Qwen3.8-Unleashed

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What Qwen3.8-Unleashed is

Qwen 3.8 27B UNLEASHED — GGUF ### ✅ All 9 tiers verified and live (2026-08-21) UD-IQ1M and UD-IQ2S were briefly published broken, then withdrawn, and are now rebuilt, load-tested, and back. If you downloaded either before 2026-08-21 22:00 UTC, re-download. No other tier was ever affected. If you are quantizing this model yourself, this is the part worth reading. Upstream builds low-bit tiers with a different architecture, not just different bit widths: | | Q3KXL and above | IQ1M / IQ2S | |---|---|---| | tensors | 506 | 498 | | highest block | 64 (MTP head at q6k) | 63 (MTP pruned) | | blockcount | 65 | 64 | | nextnpredictlayers | 1 | 0 | Their imatrix has 496 entries because it matches that pruned topology. Quantizing a 65-block f16 against it fails with Missing importance matrix for tensor blk.64.attnk.weight. --prune-layers 64 alone is not enough — it removes the tensors but leaves the model declaring an MTP head, so loading dies on blk.63.nextn.ehproj.weight not found: a structurally valid file containing an unloadable model. You also have to rewrite the metadata: bash llama-quantize --imatrix <imatrix --tensor-type-file IQ1M.txt \ --prune-layers 64 \ --override-kv qwen35.blockcount=int:64 \ --override-kv qwen35.nextnpredictlayers=int:0 \ model-f16.gguf out.gguf IQ1M 12 Every file here is now gated on exit code → magic bytes → an actual load test before upload. Header checks alone let two broken builds through; only loading the model catches it. Uncensored Qwen3.8-27B, quantized with per-tensor dynamic bit allocation. Most uncensored GGUFs are uniform quants — every layer gets the same treatment. These use a per-tensor type map, so sensitive tensors keep precision while tolerant ones compress hard. The result: a Q3 that outperforms a Q4 3.3 GB larger, with full 250k-token retrieval. Provenance, stated plainly. The per-tensor quant map was extracted from unsloth/Qwen3.8-27B-GGUF (Unsloth Dynamic 3.0) by reading the tensor-type table from their published files, then applied to JonathanColetti/Qwen3.8-27B-Uncensored weights with that repo's imatrix. The recipe is Unsloth's work, not ours — we did not independently derive it. All upstream projects are Apache 2.0.

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

How it works

How language models work

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys qwen3-8-unleashed for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (qwen3-8-unleashed below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/chat/completions \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"qwen3-8-unleashed","messages":[{"role":"user","content":"Hello"}]}'

Details

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Licence

Open weights

Sources

Weights & code

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