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Model: eulogik/Bharat-Tiny-LLM-fused
Source: Original Platform

README.md

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+---
+license: apache-2.0
+language:
+  - hi
+  - en
+  - hne
+tags:
+  - hinglish
+  - hindi
+  - indian-languages
+  - fused
+  - pytorch
+  - fp16
+  - qwen
+  - qwen2
+  - bharat
+  - indic-nlp
+base_model: Qwen/Qwen2.5-1.5B
+library_name: transformers
+pipeline_tag: text-generation
+---
+
+# Bharat-Tiny-LLM (fused · fp16)
+
+This is the **full-precision fused model** for [Bharat-Tiny-LLM](https://huggingface.co/eulogik/Bharat-Tiny-LLM) —
+the LoRA adapter merged into the base [Qwen2.5-1.5B](https://huggingface.co/Qwen/Qwen2.5-1.5B) weights,
+in PyTorch `float16`.
+
+Use this repo when you want to:
+- run inference on CPU / CUDA with `transformers`,
+- fine-tune further, or
+- produce your own quantized builds (GGUF, MLX, etc.).
+
+> Built by [eulogik](https://eulogik.com)
+
+## For most users
+
+You probably want a smaller, ready-to-run build instead:
+
+| Build | Repo | Size | Use |
+|-------|------|------|-----|
+| **MLX 4-bit** (edge / Apple Silicon) | [`eulogik/Bharat-Tiny-LLM`](https://huggingface.co/eulogik/Bharat-Tiny-LLM) | ~880 MB | Recommended for Mac / on-device |
+| **GGUF Q4_K_M** (llama.cpp, Android / Pi / CPU) | [`eulogik/Bharat-Tiny-LLM-GGUF`](https://huggingface.co/eulogik/Bharat-Tiny-LLM-GGUF) | ~1.06 GB | Cross-platform, llama.cpp |
+| **PyTorch fp16** (this repo) | `eulogik/Bharat-Tiny-LLM-fused` | ~3.3 GB | Server / fine-tuning base |
+
+## Quick start (transformers)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("eulogik/Bharat-Tiny-LLM-fused")
+tokenizer = AutoTokenizer.from_pretrained("eulogik/Bharat-Tiny-LLM-fused")
+
+messages = [{"role": "user", "content": "Chai peete hain?"}]
+prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
+inputs = tokenizer(prompt, return_tensors="pt")
+out = model.generate(
+    **inputs,
+    max_new_tokens=256,
+    temperature=0.3,
+    top_p=0.85,
+    repetition_penalty=1.25,
+    no_repeat_ngram_size=3,
+    do_sample=True,
+)
+print(tokenizer.decode(out[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
+```
+
+> ⚠️ **Generation config matters.** The base Qwen2.5-1.5B emits garbled out-of-script tokens
+> at high temperature. Always use `temperature ≈ 0.3` + `repetition_penalty ≥ 1.25` +
+> `no_repeat_ngram_size = 3`. The [`bharat-tiny-llm`](https://pypi.org/project/bharat-tiny-llm/)
+> PyPI package applies these for you.
+
+## Links
+
+- 🤗 Edge model (MLX): https://huggingface.co/eulogik/Bharat-Tiny-LLM
+- 🤗 GGUF (llama.cpp): https://huggingface.co/eulogik/Bharat-Tiny-LLM-GGUF
+- 🚀 Demo: https://huggingface.co/spaces/eulogik/Bharat-Tiny-LLM
+- 💻 Source: https://github.com/eulogik/Bharat-Tiny-LLM
+- 📦 PyPI: https://pypi.org/project/bharat-tiny-llm/
+- 🏢 Built by [eulogik](https://eulogik.com)
+
+## License
+
+Apache-2.0 (base Qwen2.5-1.5B weights Apache-2.0; LoRA adapter Apache-2.0).