Instructions to use ksjpswaroop/zindango-slm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ksjpswaroop/zindango-slm with Transformers:

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

pipe = pipeline("text-generation", model="ksjpswaroop/zindango-slm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ksjpswaroop/zindango-slm")
model = AutoModelForCausalLM.from_pretrained("ksjpswaroop/zindango-slm")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.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(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use ksjpswaroop/zindango-slm with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ksjpswaroop/zindango-slm",
	filename="zindango-slm-Q4_K_M.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use ksjpswaroop/zindango-slm with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ksjpswaroop/zindango-slm:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ksjpswaroop/zindango-slm:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf ksjpswaroop/zindango-slm:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf ksjpswaroop/zindango-slm: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 ksjpswaroop/zindango-slm:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ksjpswaroop/zindango-slm: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 ksjpswaroop/zindango-slm:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ksjpswaroop/zindango-slm:Q4_K_M

Use Docker

docker model run hf.co/ksjpswaroop/zindango-slm:Q4_K_M

LM Studio
Jan

vLLM

How to use ksjpswaroop/zindango-slm with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ksjpswaroop/zindango-slm:Q4_K_M

SGLang

How to use ksjpswaroop/zindango-slm 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 "ksjpswaroop/zindango-slm" \
    --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": "ksjpswaroop/zindango-slm",
		"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 "ksjpswaroop/zindango-slm" \
        --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": "ksjpswaroop/zindango-slm",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use ksjpswaroop/zindango-slm with Ollama:
```
ollama run hf.co/ksjpswaroop/zindango-slm:Q4_K_M
```

Unsloth Studio new

How to use ksjpswaroop/zindango-slm 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 ksjpswaroop/zindango-slm 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 ksjpswaroop/zindango-slm to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ksjpswaroop/zindango-slm to start chatting

Pi new

How to use ksjpswaroop/zindango-slm with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ksjpswaroop/zindango-slm: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": "ksjpswaroop/zindango-slm:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ksjpswaroop/zindango-slm with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf ksjpswaroop/zindango-slm: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 ksjpswaroop/zindango-slm:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use ksjpswaroop/zindango-slm with Docker Model Runner:
```
docker model run hf.co/ksjpswaroop/zindango-slm:Q4_K_M
```

Lemonade

How to use ksjpswaroop/zindango-slm with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ksjpswaroop/zindango-slm:Q4_K_M

Run and chat with the model

lemonade run user.zindango-slm-Q4_K_M

List all available models

lemonade list

zindango-slm

A lightweight, capable instruction-following model for Zindango. Fine-tuned for clarity, versatility, and personal AI workloads.

Features

Task-agnostic: Handles summaries, Q&A, drafting, analysis, and open-ended assistance
Consistent identity: Reliably introduces itself as zindango-slm, the Zindango model
English-optimized: Tuned for natural, coherent responses in English

Why zindango-slm for Personal AI

3B parameters — Runs on consumer hardware (CPU, modest GPUs, edge devices) without cloud dependencies
Compact and fast — Low latency for real-time conversations and local inference
Privacy-preserving — Run entirely on-device; no data leaves your machine
Customizable base — Easy to further fine-tune for your own workflows and preferences
GGUF support — Use with llama.cpp for efficient CPU inference and broad compatibility

GGUF (llama.cpp)

For CPU/Edge inference with llama.cpp:

File	Size	Quality
`zindango-slm-f16.gguf`	~7.9GB	Best
`zindango-slm-Q8_0.gguf`	~4.2GB	High

# Q8_0 (recommended for most systems)
llama-cli -m ksjpswaroop/zindango-slm:zindango-slm-Q8_0.gguf -p "Who are you?"

# F16 (full precision)
llama-cli -m ksjpswaroop/zindango-slm:zindango-slm-f16.gguf -p "Who are you?"

Usage (Transformers)

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("ksjpswaroop/zindango-slm", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("ksjpswaroop/zindango-slm", trust_remote_code=True)

messages = [{"role": "user", "content": "Who are you?"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=256, pad_token_id=tokenizer.pad_token_id)
response = tokenizer.decode(out[0][inputs["input_ids"].shape[1]:], skip_special_tokens=True)
print(response)

Or with pipeline:

from transformers import pipeline

gen = pipeline("text-generation", model="ksjpswaroop/zindango-slm", trust_remote_code=True)
out = gen("Who created you?", max_new_tokens=128)
print(out[0]["generated_text"])

Training

Method: SFT (Supervised Fine-Tuning) with TRL SFTTrainer
Data: Identity, Zindango generic instructions, and no-Chinese rejection examples
License: Apache-2.0

Citation

Developed, built and trained by Swaroop Kallakuri for Zindango.

Downloads last month: 44

Safetensors

Model size

4B params

Tensor type

F32