README.md

metadata

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: []

See axolotl config

axolotl version: 0.3.0

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>"

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

Detailed results can be found here

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