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Model: Local-Novel-LLM-project/Ninja-v1-128k Source: Original Platform
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README.md
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---
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license: apache-2.0
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language:
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- en
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- ja
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tags:
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- finetuned
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library_name: transformers
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pipeline_tag: text-generation
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---
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<img src="./ninjalogo.svg" width="100%" height="20%" alt="">
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# Our Models
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- [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1)
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- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1)
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- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)
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- [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k)
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- [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k)
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## Model Card for Ninja-v1-128k
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The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1
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Ninja-128k has the following changes compared to Mistral-7B-v0.1.
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- 128k context window (8k context in v0.1)
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- Achieving both high quality Japanese and English generation
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- Memory ability that does not forget even after long-context generation
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This model was created with the help of GPUs from the first LocalAI hackathon.
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We would like to take this opportunity to thank
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## List of Creation Methods
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- Chatvector for multiple models
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- Simple linear merging of result models
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- Domain and Sentence Enhancement with LORA
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- Context expansion
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## Instruction format
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Ninja adopts the prompt format from Vicuna and supports multi-turn conversation.
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The prompt should be as following:
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```
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USER: Hi ASSISTANT: Hello.</s>
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USER: Who are you?
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ASSISTANT: I am ninja.</s>
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```
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## Example prompts to improve (Japanese)
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- BAD: あなたは○○として振る舞います
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- GOOD: あなたは○○です
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- BAD: あなたは○○ができます
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- GOOD: あなたは○○をします
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## Performing inference
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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import torch
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model_id = "Local-Novel-LLM-project/Ninja-v1-128k"
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new_tokens = 1024
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model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- "
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prompt = input("Enter a prompt: ")
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system_prompt += prompt + "\n-------- "
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model_inputs = tokenizer([system_prompt], return_tensors="pt").to("cuda")
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generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)
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print(tokenizer.batch_decode(generated_ids)[0])
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````
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## Merge recipe
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- WizardLM2 - mistralai/Mistral-7B-v0.1
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- NousResearch/Yarn-Mistral-7b-128k - mistralai/Mistral-7B-v0.1
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- Elizezen/Antler-7B - stabilityai/japanese-stablelm-instruct-gamma-7b
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- NTQAI/chatntq-ja-7b-v1.0
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The characteristics of each model are as follows.
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- WizardLM2: High quality multitasking model
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- Yarn-Mistral-7b-128k: Mistral model with 128k context window
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- Antler-7B: Model specialized for novel writing
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- NTQAI/chatntq-ja-7b-v1.0 High quality Japanese specialized model
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## Other points to keep in mind
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- The training data may be biased. Be careful with the generated sentences.
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- Set trust_remote_code to True for context expansion with YaRN.
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- Memory usage may be large for long inferences.
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- If possible, we recommend inferring with llamacpp rather than Transformers.
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config.json
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{
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"_name_or_path": "NousResearch/Yarn-Mistral-7b-128k",
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"architectures": [
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"MistralForCausalLM"
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],
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"auto_map": {
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"AutoConfig": "configuration_mistral.MistralConfig",
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"AutoModelForCausalLM": "modeling_mistral_yarn.MistralForCausalLM"
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},
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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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"max_sequence_length": 131072,
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"model_type": "mistral",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 8,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 16.0,
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"finetuned": true,
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"original_max_position_embeddings": 8192,
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"type": "yarn"
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},
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"rope_theta": 10000.0,
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"sliding_window": 131072,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.35.0.dev0",
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"use_cache": true,
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"vocab_size": 32000
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}
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configuration_mistral.py
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# coding=utf-8
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# Copyright 2023 Mistral AI and the HuggingFace Inc. team. All rights reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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||||||
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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||||||
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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""" Mistral model configuration"""
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from transformers.configuration_utils import PretrainedConfig
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from transformers.utils import logging
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logger = logging.get_logger(__name__)
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MISTRAL_PRETRAINED_CONFIG_ARCHIVE_MAP = {
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"mistralai/Mistral-7B-v0.1": "https://huggingface.co/mistralai/Mistral-7B-v0.1/resolve/main/config.json",
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"mistralai/Mistral-7B-Instruct-v0.1": "https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1/resolve/main/config.json",
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}
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class MistralConfig(PretrainedConfig):
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r"""
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This is the configuration class to store the configuration of a [`MistralModel`]. It is used to instantiate an
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Mistral model according to the specified arguments, defining the model architecture. Instantiating a configuration
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with the defaults will yield a similar configuration to that of the Mistral-7B-v0.1 or Mistral-7B-Instruct-v0.1.
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[mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
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[mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)
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Configuration objects inherit from [`PretrainedConfig`] and can be used to control the model outputs. Read the
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documentation from [`PretrainedConfig`] for more information.
