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• Like all LLMs, it can produce inaccurate, outdated, or biased content and can state wrong things confidently.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

python
Copy code
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.