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dpateldev7
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Model Details
Model Provider
dpateldev7
Model Tree
Qwen/Qwen3-14B
Input Modalities
Output Modalities
Supported Functionality
<think>...</think> chain-of-thought, matching Qwen3's native thinking formatFine-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.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.
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.
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.
If you use Northstar, please also credit the base model (Qwen3) and the datasets listed above.
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))