Instructions to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RichardErkhov/MadKFC_-_MergedCPsyModel-gguf",
	filename="MergedCPsyModel.IQ3_M.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 RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with llama.cpp:

Install from brew

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

Install from WinGet (Windows)

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

Use Docker

docker model run hf.co/RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M

LM Studio
Jan
Ollama
How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with Ollama:
```
ollama run hf.co/RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M
```

Unsloth Studio new

How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf 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 RichardErkhov/MadKFC_-_MergedCPsyModel-gguf 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 RichardErkhov/MadKFC_-_MergedCPsyModel-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RichardErkhov/MadKFC_-_MergedCPsyModel-gguf to start chatting

Pi new

How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with Docker Model Runner:
```
docker model run hf.co/RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M
```

Lemonade

How to use RichardErkhov/MadKFC_-_MergedCPsyModel-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RichardErkhov/MadKFC_-_MergedCPsyModel-gguf:Q4_K_M

Run and chat with the model

lemonade run user.MadKFC_-_MergedCPsyModel-gguf-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.

Quantization made by Richard Erkhov.

Github

Discord

Request more models

MergedCPsyModel - GGUF

Model creator: https://huggingface.co/MadKFC/
Original model: https://huggingface.co/MadKFC/MergedCPsyModel/

Name	Quant method	Size
MergedCPsyModel.Q2_K.gguf	Q2_K	2.96GB
MergedCPsyModel.IQ3_XS.gguf	IQ3_XS	3.28GB
MergedCPsyModel.IQ3_S.gguf	IQ3_S	3.43GB
MergedCPsyModel.Q3_K_S.gguf	Q3_K_S	3.41GB
MergedCPsyModel.IQ3_M.gguf	IQ3_M	3.52GB
MergedCPsyModel.Q3_K.gguf	Q3_K	3.74GB
MergedCPsyModel.Q3_K_M.gguf	Q3_K_M	3.74GB
MergedCPsyModel.Q3_K_L.gguf	Q3_K_L	4.03GB
MergedCPsyModel.IQ4_XS.gguf	IQ4_XS	4.18GB
MergedCPsyModel.Q4_0.gguf	Q4_0	4.34GB
MergedCPsyModel.IQ4_NL.gguf	IQ4_NL	4.38GB
MergedCPsyModel.Q4_K_S.gguf	Q4_K_S	4.37GB
MergedCPsyModel.Q4_K.gguf	Q4_K	4.58GB
MergedCPsyModel.Q4_K_M.gguf	Q4_K_M	4.58GB
MergedCPsyModel.Q4_1.gguf	Q4_1	4.78GB
MergedCPsyModel.Q5_0.gguf	Q5_0	5.21GB
MergedCPsyModel.Q5_K_S.gguf	Q5_K_S	5.21GB
MergedCPsyModel.Q5_K.gguf	Q5_K	5.34GB
MergedCPsyModel.Q5_K_M.gguf	Q5_K_M	5.34GB
MergedCPsyModel.Q5_1.gguf	Q5_1	5.65GB
MergedCPsyModel.Q6_K.gguf	Q6_K	6.14GB
MergedCPsyModel.Q8_0.gguf	Q8_0	7.95GB

Original model description:

tags: - autotrain - text-generation-inference - text-generation - peft library_name: transformers base_model: meta-llama/Meta-Llama-3.1-8B-Instruct widget: - messages: - role: user content: What is your favorite condiment? license: other

Model Trained Using AutoTrain

This model was trained using AutoTrain. For more information, please visit AutoTrain.

Usage


from transformers import AutoModelForCausalLM, AutoTokenizer

model_path = "PATH_TO_THIS_REPO"

tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    device_map="auto",
    torch_dtype='auto'
).eval()

# Prompt content: "hi"
messages = [
    {"role": "user", "content": "hi"}
]

input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)

# Model response: "Hello! How can I assist you today?"
print(response)

Downloads last month: -

GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

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