Text Generation
PEFT
Safetensors
Eval Results
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@@ -106,12 +106,13 @@ We recommend users of this model to develop guardrails and to take appropriate p
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  This requires a GPU with at least 27GB memory.
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  ```python
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  import torch
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  from peft import PeftModel, PeftConfig
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- from transformers import AutoModelForCausalLM, AutoTokenizer
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- # load the model
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  peft_model_id = "dfurman/falcon-40b-chat-oasst1"
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  config = PeftConfig.from_pretrained(peft_model_id)
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@@ -134,8 +135,11 @@ tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
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  tokenizer.pad_token = tokenizer.eos_token
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  model = PeftModel.from_pretrained(model, peft_model_id)
 
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- # run the model
 
 
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  prompt = """<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB.
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  <bot>:"""
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@@ -149,13 +153,17 @@ batch = batch.to('cuda:0')
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  with torch.cuda.amp.autocast():
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  output_tokens = model.generate(
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- input_ids = batch.input_ids,
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  max_new_tokens=200,
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- temperature=0.7,
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- top_p=0.7,
 
 
 
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  num_return_sequences=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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  generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)
 
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  This requires a GPU with at least 27GB memory.
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+ ### First, Load the Model
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+
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  ```python
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  import torch
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  from peft import PeftModel, PeftConfig
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+ from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
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  peft_model_id = "dfurman/falcon-40b-chat-oasst1"
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  config = PeftConfig.from_pretrained(peft_model_id)
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  tokenizer.pad_token = tokenizer.eos_token
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  model = PeftModel.from_pretrained(model, peft_model_id)
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+ ```
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+ ### Next, Run the Model
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+
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+ ```python
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  prompt = """<human>: My name is Daniel. Write a short email to my closest friends inviting them to come to my home on Friday for a dinner party, I will make the food but tell them to BYOB.
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  <bot>:"""
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  with torch.cuda.amp.autocast():
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  output_tokens = model.generate(
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+ inputs=batch.input_ids,
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  max_new_tokens=200,
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+ do_sample=False,
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+ use_cache=True,
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+ temperature=1.0,
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+ top_k=50,
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+ top_p=1.0,
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  num_return_sequences=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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+ bos_token_id=tokenizer.eos_token_id,
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  )
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  generated_text = tokenizer.decode(output_tokens[0], skip_special_tokens=True)