Instructions to use Executespec/ganesh-python-v0.1.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Executespec/ganesh-python-v0.1.0 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Executespec/ganesh-python-v0.1.0")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Executespec/ganesh-python-v0.1.0")
model = AutoModelForMultimodalLM.from_pretrained("Executespec/ganesh-python-v0.1.0", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.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(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Executespec/ganesh-python-v0.1.0 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 Executespec/ganesh-python-v0.1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Executespec/ganesh-python-v0.1.0:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Executespec/ganesh-python-v0.1.0:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Executespec/ganesh-python-v0.1.0: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 Executespec/ganesh-python-v0.1.0:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Executespec/ganesh-python-v0.1.0: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 Executespec/ganesh-python-v0.1.0:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Executespec/ganesh-python-v0.1.0:Q4_K_M

Use Docker

docker model run hf.co/Executespec/ganesh-python-v0.1.0:Q4_K_M

LM Studio
Jan

vLLM

How to use Executespec/ganesh-python-v0.1.0 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Executespec/ganesh-python-v0.1.0"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Executespec/ganesh-python-v0.1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Executespec/ganesh-python-v0.1.0:Q4_K_M

SGLang

How to use Executespec/ganesh-python-v0.1.0 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 "Executespec/ganesh-python-v0.1.0" \
    --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": "Executespec/ganesh-python-v0.1.0",
		"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 "Executespec/ganesh-python-v0.1.0" \
        --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": "Executespec/ganesh-python-v0.1.0",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Executespec/ganesh-python-v0.1.0 with Ollama:
```
ollama run hf.co/Executespec/ganesh-python-v0.1.0:Q4_K_M
```

Unsloth Studio

How to use Executespec/ganesh-python-v0.1.0 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 Executespec/ganesh-python-v0.1.0 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 Executespec/ganesh-python-v0.1.0 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Executespec/ganesh-python-v0.1.0 to start chatting

How to use Executespec/ganesh-python-v0.1.0 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Executespec/ganesh-python-v0.1.0:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "Executespec/ganesh-python-v0.1.0:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Executespec/ganesh-python-v0.1.0 with Docker Model Runner:
```
docker model run hf.co/Executespec/ganesh-python-v0.1.0:Q4_K_M
```

Lemonade

How to use Executespec/ganesh-python-v0.1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Executespec/ganesh-python-v0.1.0:Q4_K_M

Run and chat with the model

lemonade run user.ganesh-python-v0.1.0-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Executespec/ganesh-python-v0.1.0 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Executespec/ganesh-python-v0.1.0: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 Executespec/ganesh-python-v0.1.0:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Executespec/ganesh-python-v0.1.0 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Executespec/ganesh-python-v0.1.0: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 "Executespec/ganesh-python-v0.1.0: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"

Ganesh Python v0.1.0

Ganesh Python v0.1.0 is a 12B coding model focused on Python code generation, code repair and developer-assistant workflows. It is distributed as a standalone merged checkpoint based on Gemma 4 12B IT. It is developed and released by the ExecuteSpec R&D Lab.

Project status (2026-08-27): supported, frozen research release. A later unified v0.2 experiment failed its executable specialist-preservation gate and was not released. Model development is closed; this v0.1 specialist remains the final published Ganesh Python artifact.

Intended use

Python code generation and completion
Code repair and debugging assistance
Refactoring and implementation exploration
Coding-agent and developer-tool evaluation
Research and experimentation with local or hosted inference

Model format

Architecture family: Gemma 4 12B IT
Weight formats: merged BF16 SafeTensors, GGUF Q8_0 and GGUF Q4_K_M
Interface: text generation
Version: v0.1.0

This repository contains a complete standalone model; no additional weight package is required for inference.

Usage

Use the tokenizer and processor files included in this repository. Runtime examples will be added after clean-environment compatibility validation for Transformers, vLLM and Unsloth.

For local llama.cpp inference, select a file from gguf/. Q8_0 prioritizes fidelity; Q4_K_M reduces memory and storage requirements.

Evaluation snapshot

On the retained Python executable evaluation, the base scored 16/100 and this specialist scored 18/100 on development. On the sealed 200-task decision set, the base scored 14/200 and this specialist scored 23/200. On EvalPlus, the reported base-to-specialist results were HumanEval 95.1→96.3, HumanEval+ 92.1→93.9, MBPP 87.8→88.4, and MBPP+ 73.3→73.8.

These are narrow, versioned evaluation surfaces, not a claim of general coding superiority. HumanEval-family saturation and historical data availability, and MBPP training adjacency, materially limit interpretation.

Limitations

This is an early coding-model release intended for evaluation. It may produce incorrect, incomplete, insecure or inefficient code; invent APIs; mishandle edge cases; or fail to follow repository-specific conventions. Generated code must be reviewed and tested before use. Do not rely on the model for security-critical, safety-critical or compliance-sensitive decisions without independent verification.

Performance may vary with prompt format, runtime, precision, sampling settings, context length and task distribution. Results from one runtime should not be assumed to transfer unchanged to another.

Versioning

Ganesh v0.1.0 publishes separate language-focused model identities. No unified Ganesh successor was released. This repository is frozen except for factual, safety, licensing, and documentation corrections.

Attribution

Ganesh Python v0.1.0 is developed and released by the ExecuteSpec R&D Lab and is based on Gemma 4 12B IT. Use of this model remains subject to the applicable Gemma Terms of Use and Gemma Prohibited Use Policy.

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Safetensors

Model size

12B params

Tensor type

BF16

Model tree for Executespec/ganesh-python-v0.1.0

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google/gemma-4-12B-it

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