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

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

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

diffusiongemma-agent-iq3-cuda13 / payload /app /docs /local_agent_stack.md

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	# Local Agent Stack

	This repo now has fifteen layers around the local DiffusionGemma runtime. The
	preferred entrypoint is:

	```bash
	cd /root/diffusiongemma-agent
	scripts/dg_agent.sh status
	scripts/dg_agent.sh up
	scripts/dg_agent.sh doctor
	scripts/dg_agent.sh capabilities --live
	```

	Docs: `docs/dg_agent_cli.md`.

	OSS wrapper matrix: `docs/oss_agent_wrappers.md`.

	## Last Verified Runtime State

	Checked on 2026-07-14 from the Windows checkout
	`C:\Users\alexg\Downloads\diffusiongemma-agent` with the active backend in WSL.
	Runtime availability is time-sensitive; recheck with `scripts/dg_agent.sh status`
	or `scripts/dg_agent.sh doctor` before relying on these endpoints.

	Runtime:

	```text
	backend http://127.0.0.1:4100/healthz
	proxy http://127.0.0.1:8090/healthz
	litellm http://127.0.0.1:4100/v1/models
	model diffusiongemma-26b-a4b-it-iq3m-fullgpu
	gpu full offload profile, ngl=999, maxtok=768, about 14.7 GiB VRAM loaded
	```

	Installed and audited OSS agent surfaces:

	```text
	Aider active WSL runtime, 0.86.2
	OpenCode .tools/opencode/node_modules/.bin/opencode plus opencode-linux-x64, 1.17.20; provider/run/MCP/ACP smoke pass
	MCP SDK active WSL runtime, 1.28.1
	Serena WSL bridge, 1.5.3, live Pyright symbols
	Qwen Code .tools/qwen-code/node_modules/.bin/qwen, 0.19.10, WSL runner + MCP
	LangGraph WSL Python 3.14 venv from local cp314 wheelhouse; smoke-import/dry-run pass
	Watchdog repo-local status/up/watchdog stack-control scripts restored and smoke pass
	```

	Core verification completed on this checkout:

	```bash
	python scripts/dg_agent.py smoke --suite qwen-code --timeout 180
	python scripts/dg_agent.py smoke --suite mcp --suite serena-mcp --timeout 180
	python scripts/dg_agent.py smoke --suite opencode-provider --suite opencode-run --suite opencode-mcp --suite opencode-acp --timeout 180
	python scripts/dg_agent.py smoke --suite langgraph --timeout 240
	python scripts/dg_agent.py smoke --suite watchdog --suite stack-control --timeout 60
	python scripts/dg_agent.py smoke --suite wrappers --timeout 180
	```

	OpenCode is installed from the Windows npm payload for PowerShell usage and the
	Linux optional package `opencode-linux-x64` for WSL MCP process spawning. Live
	`opencode mcp list` verifies `dg_agent` and `repomix` as connected. Serena has
	a separate MCP smoke with live Pyright symbols and is intended for IDE bundles.

	The verified Aider end-to-end scenario changed only an allowed Python file
	through the GPU gateway, then passed an independent WSL Python assertion. The
	temporary Aider histories stayed outside the target repository. Optional
	OpenHands, AutoGen, smolagents, CrewAI, Open Interpreter,
	LlamaIndex, and SWE-family wrappers remain separate experiments and
	must be smoke-tested individually before use.

	External clients should prefer the client pack plus MCP resources instead of
	large prompts. The exported `local_agent` profile includes artifact commands:
	`scripts/dg_agent.sh agent-runs list`,
	`scripts/dg_agent.sh agent-runs show --latest`, and
	`scripts/dg_agent.sh agent-runs artifact transcript --latest`.

	## 1. Fast Backend

	```text
	http://127.0.0.1:4100/v1
	```

	Start from Windows:

	```powershell
	powershell -ExecutionPolicy Bypass -File \\wsl.localhost\Ubuntu-24.04\root\diffusiongemma-agent\scripts\start_agent_fast_service_windows.ps1
	```

	## 2. Aider-Compatible Model Proxy

	```text
	http://127.0.0.1:8090/v1
	```

	This proxy compresses Aider prompts, repairs malformed output fences, applies
	exact single-file return constraints when the model output is malformed, and
	limits the model output to a size the current DG profile can handle.

	Start:

	```powershell
	powershell -ExecutionPolicy Bypass -File \\wsl.localhost\Ubuntu-24.04\root\diffusiongemma-agent\scripts\start_aider_proxy_windows.ps1
	```

	## 3. Reliable Automated Edits

	Use the agent profile for the normal Codex-like local workflow:

	```bash
	/root/diffusiongemma-agent/scripts/dg_agent.sh agent \
	--repo /path/to/repo \
	--task "Fix the failing test and keep the public API unchanged" \
	--file path/to/file.py
	```

	This wraps the context packer, task runner, automatic verification, rollback on
	failure, deterministic repair fallbacks for exact small edits, and preserved
	session artifacts into one command.

