Instructions to use NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use NANI-Nithin/Qwen3-0.6B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "NANI-Nithin/Qwen3-0.6B-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": "NANI-Nithin/Qwen3-0.6B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M

Ollama
How to use NANI-Nithin/Qwen3-0.6B-GGUF with Ollama:
```
ollama run hf.co/NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use NANI-Nithin/Qwen3-0.6B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Qwen3-0.6B-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": "NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use NANI-Nithin/Qwen3-0.6B-GGUF with Docker Model Runner:
```
docker model run hf.co/NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M
```

Lemonade

How to use NANI-Nithin/Qwen3-0.6B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-0.6B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-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 NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use NANI-Nithin/Qwen3-0.6B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf NANI-Nithin/Qwen3-0.6B-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 "NANI-Nithin/Qwen3-0.6B-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"

NANI-Nithin commited on 6 days ago

Commit

b3d3323

verified ·

1 Parent(s): 8076590

Upload README.md with huggingface_hub

Browse files

Files changed (1) hide show

README.md +82 -0

README.md ADDED Viewed

	@@ -0,0 +1,82 @@

+---
+base_model: Qwen/Qwen3-0.6B
+library_name: gguf
+pipeline_tag: text-generation
+tags:
+  - gguf
+  - llama.cpp
+  - quantized
+  - imatrix
+---
+# Qwen3-0.6B GGUF
+GGUF quantizations of [Qwen/Qwen3-0.6B](https://huggingface.co/Qwen/Qwen3-0.6B), covering 30 files (12.8 GB total).
+## Files
+| File | Quant | Size | Notes |
+|---|---|---:|---|
+| [Qwen3-0.6B-BF16.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-BF16.gguf) | `BF16` | 1.41 GB | Full precision source. Every quant below is cut from this file. |
+| [Qwen3-0.6B-Q8_0.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q8_0.gguf) | `Q8_0` | 0.75 GB | Effectively lossless. Use when disk and RAM are not the constraint. |
+| [Qwen3-0.6B-Q6_K.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q6_K.gguf) | `Q6_K` | 0.58 GB | Near-lossless; the last stop before quality becomes measurable. |
+| [Qwen3-0.6B-Q5_K_M.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q5_K_M.gguf) | `Q5_K_M` | 0.51 GB | Very good quality, noticeably smaller than Q6_K. |
+| [Qwen3-0.6B-Q5_K_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q5_K_S.gguf) | `Q5_K_S` | 0.51 GB | Slightly smaller than Q5_K_M for a slight quality cost. |
+| [Qwen3-0.6B-Q5_1.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q5_1.gguf) | `Q5_1` | 0.54 GB | Legacy. Prefer Q5_K_M. |
+| [Qwen3-0.6B-Q5_0.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q5_0.gguf) | `Q5_0` | 0.51 GB | Legacy. Prefer Q5_K_M. |
+| [Qwen3-0.6B-Q4_K_M.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q4_K_M.gguf) | `Q4_K_M` | 0.45 GB | The usual default. Best quality-per-byte for most people. |
+| [Qwen3-0.6B-Q4_K_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q4_K_S.gguf) | `Q4_K_S` | 0.44 GB | A little smaller than Q4_K_M, a little worse. |
+| [Qwen3-0.6B-IQ4_NL.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ4_NL.gguf) | `IQ4_NL` | 0.44 GB | Non-linear 4-bit; good on hardware without fast K-quant kernels. |
+| [Qwen3-0.6B-IQ4_XS.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ4_XS.gguf) | `IQ4_XS` | 0.42 GB | Best sub-4.5bpw option; usually beats Q4_K_S at a smaller size. |
+| [Qwen3-0.6B-Q4_1.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q4_1.gguf) | `Q4_1` | 0.47 GB | Legacy. Prefer Q4_K_M. |
+| [Qwen3-0.6B-Q4_0.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q4_0.gguf) | `Q4_0` | 0.44 GB | Legacy round-to-nearest. Prefer Q4_K_M unless a runtime needs this. |
