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

bash

# 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)

python

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:

python

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.