Instructions to use qbz506/nyaya-deepseek-8b-stage1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use qbz506/nyaya-deepseek-8b-stage1 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("unsloth/deepseek-r1-distill-llama-8b-bnb-4bit")
model = PeftModel.from_pretrained(base_model, "qbz506/nyaya-deepseek-8b-stage1")

Transformers

How to use qbz506/nyaya-deepseek-8b-stage1 with Transformers:

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

pipe = pipeline("text-generation", model="qbz506/nyaya-deepseek-8b-stage1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("qbz506/nyaya-deepseek-8b-stage1", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use qbz506/nyaya-deepseek-8b-stage1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "qbz506/nyaya-deepseek-8b-stage1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "qbz506/nyaya-deepseek-8b-stage1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/qbz506/nyaya-deepseek-8b-stage1

SGLang

How to use qbz506/nyaya-deepseek-8b-stage1 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 "qbz506/nyaya-deepseek-8b-stage1" \
    --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": "qbz506/nyaya-deepseek-8b-stage1",
		"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 "qbz506/nyaya-deepseek-8b-stage1" \
        --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": "qbz506/nyaya-deepseek-8b-stage1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use qbz506/nyaya-deepseek-8b-stage1 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 qbz506/nyaya-deepseek-8b-stage1 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 qbz506/nyaya-deepseek-8b-stage1 to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for qbz506/nyaya-deepseek-8b-stage1 to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="qbz506/nyaya-deepseek-8b-stage1",
    max_seq_length=2048,
)

Docker Model Runner
How to use qbz506/nyaya-deepseek-8b-stage1 with Docker Model Runner:
```
docker model run hf.co/qbz506/nyaya-deepseek-8b-stage1
```

Model Card for stage_1

This model is a fine-tuned version of unsloth/deepseek-r1-distill-llama-8b-bnb-4bit. It has been trained using TRL.

Quick start

from transformers import pipeline

question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="None", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])

Training procedure

This model was trained with SFT.

Framework versions

PEFT 0.18.1
TRL: 0.26.1
Transformers: 5.0.0
Pytorch: 2.10.0a0+b558c986e8.nv25.11
Datasets: 4.3.0
Tokenizers: 0.22.1

Paper: Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya

Citations

If you use this model/dataset, please cite:

@misc{sathish2026pramanafinetuninglargelanguage,
      title={Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya}, 
      author={Sharath Sathish},
      year={2026},
      eprint={2604.04937},
      archivePrefix={arXiv},
      primaryClass={cs.AI},
      url={https://arxiv.org/abs/2604.04937}, 
}
}

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Paper for qbz506/nyaya-deepseek-8b-stage1

Pramana: Fine-Tuning Large Language Models for Epistemic Reasoning through Navya-Nyaya

Paper • 2604.04937 • Published Feb 14 • 1