+| [Qwen3-0.6B-Q3_K_L.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q3_K_L.gguf) | `Q3_K_L` | 0.41 GB | Small, with real quality loss. Usable when RAM is tight. |
+| [Qwen3-0.6B-Q3_K_M.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q3_K_M.gguf) | `Q3_K_M` | 0.39 GB | Smaller again; noticeable degradation. |
+| [Qwen3-0.6B-IQ3_M.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ3_M.gguf) | `IQ3_M` | 0.38 GB | Strong at ~3.7bpw, clearly better than Q3_K_M. |
+| [Qwen3-0.6B-IQ3_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ3_S.gguf) | `IQ3_S` | 0.36 GB | Slightly smaller than IQ3_M. |
+| [Qwen3-0.6B-Q3_K_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q3_K_S.gguf) | `Q3_K_S` | 0.36 GB | Aggressive. Prefer IQ3_M at a similar size. |
+| [Qwen3-0.6B-IQ3_XS.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ3_XS.gguf) | `IQ3_XS` | 0.35 GB | Aggressive but coherent. |
+| [Qwen3-0.6B-IQ3_XXS.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ3_XXS.gguf) | `IQ3_XXS` | 0.32 GB | Very aggressive; imatrix carries it. |
+| [Qwen3-0.6B-Q2_K.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q2_K.gguf) | `Q2_K` | 0.32 GB | Very small, heavily degraded. For experimentation. |
+| [Qwen3-0.6B-IQ2_M.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ2_M.gguf) | `IQ2_M` | 0.31 GB | The smallest size most people find usable. |
+| [Qwen3-0.6B-Q2_K_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q2_K_S.gguf) | `Q2_K_S` | 0.31 GB | Smaller than Q2_K, requires the imatrix. |
+| [Qwen3-0.6B-IQ2_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ2_S.gguf) | `IQ2_S` | 0.30 GB | Below the usual usability line. |
+| [Qwen3-0.6B-IQ2_XS.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ2_XS.gguf) | `IQ2_XS` | 0.27 GB | Experimental. |
+| [Qwen3-0.6B-IQ2_XXS.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ2_XXS.gguf) | `IQ2_XXS` | 0.26 GB | Experimental. |
+| [Qwen3-0.6B-Q2_0.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q2_0.gguf) | `Q2_0` | 0.32 GB | Extreme, group-64. Included for completeness. |
+| [Qwen3-0.6B-IQ1_M.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ1_M.gguf) | `IQ1_M` | 0.25 GB | Extreme. Expect substantial degradation. |
+| [Qwen3-0.6B-IQ1_S.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-IQ1_S.gguf) | `IQ1_S` | 0.24 GB | Extreme. Expect substantial degradation. |
+| [Qwen3-0.6B-Q1_0.gguf](https://huggingface.co/NANI-Nithin/Qwen3-0.6B-GGUF/blob/main/Qwen3-0.6B-Q1_0.gguf) | `Q1_0` | 0.20 GB | Extreme. Included for completeness. |
+## Which one should I download?
+Pick the largest file that leaves a couple of gigabytes of headroom on the device you will run it on — the model has to fit in RAM (or VRAM, if you are offloading) alongside the KV cache and the OS.
+- Plenty of memory: **Q6_K** or **Q8_0**.
+- The usual choice: **Q4_K_M**.
+- Tight on memory: **IQ4_XS**, then **IQ3_M**, then **IQ2_M**.
+- The `IQ*` files are imatrix-guided and generally beat a `Q*` file of similar size, at the cost of slightly slower inference on some hardware.
+## Quantization details
+- Importance matrix computed with `llama-imatrix` over 500 rows of [Salesforce/wikitext](https://huggingface.co/datasets/Salesforce/wikitext) (`wikitext-2-raw-v1`).
+- The matrix was computed on the **BF16** weights.
+- K-quants below 6 bit and the whole `IQ` set are imatrix-guided. `Q4_0`/`Q4_1`/`Q5_0`/`Q5_1` are legacy round-to-nearest and ignore it; `Q6_K`/`Q8_0` are near-lossless and do not need it.
+- All files are cut from the same BF16 GGUF, so differences between them are quantization only.
+## Usage
+```bash
+llama-cli -hf NANI-Nithin/Qwen3-0.6B-GGUF:Q4_K_M -p "Hello"
+```
+Or download one file and point at it directly:
+```bash
+huggingface-cli download NANI-Nithin/Qwen3-0.6B-GGUF Qwen3-0.6B-Q4_K_M.gguf --local-dir .
+llama-cli -m Qwen3-0.6B-Q4_K_M.gguf -p "Hello"
+```
+---
+Quantized with [llama.cpp](https://github.com/ggml-org/llama.cpp) by AgentQuantix on 2026-09-04. Licensing follows the base model.