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ProKope-AI
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ProKope-AI
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ProKope-7B is a 7.6B-parameter autoregressive language model developed by ProKope AI for multi-step analytical reasoning, mathematical problem solving, and strict instruction following.
Built on the Qwen2ForCausalLM architecture with a 128K native context window (extensible to 512K via YaRN), ProKope-7B is compatible out of the box with transformers, vLLM, SGLang, and llama.cpp.
Evaluated via EleutherAI's lm-evaluation-harness across standard mathematical reasoning and Open LLM Leaderboard v2 benchmarks:
| Benchmark | Dataset | Setting / Metric | ProKope-7B | Chocolatine-Fusion-14B (8.4B) | Qwen2.5-Dyanka-7B | Qwen2.5-7B-Instruct | Llama-3.1-8B-Instruct |
|---|---|---|---|---|---|---|---|
| GSM8K | openai/gsm8k | 5-shot (strict-match) | 81.50% | — | — | 82.71% | 84.53% |
| IFEval | google/IFEval | 0-shot (prompt_strict_acc) | 70.61% | 79.90% | 76.40% |
| Property | Value |
|---|---|
| Architecture | Qwen2ForCausalLM |
| Total Parameters | 7.61B (6.53B non-embedding parameters) |
| Layers | 28 Transformer decoder blocks |
| Attention Heads | 28 Query / 4 Key-Value (GQA) |
| Hidden / Intermediate Size | 3,584 / 18,944 (SwiGLU) |
| Vocabulary Size | 152,064 |
| Context Length | 131,072 tokens native (up to 524,288 with YaRN) |
transformers)python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "ProKope-AI/ProKope-7B" tokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.bfloat16, device_map="auto",) messages = [ {"role": "system", "content": "You are a helpful and precise analytical assistant."}, {"role": "user", "content": "Explain why grouped-query attention reduces KV-cache memory overhead during long-context decoding."},] prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): outputs = model.generate(**inputs, max_new_tokens=512, do_sample=False) print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))vLLMbash
vllm serve ProKope-AI/ProKope-7B \ --dtype bfloat16 \ --max-model-len 32768python
from transformers import AutoConfig, AutoModelForCausalLMimport torch model_id = "ProKope-AI/ProKope-7B"config = AutoConfig.from_pretrained(model_id)config.rope_scaling = { "type": "yarn", "factor": 4.0, "original_max_position_embeddings": 131072,}config.max_position_embeddings = 524288 model = AutoModelForCausalLM.from_pretrained( model_id, config=config, torch_dtype=torch.bfloat16, device_map="auto",)bibtex
@misc{canto2026prokope7b, author = {Alan Canto and Wesley Foreman}, title = {ProKope-7B: Reasoning and Instruction-Tuned Language Model}, howpublished = {Hugging Face}, year = {2026}, url = {https://huggingface.co/ProKope-AI/ProKope-7B}}| 74.80% |
| 76.10% |
| BBH | lukaemon/bbh | 3-shot CoT (acc_norm) | 53.31% | 49.50% | 36.62% | 42.10% | 40.80% |
| MuSR | TAUR-Lab/MuSR | 0-shot CoT (acc_norm) | 44.84% | 24.10% | 15.51% | 12.80% | 11.20% |
| MMLU-Pro | TIGER-Lab/MMLU-Pro | 5-shot CoT (acc) | 44.03% | 38.80% | 37.51% | 32.20% | 31.90% |
| GPQA Diamond | Idavidrein/gpqa | 0-shot CoT (acc_norm) | 36.87% | 15.30% | 8.95% | 10.71% | 13.52% |
| MATH Level 5 | lighteval/MATH-Hard | 4-shot CoT (exact_match) | 18.86% | 34.56% | 48.79% | 35.05% | 29.40% |
| Leaderboard v2 Avg | 6-Task Macro Average | — | 44.75% | 40.36% | 37.30% | 34.61% | 33.82% |
| Recommended VRAM | 16 GB+ for bfloat16 inference |
| Precision | torch.bfloat16 (4 .safetensors shards, 15.2 GB) |
| License | Apache 2.0 (Weights gated under manual review) |