daekeun-ml
/

phi-2-upscaled-4B-instruct-v0.1

@@ -1,201 +1,83 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
-- **Developed by:** [More Information Needed]
-- **Funded by [optional]:** [More Information Needed]
-- **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
-- **Language(s) (NLP):** [More Information Needed]
-- **License:** [More Information Needed]
-- **Finetuned from model [optional]:** [More Information Needed]
-### Model Sources [optional]
-<!-- Provide the basic links for the model. -->
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
-[More Information Needed]
-### Downstream Use [optional]
-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
-[More Information Needed]
-### Out-of-Scope Use
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-[More Information Needed]
-## Bias, Risks, and Limitations
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-[More Information Needed]
-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
 ## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
-### Training Data
-<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
-[More Information Needed]
-### Training Procedure
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
-#### Preprocessing [optional]
-[More Information Needed]
-#### Training Hyperparameters
-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
-#### Speeds, Sizes, Times [optional]
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-[More Information Needed]
-## Evaluation
-<!-- This section describes the evaluation protocols and provides the results. -->
-### Testing Data, Factors & Metrics
-#### Testing Data
-<!-- This should link to a Dataset Card if possible. -->
-[More Information Needed]
-#### Factors
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-[More Information Needed]
-#### Metrics
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-[More Information Needed]
-### Results
-[More Information Needed]
-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
-[More Information Needed]
-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
-- **Hardware Type:** [More Information Needed]
-- **Hours used:** [More Information Needed]
-- **Cloud Provider:** [More Information Needed]
-- **Compute Region:** [More Information Needed]
-- **Carbon Emitted:** [More Information Needed]
-## Technical Specifications [optional]
-### Model Architecture and Objective
-[More Information Needed]
-### Compute Infrastructure
-[More Information Needed]
-#### Hardware
-[More Information Needed]
-#### Software
-[More Information Needed]
-## Citation [optional]
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
-**BibTeX:**
-[More Information Needed]
-**APA:**
-[More Information Needed]
-## Glossary [optional]
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-[More Information Needed]
-## More Information [optional]
-[More Information Needed]
-## Model Card Authors [optional]
-[More Information Needed]
-## Model Card Contact
-[More Information Needed]

 ---
 library_name: transformers
+license: apache-2.0
+datasets:
+- Intel/orca_dpo_pairs
+- wikipedia
+- Open-Orca/OpenOrca
+language:
+- en
 ---
+# phi-2-upscaled-4B-instruct-v0.1
 ## Model Details
+This model is a model that performed continued pre-training and fine-tuning (instruction tuning) using the depth up-scaling (DUS) technique disclosed by Upstage.
+### DUS(Depth Up-Scaling) and continued pre-training
+Similar to the methodology disclosed in the paper, we expanded from 32 transformer blocks to 48 blocks and then continued pre-training with the public dataset. Pre-training was performed for 3 days using 4 ml.g5.48xlarge instances from AWS (NVIDIA A10G GPU x 32ea). For pre-training, we used a sample set from Wikipedia.
+### Fine-tuning
+After performing pre-training, instruction tuning and alignment tuning were performed sequentially. This process only took about 10 hours using ml.g5.24xlarge (NVIDIA A10G GPU x 4ea). The dataset used for instruction tuning is a sample set of the OpenOrca dataset, and the dataset used for alignment tuning is Intel's orca_dpo_pairs dataset.
+### References
+- Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
+- Paper: [SOLAR 10.7B](https://arxiv.org/abs/2312.15166)
 ## How to Get Started with the Model
+Since this model used ChatGPT's ChatML template, <im_start> and <im_end> tokens were added.
+You can use Hugging Face's chat template to create the prompt, but you can also create the prompt yourself with the code snippet below.
+```python
+def create_inference_prompt(text):
+     string = f"""<|im_start|>system
+You are a helpful AI assistant.<|im_end|>
+<|im_start|>user
+{text}<|im_end|>
+<|im_start|>assistant
+"""
+     return string
+```
+If you want to simply see the inference results, please use the code snippet below.
+```python
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
+import torch
+torch.set_default_device("cuda")
+model_path = "daekeun-ml/phi-2-upscaled-4B-instruct-v0.1"
+model = AutoModelForCausalLM.from_pretrained(
+    model_path,
+    torch_dtype="auto",
+    trust_remote_code=True)
+tokenizer = AutoTokenizer.from_pretrained(
+    model_path,
+    use_fast=True,
+    trust_remote_code=True
+)
+# Format prompt
+message = [
+    {"role": "system", "content": "You are a helpful AI assistant. Generate appropriate answers to given questions."},
+    {"role": "user", "content": "What is a Large Language Model?"}
+]
+prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
+inputs = tokenizer(prompt, return_tensors="pt", return_attention_mask=False)
+outputs = model.generate(**inputs, max_new_tokens=200, do_sample=True, top_p=0.9, temperature=0.5, repetition_penalty=1.2)
+text = tokenizer.batch_decode(outputs)[0]
+print(text)
+```
+## Notes
+### License
+Apache 2.0; The license of phi-2 is MIT, but the license of the orca dataset used for training is apache 2.0.
+### Caution
+This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!