Update README.md

README.md

@@ -1,3 +1,109 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model:
+- meta-llama/Llama-3.1-8B-Instruct
+---
+
+# Llama-3.1-8B-Instruct-KV-Cache-FP8
+
+## Model Overview
+- **Model Architecture:** nm-testing/Llama-3.1-8B-Instruct-KV-Cache-FP8
+  - **Input:** Text
+  - **Output:** Text
+- **Release Date:** 
+- **Version:** 1.0
+- **Model Developers:**: Red Hat
+
+FP8 KV Cache Quantization of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct).
+
+### Model Optimizations
+
+This model was obtained by quantizing the KV Cache of weights and activations of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) to FP8 data type.
+
+
+## Deployment
+
+### Use with vLLM
+
+1. Initialize vLLM server:
+```
+vllm serve RedHatAI/Llama-3.1-8B-Instruct-KV-Cache-FP8 --tensor_parallel_size 1
+```
+
+2. Send requests to the server:
+
+```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://<your-server-host>:8000/v1"
+
+client = OpenAI(
+    api_key=openai_api_key,
+    base_url=openai_api_base,
+)
+
+model = "RedHatAI/Llama-3.1-8B-Instruct-KV-Cache-FP8"
+
+messages = [
+    {"role": "user", "content": "Explain quantum mechanics clearly and concisely."},
+]
+
+
+outputs = client.chat.completions.create(
+    model=model,
+    messages=messages,
+)
+
+generated_text = outputs.choices[0].message.content
+print(generated_text)
+```
+
+<!-- ## Creation
+
+This model was quantized using the [llm-compressor](https://github.com/vllm-project/llm-compressor) library as shown below.
+
+<details>
+  <summary>Creation details</summary>
+
+```python
+from transformers import AutoProcessor, Qwen3ForCausalLM
+
+from llmcompressor import oneshot
+from llmcompressor.modeling import replace_modules_for_calibration
+from llmcompressor.modifiers.quantization import QuantizationModifier
+
+MODEL_ID = "Qwen/Qwen3-8B"
+
+# Load model.
+model = Qwen3ForCausalLM.from_pretrained(MODEL_ID, dtype="auto")
+processor = AutoProcessor.from_pretrained(MODEL_ID)
+model = replace_modules_for_calibration(model)
+
+# Configure the quantization algorithm and scheme.
+# In this case, we:
+#   * quantize the weights to fp8 with per-block quantization
+#   * quantize the activations to fp8 with dynamic token activations
+recipe = QuantizationModifier(
+    targets="Linear",
+    scheme="FP8_BLOCK",
+    ignore=["lm_head"],
+)
+
+# Apply quantization.
+oneshot(model=model, recipe=recipe)
+
+# Save to disk in compressed-tensors format.
+SAVE_DIR = MODEL_ID.rstrip("/").split("/")[-1] + "-FP8-block"
+model.save_pretrained(SAVE_DIR)
+processor.save_pretrained(SAVE_DIR)
+```
+</details> -->
+
+
+## Evaluation
+
+
+The model was evaluated on the RULER and long-context benchmarks (LongBench), using [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness).
+[vLLM](https://docs.vllm.ai/en/stable/) was used for all evaluations.