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"
Download benchmarks/scripts/13_swe_native.py from Voidstackloop/Tessera-125B-MoE: direct link, hf CLI and curl.
- Browser
- Download file 15.4 kB
-
https://huggingface.co/Voidstackloop/Tessera-125B-MoE/resolve/main/benchmarks/scripts/13_swe_native.py
- Command line
-
hf download hf://Voidstackloop/Tessera-125B-MoE/benchmarks/scripts/13_swe_native.py
-
curl -L -o 13_swe_native.py https://huggingface.co/Voidstackloop/Tessera-125B-MoE/resolve/main/benchmarks/scripts/13_swe_native.py
15.4 kB
| #!/usr/bin/env python3 | |
| # -*- coding: utf-8 -*- | |
| """ | |
| 13_swe_native.py - SWE-bench Verified (Mini) WITHOUT Docker or conda, on a plain Linux VM, driving the real Claude Code CLI. | |
| It reuses swebench 4.0.4's own per-repo specs, setup/eval scripts and log parsers (pip install --no-deps --target /content/sb4 swebench==4.0.4), | |
| translating the conda lines to `uv venv` environments, one /testbed checkout at a time. | |
| python 13_swe_native.py list the instances of SWE-bench Verified Mini with their repo / version | |
| python 13_swe_native.py gold INSTANCE_ID build the environment and check that the GOLD patch resolves the instance | |
| python 13_swe_native.py agent --ids a,b,c --out results_swe_k448.json --endpoint http://127.0.0.1:18080 --model k448 --user agent | |
| [--max-turns 60] [--timeout 2700] Claude Code solves each instance, the patch is graded by the official parsers | |
| A resolved instance = every FAIL_TO_PASS test passes and no PASS_TO_PASS test breaks (the official SWE-bench definition). THE MODEL'S COMMANDS | |
| AND THE REPOSITORIES' TESTS ARE EXECUTED: use a disposable VM; the agent runs as a separate unix user. | |
| """ | |
| import argparse | |
| import json | |
| import os | |
| import re | |
| import shlex | |
| import shutil | |
| import subprocess | |
| import sys | |
| import time | |
| from pathlib import Path | |
| sys.path.insert(0, "/content/sb4") | |
| ENVS, TESTBED, WORK = Path("/content/sb_envs"), Path("/testbed"), Path("/content/sb_work") | |
| CONSTRAINTS = Path("/content/sb_constraints.txt") | |
| PY_MAP = {"3.5": "3.8", "3.6": "3.8", "3.7": "3.8"} # uv offers CPython 3.8+ ; these old pins run fine on 3.8 | |
| def rows(): | |
| from datasets import load_dataset | |
| return [dict(r) for r in load_dataset("MariusHobbhahn/swe-bench-verified-mini", split="test")] | |
| def get(iid): | |
| for r in rows(): | |
| if r["instance_id"] == iid: | |
| return r | |
| sys.exit(f"unknown instance {iid}") | |
| def test_spec(inst): | |
| from swebench.harness.test_spec.test_spec import make_test_spec | |
| return make_test_spec(inst) | |
| def env_dir(inst): | |
| return ENVS / f"{inst['repo'].replace('/', '__')}__{inst['version']}__v4" | |
| def adapt(script, venv, py): | |
| """conda lines -> uv venv lines (the scripts are plain bash otherwise).""" | |
| s = script.replace("source /opt/miniconda3/bin/activate\n", "") | |
| s = re.sub(r"conda create -n testbed python=([\d.]+)\s+-y", lambda m: f"uv venv --seed --python {PY_MAP.get(m.group(1), m.group(1))} {venv} && {venv}/bin/python -m pip install 'setuptools<70'", s) | |
| s = s.replace("conda activate testbed", f"source {venv}/bin/activate") | |
| s = s.replace('echo "Current environment: $CONDA_DEFAULT_ENV"', "true") | |
| # sphinx: tox would build its own interpreter without pytest here; run what its tox.ini runs, directly in the venv | |
| s = re.sub(r"tox --current-env -e\S+ -v -- ", "PYTHONWARNINGS=all python -X dev -m pytest --durations 25 -rA ", s) | |
| return s | |
| def bash(script, log, timeout, env=None): | |
| Path(log).parent.mkdir(parents=True, exist_ok=True) | |
| f = Path(log).with_suffix(".sh") | |
| f.write_text(script, encoding="utf-8") | |
| try: | |
| r = subprocess.run(["bash", str(f)], stdout=subprocess.PIPE, stderr=subprocess.STDOUT, text=True, timeout=timeout, errors="replace", | |
