h-j-han
commited on
Commit
·
191b5d9
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Parent(s):
ed2e3e6
Fix new line issue & Match vocab type to base model
Browse files- README.md +4 -3
- config.json +1 -1
- model-00001-of-00003.safetensors +2 -2
- model-00003-of-00003.safetensors +2 -2
- model.safetensors.index.json +1 -1
- tokenizer.json +0 -0
README.md
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@@ -14,7 +14,6 @@ base_model:
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VocADT is a solution for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embeddings while keeping the model’s weights fixed.
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VocADT offers a flexible and scalable solution without requiring external resources or language constraints.
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## New Vocabulary Adapted Models
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Only the input/output embeddings are replaced, while all other original weights of base model remain fixed.
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These are the merged version: after training the adapters, we merge the original embeddings with the adapter to generate the new embeddings.
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@@ -29,10 +28,10 @@ These are the merged version: after training the adapters, we merge the original
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# model_name = "mistralai/Mistral-7B-v0.1 # Base Model
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model_name = "h-j-han/Mistral-7B-VocADT-50k-Latin" # Vocabulary Adapted Model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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prefix = "\nEnglish: Hello!\nSwahili: Habari!\nEnglish: What's your name?\nSwahili: Jina lako ni nani?\nEnglish: "
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line = "My name is Amani."
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@@ -40,6 +39,8 @@ suffix = f"\nSwahili:"
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prompt = prefix + line + suffix
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inputs = tokenizer(prompt, return_tensors="pt")
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outputs = model.generate(**inputs, max_new_tokens=5)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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VocADT is a solution for vocabulary adaptation using adapter modules that are trained to learn the optimal linear combination of existing embeddings while keeping the model’s weights fixed.
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VocADT offers a flexible and scalable solution without requiring external resources or language constraints.
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## New Vocabulary Adapted Models
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Only the input/output embeddings are replaced, while all other original weights of base model remain fixed.
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These are the merged version: after training the adapters, we merge the original embeddings with the adapter to generate the new embeddings.
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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# model_name = "mistralai/Mistral-7B-v0.1" # Base Model
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model_name = "h-j-han/Mistral-7B-VocADT-50k-Latin" # Vocabulary Adapted Model
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
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prefix = "\nEnglish: Hello!\nSwahili: Habari!\nEnglish: What's your name?\nSwahili: Jina lako ni nani?\nEnglish: "
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line = "My name is Amani."
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prompt = prefix + line + suffix
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inputs = tokenizer(prompt, return_tensors="pt")
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for item in inputs:
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inputs[item] = inputs[item].cuda()
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outputs = model.generate(**inputs, max_new_tokens=5)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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config.json
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"torch_dtype": "bfloat16",
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"transformers_version": "4.43.0.dev0",
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"use_cache": true,
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"vocab_size":
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}
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"torch_dtype": "bfloat16",
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"transformers_version": "4.43.0.dev0",
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"use_cache": true,
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"vocab_size": 50000
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}
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model-00001-of-00003.safetensors
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size 4973177712
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model-00003-of-00003.safetensors
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size 4889316136
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model.safetensors.index.json
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@@ -1,6 +1,6 @@
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{
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"metadata": {
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"total_size":
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},
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"weight_map": {
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"lm_head.weight": "model-00003-of-00003.safetensors",
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{
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"metadata": {
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},
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"weight_map": {
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"lm_head.weight": "model-00003-of-00003.safetensors",
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tokenizer.json
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