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Model: arcee-ai/Mistral-7B-Instruct-v0.2-expanded Source: Original Platform
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README.md
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---
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library_name: transformers
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tags:
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- mergekit
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- block expansion
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- progressive mistral
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- arcee cpt
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---
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# Mistral-7B-Instruct-v0.2-expanded
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This method employs mergekit's passthrough method to expand blocks within the "mistralai/Mistral-7B-Instruct-v0.2" model. For every 5th layer,
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a new layer is added, with the `o_proj` and `down_proj` parameters of these added layers initialized to zero, mirroring the approach used in LLaMA Pro.
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### It's important to note that this configuration has not undergone fine-tuning. So this won't work. Therefore, when fine-tuning, ensure that only every 5th layer is trainable,while all other layers remain frozen.
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## 🧩 Configuration
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```yaml
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slices:
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [0, 4]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [3, 4]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [4, 8]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [7, 8]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [8, 12]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [11, 12]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [12, 16]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [15, 16]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [16, 20]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [19, 20]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [20, 24]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [23, 24]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [24, 28]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [27, 28]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [28, 32]
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- sources:
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- model: mistralai/Mistral-7B-Instruct-v0.2
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layer_range: [31, 32]
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parameters:
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scale:
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- filter: o_proj
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value: 0.0
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- filter: down_proj
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value: 0.0
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- value: 1.0
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merge_method: passthrough
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dtype: bfloat16
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```
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# Function to freeze layers
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```
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from transformers import AutoModelForCausalLM
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def enable_grad_only_every_nth(model, n):
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"""
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This function configures the specified model to enable gradient calculations exclusively for every nth layer, starting
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from the first layer (0-indexed), to accommodate newly added blocks for training. Concurrently, it freezes the gradients
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for all other components of the model, including the embedding layers and the model's head. This setup is particularly
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useful for fine-tuning processes where only a subset of layers are targeted for updates, ensuring efficient training and
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adaptation of newly integrated layers while maintaining the pre-trained behavior of other model components.
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"""
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# Freeze embeddings.
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for param in model.model.embed_tokens.parameters():
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param.requires_grad = False
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# Freeze lm_head.
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for param in model.lm_head.parameters():
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param.requires_grad = False
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# Enable gradients for every nth layer
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layers = model.model.layers # Access the ModuleList containing the layers
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for index, layer in enumerate(layers):
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if (index + 1) % n == 0: # Enables gradients for every nth layer, starting from the layer after the 0th
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for param in layer.parameters():
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param.requires_grad = True
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else:
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for param in layer.parameters():
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param.requires_grad = False
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model = transformers.AutoModelForCausalLM.from_pretrained(
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"arcee-ai/Mistral-7B-Instruct-v0.2-expanded"
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)
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# Update layer gradients, specify the correct value for n based on your model's architecture
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n =5
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enable_grad_only_every_nth(model, n)
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```
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config.json
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{
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"_name_or_path": "/Users/gayalshamane/Documents/mergekit/mistral_expanded_it",
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"architectures": [
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"MistralForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 14336,
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"max_position_embeddings": 32768,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 40,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_theta": 1000000.0,
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"sliding_window": null,
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"tie_word_embeddings": false,
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"torch_dtype": "float32",
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"transformers_version": "4.38.2",
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"use_cache": true,
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"vocab_size": 32000
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}
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configuration.json
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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.38.2"
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}
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23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
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|
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|
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|
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|
||||||
|
},
|
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|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
91122
tokenizer.json
Normal file
91122
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": true,
|
||||||
|
"add_eos_token": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [],
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"chat_template": "{{ bos_token }}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '[INST] ' + message['content'] + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ message['content'] + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"legacy": true,
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": null,
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"spaces_between_special_tokens": false,
|
||||||
|
"tokenizer_class": "LlamaTokenizer",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"use_default_system_prompt": false
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user