---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model-index:
- name: empower-functions-clean-data-one-more-functions
  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: qlora
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
bf16: true
chat_template: inst
dataset_prepared_path: last_run_prepared
datasets:
- conversation: mistral
  path: 659f8b7bb7c243ab879f8bc17876ce4a/data/with_function_response/more_functions/one_more_function/function_used_training.jsonl
  type: sharegpt
- conversation: mistral
  path: 659f8b7bb7c243ab879f8bc17876ce4a/data/with_function_response/original_clean/function_not_used_training.jsonl
  type: sharegpt
debug: null
eval_max_new_tokens: 256
eval_steps: 0.05
eval_table_size: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: liuylhf/empower-functions-clean-data-one-more-functions
learning_rate: 0.0002
load_in_4bit: true
load_in_8bit: false
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_model_dir: null
lora_r: 32
lora_target_modules:
- q_proj
- k_proj
- v_proj
- o_proj
loss_watchdog_patience: 3
loss_watchdog_threshold: 5.0
lr_scheduler: cosine
micro_batch_size: 2
model_config:
  output_router_logits: true
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: paged_adamw_8bit
output_dir: 659f8b7bb7c243ab879f8bc17876ce4a/model
pad_to_sequence_len: true
sample_packing: true
save_steps: 0.1
sequence_len: 4096
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.01
wandb_log_model: end
wandb_name: more-tools
wandb_project: function-call
warmup_steps: 10
weight_decay: 0.0

```

</details><br>

# empower-functions-clean-data-one-more-functions

This model is a fine-tuned version of [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0863

## 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: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0157        | 0.0   | 1    | 2.1200          |
| 0.153         | 0.05  | 23   | 0.1454          |
| 0.1236        | 0.1   | 46   | 0.1160          |
| 0.1043        | 0.15  | 69   | 0.1073          |
| 0.1163        | 0.2   | 92   | 0.1035          |
| 0.1072        | 0.25  | 115  | 0.0996          |
| 0.0988        | 0.31  | 138  | 0.0978          |
| 0.0962        | 0.36  | 161  | 0.0963          |
| 0.0823        | 0.41  | 184  | 0.0939          |
| 0.0785        | 0.46  | 207  | 0.0938          |
| 0.0941        | 0.51  | 230  | 0.0918          |
| 0.0968        | 0.56  | 253  | 0.0905          |
| 0.0856        | 0.61  | 276  | 0.0899          |
| 0.0965        | 0.66  | 299  | 0.0895          |
| 0.0894        | 0.71  | 322  | 0.0881          |
| 0.086         | 0.76  | 345  | 0.0872          |
| 0.0941        | 0.82  | 368  | 0.0869          |
| 0.0894        | 0.87  | 391  | 0.0867          |
| 0.0782        | 0.92  | 414  | 0.0864          |
| 0.0815        | 0.97  | 437  | 0.0863          |


### Framework versions

- PEFT 0.9.0
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.0