Update README.md

README.md

@@ -17,13 +17,14 @@ pipeline_tag: text-generation
 * **Language(s)**: English
 * **Library**: [HuggingFace Transformers](https://github.com/huggingface/transformers)
 * **License**: Fine-tuned checkpoints is licensed under the Non-Commercial Creative Commons license ([CC BY-NC-4.0](https://creativecommons.org/licenses/by-nc/4.0/))
-* **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/Llama-2-70b-instruct-1024/discussions)
+* **Where to send comments**: Instructions on how to provide feedback or comments on a model can be found by opening an issue in the [Hugging Face community's model repository](https://huggingface.co/upstage/Llama-2-70b-instruct-v2/discussions)
 * **Contact**: For questions and comments about the model, please email [contact@upstage.ai](mailto:contact@upstage.ai)
 
 ## Dataset Details
 
 ### Used Datasets
 - Orca-style dataset
+- Alpaca-Style Dataset
 
 
 ### Prompt Template
@@ -35,11 +36,35 @@ pipeline_tag: text-generation
 ### Assistant:
 {Assistant}
 ```
+### Usage
+
+*Tested on A100 80GB*
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
+tokenizer = AutoTokenizer.from_pretrained("upstage/Llama-2-70b-instruct-v2")
+model = AutoModelForCausalLM.from_pretrained(
+    "upstage/Llama-2-70b-instruct-v2",
+    device_map='auto',
+    torch_dtype=torch.float16,
+    load_in_8bit=True,
+    rope_scaling={'type': 'dynamic', 'factor': 2} # longer inputs possible
+)
+prompt = "### User:\nThomas is very healthy, but he has to go to the hospital every day. What could be the reasons?\n\n### Assistant:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+del inputs['token_type_ids']
+streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
+output = model.generate(**inputs, streamer=streamer, use_cache=True, max_new_tokens=float('inf'))
+output_text = tokenizer.decode(output[0], skip_prompt=True, skip_special_tokens=True)
+```
+
+Our model can handle >10k tokens thanks to the rope_scaling option.
 
 ## Hardware and Software
 
 * **Hardware**: We utilized an A100x8 * 4 for training our model
-* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer)
+* **Training Factors**: We fine-tuned this model using a combination of the [DeepSpeed library](https://github.com/microsoft/DeepSpeed) and the [HuggingFace trainer](https://huggingface.co/docs/transformers/main_classes/trainer) / [HuggingFace Accelerate](https://huggingface.co/docs/accelerate/index)
 
 ## Evaluation Results
 
@@ -49,7 +74,7 @@ We evaluated our model on four benchmark datasets, which include `ARC-Challenge`
 We used the [lm-evaluation-harness repository](https://github.com/EleutherAI/lm-evaluation-harness), specifically commit [b281b0921b636bc36ad05c0b0b0763bd6dd43463](https://github.com/EleutherAI/lm-evaluation-harness/tree/b281b0921b636bc36ad05c0b0b0763bd6dd43463).
 
 ### Main Results
-| Model                                         | Average | ARC   | HellaSwag | MMLU  | TruthfulQA |       | MT_Bench |
+| Model                                         | H4 Average | ARC   | HellaSwag | MMLU  | TruthfulQA |       | MT_Bench |
 |-----------------------------------------------|---------|-------|-----------|-------|------------|-------|----------|
 | **Llama-2-70b-instruct-v2** (***Ours***, ***Local Reproduction***) | **72.7** | **71.6** | **87.7** | **69.7** | **61.6** |       | 7.440625 |
 | Llama-2-70b-instruct (Ours, Local Reproduction) | 72.0 | 70.7 | 87.4 | 69.3 | 60.7 |       | 7.24375  |