pszemraj/Mistral-7B-v0.3-sarcasm-scrolls-4k
018
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<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/> <details><summary>See axolotl config</summary>
axolotl version: 0.4.1
base_model: mistralai/Mistral-7B-v0.3
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
strict: false
# dataset
datasets:
- path: BEE-spoke-data/sarcasm-scrolls
type: completion # format from earlier
field: text # Optional[str] default: text, field to use for completion data
val_set_size: 0.025
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false
# WANDB
wandb_project: sarcasm-scrolls
wandb_entity: pszemraj
wandb_watch: gradients
wandb_name: Mistral-7B-v0.3-sarcasm-scrolls
hub_model_id: pszemraj/Mistral-7B-v0.3-sarcasm-scrolls-2
hub_strategy: every_save
gradient_accumulation_steps: 16
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_torch_fused # paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 2e-5
load_in_8bit: false
load_in_4bit: false
bf16: auto
fp16:
tf32: true
torch_compile: true # requires >= torch 2.0, may sometimes cause problems
torch_compile_backend: inductor # Optional[str]
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
logging_steps: 5
xformers_attention:
flash_attention: true
warmup_steps: 20
# hyperparams for freq of evals, saving, etc
evals_per_epoch: 4
saves_per_epoch: 4
save_safetensors: true
save_total_limit: 1 # Checkpoints saved at a time
output_dir: ./output-axolotl/output-model-theta
resume_from_checkpoint:
deepspeed:
weight_decay: 0.04
special_tokens:
</details><br>
Mistral-7B-v0.3-sarcasm-scrolls @ ctx 4k
This model is a fine-tuned version of mistralai/Mistral-7B-v0.3 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 2.2825
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: 2e-05
- trainbatchsize: 1
- evalbatchsize: 1
- seed: 42
- gradientaccumulationsteps: 16
- totaltrainbatch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lrschedulertype: cosine
- lrschedulerwarmup_steps: 20
- num_epochs: 2
Training results
Framework versions
- Transformers 4.41.1
- Pytorch 2.3.0+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1