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LICENSE.txt

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+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+                            Preamble
+
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+IS WITH YOU.  SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF
+ALL NECESSARY SERVICING, REPAIR OR CORRECTION.
+
+  16. Limitation of Liability.
+
+  IN NO EVENT UNLESS REQUIRED BY APPLICABLE LAW OR AGREED TO IN WRITING
+WILL ANY COPYRIGHT HOLDER, OR ANY OTHER PARTY WHO MODIFIES AND/OR CONVEYS
+THE PROGRAM AS PERMITTED ABOVE, BE LIABLE TO YOU FOR DAMAGES, INCLUDING ANY
+GENERAL, SPECIAL, INCIDENTAL OR CONSEQUENTIAL DAMAGES ARISING OUT OF THE
+USE OR INABILITY TO USE THE PROGRAM (INCLUDING BUT NOT LIMITED TO LOSS OF
+DATA OR DATA BEING RENDERED INACCURATE OR LOSSES SUSTAINED BY YOU OR THIRD
+PARTIES OR A FAILURE OF THE PROGRAM TO OPERATE WITH ANY OTHER PROGRAMS),
+EVEN IF SUCH HOLDER OR OTHER PARTY HAS BEEN ADVISED OF THE POSSIBILITY OF
+SUCH DAMAGES.
+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
+above cannot be given local legal effect according to their terms,
+reviewing courts shall apply local law that most closely approximates
+an absolute waiver of all civil liability in connection with the
+Program, unless a warranty or assumption of liability accompanies a
+copy of the Program in return for a fee.
+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
+possible use to the public, the best way to achieve this is to make it
+free software which everyone can redistribute and change under these terms.
+
+  To do so, attach the following notices to the program.  It is safest
+to attach them to the start of each source file to most effectively
+state the exclusion of warranty; and each file should have at least
+the "copyright" line and a pointer to where the full notice is found.
+
+    Sarvam-Translate
+    Copyright (C) 2025  AxonWise Private Limited
+
+    This program is free software: you can redistribute it and/or modify
+    it under the terms of the GNU General Public License as published by
+    the Free Software Foundation, either version 3 of the License, or
+    (at your option) any later version.
+
+    This program is distributed in the hope that it will be useful,
+    but WITHOUT ANY WARRANTY; without even the implied warranty of
+    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+    GNU General Public License for more details.
+
+    You should have received a copy of the GNU General Public License
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
+notice like this when it starts in an interactive mode:
+
+    Sarvam-Translate  Copyright (C) 2025  AxonWise Private Limited
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.md

