---
tags:
- generated_from_trainer
base_model: Locutusque/TinyMistral-248M-v2
model-index:
- name: TinyMistral-v2-Test1/
  results: []
datasets:
- JeanKaddour/minipile
- epfl-llm/guidelines
license: apache-2.0
language:
- en
---

<!-- 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. -->

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<details><summary>See axolotl config</summary>

axolotl version: `0.3.0`
```yaml
base_model: Locutusque/TinyMistral-248M-v2
model_type: MistralForCausalLM
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

dataset_processes: 20

datasets:
  - path: epfl-llm/guidelines
    type: completion
    field: clean_text
  - path: JeanKaddour/minipile
    type: completion
    field: text
  
dataset_prepared_path: TinyMistral-FFT-data
val_set_size: 0.001
output_dir: ./TinyMistral-FFT

sequence_len: 2048
sample_packing: false
pad_to_sequence_len: true

adapter:
lora_model_dir:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
lora_fan_in_fan_out:

# wandb configuration
wandb_project: TinyMistral-FFT
wandb_watch:
wandb_run_id:
wandb_log_model: 

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: constant
cosine_min_lr_ratio: 

learning_rate: 0.00005

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

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: false
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: false
flash_attn_cross_entropy: false
flash_attn_rms_norm: true
flash_attn_fuse_qkv: false
flash_attn_fuse_mlp: true

warmup_steps: 10
evals_per_epoch: 100
# eval_steps: 10
eval_table_size:
saves_per_epoch: 50
debug:
deepspeed: #deepspeed/zero2.json # multi-gpu only
weight_decay: 0

# tokens:


special_tokens:
  bos_token: "<|bos|>"
  eos_token: "<|endoftext|>"
  unk_token: "<unk>"
```

</details><br>

# TinyMistral-StructureEvaluator

This model was further trained on the epfl-llm/guidelines and JeanKaddour/minipile datasets.

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 39460

### Training results



### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0