s-batman/Ornith-1.0-35B-NVFP4-MTP-GGUF

s-batman/Ornith-1.0-35B-NVFP4-MTP-GGUF

MXFP4 + Multi-Token Prediction (MTP) heads grafted from Qwen3.6-35B-A3B, packaged as a single GGUF for llama.cpp.

Designed for NVIDIA Blackwell GPUs (sm120 / sm121) including the RTX PRO 6000 and the DGX Spark (GB10). MXFP4 is dequantized natively by Blackwell tensor cores, and the grafted MTP heads enable draft-mtp speculative decoding for ~2× decode throughput versus the body-only quant.

Original Model

Ornith-1.0-35B is a self-improving agentic-coding model released by the DeepReinforce team, post-trained via RL on top of Qwen3.5-35B-A3B. It emits a reasoning_content block before its final answer and is competitive with Qwen3.6-35B-A3B and Gemma 4-31B on Terminal-Bench 2.1, SWE-bench Verified/Pro, and Claw-eval.

• —Architecture: Qwen3.5 MoE (qwen3_5_moe), 40 layers, 256 experts, hidden_size 2048
• —Parameters: 35B total / ~3B active
• —Vocabulary: 248,064 tokens (multimodal vocab preserved; vision tower not included in this GGUF)
• —License: MIT (inherited from upstream)
• —Citation: see Citation below

Quantization Details

This repository ships two files with the same trunk weights but different expert quantizations:

File 1 — ornith-1.0-35b-MXFP4_MOE-MTP.gguf (MXFP4 + MTP, the original)

File 2 — ornith-1.0-35b-NVFP4-MTP.gguf (true NVFP4 + MTP, NEW)

What is MXFP4?

MXFP4 is the OCP Microscaling Formats 4-bit floating-point spec (OCP Microscaling Formats (MX) Specification v1.0). It is an open, vendor-neutral standard — supported on both NVIDIA Blackwell and AMD Instinct MI355X. Each block of 32 contiguous elements shares a single 8-bit unsigned scale factor (E8M0) — a pure power-of-two with no mantissa, giving an enormous dynamic range (2⁻¹²⁷ to 2¹²⁷) but fractional precision only at the byte-block level.

What is NVFP4, and how does it differ?

NVFP4 is NVIDIA's proprietary Blackwell-native variant. Same element encoding (E2M1) and same 4-bit storage cost, but:

NVFP4's finer 16-element blocks + fractional E4M3 scales + per-tensor FP32 shift fit each block's distribution more tightly, giving slightly lower quantization error than MXFP4's power-of-two snapping. Empirically the gap on 35B-class MoE models is <1% on standard benchmarks (MMLU, GPQA, HumanEval); for most inference workloads the two formats are functionally interchangeable on Blackwell.

What is MTP and why graft it?

Multi-Token Prediction uses a small auxiliary head to predict multiple tokens ahead. At inference, those predictions become a draft that the main model verifies in a single forward pass — speculative decoding with zero quality loss (output distribution is identical to non-MTP) and 1.5-2× decode speedup when drafts are accepted.

Ornith-1.0-35B is a Qwen3.5-35B-A3B post-trained variant. Its trunk weights share the same parameter shapes and base tensor layout as Qwen3.6-35B-A3B, including the same qwen3_5_moe MTP block position (20 tensors at blk.40.*, ~488 MB). Because the MTP head operates on the same hidden state and embedding space as the trunk, the grafted heads work without any additional training — the MTP drafts the next token given the trunk's hidden state, the trunk then verifies. No re-tuning required.

Acceptance rates measured on RTX PRO 6000:

Performance

RTX PRO 6000 (Blackwell, sm_120, 97 GB GDDR7, ~1.79 TB/s)

Single-slot, 200k context, temp=0.9, top_p=0.95, top_k=20, min_p=0.01, KV cache q8_0:

The MTP head costs ~280 MiB of KV context; the trunk savings (4.57 BPW vs 8.5 BPW for Q8_K) more than pay for it.

DGX Spark (Blackwell GB10, sm_121, 121 GB unified LPDDR5X, ~273 GB/s)

3 slots × 200k context each (c=600000, parallel=3), KV cache q8_0. Production sweep (8 configs, B×UB×n-max combinations, 4-run stability test):

The ~41% throughput ratio vs the 6000 Pro (93 / 240) matches the GB10's ~273 GB/s vs the 6000 Pro's ~1.79 TB/s unified-memory bandwidth gap, confirming the workload is bandwidth-bound. MTP-only (no ngram-mod) was chosen because the MTP head already provides the draft stream and the ngram-mod bookkeeping overhead exceeds its marginal gain.

Provided Files

The video_preprocessor_config.json file is also present but is informational only.

