---

license: apache-2.0

language:

• en

base_model: empero-ai/Qwythos-9B-Claude-Mythos-5-1M

pipeline_tag: text-generation

library_name: gguf

tags:

• qwen
• qwen3.5
• 9b
• quantized
• quantization
• gguf
• llama-cpp
• q5_k_m
• q8_0
• q6_k
• imatrix
• hybrid-quantization
• selective-quantization
• shq
• SHQ8
• lightweight
• reasoning
• llm
• open-source
• empero
• claude-mythos-5
• deltanet
• gated-attention
• vision
• mtp
• multi-token-prediction
• draft-model

---

Qwythos-9B-Claude-Mythos-5-1M MTP — SHQ8 (Selective Hybrid Quants)

> v2 changelog (from upstream): tokenizer metadata normalized for Qwen3.5 GGUF runtimes; embedded chat template updated for reliable tool/function calling and OpenCode-style agent loops; Qwythos/Empero identity prompt embedded in the template; MTP variants with --spec-type draft-mtp support; Q4/Q8 tool-calling, MTP draft speculation, 1M-context allocation, and vision projector smoke-tested with current llama.cpp.

> Note: File names contain Q5_K_M for HF parser compatibility only. These are not pure Q5_K_M — they're selective hybrid quants using Q8_0, Q6_K, IQ4_XS, Q5_K, and F16 across different tensor types. See each section for the exact per-tensor map.

> Selective hybrid quantizations for Empero's Qwythos-9B-Claude-Mythos-5-1M with built-in MTP head — a full-parameter reasoning fine-tune of Qwen3.5-9B with 1M context, vision support, and multi-token prediction for faster speculative decoding.

> Uses the exact same SHQ8 method and formulas as Qwable-9B-Claude-Fable-5-SHQ8-GGUF — same architecture, same imatrix, (almost) same quantization strategies.

Available Quants

| Quant | Size | Description |

|---|---|---|

| M-SHQ8-MTP-OptA-Q5_K_M.gguf | 6.40 GB | Quality champion — Q8_0 attention + MTP head |

| M-SHQ8-MTP-v2-Q5_K_M.gguf | 5.83 GB | Compact champion — tiered precision + MTP head |

Both variants preserve the MTP head (blk.32) in Q8_0 for maximum speculation accuracy. The MTP head adds ~247 MiB over the non-MTP versions.

Perplexity

Same architecture as v1, same imatrix — PPL matches the non-MTP quants exactly (MTP head doesn't affect base model perplexity):

| Quant | PPL (ctx=1024) |

|---|---|

| Q6_K (baseline) | 7.5876 ± 0.04948 |

| SHQ8-OptA | 7.4831 ± 0.04827 |

| SHQ8-v2 | 7.6542 ± 0.05003 |

Key finding: OptA beats Q6_K by −0.105 PPL at smaller size. v2 is +0.067 vs Q6_K but 18% smaller.

Speed (with MTP Speculation)

| Quant | Tokens/sec (GTX 1070) |

|---|---|

| MTP-OptA | 30–41 t/s |

| MTP-v2 | 33–44 t/s |

MTP speculation provides a significant speedup over the non-MTP versions (~26/28 t/s) by predicting 2 tokens ahead. Numbers vary depending on prompt length and batch composition.

Architecture

Identical to Qwable-9B-Claude-Fable-5 — same Qwen3.5-9B backbone + MTP:

| Property | Value |

|---|---|

| Layers | 32 (24 Gated DeltaNet + 8 Full Attention) |

| MTP block | blk.32 — Full Attention + FFN + projection head |

| Hidden dim | 4096 |

| FFN intermediate | 12288 |

| Vocabulary | 248,320 |

| Full Attention | blk.3, 7, 11, 15, 19, 23, 27, 31 |

| DeltaNet | all others |

| Context | 1,048,576 (YaRN factor 4.0) |

MTP Head (blk.32)

blk.32 is a full transformer block dedicated to multi-token prediction. It re-encodes the main model's output through attention + FFN, then projects it via nextn.eh_proj to predict the next token's embedding:

blk.32 — Full Attention block:
├── attn_q/k/v + output    112 MiB
├── ffn_gate/up/down       288 MiB
└── nextn.eh_proj [8192→4096]   64 MiB   ← MTP projection head

Total MTP overhead: 464 MiB in BF16 (~260 MiB in Q8_0). The MTP head is quantized at Q8_0 to preserve speculation quality.

Imatrix

Reuses Qwable-9B-Claude-Fable-5.imatrix.gguf — same architecture, same tensor layout for the base model. MTP head tensors (blk.32) are set to Q8_0 explicitly, bypassing imatrix.

Quantization Commands

OptA-MTP

~/llm-tools/llama.cpp/build/bin/llama-quantize \
  --imatrix /mnt/everything/qwen/source/Qwable-9B-Claude-Fable-5.imatrix.gguf \
  --output-tensor-type Q5_K \
  --token-embedding-type Q4_K \
  --tensor-type "output_norm.*=Q8_0" \
  --tensor-type "blk\.32\.nextn\..*=Q8_0" \
  --tensor-type "blk\.32\.attn_q\.weight=Q8_0" \
  --tensor-type "blk\.32\.attn_k\.weight=Q8_0" \
  --tensor-type "blk\.32\.attn_v\.weight=Q8_0" \
  --tensor-type "blk\.32\.attn_output\.weight=Q8_0" \
  --tensor-type "blk\.32\.ffn_down\.weight=Q8_0" \
  --tensor-type "blk\.32\.ffn_gate\.weight=Q8_0" \
  --tensor-type "blk\.32\.ffn_up\.weight=Q8_0" \
  --tensor-type "blk\.32\..*norm.*=F16" \
  --tensor-type ".*attn_q_norm.*=Q8_0" \
  --tensor-type ".*attn_k_norm.*=Q8_0" \
  --tensor-type ".*ssm_conv1d.*=Q8_0" \
  --tensor-type "blk\.\d+\.attn_gate=Q8_0" \
  --tensor-type "blk\.\d+\.attn_qkv=Q8_0" \
  --tensor-type "blk\.\d+\.ssm_alpha=Q8_0" \
  --tensor-type "blk\.\d+\.ssm_beta=Q8_0" \
  --tensor-type "blk\.31\.ffn_down=Q8_0" \
  --tensor-type ".*attn_norm.*=F16" \
  --tensor-type ".*post_attention_norm.*=F16" \
  --tensor-type ".*ssm_norm.*=F16" \
  --tensor-type ".*ssm_dt.*=F16" \
  --tensor-type ".*ssm_a=F16" \
  /mnt/everything/qwen/source/Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf \
  /mnt/everything/qwen/output/M-SHQ8-MTP-OptA-Q5_K_M.gguf \
  Q5_K_M

