metadata
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B
datasets:
- allenai/tulu-3-sft-mixture
QuantFactory/Teleut-7b-GGUF
This is quantized version of allura-org/Teleut-7b created using llama.cpp
Original Model Card
Teleut 7b
A replication attempt of Tulu 3 on the Qwen 2.5 base models.
Evals (so far)
| Teleut 7B (measured) | Tülu 3 SFT 8B (reported) | Qwen 2.5 7B Instruct (reported) | Ministral 8B (reported) | Mistral 7B v0.3 (reported) | |
|---|---|---|---|---|---|
| BBH (3 shot, CoT) | 64.4% | 67.9% | 21.7% | 56.2% | 47.0%NLL |
| GSM8K (8 shot, CoT) | 78.5% | 76.2% | 83.8% | 80.0% | xx.x% |
| IFEval (prompt loose) | 66.3% | 72.8% | 74.7% | 56.4% | 53.0% |
| MMLU (0 shot, CoT) | 73.2% | 65.9% | 76.6% | 68.5% | 30.7%5-shot |
| MMLU Pro (0 shot, CoT) | 48.3% | 44.3% | 56.3%Unknown | 32.9%5-shot | 30.7%5-shot |
| PopQA (15 shot) | 18.9% | 29.3% | 18.1% | 20.2% | xx.x% |
| TruthfulQA | 47.2% | 46.8% | 63.1% | 55.5% | xx.x% |
Credits
Big thanks to Retis Labs for being providing my 8xH100 polycule used to train and test this model!
Another big thanks to AllenAI for publishing the Tülu 3 data and model series (as well as the paper and details on training), as well as Alibaba for training the original Qwen 2.5 base model series!
@article{lambert2024tulu3,
title = {Tülu 3: Pushing Frontiers in Open Language Model Post-Training},
author = {
Nathan Lambert and
Jacob Morrison and
Valentina Pyatkin and
Shengyi Huang and
Hamish Ivison and
Faeze Brahman and
Lester James V. Miranda and
Alisa Liu and
Nouha Dziri and
Shane Lyu and
Yuling Gu and
Saumya Malik and
Victoria Graf and
Jena D. Hwang and
Jiangjiang Yang and
Ronan Le Bras and
Oyvind Tafjord and
Chris Wilhelm and
Luca Soldaini and
Noah A. Smith and
Yizhong Wang and
Pradeep Dasigi and
Hannaneh Hajishirzi
},
year = {2024},
email = {tulu@allenai.org}
}
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3.5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- optimizer: Use paged_ademamix_8bit and the args are: No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 370
- num_epochs: 1
Framework versions
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
Configuration
See axolotl config
axolotl version: 0.5.2
base_model: Qwen/Qwen2.5-7B
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_fused_linear_cross_entropy: true
strict: false
chat_template: chatml
datasets:
- path: allenai/tulu-3-sft-mixture
type: chat_template
split: train
field_messages: messages
dataset_prepared_path: last_run_prepared
#val_set_size: 0.02
output_dir: ./ckpts
sequence_len: 8192
#sample_packing: true
pad_to_sequence_len: true
wandb_project: qwen-2.5-7b-sft
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 8
num_epochs: 1
optimizer: paged_ademamix_8bit
lr_scheduler: cosine
learning_rate: 3.5e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
deepspeed: deepspeed_configs/zero3_bf16.json
warmup_steps: 370
#evals_per_epoch: 4
eval_table_size:
saves_per_epoch: 2
debug:
weight_decay: 0.0