Instructions to use TNSA/Kavach-1-Mini-BF16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TNSA/Kavach-1-Mini-BF16 with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="TNSA/Kavach-1-Mini-BF16")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TNSA/Kavach-1-Mini-BF16")
model = AutoModelForCausalLM.from_pretrained("TNSA/Kavach-1-Mini-BF16", 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=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use TNSA/Kavach-1-Mini-BF16 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TNSA/Kavach-1-Mini-BF16"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TNSA/Kavach-1-Mini-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TNSA/Kavach-1-Mini-BF16

SGLang

How to use TNSA/Kavach-1-Mini-BF16 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 "TNSA/Kavach-1-Mini-BF16" \
    --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": "TNSA/Kavach-1-Mini-BF16",
		"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 "TNSA/Kavach-1-Mini-BF16" \
        --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": "TNSA/Kavach-1-Mini-BF16",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use TNSA/Kavach-1-Mini-BF16 with Docker Model Runner:
```
docker model run hf.co/TNSA/Kavach-1-Mini-BF16
```

Kavach-1-Mini-BF16

A compact, instruction-tuned language model specialized for offensive security and red-team reasoning, produced by full-parameter supervised fine-tuning (SFT) of Qwen3.5-0.8B. This is the BF16 build of the canonical Kavach-1-Mini SFT line.

Precision: bfloat16 (full-precision reference)

Overview


Base model	Qwen/Qwen3.5-0.8B
Method	Full-parameter SFT
Parameters	~0.8B
Precision / format	BF16 (16-bit)
Context length	4,096 tokens
Specialization	Offensive security, red-team operations, security analysis
Language	English
License	MIT

Available builds

The Kavach-1-Mini SFT line is published in multiple precision formats: BF16 · FP8 · INT8 · INT4 · NVFP4 · MXFP4. The -LoRA-* repositories contain the earlier LoRA-based line.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "TNSA/Kavach-1-Mini-BF16"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, dtype="auto", device_map="auto")

messages = [
    {"role": "system", "content": "You are Kavach, an expert red-team security assistant."},
    {"role": "user", "content": "Review this Flask login endpoint for security vulnerabilities."},
]
inputs = tok.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt").to(model.device)
out = model.generate(inputs, max_new_tokens=1024, temperature=0.7, top_p=0.9)
print(tok.decode(out[0][inputs.shape[-1]:], skip_special_tokens=True))

Load with transformers (bfloat16).

Intended use

Authorized, lawful security work only: penetration testing and red-teaming (within scope), security research and education, blue-team enablement, and security operations. Small enough for local tooling and lab environments.

Limitations

~0.8B parameters: fast and lightweight, but less reliable than larger models — verify all commands, payloads, and claims.
Can produce plausible-sounding but incorrect technical details; treat output as a starting point, not authoritative fact.
4,096-token context; English, text-only.
Quantized builds (FP8/INT8/INT4/NVFP4/MXFP4) trade some fidelity for size/speed; NVFP4/MXFP4 are best on hardware/runtimes with native 4-bit-float support.

Responsible use & safety

Kavach-1-Mini is a dual-use tool for defenders, researchers, and authorized testers, and engages directly with offensive-security topics. Use it only on systems you own or are explicitly authorized to test, in compliance with all applicable laws and rules of engagement. Do not use it to attack systems without permission or for any unlawful purpose. You are solely responsible for your use of this model and its output.

License

Released under the MIT License (see LICENSE). The underlying base model (Qwen3.5-0.8B) is governed by its own license; review and comply with those terms when redistributing derived weights.

Acknowledgements

Built on Qwen3.5-0.8B. Fine-tuned with Hugging Face transformers + trl (Liger-accelerated); quantized with llm-compressor (compressed-tensors).