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

Libraries

How to use knifeayumu/LLM_Collection with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="knifeayumu/LLM_Collection",
	filename="Command-A-03-2025_111B/Fallen-Command-A-111B-v1-IQ4_XS-00001-of-00002.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use knifeayumu/LLM_Collection with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf knifeayumu/LLM_Collection:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf knifeayumu/LLM_Collection:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf knifeayumu/LLM_Collection:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf knifeayumu/LLM_Collection: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 knifeayumu/LLM_Collection:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf knifeayumu/LLM_Collection: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 knifeayumu/LLM_Collection:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf knifeayumu/LLM_Collection:Q4_K_M

Use Docker

docker model run hf.co/knifeayumu/LLM_Collection:Q4_K_M

LM Studio
Jan
Ollama
How to use knifeayumu/LLM_Collection with Ollama:
```
ollama run hf.co/knifeayumu/LLM_Collection:Q4_K_M
```

Unsloth Studio new

How to use knifeayumu/LLM_Collection 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 knifeayumu/LLM_Collection 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 knifeayumu/LLM_Collection to start chatting

Using HuggingFace Spaces for Unsloth

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

Docker Model Runner
How to use knifeayumu/LLM_Collection with Docker Model Runner:
```
docker model run hf.co/knifeayumu/LLM_Collection:Q4_K_M
```

Lemonade

How to use knifeayumu/LLM_Collection with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull knifeayumu/LLM_Collection:Q4_K_M

Run and chat with the model

lemonade run user.LLM_Collection-Q4_K_M

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

Notable Deleted Models:

OnlyChat-Miqu-v1.q4_k_m.gguf (It was up for few hours [?] by OnlyThings and got deleted on HF)
NeteLegacy-13B.q5_k_m.gguf (I believe it's the first version of Nete by Undi95, deleted due to of being too NovelAI)

Downloads last month: 7,222

GGUF

Model size

32B params

Architecture

command-r

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

Inference Providers NEW

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

knifeayumu
/

LLM_Collection

Notable Deleted Models:

Collection including knifeayumu/LLM_Collection

Archives