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jrestro/
deepseek-r1-openthoughts-8b

Name: deepseek-r1-openthoughts-8b
Availability: InStock

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DeepSeek-R1 OpenThoughts Fine-tuned Model

🤖 Model Information

Base Model: DeepSeek-R1-Distill-Llama-8B
Training Dataset: OpenThoughts-114k
Training Status: ✅ Complete (561/561 steps)
Training Time: 12:39 minutes on H200 SXM GPU
Total Model Size: ~5.1GB

📁 Model Files

model-00001-of-00004.safetensors (4.6GB) - Main model weights
model-00002-of-00004.safetensors (390MB) - Additional model weights
config.json (904B) - Model configuration
generation_config.json (181B) - Generation settings

🚀 How to Use

Option 1: Use with Transformers Library

python

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

# Load the model and tokenizer
model_path = "./trained_models/deepseek-r1-openthoughts"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
    model_path,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Generate text
prompt = "What is the meaning of life?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=200, temperature=0.7)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)

Option 2: Integration with Your AI Automotive Assistant

This model can be integrated into your main AI service by:

Loading it in ai_service.py
Using it for enhanced automotive diagnostics
Providing more thoughtful responses to technical questions

🔧 Technical Details

Architecture: Llama-based with DeepSeek-R1 optimizations
Training: Fine-tuned on OpenThoughts-114k dataset
Capabilities: Enhanced reasoning and thought processes
Hardware Requirements:
- Minimum: 8GB GPU memory
- Recommended: 16GB+ GPU memory for optimal performance

📊 Training Results

Training Steps: 561/561 (100% complete)
Checkpoints: 2 saved (checkpoint-500, checkpoint-561)
Training Environment: RunPod H200 SXM GPU
Training Framework: Transformers + PyTorch

🎯 Use Cases

Enhanced automotive diagnostic reasoning
Improved customer communication
Better technical problem-solving
More detailed repair explanations
Advanced troubleshooting assistance

📝 Notes

All model files must be kept together in the same directory
The model is ready for immediate use or further fine-tuning
Compatible with standard Hugging Face transformers workflows
Trained specifically for reasoning-heavy tasks

Model trained on: January 15, 2025
Training completed by: ChuchBot AI Automotive Assistant Project

jrestro/deepseek-r1-openthoughts-8b