Update README.md

README.md

@@ -3,146 +3,141 @@ library_name: transformers
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-# Model Card for Model ID
-
-<!-- Provide a quick summary of what the model is/does. -->
-
+# Model Card for plutus-8B-instruct
 
+This model is an instruction-tuned large language model specialized for Greek financial texts. It is based on the ilsp/Llama-Krikri-8B-Instruct model and has been fine-tuned using Low-Rank Adaptation (LoRA) on a mixed Greek financial dataset.
 
 ## 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.
+This model card describes plutus-8B-instruct, a model developed by The Fin AI and finetuned to serve Greek-centric financial language tasks. The model leverages parameter-efficient fine-tuning (PEFT) via LoRA and is designed to generate or understand financial texts in Greek. The training was carried out with a local command-line backend with logging managed via TensorBoard.
 
 - **Developed by:** The Fin AI
 - **Funded by [optional]:** [More Information Needed]
 - **Shared by [optional]:** [More Information Needed]
-- **Model type:** [More Information Needed]
+- **Model type:** Instruction-tuned large language model, specialized for low-resource domains such as Greek finance
 - **Language(s) (NLP):** Greek
 - **License:** Apache License 2.0
 - **Finetuned from model [optional]:** ilsp/Llama-Krikri-8B-Instruct
 
 ### Model Sources [optional]
 
-<!-- Provide the basic links for the model. -->
-
 - **Repository:** https://huggingface.co/TheFinAI/plutus-8B-instruct
 - **Paper [optional]:** Plutus: Benchmarking Large Language Models in Low-Resource Greek Finance
 - **Demo [optional]:** https://huggingface.co/spaces/TheFinAI/plutus-8B-instruct
 
 ## 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]
+Plutus-8B-instruct can be directly applied in Greek finance applications such as answering user queries, summarizing financial reports, or generating context-aware financial planning text. Users should ensure that input texts align with the financial domain to achieve optimal performance.
 
 ### 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]
+The model can be integrated into larger systems such as chatbots, recommendation systems, or data analysis pipelines focusing on Greek financial markets. Fine-tuning on domain-specific datasets might further improve performance for specialized tasks.
 
 ### Out-of-Scope Use
 
-<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
-
-[More Information Needed]
+This model is not designed for non-financial applications or for languages other than Greek, and may not perform reliably if used outside of the Greek financial context. It should not be used for high-stakes financial decision-making without additional verification.
 
 ## Bias, Risks, and Limitations
 
-<!-- This section is meant to convey both technical and sociotechnical limitations. -->
-
-[More Information Needed]
+As with many LLMs, plutus-8B-instruct may exhibit biases present in the training data, and its outputs may require human review in sensitive contexts. Users should be aware that the model may generate plausible-sounding yet factually inaccurate or biased financial advice.
 
 ### 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.
+Users (both direct and downstream) should critically evaluate the outputs in the context of local regulations and financial best practices. It is recommended to apply additional validation measures when deploying the model in production environments.
 
 ## How to Get Started with the Model
 
-Use the code below to get started with the model.
+To load and run plutus-8B-instruct, you can use the Hugging Face Transformers library. For example:
+
+--------------------------------------------------------------------
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "TheFinAI/plutus-8B-instruct"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+prompt = "Παρακαλώ δώσε μου ανάλυση για την οικονομική κατάσταση της Ελλάδας."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+--------------------------------------------------------------------
 
-[More Information Needed]
+Additional details for running with mixed precision, LoRA configuration, or int4 quantization can be found in the training documentation.
 
 ## 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]
+The model was fine-tuned using the dataset available at https://huggingface.co/collections/TheFinAI/plutus-benchmarking-greek-financial-llms-67bc718fb8d897c65f1e87db. The training split ("train") was used, and the dataset contains various Greek financial texts.
 
 ### Training Procedure
 
-<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+The finetuning process used parameter-efficient fine-tuning (PEFT) with LoRA. The base model was ilsp/Llama-Krikri-8B-Instruct, and the training was executed on a local CLI backend with logs monitored via TensorBoard.
 
 #### Preprocessing [optional]
 
-[More Information Needed]
-
+Inputs were tokenized with a specialized tokenizer configured for the Greek language, and a chat template was applied to structure the conversational data accordingly. Padding was applied to the right as per the configuration.
 
