Instructions to use dpateldev7/northstar with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dpateldev7/northstar with Transformers:

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

pipe = pipeline("text-generation", model="dpateldev7/northstar")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dpateldev7/northstar")
model = AutoModelForCausalLM.from_pretrained("dpateldev7/northstar", 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 dpateldev7/northstar with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dpateldev7/northstar"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dpateldev7/northstar",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dpateldev7/northstar

SGLang

How to use dpateldev7/northstar 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 "dpateldev7/northstar" \
    --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": "dpateldev7/northstar",
		"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 "dpateldev7/northstar" \
        --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": "dpateldev7/northstar",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio

How to use dpateldev7/northstar 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 dpateldev7/northstar 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 dpateldev7/northstar to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for dpateldev7/northstar to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="dpateldev7/northstar",
    max_seq_length=2048,
)

Docker Model Runner
How to use dpateldev7/northstar with Docker Model Runner:
```
docker model run hf.co/dpateldev7/northstar
```

Northstar

Northstar is a 14B reasoning-focused language model, fine-tuned from Qwen3-14B to think carefully step by step before answering. It is aimed at analytical and technical work — math, coding, logic, and structured explanation.

Model details

Base model: Qwen/Qwen3-14B (14.8B parameters, Apache-2.0)
Fine-tuning method: QLoRA (4-bit), merged to 16-bit, via Unsloth
Reasoning format: responses include <think>...</think> chain-of-thought, matching Qwen3's native thinking format
Language: primarily English
License: Apache-2.0

Training data

Fine-tuned on expert reasoning traces from:

angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k (Apache-2.0) — expert chain-of-thought across coding, math, sciences, and humanities.
A subset of the main split of Jackrong/GLM-5.1-Reasoning-1M-Cleaned (Apache-2.0) — general reasoning traces.

These datasets are distilled from the outputs of other large language models (Claude and GLM, respectively), and were used under the terms set by their authors.

Intended use

Reasoning-heavy assistant tasks: working through math and logic problems, debugging and explaining code, and structured analysis. Northstar is a community fine-tune, not a frontier model.

⚠️ Limitations and safety — please read before deploying

Northstar was fine-tuned on capability-focused data that deliberately contains no refusals or safety hedging. As a result it may be more willing to comply with harmful or inappropriate requests than the base Qwen3-14B, and it has not been through a dedicated safety-alignment stage. If you deploy it anywhere user-facing, add your own moderation/safety layer.
Like all LLMs, it can produce inaccurate, outdated, or biased content and can state wrong things confidently. Verify anything important.
It inherits the biases and limitations of its base model and training data.

How to use

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "dpateldev7/northstar"
tok = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

messages = [{"role": "user", "content": "Think step by step: what is 17% of 340?"}]
inputs = tok.apply_chat_template(
    messages, tokenize=True, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
out = model.generate(inputs, max_new_tokens=1024)
print(tok.decode(out[0], skip_special_tokens=True))

For thinking mode, Qwen3 recommends sampling with temperature=0.6, top_p=0.95.

A quantized GGUF build for local use (Ollama / LM Studio / llama.cpp) is available at dpateldev7/northstar-gguf.

Acknowledgements

Base model: Qwen3 by Alibaba Cloud (Apache-2.0).
Fine-tuning framework: Unsloth.
Training data: the dataset authors linked above.

Citation

If you use Northstar, please also credit the base model (Qwen3) and the datasets listed above.

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Safetensors

Model size

15B params

Tensor type

BF16

Model tree for dpateldev7/northstar

Base model

Qwen/Qwen3-14B-Base

Finetuned

Qwen/Qwen3-14B