[MXFp4] Add E2E Example Script for Llama3 (#2042)

# Summary
- Requires: vllm-project/compressed-tensors#509
- Add script to generate an mxfp4 quantized model
- This feature is currently experimental as support has not landed or
tested in vLLM

# Testing:
Sample Model: 
- nm-testing/Meta-Llama-3-8B-Instruct-MXFP4

Sample Generation (Transformers):

```bash
========== SAMPLE GENERATION ==============
<|begin_of_text|>Hello my name is Sophia and I am a 3rd year student at the University of California, Berkeley. I am a double major in Linguistics and Psychology, with a minor in Education. I am very interested in the way that language and culture interact, and I believe that education is the key to creating a more just and equitable society.
I am a native speaker of English, and I have also studied Spanish, French, and Mandarin Chinese. I am very interested in the way that language can be used to bring
==========================================

```


Sample Config:
```yaml
"quantization_config": {
    "config_groups": {
        "group_0": {
            "format": "mxfp4-pack-quantized",
            "input_activations": {
                "actorder": null,
                "block_structure": null,
                "dynamic": true,
                "group_size": 32,
                "num_bits": 4,
                "observer": null,
                "observer_kwargs": {},
                "scale_dtype": "torch.uint8",
                "strategy": "group",
                "symmetric": true,
                "type": "float",
                "zp_dtype": null
            },
            "output_activations": null,
            "targets": [
                "Linear"
            ],
            "weights": {
                "actorder": null,
                "block_structure": null,
                "dynamic": false,
                "group_size": 32,
                "num_bits": 4,
                "observer": "minmax",
                "observer_kwargs": {},
                "scale_dtype": "torch.uint8",
                "strategy": "group",
                "symmetric": true,
                "type": "float",
                "zp_dtype": null
                }
        }
    },
    "format": "mxfp4-pack-quantized",
}


```

---------

Signed-off-by: Dipika Sikka <[email protected]>
Co-authored-by: gemini-code-assist[bot] <176961590+gemini-code-assist[bot]@users.noreply.github.com>

Author: dsikka

experimental/README.md

@@ -0,0 +1,3 @@
+# Experimental Features
+
+This folder aims to highlight features that are a work-in-progress or are supported in LLM Compressor and/or Compressed-Tensors but lack full support in downstream libraries like vLLM.

experimental/mxfp4/llama3_mxfp4.py

@@ -0,0 +1,35 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+from llmcompressor import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.utils import dispatch_for_generation
+
+MODEL_ID = "meta-llama/Meta-Llama-3-8B-Instruct"
+
+# Load model.
+model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype="auto")
+tokenizer = AutoTokenizer.from_pretrained(MODEL_ID)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp4 with per group 32 via ptq
+recipe = QuantizationModifier(targets="Linear", scheme="MXFP4", ignore=["lm_head"])
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+print("\n\n")
+print("========== SAMPLE GENERATION ==============")
+dispatch_for_generation(model)
+input_ids = tokenizer("Hello my name is", return_tensors="pt").input_ids.to(
+    model.device
+)
+output = model.generate(input_ids, max_new_tokens=100)
+print(tokenizer.decode(output[0]))
+print("==========================================\n\n")
+
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-MXFP4"
+model.save_pretrained(SAVE_DIR, save_compressed=True)
+tokenizer.save_pretrained(SAVE_DIR)