Instructions to use reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF",
	filename="qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-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 reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-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 reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M

Use Docker

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

LM Studio
Jan

vLLM

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

Install from pip and serve model

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

Use Docker

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

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

Unsloth Studio

How to use reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF to start chatting

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

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-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 reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF:Q4_K_M

Run Hermes

hermes

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

Lemonade

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

Pull the model

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

Run and chat with the model

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

List all available models

lemonade list

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

GGUF quantizations of reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT for local and edge deployment via llama.cpp and compatible runtimes.

Available Quantizations

File	Quant	Size	Description
`qwen3-1.7b-stem-proof-f16.gguf`	F16	~3.8 GB	Full precision reference
`qwen3-1.7b-distilled-30b-sft-Q8_0.gguf`	Q8_0	~2.1 GB	Near-lossless, desktop
`qwen3-1.7b-distilled-30b-sft-Q5_K_M.gguf`	Q5_K_M	~1.4 GB	Balanced quality and size
`qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf`	Q4_K_M	~1.2 GB	Mobile, edge, fastest inference

Recommended: Q5_K_M for desktop use, Q4_K_M for mobile/edge.

About the Model

This is a two-stage model:

Stage 1 — DISC-Informed Knowledge Distillation: Qwen3-1.7B distilled from Qwen3-30B-A3B-Instruct on 6,122 STEM chain-of-thought samples using proof-weighted cross-entropy loss (2.5x → 1.5x decay on derivation tokens) and KL divergence at T=2.0. The distillation emphasized multi-step reasoning over final-answer pattern matching.

Stage 2 — Legal SFT: Follow-up supervised fine-tuning on Alignment-Lab-AI/Lawyer-Instruct to add instruction-following capability and legal domain knowledge on top of the STEM reasoning backbone.

The result is a 1.7B model that fits on a phone and can do structured derivations, legal reasoning, and instruction-following.

Attribute	Value
Base model	Qwen/Qwen3-1.7B
Teacher model	Qwen/Qwen3-30B-A3B-Instruct-2507
Distillation data	6,122 STEM CoT samples (12 datasets from 0xZee)
SFT data	Alignment-Lab-AI/Lawyer-Instruct
Developer	Reaperdoesntrun / Convergent Intelligence LLC: Research Division

Usage

llama.cpp CLI

./llama-cli -m qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf \
  -p "### Instruction:\nExplain the doctrine of promissory estoppel and provide a worked example.\n\n### Response:\n" \
  -n 512 --temp 0.0

llama.cpp Python

from llama_cpp import Llama

llm = Llama(model_path="qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf", n_ctx=1024)

output = llm(
    "### Instruction:\nProve that the sum of two even numbers is even.\n\n### Response:\n",
    max_tokens=512,
    temperature=0.0,
)
print(output["choices"][0]["text"])

Ollama

# Create a Modelfile
echo 'FROM ./qwen3-1.7b-distilled-30b-sft-Q4_K_M.gguf' > Modelfile
ollama create stem-legal -f Modelfile
ollama run stem-legal "What is res judicata?"

LM Studio

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

Prompt Formats

This model responds to two prompt formats from its two training stages:

STEM derivation (from distillation):

Solve the following problem carefully and show a rigorous derivation.

Problem:
[Your math/physics/engineering problem]

Proof:

Instruction-following (from SFT):

### Instruction:
[Your question or task]

### Response:

Limitations

This is a 1.7B model — it punches above its weight on structured reasoning but has hard limits. It can produce fluent but incorrect derivations. It is not a substitute for formal proof verification, legal counsel, or professional engineering analysis. Verify all outputs independently. Performance is strongest on physics, differential equations, and legal instruction-following. Weaker on underrepresented domains (molecular biology, physiology).

Source Model

Full training details, methodology, hyperparameters, and the DISC-informed distillation approach are documented in the source model card:

reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT

Citation

@misc{colca2026distilledsft,
  title={Qwen3-1.7B Distilled 30B-A3B SFT: STEM Reasoning + Legal Instruction Following},
  year={2026},
  publisher={HuggingFace},
  url={https://huggingface.co/reaperdoesntknow/Qwen3-1.7B-Distilled-30B-A3B-SFT-GGUF},
  note={Convergent Intelligence LLC: Research Division}
}

Convergent Intelligence LLC: Research Division "Where classical analysis fails to see, we begin."

Convergent Intelligence Portfolio

Part of the Qwen3 1.7B Distillation Series by Convergent Intelligence LLC: Research Division

Mathematical Foundations

This is a GGUF-quantized variant. The mathematical foundations (Discrepancy Calculus, Topological Knowledge Distillation) are documented in the source model's card. The discrepancy operator $Df(x)$ and BV decomposition that inform the training pipeline are preserved through quantization — the structural boundaries detected by DISC during training are baked into the weights, not dependent on precision.

Related Models

Model	Downloads	Format
Qwen3-1.7B-Distilled-30B-A3B	96	HF
Qwen3-1.7B-Distilled-30B-A3B-SFT	65	HF
Qwen3-1.7B-Thinking-Distil	501	HF

Top Models from Our Lab

Model	Downloads
LFM2.5-1.2B-Distilled-SFT	342
Qwen3-1.7B-Coder-Distilled-SFT	302
Qwen3-0.6B-Distilled-30B-A3B-Thinking-SFT-GGUF	203
Qwen3-1.7B-Coder-Distilled-SFT-GGUF	194
SMOLM2Prover-GGUF	150

Total Portfolio: 41 models | 2,781 total downloads

Last updated: 2026-03-28 12:55 UTC

DistilQwen Collection

This model is part of the DistilQwen proof-weighted distillation series. Collection: 9 models | 2,788 downloads

Teacher Variant Comparison

Teacher	Student Size	Strength	Models
Qwen3-30B-A3B (Instruct)	1.7B	Instruction following, structured output, legal reasoning	3 (833 DL) ← this model
Qwen3-30B-A3B (Thinking)	0.6B	Extended deliberation, higher-entropy distributions, proof derivation	3 (779 DL)
Qwen3-30B-A3B (Coder)	1.7B	Structured decomposition, STEM derivation, logical inference	2 (825 DL)

Methodology

The only BF16 collection in the portfolio. While the broader Convergent Intelligence catalog (43 models, 12,000+ downloads) was trained on CPU at FP32 for $24 total compute, the DistilQwen series was trained on H100 at BF16 with a 30B-parameter teacher. Same methodology, premium hardware. This is what happens when you give the pipeline real compute.

All models use proof-weighted knowledge distillation: 55% cross-entropy with decaying proof weights (2.5× → 1.5×), 45% KL divergence at T=2.0. The proof weight amplifies loss on reasoning-critical tokens, forcing the student to allocate capacity to structural understanding rather than surface-level pattern matching.

Full methodology: Structure Over Scale (DOI: 10.57967/hf/8165)