Built on advanced transformer architecture and fine-tuned with proprietary datasets, ArcMind delivers dialogue experiences that feel authentically human — understanding nuance, maintaining context, and responding with genuine coherence.
1Architecture:
2 Base: Transformer-based Language Model
3 Parameters: 14B
4 Context Window: 8,192 tokens
5 Training: Supervised Fine-Tuning + RLHF
6
7Training Data:
8 - High-quality conversational datasets
9 - Multi-turn dialogue scenarios
10 - Emotionally nuanced interactions
11 - Hinglish code-switching examples
12
13Optimization:
14 - Memory-efficient attention mechanisms
15 - Quantization-ready architecture
16 - Optimized for CPU and GPU inference
1from transformers import AutoModelForCausalLM, AutoTokenizer
2
3# Load ArcMind
4model = AutoModelForCausalLM.from_pretrained("ArcDevs/ArcMind")
5tokenizer = AutoTokenizer.from_pretrained("ArcDevs/ArcMind")
6
7# Generate response
8prompt = "Hello! How are you today?"
9inputs = tokenizer(prompt, return_tensors="pt")
10outputs = model.generate(**inputs, max_length=200, temperature=0.7)
11response = tokenizer.decode(outputs[0], skip_special_tokens=True)
12
13print(response)
While ArcMind represents significant advancement in conversational AI, users should be aware:
1@software{arcmind2024,
2 title = {ArcMind: Human-Centric Conversational Language Model},
3 author = {ArcDevs Team},
4 year = {2024},
5 url = {https://huggingface.co/ArcDevs/ArcMind},
6 organization = {ArcDevs}
7}
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Crafting Intelligence From The Dark
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© 2024 ArcDevs. Licensed under Apache-2.0.