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: 3,765 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 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
14_msg_proxy.py - a small logging / rewriting proxy between Claude Code and llama-server's /v1/messages endpoint.
python 14_msg_proxy.py listens on 127.0.0.1:18082, forwards to 127.0.0.1:18080, streams the answer back, and appends every
/v1/messages request body to /content/proxy_log.jsonl. fix() is the hook for rewriting a request
(the chat template of Qwen3.8 rejects some shapes that Claude Code sends).
"""
import http.client
import http.server
import json
import time
UP = ("127.0.0.1", 18080)
CRLF = bytes([13, 10])
def fix(d):
"""Qwen3.8's chat template raises 'System message must be at the beginning' for a system-role entry in `messages`, which Claude Code sends
after the user turn. Fold every such entry into the last user message as an extra text block (nothing is dropped)."""
msgs = d.get("messages") or []
keep, extra = [], []
for m in msgs:
if m.get("role") == "system":
c = m.get("content")
text = c if isinstance(c, str) else chr(10).join(b.get("text", "") for b in c if isinstance(b, dict) and b.get("type") == "text")
if text.strip():
extra.append(text)
else:
keep.append(m)
if len(keep) != len(msgs):
block = dict(type="text", text=(chr(10) * 2).join(extra) if extra else "(no content)")
for m in reversed(keep):
if m.get("role") == "user":
c = m.get("content")
m["content"] = ([dict(type="text", text=c)] if isinstance(c, str) else list(c)) + [block]
break
else:
keep.append(dict(role="user", content=[block]))
d["messages"] = keep
return d
class H(http.server.BaseHTTPRequestHandler):
protocol_version = "HTTP/1.1"
def log_message(self, *a):
pass
def handle_any(self):
n = int(self.headers.get("Content-Length") or 0)
body = self.rfile.read(n) if n else b""
if self.path.startswith("/v1/messages") and body:
try:
d = json.loads(body)
with open("/content/proxy_log.jsonl", "a") as f:
f.write(json.dumps(dict(t=time.time(), path=self.path, req=d))[:600000] + "\n")
body = json.dumps(fix(d)).encode()
except Exception as e:
open("/content/proxy_err.log", "a").write(repr(e) + "\n")
c = http.client.HTTPConnection(*UP, timeout=7200)
hdr = dict((k, v) for k, v in self.headers.items() if k.lower() not in ("host", "content-length", "connection", "accept-encoding", "transfer-encoding"))
c.request(self.command, self.path, body, hdr)
r = c.getresponse()
with open("/content/proxy_status.log", "a") as f:
f.write(f"{time.time():.0f} {self.command} {self.path} {r.status}\n")
self.send_response(r.status)
for k, v in r.getheaders():
if k.lower() not in ("transfer-encoding", "connection", "content-length"):
self.send_header(k, v)
self.send_header("Transfer-Encoding", "chunked")
self.end_headers()
while True:
chunk = r.read1(65536)
if not chunk:
break
self.wfile.write(b"%x" % len(chunk) + CRLF + chunk + CRLF)
self.wfile.flush()
self.wfile.write(b"0" + CRLF + CRLF)
self.wfile.flush()
do_GET = do_POST = do_PUT = do_DELETE = handle_any
if __name__ == "__main__":
http.server.ThreadingHTTPServer(("127.0.0.1", 18082), H).serve_forever()
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