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Model: hiig-ai-lab/simba_best_092024
Source: Original Platform

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README.md

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+---
+license: apache-2.0
+language:
+- de
+pipeline_tag: text-generation
+tags:
+- german
+- deutsch
+- simplification
+- vereinfachung
+---
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+We fine-tuned [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) with a set of ca. 800 newspaper articles which have been simplified by the Austrian Press Agency. 
+Our aim was to have a model which can simplify German-language text.
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **Developed by:** Members of the [Public Interest AI research group](https://publicinterest.ai/), [HIIG Berlin](https://www.hiig.de/)
+- **Model type:** simplification model, text generation
+- **Language(s) (NLP):** German
+- **License:** Apache 2.0
+- **Finetuned from model:** meta-llama/Meta-Llama-3-8B-Instruct
+
+### Model Sources
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** https://github.com/fhewett/simba
+<!-- - **Paper [optional]:** [More Information Needed] -->
+- **Project website:** https://publicinterest.ai/tool/simba
+
+## 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. -->
+
+This model works best for simplifying German-language newspaper articles (news items, not commentaries or editorials). It may work for other types of texts.
+
+### Downstream Use
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+We have fine-tuned using only newspaper articles. We have not yet performed extensive out-of-domain testing, but believe that the model's capabilities could be improved by fine-tuning on more diverse data. Contact us if you have a dataset which you think could work (parallel texts, German standard & German simplified).
+
+<!-- ### Out-of-Scope Use -->
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+As with most text generation models, the model sometimes produces information that is incorrect. 
+
+### Recommendations
+
+<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
+
+Please check manually that your output text corresponds to the input text, as factual inconsistencies may have arisen.
+
+## How to Get Started with the Model
+
+We offer two tools to interact with our model: an online app and a browser extension. They can be viewed and used [here](https://publicinterest.ai/tool/simba?lang=en).
+
+Alternatively, to load the model using transformers:
+
+```
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+device = "cuda"
+tokenizer = AutoTokenizer.from_pretrained("hiig-piai/simba_best_092024")
+model = AutoModelForCausalLM.from_pretrained("hiig-piai/simba_best_092024", torch_dtype=torch.float16).to(device)
+```
+
+We used the following prompt at inference to test our model:
+
+```
+<|begin_of_text|><|start_header_id|>system<|end_header_id|>
+Du bist ein hilfreicher Assistent und hilfst dem User, Texte besser zu verstehen.<|eot_id|><|start_header_id|>user<|end_header_id|>
+Kannst du bitte den folgenden Text zusammenfassen und sprachlich auf ein A2-Niveau in Deutsch vereinfachen? Schreibe maximal 5 Sätze.
+{input_text}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
+```
+
+## 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. -->
+
+A sample of the data used to train our model can be found [here](https://github.com/fhewett/apa-rst/tree/main/original_texts).
+
+#### Training Hyperparameters
+
+- **Training regime:** Our training script can be found [here](https://github.com/fhewett/simba/blob/main/models/train_simba.py). <!--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. -->
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+#### Summary 
+
+
+<!-- ## Citation [optional]
+
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]-->
+
+## Model Card Contact
+
+simba -at- hiig.de

config.json

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+{
+  "_name_or_path": "meta-llama/Meta-Llama-3-8B-Instruct",
+  "architectures": [
+    "LlamaForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 128000,
+  "eos_token_id": 128009,
+  "hidden_act": "silu",
+  "hidden_size": 4096,
+  "initializer_range": 0.02,
+  "intermediate_size": 14336,
+  "max_position_embeddings": 8192,
+  "model_type": "llama",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 32,
+  "num_key_value_heads": 8,
+  "pretraining_tp": 1,
+  "rms_norm_eps": 1e-05,
+  "rope_scaling": null,
+  "rope_theta": 500000.0,
+  "tie_word_embeddings": false,
+  "torch_dtype": "float16",
+  "transformers_version": "4.38.1",
+  "use_cache": true,
+  "vocab_size": 128256
+}

generation_config.json

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+{
+  "bos_token_id": 128000,
+  "do_sample": true,
+  "eos_token_id": [
+    128001,
+    128009
+  ],
+  "max_length": 4096,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.38.1"
+}

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