Update README.md

README.md

@@ -5,199 +5,75 @@ base_model: sysong11/dapt-kogpt
 
 # Model Card for Model ID
 
-<!-- Provide a quick summary of what the model is/does. -->
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-
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-## Model Details
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-### Model Description
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-<!-- Provide a longer summary of what this model is. -->
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-- **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]
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-### Model Sources [optional]
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-<!-- Provide the basic links for the model. -->
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-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
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-## Uses
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-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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-### Direct Use
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-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-[More Information Needed]
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-### Downstream Use [optional]
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-<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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-### Out-of-Scope Use
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-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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-## Bias, Risks, and Limitations
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-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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-### Recommendations
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-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
+This repo contains a low-rank adapter for [domain-adapted KoGPT](https://huggingface.co/sysong11/dapt-kogpt) fit on [a small supervised tuning dataset for summarization](https://huggingface.co/datasets/sysong11/sum_train_rev).
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
-
-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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. -->
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-[More Information Needed]
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-### Training Procedure 
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-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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-#### Preprocessing [optional]
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-[More Information Needed]
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-#### Training Hyperparameters
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-- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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-#### Speeds, Sizes, Times [optional]
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-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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-[More Information Needed]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-[More Information Needed]
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-#### Factors
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-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-<!-- Relevant interpretability work for the model goes here -->
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-[More Information Needed]
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-## Environmental Impact
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-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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-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).
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-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-### Compute Infrastructure
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-#### Hardware
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-[More Information Needed]
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-#### Software
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-## Citation [optional]
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-**BibTeX:**
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-**APA:**
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-## Glossary [optional]
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-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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-[More Information Needed]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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+```python
+import json
+from random import randrange
+import torch
+
+from peft import LoraConfig, get_peft_model
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel
+
+model1 = AutoModelForCausalLM.from_pretrained(
+    "sysong11/dapt-kogpt", torch_dtype="auto", device_map="auto"
+)
+
+
+lora_path = "sysong11/dapt-kogpt-orca-sum-adapter"
+model2 = PeftModel.from_pretrained(model1, lora_path, device_map="auto")
+tokenizer = AutoTokenizer.from_pretrained(lora_path)
+
+
+test_data = []
+with open("./datasets/test.json", "rb") as f:
+    for line in f:
+        test_data.append(json.loads(line))
+
+
+prompt_template = """\
+<|im_start|>system
+{system_prompt}<|im_end|>
+<|im_start|>user
+{prompt}<|im_end|>
+<|im_start|>assistant"""
+
+msg = "Q:다음 문서를 요약 하세요, Context:{context}"
+
+ix = randrange(len(test_data))
+print(ix)
+datapoint = test_data[ix]
+ref = test_data[ix]["summary_text"]
+system_prompt = "You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can."
+tokens = tokenizer.encode(
+    prompt_template.format(
+        system_prompt=system_prompt,
+        prompt=msg.format(context=datapoint["original_text"]),
+    ),
+    return_tensors="pt",
+).to(device="cuda", non_blocking=True)
+
+gen_tokens = model2.generate(
+    input_ids=tokens,
+    do_sample=False,
+    temperature=0.5,
+    max_length=1024,
+    pad_token_id=63999,
+    eos_token_id=63999,
+)
+inputs = tokenizer.batch_decode([gen_tokens[0][: tokens[0].shape[0]]])[0]
+generated = tokenizer.batch_decode([gen_tokens[0][tokens[0].shape[0] :]])[0].replace(
+    "<|im_end|>", ""
+)
+print(inputs)
+print("generated:")
+print(generated)
+
+
+```
 
 ### Framework versions