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---
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-staging
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: ./data/with_function_response/more_functions/function_not_used_training_small.jsonl
type: sharegpt
- conversation: mistral
path: ./data/with_function_response/more_functions/function_used_training_small.jsonl
type: sharegpt
- conversation: mistral
path: ./data/with_function_response/parallel_call/parallel_data_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-more-tools-staging
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: 2
optimizer: paged_adamw_8bit
output_dir: 2af0968cad514d6e9d5fb8448230e1c6/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: 0.025
wandb_log_model: end
wandb_name: mixtral-instruct-lora-no-negative
wandb_project: function-call
warmup_steps: 10
weight_decay: 0.0
```
</details><br>
# empower-functions-more-tools-staging
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: 0.0904
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8305 | 0.0 | 1 | 1.9892 |
| 0.1336 | 0.1 | 27 | 0.1444 |
| 0.1006 | 0.2 | 54 | 0.1193 |
| 0.102 | 0.3 | 81 | 0.1120 |
| 0.0798 | 0.41 | 108 | 0.1068 |
| 0.0976 | 0.51 | 135 | 0.1051 |
| 0.1145 | 0.61 | 162 | 0.1021 |
| 0.0984 | 0.71 | 189 | 0.1008 |
| 0.0817 | 0.81 | 216 | 0.1006 |
| 0.0841 | 0.91 | 243 | 0.0971 |
| 0.0983 | 1.02 | 270 | 0.0967 |
| 0.0948 | 1.09 | 297 | 0.0956 |
| 0.093 | 1.2 | 324 | 0.0944 |
| 0.0991 | 1.3 | 351 | 0.0938 |
| 0.072 | 1.4 | 378 | 0.0927 |
| 0.0831 | 1.5 | 405 | 0.0920 |
| 0.0768 | 1.6 | 432 | 0.0912 |
| 0.0915 | 1.7 | 459 | 0.0905 |
| 0.082 | 1.81 | 486 | 0.0906 |
| 0.0702 | 1.91 | 513 | 0.0904 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0 |