Create README.md

README.md

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+---
+license: wtfpl
+language: es
+tags:
+- gpt-j
+- spanish
+- gpt-j-6b
+---
+
+# BERTIN-GPT-J-6B with 8-bit weights (Quantized)
+
+This model (and model card) is an adaptation of [hivemind/gpt-j-6B-8bit](https://huggingface.co/hivemind/gpt-j-6B-8bit), so all credits to him/her.
+
+This is a version of the **latest checkpoint (1M steps)** **bertin-project/bertin-gpt-j-6B** that is modified so you can generate **and fine-tune the model in Colab or equivalent desktop GPU (e.g. single 1080Ti)**.
+
+Here's how to run it: [![colab](https://camo.githubusercontent.com/84f0493939e0c4de4e6dbe113251b4bfb5353e57134ffd9fcab6b8714514d4d1/68747470733a2f2f636f6c61622e72657365617263682e676f6f676c652e636f6d2f6173736574732f636f6c61622d62616467652e737667)](https://colab.research.google.com/drive/1ft6wQU0BhqG5PRlwgaZJv2VukKKjU4Es)
+
+__The [original GPT-J](https://huggingface.co/EleutherAI/gpt-j-6B/tree/main)__ takes 22+ GB memory for float32 parameters alone, and that's before you account for gradients & optimizer. Even if you cast everything to 16-bit, it will still not fit onto most single-GPU setups short of A6000 and A100. You can inference it [on TPU](https://colab.research.google.com/github/kingoflolz/mesh-transformer-jax/blob/master/colab_demo.ipynb) or CPUs, but fine-tuning is way more expensive.
+
+Here, we apply several techniques to make GPT-J usable and fine-tunable on a single GPU with ~11 GB memory:
+- large weight tensors are quantized using dynamic 8-bit quantization and de-quantized just-in-time for multiplication
+- using gradient checkpoints to store one only activation per layer: using dramatically less memory at the cost of 30% slower training
+- scalable fine-tuning with [LoRA](https://arxiv.org/abs/2106.09685) and [8-bit Adam](https://arxiv.org/abs/2110.02861)
+
+In other words, all of the large weight-matrices are frozen in 8-bit, and you only train small adapters and optionally 1d tensors (layernorm scales, biases).
+
+![img](https://i.imgur.com/n4XXo1x.png)
+
+
+__Does 8-bit affect model quality?__ Technically yes, but the effect is negligible in practice. [This notebook measures wikitext test perplexity](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/check_perplexity.ipynb) and it is nigh indistinguishable from the original GPT-J. Quantized model is even slightly better, but that is not statistically significant.
+
+Our code differs from other 8-bit methods in that we use **8-bit only for storage, and all computations are performed in float16 or float32**. As a result, we can take advantage of nonlinear quantization that fits to each individual weight distribution. Such nonlinear quantization does not accelerate inference, but it allows for much smaller error.
+
+
+__What about performance?__ Both checkpointing and de-quantization has some overhead, but it's surprisingly manageable. Depending on GPU and batch size, the quantized model is 1-10% slower than the original model on top of using gradient checkpoints (which is 30% overhead). In short, this is because block-wise quantization from bitsandbytes is really fast on GPU.
+
+
+### How should I fine-tune the model?
+
+We recommend starting with the original hyperparameters from [the LoRA paper](https://arxiv.org/pdf/2106.09685.pdf).
+On top of that, there is one more trick to consider: the overhead from de-quantizing weights does not depend on batch size.
+As a result, the larger batch size you can fit, the more efficient you will train.
+
+
+### Where can I train for free?
+
+You can train fine in colab, but if you get a K80, it's probably best to switch to other free gpu providers: [kaggle](https://towardsdatascience.com/amazon-sagemaker-studio-lab-a-great-alternative-to-google-colab-7194de6ef69a), [aws sagemaker](https://towardsdatascience.com/amazon-sagemaker-studio-lab-a-great-alternative-to-google-colab-7194de6ef69a) or [paperspace](https://docs.paperspace.com/gradient/more/instance-types/free-instances). For intance, this is the same notebook [running in kaggle](https://www.kaggle.com/justheuristic/dmazur-converted) using a more powerful P100 instance.
+
+
+### Can I use this technique with other models?
+
+The model was converted using [this notebook](https://nbviewer.org/urls/huggingface.co/hivemind/gpt-j-6B-8bit/raw/main/convert-gpt-j.ipynb). It can be adapted to work with other model types. However, please bear in mind that some models replace Linear and Embedding with custom alternatives that require their own BNBWhateverWithAdapters.
+
+### How to use
+
+```sh
+wget https://huggingface.co/mrm8488/bertin-gpt-j-6B-ES-8bit/resolve/main/utils.py -O Utils.py
+pip install transformers
+pip install bitsandbytes-cuda111==0.26.0
+```
+
+```py 
+import transformers
+import torch
+
+from Utils import GPTJBlock, GPTJForCausalLM
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+
+transformers.models.gptj.modeling_gptj.GPTJBlock = GPTJBlock  # monkey-patch GPT-J
+
+tokenizer = transformers.AutoTokenizer.from_pretrained("mrm8488/bertin-gpt-j-6B-ES-8bit")
+model = GPTJForCausalLM.from_pretrained("hivemind/gpt-j-6B-8bit", pad_token_id=tokenizer.eos_token_id, low_cpu_mem_usage=True).to(device)
+
+
+prompt = tokenizer("El sentido de la vida es", return_tensors='pt')
+prompt = {key: value.to(device) for key, value in prompt.items()}
+
+out = model.generate(**prompt, max_length=64, do_sample=True)
+
+print(tokenizer.decode(out[0]))
+```