Instructions to use aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13 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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M # Run inference directly in the terminal: llama cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M # Run inference directly in the terminal: llama cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
Use Docker
docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "aogavrilov/diffusiongemma-agent-iq3-cuda13" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "aogavrilov/diffusiongemma-agent-iq3-cuda13", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
- Ollama
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Ollama:
ollama run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
- Unsloth Studio
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for aogavrilov/diffusiongemma-agent-iq3-cuda13 to start chatting
- Pi
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
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 aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
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 "aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M" \ --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"
- Docker Model Runner
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Docker Model Runner:
docker model run hf.co/aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
- Lemonade
How to use aogavrilov/diffusiongemma-agent-iq3-cuda13 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull aogavrilov/diffusiongemma-agent-iq3-cuda13:Q4_K_M
Run and chat with the model
lemonade run user.diffusiongemma-agent-iq3-cuda13-Q4_K_M
List all available models
lemonade list
File size: 12,514 Bytes
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from __future__ import annotations
import argparse
import hashlib
import json
import os
import subprocess
import tempfile
import time
from pathlib import Path
from typing import Any
DG_ROOT = Path(__file__).resolve().parents[1]
DEFAULT_CONFIG = DG_ROOT / "configs" / "client_profiles" / "haystack.dg.json"
SKIP_DIRS = {".git", ".dg-agent", ".venv", "node_modules", "__pycache__", "runlogs", ".tools"}
TEXT_SUFFIXES = {
".c",
".cc",
".cpp",
".cs",
".css",
".go",
".h",
".hpp",
".html",
".java",
".js",
".json",
".jsx",
".md",
".py",
".rs",
".sh",
".toml",
".ts",
".tsx",
".txt",
".yaml",
".yml",
}
INDEX_VERSION = 1
DEFAULT_INDEX_ROOT = DG_ROOT / "runlogs" / "dg-retrieval-index"
def load_config(path: Path) -> dict[str, Any]:
return json.loads(path.read_text(encoding="utf-8"))
def import_haystack() -> tuple[Any, Any, Any, Any, Any, Any]:
from haystack import Document
from haystack.components.generators.chat import OpenAIChatGenerator
from haystack.components.retrievers.in_memory import InMemoryBM25Retriever
from haystack.dataclasses import ChatMessage
from haystack.document_stores.in_memory import InMemoryDocumentStore
from haystack.utils import Secret
return Document, InMemoryDocumentStore, InMemoryBM25Retriever, OpenAIChatGenerator, ChatMessage, Secret
def generator_kwargs(config: dict[str, Any]) -> dict[str, Any]:
generation_kwargs = dict(config.get("generation_kwargs") or {})
generation_kwargs["max_tokens"] = int(os.environ.get("HAYSTACK_MAX_TOKENS") or config.get("max_tokens") or generation_kwargs.get("max_tokens") or 256)
generation_kwargs["temperature"] = float(os.environ.get("HAYSTACK_TEMPERATURE") or config.get("temperature") or generation_kwargs.get("temperature") or 0.0)
return {
"model": os.environ.get("HAYSTACK_MODEL") or config["model"],
"api_base_url": os.environ.get("OPENAI_BASE_URL") or config["api_base_url"],
"api_key": os.environ.get("OPENAI_API_KEY") or config["api_key"],
"generation_kwargs": generation_kwargs,
}
def rg_files(repo: Path) -> list[str]:
try:
proc = subprocess.run(["rg", "--files"], cwd=repo, text=True, capture_output=True, timeout=20)
if proc.returncode == 0 and proc.stdout.strip():
return [line.strip() for line in proc.stdout.splitlines() if line.strip()]
except (FileNotFoundError, subprocess.SubprocessError):
pass
files: list[str] = []
for path in repo.rglob("*"):
if not path.is_file():
continue
if any(part in SKIP_DIRS for part in path.parts):
continue
files.append(path.relative_to(repo).as_posix())
return files
def should_include(rel: str) -> bool:
path = Path(rel)
if any(part in SKIP_DIRS for part in path.parts):
return False
return path.suffix.lower() in TEXT_SUFFIXES or path.name in {"Dockerfile", "Makefile", "README"}
