Create README.md

README.md

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+---
+base_model:
+- microsoft/Phi-3.5-vision-instruct
+---
+
+## Eval
+
+```
+vllm serve nm-testing/Phi-3.5-vision-instruct-W8A8-Dynamic-Per-Token --trust-remote-code --max-model-len 100000
+```
+
+```
+python -m eval.run eval_vllm --model_name nm-testing/Phi-3.5-vision-instruct-W8A8-Dynamic-Per-Token --url http://0.0.0.0:8000 --output_dir output/ --eval_name "chartqa"
+...
+================================================================================
+Metrics:
+{
+    "explicit_prompt_relaxed_correctness": 0.6472,
+    "anywhere_in_answer_relaxed_correctness": 0.6616
+}
+================================================================================
+```
+
+## Creation
+
+```python
+from datasets import load_dataset
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+from llmcompressor.modifiers.quantization import GPTQModifier
+# from llmcompressor.modifiers.smoothquant import SmoothQuantModifier
+from llmcompressor.transformers import oneshot, wrap_hf_model_class
+
+# Select model and load it.
+MODEL_ID = "microsoft/Phi-3.5-vision-instruct"
+model_class = wrap_hf_model_class(AutoModelForCausalLM)
+model = model_class.from_pretrained(
+    MODEL_ID,
+    device_map="auto",
+    torch_dtype="auto",
+    trust_remote_code=True, 
+    _attn_implementation="eager",
+)
+processor = AutoTokenizer.from_pretrained(MODEL_ID, trust_remote_code=True)
+
+# Select calibration dataset.
+DATASET_ID = "HuggingFaceH4/ultrachat_200k"
+DATASET_SPLIT = "train_sft"
+
+# 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 = 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": processor.apply_chat_template(
+            example["messages"],
+            tokenize=False,
+        )
+    }
+
+
+ds = ds.map(preprocess)
+
+
+# Tokenize inputs.
+def tokenize(sample):
+    return processor(
+        sample["text"],
+        padding=False,
+        max_length=MAX_SEQUENCE_LENGTH,
+        truncation=True,
+        add_special_tokens=False,
+    )
+
+
+ds = ds.map(tokenize, remove_columns=ds.column_names)
+print(ds)
+
+# Configure algorithms. In this case, we:
+#   * apply SmoothQuant to make the activations easier to quantize
+#   * quantize the weights to int8 with GPTQ (static per channel)
+#   * quantize the activations to int8 (dynamic per token)
+# Note: set sequential_update: true in the recipe to reduce memory
+ignore=["re:.*lm_head", "re:model.vision_embed_tokens.*"]
+recipe = [
+    # SmoothQuantModifier(smoothing_strength=0.8, ignore=ignore),
+    GPTQModifier(targets="Linear", scheme="W8A8", ignore=ignore),
+]
+
+# Apply algorithms.
+oneshot(
+    model=model,
+    dataset=ds,
+    recipe=recipe,
+    max_seq_length=MAX_SEQUENCE_LENGTH,
+    num_calibration_samples=NUM_CALIBRATION_SAMPLES,
+    trust_remote_code_model=True,
+)
+
+# Confirm generations of the quantized model look sane.
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+input_ids = processor("Hello my name is", return_tensors="pt").input_ids.to("cuda")
+output = model.generate(input_ids, max_new_tokens=100)
+print(processor.decode(output[0]))
+print("==========================================\n\n")
+
+# Save to disk compressed.
+SAVE_DIR = MODEL_ID.split("/")[1] + "-W8A8-Dynamic-Per-Token"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+processor.save_pretrained(SAVE_DIR)
+```