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Guilherme34
/
Reasoner-Hermes3-70b-Lora

PEFT
Safetensors
llama
Generated from Trainer
4-bit precision
bitsandbytes
Model card Files Community
Reasoner-Hermes3-70b-Lora
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---
base_model: NousResearch/Hermes-3-Llama-3.1-70B
library_name: peft
license: llama3
tags:
- generated_from_trainer
model-index:
- name: outputs/lora-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/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
base_model: NousResearch/Hermes-3-Llama-3.1-70B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

chat_template: llama3
datasets:
  - path: Guilherme34/Reasoner-Dataset-roles-format
    type: chat_template
    chat_template: llama3
    field_messages: messages
    message_field_role: role
    message_field_content: content
    roles:
      system:
        - system
      user:
        - user
      assistant:
        - assistant

dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out

sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true

adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:

warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
   pad_token: <|end_of_text|>

```

</details><br>

# outputs/lora-out

This model is a fine-tuned version of [NousResearch/Hermes-3-Llama-3.1-70B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6269

## 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: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4145        | 0.0833 | 1    | 1.3638          |
| 1.4133        | 0.25   | 3    | 1.3479          |
| 1.1718        | 0.5    | 6    | 1.0840          |
| 0.8807        | 0.75   | 9    | 0.8536          |
| 0.7696        | 1.0    | 12   | 0.7617          |
| 0.5582        | 1.25   | 15   | 0.7075          |
| 0.5734        | 1.5    | 18   | 0.6850          |
| 0.5593        | 1.75   | 21   | 0.6519          |
| 0.5131        | 2.0    | 24   | 0.6315          |
| 0.4138        | 2.25   | 27   | 0.6263          |
| 0.3607        | 2.5    | 30   | 0.6266          |
| 0.3951        | 2.75   | 33   | 0.6272          |
| 0.345         | 3.0    | 36   | 0.6269          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1