Instructions to use yashwork-byte/gemma-3-4b-it-voice-digest-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use yashwork-byte/gemma-3-4b-it-voice-digest-GGUF with 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 yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
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 yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
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 yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
Use Docker
docker model run hf.co/yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use yashwork-byte/gemma-3-4b-it-voice-digest-GGUF with Ollama:
ollama run hf.co/yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
- Unsloth Studio
How to use yashwork-byte/gemma-3-4b-it-voice-digest-GGUF with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for yashwork-byte/gemma-3-4b-it-voice-digest-GGUF to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for yashwork-byte/gemma-3-4b-it-voice-digest-GGUF to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for yashwork-byte/gemma-3-4b-it-voice-digest-GGUF to start chatting
- Atomic Chat new
- Docker Model Runner
How to use yashwork-byte/gemma-3-4b-it-voice-digest-GGUF with Docker Model Runner:
docker model run hf.co/yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
- Lemonade
How to use yashwork-byte/gemma-3-4b-it-voice-digest-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull yashwork-byte/gemma-3-4b-it-voice-digest-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.gemma-3-4b-it-voice-digest-GGUF-Q4_K_M
List all available models
lemonade list
gemma-3-4b-it-voice-digest-GGUF
Q4_K_M GGUF (~2.4 GB) of the merged
gemma-3-4b-it-voice-digest LoRA:
a WhatsApp-voice-note digester for Hindi / Hinglish / Tamil / Bengali that
emits schema-exact JSON (English summary, translation, confirmed|tentative
action items — empty list for pure chat). Runs on llama.cpp anywhere,
including phones; ~25 s per note on an 8 GB M2 Air, all offline.
See the adapter card for the required prompt format (system prompt,
From: {sender} line, and the three instruction variants that select
full / no-translation / translation-only output), training data, and the
full evaluation. Headline: end-to-end action-item F1 0.76, hallucination
0.10 on pure-chat notes, 0 schema-parse failures, measured through this
exact quantized file with a JSON grammar.
Quick start (llama-cpp-python)
from llama_cpp import Llama
llm = Llama.from_pretrained("yashwork-byte/gemma-3-4b-it-voice-digest-GGUF",
"gemma-3-4b-it-ft-Q4_K_M.gguf", n_ctx=2048, n_gpu_layers=-1)
out = llm.create_chat_completion(
messages=[{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": f"Digest this voice note transcript.\n\nFrom: Papa\n{transcript}"}],
response_format={"type": "json_object"}, temperature=0.1)
Gemma derivative — subject to the Gemma Terms of Use.
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