ARTEXIT/Llama-PLLuM-8B-instruct-ArtexIT-reasoning-GGUF

Llama-PLLuM-8B-instruct-ArtexIT-reasoning

Built with Llama

This repository contains a GRPO fine‑tune of [CYFRAGOVPL/Llama-PLLuM-8B-instruct] trained on GSM8K (MIT). We publish both Hugging Face (safetensors) and GGUF artifacts (Q8_0, Q5_K_M) for use with llama.cpp.

What is this?

• Base: Meta Llama 3.1 → PLLuM 8B Instruct (Polish) → GRPO fine‑tune (math / word problems).
• Context: ~131k (based on GGUF header).
• Message format: Llama [INST] ... [/INST] + explicit reasoning / answer tags (see below).
• Default chat template: The tokenizer includes a default system instruction enforcing the two‑block format.

Prompt format

The model expects Llama chat formatting and supports explicit tags:

• Reasoning: <think> ... </think>
• Final answer: <answer> ... </answer>

Example

[INST] Rozwiąż: 12 * 13 = ? [/INST]
<think>12*13 = 156.</think>
<answer>156</answer>

Quickstart

Transformers (PyTorch)

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "ARTEXIT/Llama-PLLuM-8B-instruct-ArtexIT-reasoning"
tok = AutoTokenizer.from_pretrained(repo, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(repo, torch_dtype="auto", device_map="auto")

prompt = tok.apply_chat_template(
    [{"role": "user", "content": "Podaj 3 miasta w Polsce."}],
    add_generation_prompt=True,
    tokenize=False,
)
inputs = tok(prompt, return_tensors="pt").to(model.device)
out = model.generate(**inputs, max_new_tokens=64)
print(tok.decode(out[0], skip_special_tokens=False))

Training (brief)

• Method: GRPO (policy‑gradient reinforcement learning with multiple reward functions).
• Data: openai/gsm8k — License: MIT.
• Goal: consistent two‑block outputs (reasoning + final answer) using the training tags.

License & Attribution

This repository contains derivatives of Llama 3.1 and PLLuM:

• Llama 3.1 Community License applies. When redistributing, you must:
  • include a copy of the license and prominently display “Built with Llama”,
  • include “Llama” at the beginning of any distributed model’s name if it was created, trained or fine‑tuned using Llama materials,
  • keep a NOTICE file with the following line:
    Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.
  • comply with the Acceptable Use Policy (AUP).
• PLLuM: please cite the PLLuM work (see Citation below).
• Data: GSM8K is MIT‑licensed; include dataset attribution.

This repo includes:

• LICENSE — full text of the Llama 3.1 Community License
• USE_POLICY.md — pointer to the official Acceptable Use Policy
• NOTICE — required Llama attribution line

If your (or your affiliates’) products exceeded 700M monthly active users on the Llama 3.1 release date, you must obtain a separate license from Meta before exercising the rights in the Llama 3.1 license.

Citation

If you use PLLuM in research or deployments, please cite:

@unpublished{pllum2025,
    title={PLLuM: A Family of Polish Large Language Models},
    author={PLLuM Consortium},
    year={2025}
}