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@@ -71,10 +71,10 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
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  import flash_attn
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  import time
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- tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-RC4-e4")
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  model = AutoModelForCausalLM.from_pretrained(
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- "NousResearch/DeepHermes-3-RC4-e4",
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  torch_dtype=torch.float16,
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  device_map="auto",
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  attn_implementation="flash_attention_2",
@@ -110,10 +110,10 @@ from transformers import AutoTokenizer, AutoModelForCausalLM
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  import flash_attn
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  import time
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- tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-RC4-e4")
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  model = AutoModelForCausalLM.from_pretrained(
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- "NousResearch/DeepHermes-3-RC4-e4",
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  torch_dtype=torch.float16,
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  device_map="auto",
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  attn_implementation="flash_attention_2",
@@ -141,7 +141,7 @@ print(f"Response: {response}")
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  You can also run this model with vLLM, by running the following in your terminal after `pip install vllm`
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- `vllm serve NousResearch/Hermes-3-Llama-3.1-8B`
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  You may then use the model over API using the OpenAI library just like you would call OpenAI's API.
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  import flash_attn
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  import time
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+ tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-Llama-3-8B-Preview")
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  model = AutoModelForCausalLM.from_pretrained(
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+ "NousResearch/DeepHermes-3-Llama-3-8B-Preview",
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  torch_dtype=torch.float16,
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  device_map="auto",
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  attn_implementation="flash_attention_2",
 
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  import flash_attn
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  import time
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+ tokenizer = AutoTokenizer.from_pretrained("NousResearch/DeepHermes-3-Llama-3-8B-Preview")
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  model = AutoModelForCausalLM.from_pretrained(
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+ "NousResearch/DeepHermes-3-Llama-3-8B-Preview",
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  torch_dtype=torch.float16,
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  device_map="auto",
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  attn_implementation="flash_attention_2",
 
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  You can also run this model with vLLM, by running the following in your terminal after `pip install vllm`
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+ `vllm serve NousResearch/DeepHermes-3-Llama-3-8B-Preview`
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  You may then use the model over API using the OpenAI library just like you would call OpenAI's API.
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