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compress.py

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+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import re
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import oneshot
+
+# Select model and load it.
+MODEL_ID = "ArliAI/Llama-3.1-70B-ArliAI-RPMax-v1.3"
+
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Select calibration dataset.
+DATASET_ID = "openerotica/erotiquant3"
+DATASET_SPLIT = "train"
+
+# Select number of samples. 512 samples is a good place to start.
+# Increasing the number of samples can improve accuracy.
+NUM_CALIBRATION_SAMPLES = 512
+MAX_SEQUENCE_LENGTH = 4096
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+
+def preprocess(example):
+    result = []
+    matches = re.findall(r'(SYSTEM|USER|ASSISTANT):\s*((?:(?!SYSTEM|USER|ASSISTANT:).|\n)+)', example['text'], re.DOTALL)
+
+    # Loop through the matches and create a dictionary for each role and its content
+    for role, content in matches:
+        result.append({"role": role.lower(), "content": content.strip()})
+
+    text = tokenizer.apply_chat_template(result, tokenize=False, add_generation_prompt=False)
+    tokens = tokenizer.apply_chat_template(result, tokenize=True, add_generation_prompt=False)
+
+    return {
+        "chat": result,
+        "text": text,
+        "tokens": tokens,
+    }
+
+ds = ds.map(preprocess)
+
+def filter_short_rows(example):
+    result = len(example['tokens']) > MAX_SEQUENCE_LENGTH
+    if result == False:
+        print(f"length: {len(example['tokens'])}")
+    return result
+
+ds = ds.filter(filter_short_rows)
+
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return tokenizer(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+
+# Configure the quantization algorithm to run.
+#   * quantize the weights to 4 bit with GPTQ with a group size 128
+recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])
+
+model = AutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    torch_dtype="auto",
+)
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES
+)
+
+print('SAVING')
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-W4A16-G128"
+model.save_pretrained(SAVE_DIR, save_compressed=True, skip_compression_stats=True)
+tokenizer.save_pretrained(SAVE_DIR)
+
+print('Saved')