Instructions to use Voidstackloop/Tessera-125B-MoE with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Voidstackloop/Tessera-125B-MoE 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 Voidstackloop/Tessera-125B-MoE:IQ2_S
# Run inference directly in the terminal:
llama cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S
# Run inference directly in the terminal:
llama cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

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 Voidstackloop/Tessera-125B-MoE:IQ2_S
# Run inference directly in the terminal:
./llama-cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

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 Voidstackloop/Tessera-125B-MoE:IQ2_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

Use Docker

docker model run hf.co/Voidstackloop/Tessera-125B-MoE:IQ2_S

Llama new
LM Studio
Jan
Ollama
How to use Voidstackloop/Tessera-125B-MoE with Ollama:
```
ollama run hf.co/Voidstackloop/Tessera-125B-MoE:IQ2_S
```
Unsloth Desktop

How to use Voidstackloop/Tessera-125B-MoE with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

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": "Voidstackloop/Tessera-125B-MoE:IQ2_S"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Voidstackloop/Tessera-125B-MoE with Docker Model Runner:
```
docker model run hf.co/Voidstackloop/Tessera-125B-MoE:IQ2_S
```

Lemonade

How to use Voidstackloop/Tessera-125B-MoE with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Voidstackloop/Tessera-125B-MoE:IQ2_S

Run and chat with the model

lemonade run user.Tessera-125B-MoE-IQ2_S

List all available models

lemonade list

Hermes Agent

How to use Voidstackloop/Tessera-125B-MoE with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

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 Voidstackloop/Tessera-125B-MoE:IQ2_S

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Voidstackloop/Tessera-125B-MoE with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Voidstackloop/Tessera-125B-MoE:IQ2_S

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 "Voidstackloop/Tessera-125B-MoE:IQ2_S" \
  --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"

Tessera-125B-MoE, 448 experts (EXPERIMENTAL)

Quantized, expert-pruned GGUF of Qwen/Qwen3.8-Flash-Next (architecture qwen4exp: 48 layers, 512 routed experts per layer, 10 active).

Experts: 448 of 512 kept per layer, chosen by routed share on English, code, math and reasoning calibration text. The pruning is a byte slice of the finished quantized tensors (kept experts are bit-identical to the 512-expert file, no requantization); router rows were removed the same way. expert_count = 448, n_expert_used = 10.
Quantization (llama-quantize --pure with a domain importance matrix): gate/up experts IQ2_S, down experts IQ4_NL, body Q6_K, per-layer n-gram table Q4_0 (loaded lazily with -lzm on), shared experts / hc / ple Q8_0.
Qwen3.8-Flash-Next-k448-IQ2_S.gguf is 70.5 GiB; mtp-Qwen3.8-Flash-Next-balanced.gguf is the multi-token-prediction draft head (not pruned), for --spec-type draft-mtp.
SHA256SUMS lists the checksums of both GGUF files.
Needs a llama.cpp build with qwen4exp support (commit 7894e8b or newer).

Evaluation (2026-10-26)

Everything below was run by the author on this exact file with llama.cpp. Each row names the setting; "best" means the best setting we tried for that benchmark, not a cherry-picked run, and every number comes from a single run. Samples are small (60 tasks per benchmark), so a 95% interval is roughly +/-12 points. Per-task tables are in benchmarks/results/, the scripts in benchmarks/scripts/, the full write-up in benchmarks/REPORT.md.

Benchmark	Best result	Setting	Notes
LiveCodeBench v6 (60 of 175 problems, 2025-01 to 2025-04), best	54/60 = 90.0%	thinking, 16,384-token budget + up to 2 repair turns	Chained estimate, not one clean run: 41 passes from the 4k run + 9 rescued by rerunning the 21 failures at 16k + 4 rescued by repair turns. Repair turns show the model the first failing visible sample test (hidden tests only score the result), so this is an agent-style loop, not single-shot.
LiveCodeBench v6, clean single attempt	49/60 = 81.7%	thinking, 16,384-token budget, one attempt, all 60 rerun	The comparable number for single-shot leaderboards. 4k budget: 41/60 (68.3%); no-think: 38/60 (63.3%). 16k vs 4k paired p = 0.021.
CRUXEval (60 tasks)	58/60 = 96.7%	any setting	At its ceiling in every run.
Coding total (LiveCodeBench + CRUXEval, 120 tasks)	112/120 = 93.3% (estimate, with repair turns); clean single attempt 107/120 = 89.2%	thinking, 16k budget (+ repair turns for the 112)	4k: 99/120; no-think: 96/120.
HumanEval+ / MBPP (60 tasks)	55/60 = 91.7%	no-think, greedy	HumanEval+ 22/24, MBPP 33/36.
MATH-500 (45 tasks)	39/45 = 86.7%	no-think, greedy, 2,048-token cap	AIME 0/15 in the same run: every answer hit the token cap, so it is not a measure of ability. Thinking mode on math was only tested on a few failures.
MMLU-Pro (40) + BBH (20)	50/60 = 83.3%	no-think	BBH 20/20, MMLU-Pro 30/40. Thinking with a 4k budget rescued 5 of the 10 failures (about 55/60 as an estimate); a 16k budget rescued 3 of 10.
English (RACE, CoLA, rule-following; 60 tasks)	52/60 = 86.7%	no-think	Quick set; not compared with other models.

Against the unsloth UD-IQ4_XS quant (87 GiB)

No-think greedy on a 120-task suite (HumanEval+, MATH-500, MMLU-Pro; 40 each): Q4 103/120, this model 101/120, paired sign test p = 0.754. It matches Q4 within noise; it does not beat it. Same suite on the same machine: 11.7 vs 8.2 tokens/s, and the file is 17 GiB smaller. The full-precision parent was not run by us; vendor-reported LiveCodeBench v6 for the parent is 91.9.

Speed on an RTX 4060 Laptop (8 GB) with 96 GB RAM, native Windows llama.cpp with a GPU expert cache

About 22 tokens/s decoding short code answers (MTP draft head, or a large GPU expert cache without MTP); 18 to 19 tokens/s with a 128k context window configured; 14 to 16 tokens/s for prose. Prefill about 125 to 165 tokens/s.

Caveats

One run per setting, 60 tasks per benchmark, a few problems of run-to-run noise.
The thinking-budget results are fixed-budget runs; the hard LiveCodeBench problems use the whole budget.
Competitor numbers are not included because we did not run other models ourselves.
This is an experimental, pruned, 2-bit-class quantization: expect weaker results than the full-precision parent on hard tasks.

Downloads last month: 707

GGUF

Model size

162B params

Architecture

qwen4exp

Hardware compatibility

2-bit

View +1 variant

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for Voidstackloop/Tessera-125B-MoE

Base model

Qwen/Qwen3.8-Flash-Next

Quantized

(523)

this model