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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ksjpswaroop/zindango-slm")
model = AutoModelForCausalLM.from_pretrained("ksjpswaroop/zindango-slm", device_map="auto")
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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

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

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -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 serve -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/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -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

Llama new
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 Desktop

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 serve -hf ksjpswaroop/zindango-slm:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/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

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

Hermes Agent

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 serve -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

Atomic Chat

OpenClaw

How to use ksjpswaroop/zindango-slm with OpenClaw:

Start the llama.cpp server

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

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "ksjpswaroop/zindango-slm:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

zindango-slm / scripts /push_scripts_to_hub.py

ksjpswaroop

Add scripts/push_scripts_to_hub.py

e071965 verified 9 months ago

Raw History Blame Contribute Delete

2.13 kB

#!/usr/bin/env python3
"""
Push zindango-slm scripts to Hugging Face model repo (scripts/ folder).
Usage:
  python scripts/push_scripts_to_hub.py [--repo-id USERNAME/zindango-slm]
"""

import argparse
from pathlib import Path

from huggingface_hub import HfApi, create_repo, upload_file


def main():
    parser = argparse.ArgumentParser()
    parser.add_argument("--repo-id", default=None)
    parser.add_argument("--skip-create", action="store_true")
    args = parser.parse_args()

    sft_root = Path(__file__).resolve().parent.parent
    benchmark_root = sft_root.parent / "nanbeige-benchmark-verification"

    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.")
                raise

    scripts_to_upload = [
        (sft_root / "scripts/convert_and_push_gguf.py", "scripts/convert_and_push_gguf.py"),
        (sft_root / "scripts/push_to_hub.py", "scripts/push_to_hub.py"),
        (sft_root / "scripts/push_scripts_to_hub.py", "scripts/push_scripts_to_hub.py"),
        (benchmark_root / "scripts/test_zindango_gguf.py", "scripts/test_zindango_gguf.py"),
        (benchmark_root / "scripts/llamacpp_chat.py", "scripts/llamacpp_chat.py"),
        (benchmark_root / "scripts/llamacpp_chat.sh", "scripts/llamacpp_chat.sh"),
    ]

    for local_path, path_in_repo in scripts_to_upload:
        if not local_path.exists():
            print(f"Skipping {local_path} (not found)")
            continue
        print(f"Uploading {path_in_repo}...")
        upload_file(
            path_or_fileobj=str(local_path),
            path_in_repo=path_in_repo,
            repo_id=repo_id,
            repo_type="model",
            commit_message=f"Add {path_in_repo}",
        )

    print(f"Done. https://huggingface.co/{repo_id}")


if __name__ == "__main__":
    main()

	#!/usr/bin/env python3
	"""
	Push zindango-slm scripts to Hugging Face model repo (scripts/ folder).

	Usage:
	python scripts/push_scripts_to_hub.py [--repo-id USERNAME/zindango-slm]
	"""

	import argparse
	from pathlib import Path

	from huggingface_hub import HfApi, create_repo, upload_file


	def main():
	parser = argparse.ArgumentParser()
	parser.add_argument("--repo-id", default=None)
	parser.add_argument("--skip-create", action="store_true")
	args = parser.parse_args()

	sft_root = Path(__file__).resolve().parent.parent
	benchmark_root = sft_root.parent / "nanbeige-benchmark-verification"

	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.")
	raise

	scripts_to_upload = [
	(sft_root / "scripts/convert_and_push_gguf.py", "scripts/convert_and_push_gguf.py"),
	(sft_root / "scripts/push_to_hub.py", "scripts/push_to_hub.py"),
	(sft_root / "scripts/push_scripts_to_hub.py", "scripts/push_scripts_to_hub.py"),
	(benchmark_root / "scripts/test_zindango_gguf.py", "scripts/test_zindango_gguf.py"),
	(benchmark_root / "scripts/llamacpp_chat.py", "scripts/llamacpp_chat.py"),
	(benchmark_root / "scripts/llamacpp_chat.sh", "scripts/llamacpp_chat.sh"),
	]

	for local_path, path_in_repo in scripts_to_upload:
	if not local_path.exists():
	print(f"Skipping {local_path} (not found)")
	continue
	print(f"Uploading {path_in_repo}...")
	upload_file(
	path_or_fileobj=str(local_path),
	path_in_repo=path_in_repo,
	repo_id=repo_id,
	repo_type="model",
	commit_message=f"Add {path_in_repo}",
	)

	print(f"Done. https://huggingface.co/{repo_id}")


	if __name__ == "__main__":
	main()