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code-llama-7b-spider-qpl-lora
beneyal/code-llama-7b-spider-qpl-lora
code-llama-7b-spider-qpl-lora is a machine learning model from beneyal. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for peft. The card lists the license as llama2.
should probably proofread and complete it, then remove this comment. --
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At a glance
- Library
- peft
- License
- llama2
- Model type
- llama
- Access
- Public
- Created
- May 28, 2024
- Updated
- May 28, 2024
- SHA
- 84996587
Base models
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: 0.4.0
base_model: codellama/CodeLlama-7b-hf
model_type: LlamaForCausalLM
tokenizer_type: CodeLlamaTokenizer
load_in_8bit: true
load_in_4bit: false
strict: false
datasets:
- path: beneyal/spider-qpl-alpaca
type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./outputs/lora-out
sequence_len: 4096
sample_packing: false
pad_to_sequence_len: true
adapter: lora
lora_model_dir:
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project: spider-qpl
wandb_entity:
wandb_watch:
wandb_name: codellama-7b-lora
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
s2_attention:
warmup_steps: 10
evals_per_epoch: 4
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
outputs/lora-out
This model is a fine-tuned version of codellama/CodeLlama-7b-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2055
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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 2.0333 | 0.0013 | 1 | 1.9589 |
| 0.0474 | 0.2505 | 195 | 0.1516 |
| 0.0585 | 0.5010 | 390 | 0.1433 |
| 0.0321 | 0.7514 | 585 | 0.1540 |
| 0.0195 | 1.0019 | 780 | 0.1493 |
| 0.0314 | 1.2524 | 975 | 0.1599 |
| 0.0053 | 1.5029 | 1170 | 0.1737 |
| 0.0095 | 1.7534 | 1365 | 0.1667 |
| 0.0237 | 2.0039 | 1560 | 0.1730 |
| 0.0131 | 2.2543 | 1755 | 0.1917 |
| 0.0038 | 2.5048 | 1950 | 0.1907 |
| 0.0089 | 2.7553 | 2145 | 0.1851 |
| 0.0025 | 3.0058 | 2340 | 0.1894 |
| 0.0018 | 3.2563 | 2535 | 0.2001 |
| 0.0039 | 3.5067 | 2730 | 0.2026 |
| 0.0014 | 3.7572 | 2925 | 0.2055 |
Framework versions
- PEFT 0.10.0
- Transformers 4.40.2
- Pytorch 2.1.2+cu118
- Datasets 2.19.1
- Tokenizers 0.19.1
- Library
- peft
- Type
- llama
- License
- llama2
- Created
- May 28, 2024
- Updated
- May 28, 2024