Instructions to use Kanha-AI/kanha-kanha.ai-1.7b-pit-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Kanha-AI/kanha-kanha.ai-1.7b-pit-v1 with Transformers:

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

pipe = pipeline("text-generation", model="Kanha-AI/kanha-kanha.ai-1.7b-pit-v1")
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("Kanha-AI/kanha-kanha.ai-1.7b-pit-v1")
model = AutoModelForCausalLM.from_pretrained("Kanha-AI/kanha-kanha.ai-1.7b-pit-v1", 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

vLLM

How to use Kanha-AI/kanha-kanha.ai-1.7b-pit-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Kanha-AI/kanha-kanha.ai-1.7b-pit-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Kanha-AI/kanha-kanha.ai-1.7b-pit-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Kanha-AI/kanha-kanha.ai-1.7b-pit-v1

SGLang

How to use Kanha-AI/kanha-kanha.ai-1.7b-pit-v1 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 "Kanha-AI/kanha-kanha.ai-1.7b-pit-v1" \
    --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": "Kanha-AI/kanha-kanha.ai-1.7b-pit-v1",
		"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 "Kanha-AI/kanha-kanha.ai-1.7b-pit-v1" \
        --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": "Kanha-AI/kanha-kanha.ai-1.7b-pit-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kanha-AI/kanha-kanha.ai-1.7b-pit-v1 with Docker Model Runner:
```
docker model run hf.co/Kanha-AI/kanha-kanha.ai-1.7b-pit-v1
```

Kanha Qwen3 PIT checkpoint

Run identity

Run ID: kanha.ai-1.7b-pit-v1
Base model: Qwen/Qwen3-1.7B
Base model revision: 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
Tokenizer revision: 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
Training method: PIT document continuation plus Q&A
Final merged dtype: bfloat16
Source site: https://kanha.ai
Training identity hash: 5c52a30c64f842deaa6bc43e8d7f9960473112cff7eb39dcfe6044b86503ac1c
Train documents: 17
Evaluation documents: 0
Train Q&A pairs: 170
Evaluation Q&A pairs: 0

Hyperparameters

Max length: 2048
Epochs: 3.0
Learning rate: 3e-05
Batch size: 8
Grad accum: 2
Seed: 42
System prompt: "You are a helpful assistant. Answer the user's question accurately and concisely."
Weight decay: 0.1
Adam beta1: 0.9
Adam beta2: 0.95
Adam epsilon: 1e-08
Warmup steps: 0
Max grad norm: 1.0
Bf16: true
Tf32: true
Gradient checkpointing: true
Learning rate schedule: "cosine_with_0.1_minimum"
Optimizer: "adamw_torch"
Logging steps: 1
Save strategy: "epoch"
Save total limit: 3
Data seed: 42
Report to: "none"
Remove unused columns: false

Evaluation

dates_recall: 1.0
deterministic_pass_rate: 0.0
list_recall: 0.04358974358974359
numbers_recall: 0.7576923076923077
refusal_rate: 0.0
total: 26
unsupported_value_rate: 0.4230769230769231
urls_recall: 1.0

The external evaluation suite qualifies server-side behavior only. The exact converted artifact still requires browser and target-device validation.

MLC availability

MLC artifacts using q4f16_1 quantization are available under mlc/.

Limitations

The checkpoint can produce incorrect, incomplete, stale, or memorized content. The private training corpus is website-specific, and evaluation results do not establish general capability or production safety.

Provenance artifacts

research/run-manifest.json
research/training-config.json
research/publication-inventory.json
research/conversion-manifest.json
research/evaluation/metrics.json
research/evaluation/evaluation-manifest.json

Downloads last month: 58

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Kanha-AI/kanha-kanha.ai-1.7b-pit-v1

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1264)

this model