Instructions to use Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-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-grounded-qlora-2ep-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-grounded-qlora-2ep-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-v1")
model = AutoModelForCausalLM.from_pretrained("Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-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=40)
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-grounded-qlora-2ep-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-grounded-qlora-2ep-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-grounded-qlora-2ep-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-grounded-qlora-2ep-v1

SGLang

How to use Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-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-grounded-qlora-2ep-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-grounded-qlora-2ep-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-grounded-qlora-2ep-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-grounded-qlora-2ep-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-v1 with Docker Model Runner:
```
docker model run hf.co/Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-v1
```

Kanha Qwen3 experiment

Run identity

Run ID: kanha.ai-1.7b-grounded-qlora-2ep-v1
Base model: Qwen/Qwen3-1.7B
Base model revision: 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
Tokenizer revision: 70d244cc86ccca08cf5af4e1e306ecf908b1ad5e
Training method: qlora
Final merged dtype: bfloat16
Source site: https://kanha.ai
Dataset hash: 5344dbb7a1d3267d4b370aac7ecf316329d43829335c95ec38304533bf91c958
Train split: 210 records (cec1d43395a9366ae4e54ebf312eb8c4a9a1b7f2dc101f93591e615b865bfcea)
Validation split: 45 records (5d55f68556ad3b66c8f1b3069fda424f0d8e5a00785829813153b3890c71d015)
Holdout split: 0 records (e3b0c44298fc1c149afbf4c8996fb92427ae41e4649b934ca495991b7852b855)

Hyperparameters

Maximum sequence length: 2048
Seed: 42
Epochs: 2.0
Learning rate: 0.0001
Per-device batch size: 8
Gradient accumulation steps: 2
Warmup ratio: 0.05
Assistant-only loss: true
LoRA rank: 16
LoRA alpha: 16
LoRA dropout: 0.05
LoRA targets: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj

Evaluation

dates_recall: 1.0
deterministic_pass_rate: 0.4230769230769231
list_recall: 0.6051282051282051
numbers_recall: 0.9717948717948718
refusal_rate: 0.11538461538461539
total: 26
unsupported_value_rate: 0.038461538461538464
urls_recall: 1.0

Deterministic scoring and the server-side Transformers benchmark are not browser qualification. Validate the exact converted model in the target browser and device environments.

MLC availability

MLC artifacts using q4f16_1 quantization are available under mlc/.

Grounded inference contract

Inference requires retrieved source context. A bare question without retrieved source context is outside the trained and evaluated contract.

Prompt contract identity: d0dcb1de0b9d601267a3cbf39420b1f0a2c5749d80b96669a2e9fcf19a280bfb
Chat formatting: The model's native chat template is used with thinking disabled (enable_thinking=False).

System prompt (exact):

Answer only from the supplied context. Be concise. If the answer is absent from the context, respond exactly: I can't answer that from the provided context.

User template (exact):

Context:
<context>

Question:
<question>

Refusal string (exact):

I can't answer that from the provided context.

Intended use

This checkpoint is intended for research comparing training methods on the same Kanha website-derived dataset and for controlled evaluation of website question answering.

Limitations

The checkpoint can produce incorrect, incomplete, or stale answers. It may memorize training content. Review outputs, test representative failure cases, and qualify the exact runtime before any user-facing use.

Provenance artifacts

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

Downloads last month: 423

Safetensors

Model size

2B params

Tensor type

BF16

Model tree for Kanha-AI/kanha-kanha.ai-1.7b-grounded-qlora-2ep-v1

Base model

Qwen/Qwen3-1.7B-Base

Finetuned

Qwen/Qwen3-1.7B

Finetuned

(1079)

this model