co/datasets/OpenAssistant/oasst1) (License: [Apache 2.0](https://huggingface.co/datasets/OpenAssistant/oasst1/blob/main/LICENSE))
 - **License:** Apache 2.0 inherited from "Base Model" and "Dataset"
 
-The model was fine-tuned in 4-bit precision using `peft` adapters, `transformers`, and `bitsandbytes`. Training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant. The run took approximately 10 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory. See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-40b-openassistant-peft/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model. 
+The model was fine-tuned in 4-bit precision using `peft` adapters, `transformers`, and `bitsandbytes`. Training relied on a method called "Low Rank Adapters" ([LoRA](https://arxiv.org/pdf/2106.09685.pdf)), specifically the [QLoRA](https://arxiv.org/abs/2305.14314) variant. The run took approximately 10 hours and was executed on a workstation with a single A100-SXM NVIDIA GPU with 37 GB of available memory. See attached [Colab Notebook](https://huggingface.co/dfurman/Falcon-40B-Chat-v0.1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code and hyperparams used to train the model. 
 
 ### Model Date
 
@@ -43,7 +50,7 @@ To prompt the chat model, use the following format:
 <bot>:"""
 ```
 
-**Falcon-40b-openassistant-peft**:
+**Falcon-40B-Chat-v0.1**:
 ```
 Dear Friends,
 
@@ -63,7 +70,7 @@ Daniel
 <bot>:
 ```
 
-**Falcon-40b-openassistant-peft**:
+**Falcon-40B-Chat-v0.1**:
 ```
 Here is a list of things to do in San Francisco:
 
@@ -126,7 +133,7 @@ import torch
 from peft import PeftModel, PeftConfig
 from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
 
-peft_model_id = "dfurman/falcon-40b-openassistant-peft"
+peft_model_id = "dfurman/Falcon-40B-Chat-v0.1"
 config = PeftConfig.from_pretrained(peft_model_id)
 
 bnb_config = BitsAndBytesConfig(
@@ -186,7 +193,7 @@ print(generated_text.split("<human>: ")[1].split("<bot>: ")[-1])
 
 ## Reproducibility
 
-See attached [Colab Notebook](https://huggingface.co/dfurman/falcon-40b-openassistant-peft/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code (and hyperparams) used to train the model. 
+See attached [Colab Notebook](https://huggingface.co/dfurman/Falcon-40B-Chat-v0.1/blob/main/finetune_falcon40b_oasst1_with_bnb_peft.ipynb) for the code (and hyperparams) used to train the model. 
 
 ### CUDA Info