 #### 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 -->
+- **Block size:** 4096
+- **Model max length:** 42000
+- **Epochs:** 3
+- **Batch size:** 1
+- **Learning rate:** 0.0005
+- **PEFT (LoRA):** Enabled with:
+  - lora_r: 16
+  - lora_alpha: 32
+  - lora_dropout: 0
+- **Quantization:** int4
+- **Target Modules:** all-linear
+- **Optimizer:** adamw_torch
+- **Scheduler:** cosine
+- **Gradient accumulation:** 4
+- **Mixed precision:** bf16
 
 #### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
-
-[More Information Needed]
+Training was performed on locally available hardware. Specific details on throughput, training runtime, and checkpoint sizes will be provided in subsequent documentation.
 
 ## 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]
+Evaluation was performed on reserved portions of the training dataset and additional financial texts. For detailed information, users should refer to the corresponding Dataset Card for https://huggingface.co/collections/TheFinAI/plutus-benchmarking-greek-financial-llms-67bc718fb8d897c65f1e87db
 
 #### Factors
 
-<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
-
-[More Information Needed]
+The evaluation considered multiple aspects including:
+- Domain-specific performance (Greek financial texts)
+- General language understanding within Greek
+- Response coherence and factual relevance
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+Evaluation metrics included standard language generation measures as well as domain-specific qualitative assessments. Further discussion on the metrics used will be provided in future updates.
 
 ### Results
 
-[More Information Needed]
+Preliminary evaluations indicate that the model generates coherent and context-aware responses in Greek, with particular aptitude in understanding and generating financial-related texts. Detailed metric numbers and error analyses are under review and will be added when available.
 
 #### Summary
 
-
+In summary, plutus-8B-instruct is a domain-specific, instruction-tuned large language model optimized for Greek finance applications. Although it performs well in targeted scenarios, users are advised to validate its outputs carefully.
 
 ## Model Examination [optional]
 
-<!-- Relevant interpretability work for the model goes here -->
-
-[More Information Needed]
+Future updates may include interpretability analyses, such as attention visualizations and performance breakdowns by subpopulation or domain.
 
 ## 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).
+Carbon emissions can be estimated using the Machine Learning Impact Calculator (https://mlco2.github.io/impact#compute) based on the following details:
 
 - **Hardware Type:** [More Information Needed]
 - **Hours used:** [More Information Needed]
@@ -154,46 +149,53 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
 
 ### Model Architecture and Objective
 
-[More Information Needed]
+The model architecture is derived from ilsp/Llama-Krikri-8B-Instruct and fine-tuned using LoRA adaptations. Its objective is to generate and comprehend Greek language texts with an emphasis on financial contexts in an instruction-based setting.
 
 ### Compute Infrastructure
 
-[More Information Needed]
+The model training and fine-tuning were executed using a local CLI environment, with logs monitored via TensorBoard.
 
 #### Hardware
 
-[More Information Needed]
+Specific hardware details are under documentation review. Future updates will include the configuration of GPUs/CPUs used.
 
 #### Software
 
-[More Information Needed]
+- **Framework:** Hugging Face Transformers
+- **Backend:** Local CLI 
+- **Mixed Precision:** bf16
 
 ## 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]
+@misc{TheFinAI_plutus8B,
+  author       = {The Fin AI},
+  title        = {plutus-8B-instruct: Instruction-tuned LLM for Greek finance},
+  howpublished = {\\url{https://huggingface.co/TheFinAI/plutus-8B-instruct}},
+  year         = {2023},
+  note         = {Based on ilsp/Llama-Krikri-8B-Instruct, fine-tuned using LoRA on Greek financial datasets.}
+}
 
 **APA:**
 
-[More Information Needed]
+The Fin AI. (2023). plutus-8B-instruct: Instruction-tuned LLM for Greek finance. Retrieved from https://huggingface.co/TheFinAI/plutus-8B-instruct
 
 ## Glossary [optional]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
-
-[More Information Needed]
+- PEFT: Parameter-Efficient Fine-Tuning.
+- LoRA: Low-Rank Adaptation, a technique to reduce the number of trainable parameters.
+- BF16: bfloat16, a mixed precision format used to optimize training speed.
+- Int4 Quantization: A lower precision format aimed at reducing model size and inference latency.
 
 ## More Information [optional]
 
-[More Information Needed]
+For more details regarding training logs, dataset preparations, and further technical insights, please refer to the associated GitHub repositories and documentation provided by The Fin AI.
 
 ## Model Card Authors [optional]
 
-[More Information Needed]
+The model card was prepared by The Fin AI team with inputs from the Hugging Face community.
 
 ## Model Card Contact
 
-[More Information Needed]
+For additional questions or feedback, please contact The Fin AI team