Create README.md

README.md

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+---
+license: bigscience-bloom-rail-1.0
+language:
+- es
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- alpaca
+- bloom
+- LLM
+datasets:
+- tatsu-lab/alpaca
+inference: false
+widget:
+- text: "Below is an instruction that describes a task, paired with an input that provides further context.\nWrite a response that appropriately completes the request.\n### Instruction:\nTell me about alpacas"
+---
+
+<div style="text-align:center;width:250px;height:250px;">
+    <img src="here_our_logo">
+</div>
+
+
+
+# Chivoom: Spanish Alpaca (Chiva) 🐐 + BLOOM 💮
+
+
+## Adapter Description
+This adapter was created with the [PEFT](https://github.com/huggingface/peft) library and allowed the base model **BigScience/BLOOM 7B1** to be fine-tuned on the **Stanford's Alpaca Dataset** (translated to Spanish) by using the method **LoRA**.
+
+## Model Description
+BigScience Large Open-science Open-access Multilingual Language Model
+
+[BLOOM 7B1](https://huggingface.co/bigscience/bloom-7b1)
+
+## Training data
+
+We translated to Spanish the Alpaca dataset.
+
+Alpaca is a dataset of **52,000** instructions and demonstrations generated by OpenAI's `text-davinci-003` engine. This instruction data can be used to conduct instruction-tuning for language models and make the language model follow instruction better.
+
+The authors built on the data generation pipeline from [Self-Instruct framework](https://github.com/yizhongw/self-instruct) and made the following modifications:
+
+- The `text-davinci-003` engine to generate the instruction data instead of `davinci`.
+- A [new prompt](https://github.com/tatsu-lab/stanford_alpaca/blob/main/prompt.txt) was written that explicitly gave the requirement of instruction generation to `text-davinci-003`.
+- Much more aggressive batch decoding was used, i.e., generating 20 instructions at once, which significantly reduced the cost of data generation.
+- The data generation pipeline was simplified by discarding the difference between classification and non-classification instructions.
+- Only a single instance was generated for each instruction, instead of 2 to 3 instances as in Self-Instruct.
+
+This produced an instruction-following dataset with 52K examples obtained at a much lower cost (less than $500). 
+In a preliminary study, the authors also found that the 52K generated data to be much more diverse than the data released by [Self-Instruct](https://github.com/yizhongw/self-instruct/blob/main/data/seed_tasks.jsonl).
+
+### Training procedure
+
+TBA
+
+## How to use
+```py
+import torch
+from peft import PeftModel, PeftConfig
+from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
+
+peft_model_id = "platzi/chivoom"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained("bigscience/bloom-7b1")
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+model.eval()
+
+# Based on the inference code by `tloen/alpaca-lora`
+def generate_prompt(instruction, input=None):
+    if input:
+        return f"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Input:
+{input}
+### Response:"""
+    else:
+        return f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+### Instruction:
+{instruction}
+### Response:"""
+
+def generate(
+        instruction,
+        input=None,
+        temperature=0.1,
+        top_p=0.75,
+        top_k=40,
+        num_beams=4,
+        **kwargs,
+):
+    prompt = generate_prompt(instruction, input)
+    inputs = tokenizer(prompt, return_tensors="pt")
+    input_ids = inputs["input_ids"].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,
+            generation_config=generation_config,
+            return_dict_in_generate=True,
+            output_scores=True,
+            max_new_tokens=256,
+        )
+    s = generation_output.sequences[0]
+    output = tokenizer.decode(s)
+    return output.split("### Response:")[1].strip().split("Below")[0]
+
+instruction = "¿Qué es un chivo?"
+
+print("Instruction:", instruction)
+print("Response:", generate(instruction))
+``