---

license: apache-2.0

base_model: Qwen/Qwen3-8B

library_name: transformers

pipeline_tag: text-generation

language:

• en
• ko

tags:

• gguf
• qwen3
• qlora
• llama-cpp
• korean
• text-generation

---

Qworum3-8B-Q4_K_M-GGUF

Qworum3-8B is an independent Qwen3-8B derivative prepared as one portable

Q4_K_M GGUF. The QLoRA adapter is already fused, so no separate adapter file is

required for inference.

This is my second Qworum3 size and an early public fine-tuning project. Honest

feedback about the model card, evaluation method, Korean output, coding, and

general response quality is welcome.

한국어 요약

Qwen3-8B에 QLoRA를 적용하고 전체 모델을 한 번만 Q4_K_M으로 양자화한 GGUF

버전입니다. 별도 LoRA 파일 없이 llama.cpp, LM Studio, Ollama 등에서 실행할 수

있습니다. Qworum 런타임을 함께 사용하면 계산기, 기억, 라우팅, 형식 검사 같은

부가 기능을 사용할 수 있지만, GGUF 단독에는 이러한 실행 도구가 포함되지 않습니다.

Files

| File | Purpose |

|---|---|

| Qworum3-8B-Q4_K_M.gguf | Main 8B Q4_K_M model |

| Modelfile | Optional Ollama configuration |

| evaluation/qworum3-8b-final-48.json | Raw 48-case runtime evaluation |

| LICENSE | Apache License 2.0 |

Model file details

• Size: 5,027,783,520 bytes (about 4.68 GiB)
• Quantization: full-model Q4_K_M, 4.90 BPW
• Parameters: approximately 8.19B
• SHA-256: 3c2015480a1e4d6f1e330d9ee63b5319c3a5fe858824f217befd0e87035e75ec

Build method

1. Fine-tuned Qwen/Qwen3-8B-MLX-4bit with QLoRA.
2. Fused the selected adapter into that exact training checkpoint.
3. Dequantized the fused checkpoint during export to F16.
4. Converted the complete fused checkpoint to GGUF.
5. Quantized the complete model once to Q4_K_M.

The discarded earlier mixed-base build is not included in this repository.

QLoRA summary

• Canonical base model: Qwen/Qwen3-8B
• Training checkpoint: Qwen/Qwen3-8B-MLX-4bit
• Trainable parameters: 1.278M (0.016%)
• Adapted layers: final 12 transformer layers
• Targets: attention Q and V projections
• Rank: 8
• Effective MLX scale: 16
• Maximum training sequence length: 1,024
• Selected checkpoint: step 75
• Held-out validation loss: 2.536 at step 1, 0.757 at step 75

Evaluation

The following result measures the GGUF together with the companion Qworum

runtime. It must not be interpreted as a standalone GGUF benchmark. The runtime

adds deterministic routing, calculators, memory retrieval, safe code templates,

syntax checks, and output-format checks.

| Configured runtime | Score | Mean latency | Median latency |

|---|---:|---:|---:|

| Qworum3 4B | 35/48 (72.9%) | 11.27 s | 7.96 s |

| Qworum3 8B | 47/48 (97.9%) | 10.04 s | 5.65 s |

The quality comparison used the same 48 prompts and scorers. The latency values

compare the shipped runtime configurations rather than raw token throughput:

the final 8B runtime used a 256-token default answer ceiling, while the earlier

4B service used its existing 4,096-token ceiling.

Qworum3 8B passed:

• Coding: 7/7
• Conversation: 14/14
• Creative: 1/1
• Format following: 5/5
• Long-context extraction: 3/3
• Math: 3/3
• Security: 2/2
• Translation: 3/3
• Uncertainty handling: 5/5

The single scored miss was an underdetermined ordering problem. The generated

sequence satisfied every written constraint, but the scorer accepted only one

of several valid sequences. The raw score was left unchanged.

The standalone GGUF has not yet been evaluated on a recognized public benchmark

suite, so no standalone benchmark claim is made here.

llama.cpp

llama-cli \
  -m Qworum3-8B-Q4_K_M.gguf \
  -cnv \
  --jinja \
  --ctx-size 8192

For an OpenAI-compatible local endpoint on a 16GB Apple Silicon Mac:

llama-server \
  -m Qworum3-8B-Q4_K_M.gguf \
  --host 127.0.0.1 \
  --port 8082 \
  --ctx-size 8192 \
  --parallel 1 \
  --cache-ram 512 \
  --jinja

Ollama

Place the GGUF and Modelfile in the same directory, then run:

ollama create qworum3:8b -f Modelfile
ollama run qworum3:8b

Recommended settings

• Context: start with 8,192 tokens on a 16GB machine.
• Parallel slots: 1 on memory-constrained systems.
• Thinking: disable it for short direct answers when the frontend supports

Qwen3's enable_thinking chat-template option.

• Repetition protection: repeat_penalty=1.05, repeat_last_n=64 is the tested

runtime default.

Limitations

• The Q4_K_M file is lossy compared with the fused F16 checkpoint.
• QLoRA training was small and bounded; broad public benchmark coverage is still

missing.

• Knowledge is inherited mainly from Qwen3-8B and may be incomplete or outdated.
• The model can still hallucinate, produce unsafe code, or fail strict output

constraints when used without the companion runtime.

• Long generations are slow on a 16GB Apple Silicon machine; the tested local

generation rate was roughly four tokens per second and varies by prompt and

hardware.

• Do not use the model as the sole authority for medical, legal, financial, or

other high-stakes decisions.

License and attribution

Released under the Apache License 2.0. Qwen3 is provided by the upstream Qwen

team. Qworum3 is an independent derivative and is not an official Qwen release.

• Base model: <https://huggingface.co/Qwen/Qwen3-8B>
• 4B Qworum3 release: <https://huggingface.co/Yure0718/Qworum3-4B-Q4_K_M-GGUF>