@@ -1,5 +1,158 @@
----
-license: other
-license_name: other
-license_link: LICENSE
----
+---
+library_name: transformers
+license: gpl-3.0
+language:
+- as
+- bn
+- brx
+- doi
+- gom
+- gu
+- en
+- hi
+- kn
+- ks
+- mai
+- ml
+- mni
+- mr
+- ne
+- or
+- pa
+- sa
+- sat
+- sd
+- ta
+- te
+- ur
+base_model:
+- google/gemma-3-4b-it
+base_model_relation: finetune
+pipeline_tag: translation
+---
+
+# Sarvam-Translate
+<p align="center">
+  <a href="https://dashboard.sarvam.ai/translate"
+     target="_blank" rel="noopener noreferrer">
+    <img
+      src="https://img.shields.io/badge/🚀 Try on Sarvam&nbsp;Playground-1488CC?style=for-the-badge&logo=rocket"
+      alt="Try on Sarvam Playground"
+    />
+  </a>
+</p>
+Sarvam-Translate is an advanced translation model built by Sarvam AI in partnership with AI4Bharat, specifically designed for comprehensive, document-level translation across the 22 official Indian languages, built on Gemma3-4B-IT. It addresses modern translation needs by moving beyond isolated sentences to handle long-context inputs, diverse content types, and various formats. Sarvam-Translate aims to provide high-quality, contextually aware translations for Indian languages, which have traditionally lagged behind high-resource languages in LLM performance.
+
+Learn more about Sarvam-Translate in our detailed [blog post](https://www.sarvam.ai/blogs/sarvam-translate).
+
+## Key Features
+- **Comprehensive Indian Language Support**: Focus on the 22 official Indian languages, ensuring nuanced and accurate translations.
+- **Advanced Document-Level Translation**: Translates entire documents, web pages, speeches, textbooks, and scientific articles, not just isolated sentences. Maximum context length: 8k tokens
+- **Versatile Format Handling**: Processes a wide array of input formats, including markdown, digitized content (handling OCR errors), documents with embedded math and chemistry equations, and code files (translating only comments).
+- **Context-Aware & Inclusive**: Engineered to respect different contexts, formats, styles (formal/informal), and ensure inclusivity (e.g., appropriate gender attribution).
+
+## Supported languages list
+
+`Assamese`, `Bengali`, `Bodo`, `Dogri`, `Gujarati`, `English`, `Hindi`, `Kannada`, `Kashmiri`, `Konkani`, `Maithili`, `Malayalam`, `Manipuri`, `Marathi`, `Nepali`, `Odia`, `Punjabi`, `Sanskrit`, `Santali`, `Sindhi`, `Tamil`, `Telugu`, `Urdu`
+
+## Quickstart
+The following code snippet demonstrates how to use Sarvam-Translate using Transformers.
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "sarvamai/sarvam-translate"
+
+# Load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name).to('cuda:0')
+
+# Translation task
+tgt_lang = "Hindi"
+input_txt = "Be the change you wish to see in the world."
+
+# Chat-style message prompt
+messages = [
+    {"role": "system", "content": f"Translate the text below to {tgt_lang}."},
+    {"role": "user", "content": input_txt}
+]
+
+# Apply chat template to structure the conversation
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+# Tokenize and move input to model device
+model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+# Generate the output
+generated_ids = model.generate(
+    **model_inputs,
+    max_new_tokens=1024,
+    do_sample=True,
+    temperature=0.01,
+    num_return_sequences=1
+)
+output_ids = generated_ids[0][len(model_inputs.input_ids[0]):].tolist()
+output_text = tokenizer.decode(output_ids, skip_special_tokens=True)
+
+print("Input:", input_txt)
+print("Translation:", output_text)
+
+```
+
+## vLLM Deployment
+
+
+### Server:
+```bash
+vllm serve sarvamai/sarvam-translate --port 8000 --dtype bfloat16 --max-model-len 8192
+```
+
+### Client:
+```python
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+models = client.models.list()
+model = models.data[0].id
+
+
+tgt_lang = 'Hindi'
+input_txt = 'Be the change you wish to see in the world.'
+messages = [{"role": "system", "content": f"Translate the text below to {tgt_lang}."}, {"role": "user", "content": input_txt}]
+
+
+response = client.chat.completions.create(model=model, messages=messages, temperature=0.01)
+output_text = response.choices[0].message.content
+
+print("Input:", input_txt)
+print("Translation:", output_text)
+```
+
+## With Sarvam APIs
+
+Refer our [python client documentation](https://pypi.org/project/sarvamai/).
+
+Sample code:
+
+```python
+from sarvamai import SarvamAI
+client = SarvamAI()
+response = client.text.translate(
+    input="Be the change you wish to see in the world.",
+    source_language_code="en-IN",
+    target_language_code="hi-IN",
+    speaker_gender="Male",
+    model="sarvam-translate:v1",
+)
+```

added_tokens.json

@@ -0,0 +1,3 @@
+{
+  "<image_soft_token>": 262144
+}

chat_template.jinja

@@ -0,0 +1,47 @@
+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{'<start_of_turn>model
+'}}
+{%- endif -%}

config.json

@@ -0,0 +1,62 @@
+{
+  "architectures": [
+    "Gemma3ForConditionalGeneration"
+  ],
+  "boi_token_index": 255999,
+  "eoi_token_index": 256000,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "image_token_index": 262144,
+  "initializer_range": 0.02,
+  "mm_tokens_per_image": 256,
+  "model_type": "gemma3",
+  "text_config": {
+    "attention_bias": false,
+    "attention_dropout": 0.0,
+    "attn_logit_softcapping": null,
+    "cache_implementation": "hybrid",
+    "final_logit_softcapping": null,
+    "head_dim": 256,
+    "hidden_activation": "gelu_pytorch_tanh",
+    "hidden_size": 2560,
+    "initializer_range": 0.02,
+    "intermediate_size": 10240,
+    "max_position_embeddings": 131072,
+    "model_type": "gemma3_text",
+    "num_attention_heads": 8,
+    "num_hidden_layers": 34,
+    "num_key_value_heads": 4,
+    "query_pre_attn_scalar": 256,
+    "rms_norm_eps": 1e-06,
+    "rope_local_base_freq": 10000.0,
+    "rope_scaling": {
+      "factor": 8.0,
+      "rope_type": "linear"
+    },
+    "rope_theta": 1000000.0,
+    "sliding_window": 1024,
+    "sliding_window_pattern": 6,
+    "torch_dtype": "bfloat16",
+    "use_cache": true,
+    "vocab_size": 262208
+  },
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.3",
+  "vision_config": {
+    "attention_dropout": 0.0,
+    "hidden_act": "gelu_pytorch_tanh",
+    "hidden_size": 1152,
+    "image_size": 896,
+    "intermediate_size": 4304,
+    "layer_norm_eps": 1e-06,
+    "model_type": "siglip_vision_model",
+    "num_attention_heads": 16,
+    "num_channels": 3,
+    "num_hidden_layers": 27,
+    "patch_size": 14,
+    "torch_dtype": "bfloat16",
+    "vision_use_head": false
+  }
+}

