---
library_name: peft
base_model: NousResearch/Yarn-Llama-2-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: ee3d2682-aeb0-4ff6-b3b6-558aa083df56
  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: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 7006679e7e87bef3_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/7006679e7e87bef3_train_data.json
  type:
    field_input: input
    field_instruction: intent
    field_output: target
    format: '{instruction} {input}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: mamung/ee3d2682-aeb0-4ff6-b3b6-558aa083df56
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 3
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 2
max_steps: 100
micro_batch_size: 2
mlflow_experiment_name: /tmp/7006679e7e87bef3_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
  adam_beta1: 0.9
  adam_beta2: 0.95
  adam_epsilon: 1.0e-05
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: eddysang
wandb_mode: online
wandb_name: 6b37e662-fa63-43cd-b5e0-33f7aa9694e8
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 6b37e662-fa63-43cd-b5e0-33f7aa9694e8
warmup_steps: 20
weight_decay: 0.02
xformers_attention: false

```

</details><br>

# ee3d2682-aeb0-4ff6-b3b6-558aa083df56

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: 0.0501

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 64
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- training_steps: 100

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log        | 0.0167 | 1    | 1.4724          |
| 25.533        | 0.1507 | 9    | 0.4807          |
| 6.8537        | 0.3014 | 18   | 0.1697          |
| 3.9352        | 0.4521 | 27   | 0.1072          |
| 3.0822        | 0.6028 | 36   | 0.0906          |
| 2.7679        | 0.7535 | 45   | 0.0738          |
| 2.3485        | 0.9042 | 54   | 0.0690          |
| 1.8185        | 1.0602 | 63   | 0.0584          |
| 1.566         | 1.2109 | 72   | 0.0573          |
| 1.4988        | 1.3616 | 81   | 0.0523          |
| 1.6353        | 1.5123 | 90   | 0.0507          |
| 1.5689        | 1.6630 | 99   | 0.0501          |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1