LLäMmlein 7B is a German LLaMa model trained from scratch using our adapted
Tinyllama codebase on the German portion of
RedPajama V2.
To enhance data quality, we additionally deduplicated the dataset on paragraph level and filtered it using a token-to-word ratio filter. The resulting dataset can be found
here.
We provide three model sizes:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2model_id = "LSX-UniWue/LLaMmlein_7B"
3tokenizer = AutoTokenizer.from_pretrained(model_id)
4model = AutoModelForCausalLM.from_pretrained(model_id)
In addition to the final model checkpoint, we publish intermediate checkpoints throughout the full training process as unique branches in this repository.
A specific checkpoint can be loaded like this:
1from transformers import AutoTokenizer, AutoModelForCausalLM
2model_id = "LSX-UniWue/LLaMmlein_7B"
3revision = "iter-00420000-ckpt"
4tokenizer = AutoTokenizer.from_pretrained(model_id, revision=revision)
5model = AutoModelForCausalLM.from_pretrained(model_id, revision=revision)
Next to the model itself each branch contains all datapoints that were used to train the model up to that point.
In the correspinding folder, named after the checkpoint, you can find several .log files (depending on the number of GPUs) of the following format:
1{"time": 1739809392.679516,
2 "iter_num": 0,
3 "data_id": ["sha1:EDQMBYDCYBLDAZH3MGYM276BM2DEHPPJ", "sha1:SAJCI75DRHZZFGQORV66NB5FVWUAVLFH", "sha1:7RBZV2MCEM4TUGBBWGTFQAKTWUOGETZU", "sha1:234M32IMLZF7455AKOFWDP6HT6YXAYB4", "sha1:2BIZ7LLSHRK5GUGPZM2GM55APTDKBUG2", "sha1:OF7OI77ZT7ROXGMB6LL4RSRANX7REAYK", "sha1:LGPUOCOV3MKETI5F3IHVGZPD4M26NNJL", "sha1:SHIHUW7FJTP5YHFFV2JZ2CAHUVMKK7XG"],
4 "file_id": [0, 0, 0, 0, 0, 0, 0, 0],
5 "process_rank": 0}
Note: Our earlier models from the paper, which do not include data logging, are available at:
We release the LLäMmlein models under a research-only RAIL-M license. See
license.md for details.
Data Take Down