Downloads · 30 days
5
9% of all-time downloads
stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052
G-ML-Hyly/stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052
stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052 is a machine learning model from G-ML-Hyly. 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. --
All-time downloads
57
Public
Repo size
627 MB
Likes
0
Public
Repo makeup
Click a slice to open those files.
.safetensors125 MB · 98%
At a glance
- Library
- peft
- License
- llama2
- Access
- Public
- Created
- Dec 20, 2023
- Updated
- Dec 20, 2023
- SHA
- 75f9d48a
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. -->
stg-cli13b-t6-cdp-ca.mt.him.cln.inter-b4s1e1-20231220-1052
This model is a fine-tuned version of codellama/CodeLlama-13b-Instruct-hf on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.0472
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: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3435 | 0.03 | 100 | 0.0703 |
| 0.0654 | 0.07 | 200 | 0.0586 |
| 0.0579 | 0.1 | 300 | 0.0563 |
| 0.0567 | 0.14 | 400 | 0.0562 |
| 0.0551 | 0.17 | 500 | 0.0547 |
| 0.0547 | 0.21 | 600 | 0.0526 |
| 0.0532 | 0.24 | 700 | 0.0516 |
| 0.0534 | 0.28 | 800 | 0.0515 |
| 0.0521 | 0.31 | 900 | 0.0520 |
| 0.0522 | 0.35 | 1000 | 0.0517 |
| 0.0518 | 0.38 | 1100 | 0.0511 |
| 0.051 | 0.42 | 1200 | 0.0502 |
| 0.0517 | 0.45 | 1300 | 0.0494 |
| 0.0506 | 0.49 | 1400 | 0.0499 |
| 0.0511 | 0.52 | 1500 | 0.0496 |
| 0.05 | 0.56 | 1600 | 0.0493 |
| 0.05 | 0.59 | 1700 | 0.0497 |
| 0.049 | 0.63 | 1800 | 0.0485 |
| 0.0487 | 0.66 | 1900 | 0.0484 |
| 0.0492 | 0.7 | 2000 | 0.0483 |
| 0.0493 | 0.73 | 2100 | 0.0481 |
| 0.0483 | 0.77 | 2200 | 0.0478 |
| 0.048 | 0.8 | 2300 | 0.0478 |
| 0.048 | 0.83 | 2400 | 0.0476 |
| 0.0476 | 0.87 | 2500 | 0.0474 |
| 0.0471 | 0.9 | 2600 | 0.0473 |
| 0.0472 | 0.94 | 2700 | 0.0472 |
| 0.0469 | 0.97 | 2800 | 0.0472 |
Framework versions
- Transformers 4.37.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
Training procedure
The following bitsandbytes quantization config was used during training:
- quant_method: QuantizationMethod.BITS_AND_BYTES
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
Framework versions
- PEFT 0.6.2
- Library
- peft
- License
- llama2
- Created
- Dec 20, 2023
- Updated
- Dec 20, 2023