Create README.md

README.md

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+---
+model-index:
+- name: lince-zero
+  results: []
+license: apache-2.0
+language:
+- es
+pipeline_tag: text-generation
+datasets:
+- tatsu-lab/alpaca
+- databricks/databricks-dolly-15k
+library_name: transformers
+inference: false
+---
+
+# Llama-2-ft-instruct-es
+
+[Llama 2 (7B)](https://huggingface.co/meta-llama/Llama-2-7b) fine-tuned on [Clibrain](https://huggingface.co/clibrain)'s  Spanish instructions dataset.
+
+
+# Model Details
+
+Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B pretrained model. Links to other models can be found in the index at the bottom.
+
+
+# Example of Usage
+
+```py
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, AutoTokenizer, GenerationConfig
+
+model_id = "clibrain/Llama-2-ft-instruct-es"
+
+model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True).to("cuda")
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+
+def create_instruction(instruction, input_data=None, context=None):
+    sections = {
+        "Instrucción": instruction,
+        "Entrada": input_data,
+        "Contexto": context,
+    }
+
+    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"
+    prompt = system_prompt
+
+    for title, content in sections.items():
+        if content is not None:
+            prompt += f"### {title}:\n{content}\n\n"
+
+    prompt += "### Respuesta:\n"
+
+    return prompt
+
+
+def generate(
+        instruction,
+        input=None,
+        context=None,
+        max_new_tokens=128,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs
+):
+    
+    prompt = create_instruction(instruction, input, context)
+    print(prompt.replace("### Respuesta:\n", ""))
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].to("cuda")
+    attention_mask = inputs["attention_mask"].to("cuda")
+    generation_config = GenerationConfig(
+        temperature=temperature,
+        top_p=top_p,
+        top_k=top_k,
+        num_beams=num_beams,
+        **kwargs,
+    )
+    with torch.no_grad():
+        generation_output = model.generate(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=max_new_tokens,
+            early_stopping=True
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Respuesta:")[1].lstrip("\n")
+
+instruction = "Dame una lista de lugares a visitar en España."
+print(generate(instruction))
+```