euhidaman/embervlm-small

EmberVLM: Small (~137M parameters)

🔥 Efficient Vision-Language Model for Edge Deployment & Robotic Applications

This model is currently in training - STAGE2 (Epoch 5).

📊 Current Training Status

• Stage: Multimodal Instruction Tuning - Following complex instructions
• Epoch: 5
• Last Updated: 2026-02-07 00:12:34 UTC

Latest Metrics

• instruction_loss: 0.0000
• loss: 2.0480

🏗️ Model Architecture

• Size: Small (~137M parameters)
• Total Parameters: 206,720,658
• Trainable Parameters: 50,149,074 (24.3%)
• Vision Encoder: dinov2_small
• Language Model: SmolLM-135M (135M params)

🎯 Training Curriculum

EmberVLM follows a 4-stage training curriculum:

1. ✅ Stage 1: Visual-Language Alignment - Grounding vision and language
2. ✅ Stage 2: Multimodal Instruction Tuning - Following instructions
3. ✅ Stage 3: Robot Fleet Selection - Task-robot matching
4. ⏳ Stage 4: Chain-of-Thought Reasoning - Reasoning generation

Current Stage: STAGE2

💻 Usage

from transformers import AutoTokenizer
from embervlm import EmberVLM
from PIL import Image

# Load model and tokenizer
model = EmberVLM.from_pretrained("euhidaman/embervlm-small")
tokenizer = AutoTokenizer.from_pretrained("euhidaman/embervlm-small")

# Load image
image = Image.open("scene.jpg")

# Generate response
prompt = "<image>Describe what you see and select the best robot for this task."
outputs = model.generate(
    image=image,
    prompt=prompt,
    tokenizer=tokenizer,
    max_new_tokens=256
)

print(outputs)

🎓 Training Details

• Vision Backbone: dinov2_small
• Language Backbone: smollm_135m
• Optimization: AdamW with cosine learning rate schedule
• Mixed Precision: bfloat16
• Distributed Training: Multi-GPU with DDP
• Class Balancing: Focal loss for robot selection (Stage 3)
• Reasoning: Chain-of-thought with reinforcement learning (Stage 4)

🌍 Environmental Impact

This model is designed for edge deployment to minimize energy consumption.

🎯 Intended Use

• Primary: Edge deployment on resource-constrained devices
• Applications:
  • Robotic vision-language understanding
  • Real-time multimodal reasoning
  • Robot fleet selection and task planning
  • Mobile/embedded AI systems

⚠️ Limitations

• Model is still in training - performance will improve as training progresses
• Optimized for efficiency over maximum accuracy
• Best suited for edge/mobile deployment scenarios
• Training focused on robot-centric scenarios

📚 Citation

@software{embervlm_2026,
  title = {EmberVLM: Efficient Vision-Language Model for Edge Deployment},
  author = {EmberVLM Team},
  year = {2026},
  url = {https://huggingface.co/euhidaman/embervlm-small}
}

📝 License

Apache 2.0

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Note: This is a checkpoint from stage2 training (epoch 5). The model will be updated after each epoch with improved performance.