Agents-A1 is a 35B-parameter Mixture-of-Experts agentic model from InternScience, post-trained on top of Qwen3.5-35B-A3B via a three-stage paradigm: full-domain SFT → domain-level teacher training → multi-teacher multi-domain on-policy distillation.

Despite operating in the ~35B model class, Agents-A1 delivers highly competitive performance against frontier-scale systems such as GPT-5.5, DeepSeek-V4-pro, and Kimi-K2.6 — achieving SOTA on Seal-0 (56.4), HiPhO (46.4), FrontierScience-Olympiad (79.0), IFBench (80.6), IFEval (94.8), and best-among-comparable on BrowseComp (75.5), XBench-DS-2510 (86.0), GAIA (96.0), SciCode (44.3), HLE (47.6), and MolBench-bind (56.8).

This GGUF package includes the mmproj-F16.gguf vision projector for multimodal (image + text) capabilities with llama.cpp. MTP layers are extracted from Qwen3.5-35B-A3B and injected into Agents-A1's safetensors (see MTP Extraction & Injection section). License: Apache-2.0.

The released InternScience/Agents-A1 checkpoint is a 40-layer Qwen3.5-35B-A3B MoE without MTP (Multi-Token Prediction) layers. To enable MTP acceleration in llama.cpp (which speeds up long-context generation by 10–30%), we extract the 1 MTP layer from Qwen3.5-35B-A3B and inject it into Agents-A1's safetensors before GGUF conversion.

The full pipeline has 4 steps: (1) Extract 785 MTP tensors from Qwen3.5-35B-A3B (filter keys containing "mtp"); (2) append as a new shard model-15-of-15.safetensors and update model.safetensors.index.json's metadata.total_size and weight_map (do not modify the original 14 shards); (3) convert to BF16 GGUF via the master-branch llama.cpp convert_hf_to_gguf.py — the master build auto-detects regular layers blk.0–39 + MTP layer blk.40.nextn.* (785 tensors); (4) quantize with APEX llama-quantize using qwen36_35b_mtp_*.txt configs which already include the blk.40 override (MTP kept at Q8_0 across all tiers — no manual patching needed). The imatrix Qwen3.5-35B-A3B.imatrix.gguf is reused directly (same architecture, compatible weights).

Reproduction command (example: I-Compact tier)

RTX 5070 Ti 16GB + 128GB RAM · No-thinking mode achieves higher ceiling (BugFind +12) but suffers more retries on complex agent scenarios.

llama.cpp (text only)

llama.cpp (vision + text)

vLLM

SGLang

From the official Agents-A1 model card:

These GGUF files are quantized using APEX, an MoE-aware mixed-precision quantization technique. APEX classifies every tensor by its role — routed expert, shared expert, SSM, or attention — and applies a layer-wise precision gradient, giving sensitive edge layers (including the MTP layer) higher precision and compressing redundant middle layers more aggressively.

APEX beats Q8_0 perplexity at half the size — and even beats F16 in some cases.

The qwen36_35b_mtp_*.txt configs include overrides for blk.40 (the MTP layer), preserving it at Q8_0 across all four I- tiers. The same Qwen3.5-35B-A3B.imatrix.gguf is reused (same architecture, compatible MoE expert layout).

BF16 source: Agents-A1-MTP-BF16.gguf (66.19 GB). imatrix: Qwen3.5-35B-A3B.imatrix.gguf (reused from base model).