def source_manifest(repo: Path, config: dict[str, Any]) -> list[dict[str, Any]]:
max_files = int(config.get("max_files") or 120)
manifest: list[dict[str, Any]] = []
for rel in rg_files(repo):
if len(manifest) >= max_files:
break
if not should_include(rel):
continue
target = (repo / rel).resolve()
if repo not in target.parents and target != repo:
continue
try:
stat = target.stat()
except OSError:
continue
manifest.append({"path": rel, "size": stat.st_size, "mtime_ns": stat.st_mtime_ns})
return manifest
def source_documents(repo: Path, config: dict[str, Any], manifest: list[dict[str, Any]]) -> list[dict[str, str]]:
max_file_chars = int(config.get("max_file_chars") or 4000)
docs: list[dict[str, str]] = []
for item in manifest:
rel = str(item["path"])
target = (repo / rel).resolve()
try:
text = target.read_text(encoding="utf-8", errors="replace")
except OSError:
continue
text = text.strip()
if not text:
continue
if len(text) > max_file_chars:
text = text[:max_file_chars] + "\n...[truncated]"
docs.append({"path": rel, "content": text})
return docs
def index_path(repo: Path, value: str) -> Path:
if value:
return Path(value).resolve()
digest = hashlib.sha256(str(repo).encode("utf-8")).hexdigest()[:16]
return (DEFAULT_INDEX_ROOT / digest).resolve()
def load_json(path: Path) -> dict[str, Any] | None:
try:
data = json.loads(path.read_text(encoding="utf-8"))
except (OSError, ValueError):
return None
return data if isinstance(data, dict) else None
def atomic_write_json(path: Path, data: dict[str, Any]) -> None:
path.parent.mkdir(parents=True, exist_ok=True)
with tempfile.NamedTemporaryFile("w", encoding="utf-8", delete=False, dir=path.parent, suffix=".tmp") as handle:
json.dump(data, handle, ensure_ascii=False, indent=2)
handle.write("\n")
temp_name = handle.name
Path(temp_name).replace(path)
def load_or_build_index(repo: Path, config: dict[str, Any], directory: Path, rebuild: bool) -> tuple[list[dict[str, str]], dict[str, Any]]:
manifest = source_manifest(repo, config)
cache_file = directory / "documents.json"
cached = None if rebuild else load_json(cache_file)
cache_hit = bool(
cached
and cached.get("version") == INDEX_VERSION
and cached.get("manifest") == manifest
and isinstance(cached.get("documents"), list)
)
if cache_hit:
documents = [item for item in cached["documents"] if isinstance(item, dict) and isinstance(item.get("path"), str) and isinstance(item.get("content"), str)]
else:
documents = source_documents(repo, config, manifest)
atomic_write_json(
cache_file,
{
"version": INDEX_VERSION,
"created_at": time.time(),
"repo": str(repo),
"manifest": manifest,
"documents": documents,
},
)
return documents, {
"index_dir": str(directory),
"index_file": str(cache_file),
"cache_hit": cache_hit,
"manifest_files": len(manifest),
"indexed_documents": len(documents),
}
def as_haystack_documents(documents: list[dict[str, str]]) -> list[Any]:
Document, *_ = import_haystack()
return [Document(content=f"File: {item['path']}\n\n{item['content']}", meta={"path": item["path"]}) for item in documents]
def retrieve_context(repo: Path, config: dict[str, Any], query: str, directory: Path, rebuild: bool) -> tuple[list[Any], dict[str, Any]]:
_Document, InMemoryDocumentStore, InMemoryBM25Retriever, *_ = import_haystack()
source_docs, index_stats = load_or_build_index(repo, config, directory, rebuild)
docs = as_haystack_documents(source_docs)
docstore = InMemoryDocumentStore()
if docs:
docstore.write_documents(docs)
retriever = InMemoryBM25Retriever(document_store=docstore)
top_k = int(config.get("top_k") or 4)
retrieved = retriever.run(query=query, top_k=top_k).get("documents", [])
stats = {
**index_stats,
"retrieved_documents": len(retrieved),
"top_k": top_k,
"paths": [doc.meta.get("path", "") for doc in retrieved],
}
return retrieved, stats
def build_messages(config: dict[str, Any], task: str, docs: list[Any]) -> list[Any]:
*_prefix, ChatMessage, _Secret = import_haystack()
max_prompt_chars = int(config.get("max_prompt_chars") or 14000)
chunks: list[str] = []
total = 0
for doc in docs:
content = str(doc.content)
remaining = max_prompt_chars - total
if remaining <= 0:
break
if len(content) > remaining:
content = content[:remaining] + "\n...[truncated]"
chunks.append(content)
total += len(content)
context = "\n\n---\n\n".join(chunks) if chunks else "No repository context retrieved."
system = (
"You are a bounded repository assistant running through Haystack RAG over "
"a small-context local model. Answer from retrieved context only. For code "
"edits, recommend dg_agent.sh agent/session/task instead of inventing broad patches."
