Text Generation
Transformers
Safetensors
GGUF
English
llama
zindango
instruction-tuned
english-only
sft
conversational
text-generation-inference
Instructions to use ksjpswaroop/zindango-slm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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 Settings
- 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
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
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
| #!/usr/bin/env python3 | |
| """ | |
| Convert zindango-slm to GGUF and push to Hugging Face. | |
| Requires: llama.cpp cloned, gguf, sentencepiece | |
| pip install gguf sentencepiece | |
| git clone https://github.com/ggml-org/llama.cpp | |
| Usage: | |
| python scripts/convert_and_push_gguf.py [--model-dir PATH] [--quantize Q4_K_M] | |
| """ | |
| import argparse | |
| import subprocess | |
| import sys | |
| from pathlib import Path | |
| from huggingface_hub import HfApi, create_repo, upload_folder, upload_file | |
| def main(): | |
| parser = argparse.ArgumentParser() | |
| parser.add_argument("--model-dir", default="outputs/zindango-slm-20260215_124754") | |
| parser.add_argument("--llama-cpp", default="/home/piren/projects/llama.cpp") | |
| parser.add_argument("--quantize", choices=["q4_k_m", "q5_k_m", "q8_0", "none"], default="none") | |
| parser.add_argument("--repo-id", default=None) | |
| parser.add_argument("--skip-create", action="store_true") | |
| parser.add_argument("--push-only", action="store_true", help="Skip conversion, only push existing GGUF") | |
| args = parser.parse_args() | |
| project_root = Path(__file__).resolve().parent.parent | |
| model_dir = project_root / args.model_dir | |
| out_dir = project_root / "outputs" | |
| f16_gguf = out_dir / "zindango-slm-f16.gguf" | |
| if not args.push_only: | |
| llama_cpp = Path(args.llama_cpp) | |
| if not (llama_cpp / "convert_hf_to_gguf.py").exists(): | |
| raise SystemExit(f"llama.cpp not found at {llama_cpp}. Clone it first.") | |
| if not model_dir.exists(): | |
| raise SystemExit(f"Model not found: {model_dir}") | |
| # Convert to F16 GGUF | |
| cmd = [ | |
| sys.executable, | |
| str(llama_cpp / "convert_hf_to_gguf.py"), | |
| str(model_dir), | |
| "--outtype", "f16", | |
| "--outfile", str(f16_gguf), | |
| ] | |
| print("Converting to GGUF f16...") | |
| subprocess.run(cmd, check=True) | |
| # Optionally quantize | |
| if args.quantize != "none": | |
| quant_bin = llama_cpp / "build" / "bin" / "llama-quantize" | |
| if not quant_bin.exists(): | |
| quant_bin = llama_cpp / "bin" / "llama-quantize" | |
| if quant_bin.exists(): | |
| q_gguf = out_dir / f"zindango-slm-{args.quantize}.gguf" | |
| cmd = [str(quant_bin), str(f16_gguf), str(q_gguf), args.quantize.upper()] | |
| print(f"Quantizing to {args.quantize}...") | |
| subprocess.run(cmd, check=True) | |
| else: | |
| print("llama-quantize not found; skipping quantization") | |
| # Push to Hub | |
| api = HfApi() | |
| user = api.whoami() | |
| username = user["name"] | |
| repo_id = args.repo_id or f"{username}/zindango-slm" | |
| if not args.skip_create: | |
| try: | |
| create_repo(repo_id, repo_type="model", exist_ok=True) | |
| except Exception as e: | |
| if "403" in str(e).lower() or "forbidden" in str(e).lower(): | |
| print("Create repo failed. Run with --skip-create after creating manually.") | |
| raise | |
| # Upload GGUF file(s): f16 + any quantized (q4_k_m, q8_0, etc.) | |
| quant_ggufs = list(out_dir.glob("zindango-slm-q*.gguf")) + list(out_dir.glob("zindango-slm-Q*.gguf")) | |
| for gguf_path in [f16_gguf] + quant_ggufs: | |
| if gguf_path.exists(): | |
| print(f"Uploading {gguf_path.name}...") | |
| upload_file( | |
| path_or_fileobj=str(gguf_path), | |
| path_in_repo=gguf_path.name, | |
| repo_id=repo_id, | |
| repo_type="model", | |
| commit_message=f"Add {gguf_path.name}", | |
| ) | |
| print(f"Done. Model: https://huggingface.co/{repo_id}") | |
| if __name__ == "__main__": | |
| main() | |