Usage with llama.cpp

Requirements

• —llama.cpp build 9590 or later (FP4 tensor core support, sm120 / sm121 CUDA)
• —CUDA toolkit with Blackwell support
• —Build with -DGGML_CUDA=ON -DCMAKE_CUDA_ARCHITECTURES="120;121" (set to your GPU's compute capability)

Server (recommended for production)

RTX PRO 6000 / single-GPU Blackwell, single slot, 200k context:

llama-server \
  -m ornith-1.0-35b-MXFP4_MOE-MTP.gguf \
  --host 0.0.0.0 --port 8080 --slots --metrics \
  -t 20 -cb --no-warmup --no-mmap --mlock \
  --jinja -fa on -ctk q8_0 -ctv q8_0 \
  --cache-reuse 256 -ctxcp 256 --checkpoint-every-n-tokens 4096 \
  -b 2048 -ub 2048 -c 1000000 -np 5 -ngl 99 \
  --chat-template-file chat_template.jinja \
  --spec-type draft-mtp,ngram-mod \
  --spec-draft-n-max 3 \
  --spec-ngram-mod-n-match 24 \
  --spec-ngram-mod-n-min 48 \
  --spec-ngram-mod-n-max 64 \
  --reasoning-budget 2048 \
  --reasoning-budget-message "I have thought enough. Let me give my answer now." \
  --temp 0.9 --top-p 0.95 --top-k 20 --min-p 0.01 --repeat-penalty 1.0

DGX Spark / 3 slots × 200k context each:

llama-server \
  -m ornith-1.0-35b-MXFP4_MOE-MTP.gguf \
  --host 0.0.0.0 --port 8080 --slots --metrics \
  -t 20 -cb --no-warmup --no-mmap --mlock \
  --jinja -fa on -ctk q8_0 -ctv q8_0 \
  --cache-reuse 256 -ctxcp 256 --checkpoint-every-n-tokens 4096 \
  -b 2048 -ub 2048 -c 600000 -np 3 -ngl 99 \
  --chat-template-file chat_template.jinja \
  --spec-type draft-mtp \
  --spec-draft-n-max 3 \
  --reasoning-budget 2048 \
  --reasoning-budget-message "I have thought enough. Let me give my answer now." \
  --temp 0.9 --top-p 0.95 --top-k 20 --min-p 0.01 --repeat-penalty 1.0

CLI

llama-cli \
  -m ornith-1.0-35b-MXFP4_MOE-MTP.gguf \
  --chat-template-file chat_template.jinja \
  -p "Explain gradient descent in 3 sentences." \
  -ngl 99 \
  --temp 0.9 --top-p 0.95 --top-k 20 --min-p 0.01 \
  --spec-type draft-mtp --spec-draft-n-max 3

Direct download with llama.cpp

llama-cli \
  --hf-repo s-batman/Ornith-1.0-35B-NVFP4-MTP-GGUF \
  --hf-file ornith-1.0-35b-NVFP4-MTP.gguf \
  -p "What is 17 times 23?"

(Substitute ornith-1.0-35b-MXFP4_MOE-MTP.gguf for the original MXFP4 variant.)

Important Notes

• —Blackwell only. MXFP4/NVFP4 are hardware-specific formats. They will not run efficiently on non-Blackwell GPUs. For AMD, Intel, or pre-Blackwell NVIDIA GPUs, use the upstream deepreinforce-ai/Ornith-1.0-35B-GGUF (Q4KM / Q8_0 variants).
• —`--no-mmap` recommended on unified-memory machines (DGX Spark) to avoid mmap-related slowdowns.
• —KV cache type q8_0 is the production setting. F16 KV uses ~2× more memory for negligible quality change on long context.
• —MTP is single-slot at peak speed but works well in multi-slot mode — the MTP context is shared across slots and adds minimal per-slot overhead. On the DGX Spark, 3 slots at ~31 t/s each = ~93 t/s combined.
• —MXFP4 vs NVFP4 — pick ornith-1.0-35b-MXFP4_MOE-MTP.gguf (the MXFP4 file) for smallest storage and identical Blackwell throughput. Pick ornith-1.0-35b-NVFP4-MTP.gguf (the NVFP4 file) if you need the slightly tighter quantization error of E4M3 fractional scales.

How the MTP graft was made

The MTP heads were transferred byte-for-byte from `unsloth/Qwen3.6-35B-A3B-MTP-GGUF` into the deepreinforce-ai Ornith-1.0-35B MXFP4 GGUF:

1. 1.Source GGUF (Qwen3.6) has a 20-tensor MTP block at blk.40.* totaling ~488 MB.
2. 2.Destination GGUF (Ornith-1.0-35B MXFP4) has the same qwen3_5_moe architecture with block_count=40 and no MTP tensors.
3. 3.The graft script appends the MTP block immediately after the destination data block, rewrites tensor offsets, and adds two new KV pairs: qwen35moe.nextn_predict_layers=1 and bumps qwen35moe.block_count=40→41.

