Text Generation
Transformers
English
qwen2
code-generation
python
fine-tuning
Qwen
tools
agent-framework
multi-agent
conversational
Eval Results (legacy)
Instructions to use my-ai-stack/Stack-2-9-finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use my-ai-stack/Stack-2-9-finetuned with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="my-ai-stack/Stack-2-9-finetuned") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("my-ai-stack/Stack-2-9-finetuned") model = AutoModelForCausalLM.from_pretrained("my-ai-stack/Stack-2-9-finetuned") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use my-ai-stack/Stack-2-9-finetuned with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "my-ai-stack/Stack-2-9-finetuned" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
- SGLang
How to use my-ai-stack/Stack-2-9-finetuned 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 "my-ai-stack/Stack-2-9-finetuned" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "my-ai-stack/Stack-2-9-finetuned" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "my-ai-stack/Stack-2-9-finetuned", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use my-ai-stack/Stack-2-9-finetuned with Docker Model Runner:
docker model run hf.co/my-ai-stack/Stack-2-9-finetuned
File size: 18,949 Bytes
b6ae7b8 | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386 387 388 389 390 391 392 393 394 395 396 397 398 399 400 401 402 403 404 405 406 407 408 409 410 411 412 413 414 415 416 417 418 419 420 421 422 423 424 425 426 427 428 429 430 431 432 433 434 435 436 437 438 439 440 441 442 443 444 445 446 447 448 449 450 451 452 453 454 455 456 457 458 459 460 461 462 463 464 465 466 467 468 469 470 471 472 473 474 475 476 477 478 479 480 481 482 483 484 485 486 487 488 489 490 491 492 493 494 495 496 497 498 499 500 501 502 503 504 505 506 507 508 509 510 511 512 513 514 515 516 517 518 519 520 521 522 523 | #!/usr/bin/env python3
"""
Stack 2.9 - Core Agent Logic Module
Query understanding, tool selection, response generation, and self-reflection loop.
"""
import os
import json
import re
import asyncio
from pathlib import Path
from typing import Any, Dict, List, Optional, Union, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from .tools import TOOLS, get_tool, list_tools, get_tool_schemas
from .context import ContextManager, create_context_manager
class QueryIntent(Enum):
"""Intents recognized by the agent."""
FILE_READ = "file_read"
FILE_WRITE = "file_write"
FILE_EDIT = "file_edit"
FILE_SEARCH = "file_search"
GIT_OPERATION = "git_operation"
CODE_EXECUTION = "code_execution"
WEB_SEARCH = "web_search"
MEMORY = "memory"
TASK = "task"
QUESTION = "question"
GENERAL = "general"
@dataclass
class ToolCall:
"""Represents a tool call."""
tool_name: str
arguments: Dict[str, Any]
result: Optional[Dict[str, Any]] = None
success: bool = False
error: Optional[str] = None
@dataclass
class AgentResponse:
"""Represents the agent's response."""
content: str
tool_calls: List[ToolCall] = field(default_factory=list)
context_used: List[str] = field(default_factory=list)
confidence: float = 1.0
needs_clarification: bool = False
clarification_needed: Optional[str] = None
class QueryUnderstanding:
"""Understands user queries and maps them to intents and tools."""
