Add LoRA adapter + tokenizer

.gitattributes

@@ -33,3 +33,5 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+model.SRC filter=lfs diff=lfs merge=lfs -text
+model.TGT filter=lfs diff=lfs merge=lfs -text

README.md

@@ -0,0 +1,206 @@
+---
+base_model: ai4bharat/indictrans2-indic-en-dist-200M
+library_name: peft
+tags:
+- base_model:adapter:ai4bharat/indictrans2-indic-en-dist-200M
+- lora
+- transformers
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+
+## Uses
+
+<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
+
+### Direct Use
+
+<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+[More Information Needed]
+
+## Training Details
+
+### Training Data
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
+
+[More Information Needed]
+
+### Training Procedure
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
+#### Metrics
+
+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
+
+[More Information Needed]
+
+### Results
+
+[More Information Needed]
+
+#### Summary
+
+
+
+## Model Examination [optional]
+
+<!-- Relevant interpretability work for the model goes here -->
+
+[More Information Needed]
+
+## Environmental Impact
+
+<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
+
+Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
+
+#### Hardware
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
+## Model Card Contact
+
+[More Information Needed]
+### Framework versions
+
+- PEFT 0.17.0

adapter_config.json

@@ -0,0 +1,39 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "ai4bharat/indictrans2-indic-en-dist-200M",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_bias": false,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "k_proj",
+    "v_proj",
+    "out_proj",
+    "q_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "SEQ_2_SEQ_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:8b0fef8ce41988d21fa37bec2f53ee9d8776959caeaaed70c6bd074138ac7cb1
+size 14216888

model.SRC

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:ac9257c8e76b8b607705b959cc3d075656ea33032f7a974e467b8941df6e98d4
+size 3256903

model.TGT

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:3cedc5cbcc740369b76201942a0f096fec7287fee039b55bdb956f301235b914
+size 759425

special_tokens_map.json

@@ -0,0 +1,30 @@
+{
+  "bos_token": {
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenization_indictrans.py

