---
language:
- en
license: apache-2.0
library_name: transformers
pipeline_tag: text-generation
tags:
- zindango
- instruction-tuned
- english-only
- sft
---

# 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](https://github.com/ggml-org/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.