final_code.py

@@ -0,0 +1,173 @@
+import torch
+from torch.utils.data import Dataset, DataLoader
+from transformers import MT5ForConditionalGeneration, MT5Tokenizer, AdamW
+from transformers import AutoModel, AutoTokenizer
+from sklearn.metrics.pairwise import cosine_similarity
+import pandas as pd
+import matplotlib.pyplot as plt
+import numpy as np
+from huggingface_hub import HfApi, HfFolder, Repository, notebook_login, create_repo, upload_folder
+import os
+import shutil
+
+# ========== CONFIG ==========
+HF_USERNAME = "aarath97"
+HF_REPO = "mt5-dogri-translation"
+MODEL_NAME = "google/mt5-large"
+BATCH_SIZE = 2
+LR = 1e-5
+DPO_STEPS = 100
+HGRL_STEPS = 100
+COMBINED_STEPS = 50
+GAMMA = 3.5
+ALPHA = 0.5
+BETA = 0.5
+
+# ========== LOAD DATA ==========
+df = pd.read_excel("dogri_train.xlsx")
+train_data = list(zip(df['Dogri'], df['English'], df['Unpreffered']))
+
+# ========== TOKENIZERS ==========
+tokenizer = MT5Tokenizer.from_pretrained(MODEL_NAME)
+sbert = AutoModel.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+sbert_tokenizer = AutoTokenizer.from_pretrained("sentence-transformers/all-MiniLM-L6-v2")
+
+# ========== UTILITIES ==========
+def compute_similarity(sent1, sent2):
+    emb1 = sbert(**sbert_tokenizer(sent1, return_tensors='pt')).last_hidden_state.mean(1)
+    emb2 = sbert(**sbert_tokenizer(sent2, return_tensors='pt')).last_hidden_state.mean(1)
+    return cosine_similarity(emb1.detach().numpy(), emb2.detach().numpy())[0][0]
+
+def hyper_gamma_reward(rho):
+    return rho * np.exp(-GAMMA * (1 - rho))
+
+# ========== DATASET ==========
+class DogriDataset(Dataset):
+    def __init__(self, data):
+        self.data = data
+
+    def __len__(self):
+        return len(self.data)
+
+    def __getitem__(self, idx):
+        return self.data[idx]
+
+dataloader = DataLoader(DogriDataset(train_data), batch_size=BATCH_SIZE, shuffle=True)
+
+# ========== TRAINING ==========
+model = MT5ForConditionalGeneration.from_pretrained(MODEL_NAME).to("cuda")
+optimizer = AdamW(model.parameters(), lr=LR)
+
+dpo_losses, hgrl_losses, final_losses = [], [], []
+
+# ---- DPO Training ----
+for step in range(DPO_STEPS):
+    batch = next(iter(dataloader))
+    loss_batch = []
+    for src, ref, unpref in zip(*batch):
+        input_ids = tokenizer(src, return_tensors='pt', truncation=True, padding=True).input_ids.to("cuda")
+        ref_ids = tokenizer(ref, return_tensors='pt', truncation=True, padding=True).input_ids.to("cuda")
+        unpref_ids = tokenizer(unpref, return_tensors='pt', truncation=True, padding=True).input_ids.to("cuda")
+
+        ref_logprob = model(input_ids=input_ids, labels=ref_ids).loss
+        unpref_logprob = model(input_ids=input_ids, labels=unpref_ids).loss
+
+        logit_diff = -ref_logprob.item() + unpref_logprob.item()
+        beta = 1.0
+        loss = -torch.log(torch.sigmoid(torch.tensor(beta * logit_diff)))
+        loss_batch.append(loss)
+
+    loss_val = torch.stack(loss_batch).mean()
+    loss_val.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+    dpo_losses.append(loss_val.item())
+
+# ---- HGRL Training ----
+for step in range(HGRL_STEPS):
+    batch = next(iter(dataloader))
+    loss_batch = []
+    for src, ref, _ in zip(*batch):
+        input_ids = tokenizer(src, return_tensors='pt').input_ids.to("cuda")
+        gen_ids = model.generate(input_ids)
+        gen_text = tokenizer.decode(gen_ids[0], skip_special_tokens=True)
+
+        rho = compute_similarity(gen_text, ref)
+        reward = hyper_gamma_reward(rho)
+
+        labels = tokenizer(gen_text, return_tensors='pt').input_ids.to("cuda")
+        logprob = model(input_ids=input_ids, labels=labels).loss
+
+        loss = -reward * logprob
+        loss_batch.append(loss)
+
+    loss_val = torch.stack(loss_batch).mean()
+    loss_val.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+    hgrl_losses.append(loss_val.item())
+
+# ---- Combined Training ----
+for step in range(COMBINED_STEPS):
+    batch = next(iter(dataloader))
+    loss_dpo_batch, loss_hgrl_batch = [], []
+    for src, ref, unpref in zip(*batch):
+        input_ids = tokenizer(src, return_tensors='pt').input_ids.to("cuda")
+        ref_ids = tokenizer(ref, return_tensors='pt').input_ids.to("cuda")
+        unpref_ids = tokenizer(unpref, return_tensors='pt').input_ids.to("cuda")
+
+        logprob_ref = model(input_ids=input_ids, labels=ref_ids).loss
+        logprob_unpref = model(input_ids=input_ids, labels=unpref_ids).loss
+        dpo_loss = -torch.log(torch.sigmoid(torch.tensor(logprob_unpref.item() - logprob_ref.item())))
+        loss_dpo_batch.append(dpo_loss)
+
+        gen_ids = model.generate(input_ids)
+        gen_text = tokenizer.decode(gen_ids[0], skip_special_tokens=True)
+        rho = compute_similarity(gen_text, ref)
+        reward = hyper_gamma_reward(rho)
+
+        labels = tokenizer(gen_text, return_tensors='pt').input_ids.to("cuda")
+        logprob = model(input_ids=input_ids, labels=labels).loss
+        hgrl_loss = -reward * logprob
+        loss_hgrl_batch.append(hgrl_loss)
+
+    loss_dpo_mean = torch.stack(loss_dpo_batch).mean()
+    loss_hgrl_mean = torch.stack(loss_hgrl_batch).mean()
+    combined_loss = ALPHA * loss_dpo_mean + BETA * loss_hgrl_mean
+    combined_loss.backward()
+    optimizer.step()
+    optimizer.zero_grad()
+    final_losses.append(combined_loss.item())
+
+# ========== SAVE OUTPUTS ==========
+plt.plot(dpo_losses, label="DPO")
+plt.plot(hgrl_losses, label="HGRL")
+plt.plot(final_losses, label="Combined")
+plt.xlabel("Steps")
+plt.ylabel("Loss")
+plt.legend()
+plt.savefig("loss_curve.png")
+
+with open("loss_report.txt", "w") as f:
+    f.write("DPO Final Loss: {:.4f}\n".format(dpo_losses[-1]))
+    f.write("HGRL Final Loss: {:.4f}\n".format(hgrl_losses[-1]))
+    f.write("Combined Final Loss: {:.4f}\n".format(final_losses[-1]))
+
+# ========== TEST AND SAVE TRANSLATIONS ==========
+test_df = pd.read_excel("in22conv.xlsx")
+test_outputs = []
+for line in test_df.iloc[:, 0].tolist():
+    input_ids = tokenizer(line, return_tensors='pt').input_ids.to("cuda")
+    outputs = model.generate(input_ids)
+    translation = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    test_outputs.append(translation)
+
+output_df = pd.DataFrame({"Dogri": test_df.iloc[:, 0], "English": test_outputs})
+output_df.to_excel("translated_output.xlsx", index=False)
+
+# ========== PUSH TO HUGGING FACE ==========
+model.save_pretrained("mt5-dogri")
+tokenizer.save_pretrained("mt5-dogri")
+create_repo(f"{HF_USERNAME}/{HF_REPO}", private=False, exist_ok=True)
+upload_folder(repo_id=f"{HF_USERNAME}/{HF_REPO}", folder_path="mt5-dogri")
+print("Model uploaded successfully!")

generation_config.json

@@ -0,0 +1,13 @@
+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.52.3"
+}

gitattributes

@@ -0,0 +1,36 @@
+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz 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
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

model-00001-of-00002.safetensors

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