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---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.3
model-index:
- name: outputs/dadjoke-mistral-qlora-out
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false
val_set_size: 0.01
datasets:
- path: shuttie/reddit-dadjokes
split: train
type: alpaca
dataset_prepared_path: last_run_prepared
output_dir: ./outputs/dadjoke-mistral-qlora-out
adapter: qlora
lora_model_dir:
sequence_len: 256
sample_packing: false
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 60
num_epochs: 1
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.00005
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
xformers_attention:
flash_attention: true
logging_steps: 10
warmup_steps: 10
evals_per_epoch: 10
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: false
fsdp_transformer_layer_cls_to_wrap: MistralDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
special_tokens:
# torch_compile: true
# chat_template: chatml
```
</details><br>
# outputs/dadjoke-mistral-qlora-out
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.2797
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 60
- eval_batch_size: 60
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 120
- total_eval_batch_size: 120
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0008 | 1 | 2.9205 |
| 2.3515 | 0.1001 | 122 | 2.3554 |
| 2.2695 | 0.2002 | 244 | 2.3219 |
| 2.3065 | 0.3002 | 366 | 2.3112 |
| 2.2109 | 0.4003 | 488 | 2.2974 |
| 2.2043 | 0.5004 | 610 | 2.2941 |
| 2.2672 | 0.6005 | 732 | 2.2878 |
| 2.2259 | 0.7006 | 854 | 2.2825 |
| 2.2386 | 0.8007 | 976 | 2.2820 |
| 2.247 | 0.9007 | 1098 | 2.2797 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |