Instructions to use SC117/Agents-A1-MTP-APEX-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use SC117/Agents-A1-MTP-APEX-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="SC117/Agents-A1-MTP-APEX-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("SC117/Agents-A1-MTP-APEX-GGUF", dtype="auto")

llama-cpp-python

How to use SC117/Agents-A1-MTP-APEX-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="SC117/Agents-A1-MTP-APEX-GGUF",
	filename="Agents-A1-MTP-APEX-I-Balanced.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use SC117/Agents-A1-MTP-APEX-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16
# Run inference directly in the terminal:
llama cli -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16
# Run inference directly in the terminal:
./llama-cli -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Use Docker

docker model run hf.co/SC117/Agents-A1-MTP-APEX-GGUF:BF16

LM Studio
Jan

vLLM

How to use SC117/Agents-A1-MTP-APEX-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "SC117/Agents-A1-MTP-APEX-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Agents-A1-MTP-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/SC117/Agents-A1-MTP-APEX-GGUF:BF16

SGLang

How to use SC117/Agents-A1-MTP-APEX-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "SC117/Agents-A1-MTP-APEX-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Agents-A1-MTP-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "SC117/Agents-A1-MTP-APEX-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "SC117/Agents-A1-MTP-APEX-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use SC117/Agents-A1-MTP-APEX-GGUF with Ollama:
```
ollama run hf.co/SC117/Agents-A1-MTP-APEX-GGUF:BF16
```

Unsloth Studio

How to use SC117/Agents-A1-MTP-APEX-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SC117/Agents-A1-MTP-APEX-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for SC117/Agents-A1-MTP-APEX-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for SC117/Agents-A1-MTP-APEX-GGUF to start chatting

How to use SC117/Agents-A1-MTP-APEX-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "SC117/Agents-A1-MTP-APEX-GGUF:BF16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use SC117/Agents-A1-MTP-APEX-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default SC117/Agents-A1-MTP-APEX-GGUF:BF16

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use SC117/Agents-A1-MTP-APEX-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf SC117/Agents-A1-MTP-APEX-GGUF:BF16

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "SC117/Agents-A1-MTP-APEX-GGUF:BF16" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use SC117/Agents-A1-MTP-APEX-GGUF with Docker Model Runner:
```
docker model run hf.co/SC117/Agents-A1-MTP-APEX-GGUF:BF16
```

Lemonade

How to use SC117/Agents-A1-MTP-APEX-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull SC117/Agents-A1-MTP-APEX-GGUF:BF16

Run and chat with the model

lemonade run user.Agents-A1-MTP-APEX-GGUF-BF16

List all available models

lemonade list

Agents-A1-MTP-APEX-GGUF / README.md

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	---
	library_name: transformers
	license: apache-2.0
	license_link: https://huggingface.co/InternScience/Agents-A1/blob/main/LICENSE
	pipeline_tag: text-generation
	tags:
	- qwen3_5_moe
	- qwen3_5
	- reasoning
	- agentic
	- mtp
	- apex
	- quantization
	- gguf
	- multimodal
	base_model:
	- InternScience/Agents-A1
	---
	<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; margin-bottom: 24px;">
	<div style="background: #f5f5f7; border-radius: 20px; padding: 36px 32px; margin-bottom: 20px; text-align: center; position: relative; overflow: hidden;">
	<div style="position: absolute; top: -30px; right: -30px; width: 120px; height: 120px; background: #bbf7d0; border-radius: 50%;"></div>
	<div style="position: absolute; bottom: -20px; left: 40px; width: 80px; height: 80px; background: #86efac; border-radius: 50%;"></div>
	<div style="position: absolute; top: 50%; left: -15px; width: 60px; height: 60px; background: #bbf7d0; border-radius: 50%;"></div>
	<div style="display: inline-flex; gap: 8px; margin-bottom: 16px; position: relative; z-index: 1;">
	<span style="background: #10b981; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">APEX</span>
	<span style="background: #6366f1; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">MTP</span>
	<span style="background: #0ea5e9; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">Vision</span>
	<span style="background: #f97316; color: white; font-size: 11px; font-weight: 600; padding: 5px 14px; border-radius: 20px;">Apache-2.0</span>
	</div>
	<h1 style="margin: 0 0 8px 0; font-size: 32px; font-weight: 700; color: #064e3b; letter-spacing: -0.5px; border: none; position: relative; z-index: 1;">Agents-A1-MTP-APEX</h1>
	<p style="margin: 8px 0 0 0; font-size: 14px; position: relative; z-index: 1;"><span style="color: #6b7280;">English</span> \| <a href="https://huggingface.co/SC117/Agents-A1-MTP-APEX-GGUF/blob/main/README_zh.md" style="color: #10b981; text-decoration: none;">📖 中文文档</a></p>
	<p style="margin: 0; font-size: 15px; color: #6b7280; position: relative; z-index: 1;">35B agentic MoE that reaches trillion-parameter performance · APEX-quantized GGUFs + BF16 + mmproj</p>
	</div>
	</div>

