| --- |
| license: gemma |
| base_model: unsloth/gemma-4-E4B-it |
| language: |
| - hi |
| - en |
| tags: |
| - hindi |
| - indic |
| - gemma |
| - unsloth |
| - text-generation |
| datasets: |
| - ai4bharat/indic-instruct-data-v0.1 |
| pipeline_tag: text-generation |
| library_name: transformers |
| --- |
| |
| # 🇮🇳 Gemma-4-E4B-Hindi-Instruct (16-bit) |
|
|
| A Hindi instruction-tuned fine-tune of **Gemma 4 E4B**. This is the merged 16-bit model for use with 🤗 Transformers / vLLM / further fine-tuning. |
|
|
| For local CPU/edge use, see the **GGUF** build. |
|
|
| > Part of my **Hindi LLM Series** — small, openly-documented Indic models that actually follow instructions in Hindi and run on your own machine. |
|
|
| --- |
|
|
| ## Usage (Transformers) |
|
|
| ```python |
| from transformers import AutoModelForCausalLM, AutoProcessor |
| import torch |
| |
| model_id = "pankajpandey-dev/gemma-4-e4b-hindi-instruct" |
| model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.bfloat16, device_map="auto") |
| proc = AutoProcessor.from_pretrained(model_id) |
| |
| msgs = [{"role": "user", "content": [{"type": "text", "text": "मशीन लर्निंग को आसान शब्दों में समझाओ।"}]}] |
| inputs = proc.apply_chat_template(msgs, add_generation_prompt=True, tokenize=True, |
| return_dict=True, return_tensors="pt").to(model.device) |
| out = model.generate(**inputs, max_new_tokens=256, use_cache=True) |
| print(proc.decode(out[0], skip_special_tokens=True)) |
| ``` |
|
|
| --- |
|
|
| ## Example outputs |
|
|
| **Prompt:** भारत के बारे में एक रोचक तथ्य बताओ। |
| > भारत दुनिया में सबसे अधिक भाषाओं वाले देशों में से एक है — 22 आधिकारिक भाषाएँ और 1,000 से अधिक बोलियाँ। हिंदी एक इंडो-आर्यन भाषा है, जबकि तमिल एक द्रविड़ भाषा है। |
|
|
| --- |
|
|
| ## Training details |
|
|
| | | | |
| |---|---| |
| | Base model | `unsloth/gemma-4-E4B-it` | |
| | Method | LoRA (r=16, α=16), response-only loss | |
| | Framework | [Unsloth](https://github.com/unslothai/unsloth) | |
| | Data | ~10k Hindi instruction pairs (AI4Bharat indic-instruct: anudesh + dolly, hi splits) | |
| | Epochs | 2 | |
| | LR / schedule | 1e-4, cosine | |
| | Precision | bf16 (4-bit QLoRA base) | |
| | Hardware | Single NVIDIA L4 (24 GB) | |
| | Final train loss | ~0.29 | |
|
|
| Trained text-only (vision layers frozen), single-BOS chat template to avoid double-BOS corruption. |
|
|
| --- |
|
|
| ## Related repos |
|
|
| - GGUF (Q4/Q5/Q8): [`pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF`](https://huggingface.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct-GGUF) |
| - LoRA adapter: [`pankajpandey-dev/gemma-4-e4b-hindi-instruct-lora`](https://huggingface.co/pankajpandey-dev/gemma-4-e4b-hindi-instruct-lora) |
|
|
| --- |
|
|
| ## Provenance & license (please read) |
|
|
| Mixed-license lineage — review all before redistribution or commercial use: |
|
|
| - **Weights** derive from **Gemma 4**, under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms). |
| - **Data** from [AI4Bharat indic-instruct-data-v0.1](https://huggingface.co/datasets/ai4bharat/indic-instruct-data-v0.1): |
| - **Dolly** split — from `databricks-dolly-15k`, **CC-BY-SA-3.0**. |
| - **Anudesh** split — responses from **Llama-2-70B**, so the **Llama 2 Community License** applies. |
|
|
| Raw training data is not redistributed here. You are responsible for complying with the Gemma, Llama 2, and CC-BY-SA terms. |
|
|
| --- |
|
|
| ## Limitations |
|
|
| - ~8B-class model: strong Hindi fluency, but can hallucinate facts and occasionally repeat phrasing on long open-ended generation. |
| - Tuned for single-turn Hindi instructions; long multi-turn chat is not the focus. |
| - Not safety-aligned for production. |
|
|
| ## Acknowledgements |
|
|
| Base model by Google (Gemma 4). Data by AI4Bharat. Fine-tuning with Unsloth. 🙏 |
|
|
