LFM2-1.2B-RAG-GGUF

<span style="color: #7FFF7F;">LFM2-1.2B-RAG GGUF Models</span>

<span style="color: #7F7FFF;">Model Generation Details</span>

This model was generated using llama.cpp at commit ee09828cb.

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LFM2-1.2B-RAG

Based on LFM2-1.2B, LFM2-1.2B-RAG is specialized in answering questions based on provided contextual documents, for use in RAG (Retrieval-Augmented Generation) systems.

Use cases:

• Chatbot to ask questions about the documentation of a particular product.
• Custom support with an internal knowledge base to provide grounded answers.
• Academic research assistant with multi-turn conversations about research papers and course materials.

You can find more information about other task-specific models in this blog post.

📄 Model details

Generation parameters: We recommend using greedy decoding with a temperature=0.

System prompt: The system prompt is optional. You can force the output's language, for example, using "Always respond in English, regardless of the user's input language." By default, the output's language follows the user prompt's language.

Supported languages: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish.

Training approach: We fine-tuned the LFM2-1.2B-RAG model on a dataset that includes 1M+ samples of multi-turn interactions and multi-document samples consisting of a mix of curated open source documents as well as generated synthetic ones.

Chat template: LFM2 uses a ChatML-like chat template as follows:

<|startoftext|><|im_start|>user
Use the following context to answer questions:
Beach soccer differs significantly from its grass-rooted counterpart. [...]<|im_end|>
<|im_start|>assistant
Each team in a beach soccer match consists of five players, including a goalkeeper.{<|im_end|>

You can automatically apply it using the dedicated .apply_chat_template() function from Hugging Face transformers.

[!WARNING] ⚠️ The model supports both single-turn and multi-turn conversations.

<center> <div style="text-align: center;"> <img src="https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/Thv4Lq6wPYKNEAAJq_DtP.png" style="width: 100%; max-width: 50%; height: auto; display: inline-block;" /> </div> </center>

RAG systems enable AI solutions to include new, up-to-date, and potentially proprietary information in LLM responses that was not present in the training data. When a user asks a question, the retrieval component locates and delivers related documents from a knowledge base, and then the RAG generator model answers the question based on facts from those contextual documents.

🏃 How to run

• Hugging Face: LFM2-1.2B
• llama.cpp: LFM2-1.2B-Extract-GGUF
• LEAP: LEAP model library

📬 Contact

If you are interested in custom solutions with edge deployment, please contact our sales team.

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<span id="testllm" style="color: #7F7FFF;">🚀 If you find these models useful</span>

Help me test my AI-Powered Quantum Network Monitor Assistant with quantum-ready security checks:

👉 Quantum Network Monitor

The full Open Source Code for the Quantum Network Monitor Service available at my github repos ( repos with NetworkMonitor in the name) : Source Code Quantum Network Monitor. You will also find the code I use to quantize the models if you want to do it yourself GGUFModelBuilder

💬 How to test:
Choose an AI assistant type:

• TurboLLM (GPT-4.1-mini)
• HugLLM (Hugginface Open-source models)
• TestLLM (Experimental CPU-only)

What I’m Testing

I’m pushing the limits of small open-source models for AI network monitoring, specifically:

• Function calling against live network services
• How small can a model go while still handling:
  • Automated Nmap security scans
  • Quantum-readiness checks
  • Network Monitoring tasks

🟡 TestLLM – Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space):

• ✅ Zero-configuration setup
• ⏳ 30s load time (slow inference but no API costs) . No token limited as the cost is low.
• 🔧 Help wanted! If you’re into edge-device AI, let’s collaborate!

Other Assistants

🟢 TurboLLM – Uses gpt-4.1-mini :

• **It performs very well but unfortunatly OpenAI charges per token. For this reason tokens usage is limited.
• Create custom cmd processors to run .net code on Quantum Network Monitor Agents
• Real-time network diagnostics and monitoring
• Security Audits
• Penetration testing (Nmap/Metasploit)

🔵 HugLLM – Latest Open-source models:

• 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita.

💡 Example commands you could test:

1. "Give me info on my websites SSL certificate"
2. "Check if my server is using quantum safe encyption for communication"
3. "Run a comprehensive security audit on my server"
4. '"Create a cmd processor to .. (what ever you want)" Note you need to install a Quantum Network Monitor Agent to run the .net code on. This is a very flexible and powerful feature. Use with caution!

Final Word

I fund the servers used to create these model files, run the Quantum Network Monitor service, and pay for inference from Novita and OpenAI—all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is open source. Feel free to use whatever you find helpful.

If you appreciate the work, please consider buying me a coffee ☕. Your support helps cover service costs and allows me to raise token limits for everyone.

I'm also open to job opportunities or sponsorship.

Thank you! 😊