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formula_llama_3_1_8b_4bits_r4
wangd12/formula_llama_3_1_8b_4bits_r4
formula_llama_3_1_8b_4bits_r4 is a machine learning model from wangd12. Use it for the machine learning task on the model card, and read the license before you ship it in a product.
should probably proofread and complete it, then remove this comment. --
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Public
Updated Jun 5, 2025
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4.7 MB
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At a glance
- Access
- Public
- Created
- Jun 5, 2025
- Updated
- Jun 5, 2025
- SHA
- 12529d89
Model card
From the Hugging Face model README
<!-- 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. -->
<details><summary>See axolotl config</summary>
axolotl version: 0.9.1
base_model: meta-llama/Llama-3.1-8B-Instruct
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.0001
load_in_8bit: false
load_in_4bit: true
adapter: lora
lora_model_dir: null
lora_r: 4
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
- q_proj
- v_proj
- k_proj
datasets:
- path: /workspace/FinLoRA/data/train/formula_train.jsonl
type:
system_prompt: ''
field_system: system
field_instruction: context
field_output: target
format: '[INST] {instruction} [/INST]'
no_input_format: '[INST] {instruction} [/INST]'
dataset_prepared_path: null
val_set_size: 0.02
output_dir: /workspace/FinLoRA/lora/axolotl-output/formula_llama_3_1_8b_4bits_r4
peft_use_dora: false
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: false
wandb_project: finlora_models
wandb_entity: null
wandb_watch: gradients
wandb_name: formula_llama_3_1_8b_4bits_r4
wandb_log_model: 'false'
bf16: auto
tf32: false
gradient_checkpointing: true
resume_from_checkpoint: null
logging_steps: 500
flash_attention: false
deepspeed: deepspeed_configs/zero1.json
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
weight_decay: 0.0
special_tokens:
pad_token: <|end_of_text|>
chat_template: llama3
workspace/FinLoRA/lora/axolotl-output/formula_llama_3_1_8b_4bits_r4
This model is a fine-tuned version of meta-llama/Llama-3.1-8B-Instruct on the /workspace/FinLoRA/data/train/formula_train.jsonl dataset. It achieves the following results on the evaluation set:
- Loss: 1.6143
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: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 5
- gradient_accumulation_steps: 2
- total_train_batch_size: 40
- total_eval_batch_size: 20
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| No log | 0.05 | 1 | 3.8659 |
| No log | 0.25 | 5 | 3.6317 |
| No log | 0.5 | 10 | 2.6735 |
| No log | 0.75 | 15 | 1.7570 |
| No log | 1.0 | 20 | 1.6143 |
Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.8.0.dev20250319+cu128
- Datasets 3.5.1
- Tokenizers 0.21.1
- Created
- Jun 5, 2025
- Updated
- Jun 5, 2025