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Args:
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vocab_size (`int`, *optional*, defaults to 32000):
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Vocabulary size of the Mistral model. Defines the number of different tokens that can be represented by the
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`inputs_ids` passed when calling [`MistralModel`]
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hidden_size (`int`, *optional*, defaults to 4096):
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Dimension of the hidden representations.
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intermediate_size (`int`, *optional*, defaults to 14336):
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Dimension of the MLP representations.
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num_hidden_layers (`int`, *optional*, defaults to 32):
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Number of hidden layers in the Transformer encoder.
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num_attention_heads (`int`, *optional*, defaults to 32):
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Number of attention heads for each attention layer in the Transformer encoder.
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num_key_value_heads (`int`, *optional*, defaults to 8):
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This is the number of key_value heads that should be used to implement Grouped Query Attention. If
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`num_key_value_heads=num_attention_heads`, the model will use Multi Head Attention (MHA), if
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`num_key_value_heads=1 the model will use Multi Query Attention (MQA) otherwise GQA is used. When
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converting a multi-head checkpoint to a GQA checkpoint, each group key and value head should be constructed
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by meanpooling all the original heads within that group. For more details checkout [this
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paper](https://arxiv.org/pdf/2305.13245.pdf). If it is not specified, will default to `8`.
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hidden_act (`str` or `function`, *optional*, defaults to `"silu"`):
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The non-linear activation function (function or string) in the decoder.
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max_position_embeddings (`int`, *optional*, defaults to `4096*32`):
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The maximum sequence length that this model might ever be used with. Mistral's sliding window attention
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allows sequence of up to 4096*32 tokens.
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initializer_range (`float`, *optional*, defaults to 0.02):
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The standard deviation of the truncated_normal_initializer for initializing all weight matrices.
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rms_norm_eps (`float`, *optional*, defaults to 1e-06):
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The epsilon used by the rms normalization layers.
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use_cache (`bool`, *optional*, defaults to `True`):
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Whether or not the model should return the last key/values attentions (not used by all models). Only
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relevant if `config.is_decoder=True`.
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pad_token_id (`int`, *optional*):
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The id of the padding token.
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bos_token_id (`int`, *optional*, defaults to 1):
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The id of the "beginning-of-sequence" token.
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eos_token_id (`int`, *optional*, defaults to 2):
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The id of the "end-of-sequence" token.
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tie_word_embeddings (`bool`, *optional*, defaults to `False`):
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Whether the model's input and output word embeddings should be tied.
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rope_scaling (`Dict`, *optional*):
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Dictionary containing the scaling configuration for the RoPE embeddings. Currently supports three scaling
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strategies: linear and dynamic. Their scaling factor must be an float greater than 1. The expected format
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is `{"type": strategy name, "factor": scaling factor}`.
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rope_theta (`float`, *optional*, defaults to 10000.0):
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The base period of the RoPE embeddings.
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sliding_window (`int`, *optional*, defaults to 4096):
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Sliding window attention window size. If not specified, will default to `4096`.
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```python
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>>> from transformers import MistralModel, MistralConfig
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>>> # Initializing a Mistral 7B style configuration
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>>> configuration = MistralConfig()
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>>> # Initializing a model from the Mistral 7B style configuration
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>>> model = MistralModel(configuration)
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>>> # Accessing the model configuration
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>>> configuration = model.config
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```"""
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model_type = "mistral"
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keys_to_ignore_at_inference = ["past_key_values"]
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def __init__(
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self,
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vocab_size=32000,
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hidden_size=4096,
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intermediate_size=14336,
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num_hidden_layers=32,
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num_attention_heads=32,
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num_key_value_heads=8,
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hidden_act="silu",
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max_position_embeddings=4096 * 32,
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initializer_range=0.02,
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rms_norm_eps=1e-6,
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use_cache=True,
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pad_token_id=None,
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bos_token_id=1,
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eos_token_id=2,
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tie_word_embeddings=False,
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rope_scaling=None,
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rope_theta=10000.0,
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sliding_window=4096,
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**kwargs,
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):
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self.vocab_size = vocab_size
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self.max_position_embeddings = max_position_embeddings
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self.hidden_size = hidden_size
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self.intermediate_size = intermediate_size
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self.num_hidden_layers = num_hidden_layers
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self.num_attention_heads = num_attention_heads
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self.sliding_window = sliding_window
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# for backward compatibility
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if num_key_value_heads is None:
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num_key_value_heads = num_attention_heads
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self.num_key_value_heads = num_key_value_heads
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self.hidden_act = hidden_act
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self.initializer_range = initializer_range
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self.rms_norm_eps = rms_norm_eps
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self.use_cache = use_cache
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self.rope_scaling = rope_scaling
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self._rope_scaling_validation()
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self.rope_theta = rope_theta
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super().__init__(
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pad_token_id=pad_token_id,
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bos_token_id=bos_token_id,
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eos_token_id=eos_token_id,
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tie_word_embeddings=tie_word_embeddings,
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**kwargs,
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)
|
||||||
|
|
||||||
|
def _rope_scaling_validation(self):
|
||||||
|
"""
|
||||||
|
Validate the `rope_scaling` configuration.
