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---
library_name: peft
base_model: NousResearch/Yarn-Llama-2-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b8e4695d-1b90-40d0-8de9-41f916bd963a
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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Llama-2-7b-128k
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 0cac7cd61c65adcd_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/0cac7cd61c65adcd_train_data.json
type:
field_instruction: prompt
field_output: model
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: true
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: leixa/b8e4695d-1b90-40d0-8de9-41f916bd963a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 3
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: true
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mlflow_experiment_name: /tmp/0cac7cd61c65adcd_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: false
sample_packing: false
saves_per_epoch: 4
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: leixa-personal
wandb_mode: online
wandb_name: b8e4695d-1b90-40d0-8de9-41f916bd963a
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b8e4695d-1b90-40d0-8de9-41f916bd963a
warmup_steps: 10
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# b8e4695d-1b90-40d0-8de9-41f916bd963a
This model is a fine-tuned version of [NousResearch/Yarn-Llama-2-7b-128k](https://huggingface.co/NousResearch/Yarn-Llama-2-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3428
## 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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0003 | 1 | 6.2431 |
| 11.8099 | 0.0036 | 13 | 1.6349 |
| 1.7562 | 0.0072 | 26 | 0.4067 |
| 1.8423 | 0.0108 | 39 | 0.3774 |
| 1.3904 | 0.0144 | 52 | 0.3833 |
| 1.4535 | 0.0180 | 65 | 0.3698 |
| 1.3868 | 0.0216 | 78 | 0.3656 |
| 1.4394 | 0.0252 | 91 | 0.3515 |
| 1.3153 | 0.0288 | 104 | 0.3473 |
| 1.339 | 0.0324 | 117 | 0.3452 |
| 1.3674 | 0.0360 | 130 | 0.3436 |
| 1.3624 | 0.0396 | 143 | 0.3428 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1