Instructions to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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": "tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M

Ollama
How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF with Ollama:
```
ollama run hf.co/tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M
```
Unsloth Desktop

How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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": "tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF with Docker Model Runner:
```
docker model run hf.co/tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M
```

Lemonade

How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen3-1.7B-Distilled-30B-A3B-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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 "tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-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"

Qwen3-1.7B-Distilled-30B-A3B GGUF

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B — a 1.7B-parameter Qwen3 causal language model distilled from Qwen3-30B-A3B-Instruct using discrepancy-informed proof-weighted knowledge distillation on 6,122 STEM chain-of-thought samples.

Quantized by tinyopsec.

Model Details

Attribute	Value
Architecture	Qwen3ForCausalLM
Parameters	~2.03B
Base model	Qwen/Qwen3-1.7B
Teacher model	Qwen/Qwen3-30B-A3B-Instruct-2507
Training precision	BF16
Context length	1024 tokens (training)
License	Apache 2.0
Developer	Convergent Intelligence LLC: Research Division

About the Original Model

Qwen3-1.7B-Distilled-30B-A3B is trained with Discrepancy-Informed Knowledge Distillation (DISC v3) — a method that goes beyond standard KL divergence by treating per-token teacher-student divergence as a structured signal:

Discrepancy-Weighted KD — identifies reasoning pivot tokens via local KL jump detection and amplifies their distillation weight
DG-Limit Smoothing — stabilizes high-entropy student tokens with neighborhood averaging before KD is applied
Gap Energy Regularization — monitors and penalizes structural drift at reasoning transitions independent of mean token loss
Proof-Weighted Cross-Entropy — applies 2.5× → 1.5× decaying weight on derivation spans (Proof: to Final Answer:)

The model is particularly suited for STEM reasoning, mathematical derivations, and proof-style explanation.

Available Quantizations

File	Bits	Size (approx.)	Use Case
`model_f16.gguf`	16	~3.8 GB	Reference, maximum quality
`model_q8_0.gguf`	8	~2.0 GB	High quality, fast on capable hardware
`model_q6_k.gguf`	6	~1.6 GB	Near-lossless, good balance
`model_q5_k_m.gguf`	5	~1.4 GB	Recommended for quality-focused use
`model_q5_k_s.gguf`	5	~1.3 GB	Slightly smaller Q5 variant
`model_q4_k_m.gguf`	4	~1.2 GB	Best balance of size and quality
`model_q4_k_s.gguf`	4	~1.1 GB	Smaller Q4 variant
`model_q3_k_l.gguf`	3	~1.0 GB	Low memory, acceptable quality
`model_q3_k_m.gguf`	3	~0.9 GB	Compact, moderate quality loss
`model_q3_k_s.gguf`	3	~0.8 GB	Minimum size Q3
`model_q2_k.gguf`	2	~0.7 GB	Extreme compression, lowest quality

VRAM Requirements

Quantization	VRAM (approx.)
F16	~4.0 GB
Q8_0	~2.2 GB
Q6_K	~1.8 GB
Q5_K_M	~1.6 GB
Q4_K_M	~1.4 GB
Q3_K_M	~1.1 GB
Q2_K	~0.9 GB

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf \
  -p "Solve the following problem carefully and show a rigorous derivation.\n\nProblem:\nFind the eigenvalues of [[2,1],[1,2]].\n\nProof:\n" \
  -n 512 --temp 0.0

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=2048)

output = llm(
    "Solve the following problem carefully and show a rigorous derivation.\n\nProblem:\nProve that sqrt(2) is irrational.\n\nProof:\n",
    max_tokens=512,
    temperature=0.0,
)
print(output["choices"][0]["text"])

LM Studio

Download any .gguf file from this repository
Open LM Studio → Load Model → select the file
Use the prompt format below in the chat or playground

Ollama

ollama run hf.co/tinyopsec/Qwen3-1.7B-Distilled-30B-A3B-GGUF

Prompt Format

For best results, use the training format:

Solve the following problem carefully and show a rigorous derivation.

Problem:
{your problem here}

Proof:

For general instruction use, standard chat template also works:

<|im_start|>user
{your question}<|im_end|>
<|im_start|>assistant

Intended Uses

Mathematical derivations and proof-style explanation
STEM problem solving (physics, engineering, linear algebra, differential equations)
Educational tutoring and worked solutions
Lightweight reasoning on edge or CPU-only hardware
Generator component in verifier-generator or RAG reasoning pipelines

Limitations

Not a formal proof verifier or symbolic algebra engine
Can produce fluent but incorrect derivations
Training context is 1024 tokens — very long derivations may degrade
Domain coverage is uneven: physics and linear algebra are well-represented; molecular biology and physiology are sparse