Text Generation
Transformers
English
Spanish
iot
smart-home
home-assistant
tool-calling
agent
cybersecurity
devops
ollama
inference-pipeline
autonomous-agent
raspberry-pi
qwen3-coder
Instructions to use MrMoz33/tokioai-coder-iot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MrMoz33/tokioai-coder-iot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MrMoz33/tokioai-coder-iot")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MrMoz33/tokioai-coder-iot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MrMoz33/tokioai-coder-iot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MrMoz33/tokioai-coder-iot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MrMoz33/tokioai-coder-iot
- SGLang
How to use MrMoz33/tokioai-coder-iot with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "MrMoz33/tokioai-coder-iot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "MrMoz33/tokioai-coder-iot" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MrMoz33/tokioai-coder-iot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MrMoz33/tokioai-coder-iot with Docker Model Runner:
docker model run hf.co/MrMoz33/tokioai-coder-iot
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TokioAI Engine -- Test Suite
Tests router, verifier, memory, and full pipeline.
"""
import json
import time
import sys
from .router import classify_intent, build_system_prompt, IntentType
from .verifier import Verifier, verify_and_extract
from .memory import EngineMemory
from .schemas import validate_tool_call, extract_tool_calls_from_text
from .guided import parse_guided_output
def run_tests(verbose=False, ollama_host=None, model=None):
"""Run all tests."""
passed = 0
failed = 0
def check(name, condition, detail=""):
nonlocal passed, failed
if condition:
passed += 1
print(f" \033[32mPASS\033[0m {name}")
else:
failed += 1
print(f" \033[31mFAIL\033[0m {name} {detail}")
# ========================
print("\n=== ROUTER TESTS ===")
# ========================
intent, conf = classify_intent("check disk space")
check("tool: check disk space", intent == IntentType.TOOL_CALL, f"got {intent}")
intent, conf = classify_intent("list docker containers")
check("tool: docker containers", intent == IntentType.TOOL_CALL, f"got {intent}")
intent, conf = classify_intent("read /etc/hostname")
check("tool: read file", intent == IntentType.TOOL_CALL, f"got {intent}")
intent, conf = classify_intent("run nmap on 192.168.1.1")
check("tool: nmap scan", intent == IntentType.TOOL_CALL, f"got {intent}")
intent, conf = classify_intent("what is SQL injection")
check("text: SQL injection", intent in (IntentType.TEXT, IntentType.CODE), f"got {intent}")
intent, conf = classify_intent("hi")
check("text: greeting", intent == IntentType.TEXT, f"got {intent}")
intent, conf = classify_intent("thanks")
check("text: thanks", intent == IntentType.TEXT, f"got {intent}")
intent, conf = classify_intent("explain the TCP handshake")
check("text: TCP explanation", intent in (IntentType.TEXT, IntentType.CODE), f"got {intent}")
intent, conf = classify_intent("what is docker")
check("decision: what is docker", intent in (IntentType.DECISION, IntentType.TEXT, IntentType.TOOL_CALL), f"got {intent}")
# ========================
print("\n=== VERIFIER TESTS ===")
# ========================
v = Verifier()
# Valid tool call
r = v.verify('{"name": "execute_local", "arguments": {"command": "df -h"}}')
check("verify: valid tool call", r.valid and r.output_type == "tool_call")
# Tool call in markdown
r = v.verify('```json\n{"name": "read_file", "arguments": {"path": "/etc/hostname"}}\n```')
check("verify: markdown tool call", r.valid and r.output_type == "tool_call")
# Invalid tool name
r = v.verify('{"name": "invalid_tool", "arguments": {"x": 1}}')
check("verify: invalid tool name", r.output_type in ("text", "error"))
# Missing required param
r = v.verify('{"name": "execute_local", "arguments": {}}')
check("verify: missing required param", not r.valid)
# Pure text
r = v.verify("Docker is a containerization platform.")
check("verify: pure text", r.valid and r.output_type == "text")
# Empty
r = v.verify("")
check("verify: empty output", not r.valid)
# ========================
print("\n=== SCHEMA TESTS ===")
# ========================
ok, err = validate_tool_call("execute_local", {"command": "ls"})
check("schema: valid execute_local", ok, err)
ok, err = validate_tool_call("execute_local", {})
check("schema: missing command", not ok)
ok, err = validate_tool_call("read_file", {"path": "/tmp/test"})
check("schema: valid read_file", ok, err)
ok, err = validate_tool_call("diagnose", {"target": "local"})
check("schema: valid diagnose", ok, err)
ok, err = validate_tool_call("diagnose", {"target": "invalid"})
check("schema: invalid enum", not ok)
# Extract tool calls from messy text
calls = extract_tool_calls_from_text(
'I will check the disk. {"name": "execute_local", "arguments": {"command": "df -h"}} Done.'
