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"

Salih Yılboğa commited on 4 days ago

Commit

557c8eb

verified ·

1 Parent(s): ec0e38d

Add files using upload-large-folder tool

Browse files

Files changed (5) hide show

.gitattributes +2 -0
Qwen3.8-Flash-Next-k448-IQ2_S.gguf +3 -0
README.md +23 -0
SHA256SUMS +2 -0
mtp-Qwen3.8-Flash-Next-balanced.gguf +3 -0

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 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
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README.md ADDED Viewed

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+---
+base_model: Qwen/Qwen3.8-Flash-Next
+library_name: gguf
+tags:
+- gguf
+- moe
+- quantized
+- experimental
+---
+# 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.
+* `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.
+**Status at upload time: quality not measured yet.** Comparisons against the unsloth UD-IQ4_XS quant are being run; do not read anything into
+this file's quality until they are published. Needs a llama.cpp build with qwen4exp support (commit 7894e8b or newer).

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