---
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
language: [
    'en', 'am', 'ar', 'as', 'az', 'be', 'bg', 'bn', 'br', 'bs', 'ca', 'cs', 'cy', 'da', 'de', 'el',
    'eo', 'es', 'et', 'eu', 'fa', 'ff', 'fi', 'fr', 'fy', 'ga', 'gd', 'gl', 'gn', 'gu', 'ha', 'he',
    'hi', 'hr', 'ht', 'hu', 'hy', 'id', 'ig', 'is', 'it', 'ja', 'jv', 'ka', 'kk', 'km', 'kn', 'ko',
    'ku', 'ky', 'la', 'lg', 'li', 'ln', 'lo', 'lt', 'lv', 'mg', 'mk', 'ml', 'mn', 'mr', 'ms', 'my',
    'ne', 'nl', 'no', 'ns', 'om', 'or', 'pa', 'pl', 'ps', 'pt', 'qu', 'rm', 'ro', 'ru', 'sa', 'si',
    'sc', 'sd', 'sk', 'sl', 'so', 'sq', 'sr', 'ss', 'su', 'sv', 'sw', 'ta', 'te', 'th', 'tl', 'tn',
    'tr', 'ug', 'uk', 'ur', 'uz', 'vi', 'wo', 'xh', 'yi', 'yo', 'zu',
]
datasets: [
    'yahma/alpaca-cleaned',
    'gbharti/wealth-alpaca_lora',
    'saillab/taco-datasets',
    'xu-song/cc100-samples',
    'ontocord/fineweb-permissive-multilingual-2m',
    'MuskumPillerum/General-Knowledge',
    'yirenc/general_knowledge_boolean',
    'nampdn-ai/tiny-textbooks',
    'nampdn-ai/tiny-codes',
    'bigcode/the-stack-smol-xs',
    'm-a-p/CodeFeedback-Filtered-Instruction',
    'jtatman/python-code-dataset-500k',
    'iamtarun/python_code_instructions_18k_alpaca',
    'HuggingFaceH4/CodeAlpaca_20K',
    'gair-prox/open-web-math-pro',
    'rvv-karma/Math-QA',
    'ajibawa-2023/Maths-College',
    'microsoft/orca-math-word-problems-200k',
    'fblgit/simple-math',
    'SkunkworksAI/reasoning-0.01',
    'badrex/llm-emoji-dataset',
]
tags:
- litgpt
- litdata
---

# tangled-llama-58m-32k-base-v0.1

![logo](./misc/logo.png)

A pretrained language model based on the Llama model with about **58M** parameters. This model has been trained on **11.4B** (`11,422,750,857`) tokens from more than **0.8M** (`796,399`) dataset rows.

This model **isn't** designed for immediate use but rather for Continued Pretraining and Finetuning on a downstream task. While it can handle a context length of up to **128K** (`131,072`) tokens, it was pretrained with sequences of **2K** (`2048`) tokens.

The objective is to streamline the cognitive or reasoning core, eliminating any redundant knowledge from the model.

[loss, val_loss](https://api.wandb.ai/links/mtasic85/0i3wqsmb)

[val_ppl](https://api.wandb.ai/links/mtasic85/vz1jgu3v)

[epoch](https://api.wandb.ai/links/mtasic85/qltqthjr)

[learning_rate](https://api.wandb.ai/links/mtasic85/eqpiton4)