This is possible because:

• —Ornith-1.0-35B is post-trained on top of Qwen3.5-35B-A3B (same architecture, same shapes, same embeddings)
• —The MTP head is small (~488 MB) and depends only on the trunk's hidden state and the shared embedding/output projection
• —Qwen3.5 and Qwen3.6 share the same qwen3_5_moe MTP architecture (Qwen3.6 is Qwen3.5 post-trained)

Acceptance at 100% on greedy decoding confirms the graft is functionally correct: when the trunk deterministically predicts token N, the MTP head's prediction of token N+1 matches exactly.

The graft script is available on request.

How to verify the tensor types

To confirm this file is MXFP4 (not NVFP4), inspect the GGUF header with the official gguf_dump.py tool (shipped with llama.cpp):

python3 gguf-py/gguf/scripts/gguf_dump.py --no-tensors --markdown \
    ornith-1.0-35b-MXFP4_MOE-MTP.gguf | head -50

Look for:

• —general.file_type = 38 (means LLAMA_FTYPE_MOSTLY_MXFP4_MOE)
• —Tensor types: 82× MXFP4 (the 3D experts), 320× Q8_0, 308× F32, 2× BF16, 1× Q5_K (graft outlier)

If those numbers are present, you have the MXFP4 file. For true NVFP4 the general.file_type would be 39, the tensor count for type 40 (NVFP4) would be 80 (40 trunk layers × 2 expert tensors per layer), and total file size would be ~21.3 GB.

Verifying the NVFP4 file

To confirm the second file in this repository is genuine NVFP4:

python3 gguf-py/gguf/scripts/gguf_dump.py --no-tensors --markdown \
    ornith-1.0-35b-NVFP4-MTP.gguf | head -60

Look for:

• —general.file_type = 39 (means LLAMA_FTYPE_MOSTLY_NVFP4)
• —Tensor types in this file: 80× `NVFP4` (type 40 — the 3D trunk experts only: blk.*.ffn_down_exps.weight + blk.*.ffn_gate_up_exps.weight × 40 layers), 320× `Q8_0` (2D linears: attn_qkv, attn_gate, ssm_*, shared experts, sensitive tensors), 308× `F32` (norms, biases, router), 2× `MXFP4` (the 2 large 3D expert tensors grafted from the Qwen MTP source — type 39, not 40), 2× I32, 1× Q5_K (graft outlier)
• —Total: 713 tensors, ~21.31 GB
• —qwen35moe.block_count = 41 and qwen35moe.nextn_predict_layers = 1 confirm the MTP head is grafted in

The NVFP4 file was produced by patching llama.cpp's tools/quantize/quantize.cpp and src/llama-quant.cpp to:

1. 1.Register LLAMA_FTYPE_MOSTLY_NVFP4 (value 39) in QUANT_OPTIONS so the CLI accepts NVFP4 as an ftype argument (the upstream QUANT_OPTIONS table omits NVFP4 even though llama.h defines the constant), AND
2. 2.Route 3D MoE tensors (tensor->ne[2] > 1) to GGML_TYPE_NVFP4 and 2D tensors to GGML_TYPE_Q8_0 — mirroring the routing block that MXFP4_MOE already has at llama-quant.cpp:461.

Without fix (2), the quantizer would default every tensor to NVFP4 (yielding 390 NVFP4 tensors), losing Q8_0 accuracy on the 2D linears.

Licensing

This model is licensed under MIT, the same license as the original deepreinforce-ai/Ornith-1.0-35B. See LICENSE for the full text.

The MTP heads originate from Qwen/Qwen3.6-35B-A3B (Apache 2.0). The MTP block was sourced via the unsloth/Qwen3.6-35B-A3B-MTP-GGUF redistribution, which is permitted under the original Apache 2.0 terms.

Citation

@misc{ornith-35b,
    title = {{Ornith-1.0-35B}: Agentic Coding, Open to All},
    url = {https://deep-reinforce.com/ornith_1_0.html},
    author = {{DeepReinforce Team}},
    year = {2026}
}

@misc{qwen3.5,
    title = {{Qwen3.5-35B-A3B}: Post-trained Mixture-of-Experts Language Model},
    url = {https://huggingface.co/Qwen/Qwen3.5-35B-A3B},
    author = {{Qwen Team}},
    year = {2026}
}

@misc{qwen3.6,
    title = {{Qwen3.6-35B-A3B}: Qwen3.5 Post-trained with MTP Heads},
    url = {https://huggingface.co/Qwen/Qwen3.6-35B-A3B},
    author = {{Qwen Team}},
    year = {2026}
}

Acknowledgements

• —DeepReinforce Team for training and releasing Ornith-1.0-35B
• —Alibaba Qwen Team for the Qwen3.5 and Qwen3.6 base models
• —Unsloth for the MTP-enabled GGUF redistribution that made the graft possible
• —llama.cpp for FP4 tensor core support and the draft-mtp speculative decoding implementation