license: llama2
datasets:
- psmathur/orca_mini_v1_dataset
language:
- en
- id
🦚Merak-7B-v3-Mini-Orca🐳
Merak-7B-v3-Mini-Orca is Ichsan2895's Merak-7B-v3 fine-tuned on psmathur's orca_mini_v1_dataset. Dataset was machine translated into Bahasa Indonesia with Google Translate.
Training details
Merak-7B-v3-Mini-Orca was instruction fine-tuned on 2 x 3090-24GB for 6 hours. LoRA, DeepSpeed ZeRO-2, and FlashAttention were implemented during training using Axolotl.
| Hyperparameter | value |
|---|---|
| learning rate | 0.0004 |
| batch size | 16 |
| microbatch size | 2 |
| warmup step | 100 |
| epochs | 2 |
| weight decay | 0.0 |
| lr scheduler | cosine |
| lora alpha | 16 |
| lora rank | 16 |
| lora dropout | 0.05 |
| lora target modules | q_proj, v_proj, k_proj, o_proj |
| cutoff length | 4096 |
Training loss
| Step | Train Loss |
|---|---|
| 1 | 0.9578 |
| 100 | 0.816 |
| 200 | 0.7819 |
| 300 | 0.7279 |
| 400 | 0.732 |
| 500 | 0.7139 |
| 600 | 0.6829 |
| 700 | 0.6641 |
| 800 | 0.6553 |
Limitations and bias
Llama 2 and fine-tuned variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2 and any fine-tuned varient's potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 2 variants, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at https://ai.meta.com/llama/responsible-use-guide/