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"

Voidstackloop commited on 2 days ago

Commit

cc6cff9

verified ·

1 Parent(s): 0598ff3

Update README.md

Browse files

Files changed (1) hide show

README.md +1 -1

README.md CHANGED Viewed

@@ -36,7 +36,7 @@ roughly +/-12 points. Per-task tables are in `benchmarks/results/`, the scripts
 | 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. |
-| Agentic: SWE-bench Verified Mini (20 runnable instances) through Claude Code | **7/20 = 35%** with no web lookups; 15/20 as run | no-think, 65k context | **The 15/20 is not valid**: the sandbox had internet and the agent fetched upstream fixes in 8 of the 15 resolved instances. 7/20 counts only the instances solved without any lookup (a floor). An offline rerun has not been completed. |
 ### 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.

 | 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.