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Update README.md

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  1. README.md +8 -7
README.md CHANGED
@@ -84,7 +84,7 @@ repo_id = "jordiclive/lora-llama-33B-alpaca_gpt4-dolly_15k-vicuna-r64"
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  base_model = "decapoda-research/llama-30b-hf"
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  # Model Loading
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- def transfer_embeddings(model, embed_path, tokenizer):
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  old_embeddings = model.get_input_embeddings()
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  old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
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  new_embeddings = torch.nn.Embedding(old_num_tokens, old_embedding_dim)
@@ -93,16 +93,17 @@ def transfer_embeddings(model, embed_path, tokenizer):
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  embed_weights = torch.load(embed_path, map_location=old_embeddings.weight.device)
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  vocab_size = tokenizer.vocab_size
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  new_embeddings.weight.data[:vocab_size, :] = old_embeddings.weight.data[:vocab_size, :]
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- new_embeddings.weight.data[vocab_size : vocab_size + embed_weights.shape[0], :] = embed_weights.weight.data.to(
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  new_embeddings.weight.dtype
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  ).to(new_embeddings.weight.device)
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  model.set_input_embeddings(new_embeddings)
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  model.tie_weights()
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  def load_peft_model(model, peft_model_path, tokenizer):
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  embed_weights = hf_hub_download(peft_model_path, "extra_embeddings.pt")
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- model.resize_token_embeddings(tokenizer.vocab_size + embed_weights.shape[0])
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  model.config.eos_token_id = tokenizer.eos_token_id
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  model.config.bos_token_id = tokenizer.bos_token_id
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  model.config.pad_token_id = tokenizer.pad_token_id
@@ -112,20 +113,22 @@ def load_peft_model(model, peft_model_path, tokenizer):
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  torch_dtype=model.dtype,
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  )
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  model.eos_token_id = tokenizer.eos_token_id
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- transfer_embeddings(model, Path(peft_model_path).joinpath("extra_embeddings.pt"), tokenizer)
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  return model
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  tokenizer = transformers.AutoTokenizer.from_pretrained(repo_id)
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  model = transformers.AutoModelForCausalLM.from_pretrained(
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- base_model, torch_dtype=dtype, trust_remote_code=True, cache_dir="/mnt/data/jordiclive/data_cache"
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  )
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  model = load_peft_model(model, repo_id, tokenizer)
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  # device configuration
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  model = model.to(device)
 
 
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  # Choose Generation parameters
@@ -164,6 +167,4 @@ def generate(prompt, generation_config=generation_config, max_new_tokens=2048, d
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  generate("What is a meme, and what's the history behind this word?")
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  generate("What's the Earth total population")
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  generate("Write a story about future of AI development")
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-
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-
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  ```
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  base_model = "decapoda-research/llama-30b-hf"
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  # Model Loading
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+ def add_embeddings(model, embed_path, tokenizer):
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  old_embeddings = model.get_input_embeddings()
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  old_num_tokens, old_embedding_dim = old_embeddings.weight.size()
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  new_embeddings = torch.nn.Embedding(old_num_tokens, old_embedding_dim)
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  embed_weights = torch.load(embed_path, map_location=old_embeddings.weight.device)
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  vocab_size = tokenizer.vocab_size
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  new_embeddings.weight.data[:vocab_size, :] = old_embeddings.weight.data[:vocab_size, :]
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+ new_embeddings.weight.data[vocab_size : vocab_size + embed_weights.shape[0], :] = embed_weights.to(
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  new_embeddings.weight.dtype
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  ).to(new_embeddings.weight.device)
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  model.set_input_embeddings(new_embeddings)
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  model.tie_weights()
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+
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  def load_peft_model(model, peft_model_path, tokenizer):
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  embed_weights = hf_hub_download(peft_model_path, "extra_embeddings.pt")
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+ model.resize_token_embeddings(tokenizer.vocab_size + torch.load(embed_weights).shape[0])
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  model.config.eos_token_id = tokenizer.eos_token_id
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  model.config.bos_token_id = tokenizer.bos_token_id
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  model.config.pad_token_id = tokenizer.pad_token_id
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  torch_dtype=model.dtype,
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  )
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  model.eos_token_id = tokenizer.eos_token_id
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+ add_embeddings(model, embed_weights, tokenizer)
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  return model
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  tokenizer = transformers.AutoTokenizer.from_pretrained(repo_id)
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  model = transformers.AutoModelForCausalLM.from_pretrained(
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+ base_model, torch_dtype=dtype, trust_remote_code=True,
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  )
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  model = load_peft_model(model, repo_id, tokenizer)
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  # device configuration
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  model = model.to(device)
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+ if dtype == torch.float16:
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+ model = model.half()
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  # Choose Generation parameters
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  generate("What is a meme, and what's the history behind this word?")
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  generate("What's the Earth total population")
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  generate("Write a story about future of AI development")
 
 
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  ```