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TelperionAI
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Model Details
Model Provider
TelperionAI
Model Tree
huihui-ai/Huihui-Qwen3.8-27B-abliterated
Input Modalities
Output Modalities
Supported Functionality
| component | precision |
|---|---|
mlp.{gate,up,down}_proj, layers 0–55 | INT4, group-32, asymmetric (~4.6 effective bits) |
mlp.{gate,up,down}_proj, layers 56–63 | INT8 W8A16 |
self_attn.{q,k,v,o}_proj | INT8 W8A16 |
linear_attn.{in_proj_qkv,in_proj_z,out_proj} (GDN) | INT8 W8A16 |
lm_head, embed_tokens, norms, GDN state params, vision tower | BF16 |
AWQ per-input-channel scaling (folds into the norm weights — zero size cost), then GPTQ Hessian error compensation. Runs on anything Turing or newer; no FP8/FP4 hardware needed.
Calibration: self-distilled from the abliterated model on a balanced Nemotron-v2 prompt blend (25% code, 25% math, 20% STEM, 20% chat, 10% multilingual).
Measured against the abliterated BF16 model as its own reference — not stock Qwen — so the numbers reflect quantization damage only, not the effect of abliteration. 142,727 tokens plus 200 free greedy generations. vLLM 0.27.1, TP=2, 2×B300.
| build | size ↓ | top-1 ↑ | near-tie ↓ | moderate ↓ | confident ↓ | certain ↓ | divmed ↑ | tok/s ↑ |
|---|---|---|---|---|---|---|---|---|
| this model (INT4 AWQ+GPTQ) | 25.1 GB | 96.45% | 21.76% | 2.92% | 0.88% | 0.12% | 41 | 4551 |
| NVFP4 sibling (AWQ+AutoRound) |
Sizes are on-disk tensor bytes and include the ~0.85 GB BF16 MTP head.
Columns. top-1 is raw argmax agreement with the BF16 abliterated model. The bucket
columns are disagreement rates split by how confident the reference was at that position
(top1−top2 logprob margin): near-tie <0.5, moderate 0.5–2, confident 2–5, certain >5.
Only confident and certain are real damage. divmed is the median token index at
which free greedy generation first diverges.
Perplexity is excluded — on this model family it is anti-correlated with quality.
An earlier build of this model used the stock-Qwen calibration set and a weaker recipe, and landed at 3.54% confident damage. Regenerating the calibration from the abliterated model itself brings that to 0.88%.
That also answers a question worth stating plainly: abliterated weights are not intrinsically harder to quantize. With matched recipe and self-distilled calibration this model reaches 0.88% confident damage, against 0.93% for the same recipe on stock Qwen3.8-27B. The earlier gap was the calibration and recipe, not the abliteration.
The INT4 build is substantially more faithful than the NVFP4 one (confident 0.88% vs 1.80%,
paired McNemar z = 13.2; certain 0.12% vs 0.20%, z = 4.3), reproducing the same gap measured
on stock Qwen3.8-27B (0.93% vs 1.85%). At ~4.6 effective bits INT4 group-32 asymmetric gives
16 uniform levels plus a per-group zero point, against FP4's 8 non-uniform levels at 4.5 bits.
NVFP4's advantage is hardware: it decodes at 10702 tok/s here versus 4551. Pick the NVFP4 sibling if you are throughput-bound on Blackwell; pick this for fidelity, or on Ampere/Ada.
python
from vllm import LLMllm = LLM("TelperionAI/Huihui-Qwen3.8-27B-abliterated-INT4-AWQ-GPTQ", tensor_parallel_size=2)
The MTP head is included, in BF16, grafted from the abliterated base (not stock Qwen):
python
llm = LLM("TelperionAI/Huihui-Qwen3.8-27B-abliterated-INT4-AWQ-GPTQ", tensor_parallel_size=2,speculative_config={"method": "mtp", "num_speculative_tokens": 2})
Qwen3_5ForConditionalGeneration does not carry mtp.* in its state dict, so
llm-compressor silently drops it even though config.json declares
mtp_num_hidden_layers: 1. It is excluded from quantization via re:.*mtp.*.
Acceptance rate has not been measured; the head is verified to load and generate.
| 24.7 GB |
| 92.98% |
| 34.02% |
| 9.83% |
| 1.80% |
| 0.20% |
| 27 |
| 10702 |
| earlier build (base-model calibration) | 24.7 GB | 91.79% | 37.58% | 11.21% | 3.54% | 0.21% | 20 | 10685 |