Instructions to use lolilolikon/ai_knowledge with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lolilolikon/ai_knowledge with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="lolilolikon/ai_knowledge",
	filename="deepseek-r1-distill-qwen-1.5b.Q8_0.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use lolilolikon/ai_knowledge with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lolilolikon/ai_knowledge:Q8_0
# Run inference directly in the terminal:
llama-cli -hf lolilolikon/ai_knowledge:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf lolilolikon/ai_knowledge:Q8_0
# Run inference directly in the terminal:
llama-cli -hf lolilolikon/ai_knowledge:Q8_0

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 lolilolikon/ai_knowledge:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf lolilolikon/ai_knowledge:Q8_0

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 lolilolikon/ai_knowledge:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf lolilolikon/ai_knowledge:Q8_0

Use Docker

docker model run hf.co/lolilolikon/ai_knowledge:Q8_0

LM Studio
Jan
Ollama
How to use lolilolikon/ai_knowledge with Ollama:
```
ollama run hf.co/lolilolikon/ai_knowledge:Q8_0
```

Unsloth Studio

How to use lolilolikon/ai_knowledge 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 lolilolikon/ai_knowledge 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 lolilolikon/ai_knowledge to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for lolilolikon/ai_knowledge to start chatting

Atomic Chat new
Docker Model Runner
How to use lolilolikon/ai_knowledge with Docker Model Runner:
```
docker model run hf.co/lolilolikon/ai_knowledge:Q8_0
```

Lemonade

How to use lolilolikon/ai_knowledge with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull lolilolikon/ai_knowledge:Q8_0

Run and chat with the model

lemonade run user.ai_knowledge-Q8_0

List all available models

lemonade list

lolilolikon commited on 27 days ago

Commit

15fa0ac

verified ·

1 Parent(s): 7320220

Add README

Browse files

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README.md +3 -3

README.md CHANGED Viewed

@@ -15,9 +15,9 @@ This model was finetuned and converted to GGUF format using [Unsloth](https://gi
 - For multimodal models: `llama-mtmd-cli -hf lolilolikon/ai_knowledge --jinja`
 ## Available Model files:
-- `deepseek-r1-distill-llama-8b.Q8_0.gguf`
-## Note
-The model's BOS token behavior was adjusted for GGUF compatibility.
 This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

 - For multimodal models: `llama-mtmd-cli -hf lolilolikon/ai_knowledge --jinja`
 ## Available Model files:
+- `Qwen2.5-7B-Instruct.Q8_0.gguf`
+## Ollama
+An Ollama Modelfile is included for easy deployment.
 This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
 [<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)