---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- JeanKaddour/minipile
- epfl-llm/guidelines
base_model: Locutusque/TinyMistral-248M-v2.5
model-index:
- name: TinyMistral-v2.5-MiniPile-Guidelines-E1/
  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: `0.3.0`
```yaml
base_model: Locutusque/TinyMistral-248M-v2.5
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: 2
micro_batch_size: 4
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: true
fp16: false
tf32: false

gradient_checkpointing: false
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: True
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
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 for 1 epoch.

## 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: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- training_steps: 197279

### Training results



### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co./spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co./datasets/open-llm-leaderboard/details_Dans-DiscountModels__TinyMistral-v2.5-MiniPile-Guidelines-E1)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |29.16|
|AI2 Reasoning Challenge (25-Shot)|26.54|
|HellaSwag (10-Shot)              |25.65|
|MMLU (5-Shot)                    |23.44|
|TruthfulQA (0-shot)              |49.90|
|Winogrande (5-shot)              |49.41|
|GSM8k (5-shot)                   | 0.00|