💎 Ornith-1.5-35B-A3B - Custom Mixed Precision GGUFs with Imatrix

Ornith-1.5 extends the self-scaffolding framework introduced in Ornith-1.0 into a more complete self-improvement loop:
The model proposes new tasks, generates task-specific scaffolds, and produces solution rollouts for reinforcement learning, continuously creating new learning experiences from which it can improve.

This repository contains custom, highly optimized, multi-tier mixed precision GGUF weights for ornith-ai/Ornith-1.5-35B-A3B.

Ornith-1.5 35B is the direct successor of Ornith-1.0 35B, which achieves state-of-the-art performance among open-source models of comparable size across a broad range of agentic coding benchmarks.
It brings improved instruction following & improved thinking/reasoning, among other benefits.

[!TIP] Highly Recommended: Always keep reasoning/thinking enabled.
Ornith thoroughly plans and reasons through code edits before execution, ensuring an efficient and clean output.
Unlike baseline Qwen models, which frequently execute blindly and backtrack after generating broken code.

To learn more about Ornith 1.5, read their blog post.
To learn more about how to use Ornith 1.5 35B A3B, view the base model.
A smaller variant is also available: Ornith-1.5-9B

These quants were generated using manual layer targeting to maximize quality while shrinking the massive VRAM footprint of the Mixture of Experts layers.

📄 GGUF Files

In order of quality:

Updated 2026-08-22:

• Re-uploaded models without MTP layer

📊 Importance Matrix (Imatrix)

The imatrix is a combination of:

• bartowski/Ornith-1.5-35B-A3B-GGUF's imatrix file
• 1.6m tokens of my own markdown data from prompts, results, audits, skills & documentation generated via Ornith-1.0 35B A3B + this model

[!NOTE] The MXFP4 quantized layers include imatrix data, using this commit on-top of llama.cpp.

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🔍 Precision Matrix & Flavor Variations

Standard global quantization presets (like stock MXFP4_MOE) compress the backbone layers uniformly, which degrades the delicate reasoning capabilities of advanced agent models.
This repository provides multiple distinct manual configuration layouts to balance precision and memory constraints:

1. The Tri-Quant Hybrid Flavor (MXFP4 + Q8_0 + F16)

Ornith-1.5-35B-A3B-MXFP4_MOE_Q8_0_F16-Imatrix.gguf - Designed for maximum quality preservation, this layout implements a strict 3-Tier Precision Matrix:

• Tier 1 (Core & Mamba Gating - F16 Precision):
  • token_embd.weight, output.weight - Protects the critical input/output vocabulary mappings. Dramatically prevents text degradation.
  • ssm_alpha, ssm_beta - Protects the integrity of the Mamba state-space calculations across long-range context tokens.
• Tier 2 (Backbone & Shared - Q8_0 Precision): ssm_out, *._shexp - Keeps the attention mechanics, and all trailing shared experts at high quality, to protect the logical research loops.
• Tier 3 (Routed Experts - MXFP4 Precision): ffn_down_exps, ffn_gate_exps, ffn_up_exps - Shrink the massive background expert parameters directly to MXFP4.

2. The Dual-Quant Hybrid Flavor (MXFP4 + Q8_0)

Ornith-1.5-35B-A3B-MXFP4_MOE_Q8_0-Imatrix.gguf - Designed for a slightly leaner memory profile, this layout utilizes 2-Tier Precision:

• Tier 1 (Backbone - Q8_0 Precision): All attention blocks, Mamba structures, vocabulary embeddings, and internal routers use the universal Q8_0 format.
• Tier 2 (Experts - MXFP4 Precision): The heavy sparse expert blocks are target-quantized directly to MXFP4.

3. Bonus Single-Quant (MXFP4)

Ornith-1.5-35B-A3B-MXFP4_MOE-Only-Imatrix.gguf - Using only MXFP4, this shrinks the model down to 18.5 GB. The quality is not the best, but it can still do decent work.

• Single Tier (All Layers - MXFP4 Precision): All layers are target-quantized directly to MXFP4, for speed and a low VRAM footprint.

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📝 Exact Conversion Details

These files were converted via llama-quantize utilizing the following manual recipe parameters:

Convert SafeTensors to GGUF:

# Requires python3.12, with `pip install --upgrade transformers`
python convert_hf_to_gguf.py "Ornith-1.5-35B-A3B/" --outtype f16 --outfile "Ornith-1.5-35B-A3B_F16.gguf" --no-mtp

Generate Tri-Quant MXFP4_MOE + Q8_0 + F16:

llama-quantize \
  --tensor-type ".*_shexp\.weight=Q8_0" \
  --tensor-type "token_embd\.weight=F16" \
  --tensor-type "^output\.weight=F16" \
  --tensor-type "blk\..*\.(ssm_alpha|ssm_beta)\.weight=F16" \
  --tensor-type "blk\..*\.(ffn_down_exps|ffn_gate_exps|ffn_up_exps)\.weight=MXFP4" \
  --imatrix "imatrix.gguf" \
  "Ornith-1.5-35B-A3B_F16.gguf" \
  "Ornith-1.5-35B-A3B-A3B-MXFP4_MOE_Q8_0_F16-Imatrix.gguf" \
  Q8_0

Generate Dual-Quant MXFP4_MOE + Q8_0:

llama-quantize \
  --tensor-type ".*_shexp\.weight=Q8_0" \
  --tensor-type "blk\..*\.(ffn_down_exps|ffn_gate_exps|ffn_up_exps)\.weight=MXFP4" \
  --imatrix "imatrix.gguf" \
  "Ornith-1.5-35B-A3B_F16.gguf" \
  "Ornith-1.5-35B-A3B-A3B-MXFP4_MOE_Q8_0-Imatrix.gguf" \
  Q8_0

Generate Single-Quant MXFP4_MOE:

llama-quantize \
  --tensor-type ".*_shexp\.weight=MXFP4" \
  --tensor-type "token_embd\.weight=MXFP4" \
  --tensor-type "^output\.weight=MXFP4" \
  --tensor-type "blk\..*\.(ssm_alpha|ssm_beta|ssm_out|attn_gate|attn_qkv|ffn_down|ffn_gate|ffn_up|attn_k|attn_q|attn_v|attn_output)\.weight=MXFP4" \
  --tensor-type "blk\..*\.(ffn_down_exps|ffn_gate_exps|ffn_up_exps)\.weight=MXFP4" \
  --imatrix "imatrix.gguf" \
  "Ornith-1.5-35B-A3B_F16.gguf" \
  "Ornith-1.5-35B-A3B-MXFP4_MOE-Only-Imatrix.gguf" \
  MXFP4_MOE

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📝 Local Deployment & llama-server Configuration (config.ini)

To maintain long solid thinking/reasoning, prevent repetitive loops, have fewer hallucinations, and get higher-quality output, I recommend the following sampling settings & server parameters.

# --- Samplers (Dynamic & Expressive) ---
# Establishes the foundational pooling and filtering layers to balance creativity with logical precision.
temperature = 0.60
top-k = 35
top-p = 0.93
min-p = 0.10
top-n-sigma = 0.60

# --- Penalties (Prevent Syntax & Reasoner Corruption) ---
# Excluded from the pipeline to protect recurring folder paths and directory prefixes from corruption.
#repeat-penalty = 1.05
#presence-penalty = 1.1

# --- DRY Sampler (Protects Indentation & Structural Boilerplate) ---
# Intelligently limits phrase duplication and structural looping without punishing syntax punctuation or code dividers.
dry-multiplier = 0.8
dry-base = 1.75
dry-allowed-length = 3
dry-penalty-last-n = 1024
dry-sequence-breaker = [ "\n", "```\n", ":", "\t", "\"", "|", "-", "}", "]", "/", "\\" ]

# --- Enforced Execution Graph ---
# Clears the vast vocabulary tail early for speed and lets DRY safely block path duplication loops in a wide pool, 
# while temp prepares multi-token schemas so a late-stage Top-N-Sigma can slice out single-character spelling typos.
samplers = top_k;top_p;min_p;dry;temp;top_n_sigma

[!TIP] Accuracy Tips:

• Small amounts of details are mis-remembered during long context windows (>100k).
• Use cache-type-k = f16 as anything lower suffers from mis-remembered details (at any context size).
• If you must use q8_0 for kv cache, or the MXFP4 + Q8_0 or the MXFP4 Only variants, try tweaking top-n-sigma to increase accuracy.

[!NOTE] Compared to Ornith-1.0-35B, I have the values tuned for higher quality output.

Highly Recommended: Always keep reasoning/thinking enabled, for better quality results. The higher the budget, the more the model verifies/validates tasks.

# --- Reasoning ---
chat-template-kwargs = { "enable_thinking":true }
reasoning = on
reasoning-format = auto
reasoning-budget = 32768

This works well with 256k context window.

fit-ctx = 262144

[!TIP] For Maximum Quality at 100k+ Context:
Use the MXFP4 + Q8_0 + F16 split-quantized version.

• Preserved at F16: token_embd.weight, output.weight, *.ssm_alpha.weight, and *.ssm_beta.weight.
• Why this matters: Keeping these critical layers at full precision prevents the model from dropping most of the fine details during extreme "needle-in-a-haystack" retrieval tasks (large context windows).
• What to avoid: If output.weight or the embedding layers are quantized to Q8_0 or lower, logit precision rounds off, causing the model to lose accuracy and forget specific details in long-context scenarios, more often.

Updated 2026-09-05:

• Improved sampling settings again. Less over-confidence & higher accuracy (for coding & agentic tasks).

Updated 2026-08-29:

• Added accuracy tips section & updated notes

Earlier Changes

Updated 2026-08-26:

• Improved sampling settings again. It is now almost flawlessly calling correct tool calls, correct path names, variable names, etc. Moved top_n_sigma to the end.

Updated 2026-08-24:

• Improved sampling settings for even higher quality output, such as moving & tweaking top_n_sigma, and removing penalties (using dry instead). It can still make minor typos, but it's much better at Powershell commands and tool calls now.

Updated 2026-08-23:

• Improved sampling settings for higher quality output & added top_n_sigma+penalties

Updated 2026-08-22:

• Improved sampling settings

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ℹ️ Misc Details

I'm doing this as a side hobby, with my AMD 5900X, 64GB DDR4, RTX 3060 12GB & RTX 5060 Ti 16GB.

In addition to the above configuration, I also use:

slots = 1
parallel = 1
no-warmup = true

flash-attn = on
mlock = false
no-mmap = true
context-shift = false

batch-size = 2048
ubatch-size = 256

fit = on
fit-target = 1024
cache-ram = 4096
main-gpu = 0
split-mode = layer
n-gpu-layers = 999
n-cpu-moe = 0
tensor-split = 16,13
override-tensor = (token_embd)=CUDA0,(vision|vpm|nextn)=CPU

fit-ctx = 262144
cache-type-k = q8_0
cache-type-v = q8_0

jinja = true
chat-template = jinja
chat-template-file = chat_template.jinja

For further quality and better ssm behaviour, this configuration can help:

context-shift = false
cache-type-k = f16
cache-type-v = f16

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🤝 Support the Journey

As a passionate developer, I'm always programming, automating, or experimenting with new ideas.
I love building open-source tools, trying out new web tech, and creating things that don't yet exist, including local AI & quantizing models.

I love sharing these creations to give back to the community.
If my projects have saved you time or helped you out, consider supporting my work below!

👉 Support me on Ko-fi

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✨ Acknowledgments

• ornith-ai for the exceptional Ornith-1.5 base model.
• bartowski/Ornith-1.5-35B-A3B-GGUF for the imatrix gguf that I then combined with my own imatrix data.

📜 License

Released under MIT.

🔗 Citation

@misc{ornith_1_5,
    title = {{Ornith-1.5}: From Self-Scaffolding to Self-Improvement},
    url = {https://ornith.ai/ornith_1_5.html},
    author = {{Ornith Team}},
    year = {2026}
}