Instructions to use lilmeaty/wowxd with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use lilmeaty/wowxd with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="lilmeaty/wowxd")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("lilmeaty/wowxd") model = AutoModelForCausalLM.from_pretrained("lilmeaty/wowxd") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use lilmeaty/wowxd with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "lilmeaty/wowxd" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "lilmeaty/wowxd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/lilmeaty/wowxd
- SGLang
How to use lilmeaty/wowxd 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 "lilmeaty/wowxd" \ --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": "lilmeaty/wowxd", "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 "lilmeaty/wowxd" \ --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": "lilmeaty/wowxd", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use lilmeaty/wowxd with Docker Model Runner:
docker model run hf.co/lilmeaty/wowxd
merge
This is a merge of pre-trained language models created using mergekit.
Merge Details
Merge Method
This model was merged using the passthrough merge method.
Models Merged
The following models were included in the merge:
- ibm-granite/granite-3b-code-base-2k
- PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.1-SFT-3B
- stabilityai/stable-code-3b
- Bllossom/llama-3.2-Korean-Bllossom-3B
- ICEPVP8977/Uncensored_llama_3.2_3b_safetensors
- Qwen/Qwen2.5-3B
- chuanli11/Llama-3.2-3B-Instruct-uncensored
- Qwen/Qwen2.5-3B-Instruct
- meta-llama/Llama-3.2-3B-Instruct
- meta-llama/Llama-3.2-3B
Configuration
The following YAML configuration was used to produce this model:
slices:
- sources:
- layer_range: [0, 1]
model: ICEPVP8977/Uncensored_llama_3.2_3b_safetensors
- sources:
- layer_range: [0, 1]
model: meta-llama/Llama-3.2-3B-Instruct
- sources:
- layer_range: [0, 1]
model: meta-llama/Llama-3.2-3B
- sources:
- layer_range: [0, 1]
model: chuanli11/Llama-3.2-3B-Instruct-uncensored
- sources:
- layer_range: [0, 1]
model: PJMixers-Dev/LLaMa-3.2-Instruct-JankMix-v0.1-SFT-3B
- sources:
- layer_range: [0, 1]
model: Bllossom/llama-3.2-Korean-Bllossom-3B
- sources:
- layer_range: [0, 1]
model: Qwen/Qwen2.5-3B-Instruct
- sources:
- layer_range: [0, 1]
model: Qwen/Qwen2.5-3B
- sources:
- layer_range: [0, 1]
model: stabilityai/stable-code-3b
- sources:
- layer_range: [0, 1]
model: ibm-granite/granite-3b-code-base-2k
merge_method: passthrough
dtype: float16
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