82 lines
2.1 KiB
Python
82 lines
2.1 KiB
Python
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# minimal_open_feed.py
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# Stripped down to test the actual bottleneck
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import zmq
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import json
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import time
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print("="*60)
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print("MINIMAL OPEN FEED TEST")
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print("="*60)
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# ZMQ setup
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ctx = zmq.Context()
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sub = ctx.socket(zmq.SUB)
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sub.connect("tcp://localhost:5556")
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sub.setsockopt_string(zmq.SUBSCRIBE, "")
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print("\nWaiting for LBM data...")
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# Wait for first frame
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frame = None
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for i in range(50): # 5 seconds max
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try:
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msg = sub.recv(flags=zmq.NOBLOCK)
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frame = json.loads(msg)
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break
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except zmq.Again:
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time.sleep(0.1)
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if frame is None:
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print("ERROR: No LBM data received")
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exit(1)
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print(f"✓ Received frame: Cycle {frame['cycle']}, Coherence {frame['coherence']:.3f}")
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# Now try loading the model
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print("\nLoading v0.5 model...")
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try:
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from unsloth import FastLanguageModel
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import torch
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# Load base model + LoRA adapters
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name="unsloth/llama-3.2-3b",
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max_seq_length=512,
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dtype=torch.bfloat16,
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load_in_4bit=True,
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)
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# Apply v0.5 LoRA
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model = FastLanguageModel.get_peft_model(
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model,
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r=64,
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target_modules=["q_proj", "k_proj", "v_proj", "o_proj"],
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lora_alpha=128,
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lora_dropout=0,
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bias="none",
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use_gradient_checkpointing="unsloth",
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random_state=3407,
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)
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# Load the trained adapters
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from peft import PeftModel
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model = PeftModel.from_pretrained(model, "./kaelara_lora_v05/final")
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print("✓ v0.5 LoRA adapters applied")
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print("✓ Model loaded")
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# Test generation
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prompt = f"Coherence: {frame['coherence']:.3f}. What is the frequency?"
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inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
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outputs = model.generate(**inputs, max_new_tokens=30, temperature=0.7)
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response = tokenizer.decode(outputs[0], skip_special_tokens=True)
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print(f"\nPrompt: {prompt}")
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print(f"Response: {response}")
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print("\n✓ FULL PIPELINE WORKS")
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except Exception as e:
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print(f"✗ Error: {e}")
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import traceback
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traceback.print_exc()
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