SamuelJaja
/

llama_3.1-8b-instruct-construction-lora-a100

@@ -1,202 +1,91 @@
 ---
-base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
 library_name: peft
 ---
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-### Framework versions
-- PEFT 0.14.0

 ---
+language: en
+license: apache-2.0
 library_name: peft
+tags:
+- llama
+- llama-3
+- construction
+- building-regulations
+- lora
+- custom construction industry dataset
 ---
+# LLAMA3.1-8B-Instruct-Construction
+This is a fine-tuned version of LLAMA3.1-8B-Instruct optimized for construction industry and building regulations knowledge.
 ## Model Details
+- **Base Model:** meta-llama/Llama-3.1-8B-Instruct
+- **Fine-tuning Method:** LoRA (Low-Rank Adaptation)
+- **Training Data:** Custom dataset focusing on construction industry standards, building regulations, and safety requirements
+- **Usage:** This model is designed to answer questions about building codes, construction best practices, and regulatory compliance
+## Example Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel, PeftConfig
+import torch
+import re
+# Load the adapter configuration
+config = PeftConfig.from_pretrained("SamuelJaja/llama-3.1-8b-instruct-construction-lora-a100")
+# Load base model with quantization
+bnb_config = BitsAndBytesConfig(load_in_8bit=True)
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+# Load LoRA adapter
+model = PeftModel.from_pretrained(model, "SamuelJaja/llama-3.1-8b-instruct-construction-lora-a100")
+# Load tokenizer
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+tokenizer.pad_token = tokenizer.eos_token
+# Clean response function
+def clean_response(text):
+    return re.sub(r'\[/?INST\]', '', text).strip()
+# Generate text
+def generate_response(prompt, temperature=0.1, max_tokens=256):
+    # Format properly
+    if not prompt.startswith("[INST]"):
+        formatted_prompt = f"[INST] {prompt} [/INST]"
+    else:
+        formatted_prompt = prompt
+    inputs = tokenizer(formatted_prompt, return_tensors="pt").to("cuda")
+    outputs = model.generate(
+        input_ids=inputs.input_ids,
+        attention_mask=inputs.attention_mask,
+        max_new_tokens=max_tokens,
+        temperature=temperature,
+        top_p=0.9,
+        do_sample=False
+    )
+    full_response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    # Remove prompt from output
+    if formatted_prompt in full_response:
+        response = full_response.replace(formatted_prompt, "").strip()
+    else:
+        response = full_response
+    # Clean any remaining instruction tags
+    response = clean_response(response)
+    return response
+# Example use
+question = "What are the main requirements for fire safety in commercial buildings?"
+answer = generate_response(question)
+print(answer)
+```