Text Generation
Transformers
Safetensors
mistral
roleplay
creative-writing
Merge
mergekit
exl2
conversational
text-generation-inference
Instructions to use ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6") model = AutoModelForCausalLM.from_pretrained("ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6", 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]:])) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6
- SGLang
How to use ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6 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 "ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6" \ --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": "ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6", "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 "ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6" \ --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": "ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6 with Docker Model Runner:
docker model run hf.co/ArtusDev/Delta-Vector_Archaeo-12B-V2_EXL2_2.5bpw_H6
File size: 5,632 Bytes
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tags:
- roleplay
- creative-writing
- merge
- mergekit
base_model:
- Delta-Vector/Francois-PE-V2-Huali-12B
- Delta-Vector/Rei-V3-KTO-12B
pipeline_tag: text-generation
library_name: transformers
---
```
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'/||\' "Archaeopteryx"
```
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
@import url('https://fonts.googleapis.com/css2?family=VT323&display=swap');
body {
background: #0a0017;
margin: 0;
padding: 20px;
font-family: 'VT323', monospace;
color: #ff00aa;
text-shadow: 0 0 8px #ff00aa;
animation: glitch-flicker 0.2s infinite alternate;
}
@keyframes glitch-flicker {
0% { text-shadow: 0 0 5px #ff00aa, 0 0 15px #ff00aa; }
100% { text-shadow: 0 0 8px #ff0066, 0 0 18px #ff0066; }
}
.crt-container {
padding: 10px;
max-width: 900px;
margin: auto;
}
.crt-case {
background: linear-gradient(135deg, #130021, #20002c);
border-radius: 10px;
padding: 15px;
box-shadow:
inset 2px 2px 10px rgba(255,0,170,0.5),
2px 2px 5px rgba(255,0,170,0.3),
0 0 25px rgba(255,0,170,0.2);
}
.crt-screen {
background: #0c011a;
padding: 20px;
border-radius: 10px;
box-shadow:
inset 0 0 25px rgba(255,0,170,0.3),
0 0 15px rgba(255,0,170,0.7);
filter: contrast(1.2) brightness(1.2);
text-shadow: 0px 0px 5px #ff00aa;
animation: glow-pulse 3s infinite alternate;
}
@keyframes glow-pulse {
0% { box-shadow: inset 0 0 20px rgba(255,0,170,0.3), 0 0 15px rgba(255,0,170,0.3); }
100% { box-shadow: inset 0 0 30px rgba(255,0,170,0.5), 0 0 25px rgba(255,0,170,0.5); }
}
h2 {
color: #ff33cc;
text-align: center;
font-size: 28px;
text-shadow:
0 0 8px #ff33cc,
0 0 18px #ff0044;
}
pre {
background: rgba(255,0,170,0.1);
padding: 10px;
border-radius: 10px;
color: #ff66cc;
font-size: 14px;
box-shadow: inset 0 0 10px rgba(255,0,170,0.5);
}
.glitch {
animation: text-glitch 0.5s infinite alternate;
}
@keyframes text-glitch {
0% { transform: translateX(-2px); text-shadow: 0 0 5px #ff0066, 0 0 10px #ff33cc; }
100% { transform: translateX(2px); text-shadow: 0 0 8px #ff00aa, 0 0 20px #ff0099; }
}
.neon-link {
color: #ff66cc;
text-decoration: none;
transition: text-shadow 0.3s ease;
}
.neon-link:hover {
text-shadow: 0px 0px 15px #ff66cc, 0 0 25px rgba(255,0,170,0.5);
}
.ascii-art {
text-align: center;
font-size: 12px;
color: #ff33cc;
text-shadow: 0px 0px 5px #ff00ff;
margin-bottom: 20px;
}
.quantso-container {
display: flex;
justify-content: center;
gap: 20px;
margin-top: 20px;
}
.quantso-box {
background: rgba(255,0,170,0.1);
padding: 15px;
border-radius: 10px;
text-align: center;
box-shadow: inset 0 0 10px rgba(255,0,170,0.5);
flex: 1;
max-width: 150px;
}
</style>
</head>
<body>
<div class="crt-container">
<div class="crt-case">
<div class="crt-screen">
<p>A series of Merges made for Roleplaying & Creative Writing, This model uses Rei-V3-KTO-12B and Francois-PE-V2-Huali-12B and Slerp to merge the 2 models - as a sequel to the OG Archaeo.</p>
<h3>ChatML formatting</h3>
<pre>
"""<|im_start|>system
system prompt<|im_end|>
<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
</pre>
<h3>MergeKit Configuration</h3>
<pre>
models:
- model: Delta-Vector/Rei-V3-KTO-12B
- model: Delta-Vector/Francois-PE-V2-Huali-12B
merge_method: slerp
base_model: Delta-Vector/Rei-V3-KTO-12B
parameters:
t:
- value: 0.2
dtype: bfloat16
tokenizer_source: base
</pre>
<h3>Quants:</h3>
<div class="quantso-container">
<div class="quantso-box">
<strong>GGUF</strong><br>
<a class="neon-link" href="#">too lazy bwehh (waiting for mradermacher)/</a>
</div>
<div class="quantso-box">
<strong>EXL2</strong><br>
<a class="neon-link" href="#">nyooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooooo</a>
</div>
</div>
<h3>Credits</h3>
<p>Thank you to: Kubernetes-bad, LucyKnada, Intervitens, Samantha Twinkman, Tav, Alicat, Auri, Trappu & The rest of Anthracite</p>
</div>
</div>
</div>
</body>
</html> |