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Neura-Tech-AI
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Model Details
Model Provider
Neura-Tech-AI
Model Tree
Qwen/Qwen2.5-3B-Instruct
Input Modalities
Output Modalities
Supported Functionality
To assess Project Neuron's generation stability, execution latency, and instruction-following consistency, use the baseline quantitative evaluation pipeline below.
benchmark_eval.py)python
import timeimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_path = "Neura-Tech-AI/Neuron-V1-3B-Instruct" print("🎯 Initializing Project Neuron Evaluation Suite...")tokenizer = AutoTokenizer.from_pretrained(model_path)model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16).to("cuda") eval_prompts = [ "Tell me about Project Neuron in short. What is its scale?", "Explain quantum computing in simple Hindi lyrics.", "Write a secure python API routing block for model inference."] def run_performance_test(prompt): messages = [ {"role": "system", "content": "You are Neuron, an advanced AI system developed by Neura Tech AI."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer([text], return_tensors="pt").to("cuda") input_len = inputs.input_ids.shape[1] start_time = time.time() with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.1, do_sample=False, pad_token_id=tokenizer.eos_token_id ) latency = time.time() - start_time generated_tokens = outputs[0][input_len:] token_count = len(generated_tokens) tokens_per_second = token_count / latency response = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip() return latency, tokens_per_second, response print("\n--- Running Quantitative Evaluation Matrix ---")for i, prompt in enumerate(eval_prompts, 1): lat, tps, resp = run_performance_test(prompt) print(f"\n📊 Test Case #{i}: '{prompt}'") print(f"⏱️ Latency: {lat:.2f}s | ⚡ Speed: {tps:.2f} tokens/sec") print(f"🤖 Output:\n{resp}\n" + "-"*40)Throughput Speed: Maintains an average runtime acceleration of ~40-50 tokens/sec under stable CUDA configurations. VRAM Overhead: VRAM consumption balances at approximately ~10.5 GB to 12 GB peak during deep batch text token processing.
To prevent system prompt token leakage and enforce pure output retrieval during standard usage loops, apply explicit token slicing as shown below:
python
import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Neura-Tech-AI/Neuron-V1-3B-Instruct" # Load Standalone Tokenizer & Fused Core Weightstokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") # Standard Query Payloadmessages = [ {"role": "system", "content": "You are Project Neuron, an advanced AI system developed by Neura Tech AI."}, {"role": "user", "content": "tu kon hai be."}] # Apply Native Tokenization Layouttext = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to("cuda") # Run Stable Token Generation outputs = model.generate( **inputs, max_new_tokens=100, temperature=0.3, do_sample=True, top_p=0.9, pad_token_id=tokenizer.eos_token_id) # Input-Length Slicing for explicit assistant reply isolationinput_len = inputs.input_ids.shape[1]clean_reply = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True).strip() print(f"🤖 Project Neuron Reply:\n{clean_reply}")import timeimport torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_path = "Neura-Tech-AI/Neuron-V1-3B-Instruct" print("🎯 Initializing Project Neuron Evaluation Suite...")tokenizer = AutoTokenizer.from_pretrained(model_path)model = AutoModelForCausalLM.from_pretrained(model_path, torch_dtype=torch.float16).to("cuda") eval_prompts = [ "Tell me about Project Neuron in short. What is its scale?", "Explain quantum computing in simple Hindi lyrics.", "Write a secure python API routing block for model inference."] def run_performance_test(prompt): messages = [ {"role": "system", "content": "You are Neuron, an advanced AI system developed by Neura Tech AI."}, {"role": "user", "content": prompt} ] text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) inputs = tokenizer([text], return_tensors="pt").to("cuda") input_len = inputs.input_ids.shape[1] start_time = time.time() with torch.no_grad(): outputs = model.generate( **inputs, max_new_tokens=150, temperature=0.1, do_sample=False, pad_token_id=tokenizer.eos_token_id ) latency = time.time() - start_time generated_tokens = outputs[0][input_len:] token_count = len(generated_tokens) tokens_per_second = token_count / latency response = tokenizer.decode(generated_tokens, skip_special_tokens=True).strip() return latency, tokens_per_second, response print("\n--- Running Quantitative Evaluation Matrix ---")for i, prompt in enumerate(eval_prompts, 1): lat, tps, resp = run_performance_test(prompt) print(f"\n📊 Test Case #{i}: '{prompt}'") print(f"⏱️ Latency: {lat:.2f}s | ⚡ Speed: {tps:.2f} tokens/sec") print(f"🤖 Output:\n{resp}\n" + "-"*40)import torchfrom transformers import AutoModelForCausalLM, AutoTokenizer model_id = "Neura-Tech-AI/Neuron-V1-3B-Instruct" # Load Standalone Tokenizer & Fused Core Weightstokenizer = AutoTokenizer.from_pretrained(model_id)model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype=torch.float16, device_map="auto") # Standard Query Payloadmessages = [ {"role": "system", "content": "You are Project Neuron, an advanced AI system developed by Neura Tech AI."}, {"role": "user", "content": "tu kon hai be."}] # Apply Native Tokenization Layouttext = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)inputs = tokenizer([text], return_tensors="pt").to("cuda") # Run Stable Token Generation outputs = model.generate( **inputs, max_new_tokens=100, temperature=0.3, do_sample=True, top_p=0.9, pad_token_id=tokenizer.eos_token_id) # Input-Length Slicing for explicit assistant reply isolationinput_len = inputs.input_ids.shape[1]clean_reply = tokenizer.decode(outputs[0][input_len:], skip_special_tokens=True).strip() print(f"🤖 Project Neuron Reply:\n{clean_reply}")