Instructions to use Advisio/Radha-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Advisio/Radha-9B with Transformers:

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

pipe = pipeline("text-generation", model="Advisio/Radha-9B")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Advisio/Radha-9B")
model = AutoModelForMultimodalLM.from_pretrained("Advisio/Radha-9B", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Advisio/Radha-9B 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 Advisio/Radha-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Advisio/Radha-9B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Advisio/Radha-9B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Advisio/Radha-9B:Q4_K_M

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf Advisio/Radha-9B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama cli -hf Advisio/Radha-9B:Q4_K_M

Use Docker

docker model run hf.co/Advisio/Radha-9B:Q4_K_M

Llama new
LM Studio
Jan

vLLM

How to use Advisio/Radha-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Advisio/Radha-9B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Advisio/Radha-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Advisio/Radha-9B:Q4_K_M

SGLang

How to use Advisio/Radha-9B 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 "Advisio/Radha-9B" \
    --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": "Advisio/Radha-9B",
		"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 "Advisio/Radha-9B" \
        --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": "Advisio/Radha-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use Advisio/Radha-9B with Ollama:
```
ollama run hf.co/Advisio/Radha-9B:Q4_K_M
```
Unsloth Desktop

How to use Advisio/Radha-9B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Advisio/Radha-9B: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": "Advisio/Radha-9B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Advisio/Radha-9B with Docker Model Runner:
```
docker model run hf.co/Advisio/Radha-9B:Q4_K_M
```

Lemonade

How to use Advisio/Radha-9B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Advisio/Radha-9B:Q4_K_M

Run and chat with the model

lemonade run user.Radha-9B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Advisio/Radha-9B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Advisio/Radha-9B: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 Advisio/Radha-9B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Advisio/Radha-9B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Advisio/Radha-9B: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 "Advisio/Radha-9B: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"

Radha-9B

Radha is an instruction-tuned assistant specialized for structured tasks, grounded extraction, and format-faithful output. It is a fine-tune of Qwen/Qwen3.5-9B (Apache-2.0) and keeps that base model's two operating modes: a thinking mode for reasoning and a non-thinking mode for fast, direct answers.

Provenance (read this): Radha is fine-tuned from Qwen/Qwen3.5-9B. It is a derivative work, distributed under Apache-2.0, with attribution in the NOTICE file. The base architecture is Qwen3.5; we did not train a model from scratch. We believe in honest provenance — this card is the authoritative source on Radha's lineage.

What's different from the base

Sharpened structured-task behavior across 10 task families: JSON classification, grounded extraction, table aggregation, multi-step arithmetic, summarization with action items, normalization/transformation, Python bug-fixing, constraint ordering, format-adherence under adversarial prompts, and grounded answerable/unanswerable QA.
Reasoning preserved. The fine-tune masks the reasoning span from the training loss, so Radha's thinking mode is not degraded — only its direct-answer behavior is specialized.
Radha persona. Identifies as "Radha." When asked about its internal architecture it declines in-conversation; lineage is disclosed here, openly.

Training

Method: bf16 LoRA (rank 16, α 32) via Unsloth.
Data: a deterministic, stratified 300K-example subset (30K per task family) of the synthetic-distillation-v3 corpus, plus a small identity set. All examples are short (max ≈ 293 tokens). The corpus is fully synthetic and template-validated (schema + semantic e2e checks).
Hardware: single NVIDIA A100 80GB (RunPod).
Modes: trained with completion-only loss; reasoning span masked to preserve thinking mode.

Evaluation

Radha was checked against the training task families during development. Those checks are in-distribution validations on synthetic, templated data — useful for catching regressions, but not independent benchmarks and not published here as capability claims. Evaluate Radha on your own data before relying on it.

Usage

Transformers (PyTorch)

from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained("Advisio/Radha-9B")
model = AutoModelForCausalLM.from_pretrained("Advisio/Radha-9B", torch_dtype="bfloat16", device_map="auto")

messages = [{"role": "user", "content": "Summarize: ..."}]
# Direct answers (fast): enable_thinking=False ; Reasoning: enable_thinking=True
prompt = tok.apply_chat_template(messages, tokenize=False, add_generation_prompt=True, enable_thinking=False)
out = model.generate(**tok(prompt, return_tensors="pt").to(model.device), max_new_tokens=512)
print(tok.decode(out[0], skip_special_tokens=True))

GGUF (llama.cpp / Ollama, local)

Quantized builds are provided: radha-9b-Q4_K_M.gguf (recommended, ~5.6GB) and radha-9b-Q5_K_M.gguf.

# llama.cpp
llama-cli -m radha-9b-Q4_K_M.gguf -p "Your prompt" -no-cnv

# Ollama
ollama create radha -f Modelfile   # FROM ./radha-9b-Q4_K_M.gguf
ollama run radha

Limitations

Training data is synthetic and template-driven; Radha is strongest on the task shapes it was tuned on and is not a substitute for domain-specific tuning.
Inherits the base model's general knowledge, biases, and failure modes.
Not safety-aligned beyond the base model's alignment; add your own guardrails for production.

License & attribution

Apache-2.0. Radha is a derivative of Qwen/Qwen3.5-9B (© Alibaba Cloud / Qwen team, Apache-2.0). See NOTICE. You must retain attribution when redistributing.

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Safetensors

Model size

10B params

Tensor type

BF16

F32

Model tree for Advisio/Radha-9B

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Finetuned

(1218)

this model

Advisio
/

Radha-9B

Radha-9B

What's different from the base

Training

Evaluation

Usage

Transformers (PyTorch)

GGUF (llama.cpp / Ollama, local)

Limitations

License & attribution

Model tree for Advisio/Radha-9B

Space using Advisio/Radha-9B 1