---

license: apache-2.0

base_model:

• InternScience/Agents-A1

library_name: llama.cpp

pipeline_tag: text-generation

tags:

• gguf
• quantized
• llama-cpp
• qwen3.5-moe
• mixture-of-experts
• agents-a1
• nvfp4
• mtp
• speculative-decoding
• mmproj
• multimodal
• vision
• qwen3vl

---

Agents-A1 GGUF Quants

High quality GGUF quantizations of InternScience/Agents-A1, a 35B Qwen3.5-MoE agent model.

These files were produced from the BF16 Hugging Face checkpoint with a patched llama.cpp build that supports the qwen35moe architecture. The calibration pass used an importance matrix built from coding/instruction chat data, then each quant was benchmarked against the BF16 GGUF reference.

Recommended Files

| Use case | File | Notes |

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

| Best small general-purpose quant | agents-a1-IQ4_XS.gguf | Strong quality for size, broad llama.cpp compatibility. |

| Best single-user MTP throughput | agents-a1-IQ4_XS-MTP-graft-headQ6.gguf | IQ4_XS body with Q6_K MTP block; measured 1.22x over target-only in c1/128 chat serving. |

| Highest MTP acceptance in this run | agents-a1-Q4_K_M-MTP-graft-headQ6.gguf with SPEC_DRAFT_N_MAX=1 | 91.46% draft acceptance while still 1.15x over target-only. |

| Safer MTP quality step up | agents-a1-Q5_K_M-MTP-graft-headQ6.gguf | Q5_K_M body with the same integrated Q6_K/F32 MTP block; structurally validated, not re-profiled yet. |

| Vision / image input for Q4+ quants | mmproj-agents-a1-bf16.gguf | Shared BF16 Qwen3VL mmproj for IQ4_XS, Q4_K_M, Q5_K_M, Q6_K, Q8_0, NVFP4, and the MTP variants. |

| Fast Blackwell FP4 path | agents-a1-NVFP4.gguf | Tested on RTX PRO 6000 Blackwell. Requires runtime support for GGML_TYPE_NVFP4. |

| Safer quality step up | agents-a1-Q5_K_M.gguf | Lower KLD than IQ4_XS with larger size. |

| Closest to BF16 by KLD | agents-a1-Q6_K.gguf | Best KLD in this eval set. |

| High precision archival quant | agents-a1-Q8_0.gguf | Largest quantized file. |

Files

| Quant | File size | Notes |

|---|---:|---|

| Q3_K_M | 16.76 GB | Smallest included quant. |

| IQ4_XS | 18.73 GB | Recommended compact quant. |

| IQ4_XS-MTP-graft-headQ6 | 19.42 GB | IQ4_XS body plus integrated Q6_K/F32 MTP block. |

| NVFP4 | 19.72 GB | Blackwell-oriented FP4 GGUF, output head kept at Q6_K by quality rule. |

| Q4_K_M | 21.17 GB | Standard K-quant. |

| Q4_K_M-MTP-graft-headQ6 | 21.86 GB | Q4_K_M body plus integrated Q6_K/F32 MTP block. |

| Q5_K_M | 24.73 GB | Strong quality/size tradeoff. |

| Q5_K_M-MTP-graft-headQ6 | 25.42 GB | Q5_K_M body plus integrated Q6_K/F32 MTP block. |

| Q6_K | 28.51 GB | Lowest mean KLD in this run. |

| Q8_0 | 36.90 GB | Highest precision quant. |

| mmproj BF16 | 0.90 GB | Shared Qwen3VL vision encoder/projector for Q4-class and higher text GGUFs. |

Metrics

Hardware and runtime profile:

• GPU: single NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition, full offload
• llama.cpp flags: -ngl 99 -sm none -fa on -p 512 -n 128 -b 4096 -ub 512 -r 3
• PPL: llama-perplexity, context 2048, 64 rendered eval conversations, 3 chunks
• KLD: approximate KL(P_BF16 || P_quant) over top-64 next-token distributions on 32 prompts

The PPL eval is intentionally small, so treat PPL deltas as directional. KLD and top-1 agreement are more useful here for quant-to-BF16 comparison.

