File size: 8,881 Bytes
ad4b8fa 6980b87 6e7d32a 6980b87 6e7d32a ad4b8fa |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 |
---
license: other
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: meta-llama/Meta-Llama-3-8B
model-index:
- name: open-aditi-chat-hi-1.25-llama3
results: []
---
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
| Model | Language | Score | No# Questions |
| --- | --- | --- | --- |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | hi | 8.7148 | 554 |
| Qwen/Qwen1.5-72B-Chat-AWQ | hi | 8.3695 | 554 |
| manishiitg/open-aditi-v6-llama3 | hi | 8.2659 | 551 |
| Qwen/Qwen1.5-14B-Chat | hi | 8.2404 | 554 |
| google/gemma-7b-it | hi | 7.9152 | 554 |
| manishiitg/open-aditi-v6-gemma | hi | 7.8634 | 549 |
| Qwen/Qwen1.5-7B-Chat | hi | 7.8587 | 554 |
| manishiitg/open-aditi-hi-v3 | hi | 7.7644 | 554 |
| manishiitg/open-aditi-hi-v4 | hi | 7.6150 | 554 |
| manishiitg/open-aditi-hi-v2 | hi | 7.2518 | 554 |
| teknium/OpenHermes-2.5-Mistral-7B | hi | 7.2489 | 554 |
| ai4bharat/Airavata | hi | 6.9468 | 554 |
| 01-ai/Yi-34B-Chat | hi | 6.5801 | 554 |
| manishiitg/open-aditi-hi-v1 | hi | 4.7022 | 554 |
| sarvamai/OpenHathi-7B-Hi-v0.1-Base | hi | 4.2834 | 598 |
| Qwen/Qwen1.5-4B-Chat | hi | 4.1101 | 554 |
#### LLM Judge Language: en
| Model | Language | Score | No# Questions |
| --- | --- | --- | --- |
| Qwen/Qwen1.5-14B-Chat | en | 9.1947 | 356 |
| Qwen/Qwen1.5-72B-Chat-AWQ | en | 9.1618 | 356 |
| Qwen/Qwen1.5-7B-Chat | en | 9.1570 | 356 |
| 01-ai/Yi-34B-Chat | en | 9.1368 | 356 |
| mistralai/Mixtral-8x7B-Instruct-v0.1 | en | 9.1306 | 356 |
| manishiitg/open-aditi-v6-gemma | en | 9.1003 | 356 |
| teknium/OpenHermes-2.5-Mistral-7B | en | 9.0230 | 356 |
| manishiitg/open-aditi-v6-llama3 | en | 9.0197 | 356 |
| manishiitg/open-aditi-hi-v3 | en | 8.9615 | 356 |
| manishiitg/open-aditi-hi-v4 | en | 8.9188 | 356 |
| google/gemma-7b-it | en | 8.8191 | 356 |
| Qwen/Qwen1.5-4B-Chat | en | 8.7500 | 356 |
| google/gemma-2b-it | en | 8.4671 | 356 |
| manishiitg/open-aditi-hi-v2 | en | 8.4584 | 356 |
| ai4bharat/Airavata | en | 7.3834 | 356 |
| manishiitg/open-aditi-hi-v1 | en | 6.6559 | 356 |
| sarvamai/OpenHathi-7B-Hi-v0.1-Base | en | 5.9567 | 312 |
DHARMA TINY EVAL
============
#### Language Hi
| Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| open-aditi-hi-v2 | 0.6245 | 0.4959 | 0.3866 | 0.7192 | 0.5353 | 0.2945 | 0.4828 | 0.3457 | 0.5279 |
| open-aditi-hi-v3 | 0.6803 | 0.4553 | 0.2788 | 0.7385 | 0.5390 | 0.2178 | 0.4914 | 0.3346 | 0.5688 |
| open-aditi-hi-v4 | 0.6989 | 0.4526 | 0.2714 | 0.7231 | 0.5167 | 0.2331 | 0.5302 | 0.3123 | 0.5316 |
| open-aditi-v6-gemma | 0.7212 | 0.4146 | 0.3234 | 0.6923 | 0.4870 | 0.2638 | 0.4957 | 0.3680 | 0.4349 |
| open-aditi-v6-llama3 | 0.5688 | 0.4119 | 0.2268 | 0.6500 | 0.4498 | 0.2331 | 0.4310 | 0.3420 | 0.3792 |
