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Model: darkai-1/darkit-v3-1M Source: Original Platform
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ModelHub XC
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README.md
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README.md
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---
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language: multilingual
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license: apache-2.0
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organization: darkai
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author: darkai
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library_name: llama.cpp
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tags:
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- darkit-3
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- DarkAI Company
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- text-generation
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- programming
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- reasoning
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base_model: darkai/darkit-v3
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model_type: custom
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pipeline_tag: text-generation
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---
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# DarkIT v3
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DarkIT is a next-generation high-performance large language model engineered for **advanced programming, deep reasoning, and natural human conversation**.
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DarkIT v3 introduces major improvements in:
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- Advanced code generation
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- Complex debugging & error analysis
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- Long-context reasoning
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- Multi-language programming support
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- Instruction following for difficult technical tasks
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- Architecture understanding & code refactoring
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- Stable conversational behavior
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- Fast and efficient local inference
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- Unrestricted responses with strong adaptability
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---
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# What's New in v3
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DarkIT v3 has been significantly upgraded with a massive programming-focused training phase.
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### Major Improvements
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- Trained on over **18 million high-quality programming conversations**
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- Strongly improved coding intelligence and reasoning
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- Better understanding of software architecture and system design
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- More accurate debugging and bug fixing
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- Improved instruction consistency
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- Better long-response stability
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- Reduced hallucinations in programming tasks
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- Faster response generation quality under long prompts
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### Programming Capabilities
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DarkIT v3 performs strongly across:
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- Python
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- C++
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- JavaScript / TypeScript
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- Java
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- Rust
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- Go
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- PHP
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- SQL
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- Bash / Shell scripting
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- HTML / CSS
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- AI & Machine Learning workflows
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---
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# Key Specifications
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- **Model Family:** DarkIT Coder
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- **Version:** v3
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- **Model Size:** 15B Parameters
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- **Context Length:** 1M Tokens
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- **Format:** GGUF (optimized for efficient local deployment)
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- **Inference Support:** CPU / GPU
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- **Primary Focus:** Programming & Technical Reasoning
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---
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# Performance Notes
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- Optimized for strong local inference performance
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- Excellent balance between speed and output quality
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- Stable long-context generation
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- Enhanced code completion consistency
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- Improved logical reasoning across technical tasks
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- Designed for developer workflows and advanced prompting
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---
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# Recommended Usage
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DarkIT v3 performs best when used for:
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- Software development
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- AI engineering tasks
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- Code generation
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- Debugging large projects
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- Technical explanations
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- Automation scripting
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- Long-context programming conversations
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- Local offline AI deployment
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---
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# ⚠️ Notes
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- Designed primarily for inference deployment
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- Output quality may vary depending on quantization level and hardware
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- Best performance is achieved using structured prompts
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- Large context usage may require substantial RAM/VRAM
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---
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# About DarkAI
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DarkAI is an independent AI research initiative focused on building efficient, powerful, and scalable language models for real-world applications, with a strong focus on programming intelligence and local AI deployment.
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---
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- **Website:** https://darkai.site
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- **Telegram:** https://t.me/sii_3
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config.json
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config.json
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{
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"architectures": ["darkit-3-1m"]
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}
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93
notebook.ipynb
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notebook.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"!pip install llama-cpp-python huggingface_hub --extra-index-url https://abetlen.github.io/llama-cpp-python/whl/cu124\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"from huggingface_hub import HfApi\n",
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"from llama_cpp import Llama\n",
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"import os\n",
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"\n",
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"REPO_ID = \"darkai-1/darkit-v3-1M\"\n",
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"api = HfApi()\n",
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"\n",
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"files = api.list_repo_files(REPO_ID)\n",
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"gguf_files = [f for f in files if f.endswith(\".gguf\")]\n",
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"\n",
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"for i, f in enumerate(gguf_files):\n",
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" print(f\"[{i}] {f}\")\n",
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"\n",
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"choice = int(input(\"Select model number: \"))\n",
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"filename = gguf_files[choice]\n",
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"\n",
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"llm = Llama.from_pretrained(\n",
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" repo_id=REPO_ID,\n",
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" filename=filename,\n",
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" n_ctx=2048,\n",
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" n_batch=128,\n",
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" n_ubatch=128,\n",
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" n_threads=os.cpu_count() or 4,\n",
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" n_threads_batch=os.cpu_count() or 4,\n",
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" n_gpu_layers=-1,\n",
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" verbose=False,\n",
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" no_perf=True,\n",
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")\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm.set_cache(None)\n",
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"\n",
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"PROMPT = \"Hi how are you?\"\n",
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"\n",
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"stream = llm(\n",
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" f\"<|im_start|>user\\n{PROMPT}<|im_end|>\\n<|im_start|>assistant\\n\",\n",
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" max_tokens=128,\n",
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" temperature=0.7,\n",
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" top_p=0.8,\n",
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" top_k=20,\n",
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" stream=True,\n",
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" stop=[\n",
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" \"<|im_end|>\",\n",
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" \"<|im_start|>\",\n",
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" \"\\n\\nUser:\",\n",
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" \"\\n\\nAssistant:\"\n",
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" ],\n",
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" echo=False\n",
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")\n",
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"\n",
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"for chunk in stream:\n",
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" text = chunk[\"choices\"][0][\"text\"]\n",
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"\n",
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" if text:\n",
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" print(text, end=\"\", flush=True)\n",
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"\n",
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"print()\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 0
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}
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