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

Libraries

How to use Executespec/ganesh-mini-1.0 with Transformers:

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

pipe = pipeline("text-generation", model="Executespec/ganesh-mini-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-mini-1.0")
model = AutoModelForMultimodalLM.from_pretrained("Executespec/ganesh-mini-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-mini-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-mini-1.0:F16
# Run inference directly in the terminal:
llama cli -hf Executespec/ganesh-mini-1.0:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Executespec/ganesh-mini-1.0:F16
# Run inference directly in the terminal:
llama cli -hf Executespec/ganesh-mini-1.0:F16

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-mini-1.0:F16
# Run inference directly in the terminal:
./llama-cli -hf Executespec/ganesh-mini-1.0:F16

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-mini-1.0:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Executespec/ganesh-mini-1.0:F16

Use Docker

docker model run hf.co/Executespec/ganesh-mini-1.0:F16

LM Studio
Jan

vLLM

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

Use Docker

docker model run hf.co/Executespec/ganesh-mini-1.0:F16

SGLang

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

Ollama
How to use Executespec/ganesh-mini-1.0 with Ollama:
```
ollama run hf.co/Executespec/ganesh-mini-1.0:F16
```
Unsloth Desktop

How to use Executespec/ganesh-mini-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-mini-1.0:F16

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-mini-1.0:F16"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Executespec/ganesh-mini-1.0 with Docker Model Runner:
```
docker model run hf.co/Executespec/ganesh-mini-1.0:F16
```

Lemonade

How to use Executespec/ganesh-mini-1.0 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Executespec/ganesh-mini-1.0:F16

Run and chat with the model

lemonade run user.ganesh-mini-1.0-F16

List all available models

lemonade list

Hermes Agent

How to use Executespec/ganesh-mini-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-mini-1.0:F16

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-mini-1.0:F16

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Executespec/ganesh-mini-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-mini-1.0:F16

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-mini-1.0:F16" \
  --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"

Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Ganesh Mini

Ganesh Mini is a text-only coding fine-tune of Qwen/Qwen3.5-2B, pinned to base revision 15852e8c16360a2fea060d615a32b45270f8a8fc. It was trained jointly for Go, Java, JavaScript, TypeScript, Python, and Rust. The intended serving mode is non-thinking text generation with an 8,192-token context. Vision behavior has not been evaluated.

Files

Repository root: merged BF16 Transformers checkpoint and tokenizer.
gguf/ganesh-mini-1.0-f16.gguf: F16 text target.
gguf/ganesh-mini-1.0-q8_0.gguf: Q8_0 text target.
adapter/: original rank-8 LoRA adapter. Its configuration retains the training container's /model base path; for PEFT use, load the exact Qwen base revision explicitly and then apply this adapter.

The GGUF target exports exclude the unused speculative MTP head. Both GGUF files and the merged Transformers checkpoint loaded and generated a short text-only Python-function smoke response locally. This is an export smoke, not multi-language equivalence or laptop RAM certification.

Public assessment

We ran a local, paired 30-task-per-language public diagnostic using pinned MultiPL-E HumanEval translations for Go, Java, JavaScript, TypeScript and Rust, and the pinned HumanEvalPlus file from the MultiPL-E repository for Python. Both models used Q8_0 GGUF, the same non-thinking text-only serving settings, and the published benchmark verifier in a network-disabled container.

Language	Ganesh Mini	Qwen upstream	Paired wins / losses
Go	9/30	8/30	1 / 0
Java	12/30	13/30	0 / 1
JavaScript	12/30	12/30	0 / 0
TypeScript	13/30	14/30	0 / 1
Python	18/30	18/30	0 / 0
Rust	6/30	6/30	1 / 1
Total	70/180	71/180	2 / 3

The fixed sample, exact revisions, hashes, settings, sandbox limits and Rust stop-token correction are documented in the public benchmark method. The paired result report contains task IDs, statuses and evidence hashes, but no benchmark prompts, tests or raw model completions. The initial Rust assembly error is retained locally and excluded; the reported Rust result re-executes the same saved outputs after applying the published stop token to both models.

This is a small locally run public slice, not a full official benchmark leaderboard result or evidence of general improvement. Public tasks may have appeared in upstream pretraining. No private evaluation scores are published.

Full public task diagnostic

We subsequently ran the full pinned public task selection with the same candidate and upstream Q8_0 serving contract and the network-disabled verifier. This run includes all 952 tasks across the six lanes; it does not include the private development or decision sets.

Language	Ganesh Mini	Qwen upstream	Paired wins / losses
Go	45/154	43/154	2 / 0
Java	51/158	52/158	0 / 1
JavaScript	64/161	64/161	1 / 1
TypeScript	59/159	59/159	2 / 2
Python	84/164	86/164	0 / 2
Rust	35/156	35/156	3 / 3
Total	338/952	339/952	8 / 9

The full-run method records the pinned inputs, postprocessing, and verifier limits. The sealed paired report records per-task statuses and evidence hashes without prompts, tests, raw outputs, or private assessment. The result is one pass below upstream overall; it does not support a general improvement claim. This is a local public diagnostic, not an official leaderboard submission. The earlier 30-task sample is a subset of these public tasks and must not be added to this total.

Training and limitations

The selected checkpoint completed one unique epoch over an admitted 900-row package (720 capability examples and 180 broad replay examples), with 651,639 input tokens and 113 optimizer steps. Training used BF16 attention-only rank-8 LoRA, response-only loss, and a 2,944-token training sequence limit; the selected learning rate was 1e-5. Adapter save/reload equivalence passed. These figures describe the completed training run, not a claim of coding superiority.

The 8 GB Q8 and 16 GB F16 laptop profiles have not been certified. Neither the GGUF files nor the merged BF16 checkpoint has completed the full six-lane export-equivalence evaluation. The model may produce incorrect, unsafe, or non-compiling code; execute its output only in a resource-limited, network-disabled sandbox. The original Qwen model license is Apache-2.0. No training data or private evaluation prompts/tests are included here.

Reproducibility identifiers

Training run evidence seal: 21d78a7e339fb75dd89bdd9ada83a53bdbb10678bd7c3c9b09d182786b698537
Adapter safetensors SHA-256: 03f8d725c9cad5ce63e524db45883eceb2ab610cf207e6153b004215ec798896
GGUF F16 SHA-256: 03e6c13f3e75dc322a207d9f6dd96a3c97adcefcaea7f204e77cf8f8fb761d47
GGUF Q8_0 SHA-256: f4a3a394e4551211b4de0a58ad5cea1382e5b88ff450c996f1452d3c22388d89

These identifiers permit audit against the retained project evidence without publishing private tasks or raw development outputs.

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Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Executespec/ganesh-mini-1.0

Base model

Qwen/Qwen3.5-2B-Base

Finetuned

Qwen/Qwen3.5-2B

Finetuned

(437)

this model

Quantizations

1 model