v2-MTP

Config: configs/SHQ8-mtp_v2.sh

~/llm-tools/llama.cpp/build/bin/llama-quantize \
  --imatrix /mnt/everything/qwen/source/Qwable-9B-Claude-Fable-5.imatrix.gguf \
  --output-tensor-type Q5_K \
  --token-embedding-type Q4_K \
  --tensor-type "output_norm.*=Q8_0" \
  --tensor-type "blk\.32\.nextn\..*=Q8_0" \
  --tensor-type "blk\.32\.attn_q\.weight=Q8_0" \
  --tensor-type "blk\.32\.attn_k\.weight=Q8_0" \
  --tensor-type "blk\.32\.attn_v\.weight=Q8_0" \
  --tensor-type "blk\.32\.attn_output\.weight=Q8_0" \
  --tensor-type "blk\.32\.ffn_down\.weight=Q8_0" \
  --tensor-type "blk\.32\.ffn_gate\.weight=Q8_0" \
  --tensor-type "blk\.32\.ffn_up\.weight=Q8_0" \
  --tensor-type "blk\.32\..*norm.*=F16" \
  --tensor-type ".*attn_q_norm.*=Q8_0" \
  --tensor-type ".*attn_k_norm.*=Q8_0" \
  --tensor-type ".*ssm_conv1d.*=Q8_0" \
  --tensor-type "blk\.31\.ffn_down=Q8_0" \
  --tensor-type "blk\.31\.attn_output=Q8_0" \
  --tensor-type "blk\.0\.attn_gate=Q8_0" \
  --tensor-type "blk\.0\.attn_qkv=Q8_0" \
  --tensor-type "blk\.0\.ssm_alpha=Q8_0" \
  --tensor-type "blk\.0\.ssm_beta=Q8_0" \
  --tensor-type "blk\.(26|27|28|29|30|31)\.attn_gate=Q8_0" \
  --tensor-type "blk\.(26|27|28|29|30|31)\.attn_qkv=Q8_0" \
  --tensor-type "blk\.(26|27|28|29|30|31)\.ssm_alpha=Q8_0" \
  --tensor-type "blk\.(26|27|28|29|30|31)\.ssm_beta=Q8_0" \
  --tensor-type "blk\.(3|27|31)\.attn_q\.weight=Q8_0" \
  --tensor-type "blk\.(3|27|31)\.attn_k\.weight=Q8_0" \
  --tensor-type "blk\.(3|27|31)\.attn_v\.weight=Q8_0" \
  --tensor-type "blk\.([1-9]|1[0-9]|2[0-5])\.attn_gate=Q6_K" \
  --tensor-type "blk\.([1-9]|1[0-9]|2[0-5])\.attn_qkv=Q6_K" \
  --tensor-type "blk\.([1-9]|1[0-9]|2[0-5])\.ssm_alpha=Q6_K" \
  --tensor-type "blk\.([1-9]|1[0-9]|2[0-5])\.ssm_beta=Q6_K" \
  --tensor-type ".*attn_norm.*=F16" \
  --tensor-type ".*post_attention_norm.*=F16" \
  --tensor-type ".*ssm_norm.*=F16" \
  --tensor-type ".*ssm_dt.*=F16" \
  --tensor-type ".*ssm_a$=F16" \
  --tensor-type ".*ssm_out=IQ4_XS" \
  --tensor-type ".*attn_output=IQ4_XS" \
  --tensor-type ".*ffn_down=IQ4_XS" \
  /mnt/everything/qwen/source/Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf \
  /mnt/everything/qwen/output/M-SHQ8-MTP-v2-Q5_K_M.gguf \
  Q5_K_M

CRITICAL: MTP overrides (blk.32.) must come BEFORE generic IQ4_XS rules (.ffn_down=IQ4_XS) — first-match-wins prevents the MTP head from being downgraded.

MTP Speculative Decoding

These quants include a built-in MTP draft head for speculative decoding in llama.cpp. Activate MTP with:

--spec-type draft-mtp --spec-draft-n-max 2

The model's own MTP head acts as the draft predictor — no separate draft model needed.

# Basic MTP speculation
llama-cli \
  -m M-SHQ8-MTP-OptA-Q5_K_M.gguf \
  --spec-type draft-mtp --spec-draft-n-max 2 \
  -p "Your prompt" \
  -ngl 99 --flash-attn on \
  -c 4096

For server mode:

llama-server \
  -m M-SHQ8-MTP-OptA-Q5_K_M.gguf \
  --spec-type draft-mtp --spec-draft-n-max 2 \
  -c 65536 \
  -ngl 99 --flash-attn on \
  --cache-type-k q8_0 --cache-type-v q8_0

> MTP speculation can provide 1.5–3× speedup on token generation by predicting multiple tokens ahead and verifying them in a single forward pass. The compact v2 quant doubles as a natural draft for the OptA target.

>

> Personal note from wepiqx: I'm very happy that Empero released the MTP version so quickly — big thanks to the author for making this available. MTP support in the source model lets us build specialized draft speculation without needing a separate small model, which is a game-changer for GPU-constrained setups like my GTX 1070.

Coding Examples

All MTP quants generate full, working HTML/CSS/JS websites in a single pass at temperature 0.6 with the prompt:

> "I'm a dev, my audience is youth. I like a creative/tech style. Write the full website code. This HTML will be our foundation."

