Carpincho-30b-qlora

Model Card for Carpincho-30b

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This is Carpincho-30B qlora 4-bit checkpoint, an Instruction-tuned LLM based on LLama-30B. It is trained to answer in colloquial spanish Argentine language.

It was trained on 2x3090 (48G) for 120 hs using huggingface QLoRA code (4-bit quantization)

Model Details

The model is provided in LoRA format.

Usage

Here is example inference code, you will need to install the following requirements:

bitsandbytes==0.39.0
transformers @ git+https://github.com/huggingface/transformers.git
peft @ git+https://github.com/huggingface/peft.git
accelerate @ git+https://github.com/huggingface/accelerate.git
einops==0.6.1
evaluate==0.4.0
scikit-learn==1.2.2
sentencepiece==0.1.99
wandb==0.15.3

import time
import torch
from peft import PeftModel    
from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaTokenizer, StoppingCriteria, StoppingCriteriaList, TextIteratorStreamer

model_name = "models/huggyllama_llama-30b/"
adapters_name = 'carpincho-30b-qlora'

print(f"Starting to load the model {model_name} into memory")

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    load_in_4bit=True,
    torch_dtype=torch.bfloat16,
    device_map="sequential"
)

print(f"Loading {adapters_name} into memory")
model = PeftModel.from_pretrained(model, adapters_name)
tokenizer = LlamaTokenizer.from_pretrained(model_name)
tokenizer.bos_token_id = 1

stop_token_ids = [0]

print(f"Successfully loaded the model {model_name} into memory")

def main(tokenizer):
    prompt = '''Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
%s
### Response:
    ''' % "Hola, como estas?"

    batch = tokenizer(prompt, return_tensors="pt")
    batch = {k: v.cuda() for k, v in batch.items()}

    with torch.no_grad():
        generated = model.generate(inputs=batch["input_ids"],
                               do_sample=True, use_cache=True,
                               repetition_penalty=1.1,
                               max_new_tokens=100,
                               temperature=0.9,
                               top_p=0.95,
                               top_k=40,
                               return_dict_in_generate=True,
                               output_attentions=False,
                               output_hidden_states=False,
                               output_scores=False)
    result_text = tokenizer.decode(generated['sequences'].cpu().tolist()[0])
    print(result_text)

main(tokenizer)

Model Description

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• Developed by: Alfredo Ortega (@ortegaalfredo)
• Model type: 30B LLM QLoRA
• Language(s): (NLP): English and colloquial Argentine Spanish
• License: Free for non-commercial use, but I'm not the police.
• Finetuned from model: https://huggingface.co/huggyllama/llama-30b

Model Sources [optional]

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• Repository: https://huggingface.co/huggyllama/llama-30b
• Paper [optional]: https://arxiv.org/abs/2302.13971

Uses

<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->

This is a generic LLM chatbot that can be used to interact directly with humans.

Bias, Risks, and Limitations

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This bot is uncensored and may provide shocking answers. Also it contains bias present in the training material.

Recommendations

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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.

Model Card Contact

Contact the creator at @ortegaalfredo on twitter/github