---
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-more-tools-parallel
  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
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true

load_in_8bit: false
load_in_4bit: true
strict: false
chat_template: inst

datasets:
  - path: ./data/with_function_response/more_functions/function_used_training.jsonl
    type: sharegpt
    conversation: mistral
  - path: ./data/with_function_response/more_functions/function_not_used_training.jsonl
    type: sharegpt
    conversation: mistral    
  - path: ./data/with_function_response/parallel_call/missing_parameter_data_training.jsonl
    type: sharegpt
    conversation: mistral
  - path: ./data/with_function_response/parallel_call/parallel_data_training.jsonl
    type: sharegpt
    conversation: mistral

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ../empower-functions-more-tools-parallel

model_config:
  output_router_logits: true

adapter: qlora
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 64
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj


wandb_project: empower-functions
wandb_name: empower-functions-more-tools-parallel
wandb_log_model: end
hub_model_id: dyang415/empower-functions-more-tools-parallel


gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

gradient_checkpointing: true
logging_steps: 1
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10

eval_table_size:
eval_max_new_tokens: 256
eval_steps: 0.05
save_steps: 0.1
debug:
weight_decay: 0.0
fsdp:
fsdp_config:
```

</details><br>

# empower-functions-more-tools-parallel

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: 0.0865

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16

### 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: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.0913        | 0.0   | 1    | 2.0864          |
| 0.0992        | 0.2   | 178  | 0.1038          |
| 0.0923        | 0.4   | 356  | 0.0957          |
| 0.0847        | 0.6   | 534  | 0.0938          |
| 0.1034        | 0.8   | 712  | 0.0925          |
| 0.1062        | 1.0   | 890  | 0.0901          |
| 0.1006        | 1.19  | 1068 | 0.0894          |
| 0.084         | 1.39  | 1246 | 0.0882          |
| 0.0798        | 1.59  | 1424 | 0.0875          |
| 0.0752        | 1.79  | 1602 | 0.0849          |
| 0.0772        | 1.99  | 1780 | 0.0846          |
| 0.0824        | 2.17  | 1958 | 0.0849          |
| 0.0792        | 2.37  | 2136 | 0.0843          |
| 0.0627        | 2.57  | 2314 | 0.0837          |
| 0.0777        | 2.77  | 2492 | 0.0831          |
| 0.0636        | 2.98  | 2670 | 0.0827          |
| 0.0624        | 3.16  | 2848 | 0.0855          |
| 0.0612        | 3.36  | 3026 | 0.0861          |
| 0.0649        | 3.56  | 3204 | 0.0861          |
| 0.0641        | 3.76  | 3382 | 0.0865          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0
- Pytorch 2.0.1+cu117
- Datasets 2.17.1
- Tokenizers 0.15.0