# Intent patterns
PATTERNS = {
QueryIntent.FILE_READ: [
r"read\s+(?:the\s+)?(?:file\s+)?(.+\.py|.+\.js|.+\.txt|.+\.md|.+\.json)",
r"show\s+(?:me\s+)?(?:the\s+)?(?:content\s+of\s+)?(.+\.py|.+\.js|.+\.txt|.+\.md|.+\.json)",
r"what('s| is)\s+in\s+(.+\.py|.+\.js|.+\.txt|.+\.md|.+\.json)",
r"cat\s+(.+)",
r"view\s+(.+)",
],
QueryIntent.FILE_WRITE: [
r"write\s+(?:to\s+)?(.+\.py|.+\.js|.+\.txt|.+\.md|.+\.json)",
r"create\s+(?:file\s+)?(.+\.py|.+\.js|.+\.txt|.+\.md|.+\.json)",
r"save\s+(?:to\s+)?(.+)",
],
QueryIntent.FILE_EDIT: [
r"edit\s+(.+\.py|.+\.js|.+\.txt|.+\.md|.+\.json)",
r"modify\s+(.+)",
r"change\s+(.+)",
r"replace\s+(.+)",
],
QueryIntent.FILE_SEARCH: [
r"find\s+(?:files?\s+)?(?:named\s+)?(.+)",
r"search\s+for\s+(?:files?\s+)?(.+)",
r"where\s+is\s+(.+)",
r"locate\s+(.+)",
],
QueryIntent.GIT_OPERATION: [
r"git\s+(commit|push|pull|branch|status|log|diff)",
r"(commit|push|pull|branch)\s+(?:to\s+)?(?:the\s+)?(?:repo|repository)?",
],
QueryIntent.CODE_EXECUTION: [
r"run\s+(?:the\s+)?(?:command\s+)?(.+)",
r"execute\s+(.+)",
r"start\s+(?:the\s+)?(?:server\s+)?(.+)",
r"test\s+(?:the\s+)?(.+)",
r"lint\s+(.+)",
r"format\s+(.+)",
],
QueryIntent.WEB_SEARCH: [
r"search\s+(?:the\s+)?web\s+for\s+(.+)",
r"google\s+(.+)",
r"look\s+up\s+(.+)",
r"find\s+information\s+about\s+(.+)",
],
QueryIntent.MEMORY: [
r"(remember|recall|what do you remember)\s+(.+)",
r"(save|store)\s+(?:to\s+)?memory\s+(.+)",
r"what('s| is)\s+in\s+(?:the\s+)?memory",
],
QueryIntent.TASK: [
r"(create|add|new)\s+task\s+(.+)",
r"list\s+(?:my\s+)?tasks?",
r"(complete|finish|done)\s+task\s+(.+)",
],
QueryIntent.QUESTION: [
r"what\s+is\s+(.+)",
r"how\s+(?:do|does)\s+(.+)",
r"why\s+(.+)",
r"can\s+(.+)",
r"(?:help|explain)\s+(.+)",
],
}
def __init__(self):
self.tools = list_tools()
def parse(self, query: str) -> Dict[str, Any]:
"""Parse query and determine intent."""
query = query.strip().lower()
# Check each intent pattern
for intent, patterns in self.PATTERNS.items():
for pattern in patterns:
match = re.search(pattern, query, re.IGNORECASE)
if match:
return {
"intent": intent.value,
"matched": match.group(0),
"extracted": match.groups() if match.groups() else None,
"confidence": 0.8
}
return {
"intent": QueryIntent.GENERAL.value,
"matched": None,
"extracted": None,
"confidence": 0.5
}
def extract_file_path(self, text: str) -> Optional[str]:
"""Extract file path from text."""
# Common patterns for file paths
patterns = [
r"([a-zA-Z0-9_/\-\.]+\.py)",
r"([a-zA-Z0-9_/\-\.]+\.js)",
r"([a-zA-Z0-9_/\-\.]+\.ts)",
r"([a-zA-Z0-9_/\-\.]+\.md)",
r"([a-zA-Z0-9_/\-\.]+\.json)",
r"([a-zA-Z0-9_/\-\.]+\.txt)",
r"([a-zA-Z0-9_/\-\.]+\.yaml|\.yml)",
r"([a-zA-Z0-9_/\-\.]+)",
]
for pattern in patterns:
match = re.search(pattern, text)
if match:
return match.group(1)
return None
class ToolSelector:
"""Selects appropriate tools based on query intent."""
# Intent to tool mapping
INTENT_TOOLS = {
QueryIntent.FILE_READ: ["read"],
QueryIntent.FILE_WRITE: ["write"],
QueryIntent.FILE_EDIT: ["edit"],
QueryIntent.FILE_SEARCH: ["search", "grep"],
QueryIntent.GIT_OPERATION: ["git_status", "git_commit", "git_push", "git_pull", "git_branch", "git_log"],
QueryIntent.CODE_EXECUTION: ["run", "test", "lint", "format"],
QueryIntent.WEB_SEARCH: ["web_search", "fetch"],
QueryIntent.MEMORY: ["memory_recall", "memory_save", "memory_list"],
QueryIntent.TASK: ["create_task", "list_tasks", "update_task"],
}
def select(self, intent: str, context: Dict[str, Any]) -> List[str]:
"""Select tools for given intent."""
tools = []
try:
intent_enum = QueryIntent(intent)
tools = self.INTENT_TOOLS.get(intent_enum, [])
except ValueError:
tools = []
# Add general tools
tools.extend(["run", "context_load", "project_scan"])
return list(set(tools))
def get_tool_parameters(self, tool_name: str, query: str, context: Dict[str, Any]) -> Dict[str, Any]:
"""Extract parameters for a tool from query and context."""
