VSM-LLM-3B-Fast

VSM-LLM-3B-Fast

LoRA adapter for Qwen2.5-3B-Instruct, fine-tuned for Value Stream Mapping (VSM) generation.

Model Details

• Base Model: Qwen/Qwen2.5-3B-Instruct
• Training Method: LoRA (r=8, alpha=16)
• Task: Generate structured JSON for VSM plots
• Training Data: 20 synthetic VSM examples
• Trainable Parameters: 14.9M (0.48% of base model)

Usage

With Transformers + PEFT

from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel

# Load base model
base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-3B-Instruct")
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-3B-Instruct")

# Load LoRA adapter
model = PeftModel.from_pretrained(base_model, "nishit1945/VSM-LLM-3B-Fast")

# Generate
messages = [
    {"role": "system", "content": "You are a VSM expert."},
    {"role": "user", "content": "Generate VSM JSON..."}
]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt")
outputs = model.generate(inputs, max_new_tokens=400, temperature=0.1)

HuggingFace Inference API

curl https://router.huggingface.co/hf-inference/models/nishit1945/VSM-LLM-3B-Fast \
  -H "Authorization: Bearer YOUR_HF_TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"inputs": "VSM prompt...", "parameters": {"max_new_tokens": 400, "temperature": 0.1}}'

Training Details

• Epochs: 3
• Batch Size: 1 (gradient accumulation: 16)
• Learning Rate: 2e-4
• Optimizer: paged_adamw_8bit
• Quantization: 4-bit (training only)

Performance

• Inference Time: 2-4 seconds on CPU
• Output Format: Structured JSON with processes, flows, metrics, coordinates

License

Apache 2.0