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  ---
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  license: mit
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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  ---
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  # Ma-layala-mba
@@ -23,7 +37,7 @@ Here's a quick example to get you started with the Ma-layala-mba model:
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  from transformers import MaLayalaMbaForCausalLM, AutoTokenizer, pipeline
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  model = MaLayalaMbaForCausalLM.from_pretrained(
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- "aoxo/ma-layala-mba",
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  # load_in_8bit=True, # Set this depending on the GPU you have
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  torch_dtype=torch.bfloat16,
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  device_map={"": 0}, # Set this depending on the number of GPUs you have
@@ -31,7 +45,7 @@ model = MaLayalaMbaForCausalLM.from_pretrained(
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  )
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  model.eval()
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- tokenizer = AutoTokenizer.from_pretrained("aoxo/ma-layala-mba")
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  input_ids = tokenizer("മലയാളം പര്യായപദങ്ങളിൽ ഒരു പരീക്ഷ പേപ്പർ ഉണ്ടാക്കുക", return_tensors='pt').to(model.device)["input_ids"]
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@@ -58,6 +72,4 @@ Please be aware that this model has not undergone comprehensive detoxification o
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  ## Meet the Developers
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- Get to know the creators behind Ma-layala-mba and follow their work:
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-
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  - **Alosh Denny**
 
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  ---
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  license: mit
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+ datasets:
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+ - VishnuPJ/Malayalam_CultureX_IndicCorp_SMC
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+ library_name: transformers
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+ language:
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+ - ml
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+ tags:
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+ - mamba
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+ - ssm
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+ - s6
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+ - jamba
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+ - llm
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+ - state space models
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+ - malayalam
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+ - indic
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  ---
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  # Ma-layala-mba
 
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  from transformers import MaLayalaMbaForCausalLM, AutoTokenizer, pipeline
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  model = MaLayalaMbaForCausalLM.from_pretrained(
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+ "aoxo/Ma-layala-mba_Tiny_128M",
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  # load_in_8bit=True, # Set this depending on the GPU you have
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  torch_dtype=torch.bfloat16,
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  device_map={"": 0}, # Set this depending on the number of GPUs you have
 
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  )
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  model.eval()
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+ tokenizer = AutoTokenizer.from_pretrained("aoxo/Ma-layala-mba_Tiny_128M")
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  input_ids = tokenizer("മലയാളം പര്യായപദങ്ങളിൽ ഒരു പരീക്ഷ പേപ്പർ ഉണ്ടാക്കുക", return_tensors='pt').to(model.device)["input_ids"]
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  ## Meet the Developers
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  - **Alosh Denny**