Update README.md

README.md

@@ -21,36 +21,23 @@ pipeline_tag: text-generation
 
 # 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 type:** Decoder-based Model
+- **Language(s) (NLP):** Supported by Qwen2.5 + fine-tuned on summarries written in `en`, `fr`, `pt`, `es`
+- **License:** MIT
+- **Finetuned from model [optional]:** https://huggingface.co/Qwen/Qwen2.5-0.5B-Instruct
 
 ### Model Sources [optional]
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1TXGaz39o73nBucEQw12gbad7Tw11j2Ol?usp=sharing)
 
-<!-- Provide the basic links for the model. -->
-
-- **Repository:** [More Information Needed]
-- **Paper [optional]:** [More Information Needed]
-- **Demo [optional]:** [More Information Needed]
-
-## Uses
+- **Repository:** https://github.com/nicolay-r/distil-tuning-llm
+- **Paper [optional]:** **TBA**
+- **Demo [optional]:** https://colab.research.google.com/drive/1TXGaz39o73nBucEQw12gbad7Tw11j2Ol?usp=sharing
 
-### Direct Use
+## Usage
 
 We use [bulk-chain](https://github.com/nicolay-r/bulk-chain) for inference with the Qwen2 provider based on `transformers` **pipelines API**.
 
@@ -88,135 +75,84 @@ for record in content_it:
   print(record["summary"])
 ```
 
-
-### 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]
+* **MultiClinSum**
+  * We use the [following script](https://github.com/nicolay-r/distill-tuning-llm/blob/main/resources/download_dataset.sh) for downloading datasets.
+  * **Web**: https://temu.bsc.es/multiclinsum 
+  * **Data**: https://zenodo.org/records/15463353
+  * **BioASQ**: http://bioasq.org/ 
 
 ### 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 training procedure involves:
+1. Preparation of the `rationale` for summaries distillation.
+2. Launch of the **fine-tuning** process.
 
-#### Preprocessing [optional]
+**Fine-tuning:** Please follow this script for using `MultiClinSum` dataset for fine-tuning at GoogleColab A100 (40GB VRAM) + 80GB RAM:
+  * https://github.com/nicolay-r/distil-tuning-llm/blob/master/distil_ft_qwen25_05b_A100-40GB_80GB_std.sh
 
-[More Information Needed]
+#### Preprocessing [optional]
 
+Refer to the following script for the `fine-tuning` pre-processing:
+* https://github.com/nicolay-r/distil-tuning-llm/blob/master/resources/make_dataset_mult.py
 
 #### 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 -->
+We refer to the original parameters here:
+  * https://github.com/QwenLM/Qwen2.5-VL/tree/main/qwen-vl-finetune
+And use the following script:
+  * https://github.com/nicolay-r/distil-tuning-llm/blob/master/distil_ft_qwen25_05b_A100-40GB_80GB_std.sh
 
-#### Speeds, Sizes, Times [optional]
 
-<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+#### Speeds, Sizes, Times [optional]
 
-[More Information Needed]
+The fine-tuning procedure for `3` epochs takes around `~1 hour` using the GoogleColab A100.
 
 ## Evaluation
 
-![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/LRJU_B0rKfk3celyQ9zXq.png)
-
-
-### 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]
+We use evaluation split of the 20 documents out of the small portion the available training data across all the languages: `en`, `fr`, `pt`, `es`
 
 #### Metrics
 
-<!-- These are the evaluation metrics being used, ideally with a description of why. -->
-
-[More Information Needed]
+In this evaluation we use onle `rouge` score.
 
 ### Results
 
-[More Information Needed]
-
-#### Summary
-
-
-
-## Technical Specifications [optional]
+We launch 3 individual fine-tuning processes for `distil` and `standard` versions to showcase results variation among multiple runs.
 
-### Model Architecture and Objective
+> **Figure**: the obtained results for this model correspond to the `standard` version 🟠
 
-[More Information Needed]
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/64e62d11d27a8292c3637f86/6wZ_klTgm-SmvZCGJOaC5.png)
 
-### Compute Infrastructure
-
-[More Information Needed]
+#### Summary
 
 #### Hardware
 
-[More Information Needed]
+We experiment with model inference and launching using GoolgeColab Notebook service and related resources:
+* Fine-tuning: A100 (40GB)
+* Inference: T4 (16GB)
+
+Follow the Google Codalab Notebook at the repository:
+* https://github.com/nicolay-r/distil-tuning-llm
 
 #### Software
 
-[More Information Needed]
+This is an official repository for this card: 
+* https://github.com/nicolay-r/distil-tuning-llm
 
 ## 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]
+> **TO BE ADDED**
 
-[More Information Needed]
 
-## Model Card Contact
+## Model Card Authors 
 
-[More Information Needed]
+Nicolay Rusnachenko