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library_name: peft
license: apache-2.0
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5a9022d0-f07f-44eb-abb9-166cd1900db0
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: teknium/OpenHermes-2.5-Mistral-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 83d749d1d83f68a2_train_data.json
ds_type: json
field: prompt
path: /workspace/input_data/
split: train
type: completion
ddp_find_unused_parameters: false
debug: null
deepspeed: null
early_stopping_patience: null
ema_decay: 0.995
ema_update_after_step: 200
eps: 1.0e-06
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: 1
gradient_checkpointing: true
gradient_clipping: 0.5
gradient_normalization: true
greater_is_better: false
group_by_length: false
hub_model_id: CheapsetZero/5a9022d0-f07f-44eb-abb9-166cd1900db0
learning_rate: 0.00018
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_nan_inf_filter: true
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 11220
metric_for_best_model: eval_loss
micro_batch_size: 24
min_lr: 3.6e-05
mlflow_experiment_name: /tmp/83d749d1d83f68a2_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
reward_model_sampling_temperature: 0.7
s2_attention: null
sample_packing: false
save_total_limit: 3
saves_per_epoch: 4
sequence_len: 1024
skip_nan_gradients: true
special_tokens:
pad_token: <|im_end|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trl:
adaptive_beta: true
beta: 0.12
entropy_coeff: 0.01
gradient_normalization: true
kl_monitoring: true
max_completion_length: 1024
num_generations: 12
reward_funcs:
- rewards_1cd8f023-f9d4-4d99-93fe-b7d21bed195d.reward_short_sentences
- rewards_1cd8f023-f9d4-4d99-93fe-b7d21bed195d.reward_low_unique_words_percentage
- rewards_1cd8f023-f9d4-4d99-93fe-b7d21bed195d.reward_low_syllables_per_word
- rewards_1cd8f023-f9d4-4d99-93fe-b7d21bed195d.reward_specific_char_count_normalized
- rewards_1cd8f023-f9d4-4d99-93fe-b7d21bed195d.reward_high_syllables_per_word
reward_weights:
- 4.530131987296112
- 0.5133070523414518
- 3.9325955598986004
- 5.0
- 4.871272511499004
target_kl: 0.01
use_vllm: false
trust_remote_code: true
use_ema: true
use_peft: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: offline
wandb_name: 1cd8f023-f9d4-4d99-93fe-b7d21bed195d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1cd8f023-f9d4-4d99-93fe-b7d21bed195d
warmup_steps: 642
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 5a9022d0-f07f-44eb-abb9-166cd1900db0
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## 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: 0.00018
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- 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: 642
- training_steps: 768
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0039 | 1 | nan |
| 0.0 | 0.25 | 64 | nan |
| 0.0 | 0.5 | 128 | nan |
| 0.0 | 0.75 | 192 | nan |
| 0.0 | 1.0 | 256 | nan |
| 0.0 | 1.25 | 320 | nan |
| 0.0 | 1.5 | 384 | nan |
| 0.0 | 1.75 | 448 | nan |
| 0.0 | 2.0 | 512 | nan |
| 0.0 | 2.25 | 576 | nan |
| 0.0 | 2.5 | 640 | nan |
| 0.0 | 2.75 | 704 | nan |
| 0.0 | 3.0 | 768 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |