Text Generation
Transformers
GGUF
English
phi
knowledge-system
reasoning
expert-verification
multi-domain
zero-hallucination
spatial-memory
knowledge-tiles
phi-4
microsoft
knowledge-tiles-iath
conversational
Eval Results (legacy)
Instructions to use kofdai/nullai-knowledge-system with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use kofdai/nullai-knowledge-system with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="kofdai/nullai-knowledge-system") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("kofdai/nullai-knowledge-system") model = AutoModelForCausalLM.from_pretrained("kofdai/nullai-knowledge-system") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - llama-cpp-python
How to use kofdai/nullai-knowledge-system with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="kofdai/nullai-knowledge-system", filename="phi-4-q4_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use kofdai/nullai-knowledge-system with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -hf kofdai/nullai-knowledge-system:Q4_K_M # Run inference directly in the terminal: llama cli -hf kofdai/nullai-knowledge-system:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf kofdai/nullai-knowledge-system:Q4_K_M # Run inference directly in the terminal: llama cli -hf kofdai/nullai-knowledge-system:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf kofdai/nullai-knowledge-system:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf kofdai/nullai-knowledge-system:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf kofdai/nullai-knowledge-system:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf kofdai/nullai-knowledge-system:Q4_K_M
Use Docker
docker model run hf.co/kofdai/nullai-knowledge-system:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use kofdai/nullai-knowledge-system with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "kofdai/nullai-knowledge-system" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kofdai/nullai-knowledge-system", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/kofdai/nullai-knowledge-system:Q4_K_M
- SGLang
How to use kofdai/nullai-knowledge-system with SGLang:
Install from pip and serve model
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "kofdai/nullai-knowledge-system" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kofdai/nullai-knowledge-system", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker images
docker run --gpus all \ --shm-size 32g \ -p 30000:30000 \ -v ~/.cache/huggingface:/root/.cache/huggingface \ --env "HF_TOKEN=<secret>" \ --ipc=host \ lmsysorg/sglang:latest \ python3 -m sglang.launch_server \ --model-path "kofdai/nullai-knowledge-system" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "kofdai/nullai-knowledge-system", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Ollama
How to use kofdai/nullai-knowledge-system with Ollama:
ollama run hf.co/kofdai/nullai-knowledge-system:Q4_K_M
- Unsloth Studio
How to use kofdai/nullai-knowledge-system with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kofdai/nullai-knowledge-system to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for kofdai/nullai-knowledge-system to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for kofdai/nullai-knowledge-system to start chatting
- Atomic Chat new
- Docker Model Runner
How to use kofdai/nullai-knowledge-system with Docker Model Runner:
docker model run hf.co/kofdai/nullai-knowledge-system:Q4_K_M
- Lemonade
How to use kofdai/nullai-knowledge-system with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull kofdai/nullai-knowledge-system:Q4_K_M
Run and chat with the model
lemonade run user.nullai-knowledge-system-Q4_K_M
List all available models
lemonade list
Upload hot_cache.py with huggingface_hub
Browse files- hot_cache.py +78 -0
hot_cache.py
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from collections import OrderedDict
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class LRUCache:
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"""
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Least Recently Used (LRU) Cache.
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設計書で言及されているホットキャッシュのシンプルな実装です。
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"""
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def __init__(self, max_size: int = 20):
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"""
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Args:
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max_size (int): キャッシュの最大サイズ。
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"""
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if max_size <= 0:
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raise ValueError("max_size must be a positive integer.")
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self.max_size = max_size
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self._cache = OrderedDict()
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def __contains__(self, key):
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"'key in cache' 構文をサポートします。"
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return key in self._cache
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def __getitem__(self, key):
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"キーに対応する値を取得し、そのキーを最も最近使用されたものとしてマークします。"
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if key not in self._cache:
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raise KeyError(f"Key '{key}' not found in cache.")
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# アイテムを最後に移動させて「最近使用した」ことを示す
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self._cache.move_to_end(key)
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return self._cache[key]
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def __setitem__(self, key, value):
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"キーと値のペアをキャッシュに追加します。"
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if key in self._cache:
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# 既存のキーの場合は、最近使用したことを示すために移動
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self._cache.move_to_end(key)
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self._cache[key] = value
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# キャッシュサイズが上限を超えた場合、最も古く使用されていないアイテムを削除
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if len(self._cache) > self.max_size:
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self._cache.popitem(last=False)
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def get(self, key, default=None):
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"キーが存在しない場合に例外を送出しないバージョンの get です。"
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if key in self._cache:
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return self[key]
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return default
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@property
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def size(self):
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"現在のキャッシュサイズを返します。"
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return len(self._cache)
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# --- 使用例 ---
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if __name__ == "__main__":
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# サイズ3のキャッシュを作成
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cache = LRUCache(max_size=3)
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print("--- Cache Operations ---")
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cache['coord_1'] = "Tile 1 Data"
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cache['coord_2'] = "Tile 2 Data"
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cache['coord_3'] = "Tile 3 Data"
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print("Cache after adding 3 items:", cache._cache)
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# coord_1にアクセス -> 最近使用されたアイテムになる
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print("\nAccessing 'coord_1'...")
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_ = cache['coord_1']
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print("Cache state:", cache._cache)
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# 新しいアイテムを追加 -> 最も古く使用されていない 'coord_2' が削除される
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print("\nAdding 'coord_4', expecting 'coord_2' to be evicted...")
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cache['coord_4'] = "Tile 4 Data"
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print("Cache state:", cache._cache)
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print("\nIs 'coord_2' in cache?", 'coord_2' in cache)
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print("Is 'coord_3' in cache?", 'coord_3' in cache)
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print("Current cache size:", cache.size)
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