@@ -0,0 +1,251 @@
+import os
+import json
+
+from transformers.utils import logging
+from typing import Dict, List, Optional, Union, Tuple
+
+from sentencepiece import SentencePieceProcessor
+from transformers.tokenization_utils import PreTrainedTokenizer
+
+
+logger = logging.get_logger(__name__)
+
+# Convert LANGUAGE_TAGS to a frozen set for faster lookups
+LANGUAGE_TAGS = frozenset(
+    {
+        "asm_Beng",
+        "awa_Deva",
+        "ben_Beng",
+        "bho_Deva",
+        "brx_Deva",
+        "doi_Deva",
+        "eng_Latn",
+        "gom_Deva",
+        "gon_Deva",
+        "guj_Gujr",
+        "hin_Deva",
+        "hne_Deva",
+        "kan_Knda",
+        "kas_Arab",
+        "kas_Deva",
+        "kha_Latn",
+        "lus_Latn",
+        "mag_Deva",
+        "mai_Deva",
+        "mal_Mlym",
+        "mar_Deva",
+        "mni_Beng",
+        "mni_Mtei",
+        "npi_Deva",
+        "ory_Orya",
+        "pan_Guru",
+        "san_Deva",
+        "sat_Olck",
+        "snd_Arab",
+        "snd_Deva",
+        "tam_Taml",
+        "tel_Telu",
+        "urd_Arab",
+        "unr_Deva",
+    }
+)
+
+VOCAB_FILES_NAMES = {
+    "src_vocab_fp": "dict.SRC.json",
+    "tgt_vocab_fp": "dict.TGT.json",
+    "src_spm_fp": "model.SRC",
+    "tgt_spm_fp": "model.TGT",
+}
+
+
+class IndicTransTokenizer(PreTrainedTokenizer):
+    _added_tokens_encoder: Dict[str, int] = {}
+    _added_tokens_decoder: Dict[str, int] = {}
+    vocab_files_names = VOCAB_FILES_NAMES
+    model_input_names = ["input_ids", "attention_mask"]
+
+    def __init__(
+        self,
+        src_vocab_fp=None,
+        tgt_vocab_fp=None,
+        src_spm_fp=None,
+        tgt_spm_fp=None,
+        unk_token="<unk>",
+        bos_token="<s>",
+        eos_token="</s>",
+        pad_token="<pad>",
+        do_lower_case=False,
+        **kwargs,
+    ):
+        self.src_vocab_fp = src_vocab_fp
+        self.tgt_vocab_fp = tgt_vocab_fp
+        self.src_spm_fp = src_spm_fp
+        self.tgt_spm_fp = tgt_spm_fp
+
+        # Store token content directly instead of accessing .content
+        self.unk_token = (
+            hasattr(unk_token, "content") and unk_token.content or unk_token
+        )
+        self.pad_token = (
+            hasattr(pad_token, "content") and pad_token.content or pad_token
+        )
+        self.eos_token = (
+            hasattr(eos_token, "content") and eos_token.content or eos_token
+        )
+        self.bos_token = (
+            hasattr(bos_token, "content") and bos_token.content or bos_token
+        )
+
+        # Load vocabularies
+        self.src_encoder = self._load_json(self.src_vocab_fp)
+        self.tgt_encoder = self._load_json(self.tgt_vocab_fp)
+
+        # Validate tokens
+        if self.unk_token not in self.src_encoder:
+            raise KeyError("<unk> token must be in vocab")
+        if self.pad_token not in self.src_encoder:
+            raise KeyError("<pad> token must be in vocab")
+
+        # Pre-compute reverse mappings
+        self.src_decoder = {v: k for k, v in self.src_encoder.items()}
+        self.tgt_decoder = {v: k for k, v in self.tgt_encoder.items()}
+
+        # Load SPM models
+        self.src_spm = self._load_spm(self.src_spm_fp)
+        self.tgt_spm = self._load_spm(self.tgt_spm_fp)
+
+        # Initialize current settings
+        self._switch_to_input_mode()
+
+        # Cache token IDs
+        self.unk_token_id = self.src_encoder[self.unk_token]
+        self.pad_token_id = self.src_encoder[self.pad_token]
+        self.eos_token_id = self.src_encoder[self.eos_token]
+        self.bos_token_id = self.src_encoder[self.bos_token]
+
+        super().__init__(
+            src_vocab_file=self.src_vocab_fp,
+            tgt_vocab_file=self.tgt_vocab_fp,
+            do_lower_case=do_lower_case,
+            unk_token=unk_token,
+            bos_token=bos_token,
+            eos_token=eos_token,
+            pad_token=pad_token,
+            **kwargs,
+        )
+
+    def add_new_language_tags(self, new_tags: List[str]) -> None:
+        global LANGUAGE_TAGS
+        LANGUAGE_TAGS = frozenset(LANGUAGE_TAGS | set(new_tags))
+
+    def _switch_to_input_mode(self) -> None:
+        self.spm = self.src_spm
+        self.padding_side = "left"
+        self.encoder = self.src_encoder
+        self.decoder = self.src_decoder
+        self._tokenize = self._src_tokenize
+
+    def _switch_to_target_mode(self) -> None:
+        self.spm = self.tgt_spm
+        self.padding_side = "right"
+        self.encoder = self.tgt_encoder
+        self.decoder = self.tgt_decoder
+        self._tokenize = self._tgt_tokenize
