Create README.md

README.md

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+---
+language:
+- en
+- code
+
+
+tags:
+- pytorch
+- causal-lm
+- code-generation
+
+
+license: apache-2.0
+
+---
+
+
+# FIM-1.3B 
+
+## Model Description
+
+FIM-1.3B is the first of a series of large-scale infilling-enabled autoregressive language models trained by CarperAI. FIM-1.3B is the first of these models, and future models (both larger and smaller) trained on greater quantities of code data will be released, potentially with different architectural variations optimized for code.
+
+This is a preliminary release of an experimental artifact and should be treated as such.
+
+
+
+## Model Dimensions
+
+
+| Hyperparameter       | Value                                                                                                                                  |
+
+
+|----------------------|----------------------------------------------------------------------------------------------------------------------------------------|
+
+
+| \\(n_{parameters}\\) | 1,331,810,304                                                                                                                           |
+
+
+| \\(n_{layers}\\)     | 24                                                                                                                                     |
+
+
+| \\(d_{model}\\)      | 2,048                                                                                                                                   |
+
+
+| \\(d_{ff}\\)         | 8,192                                                                                                                                   |
+
+
+| \\(n_{heads}\\)      | 16                                                                                                                                     |
+
+
+| \\(d_{head}\\)       | 128                                                                                                                                    |
+
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+| \\(n_{ctx}\\)        | 2,048                                                                                                                                   |
+
+
+| \\(n_{vocab}\\)      | 50256                                                                                                                      |
+
+
+| Positional Encoding  | [Rotary Position Embedding (RoPE)](https://arxiv.org/abs/2104.09864)                                                                   |
+
+
+
+
+
+
+
+The model consists of 24 transformer layers with a model dimension of 2048, and a feedforward dimension of 8192. The model
+
+
+dimension is split into 16 heads, each with a dimension of 128. Rotary Position Embedding (RoPE) is used.
+
+
+The model is trained with the same tokenizer as GPT-NeoX-20b (link here), for a vocabulary of 50254 tokens.
+
+
+## Training Data
+
+The model was trained on the Pile, an 800Gb dataset composed of varied web corpora. The datasheet and paper for the Pile can be found [here] and [here] respectively
+
+
+## Training Details
+
+This model was trained for 47,000 steps at a batch size of 6,291,456 tokens per step in the [GPT-NeoX codebase](https://github.com/EleutherAI/gpt-neox). It was trained as an autoregressive language model, using cross-entropy loss to maximize the likelihood of predicting the next token correctly. 
+
+Following Bavarian et al. 2022, we train the model to additionally perform infilling via a data transformation applied randomly to 90% of input contexts at train-time. 
+
+Middle segments “to infill” were selected uniformly at random from contexts at the character level, and these contexts were then reformatted as 
+
+
+<SUF> {last 1/3rd of the context} <PRE> {first 1/3rd of the context} <MID> {middle 1/3rd of the context} <EOD>
+
+
+
+
+
+
+## How to use
+
+
+This model can be easily loaded using the `AutoModelForCausalLM` class:
+
+
+```python
+
+
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+tokenizer = AutoTokenizer.from_pretrained("CarperAI/FIM-1.3B")
+model = AutoModelForCausalLM.from_pretrained("CarperAI/FIM-1.3b")
+
+
+```
+
+### Performing Infilling
+
+Suppose we have some text that we would like to perform infilling on at a certain “cursor location”. 
+
+This would have the form {some prelude text here} <INFILLING LOCATION> {some text following cursor}.
+
+The way to perform infilling generation would be via placing the input text into this format:
+
+<SUF> {some text following cursor} <PRE> {some prelude text here} <MID> ... language model output is generated after <MID> token!
+
+
+## Intended Uses and Limitations
+
+FIM-1.3B learns a representation of the English language that can be used to extract features useful for downstream NLP and Code generation tasks. However, the model has solely been trained on a standard next-token-prediction language modeling task on its training data.
+
+## Limitations and Biases
+
+FIM-1.3B was trained on the Pile, a dataset known to contain profanity, lewd, and otherwise abrasive language. FIM-1.3B may produce socially unacceptable or otherwise harmful text. See Sections 5 and 6 of the Pile paper for a more detailed analysis of the biases in the Pile.
+
+As with all language models, it is hard to predict in advance how FIM-1.3B will respond to particular prompts, and offensive content may occur without warning. We recommend having a human curate or filter the outputs before releasing them, both to censor undesirable content and to improve the quality of the results. Code generated by FIM-1.3B should also be checked for security errors by a human before use in production.
+
+## Evaluation results
+
+We evaluate our model on a number of standard NLP datasets to verify that our infilling model performs on par with a comparable autoregressive model.
+
+We use the [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness) developed by EleutherAI.
+
+
+Report: 
+LogiQA, PIQA, SciQ, WSC, Winogrande, ARC_challenge, ARC_easy, lambada
+On FIM-1.3B, the comparable autoregressive model, 
+
+
+
+
+
+We also perform preliminary investigation on code generation and infilling capabilities by testing on HumanEval-Infilling [link to github] [Bavarian et al. 2022]
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+