Text Generation
Transformers
Safetensors
mistral
mergekit
Merge
roleplay
conversational
text-generation-inference
Instructions to use Vortex5/Prototype-X-12b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vortex5/Prototype-X-12b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Vortex5/Prototype-X-12b") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Vortex5/Prototype-X-12b") model = AutoModelForCausalLM.from_pretrained("Vortex5/Prototype-X-12b", 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=40) print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Vortex5/Prototype-X-12b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Vortex5/Prototype-X-12b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Vortex5/Prototype-X-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Vortex5/Prototype-X-12b
- SGLang
How to use Vortex5/Prototype-X-12b 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 "Vortex5/Prototype-X-12b" \ --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": "Vortex5/Prototype-X-12b", "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 "Vortex5/Prototype-X-12b" \ --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": "Vortex5/Prototype-X-12b", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Vortex5/Prototype-X-12b with Docker Model Runner:
docker model run hf.co/Vortex5/Prototype-X-12b
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README.md
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tags:
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models:
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- model: Retreatcost/KansenSakura-Eclipse-RP-12b
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- model: Retreatcost/KansenSakura-Radiance-RP-12b
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dtype: bfloat16
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tokenizer:
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source: Retreatcost/KansenSakura-Erosion-RP-12b
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tags:
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- mergekit
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---
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<style>
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.blackforge-shell {
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max-width: 1000px;
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margin: 40px auto;
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padding: 0 14px;
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background: radial-gradient(circle at 40% 60%, rgba(0,3,0,0.96) 0%, rgba(0,0,0,0.99) 100%);
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border: 2px solid rgba(0,160,70,0.35);
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border-radius: 16px;
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inset 0 0 50px rgba(0,120,50,0.2),
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font-family: "Inter", "Segoe UI", system-ui, sans-serif;
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margin: 42px 0 26px 0;
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text-align: center;
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background: linear-gradient(100deg, rgba(0,50,0,0.7), rgba(0,110,40,0.6), rgba(0,150,100,0.65));
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border: 1px solid rgba(0,200,100,0.25);
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border-radius: 14px;
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padding: 16px 12px;
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box-shadow:
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position: relative;
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overflow: hidden;
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}
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content: "";
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position: absolute;
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top: 0; left: -70%;
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width: 45%;
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background: linear-gradient(120deg, rgba(255,255,255,0.15), rgba(255,255,255,0));
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transform: skewX(-25deg);
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animation: beamSlide 8s linear infinite;
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}
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@keyframes beamSlide {
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0% { left: -70%; }
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}
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font-size: 1.3rem;
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letter-spacing: 2px;
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background: linear-gradient(90deg, #42ff90, #96ffb2, #2aff74);
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border-left: 4px solid rgba(0,200,90,0.7);
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details summary {
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background: radial-gradient(circle, rgba(0,255,100,0.25), rgba(0,20,0,0.9));
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box-shadow: inset 0 0 30px rgba(0,255,100,0.3);
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}
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</style>
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<div class="title-block" style="max-width:1000px;margin:40px auto;text-align:center;">
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<h2 style="font-size:2.3rem;">⚡ Prototype-X-12b ⚡</h2>
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</div>
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<div style="max-width:1000px;margin:0 auto 25px;padding:0 12px;text-align:center;">
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<img src="https://cdn-uploads.huggingface.co/production/uploads/6669a3a617b838fda45637b8/s83QTz1ZY8K-PeMrPmL5b.png"
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style="width:100%;max-width:960px;border-radius:14px;
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border:2px solid rgba(0,180,80,0.45);
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box-shadow:0 0 60px rgba(0,180,70,0.35), inset 0 0 12px rgba(0,255,140,0.2);" />
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</div>
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<div class="blackforge-divider"></div>
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<div class="blackforge-shell">
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<div style="padding:30px 32px;">
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<div class="title-block"><h2>01 // Overview</h2></div>
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<p><strong>Prototype-X-12b</strong> is a model merged using a custom <code>flowforge</code> method — it merges <a href="https://huggingface.co/Retreatcost/KansenSakura-Eclipse-RP-12b">KansenSakura-Eclipse-RP-12B</a> and <a href="https://huggingface.co/Retreatcost/KansenSakura-Radiance-RP-12b">KansenSakura-Radiance-RP-12B</a>, with <a href="https://huggingface.co/Retreatcost/KansenSakura-Erosion-RP-12b">KansenSakura-Erosion-RP-12B</a> as the base model.</p>
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<div class="title-block"><h2>02 // Custom Merge Method</h2></div>
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<p>It is a directional, coherence-aware merge algorithm that moves a base model along the weighted consensus direction defined by its donors rather than averaging them directly. Each donor’s influence is determined by its relative energy (the magnitude of its weight differences from the base), and the method normalizes and scales these offsets to preserve numerical stability. A small orthogonal adjustment prevents collapse when donors are highly similar, while the <b>strength</b>, <b>trust</b>, and <b>top_k</b> parameters control how far and how selectively the merge travels through parameter space. The result is a controlled shift in model behavior that reflects donor characteristics without discarding the base model’s underlying structure.</p>
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<details>
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<summary>Show YAML</summary>
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<pre>merge_method: flowforge
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models:
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- model: Retreatcost/KansenSakura-Eclipse-RP-12b
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- model: Retreatcost/KansenSakura-Radiance-RP-12b
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dtype: bfloat16
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tokenizer:
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source: Retreatcost/KansenSakura-Erosion-RP-12b
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</pre>
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</details>
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<div class="title-block"><h2>03 // Acknowledgments</h2></div>
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<ul style="list-style:none;padding-left:0;line-height:1.8;">
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<li><strong><a href="https://huggingface.co/mradermacher" target="_blank">Team Mradermacher</a></strong> — Static & imatrix quants</li>
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<li><strong><a href="https://huggingface.co/DeathGodlike" target="_blank">DeathGodlike</a></strong> — EXL3 quants</li>
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<li><strong><a>Original creators and model authors</a></strong></li>
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</ul>
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</div>
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</div>
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