This is a vanilla quantization of Qwen/Qwen3.8-27B. It is not a fine-tune, merge, ablation, alignment change, or chat-template modification. The source weights are pinned to commit 1d4bf0f2ff6012fd82039f2fa52739d0dd7c60c0.

The official checkpoint uses Qwen3_5ForConditionalGeneration / qwen3_5 as its internal architecture identifier. That string does not mean these weights came from a Qwen3.5 model.

Conversion

json

{
  "algorithm": "oMLX oQ8 near-uniform mixed-precision quantization with protected tensors",
  "bit_width": "mixed around 8.6 bpw",
  "group_size": "mode-specific; MXFP8 base uses group size 32",
  "calibration_source": "local fixed representative prompts; no benchmark answers"
}

Source tensor inventory: 1199 tensors, including 333 vision tensors and 15 source MTP tensors.
Conversion tool/runtime requirement: oMLX and standard MLX loaders / 71b9d52039c3058041c5029fdb3d3e833d13d624.
Artifact size: 30.025 GB (decimal).
Expected hardware: Apple Silicon with at least 64 GB unified memory.

Calibration source: local fixed representative prompts; no benchmark answers.

Component status

Text: passed release tests.
Vision/video: passed deterministic local image tests.
Tool calling: passed all native XML tool tests.
MTP: loaded and passed a temperature-zero equivalence and throughput A/B.
Chat template, tokenizer, processor, generation config, and special-token IDs: checked against the locked source by the structural gate.
Quality comparison: passed against the locked BF16 source using the exact same functional cases. Semantic similarity uses sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 at e8f8c211226b894fcb81acc59f3b34ba3efd5f42 as a measured proxy, not as ground-truth accuracy.
Longest recorded validation prompt: 73 prompt tokens. This is a measured test boundary, not a claim that the architectural maximum was exercised.

Validation results

json

{
  "release_gate": "PASS",
  "text": [
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true,
    true
  ],
  "tools": [
    true,
    true,
    true,
    true,
    true
  ],
  "vision": [
    true,
    true,
    true
  ],
  "mtp": {
    "passed": true,
    "backend": "oMLX Lightning MTP (integrated source MTP head)",
    "output_equivalent_temperature_zero": true,
    "baseline": {
      "text": "Here are the first ten square numbers, listed with commas:\n\n1. 1\n2. 4\n3. 9\n4. 16\n5. 25\n6. 36\n7. 49\n8. 64\n9. 81\n10. 100",
      "finish_reason": "stop",
      "usage": {
        "prompt_tokens": 21,
        "completion_tokens": 71,
        "total_tokens": 92,
        "input_tokens": 21,
        "output_tokens": 71,
        "prompt_tokens_details": {
          "cached_tokens": 0
        },
        "total_time": 8.91
      },
      "wall_seconds": 8.91301712510176,
      "generation_tps": 7.968574635241302
    },
    "mtp_measurement": {
      "text": "Here are the first ten square numbers, listed with commas:\n\n1. 1\n2. 4\n3. 9\n4. 16\n5. 25\n6. 36\n7. 49\n8. 64\n9. 81\n10. 100",
      "finish_reason": "stop",
      "usage": {
        "prompt_tokens": 21,
        "completion_tokens": 71,
        "total_tokens": 92,
        "input_tokens": 21,
        "output_tokens": 71,
        "prompt_tokens_details": {
          "cached_tokens": 0
        },
        "total_time": 3.68
      },
      "wall_seconds": 3.687670208979398,
      "generation_tps": 19.293478260869563
    },
    "baseline_tps": 7.968574635241302,
    "mtp_tps": 19.293478260869563,
    "speedup": 2.4211956521739126,
    "measured_improvement": true,
    "advertise_acceleration": true,
    "native_stats": {
      "finish_reason": "stop",
      "tokens": 72,
      "cycles": 21,
      "tokens_per_cycle": 3.43,
      "accepted_drafts": 51,
      "drafted_tokens": 51,
      "acceptance_rate": 1.0
    },
    "failure": null
  },
  "bf16_source_comparison": {
    "passed": true,
    "mean_semantic_similarity": 0.8972687065601349,
    "exact_matches": 4,
    "measurements": {
      "average_generation_tps": 13.513484058818289,
      "peak_memory_gb": 27.20964608,
      "artifact_bytes": 30024788604,
      "maximum_prompt_tokens_tested": 73,
      "loop_rate": 0.0
    },
    "evaluator": {
      "repo_id": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
      "revision": "e8f8c211226b894fcb81acc59f3b34ba3efd5f42",
      "pooling": "attention-mask mean pooling followed by L2 normalization",
      "maximum_tokens": 256
    }
  },
  "bf16_fixed_logit_comparison": {
    "positions": 106,
    "inputs_sha256": "f1a0af6b9580739ebc9efa9375ae31aa6940dafe7ff6446097ed6cf8d9ab37de",
    "mean_kl_divergence": 0.0007939068249092912,
    "reference_perplexity": 9.84727010437735,
    "candidate_perplexity": 9.89084181958065,
    "perplexity_delta": 0.043571715203301054,
    "top1_token_agreement": 1.0,
    "selection": "selected",
    "warnings": [
      "KL is measured on fixed original text, not a public benchmark.",
      "BF16 log-probabilities are stored in float16 after float32 log-softmax; reported KL therefore has finite-storage approximation error.",
      "The stock MLX-VLM logit scorer ignored 29 strict-loader extras, all proven to be under language_model.mtp; native oMLX validation separately loaded and tested MTP."
    ]
  }
}

No acceleration is advertised unless the MTP report contains a measured throughput improvement. Exact measurements are artifact-, prompt-, context-, and hardware-specific.

Inference

bash

python -m pip install "omlx @ git+https://github.com/jundot/omlx.git@71b9d52039c3058041c5029fdb3d3e833d13d624"
hf download Chungulus/Qwen3.8-27B-MLX-oQ8 --local-dir ./models/Qwen3.8-27B-MLX-oQ8
mkdir -p ./omlx-state
python - <<'PY'
import json
from pathlib import Path
model_id = 'Qwen3.8-27B-MLX-oQ8'
Path('omlx-state/model_settings.json').write_text(json.dumps({
    'version': 1, 'models': {model_id: {
        'mtp_enabled': True, 'mtp_num_draft_tokens': 3
    }}
}, indent=2) + '\n')
PY
omlx serve --model-dir ./models --base-path ./omlx-state --port 8000

Then send OpenAI-compatible multimodal chat requests to http://127.0.0.1:8000/v1/chat/completions.

Use the exact source chat-template controls for thinking (enable_thinking, reasoning_effort, and preserve_thinking) and the native Qwen tool format.

Limitations

Quantization can reduce quality, especially at very low bit widths. Runtime support for the hybrid Gated DeltaNet/full-attention graph, vision tower, projector, processor, and MTP component is format-specific. A loader that reads only a language tensor is not sufficient. Tested context length and resource measurements are recorded in validation_result.json; untested context lengths must not be inferred from the architectural maximum.

License and attribution

The parent model and this unmodified quantization are distributed under the source model's Apache-2.0 license. See the official Qwen3.8-27B repository for the upstream model card and attribution.