From c5e9080d29e5bec240b303c9439d741c4a4f96a8 Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 25 Oct 2026 14:44:12 +0800 Subject: [PATCH] =?UTF-8?q?=E5=88=9D=E5=A7=8B=E5=8C=96=E9=A1=B9=E7=9B=AE?= =?UTF-8?q?=EF=BC=8C=E7=94=B1ModelHub=20XC=E7=A4=BE=E5=8C=BA=E6=8F=90?= =?UTF-8?q?=E4=BE=9B=E6=A8=A1=E5=9E=8B?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Model: Mungert/LFM2-1.2B-Tool-GGUF Source: Original Platform --- .gitattributes | 67 +++++++++++ LFM2-1.2B-Tool-bf16.gguf | 3 + LFM2-1.2B-Tool-f16_q8_0.gguf | 3 + LFM2-1.2B-Tool-imatrix.gguf | 3 + LFM2-1.2B-Tool-iq3_m.gguf | 3 + LFM2-1.2B-Tool-iq3_xs.gguf | 3 + LFM2-1.2B-Tool-iq3_xxs.gguf | 3 + LFM2-1.2B-Tool-iq4_nl.gguf | 3 + LFM2-1.2B-Tool-iq4_xs.gguf | 3 + LFM2-1.2B-Tool-q3_k_m.gguf | 3 + LFM2-1.2B-Tool-q3_k_s.gguf | 3 + LFM2-1.2B-Tool-q4_0.gguf | 3 + LFM2-1.2B-Tool-q4_1.gguf | 3 + LFM2-1.2B-Tool-q4_k_m.gguf | 3 + LFM2-1.2B-Tool-q4_k_s.gguf | 3 + LFM2-1.2B-Tool-q5_0.gguf | 3 + 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LiquidAI/LFM2-1.2B +--- + +# LFM2-1.2B-Tool GGUF Models + + +## Model Generation Details + +This model was generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`c8dedc99`](https://github.com/ggerganov/llama.cpp/commit/c8dedc9999eccf7821a9fe5b29f10e8d075e2217). + + + + + + +--- + + + Click here to get info on choosing the right GGUF model format + + +--- + + + + + + +
+
+ Liquid AI +
+
+ + + Playground + + + + + + + + + + + + + + + + + + + Leap + + + + + + + + + + + + + + +
+
+ +# LFM2-1.2B-Tool + +Based on [LFM2-1.2B](https://huggingface.co/LiquidAI/LFM2-1.2B), LFM2-1.2B-Tool is designed for **concise and precise tool calling**. The key challenge was designing a non-thinking model that outperforms similarly sized thinking models for tool use. + +**Use cases**: + +- Mobile and edge devices requiring instant API calls, database queries, or system integrations without cloud dependency. +- Real-time assistants in cars, IoT devices, or customer support, where response latency is critical. +- Resource-constrained environments like embedded systems or battery-powered devices needing efficient tool execution. + +You can find more information about other task-specific models in this [blog post](https://www.liquid.ai/blog/introducing-liquid-nanos-frontier-grade-performance-on-everyday-devices). + +## πŸ“„ Model details + +**Generation parameters**: We recommend using greedy decoding with a `temperature=0`. + +**System prompt**: The system prompt must provide all the available tools + +**Supported languages**: English, Arabic, Chinese, French, German, Japanese, Korean, Portuguese, and Spanish. + +![68d41b9699b7e1fafd645300_Model Library-Prompt + Answer](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/dc2-lsJ5PwoA8039z-wW_.png) + +**Tool use**: It consists of four main steps: +1. **Function definition**: LFM2 takes JSON function definitions as input (JSON objects between `<|tool_list_start|>` and `<|tool_list_end|>` special tokens), usually in the system prompt +2. **Function call**: LFM2 writes Pythonic function calls (a Python list between `<|tool_call_start|>` and `<|tool_call_end|>` special tokens), as the assistant answer. +3. **Function execution**: The function call is executed and the result is returned (string between `<|tool_response_start|>` and `<|tool_response_end|>` special tokens), as a "tool" role. +4. **Final answer**: LFM2 interprets the outcome of the function call to address the original user prompt in plain text. + +Here is a simple example of a conversation using tool use: + +``` +<|startoftext|><|im_start|>system +List of tools: <|tool_list_start|>[{"name": "get_candidate_status", "description": "Retrieves the current status of a candidate in the recruitment process", "parameters": {"type": "object", "properties": {"candidate_id": {"type": "string", "description": "Unique identifier for the candidate"}}, "required": ["candidate_id"]}}]<|tool_list_end|><|im_end|> +<|im_start|>user +What is the current status of candidate ID 12345?