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data/trained-tiny-gpt/checkpoint-tiny-gpt.pth

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7dc6d3e7756554064ba1cb7785ae75395d3fb6b74362e212b0029da91c79c2f2
+size 66253943

data/trained-tiny-gpt/tokenizer.json

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localscripts/mergelines.py

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+import json
+def merge_short_lines(file_path,min_length=32):
+    merged = []
+    buffer = ""
+    
+    with open(file_path, "r", encoding="utf-8") as f:
+        for line in f:
+            line = line.strip()
+            data = json.loads(line)
+            text = data["text"]
+            buffer += " " + text.strip()
+            if len(buffer) >= min_length:
+                merged.append({"text": buffer.strip()})
+                buffer = ""
+        
+    if buffer.strip():
+        merged.append({"text": buffer.strip})
+
+    print(f"Merged {len(merged)} lines")
+    return merged

localscripts/mergelines2.py

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+import json
+from mergelines import merge_short_lines
+
+merged_data = merge_short_lines("./customchatbot-v1/data/data.jsonl")
+with open("./customchatbot-v1/data/merged_data.jsonl","w",encoding="utf-8") as out:
+    for item in merged_data:
+        out.write(json.dumps(item) + "\n")
+
+# with open("./customchatbot-v1/data/data.jsonl","r",encoding="utf-8") as out:
+ #   for item in out:
+ #       with open("./customchatbot-v1/data/backup_data.jsonl","w",encoding="utf-8") as out2:
+ #           out2.write(json.dumps(item) + "\n")

localscripts/train_custommade.py

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+import json
+import torch.nn as nn
+import torch
+from model import MiniGPT
+from dataset import DataLoader,ChatDataset,SimpleTokenizr
+from tqdm import tqdm
+
+with open("./customchatbot-v1/data/merged_data.jsonl", "r", encoding="utf-8") as f:
+    texts = [json.loads(line)["text"] for line in f if line.strip()]
+
+tokenizer = SimpleTokenizr()
+tokenizer.train(texts)
+
+model = MiniGPT(vocab_size=100)
+
+criterion = nn.CrossEntropyLoss()
+optimizer = torch.optim.Adam(model.parameters(),lr=0.001)
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+dataset = ChatDataset("./customchatbot-v1/data/merged_data.jsonl", tokenizer)
+dataloader = DataLoader(dataset, batch_size=100, shuffle=True)
+
+def Train(epochs):
+    for epoch in range(epochs):
+        model.train()
+        loop = tqdm(enumerate(dataloader),total=len(dataloader),desc="Training")
+        tloss = 0
+        for i,l in loop:
+            optimizer.zero_grad()
+            outputs = model(i)
+            loss = criterion(outputs,l)
+            loss.backward()
+
+Train(epochs=1)

minigpt.py

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+import torch
+import torch.nn.functional as F
+from model import MiniGPT
+from dataset import MiniBPETokenizr,SimpleTokenizr
+import json
+import os
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Load tokenizer
+tokenizer = SimpleTokenizr()
+tokenizer.load("./customchatbot-v1/trained-mini-gpt/tokenizer.json")
+
+# Load model
+model = MiniGPT(vocab_size=len(tokenizer))
+model.load_state_dict(torch.load("./customchatbot-v1/trained-mini-gpt/mini-gpt.pth", map_location=device) if os.path.exists("./customchatbot-v1/trained-mini-gpt/mini-gpt.pth") else torch.load("./customchatbot-v1/trained-mini-gpt/checkpoint-mini-gpt.pth", map_location=device)["model_state_dict"] )
+model.eval().to(device)
+totalparams = sum(p.numel() for p in model.parameters())
+print(f"Model total params: {totalparams:,}")
+
+def sample_token(logits, temperature=1.0):
+    logits = logits / temperature
+    logits = torch.nan_to_num(logits, nan=-1e9)
+    probs = F.softmax(logits, dim=-1)
+
+    if torch.any(torch.isnan(probs)) or torch.any(probs < 0):
+        print("⚠️ Invalid probs detected. Using uniform fallback.")
+        probs = torch.ones_like(probs) / probs.size(-1)
+
+    return torch.multinomial(probs, num_samples=1).item()
+
+def generate_reply(prompt, max_tokens=100):
+    tokens = tokenizer.encode(prompt)
+    if not tokens:
+        print("⚠️ Empty prompt after encoding.")
+        return
+    input_ids = torch.tensor(tokens, dtype=torch.long).unsqueeze(0).to(device)
+    generated = []
+
+    with torch.no_grad():
+        for _ in range(max_tokens):
+            logits = model(input_ids)
+            logits = logits[:, -1, :]
+            next_token = sample_token(logits)
+            generated.append(next_token)
+
+            next_str = tokenizer.itos.get(next_token, "")
+            encoded_text = tokenizer.encode(next_str)
+            decoded_text = tokenizer.decode(encoded_text)
+            print(decoded_text, end=" ", flush=True)
+
+            if next_str == "<END>":
+                break
+
+            input_ids = torch.cat([input_ids, torch.tensor([[next_token]]).to(device)], dim=1)
+    print()
+
+# Chat loop
+print("🧠 MiniGPT Chat (type 'exit' to quit')")
+while True:
+    user_input = input("User: ")
+    if user_input.lower() == "exit":
+        break
+    prompt = f"^User: {user_input}\nMiniGPT:"
+    print("MiniGPT: ", end="", flush=True)
+    generate_reply(prompt)