peft safetensors mixtral axolotl generated_from_trainer base_model:mistralai/Mixtral-8x7B-Instruct-v0.1 license:apache-2.0 4-bit bitsandbytes region:us
---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
model-index:
- name: mixtral-remove-negative-data
  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
adam_beta2: 0.95
adam_epsilon: 1.0e-05
adapter: qlora
base_model: mistralai/Mixtral-8x7B-Instruct-v0.1
bf16: auto
chat_template: inst
dataset_prepared_path: last_run_prepared
datasets:
- conversation: mistral
  path: dd7ba3a8030a4c7382d51a5d894f5cb4/./data/with_function_response/function_used_training.jsonl
  type: sharegpt
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 256
eval_steps: 0.2
eval_table_size: null
flash_attention: true
fp16: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: liuylhf/mixtral-remove-negative-data
hub_strategy: end
is_mistral_derived_model: true
learning_rate: 0.001
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_modules:
- q_proj
- v_proj
- k_proj
- o_proj
lr_scheduler: cosine
max_grad_norm: 1.0
micro_batch_size: 2
model_config:
  output_router_logits: true
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: paged_adamw_8bit
output_dir: dd7ba3a8030a4c7382d51a5d894f5cb4/model
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
save_steps: 0.1
sequence_len: 8192
strict: false
tf32: false
tokenizer_type: LlamaTokenizer
train_on_inputs: false
val_set_size: 0.01
wandb_log_model: end
wandb_name: mixtral-instruct-raw-data-v3
wandb_project: function-call
warmup_steps: 10
weight_decay: 0
xformers_attention: null

```

</details><br>

# mixtral-remove-negative-data

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: 0.0955

## 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: 0.001
- 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=(0.9,0.95) and epsilon=1e-05
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.9579        | 0.01  | 1    | 4.0485          |
| 0.148         | 0.2   | 36   | 0.1565          |
| 0.1075        | 0.4   | 72   | 0.1138          |
| 0.099         | 0.6   | 108  | 0.1018          |
| 0.0954        | 0.8   | 144  | 0.0969          |
| 0.0945        | 1.0   | 180  | 0.0955          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.39.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.0