	<div style="font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', Roboto, sans-serif; display: flex; flex-direction: column; gap: 20px; margin-bottom: 30px;">

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🤖</span> About Agents-A1</div>
	<div style="padding: 16px; font-size: 13px; color: #334155; line-height: 1.7;">
	<p style="margin: 0 0 12px 0;"><a href="https://huggingface.co/InternScience/Agents-A1" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">Agents-A1</a> is a 35B-parameter Mixture-of-Experts <b>agentic model</b> from <a href="https://huggingface.co/InternScience" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">InternScience</a>, post-trained on top of <a href="https://huggingface.co/Qwen/Qwen3.5-35B-A3B" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">Qwen3.5-35B-A3B</a> via a three-stage paradigm: full-domain SFT → domain-level teacher training → multi-teacher multi-domain on-policy distillation.</p>
	<p style="margin: 0 0 12px 0;">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).</p>
	<p style="margin: 0;">This GGUF package includes the <b>mmproj-F16.gguf</b> vision projector for multimodal (image + text) capabilities with llama.cpp. MTP layers are extracted from <a href="https://huggingface.co/Qwen/Qwen3.5-35B-A3B" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">Qwen3.5-35B-A3B</a> and injected into Agents-A1's safetensors (see <b>MTP Extraction & Injection</b> section). <b>License: Apache-2.0.</b></p>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🧠</span> Model Details</div>
	<div style="padding: 16px;">
	<table style="width: 100%; border-collapse: collapse; font-size: 13px;"><tbody>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Architecture</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">Qwen3.5 MoE (Mixture of Experts)</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Parameters</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">35B total, 3B active per token</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Experts</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">256 routed experts, 8 active per token</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Layers</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">40 transformer layers + 1 MTP layer</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Context</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">262,144 tokens</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">MTP Source</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">Qwen3.5-35B-A3B (1 layer, 785 tensors, injected)</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Block Count</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white;">41 (blk.0–39 + blk.40 MTP)</td></tr>
	<tr><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">License</td><td style="padding: 6px 10px; color: #4b5563; background: white;">Apache-2.0</td></tr>
	</tbody></table>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🔧</span> MTP Extraction & Injection</div>
	<div style="padding: 16px; font-size: 13px; color: #334155; line-height: 1.7;">
	<p style="margin: 0 0 12px 0;">The released <a href="https://huggingface.co/InternScience/Agents-A1" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">InternScience/Agents-A1</a> checkpoint is a <b>40-layer Qwen3.5-35B-A3B MoE</b> without MTP (Multi-Token Prediction) layers. To enable MTP acceleration in llama.cpp (which speeds up long-context generation by 10–30%), we <b>extract the 1 MTP layer from Qwen3.5-35B-A3B</b> and inject it into Agents-A1's safetensors before GGUF conversion.</p>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">Step 1 — Extract MTP tensors from Qwen3.5-35B-A3B</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre;">Source: J:\Models\Qwen3.5-35B-A3B-MTP (Qwen3.5-35B-A3B + native MTP)
	from safetensors import safe_open
	import json, os
	·
	src = r"J:\Models\Qwen3.5-35B-A3B-MTP"
	with open(os.path.join(src, "model.safetensors.index.json")) as f:
	idx = json.load(f)
	mtp_keys = [k for k in idx["weight_map"] if "mtp" in k.lower()]
	print(f"Found {len(mtp_keys)} MTP tensors") # 785</pre>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">Step 2 — Add as a new safetensors shard (N+1)</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre;">Save 785 MTP tensors as a new shard
	new_shard = "model.safetensors-15-of-15.safetensors"
	save_file({k: get_tensor(k) for k in mtp_keys}, new_shard)
	·
	Update model.safetensors.index.json:
	· metadata.total_size += new_shard_size
	· weight_map: append new_shard path for each MTP key
	· DO NOT modify existing 14 shards (avoid touching original data)</pre>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">Step 3 — Convert HF → BF16 GGUF with master llama.cpp</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre;">F:\llama.cpp\llama.cpp-master\convert_hf_to_gguf.py ^
	J:\Models\Agents-A1 ^
	--outfile J:\Models\Agents-A1-MTP-GGUF\Agents-A1-MTP-BF16.gguf ^
	--outtype f16
	·
	Master version handles Qwen3.5MoE with MTP auto:
	· Normal layers: blk.0–39
	· MTP layer: blk.40.nextn.* (785 tensors)</pre>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">Step 4 — Quantize with APEX (Q4_K_M default, MTP at Q8_0)</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre;">F:\llama.cpp\...\llama-quantize.exe ^
	--imatrix J:\Models\Qwen3.5-35B-A3B.imatrix.gguf ^
	--tensor-type-file E:\apex-quant\configs\qwen36_35b_mtp_<tier>.txt ^
	J:\Models\Agents-A1-MTP-GGUF\Agents-A1-MTP-BF16.gguf ^
	J:\Models\Agents-A1-MTP-GGUF\Agents-A1-MTP-APEX-I-<tier>.gguf ^
	Q4_K_M
	·
	APEX qwen36_35b_mtp_*.txt configs include blk.40 overrides
	(Q8_0 for MTP across all tiers) — no manual patching needed.</pre>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>📊</span> BenchLocal Results (APEX-I-Compact, 16.14 GB)</div>
	<div style="padding: 16px;">
