File size: 2,014 Bytes
043f109 85f843f 3bbb11b 095a44a 3bbb11b 043f109 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 |
---
base_model: unsloth/Mistral-Nemo-Instruct-2407
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** UsernameJustAnother
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407
Experimental RP Finetune with secret sauce dataset, rsLoRA, r = 64, on an Colab A100 instance. 30GB vRAM used, 2 epochs ~ 3hrs of training.
```
==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1
\\ /| Num examples = 8,160 | Num Epochs = 2
O^O/ \_/ \ Batch size per device = 2 | Gradient Accumulation steps = 4
\ / Total batch size = 8 | Total steps = 2,040
"-____-" Number of trainable parameters = 228,065,280
r = 64,
target_modules = ["q_proj", "k_proj", "v_proj", "o_proj",
"gate_proj", "up_proj", "down_proj",],
lora_alpha = 64,
lora_dropout = 0, # Supports any, but = 0 is optimized
bias = "none", # Supports any, but = "none" is optimized
use_gradient_checkpointing = "unsloth", # True or "unsloth" for very long context
random_state = 3407,
use_rslora = True, # lora_alpha --> 8
loftq_config = None,
per_device_train_batch_size = 2,
gradient_accumulation_steps = 4,
warmup_steps = 5,
num_train_epochs = 2,
learning_rate = 2e-5, # down from 2e-4, could go down to (5e-5 then 1e-5)
fp16 = not is_bfloat16_supported(),
bf16 = is_bfloat16_supported(),
logging_steps = 1,
optim = "adamw_8bit",
weight_decay = 0.01,
lr_scheduler_type = "linear",
seed = 3407,
```
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|