)
check("extract: tool in text", len(calls) == 1 and calls[0]["name"] == "execute_local")
# ========================
print("\n=== GUIDED TESTS ===")
# ========================
parsed = parse_guided_output('{"name": "execute_local", "arguments": {"command": "ls"}}', "tool_call")
check("guided: tool call parse", parsed["type"] == "tool_call" and parsed["name"] == "execute_local")
parsed = parse_guided_output('{"action": "text_response", "text_response": "hello"}', "decision")
check("guided: decision text", parsed["type"] == "text" and parsed["response"] == "hello")
parsed = parse_guided_output(
'{"action": "tool_call", "tool_call": {"name": "execute_local", "arguments": {"command": "ls"}}}',
"decision"
)
check("guided: decision tool", parsed["type"] == "tool_call" and parsed["name"] == "execute_local")
# ========================
print("\n=== MEMORY TESTS ===")
# ========================
import tempfile, os
tmp = os.path.join(tempfile.mkdtemp(), "test_memory.json")
mem = EngineMemory(path=tmp)
check("memory: seeded", len(mem.examples) > 0)
mem.add("check cpu usage", '{"name": "execute_local", "arguments": {"command": "top -bn1"}}',
intent="tool_call", tool_name="execute_local")
check("memory: add", len(mem.examples) > 0)
results = mem.retrieve("show cpu", intent="tool_call", top_k=3)
check("memory: retrieve", len(results) > 0, f"got {len(results)}")
stats = mem.get_stats()
check("memory: stats", stats["total"] > 0)
# Cleanup
os.unlink(tmp)
# ========================
print(f"\n=== RESULTS: {passed} passed, {failed} failed ===\n")
# ========================
# LIVE TESTS (optional -- only if model is available)
# ========================
if ollama_host or model:
from .pipeline import Pipeline, EngineConfig
config = EngineConfig(
ollama_host=ollama_host,
model_name=model or "tokioai-coder",
verbose=verbose,
)
print("=== LIVE PIPELINE TESTS ===")
print(f"Model: {config.model_name} @ {config.ollama_host}\n")
engine = Pipeline(config)
live_passed = 0
live_failed = 0
test_cases = [
("check disk space", "tool_call", "execute_local"),
("read /etc/hostname", "tool_call", "read_file"),
("find TODO in python files", "tool_call", "search_files"),
("what is SQL injection", "text", None),
("hi", "text", None),
("list running processes", "tool_call", "execute_local"),
("check system health", "tool_call", "diagnose"),
("explain what docker is", "text", None),
]
for prompt, expected_type, expected_tool in test_cases:
try:
result = engine.process(prompt)
type_match = result.output_type == expected_type
tool_match = True
if expected_tool and result.tool_calls:
tool_match = result.tool_calls[0]["name"] == expected_tool
ok = type_match and tool_match
detail = ""
if result.output_type == "tool_call" and result.tool_calls:
detail = f"-> {result.tool_calls[0]['name']}({list(result.tool_calls[0]['arguments'].keys())})"
else:
detail = f"-> text({len(result.text)} chars)"
if ok:
live_passed += 1
print(f" \033[32mPASS\033[0m [{result.latency_ms:4d}ms] [{result.attempts}att] {prompt:40s} {detail}")
else:
live_failed += 1
print(f" \033[31mFAIL\033[0m [{result.latency_ms:4d}ms] [{result.attempts}att] {prompt:40s} {detail} (expected {expected_type}/{expected_tool})")
except Exception as e:
live_failed += 1
print(f" \033[31mERROR\033[0m {prompt}: {e}")
engine.reset()
print(f"\n=== LIVE RESULTS: {live_passed} passed, {live_failed} failed ===\n")
passed += live_passed
failed += live_failed
return passed, failed
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