peft pytorch mixtral hi dataset:HuggingFaceH4/ultrachat_200k dataset:rohansolo/BB_HindiHinglishV2 base_model:mistralai/Mixtral-8x7B-v0.1 license:cc-by-nc-4.0 region:us
---
library_name: peft

license: cc-by-nc-4.0
base_model: mistralai/Mixtral-8x7B-v0.1
datasets:
- HuggingFaceH4/ultrachat_200k
- rohansolo/BB_HindiHinglishV2
model-index:
- name: BB-Mixtral-HindiHinglish-8x7B-v0.1
  results: []
language:
- hi
---





<img src="https://huggingface.co/rohansolo/BB_HindiHinglishV2/resolve/main/Images/brain_logo.PNG" width="400" />



### Model Description


Mixtral Fine-Tuned for Hindi and Hinglish as part of ongoing experiments by [bb deep learning systems](bhaiyabot.com)


- **Developed by:** [bb deep learning systems](bhaiyabot.com)
- **Language(s) (NLP):** [English, Hindi, Romanised Hindi]
- **License:** [cc-by-nc-4.0]
- **Finetuned from model:** [mistralai/Mixtral-8x7B-v0.1](mistralai/Mixtral-8x7B-v0.1)

### Model Sources [optional]

- **Paper:** [More Information Coming Soon]



## Training Details

### Training Data

A mix of [Ultrachat200k] and [rohansolo/BB_HindiHinglishV2] were used for a total of 573,014,566 tokens in Hindi, Romanised Hindi and English.

### Training Procedure 

Training Loss at the end was

0.8977639613123988

<img src="https://huggingface.co/rohansolo/BB_HindiHinglishV2/resolve/main/Images/train-loss.png" width="400" />
<img src="https://huggingface.co/rohansolo/BB_HindiHinglishV2/resolve/main/Images/train-lr.png" width="400" />


Model was trained using the follwoing Hyperparameters:


warmup_steps: 100
weight_decay: 0.05

num_epochs: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.0002


lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
  - q_proj
  - k_proj
  - v_proj
  - o_proj
  - w1
  - w2
  - w3
lora_target_linear:
lora_fan_in_fan_out:
lora_modules_to_save:
  - embed_tokens
  - lm_head


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- 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


## Environmental Impact
```
Experiments were conducted using a private infrastructure, which has a carbon efficiency of 0.432 kgCO$_2$eq/kWh. A cumulative of 94 hours of computation was performed on hardware of type A100 SXM4 80 GB (TDP of 400W).

Total emissions are estimated to be 16.24 kgCO$_2$eq of which 0 percents were directly offset.

- **Hardware Type:** [8 x A100 SXM4 80 GB]
```
