Instructions to use mradermacher/sarvam-30b-uncensored-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mradermacher/sarvam-30b-uncensored-i1-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mradermacher/sarvam-30b-uncensored-i1-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use mradermacher/sarvam-30b-uncensored-i1-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 mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/sarvam-30b-uncensored-i1-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 mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf mradermacher/sarvam-30b-uncensored-i1-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 mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf mradermacher/sarvam-30b-uncensored-i1-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 mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
Use Docker
docker model run hf.co/mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use mradermacher/sarvam-30b-uncensored-i1-GGUF with Ollama:
ollama run hf.co/mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use mradermacher/sarvam-30b-uncensored-i1-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use mradermacher/sarvam-30b-uncensored-i1-GGUF with Docker Model Runner:
docker model run hf.co/mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
- Lemonade
How to use mradermacher/sarvam-30b-uncensored-i1-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.sarvam-30b-uncensored-i1-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use mradermacher/sarvam-30b-uncensored-i1-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 mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
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 mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use mradermacher/sarvam-30b-uncensored-i1-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M
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 "mradermacher/sarvam-30b-uncensored-i1-GGUF:Q4_K_M" \ --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"
reasoning fix for ggufs
For anyone wondering why reasoning does not work in llama.cpp with these ggufs, here's a fix I made for the template and params:
default template inside gguf was not working - does not produce thinking. But we can use external one.
what was needed is detection of thinking from template - it looks for reasoning_content within template
when llama detects that, it writes
srv init: init: chat template, thinking = 1
in the console
and it also needed correct reasoning-format inside llama to wrap the thinking block
and it needed a phrase for it to jumpstart thinking within template
so the full fix:
command line parameters for llama.cpp (only these 2):
--chat-template-file sarvamNewTemplate.txt --reasoning-format deepseek-legacy
and then the new template:
{{- '[@BOS@]\n' -}}
{%- for m in messages -%}
{%- if m.role == 'system' -%}
{{- '<|start_of_turn|><|system|>\n' + m.content + '<|end_of_turn|>\n' -}}
{%- elif m.role == 'user' -%}
{{- '<|start_of_turn|><|user|>\n' + m.content + '<|end_of_turn|>\n' -}}
{%- elif m.role == 'assistant' -%}
{{- '<|start_of_turn|><|assistant|>\n' -}}
{%- if m.reasoning_content -%}
{{- '<think>\n' + m.reasoning_content + '\n</think>\n' -}}
{%- endif -%}
{{- m.content + '<|end_of_turn|>\n' -}}
{%- endif -%}
{%- endfor -%}
{%- if add_generation_prompt -%}
{{- '<|start_of_turn|><|assistant|>\n<think>\nLet me think through this carefully.\n' -}}
{%- endif -%}
now it always reasons and outputs reasoning text correctly within reasoning block in frontend
generated text is also printed correctly, outside the thinking block