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 / README.md

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	---
	license: apache-2.0
	base_model:
	- Qwen/Qwen3-14B
	datasets:
	- angrygiraffe/claude-opus-4.6-4.7-reasoning-8.7k
	- Jackrong/GLM-5.1-Reasoning-1M-Cleaned
	language:
	- en
	pipeline_tag: text-generation
	library_name: transformers
	tags:
	- reasoning
	- chain-of-thought
	- qwen3
	- unsloth
	- fine-tuned
	---

	# Northstar

	Northstar is a 14B reasoning-focused language model, fine-tuned from
	[Qwen3-14B](https://huggingface.co/Qwen/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](https://huggingface.co/Qwen/Qwen3-14B) (14.8B parameters, Apache-2.0)
	- Fine-tuning method: QLoRA (4-bit), merged to 16-bit, via [Unsloth](https://github.com/unslothai/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`](https://huggingface.co/datasets/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`](https://huggingface.co/datasets/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
	```python
	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`](https://huggingface.co/dpateldev7/northstar-gguf).

	## Acknowledgements
	- Base model: [Qwen3](https://qwenlm.github.io/blog/qwen3/) by Alibaba Cloud (Apache-2.0).
	- Fine-tuning framework: [Unsloth](https://github.com/unslothai/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.