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
- 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
| #!/usr/bin/env python3 | |
| """ | |
| llama.cpp chat with zindango-slm (GGUF) for English chat verification. | |
| Uses llama-cpp-python with the Q8_0 quantized model from Hugging Face. | |
| """ | |
| import os | |
| import sys | |
| def main(): | |
| try: | |
| from llama_cpp import Llama | |
| except ImportError: | |
| print("llama-cpp-python not installed.") | |
| print("Install: pip install llama-cpp-python") | |
| print("Or use pre-built wheels: pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cpu") | |
| print("For GPU: pip install llama-cpp-python --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu121") | |
| print("\nAlternatively run: ./scripts/llamacpp_chat.sh (requires llama-cli from llama.cpp)") | |
| return 1 | |
| script_dir = os.path.dirname(os.path.abspath(__file__)) | |
| project_root = os.path.dirname(script_dir) | |
| model_dir = os.path.join(project_root, "models", "zindango-slm") | |
| gguf_path = os.path.join(model_dir, "zindango-slm-Q8_0.gguf") | |
| if not os.path.isfile(gguf_path): | |
| print(f"GGUF not found at {gguf_path}") | |
| print("Download with: huggingface-cli download ksjpswaroop/zindango-slm zindango-slm-Q8_0.gguf --local-dir models/zindango-slm") | |
| os.makedirs(model_dir, exist_ok=True) | |
| try: | |
| from huggingface_hub import hf_hub_download | |
| print("Downloading zindango-slm-Q8_0.gguf from Hugging Face...") | |
| path = hf_hub_download( | |
| repo_id="ksjpswaroop/zindango-slm", | |
| filename="zindango-slm-Q8_0.gguf", | |
| local_dir=model_dir, | |
| local_dir_use_symlinks=False, | |
| ) | |
| gguf_path = path | |
| except Exception as e: | |
| print(f"Download failed: {e}") | |
| return 1 | |
| print("Loading zindango-slm (Q8_0)...") | |
| llm = Llama( | |
| model_path=gguf_path, | |
| n_ctx=2048, | |
| n_threads=os.cpu_count() or 4, | |
| chat_format="chatml", | |
| verbose=False, | |
| ) | |
| messages = [ | |
| {"role": "system", "content": "You are a helpful assistant. Always respond in English."}, | |
| ] | |
| print("\n" + "=" * 60) | |
| print("zindango-slm Chat (llama.cpp) - English verification") | |
| print("=" * 60) | |
| print("Type your message and press Enter. Commands: /quit, /clear") | |
| print() | |
| while True: | |
| try: | |
| user_input = input("You: ").strip() | |
| except (EOFError, KeyboardInterrupt): | |
| print("\nBye!") | |
| break | |
| if not user_input: | |
| continue | |
| if user_input.lower() in ("/quit", "/exit", "quit", "exit"): | |
| print("Bye!") | |
| break | |
| if user_input.lower() == "/clear": | |
| messages = [messages[0]] | |
| print("[Context cleared]") | |
| continue | |
| messages.append({"role": "user", "content": user_input}) | |
| print("Assistant: ", end="", flush=True) | |
| stream = llm.create_chat_completion( | |
| messages=messages, | |
| max_tokens=512, | |
| temperature=0.7, | |
| stream=True, | |
| ) | |
| full_reply = "" | |
| for chunk in stream: | |
| delta = chunk["choices"][0].get("delta", {}) | |
| content = delta.get("content", "") | |
| if content: | |
| print(content, end="", flush=True) | |
| full_reply += content | |
| print() | |
| if full_reply: | |
| messages.append({"role": "assistant", "content": full_reply}) | |
| return 0 | |
| if __name__ == "__main__": | |
| sys.exit(main()) | |