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Update README.md
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README.md
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@@ -16,6 +16,56 @@ thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources
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This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
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- Base model: [EleutherAI/pythia-70m-deduped-v0](https://huggingface.co/EleutherAI/pythia-70m-deduped-v0)
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## Usage
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This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
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- Base model: [EleutherAI/pythia-70m-deduped-v0](https://huggingface.co/EleutherAI/pythia-70m-deduped-v0)
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## Usage on CPU
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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def generate_response(prompt, model_name):
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tokenizer = AutoTokenizer.from_pretrained(
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model_name,
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use_fast=True,
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trust_remote_code=True,
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)
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.float32,
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device_map={"": "cpu"},
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trust_remote_code=True,
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)
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model.cpu().eval()
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inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cpu")
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tokens = model.generate(
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input_ids=inputs["input_ids"],
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attention_mask=inputs["attention_mask"],
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min_new_tokens=2,
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max_new_tokens=500,
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do_sample=False,
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num_beams=2,
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temperature=float(0.0),
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repetition_penalty=float(1.0),
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renormalize_logits=True
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)[0]
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tokens = tokens[inputs["input_ids"].shape[1]:]
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answer = tokenizer.decode(tokens, skip_special_tokens=True)
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return answer
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```
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After defining the function, you can define the prompt and the model from another file
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```python
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model_name = "diegomiranda/text-to-cypher"
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prompt = "Create a Cypher statement to answer the following question:Retorne os processos de Direito Tributário que se baseiam em lei 939 de 1992?<|endoftext|>"
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response = generate_response(prompt, model_name)
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print(response)
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```
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## Usage
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