Create README.md

README.md

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+```python
+import time
+from datasets import load_dataset
+from transformers import AutoTokenizer
+from rich import print
+from llmcompressor.modifiers.quantization import GPTQModifier
+from llmcompressor.transformers import SparseAutoModelForCausalLM, oneshot
+
+# Select model and load it.
+MODEL_ID = "Unbabel/TowerInstruct-7B-v0.1"
+model = SparseAutoModelForCausalLM.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    torch_dtype="auto",
+)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Select calibration dataset.
+DATASET_ID = "neuralmagic/LLM_compression_calibration"
+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 = 756
+MAX_SEQUENCE_LENGTH = 2048
+
+# Load dataset and preprocess.
+ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
+ds = ds.shuffle(seed=42).select(range(NUM_CALIBRATION_SAMPLES))
+
+
+def preprocess(example):
+    return {
+        "text": tokenizer.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# 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
+# Note: to reduce GPU memory use `sequential_update=False`
+recipe = GPTQModifier(targets="Linear", scheme="W4A16", ignore=["lm_head"])
+print(recipe)
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+)
+
+# Confirm generations of the quantized model look sane and measure generation time.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+input_text = "Translate the following text from Portuguese into English.\nPortuguese: Um grupo de investigadores lançou um novo modelo para tarefas relacionadas com tradução.\nEnglish:"
+input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
+start_time = time.time()
+output = model.generate(input_ids, max_new_tokens=100)
+end_time = time.time()
+generation_time = end_time - start_time
+print(tokenizer.decode(output[0]))
+print(f"Generation time: {generation_time:.2f} seconds")
+print("==========================================\n\n")
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-quantized.w4a16"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)
+```