Create README.md

README.md

@@ -0,0 +1,208 @@
+---
+library_name: transformers
+license: llama3.1
+base_model: RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4
+datasets:
+- trl-lib/tldr
+---
+
+# Llama-3.1-8B-tldr-FP8-dynamic
+
+## Model Overview
+- **Model Architecture:** LlamaForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Sparsity:** 2:4 
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 06/04/2025
+- **Version:** 1.0
+- **Intended Use Cases:** This model is finetuned to summarize text in the style of Reddit posts.
+- **Out-of-scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.1 Community License.
+- **Model Developers:** Red Hat (Neural Magic)
+
+This model is a quantized version of [RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4](https://huggingface.co/RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4), which is fine-tuned on the [trl-lib/tldr](https://huggingface.co/datasets/trl-lib/tldr) dataset.
+This sparse model recovers 100% of the BERTScore (0.366) obtained by the dense model [RedHatAI/Llama-3.1-8B-tldr](https://huggingface.co/RedHatAI/Llama-3.1-8B-tldr). 
+
+
+## Deployment
+
+This model can be deployed efficiently using [vLLM](https://docs.vllm.ai/en/latest/), as shown in the example below.
+
+Run the following command to start the vLLM server:
+```bash
+vllm serve RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic
+```
+
+Once your server is started, you can query the model using the OpenAI API:
+
+```python
+from openai import OpenAI
+
+# Modify OpenAI's API key and API base to use vLLM's API server.
+openai_api_key = "EMPTY"
+openai_api_base = "http://localhost:8000/v1"
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+post="""
+SUBREDDIT: r/AI
+
+TITLE: Training sparse LLMs
+
+POST: Now you can use the llm-compressor integration to axolotl to train sparse LLMs!
+
+It's super easy to use. See the example in https://huggingface.co/RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4.
+
+And there's more. You can run 2:4 sparse models on vLLM and get significant speedupts on Hopper GPUs!
+"""
+
+prompt = f"Give a TL;DR of the following Reddit post.\n<|user|>{post}\nTL;DR:\n<|assistant|>\n"
+
+completion = client.completions.create(
+  model="RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic",
+  prompt=prompt,
+  max_tokens=256,
+)
+print("Completion result:", completion)
+```
+
+## Quantization
+
+<details><summary>Quantization details</summary>
+
+This model was created by applying [llm-compressor](https://github.com/vllm-project/llm-compressor), as presented in the code snipet below.
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+from llmcompressor.transformers import oneshot
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+recipe = QuantizationModifier(targets="Linear", scheme="FP8_DYNAMIC", ignore=["lm_head"])
+
+model_stub = "RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4"
+model_name = model_stub.split("/")[-1]
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_stub, torch_dtype="auto", device_map="auto"
+)
+
+output_dir = f"./{model_name}-FP8-dynamic"
+
+oneshot(
+    model=model,
+    recipe=recipe,
+    output_dir=output_dir,
+    tokenizer=AutoTokenizer.from_pretrained(model_stub),
+)
+
+model.save_pretrained(output_dir, save_compressed=True, skip_sparsity_compression_stats=False)
+tokenizer.save_pretrained(output_dir)
+```
+</details>
+
+
+## Evaluation
+
+The model was evaluated on the test split of [trl-lib/tldr](https://huggingface.co/datasets/trl-lib/tldr) using the Neural Magic fork of [lm-evaluation-harness](https://github.com/neuralmagic/lm-evaluation-harness/tree/tldr) (tldr branch).
+One can reproduce these results by using the following command:
+
+```bash
+lm_eval --model vllm --model_args "pretrained=RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic,dtype=auto,add_bos_token" --batch-size auto --tasks tldr
+```
+
+<table>
+  <tr>
+   <th>Metric
+   </th>
+   <th>Llama-3.1-8B-Instruct
+   </th>
+   <th>Llama-3.1-8B-tldr
+   </th>
+   <th>Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic<br>(this model)
+   </th>
+  </tr>
+  <tr>
+   <td>BERTScore
+   </td>
+   <td>-0.230
+   </td>
+   <td>0.366
+   </td>
+   <td>0.366
+   </td>
+  </tr>
+  <tr>
+   <td>ROUGE-1
+   </td>
+   <td>0.059
+   </td>
+   <td>0.362
+   </td>
+   <td>0.354
+   </td>
+  </tr>
+  <tr>
+   <td>ROUGE-2
+   </td>
+   <td>0.018
+   </td>
+   <td>0.144
+   </td>
+   <td>0.140
+   </td>
+  </tr>
+  <tr>
+   <td>ROUGE-Lsum
+   </td>
+   <td>0.051
+   </td>
+   <td>0.306
+   </td>
+   <td>0.302
+   </td>
+  </tr>
+</table>
+
+## Inference Performance
+
+We evaluated the inference performance of this model using the first 1,000 samples from the training set of the [trl-lib/tldr](https://huggingface.co/datasets/trl-lib/tldr) dataset.
+Benchmarking was conducted with [vLLM](https://docs.vllm.ai/en/latest/) version `0.9.0.1` and [GuideLLM](https://github.com/neuralmagic/guidellm) version `0.2.1`.
+
+The figure below presents the **mean end-to-end latency per request** across varying request rates.
+Results are shown for this model, as well as two variants:
+- **Dense:** [Llama-3.1-8B-tldr](https://huggingface.co/RedHatAI/Llama-3.1-8B-tldr)
+- **Dense-quantized:** [Llama-3.1-8B-tldr-FP8-dynamic](https://huggingface.co/RedHatAI/Llama-3.1-8B-tldr-FP8-dynamic)
+
+![Latency](./inference_performance/latency.png)
+
+
+<details><summary><strong>Reproduction instructions</strong></summary>
+
+To replicate the benchmark:
+
+1. Generate a JSON file containing the first 1,000 training samples:
+```python
+from datasets import load_dataset
+ds = load_dataset("trl-lib/tldr", split="train").take(1000)
+ds.to_json("tldr_1000.json")
+```
+
+2. Start a vLLM server using your target model:
+```bash
+vllm serve RedHatAI/Sparse-Llama-3.1-8B-tldr-2of4-FP8-dynamic
+```
+
+3. Run the benchmark with GuideLLM:
+```
+GUIDELLM__OPENAI__MAX_OUTPUT_TOKENS=128 guidellm benchmark --target "http://localhost:8000" --rate-type sweep --data tldr_1000.json
+```
+> The average output length is approximately 30 tokens per sample. We capped the generation at 128 tokens to reduce performance skew from rare, unusually verbose completions.
+
+</details>
+
+
+