From d467883ab22fff892de8d4559c4ebfd97053904d Mon Sep 17 00:00:00 2001 From: ModelHub XC Date: Sun, 18 Oct 2026 04:26:17 +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/Midm-2.0-Base-Instruct-GGUF Source: Original Platform --- .gitattributes | 81 ++++ Midm-2.0-Base-Instruct-bf16.gguf | 3 + Midm-2.0-Base-Instruct-bf16_q8_0.gguf | 3 + Midm-2.0-Base-Instruct-f16_q8_0.gguf | 3 + Midm-2.0-Base-Instruct-iq1_m.gguf | 3 + Midm-2.0-Base-Instruct-iq1_s.gguf | 3 + Midm-2.0-Base-Instruct-iq2_m.gguf | 3 + Midm-2.0-Base-Instruct-iq2_s.gguf | 3 + Midm-2.0-Base-Instruct-iq2_xs.gguf | 3 + Midm-2.0-Base-Instruct-iq2_xxs.gguf | 3 + Midm-2.0-Base-Instruct-iq3_m.gguf | 3 + Midm-2.0-Base-Instruct-iq3_s.gguf | 3 + Midm-2.0-Base-Instruct-iq3_xs.gguf | 3 + 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generated using [llama.cpp](https://github.com/ggerganov/llama.cpp) at commit [`21c02174`](https://github.com/ggerganov/llama.cpp/commit/21c021745d781edf9c44b4972ef6cbbf53b0ecff). + + + + + +--- + +## Quantization Beyond the IMatrix + +I've been experimenting with a new quantization approach that selectively elevates the precision of key layers beyond what the default IMatrix configuration provides. + +In my testing, standard IMatrix quantization underperforms at lower bit depths, especially with Mixture of Experts (MoE) models. To address this, I'm using the `--tensor-type` option in `llama.cpp` to manually "bump" important layers to higher precision. You can see the implementation here: +๐Ÿ‘‰ [Layer bumping with llama.cpp](https://github.com/Mungert69/GGUFModelBuilder/blob/main/model-converter/tensor_list_builder.py) + +While this does increase model file size, it significantly improves precision for a given quantization level. + +### **I'd love your feedbackโ€”have you tried this? How does it perform for you?** + + + + +--- + + + Click here to get info on choosing the right GGUF model format + + +--- + + + + + + + +

+
+ Mi:dm 2.0 Base +
+

+ +

+๐Ÿค— Mi:dm 2.0 Models | +๐Ÿ“œ Mi:dm 2.0 Technical Report | +๐Ÿ“• Mi:dm 2.0 Technical Blog* +

+ +

*To be released soon

+ +
+ +# News ๐Ÿ“ข + +- ๐Ÿ”œ _(Coming Soon!) GGUF format model files will be available soon for easier local deployment._ +- โšก๏ธ`2025/07/04`: Released Mi:dm 2.0 Model collection on Hugging Face๐Ÿค—. +
+
+# Table of Contents + +- ___Overview___ + - [Mi:dm 2.0](#midm-20) + - [Quickstart](#quickstart) + - [Evaluation](#evaluation) +- ___Usage___ + - [Run on Friendli.AI](#run-on-friendliai) + - [Run on Your Local Machine](#run-on-your-local-machine) + - [Deployment](#deployment) + - [Tutorials](#tutorials) +- ___More Information___ + - [Limitation](#limitation) + - [License](#license) + - [Contact](#contact) + +
+
+ +# Overview + +### Mi:dm 2.0 + +**Mi:dm 2.0** is a __"Korea-centric AI"__ model developed using KT's proprietary technology. The term __"Korea-centric AI"__ refers to a model that deeply internalizes the unique values, cognitive frameworks, and commonsense reasoning inherent to Korean society. It goes beyond simply processing or generating Korean textโ€”it reflects a deeper understanding of the socio-cultural norms and values that define Korean society. + +Mi:dm 2.0 is released in two versions: + +- **Mi:dm 2.0 Base** + An 11.5B parameter dense model designed to balance model size and performance. + It extends an 8B-scale model by applying the Depth-up Scaling (DuS) method, making it suitable for real-world applications that require both performance and versatility. + +- **Mi:dm 2.0 Mini** + A lightweight 2.3B parameter dense model optimized for on-device environments and systems with limited GPU resources. + It was derived from the Base model through pruning and distillation to enable compact deployment. + +> [!Note] +> Neither the pre-training nor the post-training data includes KT users' data. + +
+ +### Quickstart + +Here is the code snippet to run conversational inference with the model: + +```python +import torch +from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig + +model_name = "K-intelligence/Midm-2.0-Base-Instruct" + +model = AutoModelForCausalLM.from_pretrained( + model_name, + torch_dtype=torch.bfloat16, + trust_remote_code=True, + device_map="auto" +) +tokenizer = AutoTokenizer.from_pretrained(model_name) +generation_config = GenerationConfig.from_pretrained(model_name) + +prompt = "KT์— ๋Œ€ํ•ด ์†Œ๊ฐœํ•ด์ค˜" + +# message for inference +messages = [ + {"role": "system", + "content": "Mi:dm(๋ฏฟ:์Œ)์€ KT์—์„œ ๊ฐœ๋ฐœํ•œ AI ๊ธฐ๋ฐ˜ ์–ด์‹œ์Šคํ„ดํŠธ์ด๋‹ค."}, + {"role": "user", "content": prompt} +] + +input_ids = tokenizer.apply_chat_template( + messages, + tokenize=True, + add_generation_prompt=True, + return_tensors="pt" +) + +output = model.generate( + input_ids.to("cuda"), + generation_config=generation_config, + eos_token_id=tokenizer.eos_token_id, + max_new_tokens=128, + do_sample=False, +) +print(tokenizer.decode(output[0])) +``` + +> [!NOTE] +> The `transformers` library should be version `4.45.0` or higher. + +
+ +# Evaluation + + +#### Korean + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelSociety & CultureGeneral KnowledgeInstruction Following
K-Refer*K-Refer-Hard*Ko-Sovereign*HAERAEAvg.KMMLUKo-Sovereign*Avg.Ko-IFEvalKo-MTBenchAvg.
Qwen3-4B53.642.935.850.645.750.642.546.575.963.069.4
Exaone-3.5-2.4B-inst64.067.144.461.359.243.542.443.065.474.068.9
Mi:dm 2.0-Mini-inst66.461.436.770.858.845.142.443.873.374.073.6
Qwen3-14B72.465.749.868.464.155.454.755.183.67177.3
Llama-3.1-8B-inst43.236.433.849.540.733.036.734.860.15758.5
Exaone-3.5-7.8B-inst71.669.346.972.965.252.645.649.169.179.674.4
Mi:dm 2.0-Base-inst89.686.456.381.578.457.358.057.78289.785.9
+ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelComprehensionReasoning
K-Prag*K-Refer-Hard*Ko-BestKo-Sovereign*Avg.Ko-WinograndeKo-BestLogicKorHRM8KAvg.
Qwen3-4B73.956.791.543.566.667.569.25.656.743.8
Exaone-3.5-2.4B-inst68.758.587.238.062.560.364.17.438.536.7
Mi:dm 2.0-Mini-inst69.555.480.542.561.961.764.57.739.937.4
Qwen3-14B86.774.093.952.076.877.275.46.464.548.8
Llama-3.1-8B-inst59.948.677.431.551.540.126.02.430.919.8
Exaone-3.5-7.8B-inst73.561.992.044.067.264.660.38.649.739.5
Mi:dm 2.0-Base-inst86.570.895.253.076.175.173.08.652.944.8
+ +`*` indicates KT proprietary evaluation resources. + +
+ + +#### English + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
ModelInstructionReasoningMathCodingGeneral Knowledge
IFEvalBBHGPQAMuSRAvg.GSM8KMBPP+MMLU-proMMLUAvg.
Qwen3-4B79.779.039.858.559.190.462.4-73.373.3
Exaone-3.5-2.4B-inst81.146.428.149.741.482.559.8-59.559.5
Mi:dm 2.0-Mini-inst73.644.526.651.740.983.160.9-56.556.5
 
Qwen3-14B83.983.449.857.763.688.073.470.582.776.6
Llama-3.1-8B-inst79.960.321.650.344.181.281.847.670.759.2
Exaone-3.5-7.8B-inst83.650.133.151.244.881.179.440.769.054.8
Mi:dm 2.0-Base-inst84.077.733.551.954.491.677.553.373.763.5
+ + +
+ +# Usage + +### Run on Friendli.AI +You can try our model immediately via `Friendli.AI`. Simply click `Deploy` and then `Friendli Endpoints`. + +> [!Note] +> Please note that a login to `Friendli.AI` is required after your fifth chat interaction. + +

+ Left Image + Right Image +

+ + +### Run on Your Local Machine +We provide a detailed description about running Mi:dm 2.0 on your local machine using llama.cpp, LM Studio, and Ollama. Please check our [github](https://github.com/K-intelligence-Midm/Midm-2.0) for more information + + +### Deployment + +To serve Mi:dm 2.0 using [vLLM](https://github.com/vllm-project/vllm)(`>=0.8.0`) with an OpenAI-compatible API: +```bash +vllm serve K-intelligence/Midm-2.0-Base-Instruct +``` + + +### Tutorials +To help our end-users easily use Mi:dm 2.0, we have provided comprehensive tutorials on [github](https://github.com/K-intelligence-Midm/Midm-2.0). +
+ +
+
+ +# More Information + +### Limitation +* The training data for both Mi:dm 2.0 models consists primarily of English and Korean. Understanding and generation in other languages are not guaranteed. + +* The model is not guaranteed to provide reliable advice in fields that require professional expertise, such as law, medicine, or finance. + +* Researchers have made efforts to exclude unethical content from the training data โ€” such as profanity, slurs, bias, and discriminatory language. However, despite these efforts, the model may still produce inappropriate expressions or factual inaccuracies. + + +### License + +Mi:dm 2.0 is licensed under the [MIT License](./LICENSE). + + +### Contact +Mi:dm 2.0 Technical Inquiries: midm-llm@kt.com + +
+ + + + + +--- + +# ๐Ÿš€ If you find these models useful + +Help me test my **AI-Powered Quantum Network Monitor Assistant** with **quantum-ready security checks**: + +๐Ÿ‘‰ [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) + +๐Ÿ’ฌ **How to test**: + Choose an **AI assistant type**: + - `TurboLLM` (GPT-4.1-mini) + - `HugLLM` (Hugginface Open-source models) + - `TestLLM` (Experimental CPU-only) + +### **What Iโ€™m Testing** +Iโ€™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** + +๐ŸŸก **TestLLM** โ€“ Current experimental model (llama.cpp on 2 CPU threads on huggingface docker space): +- โœ… **Zero-configuration setup** +- โณ 30s load time (slow inference but **no API costs**) . No token limited as the cost is low. +- ๐Ÿ”ง **Help wanted!** If youโ€™re into **edge-device AI**, letโ€™s collaborate! + +### **Other Assistants** +๐ŸŸข **TurboLLM** โ€“ 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) + +๐Ÿ”ต **HugLLM** โ€“ Latest Open-source models: +- ๐ŸŒ Runs on Hugging Face Inference API. Performs pretty well using the lastest models hosted on Novita. + +### ๐Ÿ’ก **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 OpenAIโ€”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) โ˜•. 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! ๐Ÿ˜Š