Text Generation
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mistral
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text-generation-inference
Instructions to use DarkArtsForge/Zepar-24B-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use DarkArtsForge/Zepar-24B-v1 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="DarkArtsForge/Zepar-24B-v1") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("DarkArtsForge/Zepar-24B-v1") model = AutoModelForCausalLM.from_pretrained("DarkArtsForge/Zepar-24B-v1", device_map="auto") messages = [ {"role": "user", "content": "Who are you?"}, ] inputs = tokenizer.apply_chat_template( messages, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt", ).to(model.device) outputs = model.generate(**inputs, max_new_tokens=256) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use DarkArtsForge/Zepar-24B-v1 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "DarkArtsForge/Zepar-24B-v1" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DarkArtsForge/Zepar-24B-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/DarkArtsForge/Zepar-24B-v1
- SGLang
How to use DarkArtsForge/Zepar-24B-v1 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 "DarkArtsForge/Zepar-24B-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DarkArtsForge/Zepar-24B-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'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 "DarkArtsForge/Zepar-24B-v1" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "DarkArtsForge/Zepar-24B-v1", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use DarkArtsForge/Zepar-24B-v1 with Docker Model Runner:
docker model run hf.co/DarkArtsForge/Zepar-24B-v1
โ ๏ธ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly. Also, use Mistral Tekken template for best results.
๐ช Zepar 24B v1
A test of new merge method adaptiveswap (based on nearswap) which aims to intelligently blend various swapping mechanisms from other methods using Pareto Frontier AI controller.
architecture: MistralForCausalLM
models:
- model: B:\24B\Morax-24B-v2
- model: B:\24B\Paimon-24B-v2
merge_method: adaptiveswap
base_model: A:\LLM\.cache\24B\!models--mistralai--Magistral-Small-2509\textonly
parameters:
mode: adaptive
blend_methods: ["cosine", "magnitude", "variance", "salience", "slerp"]
blend_strength: 1.0
precision: float32
autopilot: true
dtype: float32
out_dtype: bfloat16
tokenizer:
source: union
chat_template: auto
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