Instructions to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-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 IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF
# Run inference directly in the terminal:
llama cli -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF
# Run inference directly in the terminal:
./build/bin/llama cli -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Use Docker

docker model run hf.co/IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Llama new
LM Studio
Jan

vLLM

How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-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": "IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Ollama
How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with Ollama:
```
ollama run hf.co/IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF
```
Unsloth Desktop

How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF
```

Lemonade

How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Run and chat with the model

lemonade run user.Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-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 IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

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 IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF

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 "IsValorum/Sage-A1.5-APEX-I-MiniPlus-V2.1-GGUF" \
  --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"

Quick Navigation Index

Independently computed using dedicated cloud compute clusters, funded 100% out-of-pocket.

If you consider these releases to be of high quality and they help you in your day-to-day workflow, consider supporting on Ko-fi. Every contribution directly sustains future cloud compute hours and helps support my university studies.

Model Overview & Technical Specifications
Empirical Importance Matrix Calibration
Bundled Multimodal Vision Projector
Surgical Tensor Quantization Map
Recommended Server & Inference Configuration
Integrated MTP Speculative Decoding
Community Compute Fund & Student Support

1. Model Overview & Technical Specifications

This repository provides Sage-A1.5 APEX-I-MiniPlus V2.1, a surgical high-fidelity quantization of InternScience/Sage-A1.5.

The architecture comprises 40 hybrid layers (linear attention / DeltaNet recurrence interleaved with periodic quadratic attention anchors every 4 layers), 256 routed micro-experts per layer with top-k=8 activation, and an integrated speculative Multi-Token Prediction (MTP) block in blk.40, totaling exactly 753 tensors.

MiniPlus V2.1 is engineered for uncompromising reasoning, coding, and mathematical precision, protecting critical attention paths, routing matrices, and shared foundation experts while compressing routed micro-experts with precision guided by an empirical Importance Matrix.

Key Characteristics

Base Model: InternScience/Sage-A1.5
Architecture: qwen35moe (Hybrid Recurrent + Periodic Full Attention MoE + Integrated MTP)
Total Tensors: 753 (733 main model layers + 20 integrated MTP tensors)
Quantization Profile: APEX-I-MiniPlus V2.1
Calibration Corpus: 1,001,472 tokens (VAL-1M multi-domain corpus)
MoE Expert Coverage: 100.0% (30,720 / 30,720 expert projection matrices observed, 0 unobserved)

2. Empirical Importance Matrix Calibration

Quantization uses the official empirical Importance Matrix Sage-A1.5-VAL-1M-I-Matrix.GGUF from IsValorum/Sage-A1.5-VAL-1M-I-Matrix-GGUF.

Calibration Parameters

Hardware: Dedicated NVIDIA A100 SXM4 80GB GPU
Token Volume: 1,001,472 tokens across 489 sequential chunks (chunk size 2048)
Composition: Code (35.0%), Reasoning & Mathematics (25.0%), Multi-Domain Prose & Natural Languages (25.0%), Tool Use & Structured JSON (15.0%)
Natural Language Diversity: English (134,960 tok), Spanish (97,146 tok), Mandarin Chinese (96,322 tok), French (77,578 tok), German (73,636 tok), Russian (71,940 tok), Japanese (70,894 tok), Portuguese (69,576 tok), Arabic (69,566 tok), Italian (69,456 tok), Korean (69,444 tok)
Programming Languages: Python, JavaScript, TypeScript, C++, Rust, Go, Java, Bash, SQL, JSON Schema
MoE Coverage Metric: 100.0% expert activation across all 40 layers and 256 micro-experts

3. Bundled Multimodal Vision Projector

