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TinyMistral-v2-Test1

Text Generation
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PyTorch
English
mistral
Generated from Trainer
text-generation-inference
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TinyMistral-v2-Test1 / README.md
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metadata
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

Built with Axolotl

See axolotl config

axolotl version: 0.3.0

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>"

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