Text Generation
Transformers
mistral
Merge
mergekit
lazymergekit
bardsai/jaskier-7b-dpo-v5.6
eren23/ogno-monarch-jaskier-merge-7b
liminerity/Omningotex-7b-slerp
yleo/OgnoMonarch-7B
Instructions to use nwhamed/mergedd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nwhamed/mergedd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="nwhamed/mergedd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("nwhamed/mergedd") model = AutoModelForCausalLM.from_pretrained("nwhamed/mergedd") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use nwhamed/mergedd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "nwhamed/mergedd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "nwhamed/mergedd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/nwhamed/mergedd
- SGLang
How to use nwhamed/mergedd 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 "nwhamed/mergedd" \ --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": "nwhamed/mergedd", "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 "nwhamed/mergedd" \ --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": "nwhamed/mergedd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use nwhamed/mergedd with Docker Model Runner:
docker model run hf.co/nwhamed/mergedd
mergedd
mergedd is a merge of the following models using mergekit:
🧩 Configuration
"models": [
{
"model": "bardsai/jaskier-7b-dpo-v5.6",
"parameters": {}
},
{
"model": "eren23/ogno-monarch-jaskier-merge-7b",
"parameters": {
"density": 0.53,
"weight": 0.4
}
},
{
"model": "liminerity/Omningotex-7b-slerp",
"parameters": {
"density": 0.53,
"weight": 0.3
}
},
{
"model": "yleo/OgnoMonarch-7B",
"parameters": {
"density": 0.53,
"weight": 0.3
}
}
],
"merge_method": "dare_ties",
"base_model": "bardsai/jaskier-7b-dpo-v5.6",
"parameters": {
"int8_mask": true,
"dtype": "bfloat16"
}
}
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docker model run hf.co/nwhamed/mergedd