This is a GGUF quantisation of Qwen/Qwen3.5-4B, fine-tuned via a structured system prompt and optional retrieval layer to serve as a Q&A and recommendation assistant over a corpus of 350+ articles extracted from robertolofaro.com.
The update is up to 2026-05-05.
The model is designed to answer questions about the articles and, primarily, to act as an arguments outlining and guided brainstorming system.
Its answers should be considered just representation of the material within the source articles, coupled with the capabilities of the underlying Qwen3.5-4B model.
No answer represents or should be considered advice, as the training material did not include your own specific context and a professional assessment of your context.
Hence, before acting on the answers, consult professional advice.
Intended Use
Use
Supported
Interactive Q&A on the 350+ articles
✅
Offline / local inference (CPU)
✅
General-purpose assistant
⚠️ Not the primary intent
Commercial deployment without attribution
❌ (see license)
Primary Task
Given a natural language request such as:
"what is the potential of Italy? /nothink"
the model returns a short answer structured as an executive summary followed by guidelines and hints.
As an example, with the request above, this is the answer that you could obtain from the demo space,
as the demo is anyway set to be non-deterministic, you can get different answers each time:
Case 1
Executive Summary:
Italy is described as an "arrested development" where institutional and political structures are too weak to support change or innovation, often resulting in inefficiencies and a lack of coordination between levels of government. The potential lies not just in economic growth but also in cultural and social transformation through education, transparency, and shared leadership.
Guidelines & Hints:
Italy has a "culture of failure" due to systemic corruption and weak institutional frameworks that discourage innovation at the local level.
Success requires breaking down barriers between levels of government (national, regional, local) to foster coordinated development.
Education is critical—Italy needs a shift from "exam-centric" learning to competency-based systems focused on real-world application.
Transparency and accountability in public administration are prerequisites for sustainable change, as secrecy undermines trust and efficiency.
Local governance must be empowered with clear mandates and resources; otherwise, initiatives remain fragmented and unscalable.
Collaboration between civil society, business, and government is necessary to create a "culture of success" rather than relying on top-down directives.
Historical context (e.g., the Risorgimento) shows that long-term national vision requires continuous adaptation—not just one-off reforms.
Technology adoption must be aligned with human development; digital tools alone cannot replace systemic structural changes.
Regional diversity demands tailored solutions, not a "one-size-fits-all" approach to policy implementation.
The role of the "middle class" as both driver and beneficiary of change is often overlooked in Italy’s political landscape.
Case 2
Executive Summary:
Italy is described as a country with unique cultural and historical assets that are often underutilized due to structural barriers, lack of innovation, and outdated administrative practices. While it possesses rich resources in art, tourism, agriculture, and education, the potential remains largely unrealized unless systemic changes address inefficiencies, encourage foreign investment, and foster collaborative ecosystems between public and private sectors.
Guidelines & Hints:
Italy's cultural heritage is a core asset but often limited by bureaucratic inertia and lack of strategic integration with modern economic needs (e.g., tourism focused on static consumption rather than dynamic experiences).
Innovation and technology adoption are critical; the country suffers from "technological isolationism" where domestic companies avoid global expansion due to regulatory complexity.
Foreign investment requires a shift toward transparent, collaborative partnerships rather than transactional relationships with local entities.
Education must evolve from theory-focused models to practical skills aligned with market demands, including digital literacy and cross-cultural competencies for export-oriented roles.
Administrative reform is essential—streamlining processes like tax incentives, construction permits, and regulatory frameworks to attract and retain talent and capital.
Social cohesion and trust between citizens and institutions are foundational; without public confidence, structural reforms lack sustainable momentum.
Export-focused strategies must leverage Italy’s manufacturing excellence while adapting for global markets through localized production and digital transformation.
Sustainability requires balancing industrial growth with environmental responsibility—e.g., energy efficiency in agriculture, circular economy principles in waste management.
Digital infrastructure gaps (e.g., internet access, data standards) hinder innovation ecosystems and remote work opportunities.