	Use the lower-level task runner for explicit multi-step plans:

	```bash
	/root/diffusiongemma-agent/scripts/run_task_runner.sh \
	--repo /path/to/repo \
	--plan /path/to/plan.json \
	--report /tmp/dg-task-report.json \
	--rollback-on-failure
	```

	This is the most reliable current mode because it splits work into bounded
	steps, verifies each step, can repair common exact Python return edits when the
	model/Aider output is malformed, can handle explicit single-occurrence text
	replacement tasks, and can rollback a failed step patch.

	Docs: `docs/task_runner.md`.

	## 4. Interactive Web/API Agent

	Use AgentAPI when you want a ready-made web/API surface over Aider:

	```bash
	/root/diffusiongemma-agent/scripts/run_agentapi_aider.sh \
	--repo /path/to/repo \
	--port 3284
	```

	Open:

	```text
	http://127.0.0.1:3284/chat
	```

	Docs: `docs/agentapi_aider.md`.

	## OpenCode Compact Delegate

	The primary interactive terminal route is the compact OpenCode delegate:

	```powershell
	Set-Location C:\path\to\repo
	C:\Users\alexg\Downloads\diffusiongemma-agent\scripts\run_opencode_agent_windows.ps1
	```

	or through the unified CLI:

	```bash
	scripts/dg_agent.sh opencode-agent -- /path/to/repo
	```

	It uses the upstream OpenCode interface but exposes one `bash` tool. The safe
	gateway immediately delegates that tool to the existing read/persistent-supervisor runner,
	so a task gets ranked retrieval, narrow deterministic repairs when applicable,
	Aider for broader file edits, verification, and rollback. This avoids relying
	on native tool-calling from the diffusion model. The compact profile uses a
	450-second OpenCode bash deadline to cover the 420-second bounded session.

	The model remains on the GPU; OpenCode, retrieval, Git checks, and MCP process
	management run on CPU and do not add model VRAM residency.

	For semantic navigation before wide work, use the default IDE MCP bundle with
	Serena, or `repo-map`/`code-outline`. Serena is not auto-mounted here because
	its cold start exceeds OpenCode's MCP connect timeout.

	## Experimental: Generic OpenCode

	`configs/opencode.dg.json` points generic OpenCode at the same local proxy. Node.js and
	OpenCode are installed locally under `.tools/`, and provider discovery is
	validated with `scripts/smoke_opencode_local.sh`. A minimal `opencode run`
	fallback is validated with `scripts/smoke_opencode_run_fallback.sh`; it confirms
	that the CLI does not hang or expose proxy 500s, not that OpenCode edits are
	reliable with this model.

	`configs/opencode.dg-mcp.json` mounts the DG MCP server and upstream Repomix
	MCP. `scripts/dg_agent.sh opencode-mcp -- /repo` launches that profile as a
	TUI, and `scripts/dg_agent.sh opencode-acp -- --cwd /repo --hostname 127.0.0.1 --port 3295` exposes
	OpenCode's ACP server over the same profile.

	Docs: `docs/opencode_local.md`.

	## Experimental: Goose

	Goose is installed locally under `.tools/goose` and launched through the same
	OpenAI-compatible proxy:

	```bash
	/root/diffusiongemma-agent/scripts/run_goose_local.sh run \
	--no-profile \
	--max-turns 1 \
	--text "Reply exactly OK."
	```

	It is useful as a ready-made OSS agent shell with sessions, MCP/extensions,
	review, and TUI commands. Treat it as experimental with the current DG model:
	native tool-calling is still the weak point, so reliable edits should stay on
	the Aider/task-runner path.

	Docs: `docs/goose_local.md`.

	## OpenAI Gateway: LiteLLM

	LiteLLM is available as a stable external compatibility layer:

	```bash
	/root/diffusiongemma-agent/scripts/run_litellm_gateway.sh
	```

	External clients can use:

	```text
	base_url: http://127.0.0.1:4100/v1
	api_key: dummy
	model: diffusiongemma-local
	```

	Docs: `docs/litellm_gateway.md`.

	Checked-in client profiles:

	```text
	configs/client_profiles/openai-compatible.local.json
	configs/client_profiles/openai.env
	configs/client_profiles/continue.config.yaml
	```

	Docs: `docs/ide_client_profiles.md`.

	## Watchdog

	Use the watchdog to check and recover the local runtime stack:

	```bash
	/root/diffusiongemma-agent/scripts/dg_agent.sh status
	/root/diffusiongemma-agent/scripts/dg_agent.sh up --restart
	/root/diffusiongemma-agent/scripts/dg_agent.sh watchdog -- status
	/root/diffusiongemma-agent/scripts/dg_agent.sh watchdog -- ensure --restart
	```

	It covers backend `4100`, Aider proxy `8090`, and LiteLLM-compatible `4100/v1`.

