Instructions to use Gweizheng/Marcoro14-7B-dare with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Gweizheng/Marcoro14-7B-dare with Transformers:

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

pipe = pipeline("text-generation", model="Gweizheng/Marcoro14-7B-dare")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Gweizheng/Marcoro14-7B-dare")
model = AutoModelForCausalLM.from_pretrained("Gweizheng/Marcoro14-7B-dare", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Gweizheng/Marcoro14-7B-dare with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Gweizheng/Marcoro14-7B-dare"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Gweizheng/Marcoro14-7B-dare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Gweizheng/Marcoro14-7B-dare

SGLang

How to use Gweizheng/Marcoro14-7B-dare 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 "Gweizheng/Marcoro14-7B-dare" \
    --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": "Gweizheng/Marcoro14-7B-dare",
		"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 "Gweizheng/Marcoro14-7B-dare" \
        --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": "Gweizheng/Marcoro14-7B-dare",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Gweizheng/Marcoro14-7B-dare with Docker Model Runner:
```
docker model run hf.co/Gweizheng/Marcoro14-7B-dare
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Marcoro14-7B-dare

Marcoro14-7B-dare is a merge of the following models using mergekit:

🧩 Configuration

```yaml models:

model: mistralai/Mistral-7B-v0.1
No parameters necessary for base model
model: samir-fama/SamirGPT-v1 parameters: density: 0.53 weight: 0.4
model: abacusai/Slerp-CM-mist-dpo parameters: density: 0.53 weight: 0.3
model: EmbeddedLLM/Mistral-7B-Merge-14-v0.2 parameters: density: 0.53 weight: 0.3 merge_method: dare_ties base_model: mistralai/Mistral-7B-v0.1 parameters: int8_mask: true dtype: bfloat16 ```

Downloads last month: 75

Safetensors

Model size

7B params

Tensor type

BF16

Model tree for Gweizheng/Marcoro14-7B-dare

Quantizations

1 model

Marcoro14-7B-dare

🧩 Configuration

No parameters necessary for base model

Model tree for Gweizheng/Marcoro14-7B-dare