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Model: rudrashah/RLM-hinglish-translator Source: Original Platform
2026-07-26 13:27:10 +08:00
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--- a/README.md
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+---
+license: mit
+datasets:
+- cmu_hinglish_dog
+language:
+- en
+library_name: transformers
+pipeline_tag: translation
+tags:
+- hinglish
+- translation
+- hinglish to english
+- language translation
+- keras
+- keras nlp
+- nlp
+- transformers
+- gemma2b
+---
+# Project Hinglish - A Hinglish to English Language Translater.
+
+## Newer Model is HERE? → [RLM-hinglish-translator (v2)](https://huggingface.co/rudrashah/RLM-hinglish-translator-2)
+
+<p align="center">
+  <a href="https://www.buymeacoffee.com/rudrashah" target="_blank">
+    <img src="https://cdn.buymeacoffee.com/buttons/v2/default-yellow.png" alt="Buy Me A Coffee" style="height: 60px !important;width: 217px !important;">
+  </a>
+</p>
+
+Project Hinglish aims to develop a high-performance language translation model capable of translating Hinglish (a blend of Hindi and English commonly used in informal communication in India) to standard English.
+The model is fine-tuned over gemma-2b using PEFT(LoRA) method using the rank 128. Aim of this model is for handling the unique syntactical and lexical characteristics of Hinglish.
+
+# Fine-Tune Method:
+
+- **Fine-Tuning Approach Using PEFT (LoRA):** The fine-tuning employs Parameter-efficient Fine Tuning (PEFT) techniques, particularly using LoRA (Low-Rank Adaptation). LoRA modifies a pre-trained model efficiently by introducing low-rank matrices that adapt the model’s attention and feed-forward layers. This method allows significant model adaptation with minimal updates to the parameters, preserving the original model's strengths while adapting it effectively to the nuances of Hinglish.
+- **Dataset:** cmu_hinglish_dog + Combination of sentences taken from my own dialy life chats with friends and Uber Messages.
+
+# Example Output
+
+![Example IO](io1.png)
+
+# Usage
+``` python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("rudrashah/RLM-hinglish-translator")
+model = AutoModelForCausalLM.from_pretrained("rudrashah/RLM-hinglish-translator")
+
+template = "Hinglish:\n{hi_en}\n\nEnglish:\n{en}" #THIS IS MOST IMPORTANT, WITHOUT THIS IT WILL GIVE RANDOM OUTPUT
+input_text = tokenizer(template.format(hi_en="aapka name kya hai?",en=""),return_tensors="pt")
+
+output = model.generate(**input_text)
+print(tokenizer.decode(output[0]))
+```
+[Usage Code Notebook on kaggle](https://www.kaggle.com/code/sciencelabwork/hinglish-to-english-usage)
--- a/config.json
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+{
+  "architectures": [
+    "GemmaForCausalLM"
+  ],
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+  "bos_token_id": 2,
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+  "head_dim": 256,
+  "hidden_act": "gelu_pytorch_tanh",
+  "hidden_activation": null,
+  "hidden_size": 2048,
+  "initializer_range": 0.02,
+  "intermediate_size": 16384,
+  "max_position_embeddings": 8192,
+  "model_type": "gemma",
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+  "pad_token_id": 0,
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+  "rope_theta": 10000.0,
+  "torch_dtype": "float32",
+  "transformers_version": "4.39.3",
+  "use_cache": true,
+  "vocab_size": 256000
+}
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+}
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--- a/special_tokens_map.json
+++ b/special_tokens_map.json
@@ -0,0 +1,30 @@
+{
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+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
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+}
--- a/tokenizer.model
+++ b/tokenizer.model
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
+size 4241003
--- a/tokenizer_config.json
+++ b/tokenizer_config.json
@@ -0,0 +1,48 @@
+{
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+}