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Model: pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+- hi
+- en
+base_model: openbmb/MiniCPM5-1B
+tags:
+- hindi
+- indic
+- instruction-tuned
+- minicpm5
+- text-generation
+- conversational
+- lora
+- unsloth
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# MiniCPM5-1B-Hindi-Instruct
+
+A Hindi instruction-tuned variant of [openbmb/MiniCPM5-1B](https://huggingface.co/openbmb/MiniCPM5-1B), fine-tuned for Hindi (हिंदी) conversational and instruction-following tasks.
+
+Part of the [🇮🇳 Hindi LLM Series](https://huggingface.co/collections/pankajpandey-dev) by [@pankajpandey-dev](https://huggingface.co/pankajpandey-dev).
+
+## Model Details
+
+- **Base model:** openbmb/MiniCPM5-1B (1.1B parameters)
+- **Language:** Hindi (हिंदी), with English understanding retained from the base
+- **Fine-tuning method:** LoRA (r=32, alpha=64) merged into base weights
+- **Training framework:** [Unsloth](https://github.com/unslothai/unsloth) + TRL
+- **License:** Apache 2.0
+
+## Training Data
+
+Fine-tuned on **4,000 high-quality Hindi instruction examples** sampled from:
+
+- [`ai4bharat/indic-instruct-data-v0.1`](https://huggingface.co/datasets/ai4bharat/indic-instruct-data-v0.1) — `anudesh` (Hindi split): native crowd-sourced Hindi instructions
+- [`ai4bharat/indic-instruct-data-v0.1`](https://huggingface.co/datasets/ai4bharat/indic-instruct-data-v0.1) — `dolly` (Hindi split, filtered to chrF ≥ 60): broad instruction variety
+
+All examples ≤ 2048 tokens, formatted with the MiniCPM5 ChatML template.
+
+## Training Configuration
+
+| Hyperparameter | Value |
+|----------------|-------|
+| LoRA rank | 32 |
+| LoRA alpha | 64 |
+| LoRA dropout | 0.0 |
+| Target modules | q, k, v, o, gate, up, down |
+| Batch size (effective) | 16 |
+| Learning rate | 2e-4 |
+| LR scheduler | cosine |
+| Warmup steps | 15 |
+| Epochs | 2 |
+| Total steps | 500 |
+| Precision | fp16 (4-bit base) |
+| Hardware | NVIDIA Tesla T4 (Colab) |
+| Training time | ~60 minutes |
+| Final training loss | 1.108 |
+
+## Usage
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+
+model_id = "pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.float16,
+    device_map="auto",
+    trust_remote_code=True,
+)
+
+messages = [
+    {"role": "user", "content": "नमस्ते! बारिश के दिन पर एक छोटी कविता लिखो।"}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+).to(model.device)
+
+outputs = model.generate(
+    inputs,
+    max_new_tokens=256,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+    repetition_penalty=1.1,
+)
+print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))
+```
+
+### Recommended Generation Parameters
+
+- **temperature:** 0.7 (lower = more focused, higher = more creative)
+- **top_p:** 0.9
+- **repetition_penalty:** 1.1
+- **max_new_tokens:** 256–512 depending on task
+
+### LoRA Adapter Only
+
+If you prefer to load the LoRA adapter on top of the base model (~85 MB vs 2.2 GB), it's available in the `lora_adapter/` folder of this repo:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+base = AutoModelForCausalLM.from_pretrained("openbmb/MiniCPM5-1B", trust_remote_code=True)
+model = PeftModel.from_pretrained(base, "pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct", subfolder="lora_adapter")
+```
+
+## Example Outputs
+
+**Prompt:** बारिश के दिन पर एक छोटी कविता लिखो।
+**Response:** *(creative Hindi poetry generation)*
+
+**Prompt:** मशीन लर्निंग क्या है? सरल हिंदी में समझाइए।
+**Response:** *(simplified Hindi explanation of ML)*
+
+**Prompt:** नमस्ते! अपना परिचय दीजिए।
+**Response:** *(conversational Hindi self-introduction)*
+
+## Quantized Versions (GGUF)
+
+For running locally with llama.cpp, Ollama, LM Studio, or other GGUF-compatible inference engines.
+
+## Acknowledgements
+
+- [OpenBMB](https://huggingface.co/openbmb) for the MiniCPM5-1B base model
+- [AI4Bharat](https://huggingface.co/ai4bharat) (IIT Madras) for the indic-instruct-data dataset
+- [Unsloth](https://github.com/unslothai/unsloth) for the training framework
+
+## Citation
+
+If you use this model in your work, please cite:
+
+```bibtex
+@misc{pandey2026minicpm5hindi,
+  title  = {MiniCPM5-1B-Hindi-Instruct},
+  author = {Pankaj Pandey},
+  year   = {2026},
+  url    = {https://huggingface.co/pankajpandey-dev/MiniCPM5-1B-Hindi-Instruct}
+}
+```
+
+---
+
+*Part of an ongoing effort to bring strong open-source LLMs to Indian languages. Feedback and contributions welcome via the community tab.*