|
||||||
|
"""
|
||||||
|
if self.rope_scaling is None:
|
||||||
|
return
|
||||||
|
|
||||||
|
if not isinstance(self.rope_scaling, dict):
|
||||||
|
raise ValueError(
|
||||||
|
"`rope_scaling` must be a dictionary, "
|
||||||
|
f"got {self.rope_scaling}"
|
||||||
|
)
|
||||||
|
rope_scaling_type = self.rope_scaling.get("type", None)
|
||||||
|
rope_scaling_factor = self.rope_scaling.get("factor", None)
|
||||||
|
if rope_scaling_type is None or rope_scaling_type not in ["linear", "dynamic", "yarn", "dynamic-yarn"]:
|
||||||
|
raise ValueError(
|
||||||
|
f"`rope_scaling`'s name field must be one of ['linear', 'dynamic', 'yarn', 'dynamic-yarn'], got {rope_scaling_type}"
|
||||||
|
)
|
||||||
|
if rope_scaling_factor is None or not isinstance(rope_scaling_factor, float) or rope_scaling_factor <= 1.0:
|
||||||
|
raise ValueError(f"`rope_scaling`'s factor field must be an float > 1, got {rope_scaling_factor}")
|
||||||
|
if rope_scaling_type == "yarn" or rope_scaling_type == "dynamic-yarn":
|
||||||
|
original_max_position_embeddings = self.rope_scaling.get("original_max_position_embeddings", None)
|
||||||
|
if original_max_position_embeddings is None or not isinstance(original_max_position_embeddings, int):
|
||||||
|
raise ValueError(f"`rope_scaling.original_max_position_embeddings` must be set to an int when using yarn, and dynamic-yarn")
|
||||||
6
generation_config.json
Normal file
6
generation_config.json
Normal file
@@ -0,0 +1,6 @@
|
|||||||
|
{
|
||||||
|
"_from_model_config": true,
|
||||||
|
"bos_token_id": 1,
|
||||||
|
"eos_token_id": 2,
|
||||||
|
"transformers_version": "4.37.1"
|
||||||
|
}
|
||||||
3
model-00001-of-00003.safetensors
Normal file
3
model-00001-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:1757ce6a7bf65483e2892776e2da491c201b297e906e7d5f8b381f3701ff07f2
|
||||||
|
size 4943162336
|
||||||
3
model-00002-of-00003.safetensors
Normal file
3
model-00002-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:05c28f78b36657fccca9c99457cff021d80a6f67a7271fc581be05b2c41fffd4
|
||||||
|
size 4999819336
|
||||||
3
model-00003-of-00003.safetensors
Normal file
3
model-00003-of-00003.safetensors
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
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||||||
|
oid sha256:763f961217eb53f48559c31803e77c2a4c400dba322724ccc5ae26d30b72d5a4
|
||||||
|
size 4540516344
|
||||||
298
model.safetensors.index.json
Normal file
298
model.safetensors.index.json
Normal file
@@ -0,0 +1,298 @@
|
|||||||
|
{
|
||||||
|
"metadata": {
|
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|
"total_size": 14483464192
|
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|
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|
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|
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|
||||||
|
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|
||||||
|
}
|
||||||
1489
modeling_mistral_yarn.py
Normal file
1489
modeling_mistral_yarn.py
Normal file
File diff suppressed because it is too large
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1150
ninjalogo.svg
Normal file
1150
ninjalogo.svg
Normal file
File diff suppressed because it is too large
Load Diff
|
After Width: | Height: | Size: 109 KiB |
23
special_tokens_map.json
Normal file
23
special_tokens_map.json
Normal file
@@ -0,0 +1,23 @@
|
|||||||
|
{
|
||||||
|
"bos_token": {
|
||||||
|
"content": "<s>",
|
||||||
|
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|
||||||
|
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|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"eos_token": {
|
||||||
|
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|
||||||
|
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|
||||||
|
"normalized": false,
|
||||||
|
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|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
BIN
tokenizer.model
(Stored with Git LFS)
Normal file
Binary file not shown.
44
tokenizer_config.json
Normal file
44
tokenizer_config.json
Normal file
@@ -0,0 +1,44 @@
|
|||||||
|
{
|
||||||
|
"add_bos_token": true,
|
||||||
|
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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,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "</s>",
|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
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|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [],
|
||||||
|
"bos_token": "<s>",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "</s>",
|
||||||
|
"legacy": true,
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": null,
|
||||||
|
"padding_side": "left",
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"spaces_between_special_tokens": false,
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "LlamaTokenizer",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"use_default_system_prompt": true
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user