How to use from
llama.cpp
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Saugat20021/sahayak-voice-models
# Run inference directly in the terminal:
llama cli -hf Saugat20021/sahayak-voice-models
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Saugat20021/sahayak-voice-models
# Run inference directly in the terminal:
llama cli -hf Saugat20021/sahayak-voice-models
Use pre-built binary
# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf Saugat20021/sahayak-voice-models
# Run inference directly in the terminal:
./llama-cli -hf Saugat20021/sahayak-voice-models
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf Saugat20021/sahayak-voice-models
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Saugat20021/sahayak-voice-models
Use Docker
docker model run hf.co/Saugat20021/sahayak-voice-models
Quick Links

Sahayak Voice Models

The full model bundle behind the Sahayak voice banking assistant: TTS, STT, and LLM, one folder each. tts/ and stt/ are original fine-tunes; llm/'s base weights are an unmodified third-party public model (only its Modelfile is original). All are included here so this repo is a complete, self-contained, reproducible bundle of everything the assistant runs. Attribution for each is below.

tts/ — original fine-tune

File What it is
t3_nepali_checkpoint.pt Fine-tuned T3 (text→speech-token) model, trained on Nepali
tokenizer_np.json Nepali BPE+grapheme tokenizer for the fine-tuned T3 model

This is a Nepali fine-tune of the T3 stage of ResembleAI/chatterbox. You still need the base model's other components to use it — ve.safetensors (voice encoder), s3gen.safetensors (mel generator), conds.pt (default voice conditioning) — download those from the original ResembleAI repo; they are unmodified upstream weights and aren't re-hosted here.

stt/ — fine-tuned on the Hindi version

Based on ai4bharat/indic-conformer-600m-multilingual (ONNX conformer encoder + per-language CTC/RNNT decoder heads, 22 Indic languages including Nepali) — further fine-tuned here on the Hindi version of the model to adapt it for this assistant. All credit for the base architecture/weights belongs to the original AI4Bharat repo; the fine-tuning on top of it was done for this project.

llm/ — base weights are a mirror; the Modelfile is original

File What it is
himalaya-gemma-toolcall.gguf Same weights as himalaya-ai/himalaya-gemma-4-e2b-it-ggufnot modified/fine-tuned further here
Modelfile An Ollama Modelfile adding a tool-calling prompt template (JSON function-call format) on top of the base model — this is the only original artifact in this folder

The underlying weights are unchanged from Himalaya AI Labs' public Gemma-based Nepali model. All credit and license terms belong to that original repo.

Summary of what's actually original here

tts/t3_nepali_checkpoint.pt + tts/tokenizer_np.json (Nepali TTS fine-tune), the stt/ model (fine-tuned from the Hindi version of IndicConformer), and llm/Modelfile were produced for this project. The llm/himalaya-gemma-toolcall.gguf weights are an unmodified copy of a third-party public model, included for a complete, reproducible bundle — not a claim of authorship over those weights.

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