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  ---
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  license: apache-2.0
 
 
 
 
 
 
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  ---
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  # Model Description
@@ -109,16 +115,13 @@ write_dist_matrix(dist_matrix, max_dist, output_data_filename_, verbose=True)
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  end = time.perf_counter()
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  """
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- tokenizer = AutoTokenizer.from_pretrained('mellum-base-4b')
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- model = AutoModelForCausalLM.from_pretrained('mellum-base-4b')
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  encoded_input = tokenizer(example, return_tensors='pt', return_token_type_ids=False)
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  input_len = len(encoded_input["input_ids"][0])
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  out = model.generate(
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  **encoded_input,
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  max_new_tokens=100,
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- num_beams=1,
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- pad_token_id=tokenizer.eos_token_id,
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- eos_token_id=tokenizer.eos_token_id,
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  )
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  print("### Context")
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  print(tokenizer.decode(out[0][:input_len]))
@@ -138,4 +141,4 @@ If you use this model, please cite:
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  ```
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  # Contact
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- For questions, collaborations and requests reach us out via [email protected]
 
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  ---
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  license: apache-2.0
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+ datasets:
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+ - bigcode/the-stack
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+ - bigcode/the-stack-v2
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+ - bigcode/starcoderdata
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+ - bigcode/commitpack
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+ library_name: transformers
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  ---
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  # Model Description
 
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  end = time.perf_counter()
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  """
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+ tokenizer = AutoTokenizer.from_pretrained('JetBrains/Mellum-4b-base')
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+ model = AutoModelForCausalLM.from_pretrained('JetBrains/Mellum-4b-base')
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  encoded_input = tokenizer(example, return_tensors='pt', return_token_type_ids=False)
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  input_len = len(encoded_input["input_ids"][0])
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  out = model.generate(
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  **encoded_input,
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  max_new_tokens=100,
 
 
 
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  )
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  print("### Context")
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  print(tokenizer.decode(out[0][:input_len]))
 
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  ```
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  # Contact
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+ For questions, collaborations and requests reach us out via [email protected]