Update README.md

README.md

@@ -1,3 +1,117 @@
 ---
 license: mit
+pipeline_tag: text-generation
+tags:
+- text-generation-inference
+language:
+- en
 ---
+
+# phi-3-mini-128k-instruct-int4
+
+- Orginal model : [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
+- Quantized using [intel/auto-round](https://github.com/intel/auto-round) 
+
+## Description 
+
+**Phi-3-mini-128k-instruct-int4** is an int4 model with group_size 128 of the [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct).
+
+The above model was quantized using AutoRound(Advanced Weight-Only Quantization Algorithm for LLMs) released by [intel](https://github.com/intel).
+
+you can find out more in detail through the the [GitHub Repository](https://github.com/intel/auto-round). 
+
+
+## Training details
+
+
+### Cloning a repository(AutoRound)  
+
+```
+git clone https://github.com/intel/auto-round
+```
+
+### Enter into the examples/language-modeling folder 
+
+```
+cd auto-round/examples/language-modeling
+pip install -r requirements.txt
+```
+
+### Install FlashAttention-2  
+
+```
+
+pip install flash_attn==2.5.8
+
+```
+
+
+Here's an simplified code for quantization.
+
+```
+python main.py \
+  --model_name "microsoft/Phi-3-mini-128k-instruct" \
+  --bits 8 \
+  --group_size 128 \
+  --train_bs 1 \
+  --gradient_accumulate_steps 8 \
+  --deployment_device 'gpu' \
+  --output_dir "./save_ckpt" 
+```
+
+
+## Model inference
+
+
+### Install the necessary packages 
+
+```
+pip install auto_gptq
+pip install optimum
+pip install -U accelerate bitsandbytes datasets peft transformers
+```
+
+### Example codes 
+
+```
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+
+torch.random.manual_seed(0)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "ssuncheol/phi-3-mini-128k-instruct-int4", 
+    device_map="cuda", 
+    torch_dtype="auto", 
+    trust_remote_code=True, 
+)
+tokenizer = AutoTokenizer.from_pretrained("ssuncheol/phi-3-mini-128k-instruct-int4")
+
+messages = [
+    {"role": "system", "content": "You are a helpful digital assistant. Please provide safe, ethical and accurate information to the user."},
+    {"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'])
+```
+
+
+## License
+The model is licensed under the MIT license.
+