Instructions to use IsValorum/T-Search-APEX-I-NanoPlus-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/T-Search-APEX-I-NanoPlus-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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

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 IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

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 IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

Use Docker

docker model run hf.co/IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

LM Studio
Jan

vLLM

How to use IsValorum/T-Search-APEX-I-NanoPlus-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "IsValorum/T-Search-APEX-I-NanoPlus-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/T-Search-APEX-I-NanoPlus-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

Ollama
How to use IsValorum/T-Search-APEX-I-NanoPlus-GGUF with Ollama:
```
ollama run hf.co/IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0
```
Unsloth Desktop

How to use IsValorum/T-Search-APEX-I-NanoPlus-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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use IsValorum/T-Search-APEX-I-NanoPlus-GGUF with Docker Model Runner:
```
docker model run hf.co/IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0
```

Lemonade

How to use IsValorum/T-Search-APEX-I-NanoPlus-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull IsValorum/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

Run and chat with the model

lemonade run user.T-Search-APEX-I-NanoPlus-GGUF-Q8_0

List all available models

lemonade list

Hermes Agent

How to use IsValorum/T-Search-APEX-I-NanoPlus-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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use IsValorum/T-Search-APEX-I-NanoPlus-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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0

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/T-Search-APEX-I-NanoPlus-GGUF:Q8_0" \
  --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"

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.

T-Search APEX-I-NanoPlus GGUF

Surgical Tensor Quantization Map (Audited from GGUF)

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, bracket drops, and hallucinations.
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	`Q4_K`	Foundation knowledge backbone active on 100% of tokens; protected in linear Q4_K for fast streaming.
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	`Q4_K`	Linear AVX2 execution; zero SIMD CPU stalls during system RAM streaming.
Border MoE Down-Proj	Layers 0–1 & 38–39 (`ffn_down_exps`)	4	`Q3_K`	Linear SIMD execution optimized for token entry and exit stability.
Border MoE Gate/Up	Layers 0–1 & 38–39 (`ffn_gate/up_exps`)	8	`IQ3_XXS`	High-density boundary protection guided by imatrix.
Core MoE Down-Proj	Layers 2–37 (`ffn_down_exps`)	36	`IQ3_XXS`	Fortified 3.06 bpw residual stream; preserves core mathematical, coding, and reasoning capacity.
Core MoE Gating	Layers 2–37 (`ffn_gate_exps`)	36	`IQ2_S`	High-precision 2.50 bpw SwiGLU gating; eliminates activation noise.
Core MoE Up-Proj	Layers 2–15 (`ffn_up_exps`)	14	`IQ2_S`	Enhanced 2.50 bpw precision for sensitive early-intermediate feature extraction.
Core MoE Up-Proj	Layers 16–37 (`ffn_up_exps`)	22	`IQ2_XXS`	Extreme 2.06 bpw compression in deep MoE layers to reach exact 12.55 GB envelope.
Integrated MTP Block	`blk.40.attn_output` (MTP Speculative Output)	1	`Q6_K`	High-precision output for speculative draft token validation.
Integrated MTP Block	`blk.40.nextn.eh_proj`, `blk.40.attn_q/k/v`	4	`Q4_K`	Guarded hidden projection and attention matrices.
Integrated MTP Block	`blk.40.ffn_down/gate/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*`	12	`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).

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, you can support future Day-0 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).