| env={**os.environ, "PIP_CONSTRAINT": str(CONSTRAINTS), **(env or {})}) # one stream, in order: the grader reads the output between the START/END markers | |
| out = r.stdout | |
| code = r.returncode | |
| except subprocess.TimeoutExpired as e: | |
| out = ((e.stdout or b"").decode("utf-8", "replace") if isinstance(e.stdout, bytes) else (e.stdout or "")) + "\n[timeout]" | |
| code = 124 | |
| Path(log).write_text(out, encoding="utf-8") | |
| return code, out | |
| def prepare(inst): | |
| """Environment (cached per repo+version) and a fresh /testbed at the base commit with the repo installed.""" | |
| ts = test_spec(inst) | |
| venv = env_dir(inst) | |
| py = re.search(r"python=([\d.]+)", ts.setup_env_script) | |
| py = PY_MAP.get(py.group(1), py.group(1)) if py else "3.9" | |
| if not (venv / ".done").exists(): | |
| shutil.rmtree(venv, ignore_errors=True) | |
| ENVS.mkdir(parents=True, exist_ok=True) | |
| code, out = bash(adapt(ts.setup_env_script, venv, py), WORK / "logs" / f"{venv.name}_env.log", 3000) | |
| if code != 0 or not (venv / "bin" / "python").exists(): | |
| raise RuntimeError(f"environment build failed (exit {code}): {out[-600:]}") | |
| (venv / ".done").write_text("ok") | |
| shutil.rmtree(TESTBED, ignore_errors=True) | |
| code, out = bash(adapt(ts.install_repo_script, venv, py), WORK / "logs" / f"{inst['instance_id']}_repo.log", 2400) | |
| if code != 0 or not (TESTBED / ".git").exists(): | |
| raise RuntimeError(f"repo install failed (exit {code}): {out[-600:]}") | |
| return ts, venv | |
| def alias_docstring_lines(out): | |
| """Python 3.8 prints `test_x (mod.Class)` and the docstring line separately; add the one-line form that the dataset's test names use.""" | |
| res, prev = [], None | |
| for line in out.split("\n"): | |
| res.append(line) | |
| t = line.strip() | |
| m = re.match(r"^(test\S* \([\w.]+\))$", t) | |
| if m: | |
| prev = m.group(1) | |
| elif prev and " ... " in t: | |
| res.append(f"{prev} ... {t.rsplit(' ... ', 1)[1]}") | |
| prev = None | |
| else: | |
| prev = None | |
| return "\n".join(res) | |
| def evaluate(inst, ts, venv, patch): | |
| """Fresh checkout, apply the model patch, run the official eval script, grade with the official parser. -> (resolved, note)""" | |
| from swebench.harness.grading import get_eval_report | |
| py = re.search(r"python=([\d.]+)", ts.setup_env_script) | |
| py = PY_MAP.get(py.group(1), py.group(1)) if py else "3.9" | |
| subprocess.run("git reset --hard -q && git clean -fdq", shell=True, cwd=TESTBED) | |
| if not patch.strip(): | |
| return False, "empty patch" | |
| pf = WORK / f"{inst['instance_id']}.patch" | |
| pf.write_text(patch if patch.endswith("\n") else patch + "\n", encoding="utf-8") | |
| r = subprocess.run(["git", "apply", "-v", str(pf)], cwd=TESTBED, capture_output=True, text=True) | |
| if r.returncode != 0: | |
| return False, "patch does not apply: " + (r.stdout + r.stderr)[-200:] | |
| code, out = bash(adapt(ts.eval_script, venv, py), WORK / "logs" / f"{inst['instance_id']}_eval.log", 2700) | |
| logp = WORK / "logs" / f"{inst['instance_id']}_report.log" | |
| logp.write_text(">>>>> Applied Patch\n" + alias_docstring_lines(out), encoding="utf-8") | |
| try: | |
| rep = get_eval_report(test_spec=ts, prediction={"instance_id": inst["instance_id"], "model_name_or_path": "k448", "model_patch": patch}, | |
| test_log_path=str(logp), include_tests_status=True)[inst["instance_id"]] | |
| except Exception as e: | |
| return False, f"grading error: {e!r}" | |
| ts_ = rep.get("tests_status", {}) | |
| f2p, p2p = ts_.get("FAIL_TO_PASS", {}), ts_.get("PASS_TO_PASS", {}) | |
| note = f"F2P {len(f2p.get('success', []))}/{len(f2p.get('success', [])) + len(f2p.get('failure', []))}, P2P ok {len(p2p.get('success', []))} broken {len(p2p.get('failure', []))}" | |
