koalajun
/

Gemma-2-9b-it-Ko-Crypto-Translate

@@ -1,199 +1,77 @@
 ---
-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: mit
+language:
+- en
+- ko
+base_model:
+- google/gemma-2-9b-it
 ---
+# Model Card for Gemma-2-9b-it-Ko-Crypto-Translate
+This model has been fine-tuned on a crypto news translation task. It is designed to translate English crypto news into Korean, leveraging the Gemma-2-9b-it architecture. The model is intended for natural language processing (NLP) tasks, specifically translation, within the crypto news domain.
 ## Model Details
 ### Model Description
+This fine-tuned model is based on the **Gemma-2-9b-it** architecture and has been specifically trained to translate English crypto news into Korean. Fine-tuning was performed using a custom dataset focused on cryptocurrency news articles, ensuring the model's output is accurate in both language translation and crypto-specific terminology.
+- **Developed by:** Hyoun Jun Lee
+- **Model type:** Gemma-2-9b-it
+- **Language(s) (NLP):** English, Korean
+### Model Sources
+- **Repository:** [Hugging Face: koalajun/Gemma-2-9b-it-Ko-Crypto-Translate](https://huggingface.co/koalajun/Gemma-2-9b-it-Ko-Crypto-Translate)
 ## Uses
 ### Direct Use
+This model can be used for translating English cryptocurrency news articles into Korean. It can be integrated into applications such as financial platforms or news websites to provide real-time translation of crypto news.
+### Downstream Use
+The model can be further fine-tuned for more specific translation tasks in the financial or legal domains. Additionally, it can be used as a basis for other translation or language generation tasks that require bilingual capabilities in English and Korean.
 ### Out-of-Scope Use
+This model is not intended for general translation tasks outside the financial/crypto domain. It may not perform well in non-financial contexts, as it was fine-tuned with specialized crypto-related datasets.
 ## Bias, Risks, and Limitations
+Given the specific nature of the dataset (crypto news), the model may introduce biases related to the financial or crypto sector. The translation might also be less effective for general or non-financial text, and there could be inaccuracies in domain-specific terms.
 ### Recommendations
+Users should validate the model's output in critical applications, especially when used in real-time financial decision-making or for publications where accuracy is paramount.
 ## How to Get Started with the Model
+To use this model for inference, you can load it using the Hugging Face `transformers` library as follows:
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_name = "koalajun/Gemma-2-9b-it-Ko-Crypto-Translate"
+model = AutoModelForCausalLM.from_pretrained(model_name)
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+device = "cuda" if torch.cuda.is_available() else "cpu"
+model = model.to(device)
+# Define the input prompt for testing
+prompt = "Translate the latest crypto news from English to Korean: Bitcoin prices continue to rise, surpassing $30,000 this week."
+# Tokenize the input prompt
+inputs = tokenizer(prompt, return_tensors="pt").to(device)
+# Generate response from the model
+outputs = model.generate(inputs.input_ids, max_length=200, num_return_sequences=1)
+# Decode and print the generated text (translation)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print("Translation:", response)