| open-aditi-hi-v1 | 0.4572 | 0.3767 | 0.2230 | 0.6346 | 0.4647 | 0.1840 | 0.3405 | 0.3271 | 0.3532 |
| OpenHermes-2.5-Mistral-7B | 0.3309 | 0.4201 | 0.3197 | 0.6077 | 0.4981 | 0.2331 | 0.3276 | 0.3086 | 0.3086 |
| OpenHathi-7B-Hi-v0.1-Base | 0.2862 | 0.3333 | 0.5130 | 0.6077 | 0.4907 | 0.2301 | 0.3017 | 0.2677 | 0.1933 |
| Airavata | 0.2751 | 0.1274 | 0.2268 | 0.0615 | 0.3866 | 0.1104 | 0.2845 | 0.1450 | 0.3383 |
| gemma-7b-it | 0.1227 | 0.0786 | 0.0743 | 0.1808 | 0.1561 | 0.0491 | 0.1078 | 0.0818 | 0.0855 |
#### Language En
| Model | ARC-Easy | bigbench | truthful_qa | BoolQ | winogrande | agieval | ARC-Challenge | MMLU | openbookqa |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| OpenHermes-2.5-Mistral-7B | 0.8922 | 0.5745 | 0.3197 | 0.8346 | 0.6989 | 0.4908 | 0.7802 | 0.5911 | 0.7621 |
| open-aditi-hi-v2 | 0.8625 | 0.5149 | 0.3532 | 0.8192 | 0.6877 | 0.4571 | 0.7500 | 0.5613 | 0.7732 |
| open-aditi-hi-v4 | 0.8959 | 0.5041 | 0.2862 | 0.8423 | 0.6914 | 0.4571 | 0.7716 | 0.5651 | 0.7138 |
| open-aditi-hi-v3 | 0.8773 | 0.4986 | 0.3048 | 0.8385 | 0.6766 | 0.4663 | 0.7371 | 0.5613 | 0.7249 |
| Qwen1.5-7B-Chat | 0.8922 | 0.5122 | 0.2007 | 0.8000 | 0.6654 | 0.4294 | 0.7759 | 0.5799 | 0.7621 |
| open-aditi-v6-gemma | 0.8699 | 0.4959 | 0.2602 | 0.7385 | 0.5465 | 0.4540 | 0.7371 | 0.5167 | 0.6654 |
| open-aditi-v6-llama3 | 0.8810 | 0.4634 | 0.1822 | 0.7577 | 0.5353 | 0.4110 | 0.7457 | 0.5688 | 0.6506 |
| open-aditi-hi-v1 | 0.8104 | 0.3902 | 0.2491 | 0.6962 | 0.5539 | 0.3681 | 0.6379 | 0.5056 | 0.5911 |
| Airavata | 0.7026 | 0.4282 | 0.3123 | 0.7192 | 0.5651 | 0.3313 | 0.5172 | 0.3792 | 0.5093 |
| OpenHathi-7B-Hi-v0.1-Base | 0.4684 | 0.3062 | 0.4758 | 0.6346 | 0.5167 | 0.2577 | 0.3017 | 0.2788 | 0.2714 |
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"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
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-llama3
hf_use_auth_token: true
wandb_project: open-aditi-chat-hi-1.25-llama3
dataset_prepared_path: manishiitg
push_dataset_to_hub: manishiitg
val_set_size: .1
output_dir: /sky-notebook/manishiitg/open-aditi-chat-hi-1.25-llama3
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: 6
num_epochs: 1
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0002
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:
special_tokens:
pad_token: <|end_of_text|>
```
</details><br>
# open-aditi-chat-hi-1.25-llama3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9727
## 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: 6
- eval_batch_size: 6
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 384
- total_eval_batch_size: 48
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5388 | 0.01 | 1 | 2.5709 |
| 0.8839 | 0.5 | 88 | 1.9648 |
| 0.88 | 1.0 | 176 | 1.9727 |
### Framework versions
- PEFT 0.9.0
- Transformers 4.40.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.0 |