| return bool(rep.get("resolved")), note | |
| def cmd_goldall(a): | |
| """Gold-patch check of every instance: the ones that resolve are the runnable benchmark.""" | |
| out = Path(a.out) | |
| res = json.loads(out.read_text(encoding="utf-8")) if out.exists() else {} | |
| for inst in rows(): | |
| iid = inst["instance_id"] | |
| if iid in res: | |
| continue | |
| t0 = time.time() | |
| try: | |
| ts, venv = prepare(inst) | |
| ok, note = evaluate(inst, ts, venv, inst["patch"]) | |
| except Exception as e: | |
| ok, note = False, f"error: {e!r}"[:300] | |
| res[iid] = {"ok": ok, "note": note, "secs": round(time.time() - t0)} | |
| out.write_text(json.dumps(res, indent=1), encoding="utf-8") | |
| print(f"{iid:36s} gold_resolved={ok} {note[:100]} ({time.time() - t0:.0f} s)", flush=True) | |
| print(f"runnable {sum(v['ok'] for v in res.values())}/{len(res)}") | |
| def cmd_list(a): | |
| for r in rows(): | |
| print(f"{r['instance_id']:36s} {r['repo']:20s} {r['version']:5s} F2P={len(json.loads(r['FAIL_TO_PASS']) if isinstance(r['FAIL_TO_PASS'], str) else r['FAIL_TO_PASS'])}") | |
| def cmd_gold(a): | |
| inst = get(a.instance) | |
| t0 = time.time() | |
| ts, venv = prepare(inst) | |
| print(f"prepared in {time.time() - t0:.0f} s", flush=True) | |
| ok, note = evaluate(inst, ts, venv, inst["patch"]) | |
| print(f"GOLD {inst['instance_id']}: resolved={ok} {note} ({time.time() - t0:.0f} s)") | |
| return 0 if ok else 1 | |
| def agent_prompt(inst): | |
| return (f"<uploaded_files>\n/testbed\n</uploaded_files>\nI've uploaded a python code repository in the directory /testbed. Consider the following issue " | |
| f"description:\n\n<issue>\n{inst['problem_statement']}\n</issue>\n\nCan you help me implement the necessary changes to the repository so that the " | |
| "requirements specified in the issue are met? I've already taken care of all changes to any of the test files described in the issue, so you do NOT " | |
| "need to modify the testing logic or any of the tests in any way. Make the minimal changes to non-test files in /testbed so that the issue is resolved. " | |
| "You can run the existing tests to check your work. When you are done, stop.") | |
| def parse_stream(text): | |
| calls = errs = 0 | |
| last, final = "", {} | |
| for line in text.splitlines(): | |
| if not line.startswith("{"): | |
| continue | |
| try: | |
| ev = json.loads(line) | |
| except ValueError: | |
| continue | |
| t = ev.get("type") | |
| if t == "assistant": | |
| for b in (ev.get("message", {}).get("content") or []): | |
| if b.get("type") == "tool_use": | |
| calls += 1 | |
| elif b.get("type") == "text" and b.get("text", "").strip(): | |
| last = b["text"].strip() | |
| elif t == "user": | |
| c = ev.get("message", {}).get("content") | |
| if isinstance(c, list): | |
| errs += sum(1 for b in c if isinstance(b, dict) and b.get("type") == "tool_result" and b.get("is_error")) | |
| elif t == "result": | |
| final = ev | |
| u = final.get("usage") or {} | |
| return {"turns": final.get("num_turns", 0), "tool_calls": calls, "tool_errors": errs, "out_tok": u.get("output_tokens", 0), | |
| "in_tok": u.get("input_tokens", 0) + u.get("cache_read_input_tokens", 0) + u.get("cache_creation_input_tokens", 0), | |
| "stop": final.get("subtype") or ("no_result" if not final else "?"), "last": last[-300:]} | |
| def cmd_agent(a): | |
| ids = a.ids.split(",") if a.ids else [r["instance_id"] for r in rows()] | |
| out = Path(a.out) | |
| res = json.loads(out.read_text(encoding="utf-8")).get("results", {}) if out.exists() else {} | |
| logs = Path(str(out.with_suffix("")) + "_logs") | |
| logs.mkdir(parents=True, exist_ok=True) | |
| home = WORK / "_home" | |
| home.mkdir(parents=True, exist_ok=True) | |
| t0 = time.time() | |
| for k, iid in enumerate(ids, 1): | |
| if iid in res: | |
| continue | |
| inst = get(iid) | |
