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skymizer/mistral-7b-v0.1-csft-open-orca-on-open-orca

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

<!-- 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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>

axolotl version: 0.5.2

yaml
base_model: Open-Orca/Mistral-7B-OpenOrca
model_type: AutoModelForCausalLM
tokenizer_config: Open-Orca/Mistral-7B-OpenOrca
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
resize_token_embeddings_to_32x: true

plugins:
  - axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_glu_activation: true
liger_layer_norm: true
liger_fused_linear_cross_entropy: true

load_in_8bit: false
load_in_4bit: false
strict: false

chat_template: chatml
datasets:
  - path: skymizer/open-orca-conversations
    type: chat_template
    field_messages: messages

hf_use_auth_token: true
dataset_prepared_path: pretokenized/open-orca
output_dir: ./outputs/out

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

val_set_size: 0.005
eval_sample_packing: false
# eval_causal_lm_metrics: ["perplexity"]

wandb_project: "axolotl_mistral_sft"
wandb_entity:
wandb_watch:
wandb_name: "mistral-7B-v0.1-csft-open-orca-on-open-orca"
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 16
max_steps: 3000
optimizer: adamw_torch
lr_scheduler: cosine
learning_rate: 0.000005 
weight_decay: 0.0
adam_beta1: 0.9
adam_beta2: 0.95
adam_eps: 0.000001
max_grad_norm: 1.0

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

hub_model_id: "skymizer/mistral-7b-v0.1-csft-open-orca-on-open-orca"
save_strategy: "steps"
save_steps: 1000

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

warmup_ratio: 0.03
eval_steps: 500
# evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:

seed: 42

</details><br>

mistral-7b-v0.1-csft-open-orca-on-open-orca

This model is a fine-tuned version of Open-Orca/Mistral-7B-OpenOrca on the None dataset. It achieves the following results on the evaluation set:

  • —Loss: 2.1946

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-06
  • —trainbatchsize: 16
  • —evalbatchsize: 16
  • —seed: 42
  • —distributed_type: multi-GPU
  • —num_devices: 4
  • —gradientaccumulationsteps: 2
  • —totaltrainbatch_size: 128
  • —totalevalbatch_size: 64
  • —optimizer: Use OptimizerNames.ADAMWTORCH with betas=(0.9,0.95) and epsilon=1e-08 and optimizerargs=No additional optimizer arguments
  • —lrschedulertype: cosine
  • —lrschedulerwarmup_steps: 90
  • —training_steps: 3000

Training results

Training LossEpochStepValidation Loss
1.13110.000214.4372
0.52770.08315002.2236
0.4630.166310002.2066
0.48550.249415002.2146
0.46620.332520002.1989
0.44940.415725002.1966
0.42680.498830002.1946

Framework versions

  • —Transformers 4.46.3
  • —Pytorch 2.5.1+cu124
  • —Datasets 3.1.0
  • —Tokenizers 0.20.3