utter-project
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EuroMoE-2.6B-A0.6B-Instruct-Preview

@@ -1,199 +1,118 @@
 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
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-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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 ---
+license: apache-2.0
+language:
+- en
+- de
+- es
+- fr
+- it
+- pt
+- pl
+- nl
+- tr
+- sv
+- cs
+- el
+- hu
+- ro
+- fi
+- uk
+- sl
+- sk
+- da
+- lt
+- lv
+- et
+- bg
+- 'no'
+- ca
+- hr
+- ga
+- mt
+- gl
+- zh
+- ru
+- ko
+- ja
+- ar
+- hi
 library_name: transformers
+base_model:
+- utter-project/EuroMoE-2.6B-A0.6B-Preview
 ---
+# Model Card for EuroMoE-2.6B-A0.6B-Instruct-Preview
+⚠️ PREVIEW RELEASE: This is a preview version of EuroMoE-2.6B-A0.6B-Instruct-Preview. The model is still under development and may have limitations in performance and stability. Use with caution in production environments.
+This is the model card for EuroMoE-2.6B-A0.6B-Instruct-Preview. You can also check the pre-trained version: [EuroMoE-2.6B-A0.6B-Preview](https://huggingface.co/utter-project/EuroMoE-2.6B-A0.6B-Preview).
+- **Developed by:** Unbabel, Instituto Superior Técnico, Instituto de Telecomunicações, University of Edinburgh, Aveni, University of Paris-Saclay, University of Amsterdam, Naver Labs, Sorbonne Université.
+- **Funded by:** European Union.
+- **Model type:** A 2.6B parameter multilingual transformer MoE with 0.6B active parameters.
+- **Language(s) (NLP):** Bulgarian, Croatian, Czech, Danish, Dutch, English, Estonian, Finnish, French, German, Greek, Hungarian, Irish, Italian, Latvian, Lithuanian, Maltese, Polish, Portuguese, Romanian, Slovak, Slovenian, Spanish, Swedish, Arabic, Catalan, Chinese, Galician, Hindi, Japanese, Korean, Norwegian, Russian, Turkish, and Ukrainian.
+- **License:** Apache License 2.0.
 ## Model Details
+The EuroLLM project has the goal of creating a suite of LLMs capable of understanding and generating text in all European Union languages as well as some additional relevant languages.
+EuroMoE-2.6B-A0.6B is a 22B parameter model trained on 8 trillion tokens divided across the considered languages and several data sources: Web data, parallel data (en-xx and xx-en), and high-quality datasets.
+EuroMoE-2.6B-A0.6B-Instruct was further instruction tuned on EuroBlocks, an instruction tuning dataset with focus on general instruction-following and machine translation.
+### Model Description
+EuroMoE uses a standard MoE Transformer architecture:
+- We use grouped query attention (GQA) with 2 key-value heads, since it has been shown to increase speed at inference time while maintaining downstream performance.
+- We perform pre-layer normalization, since it improves the training stability, and use the RMSNorm, which is faster.
+- We use the SwiGLU activation function, since it has been shown to lead to good results on downstream tasks.
+- We use rotary positional embeddings (RoPE) in every layer, since these have been shown to lead to good performances while allowing the extension of the context length.
+For pre-training, we use 512 Nvidia A100 GPUs of the Leonardo supercomputer, training the model with a constant batch size of 4096 sequences, which corresponds to approximately 17 million tokens, using the Adam optimizer, and BF16 precision.
+Here is a summary of the model hyper-parameters:
+|                                      |                      |
+|--------------------------------------|----------------------|
+| Sequence Length                      |      4,096           |
+| Number of Layers                     |         24           |
+| Embedding Size                       |           1,024      |
+| Total/Active experts                  |      64/8      |
+| Expert Hidden Size                   |            512      |
+| Number of Heads                      |        8            |
+| Number of KV Heads (GQA)             |         2            |
+| Activation Function                  | SwiGLU               |
+| Position Encodings                   | RoPE (\Theta=500,000) |
+| Layer Norm                           | RMSNorm              |
+| Tied Embeddings                      | Yes                  |
+| Embedding Parameters                 | 0.13B               |
+| LM Head Parameters                   | 0.13B               |
+| Active Non-embedding Parameters             | 0.34B               |
+| Total Non-embedding Parameters             | 2.35B               |
+| Active Parameters                   | 0.6B               |
+| Total Parameters                     | 2.61B               |
+## Run the model
+    from transformers import AutoModelForCausalLM, AutoTokenizer
+    model_id = "utter-project/EuroMoE-2.6B-A0.6B-Instruct-Preview"
+    tokenizer = AutoTokenizer.from_pretrained(model_id)
+    model = AutoModelForCausalLM.from_pretrained(model_id)
+    messages = [
+        {
+            "role": "system",
+            "content": "You are EuroLLM --- an AI assistant specialized in European languages that provides safe, educational and helpful answers.",
+        },
+        {
+            "role": "user", "content": "What is the capital of Portugal? How would you describe it?"
+        },
+        ]
+    inputs = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt")
+    outputs = model.generate(inputs, max_new_tokens=1024)
+    print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 ## Bias, Risks, and Limitations
+EuroLLM-22B has not been aligned to human preferences, so the model may generate problematic outputs (e.g., hallucinations, harmful content, or false statements).