params = {}
query_lower = query.lower()
if tool_name == "read":
path = re.search(r"(?:read|show|cat|view)\s+(?:the\s+)?(?:file\s+)?(.+)", query, re.IGNORECASE)
if path:
params["path"] = path.group(1).strip()
elif tool_name == "write":
path = re.search(r"write\s+(?:to\s+)?(.+?)(?:\s+with|\s+content|$)", query, re.IGNORECASE)
if path:
params["path"] = path.group(1).strip()
# Try to extract content
content_match = re.search(r"(?:content|with):\s*(.+)$", query, re.IGNORECASE)
if content_match:
params["content"] = content_match.group(1)
elif tool_name == "git_commit":
msg = re.search(r"commit(?:\s+with)?\s+(?:message\s+)?[\"']?(.+)[\"']?", query, re.IGNORECASE)
if msg:
params["message"] = msg.group(1).strip()
elif tool_name == "web_search":
# Extract search query
patterns = [
r"search\s+(?:the\s+)?web\s+for\s+(.+)",
r"google\s+(.+)",
r"look\s+up\s+(.+)",
]
for pattern in patterns:
match = re.search(pattern, query, re.IGNORECASE)
if match:
params["query"] = match.group(1).strip()
break
return params
class ResponseGenerator:
"""Generates natural language responses."""
def __init__(self):
self.context_manager = create_context_manager()
def generate(
self,
tool_results: List[ToolCall],
intent: str,
context: Dict[str, Any]
) -> str:
"""Generate response from tool results."""
if not tool_results:
return "I couldn't find any results for your query."
responses = []
for call in tool_results:
if call.result is None:
responses.append(f"I tried to use {call.tool_name} but got no result.")
continue
if call.result.get("success"):
result = call.result
# Format based on tool
if call.tool_name == "read":
if "content" in result:
content = result["content"]
if len(content) > 500:
content = content[:500] + "..."
responses.append(f"Here's the content:\n```\n{content}\n```")
elif call.tool_name == "grep":
if "matches" in result:
matches = result["matches"]
if matches:
resp = f"Found {len(matches)} matches:\n"
for m in matches[:10]:
resp += f"- {m.get('file', '?')}:{m.get('line', '?')} - {m.get('content', '')}\n"
responses.append(resp)
else:
responses.append("No matches found.")
elif call.tool_name in ["git_status", "git_log"]:
if "files" in result:
files = result["files"]
if files:
responses.append(f"Changed files ({len(files)}):\n" + "\n".join(f" - {f}" for f in files))
else:
responses.append("No changes detected.")
elif "commits" in result:
commits = result["commits"]
if commits:
responses.append("Recent commits:\n" + "\n".join(f" - {c}" for c in commits[:5]))
elif call.tool_name == "web_search":
if "results" in result:
results = result["results"]
resp = "Search results:\n"
for r in results[:5]:
resp += f"- {r.get('title', 'Untitled')}\n"
responses.append(resp)
elif call.tool_name == "run":
stdout = result.get("stdout", "")
stderr = result.get("stderr", "")
if stdout:
responses.append(f"Output:\n```\n{stdout[:500]}\n```")
if stderr:
responses.append(f"Errors:\n```\n{stderr[:500]}\n```")
if not stdout and not stderr:
responses.append("Command executed successfully.")
elif call.tool_name == "memory_recall":
if "matches" in result:
matches = result["matches"]
if matches:
responses.append(f"Found {len(matches)} memory entries.")
else:
responses.append("No matching memories found.")
else:
# Generic success response
responses.append(f"{call.tool_name}: {json.dumps(result)[:200]}")
else:
error = call.result.get("error", "Unknown error")
responses.append(f"Error in {call.tool_name}: {error}")
return "\n\n".join(responses) or "I processed your request but have no results to show."
def generate_clarification(self, question: str) -> str:
"""Generate clarification question."""
return f"I need some clarification: {question}"
class SelfReflection:
"""Self-reflection loop for improving responses."""
def __init__(self):
self.max_iterations = 3
self.min_confidence = 0.7
def reflect(
self,
query: str,
tool_calls: List[ToolCall],
response: str
) -> Dict[str, Any]:
"""Reflect on the response and determine if improvement is needed."""