	<table style="width: 100%; border-collapse: collapse; font-size: 13px;"><thead><tr style="background: rgba(16,185,129,0.05);"><th style="padding: 7px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold;">Mode</th><th style="padding: 7px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold;">ToolCall-15</th><th style="padding: 7px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold;">BugFind-15</th><th style="padding: 7px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold;">HermesAgent-20</th><th style="padding: 7px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold;">Max</th><th style="padding: 7px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold;">Eff.</th></tr></thead><tbody>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">Thinking</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">100</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">88</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">87</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #047857; background: white;">91.2</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">71.2</td></tr>
	<tr><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">No Thinking</td><td style="padding: 6px 10px; color: #334155; background: white;">97</td><td style="padding: 6px 10px; color: #334155; background: white;">100</td><td style="padding: 6px 10px; color: #334155; background: white;">85</td><td style="padding: 6px 10px; font-weight: bold; color: #047857; background: white;">93.1</td><td style="padding: 6px 10px; color: #334155; background: white;">57.1</td></tr>
	</tbody></table>
	<p style="margin: 12px 0 0 0; font-size: 12px; color: #64748b; font-style: italic;">RTX 5070 Ti 16GB + 128GB RAM · No-thinking mode achieves higher ceiling (BugFind +12) but suffers more retries on complex agent scenarios.</p>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🚀</span> Usage</div>
	<div style="padding: 16px; font-size: 13px; color: #334155; line-height: 1.7;">
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">llama.cpp (text only)</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre-wrap;">hf download SC117/Agents-A1-MTP-APEX-GGUF --include "*.gguf" --local-dir ./models
	./llama-server -m ./models/Agents-A1-MTP-APEX-I-Compact.gguf -ngl 99 -c 131072</pre>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">llama.cpp (vision + text)</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre-wrap;">./llama-server -m ./models/Agents-A1-MTP-APEX-I-Compact.gguf --mmproj ./models/mmproj-F16.gguf -ngl 99 -c 131072</pre>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">vLLM</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre-wrap;">vllm serve SC117/Agents-A1-MTP-APEX-GGUF --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3
	·
	Tool-call variant
	vllm serve SC117/Agents-A1-MTP-APEX-GGUF --port 8000 --tensor-parallel-size 1 --max-model-len 262144 --reasoning-parser qwen3 --enable-auto-tool-choice --tool-call-parser qwen3_coder</pre>
	<p style="margin: 0 0 8px 0; font-weight: bold; color: #064e3b;">SGLang</p>
	<pre style="margin: 0; font-family: monospace; background: #f8fafc; padding: 10px 14px; border-radius: 6px; border: 1px solid #e2e8f0; font-size: 12px; color: #1e293b; white-space: pre-wrap;">python3 -m sglang.launch_server --model-path "SC117/Agents-A1-MTP-APEX-GGUF" --host 0.0.0.0 --port 30000</pre>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>🎛️</span> Recommended Sampling Parameters</div>
	<div style="padding: 16px;">
	<p style="margin: 0 0 12px 0; font-size: 13px; color: #334155;">From the <a href="https://huggingface.co/InternScience/Agents-A1#recommended-sampling-parameters" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">official Agents-A1 model card</a>:</p>
	<table style="width: 100%; border-collapse: collapse; font-size: 13px;"><tbody>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">temperature</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white; font-family: monospace;">0.85</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">top_p</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white; font-family: monospace;">0.95</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">top_k</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white; font-family: monospace;">20</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">min_p</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white; font-family: monospace;">0.0</td></tr>
	<tr><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); font-weight: bold; color: #334155; background: white;">presence_penalty</td><td style="padding: 6px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #4b5563; background: white; font-family: monospace;">1.1</td></tr>
	<tr><td style="padding: 6px 10px; font-weight: bold; color: #334155; background: white;">repetition_penalty</td><td style="padding: 6px 10px; color: #4b5563; font-family: monospace; background: white;">1.0</td></tr>
	</tbody></table>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>💡</span> What is APEX?</div>
	<div style="padding: 16px;">
	<p style="margin: 0 0 12px 0; font-size: 13px; color: #334155; line-height: 1.7;">These GGUF files are quantized using <a href="https://github.com/mudler/apex-quant" target="_blank" style="color: #047857; text-decoration: none; font-weight: 700;">APEX</a>, 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.</p>
	<p style="margin: 0; font-size: 13px; color: #334155; line-height: 1.7; font-weight: 700;">APEX beats Q8_0 perplexity at half the size — and even beats F16 in some cases.</p>
	<p style="margin: 12px 0 0 0; font-size: 13px; color: #334155;">The <code>qwen36_35b_mtp_*.txt</code> configs include overrides for <b>blk.40</b> (the MTP layer), preserving it at Q8_0 across all four I- tiers. The same <code>Qwen3.5-35B-A3B.imatrix.gguf</code> is reused (same architecture, compatible MoE expert layout).</p>
	</div>
	</div>

