---

base_model: Qwen/Qwen3.8-27B

license: apache-2.0

library_name: gguf

pipeline_tag: image-text-to-text

tags:

• qwen3.8
• qwen35
• gguf
• llama.cpp
• multimodal
• iq1_m
• q4_k

---

Qwen3.8-27B IQ1_M GGUF

A runnable multimodal GGUF quantization of Qwen/Qwen3.8-27B, produced directly from the pinned two-part BF16 GGUF source at revision f1bfb127c64f7072bdd2cad55f258b9c8b2910fe.

Quantization policy

• The 64 calibrated main transformer blocks use imatrix-aware IQ1_M.
• The auxiliary MTP block (blk.64) has no entries in the pinned importance matrix, so exactly eight two-dimensional weight tensors are manually overridden to Q4_K instead of being forced into an uncalibrated extreme-low-bit type. Other supporting tensors may independently use Q4_K or their normal GGUF types under the IQ1_M policy.
• Those eight matrices contain 424,673,280 parameters, 1.5544% of the 27,320,697,856-parameter model.
• Norm and other non-quantized tensors retain their normal GGUF types.
• The resulting complete model is 2.3045 whole-file bits per weight. This is different from the nominal IQ1_M tensor rate because metadata and the protected Q4_K/F32 tensors are included.

The eight manual block-64 Q4_K override tensors are:

• blk.64.attn_k.weight
• blk.64.attn_output.weight
• blk.64.attn_q.weight
• blk.64.attn_v.weight
• blk.64.ffn_down.weight
• blk.64.ffn_gate.weight
• blk.64.ffn_up.weight
• blk.64.nextn.eh_proj.weight

Toolchain

The quantizer is built from llama.cpp 62061f91088281e65071cc38c5f69ee95c39f14e plus the official MTP accounting fix from PR #24986, merge commit b3ce5cedf4c007b78a45befe839fa3abada03c0b. The executable SHA256 is 5d3a8456974b28569322dea7ee33941e3c9f09750e100dfce358326110b29187 and the applied patch SHA256 is 01a53c23afd4ed81b79ffc697b1c4a4b83443253b6095732eee3f8fc58e96b40. Every loaded llama/ggml shared library is pinned in QUANTIZER_TOOLCHAIN.json.

Files

• Qwen3.8-27B-IQ1_M.gguf: model GGUF, 7.33 GiB, SHA256 131cdf5c1c4b547081543382b00434e9ebf3f8eb369ef3714550086074f80bdf
• mmproj-Qwen3.8-27B-BF16.gguf: vision projector, 888.01 MiB, SHA256 83ee4f4f205fa514161778c41df1ea14144faa0f713510893b63c2395f5c2d53
• Qwen3.8-27B-IQ1_M.gguf.validation.json: structural, text-runtime and vision-runtime acceptance results
• GGUF_REPORT.json: tensor-type histogram, architecture and whole-file BPW
• MTP_Q4K_AUDIT.json: exact dry-run audit of the eight protected MTP tensors
• QUANTIZER_TOOLCHAIN.json: executable, library, patch, source and argv hashes
• PROVENANCE.md: source revisions, commands and verification details
• CHECKSUMS.sha256: hashes for every release file
• PROVENANCE.json: machine-readable model/projector identity contract consumed by the uploader

RTX 3090 CUDA runtime validation

PASS (18 August 2026). The uploaded Qwen3.8-27B-IQ1_M.gguf was loaded and generated coherent text on an NVIDIA GeForce RTX 3090 using Ollama 0.32.14 with the CUDA backend.

• CUDA offload: 66/66 layers
• Canonical output: The answer is 4, and the capital of France is Paris.
• Generation speed: 24.06 tokens/s
• Observed peak VRAM: 7,772 MiB
• Model SHA256: 131cdf5c1c4b547081543382b00434e9ebf3f8eb369ef3714550086074f80bdf

The complete request, response, raw GPU samples, runtime logs, hashes and reproduction script are in RTX3090_VALIDATION/.

llama.cpp example

llama-mtmd-cli \
  -m Qwen3.8-27B-IQ1_M.gguf \
  --mmproj mmproj-Qwen3.8-27B-BF16.gguf \
  --image image.jpg \
  -p "Describe this image."

Extreme low-bit quantization trades quality for a much smaller artifact. Use a higher-bit quant when accuracy matters more than footprint.

Multi-word RTX 3090 generation gate

A second CUDA acceptance explicitly required a string longer than one word. It passed with a 22-word grammatical response, done_reason=stop, 68% peak GPU utilization and 7,772 MiB observed VRAM. The sanitized record is MULTIWORD_RTX3090_VALIDATION.json.

Agentic/tool-use validation

The original Ollama 0.32.14 structured-call test remains preserved: it scored 0/6 because intended function calls appeared as ordinary assistant text while the API returned tool_calls=null.

A direct llama.cpp investigation separated that interface failure from the model's actual serialization ability:

• Direct llama.cpp with one selected tool and string tool_choice="required": 1/1 native structured call passed.
• Direct llama.cpp with three available tools: failed raw multi-tool selection, repeating argument text until the token limit.
• The included loopback adapter normalizes OpenAI named tool_choice objects, deterministically selects one explicit tool, and then uses llama.cpp's native structured-call serialization.
• Adapter validation: 6/6 explicit multi-tool cases passed, plus 1/1 named OpenAI tool-choice object passed. The returned functions and JSON arguments were exact.

This is a controller-plus-model result, not a claim that raw IQ1_M performs unrestricted semantic routing across arbitrary tool catalogues. The repaired path is reproducible with AGENTIC_VALIDATION/RUN_AGENTIC_ADAPTER.sh; full evidence, source, limitations and machine-readable results are in AGENTIC_VALIDATION/.

One-command RTX 3090 reproduction

RUN_RTX3090_FROM_REPO.sh verifies the exact model SHA256, installs the validated Ollama 0.32.14 release if Ollama is absent, forces the cuda_v12 path, imports the local GGUF and generates a multi-word response. It expects a working NVIDIA driver (nvidia-smi) and network access only if Ollama must be installed.