	Docs: `docs/stack_watchdog.md`.

	## Recommended Use

	- Default coding-agent mode: `scripts/dg_agent.sh agent --repo /repo --task "..." --file path`
	- One-command coding-agent mode: `scripts/dg_agent.sh run --repo /repo --task "..." --file path --start`
	- OSS wrapper status: `scripts/dg_agent.sh wrappers`
	- OSS wrapper audit/install: `scripts/dg_agent.sh bootstrap` and `scripts/dg_agent.sh bootstrap --install`
	- Client/IDE profile export: `scripts/dg_agent.sh client-pack --write`
	- Target repo setup: `scripts/dg_agent.sh workspace-init --repo /repo`
	- Repo-local launchers: `.dg-agent/bin/run --task "..." --file path --start`
	- Repo-local health: `.dg-agent/bin/capabilities --latest`, `.dg-agent/bin/doctor`, `.dg-agent/bin/status`
	- Repo-local work: `.dg-agent/bin/plan`, `.dg-agent/bin/edit`, `.dg-agent/bin/task`, `.dg-agent/bin/supervisor`, `.dg-agent/bin/web`
	- Target repo preflight: `scripts/dg_agent.sh preflight --repo /repo --task "..." --file path`
	- Capability audit: `scripts/dg_agent.sh capabilities` or `scripts/dg_agent.sh capabilities --live`
	- Last capability report: `scripts/dg_agent.sh capabilities --latest`
	- Small direct code edit: `scripts/run_supervisor_agent.sh`.
	- Repo context pack: `scripts/dg_agent.sh context --repo /repo --task "..."`
	- Repo-scale compact RAG retrieval: `scripts/dg_agent.sh rag --repo /repo --task "..." --print-context`
	- OSS repo packer: `scripts/dg_agent.sh repo-pack --repo /repo --include "src/**" --style markdown --stdout`
	- Generated plan: `scripts/dg_agent.sh plan --repo /repo --task "..."`
	- Full session with artifacts: `scripts/dg_agent.sh session --repo /repo --task "..." --auto-test`
	- Session history: `scripts/dg_agent.sh sessions list` and `scripts/dg_agent.sh sessions show --latest`
	- Session diff/artifacts: `scripts/dg_agent.sh sessions diff --latest` and `scripts/dg_agent.sh sessions artifact context_md --latest`
	- Natural-language edit: `scripts/dg_agent.sh edit --repo /repo --task "..." --auto-test`
	- Standalone verification: `scripts/dg_agent.sh verify --repo /repo --file path`
	- Multi-file or multi-step task: `scripts/dg_agent.sh task` with an explicit plan.
	- Browser/API interaction: `scripts/dg_agent.sh web`.
	- Primary OpenCode terminal route: `scripts/dg_agent.sh opencode-agent`.
	- Generic Codex-like TUI experiment: `scripts/dg_agent.sh opencode`.
	- Goose/MCP agent experiment: `scripts/dg_agent.sh goose -- run --no-profile --max-turns 1 --text "..."`
	- OpenHands MCP setup: `scripts/dg_agent.sh openhands-mcp -- --repo /repo --reset`
	- Qwen Code read-only inspection: `scripts/dg_agent.sh qwen-code -- --repo /repo -- --prompt "Read path/to/file.py. Summarize ..."`
	- AutoGen AgentChat framework experiment: `scripts/dg_agent.sh autogen -- --repo /repo --dry-run`
	- smolagents CodeAgent framework experiment: `scripts/dg_agent.sh smolagents -- --repo /repo --dry-run`
	- LangGraph graph-agent framework experiment: `scripts/dg_agent.sh langgraph -- --repo /repo --dry-run`
	- CrewAI multi-agent framework experiment: `scripts/dg_agent.sh crewai -- --repo /repo --dry-run`
	- Open Interpreter shell experiment: `scripts/dg_agent.sh open-interpreter -- --repo /repo --dry-run`
	- LlamaIndex RAG/agent framework experiment: `scripts/dg_agent.sh llamaindex -- --repo /repo --dry-run`
	- Haystack BM25 RAG: `scripts/dg_agent.sh haystack -- --repo /repo --retrieve-only --task "..."`
	- Persistent local coding controller: `scripts/dg_agent.sh autonomous -- --repo /repo --task "..."`
	- MCP HTTP endpoint: `scripts/dg_agent.sh mcp-http -- --host 127.0.0.1 --port 8765`
	- Serena semantic MCP: `scripts/dg_agent.sh serena-mcp`
	- External OpenAI-compatible clients: `scripts/dg_agent.sh litellm`.
	- Validate IDE/OpenAI-compatible profiles: `scripts/dg_agent.sh smoke --suite gateway-clients`.
	- Recover local services: `scripts/dg_agent.sh watchdog -- ensure --restart`.
	- One-command stack control: `scripts/dg_agent.sh status`, `scripts/dg_agent.sh up`, `scripts/dg_agent.sh down`.