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Scripts/Inference_llama.cpp.py

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+from llama_cpp import Llama
+
+# INSTRUCTIONS: Replace the JSON below with your material's properties
+# Common data sources: materialsproject.org, DFT calculations, experimental databases
+
+JSON_INPUT = """
+{
+  "material_id": "mp-8062",
+  "formula": "SiC",
+  "elements": [
+    "Si",
+    "C"
+  ],
+  "spacegroup": "P63mc",
+  "band_gap": 3.26,
+  "formation_energy_per_atom": -0.73,
+  "density": 3.21,
+  "volume": 41.2,
+  "nsites": 8,
+  "is_stable": true,
+  "elastic_modulus": 448,
+  "bulk_modulus": 220,
+  "thermal_expansion": 4.2e-06,
+  "electron_affinity": 4.0,
+  "ionization_energy": 6.7,
+  "crystal_system": "Hexagonal",
+  "magnetic_property": "Non-magnetic",
+  "thermal_conductivity": 490,
+  "specific_heat": 0.69,
+  "is_superconductor": false,
+  "band_gap_type": "Indirect"
+}
+"""
+
+model_path = "./" # Path to the directory containing your model weight files
+
+llm = Llama(
+    model_path=model_path,
+    n_gpu_layers=29, 
+    n_ctx=10000,
+    n_threads=4
+)
+
+topic = JSON_INPUT.strip()
+prompt = f"USER: {topic}\nASSISTANT:"
+
+
+output = llm(
+    prompt,
+    max_tokens=3000,
+    temperature=0.7,
+    top_p=0.9,
+    repeat_penalty=1.1
+)
+
+result = output.get("choices", [{}])[0].get("text", "").strip()
+
+print(result)
+
+

Scripts/Inference_safetensors.py

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+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# INSTRUCTIONS: Replace the JSON below with your material's properties
+# Common data sources: materialsproject.org, DFT calculations, experimental databases
+
+JSON_INPUT = """
+{
+  "material_id": "mp-8062",
+  "formula": "SiC",
+  "elements": [
+    "Si",
+    "C"
+  ],
+  "spacegroup": "P63mc",
+  "band_gap": 3.26,
+  "formation_energy_per_atom": -0.73,
+  "density": 3.21,
+  "volume": 41.2,
+  "nsites": 8,
+  "is_stable": true,
+  "elastic_modulus": 448,
+  "bulk_modulus": 220,
+  "thermal_expansion": 4.2e-06,
+  "electron_affinity": 4.0,
+  "ionization_energy": 6.7,
+  "crystal_system": "Hexagonal",
+  "magnetic_property": "Non-magnetic",
+  "thermal_conductivity": 490,
+  "specific_heat": 0.69,
+  "is_superconductor": false,
+  "band_gap_type": "Indirect"
+}
+"""
+
+def load_model(model_path):
+    model = AutoModelForCausalLM.from_pretrained(
+        model_path,
+        torch_dtype=torch.float16,
+        device_map="auto",
+        trust_remote_code=True
+    )
+    
+    tokenizer = AutoTokenizer.from_pretrained(
+        model_path,
+        trust_remote_code=True
+    )
+    
+    return model, tokenizer
+
+def generate_response(model, tokenizer, topic):
+    topic = topic.strip()
+    
+    prompt = f"USER: {topic}\nASSISTANT:"
+    
+    inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+    
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=3000,
+        temperature=0.7,
+        top_p=0.9,
+        repetition_penalty=1.1,
+        do_sample=True
+    )
+    
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+    
+    return response.split("ASSISTANT:")[-1].strip()
+
+def run():
+    model_path = "./" # Path to the directory containing your model weight files
+    
+    model, tokenizer = load_model(model_path)
+    
+    result = generate_response(model, tokenizer, JSON_INPUT)
+    
+    print(result)
+
+if __name__ == "__main__":
+    run()
+
+