)
user = f"Question:\n{task}\n\nRetrieved repository context:\n{context}"
return [ChatMessage.from_system(system), ChatMessage.from_user(user)]
def run_task(args: argparse.Namespace, config: dict[str, Any]) -> int:
repo = Path(args.repo).resolve()
os.chdir(repo)
docs, stats = retrieve_context(repo, config, args.task, index_path(repo, args.index_dir), args.rebuild_index)
if args.retrieve_only:
payload = {
"status": "success",
"retrieval": stats,
"documents": [{"path": doc.meta.get("path", ""), "content": str(doc.content)} for doc in docs],
}
print(json.dumps(payload, ensure_ascii=False, indent=2) if args.json else "\n\n".join(item["content"] for item in payload["documents"]))
return 0
_Document, _Store, _Retriever, OpenAIChatGenerator, _ChatMessage, Secret = import_haystack()
kwargs = generator_kwargs(config)
api_key = kwargs.pop("api_key")
generator = OpenAIChatGenerator(api_key=Secret.from_token(api_key), **kwargs)
result = generator.run(messages=build_messages(config, args.task, docs))
replies = result.get("replies", [])
answer = replies[0].text if replies else ""
payload = {"status": "success", "retrieval": stats, "result": answer}
if args.json:
print(json.dumps(payload, ensure_ascii=False, indent=2))
else:
print(answer)
return 0
def parse_args() -> argparse.Namespace:
parser = argparse.ArgumentParser(description="Run Haystack BM25 RAG with the local DiffusionGemma profile.")
parser.add_argument("--repo", default=".", help="Target repo, used as working directory")
parser.add_argument("--config", default=str(DEFAULT_CONFIG))
parser.add_argument("--task", default="")
parser.add_argument("--dry-run", action="store_true")
parser.add_argument("--smoke-import", action="store_true")
parser.add_argument("--index-dir", default="", help="Persistent document-cache directory; defaults to runlogs/dg-retrieval-index/<repo-hash>")
parser.add_argument("--rebuild-index", action="store_true", help="Discard the cached document set before retrieval")
parser.add_argument("--retrieve-only", action="store_true", help="Return Haystack BM25 documents without asking the model to generate")
parser.add_argument("--json", action="store_true")
return parser.parse_args()
def main() -> int:
args = parse_args()
repo = Path(args.repo).resolve()
config_path = Path(args.config).resolve()
config = load_config(config_path)
if args.smoke_import:
Document, InMemoryDocumentStore, InMemoryBM25Retriever, OpenAIChatGenerator, _ChatMessage, _Secret = import_haystack()
print("haystack import ok")
print(Document.__name__)
print(InMemoryDocumentStore.__name__)
print(InMemoryBM25Retriever.__name__)
print(OpenAIChatGenerator.__name__)
return 0
if args.dry_run:
data = {
"repo": str(repo),
"config": str(config_path),
"document_store": config["document_store"],
"retriever": config["retriever"],
"generator": config["generator"],
"generator_kwargs": generator_kwargs(config),
"retrieval": {
"top_k": int(config.get("top_k") or 4),
"max_files": int(config.get("max_files") or 120),
"max_file_chars": int(config.get("max_file_chars") or 4000),
"index_dir": str(index_path(repo, args.index_dir)),
"persistent_cache": True,
},
"command": f"scripts/dg_agent.sh haystack -- --repo {repo} --task '...'",
}
print(json.dumps(data, ensure_ascii=False, indent=2) if args.json else "\n".join(f"{k}: {v}" for k, v in data.items()))
return 0
if not args.task:
print("--task is required unless --dry-run or --smoke-import is used", flush=True)
return 2
return run_task(args, config)
if __name__ == "__main__":
raise SystemExit(main())
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