	<div style="border: 1px solid #cbd5e1; border-radius: 12px; overflow: hidden; background: #ffffff; box-shadow: 0 2px 4px rgba(0,0,0,0.02);">
	<div style="background: linear-gradient(135deg, #10b981 0%, #059669 100%); padding: 12px 16px; color: white; font-weight: 700; font-size: 14px; display: flex; align-items: center; gap: 8px;"><span>📦</span> APEX Quantization Tiers</div>
	<div style="padding: 16px;">
	<table style="width: 100%; border-collapse: collapse; font-size: 13px;"><thead><tr style="background: rgba(16,185,129,0.05);"><th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold; width: 35%;">File</th><th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold; width: 15%;">Size</th><th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold; width: 15%;">Profile</th><th style="padding: 8px 10px; border-bottom: 2px solid #10b981; text-align: left; color: #047857; font-weight: bold; width: 35%;">Best For</th></tr></thead><tbody>
	<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;"><code>*-APEX-I-Quality.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">21.75 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">I-Quality</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">High quality (Q6_K + iq4_xs attention)</td></tr>
	<tr><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;"><code>*-APEX-I-Balanced.gguf</code></td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">24.21 GB</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">I-Balanced</td><td style="padding: 8px 10px; box-shadow: 0 1px 0 0 rgba(128,128,128,0.15); color: #334155; background: white;">Best all-rounder (Q6_K + Q5_K experts)</td></tr>
	<tr><td style="padding: 8px 10px; color: #334155;"><code>*-APEX-I-Compact.gguf</code></td><td style="padding: 8px 10px; color: #334155;">16.14 GB</td><td style="padding: 8px 10px; color: #334155;">I-Compact</td><td style="padding: 8px 10px; color: #334155;">Best quality/size ratio (Q4_K default)</td></tr>
	<tr><td style="padding: 8px 10px; color: #334155;"><code>*-APEX-I-Mini.gguf</code></td><td style="padding: 8px 10px; color: #334155;">13.36 GB</td><td style="padding: 8px 10px; color: #334155;">I-Mini</td><td style="padding: 8px 10px; color: #334155;">Most compact, fits in 16GB VRAM (Q3_K + iq2_s)</td></tr>
	</tbody></table>
	<p style="margin: 12px 0 0 0; font-size: 12px; color: #64748b; font-style: italic;">BF16 source: <code>Agents-A1-MTP-BF16.gguf</code> (66.19 GB). imatrix: <code>Qwen3.5-35B-A3B.imatrix.gguf</code> (reused from base model).</p>
	</div>
	</div>

	</div>

	## Links

	- Original Model: https://huggingface.co/InternScience/Agents-A1
	- Base Model (MTP source): https://huggingface.co/Qwen/Qwen3.5-35B-A3B
	- Paper: https://arxiv.org/abs/2606.30616
	- APEX Quantization: https://github.com/mudler/apex-quant
	- BenchLocal Results: https://scorp1o117.github.io/benchlocal-results/

	## Citation

	```bibtex
	@misc{bai2026scalinghorizonparametersreaching,
	title={Scaling the Horizon, Not the Parameters: Reaching Trillion-Parameter Performance with a 35B Agent},
	author={Lei Bai and Zongsheng Cao and Yang Chen and Zhiyao Cui and Shangheng Du and Yue Fan and Shiyang Feng and Zijie Guo and Haonan He and Liang He and Xiaohan He and Shuyue Hu and Yusong Hu and Songtao Huang and Yichen Jiang and Hao Li and Xin Li and Dahua Lin and Weihao Lin and Fenghua Ling and Dongrui Liu and Zhuo Liu and Runmin Ma and Chunjiang Mu and others},
	year={2026},
	eprint={2606.30616},
	archivePrefix={arXiv}
	}
	```