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

Use Docker

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

LM Studio
Jan

vLLM

How to use tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-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-Coder-Distilled-SFT-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-Coder-Distilled-SFT-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

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

How to use tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-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-Coder-Distilled-SFT-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-Coder-Distilled-SFT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

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

Lemonade

How to use tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-GGUF with Lemonade:

Pull the model

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

Run and chat with the model

lemonade run user.Qwen3-1.7B-Coder-Distilled-SFT-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-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-Coder-Distilled-SFT-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-Coder-Distilled-SFT-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-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-Coder-Distilled-SFT-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-Coder-Distilled-SFT-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-Coder-Distilled-SFT — GGUF

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT, quantized by tinyopsec.

A 1.7B model built in two stages: knowledge distillation from Qwen3-Coder-30B-A3B-Instruct (30B MoE teacher) to establish a structured STEM reasoning backbone, then SFT on ~54,600 logical inference problems. Architecture: Qwen3ForCausalLM.

Available Quantizations

File	Bits	Approx. Size	Use Case
`model_f16.gguf`	16	~3.4 GB	Full precision, reference
`model_q8_0.gguf`	8	~1.8 GB	Best quality, fits in RAM easily
`model_q6_k.gguf`	6	~1.4 GB	Excellent quality, recommended
`model_q5_k_m.gguf`	5	~1.2 GB	Great balance quality/size
`model_q5_k_s.gguf`	5	~1.1 GB	Slightly smaller Q5 variant
`model_q4_k_m.gguf`	4	~1.0 GB	Good quality, very portable
`model_q4_k_s.gguf`	4	~0.95 GB	Smaller Q4 variant
`model_q3_k_l.gguf`	3	~0.85 GB	Lower quality, minimal RAM
`model_q3_k_m.gguf`	3	~0.80 GB	Minimal footprint
`model_q3_k_s.gguf`	3	~0.75 GB	Smallest Q3 variant
`model_q2_k.gguf`	2	~0.60 GB	Extreme compression, lowest quality

VRAM Requirements

Quantization	VRAM
F16	~3.4 GB
Q8_0	~1.8 GB
Q6_K	~1.4 GB
Q5_K_M	~1.2 GB
Q4_K_M	~1.0 GB
Q3_K_M	~0.80 GB
Q2_K	~0.60 GB

Usage

llama.cpp

./llama-cli -m model_q4_k_m.gguf -p "### Instruction:\nWhat can we infer from: All cats are mammals. Whiskers is a cat.\n\n### Response:" -n 256

llama-cpp-python

from llama_cpp import Llama

llm = Llama(model_path="model_q4_k_m.gguf", n_ctx=1024)
output = llm(
    "### Instruction:\nWhat can we infer from: All cats are mammals. Whiskers is a cat.\n\n### Response:",
    max_tokens=256,
    stop=["### Instruction:"],
)
print(output["choices"][0]["text"])

LM Studio

Download any .gguf file from this repo and load it directly in LM Studio.

Ollama

ollama run hf.co/tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-GGUF

Prompt Formats

Logical inference (Stage 2 — primary):

### Instruction:
[Your question or logical inference problem]

### Response:

STEM derivation (Stage 1 — also supported):

Solve the following problem carefully and show a rigorous derivation.

Problem:
[Your problem]

Proof:

Model Details

Attribute	Value
Architecture	Qwen3ForCausalLM
Parameters	~2B (1.7B effective)
Base model	Qwen/Qwen3-1.7B
Teacher model	Qwen/Qwen3-Coder-30B-A3B-Instruct
Context length	1024 tokens (training)
Precision	BF16
License	Apache 2.0

Good for: Logical inference, propositional logic, formal reasoning, STEM derivation, structured argumentation, educational tutoring, edge deployment.

Not for: General code generation, formal proof verification (use Lean/Coq), safety-critical tasks, or long context beyond 1024 tokens.

Original Model

reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT by Convergent Intelligence LLC: Research Division.

Downloads last month: 1,368

GGUF

Model size

2B params

Architecture

qwen3

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Model tree for tinyopsec/Qwen3-1.7B-Coder-Distilled-SFT-GGUF

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

reaperdoesntknow/Qwen3-1.7B-Coder-Distilled-SFT

Quantized

(2)

this model