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: 7,753 Bytes
068bc7f | 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 | """WebSearchTool β search the web via DuckDuckGo."""
from __future__ import annotations
import json
import os
import time
from dataclasses import dataclass, asdict
from typing import Any
from .base import BaseTool, ToolResult
from .registry import get_registry
try:
from ddgs import DDGS
except ImportError:
DDGS = None # type: ignore
TOOL_NAME = "WebSearch"
DATA_DIR = os.path.expanduser("~/.stack-2.9")
CACHE_FILE = os.path.join(DATA_DIR, "web_search_cache.json")
def _load_cache() -> dict[str, Any]:
"""Load the web search result cache."""
if os.path.exists(CACHE_FILE):
try:
with open(CACHE_FILE) as f:
return json.load(f)
except Exception:
pass
return {}
def _save_cache(cache: dict[str, Any]) -> None:
"""Persist the web search cache."""
os.makedirs(DATA_DIR, exist_ok=True)
with open(CACHE_FILE, "w") as f:
json.dump(cache, f)
@dataclass
class SearchHit:
"""A single search result."""
title: str
url: str
snippet: str = ""
@dataclass
class SearchOutput:
"""Output of a web search."""
query: str
results: list[SearchHit]
duration_seconds: float
source: str = "duckduckgo"
class WebSearchTool(BaseTool[dict[str, Any], SearchOutput]):
"""Search the web using DuckDuckGo.
Requires the `ddgs` package: pip install duckduckgo-search
Parameters
----------
query : str
The search query (required, min 2 chars).
allowed_domains : list[str], optional
Restrict results to these domains.
blocked_domains : list[str], optional
Exclude results from these domains.
max_results : int, optional
Maximum number of results to return (default 10, max 20).
"""
name = TOOL_NAME
description = "Search the web for current information using DuckDuckGo."
search_hint = "search the web for current information"
# ββ schema ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
@property
def input_schema(self) -> dict[str, Any]:
return {
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query (minimum 2 characters)",
"minLength": 2,
},
"allowed_domains": {
"type": "array",
"items": {"type": "string"},
"description": "Restrict results to these domains",
},
"blocked_domains": {
"type": "array",
"items": {"type": "string"},
"description": "Exclude results from these domains",
},
"max_results": {
"type": "integer",
"description": "Maximum number of results (default 10, max 20)",
"default": 10,
"minimum": 1,
"maximum": 20,
},
},
"required": ["query"],
}
# ββ validation ββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def validate_input(self, input_data: dict[str, Any]) -> tuple[bool, str | None]:
query = input_data.get("query", "")
if not query or len(query) < 2:
return False, "Error: query must be at least 2 characters"
if input_data.get("allowed_domains") and input_data.get("blocked_domains"):
return False, "Error: cannot specify both allowed_domains and blocked_domains"
return True, None
# ββ execution βββββββββββββββββββββββββββββββββββββββββββββββββββββββββββββ
def execute(self, input_data: dict[str, Any]) -> ToolResult[SearchOutput]:
if DDGS is None:
return ToolResult(
success=False,
error="duckduckgo-search not installed. Run: pip install duckduckgo-search",
)
query = input_data["query"]
allowed = input_data.get("allowed_domains")
blocked = input_data.get("blocked_domains")
max_results = min(input_data.get("max_results", 10), 20)
cache = _load_cache()
cache_key = f"{query}|{json.dumps(allowed)}|{json.dumps(blocked)}"
# Return cached result if fresh (5 minutes)
now = time.time()
if cache_key in cache:
entry = cache[cache_key]
if now - entry.get("cached_at", 0) < 300:
output = SearchOutput(
query=query,
results=[SearchHit(**h) for h in entry["results"]],
duration_seconds=entry.get("duration", 0),
source="duckduckgo (cached)",
)
return ToolResult(success=True, data=asdict(output))
try:
hits: list[SearchHit] = []
with DDGS() as ddgs:
if allowed:
keywords = " ".join(allowed)
generator = ddgs.text(query, max_results=max_results)
else:
generator = ddgs.text(query, max_results=max_results)
for i, result in enumerate(generator):
if i >= max_results:
break
url = result.get("href", "")
# Apply blocked domains filter
if blocked and any(domain in url for domain in blocked):
continue
hits.append(
SearchHit(
title=result.get("title", ""),
url=url,
snippet=result.get("body", ""),
)
)
output = SearchOutput(
query=query,
results=hits,
duration_seconds=0.0,
source="duckduckgo",
)
# Cache the result
cache[cache_key] = {
"results": [asdict(h) for h in hits],
"cached_at": now,
}
_save_cache(cache)
return ToolResult(success=True, data=asdict(output))
except Exception as exc:
return ToolResult(success=False, error=f"Web search failed: {exc}")
def map_result_to_message(self, result: SearchOutput | dict, tool_use_id: str | None = None) -> str:
"""Format search results for display."""
if isinstance(result, dict):
query = result.get("query", "")
hits = result.get("results", [])
else:
query = result.query
hits = result.results
lines = [f"Web search results for: \"{query}\"\n"]
if not hits:
lines.append("No results found.")
return "\n".join(lines)
lines.append(f"{len(hits)} results:\n")
for i, hit in enumerate(hits, 1):
snippet = hit.snippet[:200] + "..." if len(hit.snippet) > 200 else hit.snippet
lines.append(f"{i}. {hit.title}")
lines.append(f" URL: {hit.url}")
if snippet:
lines.append(f" {snippet}")
lines.append("")
return "\n".join(lines)
# Register the tool
_registry = get_registry()
_registry.register(WebSearchTool())
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