# Check if any tool call failed
failed_calls = [c for c in tool_calls if not c.success]
# Calculate confidence
success_rate = len(tool_calls) / max(len(tool_calls), 1)
confidence = success_rate
needs_reflection = (
len(failed_calls) > 0 or
confidence < self.min_confidence or
len(response) < 20
)
return {
"needs_reflection": needs_reflection,
"confidence": confidence,
"failed_calls": len(failed_calls),
"response_length": len(response),
"suggestion": self._get_suggestion(failed_calls, confidence) if needs_reflection else None
}
def _get_suggestion(self, failed_calls: List[ToolCall], confidence: float) -> str:
"""Get improvement suggestion."""
if not failed_calls:
return "Try providing more context in your query."
return f"Failed tool calls: {', '.join(c.tool_name for c in failed_calls)}"
class StackAgent:
"""
Core agent that combines all components for intelligent assistance.
"""
def __init__(self, workspace: Optional[str] = None):
self.query_understanding = QueryUnderstanding()
self.tool_selector = ToolSelector()
self.response_generator = ResponseGenerator()
self.self_reflection = SelfReflection()
self.context_manager = create_context_manager(workspace)
self.conversation_history: List[Dict[str, Any]] = []
def process(self, query: str, context: Optional[Dict] = None) -> AgentResponse:
"""Process a user query."""
context = context or {}
# Step 1: Understand query
parsed = self.query_understanding.parse(query)
intent = parsed["intent"]
confidence = parsed["confidence"]
# Step 2: Select tools
selected_tools = self.tool_selector.select(intent, context)
tool_params = {}
for tool_name in selected_tools:
tool_params[tool_name] = self.tool_selector.get_tool_parameters(tool_name, query, context)
# Step 3: Execute tools
tool_calls = []
for tool_name in selected_tools:
tool = get_tool(tool_name)
if tool is None:
continue
params = tool_params.get(tool_name, {})
try:
result = tool(**params)
call = ToolCall(
tool_name=tool_name,
arguments=params,
result=result,
success=result.get("success", False) if isinstance(result, dict) else True
)
except Exception as e:
call = ToolCall(
tool_name=tool_name,
arguments=params,
error=str(e),
success=False
)
tool_calls.append(call)
# Record in session
self.context_manager.session.add_tool_usage(tool_name, call.result)
# Step 4: Generate response
response_content = self.response_generator.generate(tool_calls, intent, context)
# Step 5: Self-reflect
reflection = self.self_reflection.reflect(query, tool_calls, response_content)
# Step 6: Add to conversation history
self.conversation_history.append({
"query": query,
"intent": intent,
"tool_calls": [c.tool_name for c in tool_calls],
"response": response_content,
"reflection": reflection,
"timestamp": datetime.now().isoformat()
})
return AgentResponse(
content=response_content,
tool_calls=tool_calls,
confidence=reflection.get("confidence", confidence),
needs_clarification=reflection.get("needs_reflection", False),
clarification_needed=reflection.get("suggestion")
)
def process_with_tools(self, query: str, forced_tools: List[str]) -> AgentResponse:
"""Process query with explicitly specified tools."""
tool_calls = []
for tool_name in forced_tools:
tool = get_tool(tool_name)
if tool is None:
continue
try:
result = tool()
call = ToolCall(
tool_name=tool_name,
arguments={},
result=result,
success=result.get("success", False) if isinstance(result, dict) else True
)
except Exception as e:
call = ToolCall(
tool_name=tool_name,
arguments={},
error=str(e),
success=False
)
tool_calls.append(call)
response_content = self.response_generator.generate(tool_calls, "general", {})
return AgentResponse(
content=response_content,
tool_calls=tool_calls,
confidence=1.0
)
def get_context(self) -> str:
"""Get current context as string."""
return self.context_manager.get_workspace_context()
def get_schemas(self) -> List[Dict[str, Any]]:
"""Get tool schemas for tool calling."""
return get_tool_schemas()
def create_agent(workspace: Optional[str] = None) -> StackAgent:
"""Factory function to create agent."""
return StackAgent(workspace)
if __name__ == "__main__":
print("Stack 2.9 Agent Module")
agent = create_agent()
print(f"Agent initialized with {len(list_tools())} tools")
# Test query
response = agent.process("list my tasks")
print(f"\nQuery: 'list my tasks'")
print(f"Response: {response.content[:200]}")
|