| Model | Size GB | Prompt tok/s | Gen tok/s | PPL | PPL delta | KLD mean | KLD p95 | Top-1 match |

|---|---:|---:|---:|---:|---:|---:|---:|---:|

| BF16 reference | 69.38 | 3418.9 | 161.8 | 1.3031 | 0.0000 | 0.0000 | 0.0000 | 32/32 |

| Q3_K_M | 16.76 | 6779.5 | 269.0 | 1.3101 | +0.0070 | 0.0655 | 0.2155 | 28/32 |

| IQ4_XS | 18.73 | 7719.5 | 258.1 | 1.3038 | +0.0007 | 0.0151 | 0.0654 | 29/32 |

| NVFP4 | 19.72 | 9064.0 | 265.1 | 1.3063 | +0.0032 | 0.0420 | 0.1473 | 31/32 |

| Q4_K_M | 21.17 | 7230.8 | 262.6 | 1.3016 | -0.0015 | 0.1225 | 0.3349 | 27/32 |

| Q5_K_M | 24.73 | 7021.4 | 257.9 | 1.3041 | +0.0010 | 0.0091 | 0.0335 | 30/32 |

| Q6_K | 28.51 | 6294.0 | 244.6 | 1.3040 | +0.0009 | 0.0049 | 0.0178 | 32/32 |

| Q8_0 | 36.90 | 7431.3 | 222.7 | 1.3036 | +0.0005 | 0.0053 | 0.0063 | 30/32 |

Charts

!Size vs generation speed

!Mean KLD

!PPL delta

!Quality vs size

Raw metric files are in metrics/; KLD reports, checksums, and the MTP audit are in reports/.

MTP Variants

The upstream Agents-A1 checkpoint used for the first GGUF release advertises

MTP in config but does not ship mtp./blk.40. tensors. The MTP variants

here graft in the Agents-A1 MTPLX MTP sidecar from

wang-yang/Agents-A1-MTPLX-Q4. The dense MTP block is preserved at Q6_K/F32

while the model body remains quantized to IQ4_XS, Q4_K_M, or Q5_K_M.

The Q5_K_M MTP artifact reuses the validated Q5_K_M body tensors and the same

Q6_K/F32 blk.40.* MTP block, with GGUF metadata updated to

qwen35moe.block_count=41 and qwen35moe.nextn_predict_layers=1.

Structural checks for the integrated MTP GGUFs:

| Check | Value |

|---|---:|

| GGUF tensors | 753 |

| qwen35moe.block_count | 41 |

| qwen35moe.nextn_predict_layers | 1 |

| blk.40.* MTP tensors | 20 |

| blk.40.nextn.* tensors | 4 |

Single-user serving profile: one RTX PRO 6000 Blackwell Max-Q 96 GB GPU,

PARALLEL=1, CTX_SIZE=8192, streaming chat completions, 12 requests,

128 max tokens, temperature=0, top_p=1.

| Quant | Mode | Aggregate tok/s | Speedup vs target-only | Draft acceptance | Mean accepted length | Acceptance by position |

|---|---:|---:|---:|---:|---:|---|

| IQ4_XS-MTP | target-only | 224.59 | 1.00x | n/a | n/a | n/a |

| IQ4_XS-MTP | draft-mtp, n_max=2 | 275.03 | 1.22x | 76.51% | 2.52 | (0.830, 0.692) |

| IQ4_XS-MTP | draft-mtp, n_max=1 | 259.58 | 1.16x | 86.47% | 1.86 | (0.865) |

| Q4_K_M-MTP | target-only | 230.48 | 1.00x | n/a | n/a | n/a |

| Q4_K_M-MTP | draft-mtp, n_max=2 | 273.80 | 1.19x | 77.18% | 2.53 | (0.847, 0.687) |

| Q4_K_M-MTP | draft-mtp, n_max=1 | 264.88 | 1.15x | 91.46% | 1.91 | (0.915) |

agents-a1-Q5_K_M-MTP-graft-headQ6.gguf is structurally validated but has not

been re-profiled for throughput or draft acceptance yet.

Recommended low-latency/single-user throughput profile: SPEC_DRAFT_N_MAX=2.

Recommended high-acceptance fallback: SPEC_DRAFT_N_MAX=1.

