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--- |
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license: apache-2.0 |
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language: |
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- es |
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pipeline_tag: text-generation |
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library_name: transformers |
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inference: false |
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--- |
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# Llama-2-13B-ft-instruct-es |
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[Llama 2 (13B)](https://huggingface.co/meta-llama/Llama-2-13b) fine-tuned on [Clibrain](https://huggingface.co/clibrain)'s Spanish instructions dataset. |
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## Model Details |
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Llama 2 is a collection of pre-trained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pre-trained model. |
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## Example of Usage |
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```py |
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import torch |
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from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig |
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model_id = "clibrain/Llama-2-13b-ft-instruct-es" |
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda") |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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def create_instruction(instruction, input_data=None, context=None): |
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sections = { |
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"Instrucci贸n": instruction, |
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"Entrada": input_data, |
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"Contexto": context, |
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} |
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system_prompt = "A continuaci贸n hay una instrucci贸n que describe una tarea, junto con una entrada que proporciona m谩s contexto. Escriba una respuesta que complete adecuadamente la solicitud.\n\n" |
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prompt = system_prompt |
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for title, content in sections.items(): |
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if content is not None: |
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prompt += f"### {title}:\n{content}\n\n" |
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prompt += "### Respuesta:\n" |
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return prompt |
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def generate( |
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instruction, |
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input=None, |
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context=None, |
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max_new_tokens=128, |
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temperature=0.1, |
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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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**kwargs |
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): |
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prompt = create_instruction(instruction, input, context) |
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print(prompt.replace("### Respuesta:\n", "")) |
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inputs = tokenizer(prompt, return_tensors="pt") |
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input_ids = inputs["input_ids"].to("cuda") |
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attention_mask = inputs["attention_mask"].to("cuda") |
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generation_config = GenerationConfig( |
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temperature=temperature, |
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top_p=top_p, |
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top_k=top_k, |
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num_beams=num_beams, |
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**kwargs, |
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) |
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with torch.no_grad(): |
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generation_output = model.generate( |
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input_ids=input_ids, |
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attention_mask=attention_mask, |
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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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max_new_tokens=max_new_tokens, |
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early_stopping=True |
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) |
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s = generation_output.sequences[0] |
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output = tokenizer.decode(s) |
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return output.split("### Respuesta:")[1].lstrip("\n") |
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instruction = "Dame una lista de lugares a visitar en Espa帽a." |
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print(generate(instruction)) |
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``` |
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## Example of Usage with `pipelines` |
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```py |
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from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline |
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model_id = "clibrain/Llama-2-13b-ft-instruct-es" |
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda") |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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pipe = pipeline(task="text-generation", model=model, tokenizer=tokenizer, max_length=200, device=0) |
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prompt = """ |
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A continuaci贸n hay una instrucci贸n que describe una tarea. Escriba una respuesta que complete adecuadamente la solicitud. |
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### Instrucci贸n: |
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Dame una lista de 5 lugares a visitar en Espa帽a. |
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### Respuesta: |
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""" |
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result = pipe(prompt) |
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print(result[0]['generated_text']) |
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``` |