Update README.md

README.md

@@ -52,7 +52,7 @@ from transformers import pipeline
 
 pipe = pipeline(
     "text-generation",
-    model="silma-ai/SILMA-9B-Instruct-v0.8",
+    model="silma-ai/SILMA-9B-Instruct-v1.0",
     model_kwargs={"torch_dtype": torch.bfloat16},
     device="cuda",  # replace with "mps" to run on a Mac device
 )
@@ -75,7 +75,7 @@ print(assistant_response)
 from transformers import AutoTokenizer, AutoModelForCausalLM
 import torch
 
-model_id = "silma-ai/SILMA-9B-Instruct-v0.8"
+model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = AutoModelForCausalLM.from_pretrained(
     model_id,
@@ -133,7 +133,7 @@ print(tokenizer.decode(outputs[0]))
 # pip install bitsandbytes accelerate
 from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
-model_id = "silma-ai/SILMA-9B-Instruct-v0.8"
+model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
 quantization_config = BitsAndBytesConfig(load_in_8bit=True)
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
@@ -162,7 +162,7 @@ print(tokenizer.decode(outputs[0]))
 # pip install bitsandbytes accelerate
 from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
 
-model_id = "silma-ai/SILMA-9B-Instruct-v0.8"
+model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
 quantization_config = BitsAndBytesConfig(load_in_4bit=True)
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)
@@ -204,7 +204,7 @@ import torch
 torch.set_float32_matmul_precision("high")
 
 # load the model + tokenizer
-model_id = "silma-ai/SILMA-9B-Instruct-v0.8"
+model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
 tokenizer = AutoTokenizer.from_pretrained(model_id)
 model = Gemma2ForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16)
 model.to("cuda")
@@ -259,7 +259,7 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
 import transformers
 import torch
 
-model_id = "silma-ai/SILMA-9B-Instruct-v0.8"
+model_id = "silma-ai/SILMA-9B-Instruct-v1.0"
 dtype = torch.bfloat16
 
 tokenizer = AutoTokenizer.from_pretrained(model_id)