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BeveledCube commited on
Commit
06b0b4a
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1 Parent(s): 564bd7c

Removed beam thing

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Files changed (7) hide show
  1. models/fast.py +1 -1
  2. models/gpt2.py +1 -1
  3. models/llama2.py +1 -1
  4. models/llama3.py +1 -1
  5. models/llamatiny.py +1 -1
  6. models/mamba.py +1 -1
  7. models/tinystories.py +1 -1
models/fast.py CHANGED
@@ -11,6 +11,6 @@ def load():
11
 
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  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
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  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/gpt2.py CHANGED
@@ -16,6 +16,6 @@ def generate(input_text):
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  attention_mask = tf.ones_like(input_ids)
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  # Generate output using the model
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- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
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  attention_mask = tf.ones_like(input_ids)
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  # Generate output using the model
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+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/llama2.py CHANGED
@@ -11,6 +11,6 @@ def load():
11
 
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  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
11
 
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  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/llama3.py CHANGED
@@ -11,6 +11,6 @@ def load():
11
 
12
  def generate(input_text):
13
  input_ids = tokenizer.encode(input_text, return_tensors="pt")
14
- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
11
 
12
  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
14
+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/llamatiny.py CHANGED
@@ -11,6 +11,6 @@ def load():
11
 
12
  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
11
 
12
  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/mamba.py CHANGED
@@ -11,6 +11,6 @@ def load():
11
 
12
  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
14
- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
16
  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
11
 
12
  def generate(input_text):
13
  input_ids = tokenizer.encode(input_text, return_tensors="pt")
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+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
models/tinystories.py CHANGED
@@ -11,6 +11,6 @@ def load():
11
 
12
  def generate(input_text):
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  input_ids = tokenizer.encode(input_text, return_tensors="pt")
14
- output_ids = model.generate(input_ids, num_beams=5, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)
 
11
 
12
  def generate(input_text):
13
  input_ids = tokenizer.encode(input_text, return_tensors="pt")
14
+ output_ids = model.generate(input_ids, no_repeat_ngram_size=2, max_new_tokens=100)
15
 
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  return tokenizer.decode(output_ids[0], skip_special_tokens=True)