Instructions to use mtrajan/sarvam-30b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use mtrajan/sarvam-30b-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 mtrajan/sarvam-30b-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mtrajan/sarvam-30b-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mtrajan/sarvam-30b-GGUF:F16
# Run inference directly in the terminal:
llama cli -hf mtrajan/sarvam-30b-GGUF:F16

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 mtrajan/sarvam-30b-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf mtrajan/sarvam-30b-GGUF:F16

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 mtrajan/sarvam-30b-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mtrajan/sarvam-30b-GGUF:F16

Use Docker

docker model run hf.co/mtrajan/sarvam-30b-GGUF:F16

LM Studio
Jan

vLLM

How to use mtrajan/sarvam-30b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mtrajan/sarvam-30b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mtrajan/sarvam-30b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mtrajan/sarvam-30b-GGUF:F16

Ollama
How to use mtrajan/sarvam-30b-GGUF with Ollama:
```
ollama run hf.co/mtrajan/sarvam-30b-GGUF:F16
```

Unsloth Studio

How to use mtrajan/sarvam-30b-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 mtrajan/sarvam-30b-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 mtrajan/sarvam-30b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mtrajan/sarvam-30b-GGUF to start chatting

How to use mtrajan/sarvam-30b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mtrajan/sarvam-30b-GGUF:F16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "mtrajan/sarvam-30b-GGUF:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

OpenClaw new

How to use mtrajan/sarvam-30b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mtrajan/sarvam-30b-GGUF:F16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "mtrajan/sarvam-30b-GGUF:F16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use mtrajan/sarvam-30b-GGUF with Docker Model Runner:
```
docker model run hf.co/mtrajan/sarvam-30b-GGUF:F16
```

Lemonade

How to use mtrajan/sarvam-30b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mtrajan/sarvam-30b-GGUF:F16

Run and chat with the model

lemonade run user.sarvam-30b-GGUF-F16

List all available models

lemonade list

Hermes Agent

How to use mtrajan/sarvam-30b-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mtrajan/sarvam-30b-GGUF:F16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default mtrajan/sarvam-30b-GGUF:F16

Run Hermes

hermes

Atomic Chat

Sarvam-30B GGUF

GGUF quantizations of sarvamai/sarvam-30b — the first publicly available GGUF for this model.

Created by applying llama.cpp PR #20275 which adds sarvam_moe architecture support to the converter and runtime.

Will this work with Ollama / LM Studio / Jan?

Not yet. These tools bundle mainline llama.cpp, which does not recognize sarvam_moe. You will see:
error loading model: unknown model architecture: 'sarvam_moe'
This GGUF requires a patched llama.cpp (with PR #20275 applied) until that PR merges into mainline. Once it does, Ollama / LM Studio / Jan will work automatically on their next update.

To build a patched llama.cpp, use mtr7x/sarvam-gguf.

Files

File	Quant	Size	BPW	Notes
sarvam-30b-q4_k_m.gguf	Q4_K_M	19 GB	4.87	Recommended — good balance of quality and size
sarvam-30b-f16.gguf	F16	60 GB	16.00	Full precision, use for further quantization

How to use

Option 1: Patch llama.cpp automatically (recommended)

git clone https://github.com/mtr7x/sarvam-gguf.git
cd sarvam-gguf
chmod +x patch_and_convert.sh
./patch_and_convert.sh

This clones llama.cpp, applies PR #20275, builds it, and you're ready to run.

Option 2: Run with patched llama.cpp directly

./llama-cli \
    --model sarvam-30b-q4_k_m.gguf \
    --n-gpu-layers 99 \
    --ctx-size 2048 \
    --temp 0.7 \
    -no-cnv \
    --prompt "भारत के बारे में बताइए।"

What does NOT work (yet)

Tool	Status	Why
Ollama	`unknown model architecture`	Waiting on PR #20275 merge
LM Studio	`unknown model architecture`	Waiting on PR #20275 merge
Jan	`unknown model architecture`	Waiting on PR #20275 merge
llama.cpp (mainline)	`unknown model architecture`	PR #20275 not yet merged
llama.cpp (patched)	Works	This is what you need

Architecture

sarvamai/sarvam-30b
├── model_type: sarvam_moe
├── 30B params, 2.4B active
├── 19 layers (1 dense + 18 MoE)
├── 128 experts + 1 shared, top-6, sigmoid routing
├── 64 query heads, 4 KV heads, head_dim=64
├── vocab_size: 262,144 (Indic-optimized)
└── Apache 2.0

Why this is needed

Sarvam open-sourced 30B and 105B under Apache 2.0, but mainline llama.cpp doesn't recognize model_type: "sarvam_moe" — the converter exits immediately. Contrary to what you might expect, sigmoid routing is already supported in llama.cpp (used by GLM4 and others). The actual blocker is a missing class registration + tensor mappings + C++ graph builder — all provided by PR #20275 (387 lines).

The domino chain

PR #20275 merges into llama.cpp       ← pending
  → GGUF can be created               ← done (this repo)
    → Ollama updates its llama.cpp     ← blocked
      → Unsloth applies dynamic quants ← blocked
        → ollama run sarvam-30b        ← blocked

Runtime support

Runtime	Status
vLLM	PR #33942 merged
SGLang	Works
llama.cpp (patched)	Works (PR #20275)
llama.cpp (mainline)	Blocked — PR pending
Ollama	Blocked on llama.cpp
LM Studio	Blocked on llama.cpp

Credits

sarvamai for open-sourcing Sarvam-30B under Apache 2.0
sumitchatterjee13 for llama.cpp PR #20275
Conversion pipeline: mtr7x/sarvam-gguf

Read the full analysis: Sarvam. Open is not sovereign

Downloads last month: 15

GGUF

Model size

32B params

Architecture

sarvam_moe

Hardware compatibility

4-bit

16-bit

Model tree for mtrajan/sarvam-30b-GGUF

Base model

sarvamai/sarvam-30b

Quantized

(30)

this model