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Model: prithivMLmods/Llama-Deepsync-1B Source: Original Platform
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README.md
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---
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license: creativeml-openrail-m
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language:
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- en
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- de
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- fr
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- it
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- pt
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- hi
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- es
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- th
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base_model:
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- meta-llama/Llama-3.2-1B-Instruct
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pipeline_tag: text-generation
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tags:
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- text-generation-inference
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- Llama
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- Code
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- CoT
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- Math
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- Deepsync
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- 3b
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library_name: transformers
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---
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<pre align="center">
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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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</pre>
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The **Llama-Deepsync-1B** is a fine-tuned version of the **Llama-3.2-1B-Instruct** base model, designed for text generation tasks that require deep reasoning, logical structuring, and problem-solving. This model leverages its optimized architecture to provide accurate and contextually relevant outputs for complex queries, making it ideal for applications in education, programming, and creative writing.
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With its robust natural language processing capabilities, **Llama-Deepsync-1B** excels in generating step-by-step solutions, creative content, and logical analyses. Its architecture integrates advanced understanding of both structured and unstructured data, ensuring precise text generation aligned with user inputs.
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- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
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- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
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- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
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- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
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# **Model Architecture**
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Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
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# **Use with transformers**
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Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
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Make sure to update your transformers installation via `pip install --upgrade transformers`.
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```python
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import torch
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from transformers import pipeline
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model_id = "prithivMLmods/Llama-Deepsync-1B"
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pipe = pipeline(
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"text-generation",
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model=model_id,
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torch_dtype=torch.bfloat16,
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device_map="auto",
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)
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messages = [
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{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
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{"role": "user", "content": "Who are you?"},
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]
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outputs = pipe(
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messages,
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max_new_tokens=256,
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)
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print(outputs[0]["generated_text"][-1])
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```
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Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
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# **Run with Ollama [Ollama Run]**
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Ollama makes running machine learning models simple and efficient. Follow these steps to set up and run your GGUF models quickly.
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## Quick Start: Step-by-Step Guide
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| Step | Description | Command / Instructions |
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|------|-------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------------|
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| 1 | **Install Ollama 🦙** | Download Ollama from [https://ollama.com/download](https://ollama.com/download) and install it on your system. |
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| 2 | **Create Your Model File** | - Create a file named after your model, e.g., `metallama`. |
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| | | - Add the following line to specify the base model: |
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| | | ```bash |
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| | | FROM Llama-3.2-1B.F16.gguf |
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| | | ``` |
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| | | - Ensure the base model file is in the same directory. |
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| 3 | **Create and Patch the Model** | Run the following commands to create and verify your model: |
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| | | ```bash |
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| | | ollama create metallama -f ./metallama |
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| | | ollama list |
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| | | ``` |
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| 4 | **Run the Model** | Use the following command to start your model: |
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| | | ```bash |
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| | | ollama run metallama |
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| | | ``` |
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| 5 | **Interact with the Model** | Once the model is running, interact with it: |
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| | | ```plaintext |
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| | | >>> Tell me about Space X. |
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| | | Space X, the private aerospace company founded by Elon Musk, is revolutionizing space exploration... |
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| | | ``` |
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## Conclusion
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With Ollama, running and interacting with models is seamless. Start experimenting today!
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config.json
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{
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"_name_or_path": "unsloth/llama-3.2-1b-instruct-bnb-4bit",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"head_dim": 64,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 131072,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 16,
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"num_key_value_heads": 8,
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"pad_token_id": 128004,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 32.0,
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"high_freq_factor": 4.0,
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"low_freq_factor": 1.0,
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"original_max_position_embeddings": 8192,
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"rope_type": "llama3"
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},
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"rope_theta": 500000.0,
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"tie_word_embeddings": true,
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"torch_dtype": "float16",
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"transformers_version": "4.47.1",
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"unsloth_version": "2024.12.11",
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"use_cache": true,
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"vocab_size": 128256
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}
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configuration.json
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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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generation_config.json
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{
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"bos_token_id": 128000,
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"do_sample": true,
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"eos_token_id": [
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128001,
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128008,
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128009
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],
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"max_length": 131072,
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"pad_token_id": 128004,
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"temperature": 0.6,
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"top_p": 0.9,
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"transformers_version": "4.47.1"
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}
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:b56eb0e9c80cdaf3eef6cd4f5308ef0187e9cc770ea65f386e7ef3acd9efd902
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size 2471645464
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special_tokens_map.json
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{
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"bos_token": {
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"content": "<|begin_of_text|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"eos_token": {
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"content": "<|eot_id|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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},
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"pad_token": {
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"content": "<|finetune_right_pad_id|>",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false
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}
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}
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tokenizer.json
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tokenizer.json
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tokenizer_config.json
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