open-aditi-v6-gemma

Preview of dataset trained on: https://huggingface.co/datasets/manishiitg/aditi-syn-v2

The synthetic dataset (https://huggingface.co/datasets/manishiitg/aditi-syn-v2) and the full data creation pipeline (https://github.com/manishiitg/aditi_dataset) have been open-sourced, enabling transparency and fostering further research in this domain. The dataset is a rich tapestry of Hinglish (a blend of Hindi and English) data, as well as a diverse array of tasks spanning tools, retrieval-augmented generation (RAG), mathematics, and reasoning – all in the Hindi language.

LMJudge Eval

https://github.com/manishiitg/IndicLMJudge

LLM Judge Language: hi

LLM Judge Language: en

DHARMA TINY EVAL

Language Hi

Language En

Task: BoolQ Metric: score

Task: ARC-Easy Metric: score

Task: openbookqa Metric: score

Task: winogrande Metric: score

Task: ARC-Challenge Metric: score

Task: truthful_qa Metric: score

Task: bigbench Metric: score

Task: MMLU Metric: score

Task: agieval Metric: score

<!-- 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. -->

<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>

<details><summary>See axolotl config</summary>

axolotl version: 0.4.0

base_model: google/gemma-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_config: philschmid/gemma-tokenizer-chatml
tokenizer_use_fast: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: manishiitg/aditi-syn-train-small-v3
    type: completion
  

# 25 has only sythentic data, and has judge removed data 
hub_model_id: manishiitg/open-aditi-chat-hi-1.25-gemma
hf_use_auth_token: true

wandb_project: open-aditi-chat-hi-1.25-gemma

dataset_prepared_path: manishiitg
push_dataset_to_hub: manishiitg
val_set_size: .1
output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25-gemma

adapter: qlora
lora_model_dir:
save_safetensors: true

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true

wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model:

gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

adam_beta2: 0.95
adam_epsilon: 0.00001
max_grad_norm: 1.0

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false


gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
auto_resume_from_checkpoints: true ## manage check point resume from here
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 10
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
save_steps: 20 ## increase based on your dataset
save_strategy: steps
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:

</details><br>

open-aditi-chat-hi-1.25-gemma

This model is a fine-tuned version of google/gemma-7B on an unknown dataset. It achieves the following results on the evaluation set:

• Loss: 2.0992

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: 4
• seed: 42
• distributed_type: multi-GPU
• num_devices: 8
• gradient_accumulation_steps: 8
• total_train_batch_size: 256
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-05
• lr_scheduler_type: cosine
• lr_scheduler_warmup_steps: 10
• num_epochs: 1

Training results

Framework versions

• PEFT 0.9.0
• Transformers 4.40.0.dev0
• Pytorch 2.1.2+cu121
• Datasets 2.18.0
• Tokenizers 0.15.0