Update README.md

README.md

@@ -19,6 +19,89 @@ pipeline_tag: text-generation
 
 [Phi4-mini](https://huggingface.co/microsoft/Phi-4-mini-instruct) model quantized with [torchao](https://huggingface.co/docs/transformers/main/en/quantization/torchao) float8 dynamic activation and float8 weight quantization (per row granularity), by PyTorch team. Use it directly, or serve using [vLLM](https://docs.vllm.ai/en/latest/) with 36% VRAM reduction, 15-20% speedup and little to no accuracy impact on H100.
 
+# Inference with vLLM
+Need to install vllm nightly to get some recent changes:
+```
+pip install vllm --pre --extra-index-url https://wheels.vllm.ai/nightly
+```
+## Command Line
+Then we can serve with the following command:
+```
+vllm serve pytorch/Phi-4-mini-instruct-int4wo-hqq --tokenizer microsoft/Phi-4-mini-instruct -O3
+```
+
+## Code Example
+```
+from vllm import LLM, SamplingParams
+
+llm = LLM(model="pytorch/Phi-4-mini-instruct-float8dq", trust_remote_code=True)
+
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
+]
+
+sampling_params = SamplingParams(
+  max_tokens=500,
+  temperature=0.0,
+)
+
+output = llm.chat(messages=messages, sampling_params=sampling_params)
+print(output[0].outputs[0].text)
+```
+
+# Inference with Transformers
+
+Install the required packages:
+```
+pip install git+https://github.com/huggingface/transformers@main
+pip install --pre torchao --index-url https://download.pytorch.org/whl/nightly/cu126
+pip install torch
+pip install accelerate
+```
+
+Example:
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+ 
+torch.random.manual_seed(0)
+
+model_path = "pytorch/Phi-4-mini-instruct-float8dq"
+
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    device_map="auto",
+    torch_dtype="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path)
+ 
+messages = [
+    {"role": "system", "content": "You are a helpful AI assistant."},
+    {"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
+    {"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
+    {"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
+]
+ 
+pipe = pipeline(
+    "text-generation",
+    model=model,
+    tokenizer=tokenizer,
+)
+ 
+generation_args = {
+    "max_new_tokens": 500,
+    "return_full_text": False,
+    "temperature": 0.0,
+    "do_sample": False,
+}
+ 
+output = pipe(messages, **generation_args)
+print(output[0]['generated_text'])
+```
 
 # Quantization Recipe