raxcore-dev/rax-3.5-chat

Rax 3.5 Chat

Developed by RaxCore - A leading developer company in Africa and beyond

Rax 3.5 Chat is an extensively enhanced conversational AI model featuring significant architectural improvements and advanced training methodologies developed by RaxCore. Built upon the Llama foundation, this model has been completely transformed through proprietary optimization techniques.

Model Details

• Model Name: Rax 3.5 Chat
• Architecture: Llama (LlamaForCausalLM)
• Parameters: ~1.1B
• Context Length: 2048 tokens
• Precision: bfloat16
• License: Apache 2.0

RaxCore Innovations

This model features several breakthrough improvements developed by RaxCore:

• Enhanced Conversational Flow: Advanced dialogue management system
• Cultural Context Awareness: Optimized for diverse global interactions
• Response Quality Optimization: Proprietary coherence enhancement algorithms
• Efficiency Improvements: Reduced inference time with maintained quality
• Robustness Enhancements: Better handling of edge cases and complex queries

Model Architecture

• Hidden Size: 2048
• Intermediate Size: 5632
• Attention Heads: 32
• Key-Value Heads: 4
• Hidden Layers: 22
• Vocabulary Size: 32,000

Usage

Quick Start

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("rax-3.5-chat")
model = AutoModelForCausalLM.from_pretrained(
    "rax-3.5-chat",
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Chat template
messages = [
    {"role": "system", "content": "You are Rax, a helpful AI assistant."},
    {"role": "user", "content": "Hello! How are you?"}
]

# Apply chat template
input_text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(input_text, return_tensors="pt")

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=256,
        temperature=0.7,
        do_sample=True,
        pad_token_id=tokenizer.eos_token_id
    )

response = tokenizer.decode(outputs[0][inputs['input_ids'].shape[1]:], skip_special_tokens=True)
print(response)

Chat Format

Rax 3.5 Chat uses the following conversation format:

<|system|>
You are Rax, a helpful AI assistant.</s>
<|user|>
Hello! How are you?</s>
<|assistant|>
Hello! I'm doing well, thank you for asking. How can I help you today?</s>

Training Details

RaxCore's advanced development process included:

• Proprietary fine-tuning algorithms developed over several days
• Enhanced dialogue optimization using RaxCore's conversational AI framework
• Advanced response coherence improvements through custom training pipelines
• Specialized African context integration for global applicability
• Performance optimization exceeding baseline capabilities by significant margins

Built upon TinyLlama foundation with extensive RaxCore enhancements

Intended Use

Rax 3.5 Chat is designed for:

• Conversational AI applications
• Chatbots and virtual assistants
• Educational and research purposes
• Creative writing assistance

Limitations

• Context window limited to 2048 tokens
• May generate incorrect or biased information
• Not suitable for production use without proper safety measures
• Requires responsible deployment practices

Ethical Considerations

Please use this model responsibly:

• Implement appropriate content filtering
• Monitor outputs for potential biases
• Ensure compliance with applicable regulations
• Consider the impact on users and society

Technical Specifications

• Framework: Transformers 4.35.0+
• Hardware Requirements: GPU with 4GB+ VRAM recommended
• Inference Speed: Optimized for real-time chat applications

Citation

If you use Rax 3.5 Chat in your research or applications, please cite:

@misc{rax35chat2024,
  title={Rax 3.5 Chat: An Enhanced Conversational AI Model},
  author={RaxCore},
  year={2024},
  note={Enhanced from TinyLlama with significant RaxCore improvements},
  organization={RaxCore - Leading developer company in Africa and beyond}
}

Contact

For questions, issues, or collaboration opportunities:

• Hugging Face: https://huggingface.co/raxcore-dev
• Website: https://www.raxcore.dev/
• Model Repository: Contact RaxCore directly

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RaxCore - A leading developer company in Africa and beyond
🌐 Website: www.raxcore.dev
🤗 Hugging Face: raxcore-dev
Rax 3.5 Chat - Powering the next generation of conversational AI