---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: andysalerno/mistral-sft-v3
model-index:
- name: rainbowfish-v7
  results: []
datasets:
- andysalerno/rainbowfish-v1
---

<!-- 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: andysalerno/mistral-sft-v3
model_type: AutoModelForCausalLM

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: andysalerno/rainbowfish-v1
    type:
      system_prompt: ""
      field_system: system
      field_instruction: input
      field_output: output
      format: "{instruction}"
      no_input_format: "{instruction}"
dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./lora-out-rainbow7

adapter: lora
lora_model_dir:

sequence_len: 2048
sample_packing: false # was true
eval_sample_packing: false
pad_to_sequence_len: false
padding_side: left

lora_r: 64
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

lora_modules_to_save:
  - embed_tokens
  - lm_head

wandb_project: axolotl
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 4
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5

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

gradient_checkpointing: true
gradient_checkpointing_kwargs:
  use_reentrant: false
# early_stopping_patience: 3
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

hub_strategy: "every_save"
hub_model_id: andysalerno/rainbowfish-v7

num_epochs: 2
warmup_steps: 100 
# warmup_ratio: 0.1
eval_steps: 200
eval_table_size:
eval_table_max_new_tokens: 128
# save_steps: 5
# max_steps: 400
saves_per_epoch: 2
debug:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<|im_start|>"
  eos_token: "<|im_end|>"
  unk_token: "<unk>"
```

</details><br>

# rainbowfish-v7

This model is a fine-tuned version of [andysalerno/mistral-sft-v3](https://huggingface.co/andysalerno/mistral-sft-v3) on the [andysalerno/rainbowfish-v1](https://huggingface.co/datasets/andysalerno/rainbowfish-v1) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6464

## 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: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 2

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6514        | 0.18  | 200  | 0.6828          |
| 0.6875        | 0.37  | 400  | 0.6691          |
| 0.6626        | 0.55  | 600  | 0.6625          |
| 0.688         | 0.74  | 800  | 0.6558          |
| 0.7143        | 0.92  | 1000 | 0.6520          |
| 0.5243        | 1.11  | 1200 | 0.6495          |
| 0.6205        | 1.29  | 1400 | 0.6482          |
| 0.6159        | 1.47  | 1600 | 0.6469          |
| 0.6287        | 1.66  | 1800 | 0.6465          |
| 0.6606        | 1.84  | 2000 | 0.6464          |


### Framework versions

- PEFT 0.8.2
- Transformers 4.38.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0