| s0 = time.time() | |
| info = {"repo": inst["repo"], "version": inst["version"]} | |
| try: | |
| ts, venv = prepare(inst) | |
| subprocess.run(["chown", "-R", f"{a.user}:{a.user}", str(TESTBED), str(home)], check=True) | |
| env = {"PATH": f"{venv}/bin:" + os.environ.get("PATH", ""), "VIRTUAL_ENV": str(venv), "HOME": str(home), "LANG": "C.UTF-8", | |
| "ANTHROPIC_BASE_URL": a.endpoint, "ANTHROPIC_AUTH_TOKEN": "local", "ANTHROPIC_API_KEY": "local", "ANTHROPIC_MODEL": a.model, | |
| "ANTHROPIC_SMALL_FAST_MODEL": a.model, "ANTHROPIC_DEFAULT_HAIKU_MODEL": a.model, "ANTHROPIC_DEFAULT_SONNET_MODEL": a.model, | |
| "ANTHROPIC_DEFAULT_OPUS_MODEL": a.model, "CLAUDE_CODE_DISABLE_NONESSENTIAL_TRAFFIC": "1", "DISABLE_TELEMETRY": "1", | |
| "DISABLE_AUTOUPDATER": "1", "DISABLE_ERROR_REPORTING": "1", "IS_SANDBOX": "1"} | |
| cmd = ["runuser", "-u", a.user, "--", "env"] + [f"{kk}={vv}" for kk, vv in env.items()] + shlex.split(a.claude) + [ | |
| "-p", agent_prompt(inst), "--output-format", "stream-json", "--verbose", "--dangerously-skip-permissions", | |
| "--max-turns", str(a.max_turns), "--model", a.model] | |
| try: | |
| r = subprocess.run(cmd, cwd=TESTBED, capture_output=True, text=True, timeout=a.timeout, errors="replace", stdin=subprocess.DEVNULL) | |
| text, err, timed_out = r.stdout, r.stderr, False | |
| except subprocess.TimeoutExpired as e: | |
| text = ((e.stdout or b"").decode("utf-8", "replace") if isinstance(e.stdout, bytes) else (e.stdout or "")) | |
| err, timed_out = "timeout", True | |
| (logs / f"{iid}.jsonl").write_text(text + ("\n[stderr]\n" + err[-2000:] if err else ""), encoding="utf-8") | |
| info.update(parse_stream(text)) | |
| info["timed_out"] = timed_out | |
| subprocess.run("git add -A && git diff --cached HEAD > /tmp/model.patch", shell=True, cwd=TESTBED) | |
| patch = Path("/tmp/model.patch").read_text(encoding="utf-8", errors="replace") | |
| info["patch_lines"] = patch.count("\n") | |
| info["pass"], info["note"] = evaluate(inst, ts, venv, patch) | |
| except Exception as e: | |
| info.update({"pass": False, "note": f"harness error: {e!r}"[:300]}) | |
| info["secs"] = round(time.time() - s0) | |
| res[iid] = info | |
| out.write_text(json.dumps({"kind": "swe", "name": a.model, "seconds": round(time.time() - t0), "results": res}, indent=1), encoding="utf-8") | |
| print(f"{k:3d}/{len(ids)} {iid:34s} {'RESOLVED' if info['pass'] else 'no':8s} turns={info.get('turns', 0):3d} calls={info.get('tool_calls', 0):3d} " | |
| f"tool_errors={info.get('tool_errors', 0):2d} patch={info.get('patch_lines', 0):3d} lines {info['secs']:5d} s {info.get('note', '')[:80]}", flush=True) | |
| n = len(res) | |
| print(f"\nresolved {sum(v['pass'] for v in res.values())}/{n}") | |
| if __name__ == "__main__": | |
| ap = argparse.ArgumentParser(description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter) | |
| sub = ap.add_subparsers(dest="cmd", required=True) | |
| sub.add_parser("list") | |
| ga = sub.add_parser("goldall") | |
| ga.add_argument("--out", default="/content/gold_all.json") | |
| g = sub.add_parser("gold") | |
| g.add_argument("instance") | |
| ag = sub.add_parser("agent") | |
| ag.add_argument("--ids") | |
| ag.add_argument("--out", default="results_swe.json") | |
| ag.add_argument("--endpoint", default="http://127.0.0.1:18080") | |
| ag.add_argument("--model", default="k448") | |
| ag.add_argument("--user", default="agent") | |
| ag.add_argument("--claude", default="claude") | |
| ag.add_argument("--max-turns", type=int, default=60) | |
| ag.add_argument("--timeout", type=int, default=2700) | |
| a = ap.parse_args() | |
| WORK.mkdir(parents=True, exist_ok=True) | |
| CONSTRAINTS.write_text("Pygments==2.18.0\n", encoding="utf-8") # the version current when the official images were built | |
| {"list": cmd_list, "gold": cmd_gold, "goldall": cmd_goldall, "agent": cmd_agent}[a.cmd](a) | |