Cultural promotion beyond tourism should include soft-power initiatives that highlight Italy’s roles as a mediator of global ideas (science, art, philosophy).
Case 3: selecting a specific category (e.g. "diritto di voto") and ask a question about the category
The question: within the articles in the selected category, what are the key issues identified for Italy?
Executive Summary
The articles identify critical structural issues in Italy regarding cultural and political industrialization, focusing on administrative inefficiency, transparency gaps, and the lack of systemic change. Key concerns include institutional inertia, corruption risks due to fragmented oversight, and the failure to implement necessary reforms despite external pressure from EU compliance requirements.
Guidelines & Hints
Industrial Culture vs. Political Reality: Italy's industrial culture lacks alignment with political reality; decisions are often delayed by bureaucratic processes rather than being made based on market needs or strategic planning.
Administrative Efficiency: Administrative bodies suffer from excessive complexity, lack of clarity in procedures, and poor coordination across departments.
Transparency and Accountability: There is a systemic weakness in transparency mechanisms and accountability frameworks, leading to corruption risks and reduced public trust.
EU Compliance vs. Local Reality: While Italy faces pressure from the EU to adopt compliance measures (e.g., VAT directives), there is resistance or failure to implement these changes due to local cultural inertia and lack of political will.
"Self-Payment" Culture: A persistent mindset where individuals assume personal responsibility for collective outcomes ("I paid the bills") without considering systemic solutions or broader implications.
Collaboration vs. Competition: Administrative processes often prioritize competition over collaboration, hindering cross-functional teamwork and reducing overall efficiency.
"Wolf to Wolf" Dynamics: Decision-making cycles are driven by short-term political maneuvering rather than long-term strategic planning, leading to inconsistent policies and fragmented outcomes.
"Grass Filings" (Fili d'erba): Initiatives often start small but require continuous support and integration into broader systems; without this continuity, they fail at scale.
Political Industrialization: Political decisions are treated as isolated events rather than part of a larger industrial or economic strategy, leading to fragmented implementation efforts.
Therefore, consider the "demo" just as a demo- if you want to use the model to brainstorm, I suggest that you download the model and create your own script.
You can actually look at an example of the system prompt below.
About the articles
The 350+ articles cover topics spanning organizational change, business transformation, knowledge management, AI adoption, and programme management, drawing on the author's 35+ years of experience in consulting and C-level advisory roles across European industrial and, in Italy, also public-sector missions.
The abstract and content of each article (including those after the update date of the model) is on GitHub.
As some articles span over multiple releases, and even across multiple sections (i.e. are mini-book drafts in disguise), there is also a list of multi-part articles where you can navigate across the sections of an article.
Access to each article is free and CC-BY-SA-4.0, this model is just to further ease access vs. the existing research facilities on the website, and to ensure permanent availability online.
Available Quantisations
Quantisation
File
Size
Recommended For
Q4_K_M
articles-Q4_K_M.gguf
~2.71 GB
CPU inference, everyday use
The Q4_K_M variant is recommended for CPU-only environments and is the one used in the companion Space.
Usage
Sample system prompt (used by the first version of the demo space)
SYSTEM_PROMPT = """You are the reference expert for the articles contained in the training of this model, all extracted from the website robertolofaro.com, and all focused on change.
#Your Mission:
When a user asks a question, your goal is to provide a structured response based ONLY on the articles provided in your training. Do not provide general advice from outside these sources.
Do not provide article titles or article IDs, provide only the concepts that articles express.
Response Format:
Executive Summary: A 2-3 sentence overview answering the core query.
Guidelines & Hints: A markdown list of specific "answers/guidelines/hints" found in the source material.
"""
You can obviously tailor the system prompt, or add other elements to be added before the user prompt (e.g. to add further material as "context") or as closing element after the user prompt.
Also, as the full content of each article within the training of the model and metadata is also available on GitHub, you could integrate with actual quotes, links to the full articles that contributed to the answer, etc, but beware of the risk of introducing hallucinations etc.
