nitrai-research/Polaris-V1

Overview

Polaris-V1 is an efficient 4-billion parameter open-weights reasoning model developed by NitrAI Research. It is aligned through reinforcement learning with multi-file code synthesis and theorem-verification rewards, engineered specifically to execute on consumer-grade hardware (3.2 GB VRAM) with a 1.59M token YaRN context window.

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📊 Comprehensive Model Benchmark Comparison

The following table compares Polaris-V1 (4B RL) against open-weights edge & distilled architectures (inclusionAI/Ling-3.0-tiny, empero-ai/Qwen3.8-9B), as well as closed frontier ceiling models (Claude Opus 5, Kimi K3).

Model	Model Tier / Type	Active Params	SWE-bench Verified (Pass@1)	WildClawBench (Agentic Traj.)	DeepSWE (Pass@1)	SWE-bench Pro (Pass@1)	Context Window	Min VRAM / Hardware
Claude Opus 5	Frontier Cloud (Ideal)	Unknown	96.8%	94.5%	89.2%	87.4%	2,000,000+	Datacenter Pod
Kimi K3	Frontier Reasoning (Ideal)	2.8T MoE	93.4%	88.2%	81.6%	79.5%	2,000,000	8× H100 Cluster
empero-ai/Qwen3.8-9B	Open Distilled (9B)	9.0B	37.8%	42.1%	32.0%	34.5%	1,000,000	~6.5 GB (Q4)
Polaris-V1 (Ours)	Local Open Weights (4B RL)	4.0B	31.4%	38.5%	26.8%	28.0%	1,592,638	3.2 GB (Q4)
inclusionAI/Ling-3.0-tiny	Edge MoE (7.9B)	1.3B	24.2%	29.0%	21.5%	19.4%	256,000	~4.8 GB (FP8)

Key Takeaway: Polaris-V1 achieves 31.4% on SWE-bench Verified and 28.0% on SWE-bench Pro at only 4B parameters. While frontier cloud systems (Claude Opus 5 / Kimi K3) set the absolute performance ceiling for datacenter clusters, Polaris-V1 provides an industry-leading capability-per-watt ratio, running locally on 3.2 GB VRAM consumer GPUs and Apple Silicon with full 1.59M YaRN context retrieval.

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⚡ Hardware & Execution Specifications

• Architecture: 4B dense causal LM + Multi-Turn RL Alignment.
• VRAM Requirements: 3.2 GB (Q4_K_M) / 4.5 GB (Q8_0) / 8.4 GB (FP16).
• Supported Context Window: 1,592,638 Tokens (1.59M) with YaRN RoPE scaling.
• Speculative Decoding: 540–720 tok/s on consumer GPUs when paired with Qwen3.5-0.8B-Draft.

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🚀 Quickstart

1. Ollama (One-Line Launch)

ollama run nitrai-research/polaris-v1

2. Hugging Face Transformers (Python)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "nitrai-research/Polaris-V1"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

prompt = "Analyze race conditions in Redis cluster partition recovery logic:"
messages = [{"role": "user", "content": prompt}]
inputs = tokenizer(tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True), return_tensors="pt").to(model.device)

outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.2)
print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))

3. llama.cpp / GGUF

./llama-cli -hf nitrai-research/Polaris-V1:polaris-v1-merged-fp16.Q4_K_M.gguf -p "<|im_start|>user\nWrite a unified diff for issue #12907 in astropy.<|im_end|>\n<|im_start|>assistant\n" -c 32768

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📜 License

Polaris-V1 is released under the Apache 2.0 License.

Developed with ❤️ by NitrAI Lab.