transformers pytorch mistral text-generation generated_from_trainer conversational base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us
---
license: apache-2.0
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
tags:
- generated_from_trainer
model-index:
- name: workspace/out-mistral-2B
  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: `0.4.0`
```yaml
adapter: null
base_model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
batch_size: 2
bf16: auto
dataset_prepared_path: null
datasets:
- ds_type: json
  path: /workspace/data.jsonl
  type: context_qa.load_v2
debug: null
deepspeed: null
early_stopping_patience: null
evals_per_epoch: 4
flash_attention: null
fp16: null
fsdp: null
fsdp_config: null
gptq_groupsize: null
gptq_model_v1: null
gradient_checkpointing: true
group_by_length: false
learning_rate: 1.0e-05
local_rank: null
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.2
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules: null
lr_scheduler: cosine
max_packed_sequence_len: null
micro_batch_size: 1
model_config:
  output_router_logits: true
model_type: MistralForCausalLM
num_epochs: 4
optimizer: adamw_bnb_8bit
output_dir: /workspace/out-mistral-2B
resume_from_checkpoint: null
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
  bos_token: <s>
  eos_token: <|im_end|>
  pad_token: </s>
tf32: true
tokenizer_type: LlamaTokenizer
torchdistx_path: null
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_log_model: Nous-Hermes-2-Mistral-7B-DPO
wandb_name: mistral
wandb_project: Ultron-llama
wandb_watch: null
warmup_steps: 40
weight_decay: 0.0
xformers_attention: true

```

</details><br>

# workspace/out-mistral-2B

This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5036

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6411        | 0.02  | 1    | 0.4803          |
| 0.5321        | 0.26  | 11   | 0.3867          |
| 0.4077        | 0.51  | 22   | 0.3591          |
| 0.4455        | 0.77  | 33   | 0.3995          |
| 0.2921        | 1.02  | 44   | 0.4368          |
| 0.3459        | 1.28  | 55   | 0.4884          |
| 0.2768        | 1.53  | 66   | 0.4978          |
| 0.4168        | 1.79  | 77   | 0.4808          |
| 0.14          | 2.05  | 88   | 0.4547          |
| 0.1132        | 2.3   | 99   | 0.4856          |
| 0.1055        | 2.56  | 110  | 0.4916          |
| 0.1385        | 2.81  | 121  | 0.4783          |
| 0.0455        | 3.07  | 132  | 0.4677          |
| 0.0211        | 3.33  | 143  | 0.4892          |
| 0.0236        | 3.58  | 154  | 0.5016          |
| 0.009         | 3.84  | 165  | 0.5036          |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0
