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--- |
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model-index: |
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- name: lince-zero |
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results: [] |
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license: apache-2.0 |
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language: |
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- es |
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thumbnail: https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png |
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pipeline_tag: text-generation |
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--- |
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<div style="text-align:center;width:250px;height:250px;"> |
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<img src="https://huggingface.co/mrm8488/falcoder-7b/resolve/main/falcoder.png" alt="falcoder logo""> |
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</div> |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# Lince Zero |
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**Lince** is model fine-tuned on a massive and original corpus of Spanish instructions. |
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## Model description 🧠 |
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TBA |
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## Training and evaluation data 📚 |
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We created an instruction dataset following the format or popular datasets in the field such as *Alpaca* and Dolly* and augmented it. |
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### Training hyperparameters ⚙ |
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TBA |
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### Training results 🗒️ |
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TBA |
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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, AutoTokenizer |
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model_id = "clibrain/lince-zero" |
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tokenizer = AutoTokenizer.from_pretrained(model_id) |
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model = AutoModelForCausalLM.from_pretrained(model_id).to("cuda") |
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def create_instruction(instruction: str, input_data: str = None, context: str = None) -> str: |
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sections = { |
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"Instrucción": 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 continuación hay una instrucción que describe una tarea, junto con una entrada que proporciona más 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) |
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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 España." |
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print(generate(instruction)) |
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``` |
