Instructions to use mradermacher/sarv-reasoning-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/sarv-reasoning-i1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="mradermacher/sarv-reasoning-i1-GGUF")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/sarv-reasoning-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/sarv-reasoning-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/sarv-reasoning-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/sarv-reasoning-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/sarv-reasoning-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use mradermacher/sarv-reasoning-i1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mradermacher/sarv-reasoning-i1-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": "mradermacher/sarv-reasoning-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M

SGLang

How to use mradermacher/sarv-reasoning-i1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "mradermacher/sarv-reasoning-i1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mradermacher/sarv-reasoning-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "mradermacher/sarv-reasoning-i1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mradermacher/sarv-reasoning-i1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use mradermacher/sarv-reasoning-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mradermacher/sarv-reasoning-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/sarv-reasoning-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/sarv-reasoning-i1-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mradermacher/sarv-reasoning-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M
```

Lemonade

How to use mradermacher/sarv-reasoning-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/sarv-reasoning-i1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.sarv-reasoning-i1-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mradermacher/sarv-reasoning-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/sarv-reasoning-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/sarv-reasoning-i1-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mradermacher/sarv-reasoning-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/sarv-reasoning-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/sarv-reasoning-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"

mradermacher commited on Aug 14

Commit

f8e4e29

verified ·

1 Parent(s): 945966d

auto-patch README.md

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@@ -1,3 +1,31 @@
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
@@ -7,3 +35,53 @@
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/artindnr/sarv-reasoning

+---
+base_model: artindnr/sarv-reasoning
+language:
+- fa
+library_name: transformers
+license: apache-2.0
+mradermacher:
+  readme_rev: 1
+quantized_by: mradermacher
+tags:
+- persian
+- farsi
+- poetry
+- poem-generation
+- reasoning
+- chain-of-thought
+- gpt-oss
+- lora
+- sarv
+- mixture-of-experts
+- mxfp4
+- text-generation
+- pytorch
+- jax
+- tf
+---
+## About
 <!-- ### quantize_version: 2 -->
 <!-- ### output_tensor_quantised: 1 -->
 <!-- ### convert_type: hf -->
 <!-- ### quants_skip:  -->
 <!-- ### skip_mmproj:  -->
 weighted/imatrix quants of https://huggingface.co/artindnr/sarv-reasoning
+<!-- provided-files -->
+***For a convenient overview and download list, visit our [model page for this model](https://hf.tst.eu/model#sarv-reasoning-i1-GGUF).***
+static quants are available at https://huggingface.co/mradermacher/sarv-reasoning-GGUF
+## Usage
+If you are unsure how to use GGUF files, refer to one of [TheBloke's
+READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
+more details, including on how to concatenate multi-part files.
+## Provided Quants
+(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
+| Link | Type | Size/GB | Notes |
+|:-----|:-----|--------:|:------|
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.imatrix.gguf) | imatrix | 0.1 | imatrix file (for creating your own quants) |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-IQ2_M.gguf) | i1-IQ2_M | 12.2 |  |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.2 | lower quality |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-Q2_K.gguf) | i1-Q2_K | 12.2 | IQ3_XXS probably better |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-IQ4_XS.gguf) | i1-IQ4_XS | 12.2 |  |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-Q2_K_S.gguf) | i1-Q2_K_S | 12.2 | very low quality |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-IQ3_M.gguf) | i1-IQ3_M | 12.3 |  |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.0 | IQ3_S probably better |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-Q4_K_S.gguf) | i1-Q4_K_S | 14.8 | optimal size/speed/quality |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-Q4_K_M.gguf) | i1-Q4_K_M | 15.9 | fast, recommended |
+| [GGUF](https://huggingface.co/mradermacher/sarv-reasoning-i1-GGUF/resolve/main/sarv-reasoning.i1-Q6_K.gguf) | i1-Q6_K | 22.3 | practically like static Q6_K |
+Here is a handy graph by ikawrakow comparing some lower-quality quant
+types (lower is better):
+![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)
+And here are Artefact2's thoughts on the matter:
+https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
+## FAQ / Model Request
+See https://huggingface.co/mradermacher/model_requests for some answers to
+questions you might have and/or if you want some other model quantized.
+## Thanks
+I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
+me use its servers and providing upgrades to my workstation to enable
+this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
+<!-- end -->