Create README.md

README.md

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+---
+license: apache-2.0
+license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct/blob/main/LICENSE
+language:
+- en
+pipeline_tag: text-generation
+library_name: transformers
+tags:
+- code
+- codeqwen
+- chat
+- qwen
+- qwen-coder
+- fp8
+- llm-compressor
+- compressed-tensors
+- vllm
+base_model:
+- Qwen/Qwen2.5-Coder-14B-Instruct
+---
+## Model Overview
+- **Model Architecture:** Qwen2ForCausalLM
+  - **Input:** Text
+  - **Output:** Text
+- **Model Optimizations:**
+  - **Weight quantization:** FP8
+  - **Activation quantization:** FP8
+- **Release Date:** 11/28/2024
+- **Version:** 1.0
+- **Model Developers:** Red Hat
+
+Quantized version of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct).
+
+### Model Optimizations
+
+This model was obtained by quantizing the weights and activations of [Qwen/Qwen2.5-Coder-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-14B-Instruct) to FP8 data type.
+This optimization reduces the number of bits per parameter from 16 to 8, reducing the disk size and GPU memory requirements by approximately 50%.
+Only the weights and activations of the linear operators within transformers blocks are quantized. 
+
+## Deployment
+
+### Use with vLLM
+
+1. Initialize vLLM server:
+```
+vllm serve RedHatAI/Qwen2.5-Coder-14B-Instruct-FP8-dynamic
+```
+
+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/Qwen2.5-Coder-14B-Instruct-FP8-dynamic"
+
+messages = [
+    [{"role": "user", "content": "Write a quick sort algorithm."}],
+]
+
+outputs = client.chat.completions.create(
+    model=model,
+    messages=messages,
+)
+
+generated_text = outputs.choices[0].message.content
+print(generated_text)
+```
+
+## Creation
+
+This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below. 
+
+<details>
+  <summary>Model Creation Code</summary>
+
+```python
+from llmcompressor.modifiers.quantization import QuantizationModifier
+from llmcompressor.transformers import oneshot
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model
+model_stub = "Qwen/Qwen2.5-Coder-14B-Instruct"
+model_name = model_stub.split("/")[-1]
+
+model = AutoModelForCausalLM.from_pretrained(model_stub, dtype="auto")
+
+tokenizer = AutoTokenizer.from_pretrained(model_stub)
+
+# Configure the quantization algorithm and scheme
+recipe = QuantizationModifier(
+    ignore=["lm_head"],
+    targets="Linear",
+    scheme="FP8_dynamic",
+)
+
+# Apply quantization
+oneshot(
+    model=model,
+    recipe=recipe,
+)
+
+# Save to disk in compressed-tensors format
+save_path = model_name + "-FP8-dynamic"
+model.save_pretrained(save_path)
+tokenizer.save_pretrained(save_path)
+print(f"Model and tokenizer saved to: {save_path}")
+```
+</details>
+