Update README.md

README.md

@@ -20,7 +20,7 @@ extra_gated_fields:
 
 ## Model Description
 
-`Stable Zephyr 3B` is a 3 billion parameter instruction tuned inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290), evaluation for this model based on
+`StableLM Zephyr 3B` is a 3 billion parameter instruction tuned inspired by [HugginFaceH4's Zephyr 7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) training pipeline this model was trained on a mix of publicly available datasets, synthetic datasets using [Direct Preference Optimization (DPO)](https://arxiv.org/abs/2305.18290), evaluation for this model based on
 [MT Bench](https://tatsu-lab.github.io/alpaca_eval/) and [Alpaca Benchmark](https://tatsu-lab.github.io/alpaca_eval/)
 
 ## Usage
@@ -29,7 +29,7 @@ Get started generating text with `Stable Zephyr 3B` by using the following code
 
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
-tokenizer = AutoTokenizer.from_pretrained("stabilityai/stable-zephyr-3b-dpo")
+tokenizer = AutoTokenizer.from_pretrained("stabilityai/stablelm-zephyr-3b-dpo")
 model = AutoModelForCausalLM.from_pretrained(
   "stable-zephyr-3b",
   trust_remote_code=True,
@@ -51,7 +51,7 @@ print(tokenizer.decode(tokens[0], skip_special_tokens=True))
 ## Model Details
 
 * **Developed by**: [Stability AI](https://stability.ai/)
-* **Model type**: `Stable Zephyr 3B` models are auto-regressive language models based on the transformer decoder architecture.
+* **Model type**: `StableLM Zephyr 3B` models are auto-regressive language models based on the transformer decoder architecture.
 * **Language(s)**: English
 * **Library**: [Alignment Handbook](https://github.com/huggingface/alignment-handbook.git)
 * **Finetuned from model**: [stabilityai/stablelm-3b-4e1t](https://huggingface.co/stabilityai/stablelm-3b-4e1t)
@@ -81,7 +81,7 @@ The dataset is comprised of a mixture of open datasets large-scale datasets avai
 
 | Model | Size | Alignment | MT-Bench (score) | AlpacaEval (win rate %) |
 |-------------|-----|----|---------------|--------------|
-| **Stable Zephyr 3B** 🪁 | 3B | DPO | 6.64 | 76.00 |
+| **StableLM Zephyr 3B** 🪁 | 3B | DPO | 6.64 | 76.00 |
 | Stable Zephyr (SFT only) | 3B | SFT | 6.04 | 71.15 | 
 | MPT-Chat |  7B |dSFT |5.42| -|
 | Xwin-LMv0.1 | 7B| dPPO| 6.19| 87.83|
@@ -181,7 +181,7 @@ The dataset is comprised of a mixture of open datasets large-scale datasets avai
 
 ### Training Infrastructure
 
-* **Hardware**: `Stable Zephyr 3B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes.
+* **Hardware**: `StableLM Zephyr 3B` was trained on the Stability AI cluster across 8 nodes with 8 A100 80GBs GPUs for each nodes.
 * **Code Base**: We use our internal script for SFT steps and used [HuggingFace Alignment Handbook script](https://github.com/huggingface/alignment-handbook) for DPO training.
 ## Use and Limitations