epfl-ml-ytf
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apertus-8b-pruned-latin-94237

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 ---
-library_name: transformers
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 ---
-# Model Card for Model ID
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 ---
+language:
+- en
+license: apache-2.0
+base_model: swiss-ai/Apertus-8B-Instruct-2509
+tags:
+- academic-project
+- pruning
+- vocabulary-pruning
+- nlp
+- llm
+- ml optimization
 ---
+# Model Card for Apertus-8B_pruned-latin-94237
+## Model Summary
+This model is a vocabulary-pruned English-only version of [swiss-ai/Apertus-8B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509).
+It was created as part of an academic project in Machine Learning to
+investigate the effects of vocabulary reduction on model size and performance.
+**Base Model:** [Apertus-8B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509)
+**Developer (Base Model):** Swiss AI Initiative (ETH Zurich, EPFL, CSCS)
+**Pruning Method:** Vocabulary Pruning (see details below)
+## Vocabulary Pruning Details
+The pruned vocabulary was obtained by only retaining tokens with characters from
+the "Latin", "Common" and "Inherited" unicode scripts. More information can be
+found on the project report.
+## Intended Use
+This model is intended for **academic research and educational purposes**, specifically to study:
+- The impact of language restriction on multilingual LLM performance.
+- Efficiency gains in memory usage and inference speed.
+- Comparative analysis between full-scale and pruned models.
+For general-purpose instruction following or production use, we recommend using the original [swiss-ai/Apertus-8B-Instruct-2509](https://huggingface.co/swiss-ai/Apertus-8B-Instruct-2509).
+## How to Use
+You can load this model using the `transformers` library. Ensure you are using a recent version of `transformers`.
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+model_id = "epfl-ml-ytf/apertus-8b-pruned-latin-94237"
+# Load the pruned tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+# Example generation
+messages = [
+    {"role": "user", "content": "Explain the concept of vocabulary pruning in one sentence."}
+]
+inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True).to(model.device)
+outputs = model.generate(inputs, max_new_tokens=100)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))