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Model: Writer/palmyra-small
Source: Original Platform

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README.md

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+---
+language:
+- en
+datasets:
+- English
+tags:
+- text generation
+- pytorch
+- causal-lm
+- Writer-data
+- NeMo
+- palmyra
+pipeline_tag: text-generation
+library_name: transformers
+license: apache-2.0
+---
+
+
+
+# Palmyra Small 128M
+
+<style>
+img {
+ display: inline;
+}
+</style>
+
+|[![Model architecture](https://img.shields.io/badge/Model%20Arch-Transformer%20Decoder-green)](#model-architecture)|[![Model size](https://img.shields.io/badge/Params-128M-green)](#model-architecture)|[![Language](https://img.shields.io/badge/Language-en--US-lightgrey#model-badge)](#datasets)
+
+
+## Model Description
+
+Palmyra Small was primarily pre-trained with English text. Note that there is still a trace amount of non-English data present within the training corpus that was accessed through CommonCrawl. A causal language modeling (CLM) objective was utilized during the process of the model's pretraining. Similar to GPT-3, Palmyra Small is a member of the same family of models that only contain a decoder. As a result, it was pre-trained utilizing the objective of self-supervised causal language modeling. Palmyra Small uses the prompts and general experimental setup from GPT-3 in order to conduct its evaluation per GPT-3.
+
+## Use case
+Palmyra Small is the fastest of Writer’s LLMs and can perform important tasks such as text parsing, simple classification, address correction, and keyword recognition. Providing more context drives even better performance.
+
+
+## Training data
+
+Palmyra Small (128M) was trained on Writer’s custom dataset.
+
+## Intended Use and Limitations
+
+Palmyra Small learns an inner representation of the English language that can be used to extract features useful for downstream tasks. However, the model is best at what it was pre-trained for which is generating text from a prompt.
+
+### How to use
+
+This model can be easily loaded using the `AutoModelForCausalLM` functionality:
+
+```python
+
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("Writer/palmyra-small")
+
+tokenizer = AutoTokenizer.from_pretrained("Writer/palmyra-small")
+
+
+```
+
+### Limitations and Biases
+
+Palmyra Small’s core functionality is to take a string of text and predict the next token. While language models are widely used for other tasks, there are many unknowns in this work. When prompting Palmyra, keep in mind that the next statistically likely token is not always the token that produces the most "accurate" text. Never rely on Palmyra Small to produce factually correct results.
+
+Palmyra Small was trained on Writer’s custom data. As with all language models, it is difficult to predict how Palmyra Small will respond to specific prompts, and offensive content may appear unexpectedly. We recommend that the outputs be curated or filtered by humans before they are released, both to censor undesirable content and to improve the quality of the results.
+
+
+## Citation and Related Information
+
+
+To cite this model:
+```
+@misc{Palmyra,
+  author = {Writer Engineering Team},
+  title = {{Palmyra-base Parameter Autoregressive Language Model}},
+  howpublished = {\url{https://dev.writer.com}},
+  year = 2023,
+  month = January 
+}
+```

config.json

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+{
+  "_name_or_path": "Writer/Writer-LLM-small",
+  "activation_function": "gelu",
+  "architectures": [
+    "GPT2LMHeadModel"
+  ],
+  "attn_pdrop": 0.1,
+  "bos_token_id": 50256,
+  "embd_pdrop": 0.1,
+  "eos_token_id": 50256,
+  "initializer_range": 0.023,
+  "layer_norm_epsilon": 1e-05,
+  "model_type": "gpt2",
+  "n_embd": 768,
+  "n_head": 12,
+  "n_inner": 3072,
+  "n_layer": 12,
+  "n_positions": 2048,
+  "reorder_and_upcast_attn": false,
+  "resid_pdrop": 0.1,
+  "scale_attn_by_inverse_layer_idx": false,
+  "scale_attn_weights": true,
+  "summary_activation": null,
+  "summary_first_dropout": 0.1,
+  "summary_proj_to_labels": true,
+  "summary_type": "cls_index",
+  "summary_use_proj": true,
+  "torch_dtype": "float32",
+  "transformers_version": "4.24.0",
+  "use_cache": true,
+  "vocab_size": 50257
+}

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special_tokens_map.json

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+}

tokenizer_config.json

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+  "add_prefix_space": false,
+  "bos_token": "<|endoftext|>",
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+}