The model by itself is capable of an overall context exceeding 200,000 tokens: hence, if you have hardware able to support more complex processing (e.g. GPU or large amounts of RAM), you can ask an AI to design a script that includes your own hardware configuration, by giving the link to this model repository, and asking to generate a script that fulfills your requirements.
Suggestion: start by listing the requirements and asking for a feasibility and qualifications.
Then, iterate on the first answer qualifying and gradually validating, so that the AI can support you in debugging or improving the code while also suggesting improvements (or introducing those that you suggest).
Overall, this back-and-forth could take few minutes, but produces significantly better results (and easier to maintain) than trying to have it all in one single run, when what you are doing is associated with content (as the model in reality represents content).
You can also decide that this model is not enough, point your AI directly to the GitHub repository with the articles, and generate your own tuned model using e.g. a different model as the base reference- CC-BY-SA-4.0 covers those uses to, no license is needed.
Sample of results by using a custom Python script
To test the potential use of the model, a script in Python has been developed that enabled also to selectively add integration with:
the AI-generated MorningNews that release on a daily basis on GitHub since March 2026
DuckDuckGo search, to allow further integration
The concept was layering first answers from articles, to "steer" then the exploration of MorningNews for recent, certified information related to the answer, and finally add also web searches.
As that experimental script is strictly conditioned by the context, to avoid having answers considered, due to the structured approach, as advice, I did not release the script online.
This is anyway an example of what you can obtain by using locally the model and integrating it with your own sources plus focused web search.
the formatting example after integrating MorningNews and web search
You are free to download the model and to use this infographic to feed an AI with the instructions of using that as a template of what an agent, Claude project, etc should deliver based on the model and your own additional content.
Beware of designing a series of tests to assess the "reasoning boundaries" and limit both hallucinations and extension of information by similarity or association.
Quick Start with Ollama
ollama run hf.co/robertolofaro/articles-model:Q4_K_M
The faiss_hnsw and qdrant files are provided for RAG use.
Quick Start with llama.cpp
The pre-compiled llama.cpp with the version supporting Qwen3.5 is shared on huggingface, and it has been built offline, tested offline with Python 3.12.3 under Ubuntu 24.04, and online with Python 3.13 within a HuggingFace space.
The Space runs the Q4_K_M quantisation on CPU hardware (no GPU required).
Limitations
The model is designed to support a system of arguments outlining and guided brainstorming using the articles within the training corpus.
Recommendations are bounded by the 350+ article in the corpus; the model will not recommend external works.
The model does not have live internet access; content reflects the corpus as indexed at build time; if you want access, you have to build the application.
Already tested application variants enabling integration with e.g. an AI-generated MorningNews and websearch with DuckDuckGo
CPU inference with Q4_K_M typically yields response times of 15–60 seconds depending on hardware; within the huggingface space, could take few minutes.
Ethical Considerations
The corpus consists entirely of original works by the author; no third-party copyrighted content is embedded.
The system is informational; it does not collect user data.
The model inherits any biases present in the Qwen3.5-4B base model; users should apply standard critical judgement to outputs.
Citation
If you use this model or the associated scripts in research or derivative work, please cite:
bibtex
1@misc{roberto_lofaro_2026,
2 author = { Roberto Lofaro },
3 title = { articles-model (Revision 7caa2c6) },
4 year = 2026,
5 url = { https://huggingface.co/robertolofaro/articles-model },
6 doi = { 10.57967/hf/8903 },
7 publisher = { Hugging Face }
8 note = {GGUF quantisation of Qwen3.5-4B, fine-tuned for arguments outlining and guided brainstorming and retrieval (FAISS-HNSW / Qdrant)}
9}
License
This model card and associated scripts are released under CC BY-SA 4.0.
The base model weights are subject to the Qwen3 License.
Published openly as part of Roberto Lofaro's AI-assisted knowledge production initiative.
GitHub · Patreon · robertolofaro.com