SHQ8-MTP-OptA — mythos-SHQ8-MTP_temp-0.6.html

A focused concept page in 369 lines:

• "NEXT_GEN" branding with Space Grotesk
• Features section with animated grid cards
• Showcase with project grid
• Clean modern aesthetic, minimal dependencies

SHQ8-MTP-OptA (rp 1.05) — mythos-SHQ8-MTP_temp-0.6-rp-1.05.html

Full website in 882 lines:

• Sticky navbar, hero with particle-style background
• About, projects, and contact sections with form validation
• More sections than rp 1.0 version
• repeat_penalty 1.05 reduces repetition in long outputs

SHQ8-MTP-v2 — mythos-SHQ8-MTP-v2_temp-0.6.html

Most feature-rich output in 986 lines:

• Full hero + projects + skills + about + contact
• Skill bars, project cards, contact form
• Most complete single-pass generation of all MTP variants
• External deps: Google Fonts

SHQ8-MTP-v2 (rp 1.05) — mythos-SHQ8-MTP-v2_temp-0.6-rp-1.05.html

Compact landing in 337 lines:

• Features section with clean card layout
• Contact section with form
• Most concise MTP output
• No external dependencies

> At temp 0.6, all MTP quants produce clean, working code. The rp 1.05 variants tend to generate more structured multi-section sites with less repetition. MTP speculation doesn't affect output quality — only generation speed.

Usage

> Recommended sampling: Start with temperature 0.6, top_k 20, top_p 0.95, min_p 0. If you encounter looping or over-thinking, set repeat_penalty to 1.05 — this solves both issues without touching temperature. Be cautious with high temperatures — this is a reasoning fine-tune and can get unstable above 1.2.

>

> Personal note from wepiqx: I've found that top_p 1.0 + min_p 0.05 often produces noticeably better results than top_p 0.95 + min_p 0. Give it a try.

llama.cpp

llama-cli \
  -m M-SHQ8-MTP-OptA-Q5_K_M.gguf \
  -p "Your prompt here" \
  -ngl 99 --flash-attn on \
  -c 4096 \
  --temp 0.6 --top-k 20 --top-p 0.95 --min-p 0

Ollama

FROM ./M-SHQ8-MTP-OptA-Q5_K_M.gguf

PARAMETER num_ctx 8192
PARAMETER temperature 0.6
PARAMETER top_k 20
PARAMETER top_p 0.95
PARAMETER min_p 0

LM Studio

1. Drag the .gguf into LM Studio
2. GPU Offload: 99 layers
3. Enable flash-attention
4. Sampling: temp 0.6, top_k 20, top_p 0.95, min_p 0

⚠️ Crucial Security & Safety Note (Uncensored Nature)

Please be aware that Qwythos-9B inherits a deeply uncensored base and is fine-tuned to engage substantively with high-stakes technical domains like offensive cybersecurity, red-teaming methodologies, clinical medicine, and advanced pharmacology without refusals, hedging, or generic disclaimers.

• For Users/Developers: This model does not surface safety boilerplate. It is critical to verify any specific identifiers, source code, or clinical data before execution or practical application.
• For Deployments: If you are using these SHQ8 quants in user-facing production applications, it is highly recommended to implement your own application-level moderation, review pipelines, or safety alignment layers depending on your target audience.

Files

| File | Size | Description |

|---|---|---|

| Qwythos-9B-Claude-Mythos-5-1M-MTP-BF16.gguf | 18 GB | BF16 source with MTP head |

| M-SHQ8-MTP-OptA-Q5_K_M.gguf | 6.40 GB | Quality champion + MTP |

| M-SHQ8-MTP-v2-Q5_K_M.gguf | 5.83 GB | Compact champion + MTP |

Config Storage

Config scripts: configs/SHQ8-mtp_optA.sh, configs/SHQ8-mtp_v2.sh

References

• Qwythos-9B-Claude-Mythos-5-1M
• Qwable-9B-Claude-Fable-5-SHQ8-GGUF — full methodology, importance analysis, PPL results