Bochkov nielsr HF Staff commited on
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Improve model card: Add library_name and GitHub link (#1)

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- Improve model card: Add library_name and GitHub link (ed2474bea9a9ae485714e0c0d3b8ff123a5d951e)


Co-authored-by: Niels Rogge <nielsr@users.noreply.huggingface.co>

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  1. README.md +6 -2
README.md CHANGED
@@ -1,5 +1,7 @@
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  ---
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  license: apache-2.0
 
 
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  tags:
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  - transformer
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  - causal-lm
@@ -7,7 +9,6 @@ tags:
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  - constructive-learning
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  - frozen-embeddings
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  - bvv
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- pipeline_tag: text-generation
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  ---
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  # Model Card for abs-bvv-2
@@ -25,6 +26,8 @@ The core idea is to demonstrate an alternative, more modular and resource-effici
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  `abs-bvv-2` represents the state of the model after 2 layers of progressive training. It has 2 Transformer blocks, a hidden dimension of 4096, and uses the `bvv241` tokenizer family.
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  ## Intended Use
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  This model is primarily an artifact for research into emergent capabilities, constructive learning, and the role of embeddings in LLMs. It can be used for text generation, but it is not fine-tuned for specific downstream tasks and may produce unpredictable outputs. It is suitable for exploring the raw capabilities of a model trained under this novel paradigm.
@@ -103,4 +106,5 @@ outputs = model.generate(
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  do_sample=True
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  )
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- print(tokenizer.decode(outputs[0], skip_special_tokens=True))
 
 
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  ---
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  license: apache-2.0
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+ pipeline_tag: text-generation
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+ library_name: transformers
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  tags:
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  - transformer
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  - causal-lm
 
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  - constructive-learning
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  - frozen-embeddings
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  - bvv
 
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  ---
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  # Model Card for abs-bvv-2
 
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  `abs-bvv-2` represents the state of the model after 2 layers of progressive training. It has 2 Transformer blocks, a hidden dimension of 4096, and uses the `bvv241` tokenizer family.
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+ **Code:** [https://github.com/Bochkov/bvv241](https://github.com/Bochkov/bvv241)
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+
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  ## Intended Use
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  This model is primarily an artifact for research into emergent capabilities, constructive learning, and the role of embeddings in LLMs. It can be used for text generation, but it is not fine-tuned for specific downstream tasks and may produce unpredictable outputs. It is suitable for exploring the raw capabilities of a model trained under this novel paradigm.
 
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  do_sample=True
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  )
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+ print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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+ ```