+
+    @staticmethod
+    def _load_spm(path: str) -> SentencePieceProcessor:
+        return SentencePieceProcessor(model_file=path)
+
+    @staticmethod
+    def _save_json(data: Union[Dict, List], path: str) -> None:
+        with open(path, "w", encoding="utf-8") as f:
+            json.dump(data, f, indent=2)
+
+    @staticmethod
+    def _load_json(path: str) -> Union[Dict, List]:
+        with open(path, "r", encoding="utf-8") as f:
+            return json.load(f)
+
+    @property
+    def src_vocab_size(self) -> int:
+        return len(self.src_encoder)
+
+    @property
+    def tgt_vocab_size(self) -> int:
+        return len(self.tgt_encoder)
+
+    def get_src_vocab(self) -> Dict[str, int]:
+        return dict(self.src_encoder, **self.added_tokens_encoder)
+
+    def get_tgt_vocab(self) -> Dict[str, int]:
+        return dict(self.tgt_encoder, **self.added_tokens_decoder)
+
+    def get_vocab(self) -> Dict[str, int]:
+        return self.get_src_vocab()
+
+    @property
+    def vocab_size(self) -> int:
+        return self.src_vocab_size
+
+    def _convert_token_to_id(self, token: str) -> int:
+        return self.encoder.get(token, self.unk_token_id)
+
+    def _convert_id_to_token(self, index: int) -> str:
+        return self.decoder.get(index, self.unk_token)
+
+    def convert_tokens_to_string(self, tokens: List[str]) -> str:
+        return "".join(tokens).replace("▁", " ").strip()
+
+    def _src_tokenize(self, text: str) -> List[str]:
+        src_lang, tgt_lang, text = text.split(" ", 2)
+        assert src_lang in LANGUAGE_TAGS, f"Invalid source language tag: {src_lang}"
+        assert tgt_lang in LANGUAGE_TAGS, f"Invalid target language tag: {tgt_lang}"
+        return [src_lang, tgt_lang] + self.spm.EncodeAsPieces(text)
+
+    def _tgt_tokenize(self, text: str) -> List[str]:
+        return self.spm.EncodeAsPieces(text)
+
+    def _decode(
+        self,
+        token_ids: Union[int, List[int]],
+        skip_special_tokens: bool = False,
+        clean_up_tokenization_spaces: bool = None,
+        spaces_between_special_tokens: bool = True,
+        **kwargs,
+    ) -> str:
+        self._switch_to_target_mode()
+        decoded_token_ids = super()._decode(
+            token_ids=token_ids,
+            skip_special_tokens=skip_special_tokens,
+            clean_up_tokenization_spaces=clean_up_tokenization_spaces,
+            spaces_between_special_tokens=spaces_between_special_tokens,
+            **kwargs,
+        )
+        self._switch_to_input_mode()
+        return decoded_token_ids
+
+    def build_inputs_with_special_tokens(
+        self, token_ids_0: List[int], token_ids_1: Optional[List[int]] = None
+    ) -> List[int]:
+        return token_ids_0 + [self.eos_token_id]
+
+    def save_vocabulary(
+        self, save_directory: str, filename_prefix: Optional[str] = None
+    ) -> Tuple[str, ...]:
+        if not os.path.isdir(save_directory):
+            logger.error(f"Vocabulary path ({save_directory}) should be a directory")
+            return ()
+
+        src_spm_fp = os.path.join(save_directory, "model.SRC")
+        tgt_spm_fp = os.path.join(save_directory, "model.TGT")
+        src_vocab_fp = os.path.join(save_directory, "dict.SRC.json")
+        tgt_vocab_fp = os.path.join(save_directory, "dict.TGT.json")
+
+        self._save_json(self.src_encoder, src_vocab_fp)
+        self._save_json(self.tgt_encoder, tgt_vocab_fp)
+
+        for fp, spm in [(src_spm_fp, self.src_spm), (tgt_spm_fp, self.tgt_spm)]:
+            with open(fp, "wb") as f:
+                f.write(spm.serialized_model_proto())
+
+        return src_vocab_fp, tgt_vocab_fp, src_spm_fp, tgt_spm_fp

tokenizer_config.json

@@ -0,0 +1,53 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "</s>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "<unk>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "auto_map": {
+    "AutoTokenizer": [
+      "tokenization_indictrans.IndicTransTokenizer",
+      null
+    ]
+  },
+  "bos_token": "<s>",
+  "clean_up_tokenization_spaces": true,
+  "do_lower_case": false,
+  "eos_token": "</s>",
+  "extra_special_tokens": {},
+  "model_max_length": 256,
+  "pad_token": "<pad>",
+  "src_vocab_file": "/root/.cache/huggingface/hub/models--ai4bharat--indictrans2-indic-en-dist-200M/snapshots/eb9e49d81077cfc5311e82ff36d8c1fc11557b5d/dict.SRC.json",
+  "tgt_vocab_file": "/root/.cache/huggingface/hub/models--ai4bharat--indictrans2-indic-en-dist-200M/snapshots/eb9e49d81077cfc5311e82ff36d8c1fc11557b5d/dict.TGT.json",
+  "tokenizer_class": "IndicTransTokenizer",
+  "unk_token": "<unk>"
+}