Training Specs

• CPU:- Intel(R) Core(TM) i3-14100
• Memory:- 16.0 GB DDR5
• GPU:- None (Intel UHD Graphics 730)

Model Overview

• Model Name: VedaX-0.7B-Base
• Organization: VedaX Labs
• Parameters: 0.7 Billion
• Architecture: Transformer-based causal language model
• Type: Base/Foundation Model
• License: Apache-2.0

Capabilities

• Text generation
• Lightweight inference
• Fine-tuning friendly

Intended Use

VedaX-0.7B-Base is intended for:

• Research and experimentation
• Educational purposes
• Building lightweight AI assistants
• Fine-tuning for downstream tasks
• Local inference applications
• Suitable for all devices

Context Length

• Context Window: 4096 tokens

Training

The model was trained on a mixture of publicly available text datasets and curated instruction-style data.

Example Usage

python
Copy code
from transformers import AutoTokenizer, AutoModelForCausalLM

model_id = "VedaX-Labs/VedaX-0.7B-Base"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

prompt = "The magic forest was"

inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_new_tokens=128
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Benchmarking(Higher number means higher score)

Limitations

• May generate inaccurate information
• Limited reasoning compared to larger models
• Responses may contain hallucinations
• Performance depends on prompting quality
• May generate repettetive words(Chances is very low)

Future Plans

• Instruction-tuned versions
• Reasoning-enhanced variants
• Multimodal support
• Larger parameter models

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Developed by VedaX Labs.