Kataren_Mistral_7.B

Overview

Kataren_Mistral_7.B is a large language model (LLM) based on the Mistral-7B architecture. Developed by R-Kentaren, this model is designed for general-purpose text generation, instruction following, and conversational tasks. It leverages the powerful capabilities of the base Mistral-7B model while incorporating custom fine-tuning or merging techniques to enhance performance in specific scenarios.

Key Features:

• Parameter Count: ~7 billion
• Architecture: Transformer-based, with sliding window attention and grouped-query attention for efficient inference.
• Vocabulary Size: 32,000 (standard Mistral tokenizer)
• Context Length: Up to 8,192 tokens
• License: Apache 2.0 (inherited from base model, unless otherwise specified)

This model is ideal for applications such as chatbots, content creation, code assistance, and creative writing. As a community-uploaded model on Hugging Face, it is provided "as-is" for experimentation and research.

Base Model and Training

• Base Model: This model is derived from mistralai/Mistral-7B-v0.1, a pretrained generative text model known for its efficiency and strong performance on benchmarks.
• Fine-Tuning/Merging Details: (Note: Specific training data, hyperparameters, or merging recipes are not publicly detailed at this time. If you are the maintainer, consider updating this section with more information.)
  • Potential use cases suggest fine-tuning on diverse datasets for improved coherence and creativity.
  • No moderation mechanisms are built-in, so outputs should be filtered for production use.

Performance

While formal benchmarks are not available, Mistral-7B derivatives typically excel in:

• Natural language understanding (e.g., MMLU, HellaSwag)
• Code generation (e.g., HumanEval)
• Reasoning tasks (e.g., GSM8K)

Users are encouraged to evaluate the model on their specific tasks and share results.

Usage Instructions

Requirements

• Python 3.8+
• Transformers library: pip install transformers
• For GPU acceleration: pip install torch (with CUDA support)
• Optional: Accelerate for distributed inference: pip install accelerate

Quick Start with Transformers

Load and use the model for text generation:

from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline

model_name = "R-Kentaren/Kataren_Mistral_7.B"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")  # Use "auto" for GPU if available

# Inference pipeline for simplicity
generator = pipeline("text-generation", model=model, tokenizer=tokenizer)

# Generate text
prompt = "Once upon a time in a distant land,"
output = generator(prompt, max_new_tokens=100, do_sample=True, temperature=0.7)
print(output[0]['generated_text'])

Advanced Usage

• Instruction Format: For instruct-tuned behavior, use the following template:

  [INST] Your instruction here [/INST]
  

• Quantization: For lower memory usage, consider GGUF or GPTQ formats if available in the repository.
• Inference Endpoints: Deploy via Hugging Face Inference API or third-party services like Replicate.

Limitations and Biases

• Hallucinations: The model may generate factually incorrect or fabricated information.
• Biases: Inherited from training data, potentially including societal biases. Use with caution in sensitive applications.
• No Safety Filters: Outputs can include harmful content; implement your own safeguards.
• Compute Requirements: Requires significant VRAM (at least 16GB for full precision on GPU).

Ethical Considerations

This model should be used responsibly. Avoid applications that could cause harm, spread misinformation, or violate privacy. xAI and Hugging Face promote ethical AI practices.

Citation

If you use this model in your work, please cite it as:

@misc{kataren_mistral_7b,
  author = {R-Kentaren},
  title = {Kataren_Mistral_7.B: A Custom Mistral-7B Variant},
  year = {2025},
  publisher = {Hugging Face},
  url = {https://huggingface.co/R-Kentaren/Kataren_Mistral_7.B}
}

Contact

For questions or contributions, reach out to the maintainer on Hugging Face or open a discussion in the repository.

Model uploaded on August 15, 2025. Updates may include improved documentation or fine-tunes.