Update README.md

README.md

@@ -5,7 +5,7 @@ datasets:
 
 ![RWKlogo.png](https://s3.amazonaws.com/moonup/production/uploads/62441d1d9fdefb55a0b7d12c/UWpP-lGRZJJDaEx_uUlDv.png)
 
-# Model card for RWKV-4 | 3B parameters trained on Pile dataset
+# Model card for RWKV-4 | 3B parameters chat version (Raven)
 
 RWKV is a project led by [Bo Peng](https://github.com/BlinkDL). Learn more about the model architecture in the blogposts from Johan Wind [here](https://johanwind.github.io/2023/03/23/rwkv_overview.html) and [here](https://johanwind.github.io/2023/03/23/rwkv_details.html). Learn more about the project by joining the [RWKV discord server](https://discordapp.com/users/468093332535640064).
 
@@ -47,8 +47,8 @@ You can use the `AutoModelForCausalLM` and `AutoTokenizer` classes to generate t
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile")
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-raven-3b")
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-raven-3b")
 
 prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
 
@@ -66,8 +66,8 @@ print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
 ```python
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile").to(0)
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-raven-3b").to(0)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-raven-3b")
 
 prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
 
@@ -89,8 +89,8 @@ print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile", torch_dtype=torch.float16).to(0)
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-raven-3b", torch_dtype=torch.float16).to(0)
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-raven-3b")
 
 prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."
 
@@ -110,8 +110,8 @@ print(tokenizer.decode(output[0].tolist(), skip_special_tokens=True))
 # pip install accelerate
 from transformers import AutoModelForCausalLM, AutoTokenizer
 
-model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-4-3b-pile", device_map="auto")
-tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-4-3b-pile")
+model = AutoModelForCausalLM.from_pretrained("RWKV/rwkv-raven-3b", device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("RWKV/rwkv-raven-3b")
 
 prompt = "\nIn a shocking finding, scientist discovered a herd of dragons living in a remote, previously unexplored valley, in Tibet. Even more surprising to the researchers was the fact that the dragons spoke perfect Chinese."