README.md

---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: unsloth/mistral-7b-v0.2
model-index:
- name: 155d3ef3-4b20-4be7-82a7-000483e76343
  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: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/mistral-7b-v0.2
bf16: auto
dataset_prepared_path: last_run_prepared
datasets:
- data_files:
  - dd8680ad4c472b16_train_data.json
  ds_type: json
  format: custom
  path: dd8680ad4c472b16_train_data.json
  type:
    field: null
    field_input: context
    field_instruction: question
    field_output: answer
    field_system: null
    format: null
    no_input_format: null
    system_format: '{system}'
    system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_strategy: 'no'
eval_table_size: null
evals_per_epoch: 4
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: taopanda-1/155d3ef3-4b20-4be7-82a7-000483e76343
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
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_linear: true
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
loss_watchdog_patience: 3
loss_watchdog_threshold: 5.0
lr_scheduler: cosine
max_steps: '2000'
micro_batch_size: 2
model_type: MistralForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: ./outputs/lora-out/taopanda-1_5ade1b66-53e7-4502-a577-24394950045b
pad_to_sequence_len: true
resume_from_checkpoint: null
sample_packing: true
save_steps: '500'
seed: 56778
sequence_len: 8192
special_tokens: null
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: fatcat87-taopanda
wandb_log_model: null
wandb_mode: online
wandb_name: taopanda-1_5ade1b66-53e7-4502-a577-24394950045b
wandb_project: subnet56
wandb_runid: taopanda-1_5ade1b66-53e7-4502-a577-24394950045b
wandb_watch: null
warmup_steps: 5
weight_decay: 0.0
xformers_attention: null

```

</details><br>

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/fatcat87-taopanda/subnet56/runs/jiwpxm9h)
# 155d3ef3-4b20-4be7-82a7-000483e76343

This model is a fine-tuned version of [unsloth/mistral-7b-v0.2](https://huggingface.co/unsloth/mistral-7b-v0.2) on the None dataset.

## 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: 56778
- 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: 5
- training_steps: 224

### Training results



### Framework versions

- PEFT 0.11.1
- Transformers 4.42.3
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1