Detailed MTP evidence is in:

• reports/agents-a1-mtp-q4-profile-summary.md
• reports/agents-a1-mtp-q4-profile-summary.json
• reports/agents-a1-q5-mtp-build-report.json
• configs/mtp_profiles.yaml

Usage

Example with the recommended compact quant:

llama-server \
  -m agents-a1-IQ4_XS.gguf \
  -ngl 99 \
  -c 8192 \
  -b 4096 \
  -ub 512 \
  --flash-attn on

NVFP4 example:

llama-server \
  -m agents-a1-NVFP4.gguf \
  -ngl 99 \
  -c 8192 \
  -b 4096 \
  -ub 512 \
  --flash-attn on

The NVFP4 artifact is a standard GGUF using the NVFP4 tensor type, but runtime support is still newer and less universal than K-quants or IQ4_XS. It was tested on a Blackwell GPU with a llama.cpp build reporting BLACKWELL_NATIVE_FP4 = 1.

MTP example:

LLAMA_SPEC_MAX_DRAFTING_SLOTS=1 \
LLAMA_MTP_FAST_BACKEND_SAMPLE=1 \
LLAMA_MTP_DRAFT_TOP_K=1 \
LLAMA_MTP_DRAFT_TOP_P=1 \
LLAMA_MTP_DRAFT_TEMP=1 \
llama-server \
  -m agents-a1-IQ4_XS-MTP-graft-headQ6.gguf \
  -ngl 99 \
  -c 8192 \
  -b 4096 \
  -ub 512 \
  --flash-attn on \
  --reasoning off \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  --spec-draft-n-min 0 \
  --spec-draft-backend-sampling

For the high-acceptance profile, change --spec-draft-n-max 2 to

--spec-draft-n-max 1. The same command shape applies to

agents-a1-Q4_K_M-MTP-graft-headQ6.gguf and

agents-a1-Q5_K_M-MTP-graft-headQ6.gguf.

Vision / mmproj

The release includes one shared multimodal projector:

• mmproj-agents-a1-bf16.gguf
• processor_config.json
• preprocessor_config.json
• video_preprocessor_config.json

The mmproj was converted from the original InternScience/Agents-A1 Hugging

Face checkpoint with llama.cpp convert_hf_to_gguf.py --mmproj --outtype bf16.

It contains the Qwen3VL vision tower/projector and is independent of the text

quantization level, so the same file is intended for Q4-class and higher text

GGUFs:

• agents-a1-IQ4_XS.gguf
• agents-a1-IQ4_XS-MTP-graft-headQ6.gguf
• agents-a1-NVFP4.gguf
• agents-a1-Q4_K_M.gguf
• agents-a1-Q4_K_M-MTP-graft-headQ6.gguf
• agents-a1-Q5_K_M.gguf
• agents-a1-Q5_K_M-MTP-graft-headQ6.gguf
• agents-a1-Q6_K.gguf
• agents-a1-Q8_0.gguf

Q3_K_M may load with the same mmproj, but it is not the recommended vision

profile because image tasks are more sensitive to text-model quantization.

Example with llama.cpp's multimodal CLI:

llama-mtmd-cli \
  -m agents-a1-Q4_K_M.gguf \
  --mmproj mmproj-agents-a1-bf16.gguf \
  --image image.jpg \
  -p "Describe the image." \
  -ngl 99 \
  -c 4096 \
  -b 1024 \
  -ub 256 \
  --chat-template chatml \
  --image-min-tokens 1024 \
  --flash-attn on

If your llama.cpp llama-server build has multimodal support enabled, the same

mmproj can be passed with --mmproj mmproj-agents-a1-bf16.gguf.

Local smoke test:

| Text GGUF | Image | Prompt | Expected | Answer | Verified |

|---|---|---|---|---|---:|

| agents-a1-Q4_K_M.gguf | llama.cpp tools/mtmd/test-1.jpeg | Look at the newspaper image. What is the main headline? Answer only with the headline text. | MEN WALK ON MOON | MEN WALK ON MOON | true |

Verification report: reports/mmproj-q4km-actual-image-verify.json.

MTP Status

The original upstream snapshot remains config-only for MTP; see

reports/mtp-weights-audit.json. The new *-MTP-graft-headQ6.gguf files are

true integrated MTP GGUFs built from the Agents-A1 MTPLX MTP sidecar.

Provenance

• Base model: InternScience/Agents-A1
• License: Apache-2.0, inherited from the base model
• Quantization source: BF16 GGUF converted from the Hugging Face checkpoint
• MTP source: wang-yang/Agents-A1-MTPLX-Q4 sidecar grafted onto the base Agents-A1 checkpoint
• Calibration: coding/instruction chat data rendered with the model chat template
• Quantizer: patched llama.cpp with Qwen3.5-MoE and NVFP4 support