Instructions to use duyntnet/ToppyCox-7B-imatrix-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use duyntnet/ToppyCox-7B-imatrix-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="duyntnet/ToppyCox-7B-imatrix-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("duyntnet/ToppyCox-7B-imatrix-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use duyntnet/ToppyCox-7B-imatrix-GGUF 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 duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf duyntnet/ToppyCox-7B-imatrix-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 duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf duyntnet/ToppyCox-7B-imatrix-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 duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M

Use Docker

docker model run hf.co/duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use duyntnet/ToppyCox-7B-imatrix-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "duyntnet/ToppyCox-7B-imatrix-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/ToppyCox-7B-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M

SGLang

How to use duyntnet/ToppyCox-7B-imatrix-GGUF 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 "duyntnet/ToppyCox-7B-imatrix-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/ToppyCox-7B-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "duyntnet/ToppyCox-7B-imatrix-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "duyntnet/ToppyCox-7B-imatrix-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use duyntnet/ToppyCox-7B-imatrix-GGUF with Ollama:
```
ollama run hf.co/duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M
```
Unsloth Desktop
Docker Model Runner
How to use duyntnet/ToppyCox-7B-imatrix-GGUF with Docker Model Runner:
```
docker model run hf.co/duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M
```

Lemonade

How to use duyntnet/ToppyCox-7B-imatrix-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull duyntnet/ToppyCox-7B-imatrix-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ToppyCox-7B-imatrix-GGUF-Q4_K_M

List all available models

lemonade list

Atomic Chat

Quantizations of https://huggingface.co/OmnicromsBrain/ToppyCox-7B

Open source inference clients/UIs

Closed source inference clients/UIs

LM Studio
More will be added...

From original readme

ToppyCox-7B is a merge of the following models using LazyMergekit:

🧩 Configuration

slices:
  - sources:
      - model: Undi95/Toppy-M-7B
        layer_range: [0, 32]
      - model: N8Programs/Coxcomb
        layer_range: [0, 32]
merge_method: slerp
base_model: Undi95/Toppy-M-7B
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

💻 Usage

!pip install -qU transformers accelerate

from transformers import AutoTokenizer
import transformers
import torch

model = "OmnicromsBrain/ToppyCox-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]

tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
    "text-generation",
    model=model,
    torch_dtype=torch.float16,
    device_map="auto",
)

outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])

Downloads last month: 135

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit