Sandiago21
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update README.md with new instructions to call and run the fine-tuned model
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README.md
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@@ -20,6 +20,7 @@ This repository contains a LLaMA-13B further fine-tuned model on conversations a
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## Model Details
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### Model Description
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@@ -95,23 +96,91 @@ def generate_prompt(instruction: str, input_ctxt: str = None) -> str:
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Use the code below to get started with the model.
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```python
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import torch
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from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
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model = LlamaForCausalLM.from_pretrained(
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-
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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generation_config = GenerationConfig(
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temperature=0.2,
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top_p=0.75,
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top_k=40,
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num_beams=4,
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max_new_tokens=
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)
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model.eval()
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response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
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print(response)
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>>> The capital city of Greece is Athens and it borders
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```
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## Training Details
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## Model Details
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Anyone can use (ask prompts) and play with the model using the pre-existing Jupyter Notebook in the **noteboooks** folder.
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### Model Description
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Use the code below to get started with the model.
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1. You can git clone the repo, which contains also the artifacts for the base model for simplicity and completeness, and run the following code snippet to load the mode:
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```python
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import torch
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from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
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MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
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config = PeftConfig.from_pretrained(MODEL_NAME)
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model = LlamaForCausalLM.from_pretrained(
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config.base_model_name_or_path,
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)
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model = PeftModel.from_pretrained(model, MODEL_NAME)
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generation_config = GenerationConfig(
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temperature=0.2,
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top_p=0.75,
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top_k=40,
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num_beams=4,
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max_new_tokens=32,
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)
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model.eval()
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if torch.__version__ >= "2":
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model = torch.compile(model)
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```
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### Example of Usage
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```python
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instruction = "What is the capital city of Greece and with which countries does Greece border?"
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input_ctxt = None # For some tasks, you can provide an input context to help the model generate a better response.
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prompt = generate_prompt(instruction, input_ctxt)
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input_ids = tokenizer(prompt, return_tensors="pt").input_ids
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input_ids = input_ids.to(model.device)
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with torch.no_grad():
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outputs = model.generate(
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input_ids=input_ids,
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generation_config=generation_config,
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return_dict_in_generate=True,
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output_scores=True,
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)
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response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
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print(response)
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>>> The capital city of Greece is Athens and it borders Turkey, Bulgaria, Macedonia, Albania, and the Aegean Sea.
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```
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2. You can also directly call the model from HuggingFace using the following code snippet:
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```python
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import torch
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from transformers import GenerationConfig, LlamaTokenizer, LlamaForCausalLM
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MODEL_NAME = "Sandiago21/llama-7b-hf-prompt-answering"
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BASE_MODEL = "decapoda-research/llama-7b-hf
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config = PeftConfig.from_pretrained(MODEL_NAME)
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model = LlamaForCausalLM.from_pretrained(
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BASE_MODEL,
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load_in_8bit=True,
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torch_dtype=torch.float16,
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device_map="auto",
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)
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tokenizer = LlamaTokenizer.from_pretrained(MODEL_NAME)
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model = PeftModel.from_pretrained(model, MODEL_NAME)
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generation_config = GenerationConfig(
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temperature=0.2,
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top_p=0.75,
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top_k=40,
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num_beams=4,
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max_new_tokens=32,
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)
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model.eval()
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response = tokenizer.decode(outputs.sequences[0], skip_special_tokens=True)
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print(response)
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>>> The capital city of Greece is Athens and it borders Turkey, Bulgaria, Macedonia, Albania, and the Aegean Sea.
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```
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## Training Details
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