metadata
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:
- Replete-AI/Everything_Instruct_Multilingual
- HuggingFaceH4/ultrachat_200k
- HuggingFaceH4/no_robots
- datatab/ultrachat_200k_serbian
- datatab/ultrafeedback_binarized_serbian
- datatab/alpaca-cleaned-serbian-full
- datatab/orca_math_world_problem_200k_serbian
- datatab/open-orca-slim-serbian
tags:
- litgpt
- litdata
tangled-llama-33m-32k-instruct-v0.1
A pretrained language model based on the Llama model with about 33M parameters. This model has been trained on 4.2B (4,252,334,823) tokens from more than 6.2M (6,271,145) 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 32K (32,768) tokens, it was pretrained with sequences of 32K (32768) tokens.
The objective is to streamline the cognitive or reasoning core, eliminating any redundant knowledge from the model.
lm-evaluation-harness
litgpt evaluate --tasks 'leaderboard' --out_dir 'evaluate-0/' --batch_size 4 --dtype 'bfloat16' out/contrain/final/
litgpt evaluate --tasks 'hellaswag,gsm8k,truthfulqa_mc2,mmlu,winogrande,arc_challenge' --out_dir 'evaluate-1/' --batch_size 4 --dtype 'bfloat16' out/contrain/final/