<|im_end|> +<|im_start|>assistant +<|tool_call_start|>[get_candidate_status(candidate_id="12345")]<|tool_call_end|>Checking the current status of candidate ID 12345.<|im_end|> +<|im_start|>tool +<|tool_response_start|>{"candidate_id": "12345", "status": "Interview Scheduled", "position": "Clinical Research Associate", "date": "2023-11-20"}<|tool_response_end|><|im_end|> +<|im_start|>assistant +The candidate with ID 12345 is currently in the "Interview Scheduled" stage for the position of Clinical Research Associate, with an interview date set for 2023-11-20.<|im_end|> +``` + +> [!WARNING] +> ⚠️ The model supports both single-turn and multi-turn conversations. + +## πŸ“ˆ Performance + +For edge inference, latency is a crucial factor in delivering a seamless and satisfactory user experience. Consequently, while test-time-compute inherently provides more accuracy, it ultimately compromises the user experience due to increased waiting times for function calls. + +Therefore, the goal was to develop a tool calling model that is competitive with thinking models, yet operates without any internal chain-of-thought process. + +![image](https://cdn-uploads.huggingface.co/production/uploads/61b8e2ba285851687028d395/gdPh7juYTsxc7wpD5iYS_.png) + +We evaluated each model on a proprietary benchmark that was specifically designed to prevent data contamination. The benchmark ensures that performance metrics reflect genuine tool-calling capabilities rather than memorized patterns from training data. + +## πŸƒ How to run + +- Hugging Face: [LFM2-350M](https://huggingface.co/LiquidAI/LFM2-350M) +- llama.cpp: [LFM2-350M-Extract-GGUF](https://huggingface.co/LiquidAI/LFM2-350M-Extract-GGUF) +- LEAP: [LEAP model library](https://leap.liquid.ai/models?model=lfm2-350M-extract) + +## πŸ“¬ Contact + +If you are interested in custom solutions with edge deployment, please contact [our sales team](https://www.liquid.ai/contact). + + + +--- + +# πŸš€ If you find these models useful + +Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**: + +πŸ‘‰ [Quantum Network Monitor](https://readyforquantum.com/?assistant=open&utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) + + +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](https://github.com/Mungert69). You will also find the code I use to quantize the models if you want to do it yourself [GGUFModelBuilder](https://github.com/Mungert69/GGUFModelBuilder) + +πŸ’¬ **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): +- βœ… **Zero-configuration setup** +- ⏳ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low. +- πŸ”§ **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) + +πŸ”΅ **HugLLM** – Latest Open-source models: +- 🌐 Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. + +### πŸ’‘ **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](https://readyforquantum.com/Download/?utm_source=huggingface&utm_medium=referral&utm_campaign=huggingface_repo_readme) 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 OpenAIβ€”all out of my own pocket. All the code behind the model creation and the Quantum Network Monitor project is [open source](https://github.com/Mungert69). Feel free to use whatever you find helpful. + +If you appreciate the work, please consider [buying me a coffee](https://www.buymeacoffee.com/mahadeva) β˜•. 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! 😊 diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file