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CodeLlama-7b-hf-manim-fine-tuned
shubham24/CodeLlama-7b-hf-manim-fine-tuned
CodeLlama-7b-hf-manim-fine-tuned is a machine learning model from shubham24. 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 26, 2024
- Updated
- May 26, 2024
- SHA
- 79abb162
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: AayushMathur/manim_python_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:
wandb_entity:
wandb_watch:
wandb_name:
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: 0.0039
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 |
|---|---|---|---|
| 0.7377 | 0.0140 | 1 | 0.7414 |
| 0.1444 | 0.2526 | 18 | 0.0560 |
| 0.0349 | 0.5053 | 36 | 0.0280 |
| 0.0429 | 0.7579 | 54 | 0.0206 |
| 0.0625 | 1.0105 | 72 | 0.0251 |
| 0.0496 | 1.2632 | 90 | 0.0157 |
| 0.032 | 1.5158 | 108 | 0.0126 |
| 0.0094 | 1.7684 | 126 | 0.0104 |
| 0.0453 | 2.0211 | 144 | 0.0087 |
| 0.0005 | 2.2737 | 162 | 0.0104 |
| 0.0373 | 2.5263 | 180 | 0.0069 |
| 0.0262 | 2.7789 | 198 | 0.0056 |
| 0.0088 | 3.0316 | 216 | 0.0048 |
| 0.0266 | 3.2842 | 234 | 0.0045 |
| 0.013 | 3.5368 | 252 | 0.0041 |
| 0.0141 | 3.7895 | 270 | 0.0039 |
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 26, 2024
- Updated
- May 26, 2024