---
license: apache-2.0
library_name: transformers
tags:
- axolotl
- generated_from_trainer
- mixtral
- nous_hermes
- wikihow
- peft
- lora
- qlora
- adapter
- finetune
base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT
model-index:
- name: Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow
  results: []
datasets:
- HuggingFaceTB/cosmopedia
pipeline_tag: text-generation
---

<!-- 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
base_model: NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT
model_type: MixtralForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

hub_model_id: MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow
hf_use_auth_token: true

load_in_4bit: true
strict: false

datasets:
  - path: HuggingFaceTB/cosmopedia
    name:
      - wikihow
    type:
      system_prompt: ""
      field_instruction: prompt
      field_output: text
      format: "[INST] {instruction} [/INST]"
      no_input_format: "[INST] {instruction} [/INST]"
    
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-out-wikihow

# save_safetensors: true

adapter: qlora
lora_model_dir: 

sequence_len: 1024
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
#  - gate
  - q_proj
#  - k_proj
  - v_proj
#  - o_proj
#  - w1
#  - w2
#  - w3

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

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:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```

</details><br>

# Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow

This model is a fine-tuned version of [NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT](https://huggingface.co/NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4559

## How to use

**PEFT**
```python
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM

model_id = "MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow"

config = PeftConfig.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained("NousResearch/Nous-Hermes-2-Mixtral-8x7B-SFT")
model = PeftModel.from_pretrained(model, model_id)
```

**Transformers**
```python
# Use a pipeline as a high-level helper
from transformers import pipeline

model_id = "MaziyarPanahi/Nous-Hermes-2-Mixtral-8x7B-SFT-Wikihow"

pipe = pipeline("text-generation", model=model_id)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)
```

## 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
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_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: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.8589        | 0.0   | 1    | 0.8705          |
| 0.5094        | 0.25  | 483  | 0.5009          |
| 0.4503        | 0.5   | 966  | 0.4734          |
| 0.4569        | 0.75  | 1449 | 0.4559          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.15.0