Instructions to use ProKope-AI/ProKope-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ProKope-AI/ProKope-7B with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ProKope-AI/ProKope-7B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ProKope-AI/ProKope-7B")
model = AutoModelForCausalLM.from_pretrained("ProKope-AI/ProKope-7B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ProKope-AI/ProKope-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ProKope-AI/ProKope-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ProKope-AI/ProKope-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ProKope-AI/ProKope-7B

SGLang

How to use ProKope-AI/ProKope-7B 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 "ProKope-AI/ProKope-7B" \
    --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": "ProKope-AI/ProKope-7B",
		"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 "ProKope-AI/ProKope-7B" \
        --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": "ProKope-AI/ProKope-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ProKope-AI/ProKope-7B with Docker Model Runner:
```
docker model run hf.co/ProKope-AI/ProKope-7B
```

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This repository is publicly accessible, but you have to accept the conditions to access its files and content.

Access to ProKope-7B weights is provided for academic evaluation, benchmarking, and commercial licensing inquiries. Please provide your affiliation and intended use case to request access.

ProKope-7B

Introduction

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.

Evaluation

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%	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%

Model Details

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)
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)

Quickstart

1. Standard Inference (`transformers`)

import torch
from 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))

2. Serving with `vLLM`

vllm serve ProKope-AI/ProKope-7B \
  --dtype bfloat16 \
  --max-model-len 32768

3. Long-Context Extension (YaRN up to 512K)

from transformers import AutoConfig, AutoModelForCausalLM
import 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",
)

Limitations & Intended Use

Intended Use: Analytical reasoning, scientific and technical question answering, code generation, and structured instruction following.
Limitations: As an autoregressive language model, outputs may contain factual inaccuracies or mathematical errors. Verify critical outputs independently.

Contact & Citation

Organization: ProKope AI
Authors: Alan Canto, Wesley Foreman
Contact: [email protected]

@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}
}

Downloads last month: 6

Safetensors

Model size

8B params

Tensor type

BF16

Datasets used to train ProKope-AI/ProKope-7B

Evaluation results

TIGER-Lab/MMLU-Pro · Mmlu Pro View evaluation results leaderboard

44.03 ^*
openai/gsm8k · Gsm8k View evaluation results leaderboard

81.5 ^*
Idavidrein/gpqa · Diamond View evaluation results leaderboard

36.87 ^*