Sage-A1.5 features multimodal visual reasoning capabilities. For visual inference, the high-precision multimodal projector Sage-A1.5-mmproj.gguf is bundled directly in this repository.

When launching llama-server or CLI tools, include --mmproj Sage-A1.5-mmproj.gguf to enable visual inputs.

4. Surgical Tensor Quantization Map

The exact tensor breakdown below has been verified directly from the compiled binary weights:

Layer Group	Sub-Component / Tensor	Qty	Precision	Engineering Rationale
Global Output Head	`output.weight`	1	`Q6_K`	Preserves near-FP16 token classification; eliminates syntax errors and bracket drops.
Global Embeddings	`token_embd.weight`	1	`Q4_K`	High-fidelity vocabulary embedding representation.
All Normalizations	`output_norm`, `attn_*_norm`, `post_attention_norm`, `ssm_norm`	131	`F32`	100% uncompressed numerical stability across all 40 layers.
Expert Routers	`blk.*.ffn_gate_inp`, `ffn_gate_inp_shexp`	80	`F32`	100% uncompressed routing fidelity across 256 micro-experts; zero router drift.
Attention Gates	`blk.*.attn_gate.weight` (30 Hybrid Layers)	30	`Q8_0`	High-precision attention gating across hybrid DeltaNet recurrence layers; eliminates crosstalk.
Shared Foundation Experts	`blk.*.ffn_{gate,down,up}_shexp` (All 40 Layers)	120	`Q5_K`	Foundation knowledge backbone active on 100% of tokens; protected in high-grade Q5_K.
Periodic Full Attention	`blk.{3,7,11,...}.attn_q/k/v` (10 Anchor Layers)	30	`Q4_K`	Full quadratic attention anchor checkpoints for deep needle-in-a-haystack retrieval.
Periodic Full Attention	`blk.{3,7,11,...}.attn_output` (10 Anchor Layers)	10	`Q6_K`	High-precision attention output projection over deep context.
Recurrent SSM Scales	`blk.*.ssm_alpha`, `ssm_a`, `ssm_conv1d`, `ssm_dt`	120	`F32`	Guarded in uncompressed FP32 to prevent DeltaNet recurrent state drift.
Linear Attention & SSM	`blk.*.attn_qkv`, `ssm_beta`, `ssm_out`	90	`Q3_K`	Fast linear attention execution; balanced memory bandwidth.
Border MoE Experts	Layers 0–9 & 30–39 (`ffn_{gate,down,up}_exps`)	60	`Q3_K`	Edge layer protection for token entry, context establishment, and output convergence.
Core MoE Experts	Layers 10–29 (`ffn_{gate,down,up}_exps`)	60	`IQ3_XXS`	High-density 3.06 bpw matrix compression guided by empirical I-Matrix.
Integrated MTP Block	`blk.40.attn_output` (Speculative Output)	1	`Q6_K`	High-precision output for speculative draft token validation.
Integrated MTP Attention	`blk.40.attn_q/k/v`, `nextn.eh_proj`	4	`Q4_K`	Guarded hidden projection and attention matrices.
Integrated MTP Shared	`blk.40.ffn_{gate,down,up}_shexp`	3	`Q5_K`	Protected shared expert for speculative draft generation.
Integrated MTP Experts	`blk.40.ffn_{gate,down,up}_exps`	3	`Q4_0`	Linear quantization for fast speculative forward passes.
Integrated MTP Norms	`blk.40._norm`, `nextn._norm`, `blk.40.ffn_gate_inp*`	9	`F32`	100% uncompressed numerical stability in speculative routing and RMSNorms.

The complete 100% audited tensor list is attached in tensor-map.tsv in this repository (753 / 753 tensors).

5. Recommended Server & Inference Configuration

Launch llama-server with the following recommended parameters:

llama-server \
  -m Sage-A1.5.APEX-I-MiniPlus-V2.1.gguf \
  --mmproj Sage-A1.5-mmproj.gguf \
  -c 32768 \
  -ngl 99 \
  --threads 16 \
  --port 8080

Recommended Generation Parameters

Temperature: 0.6 - 0.7
Top-P: 0.95
Min-P: 0.05
Repeat Penalty: 1.05

6. Integrated MTP Speculative Decoding

Sage-A1.5 incorporates a native Multi-Token Prediction (MTP) block stored directly in blk.40. This enables speculative self-decoding without requiring an external draft model:

llama-server \
  -m Sage-A1.5.APEX-I-MiniPlus-V2.1.gguf \
  --draft-max 2 \
  -c 32768 \
  -ngl 99

7. Community Compute Fund & Student Support

All IsValorum quantizations will always remain completely free and open to the public without paywalls.

However, cloud GPU compute is expensive and funded entirely out-of-pocket. If you find these handcrafted builds to be of high quality and they benefit your day-to-day workflow, consider supporting future releases on Ko-fi. Every contribution directly funds the cloud compute hours required for new model runs and helps support my university education:

(When supporting on Ko-fi, feel free to leave a note with your Hugging Face handle and the specific model you would like prioritized).