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
- llama.cpp
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
- Pi
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"
File size: 2,895 Bytes
0598ff3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 | id,pass,tokens
lcb_hard_3744,1,4410
lcb_easy_3778,1,458
lcb_easy_abc392_a,1,919
lcb_medium_arc195_a,1,4448
lcb_hard_abc397_g,0,6412
lcb_hard_arc192_e,0,6590
lcb_hard_arc196_d,0,4268
lcb_hard_abc400_e,1,4389
lcb_hard_arc194_d,0,4900
lcb_medium_3779,1,4274
lcb_hard_abc392_f,0,4437
lcb_easy_abc395_a,1,370
lcb_medium_3786,1,4515
lcb_hard_arc195_e,0,4552
lcb_hard_abc398_d,1,4364
lcb_hard_abc398_g,0,4413
lcb_medium_abc392_d,1,4493
lcb_easy_abc393_a,1,779
lcb_hard_abc391_g,1,4675
lcb_hard_abc394_g,0,4923
lcb_medium_3805,0,4727
lcb_hard_abc396_g,1,4706
lcb_easy_3817,1,360
lcb_easy_3832,1,4169
lcb_hard_3777,1,4719
lcb_hard_abc393_d,1,4230
lcb_hard_abc388_g,1,4779
lcb_hard_abc394_f,0,4531
lcb_hard_abc389_f,1,5117
lcb_medium_3677,1,4521
lcb_medium_3722,1,4362
lcb_hard_arc190_c,0,8192
lcb_hard_abc387_f,0,5298
lcb_hard_3783,1,4697
lcb_hard_arc192_d,1,5087
lcb_hard_abc393_e,1,4479
lcb_medium_abc388_c,1,3173
lcb_easy_3753,1,448
lcb_hard_3697,1,4452
lcb_hard_arc196_b,0,5168
lcb_easy_3736,1,772
lcb_hard_3789,1,4593
lcb_medium_arc192_a,0,5127
lcb_medium_abc392_c,1,822
lcb_medium_3785,1,1293
lcb_medium_3737,1,4344
lcb_medium_3748,1,1953
lcb_easy_3731,1,1419
lcb_medium_abc398_b,1,562
lcb_easy_abc400_b,1,1476
lcb_hard_arc193_b,0,4545
lcb_medium_abc399_d,0,4540
lcb_medium_abc400_d,0,4633
lcb_medium_abc399_c,1,1324
lcb_medium_3760,1,4249
lcb_medium_3776,0,5012
lcb_medium_3743,1,4275
lcb_easy_abc396_b,1,510
lcb_easy_3799,1,3001
lcb_hard_3674,0,8192
crux_o_sample_595,1,564
crux_i_sample_123,1,2206
crux_o_sample_32,1,402
crux_i_sample_198,1,982
crux_o_sample_630,1,1180
crux_i_sample_423,1,4149
crux_o_sample_388,1,428
crux_i_sample_379,1,467
crux_o_sample_64,1,838
crux_i_sample_506,1,542
crux_o_sample_560,1,425
crux_i_sample_204,1,541
crux_o_sample_760,1,319
crux_i_sample_308,1,753
crux_o_sample_379,1,421
crux_i_sample_223,1,386
crux_o_sample_184,1,322
crux_i_sample_374,1,654
crux_o_sample_385,1,726
crux_i_sample_11,1,404
crux_o_sample_155,1,1422
crux_i_sample_39,1,346
crux_o_sample_387,1,195
crux_i_sample_544,1,2334
crux_o_sample_445,0,1312
crux_i_sample_257,1,999
crux_o_sample_144,1,164
crux_i_sample_610,1,2645
crux_o_sample_728,1,274
crux_i_sample_490,1,513
crux_o_sample_670,1,694
crux_i_sample_20,1,295
crux_o_sample_694,1,623
crux_i_sample_551,1,940
crux_o_sample_747,1,394
crux_i_sample_260,1,698
crux_o_sample_286,1,795
crux_i_sample_549,1,468
crux_o_sample_650,1,230
crux_i_sample_783,1,673
crux_o_sample_114,1,536
crux_i_sample_457,1,790
crux_o_sample_611,1,287
crux_i_sample_515,1,326
crux_o_sample_405,1,355
crux_i_sample_336,1,1078
crux_o_sample_349,1,867
crux_i_sample_663,1,302
crux_o_sample_599,1,999
crux_i_sample_149,1,1678
crux_o_sample_669,1,647
crux_i_sample_703,1,787
crux_o_sample_136,0,2757
crux_i_sample_608,1,633
crux_o_sample_210,1,379
crux_i_sample_673,1,792
crux_o_sample_682,1,505
crux_i_sample_291,1,1122
crux_o_sample_561,1,282
crux_i_sample_557,1,983
|