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Browse files- README.md +108 -0
- config.json +29 -0
- model.safetensors +3 -0
- tokenizer.json +0 -0
- tokenizer_config.json +61 -0
README.md
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---
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license: apache-2.0
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library_name: transformers
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base_model: 01-ai/Yi-Coder-1.5B
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---
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# Quantized Version of 01-ai/Yi-Coder-1.5B-Chat
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This model is a quantized variant of the 01-ai/Yi-Coder-1.5B-Chat model, optimized for use with Jlama, a Java-based inference engine. The quantization process reduces the model's size and improves inference speed, while maintaining high accuracy for efficient deployment in production environments.
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For more information on Jlama, visit the [Jlama GitHub repository](https://github.com/tjake/jlama).
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---
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<div align="center">
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<picture>
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<img src="https://raw.githubusercontent.com/01-ai/Yi/main/assets/img/Yi_logo_icon_light.svg" width="120px">
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</picture>
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</div>
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<p align="center">
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<a href="https://github.com/01-ai">🐙 GitHub</a> •
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<a href="https://discord.gg/hYUwWddeAu">👾 Discord</a> •
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<a href="https://twitter.com/01ai_yi">🐤 Twitter</a> •
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<a href="https://github.com/01-ai/Yi-1.5/issues/2">💬 WeChat</a>
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<br/>
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<a href="https://arxiv.org/abs/2403.04652">📝 Paper</a> •
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<a href="https://01-ai.github.io/">💪 Tech Blog</a> •
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<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#faq">🙌 FAQ</a> •
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<a href="https://github.com/01-ai/Yi/tree/main?tab=readme-ov-file#learning-hub">📗 Learning Hub</a>
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</p>
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# Intro
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Yi-Coder is a series of open-source code language models that delivers state-of-the-art coding performance with fewer than 10 billion parameters.
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Key features:
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- Excelling in long-context understanding with a maximum context length of 128K tokens.
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- Supporting 52 major programming languages:
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```bash
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'java', 'markdown', 'python', 'php', 'javascript', 'c++', 'c#', 'c', 'typescript', 'html', 'go', 'java_server_pages', 'dart', 'objective-c', 'kotlin', 'tex', 'swift', 'ruby', 'sql', 'rust', 'css', 'yaml', 'matlab', 'lua', 'json', 'shell', 'visual_basic', 'scala', 'rmarkdown', 'pascal', 'fortran', 'haskell', 'assembly', 'perl', 'julia', 'cmake', 'groovy', 'ocaml', 'powershell', 'elixir', 'clojure', 'makefile', 'coffeescript', 'erlang', 'lisp', 'toml', 'batchfile', 'cobol', 'dockerfile', 'r', 'prolog', 'verilog'
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```
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For model details and benchmarks, see [Yi-Coder blog](https://01-ai.github.io/) and [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
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<p align="left">
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<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/yi-coder-calculator-demo.gif?raw=true" alt="demo1" width="500"/>
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</p>
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# Models
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| Name | Type | Length | Download |
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|--------------------|------|----------------|---------------------------------------------------------------------------------------------------------------------------------------------------|
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| Yi-Coder-9B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B-Chat) |
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| Yi-Coder-1.5B-Chat | Chat | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B-Chat) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B-Chat) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B-Chat) |
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| Yi-Coder-9B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-9B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-9B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-9B) |
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| Yi-Coder-1.5B | Base | 128K | [🤗 Hugging Face](https://huggingface.co/01-ai/Yi-Coder-1.5B) • [🤖 ModelScope](https://www.modelscope.cn/models/01ai/Yi-Coder-1.5B) • [🟣 wisemodel](https://wisemodel.cn/models/01.AI/Yi-Coder-1.5B) |
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# Benchmarks
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As illustrated in the figure below, Yi-Coder-9B-Chat achieved an impressive 23% pass rate in LiveCodeBench, making it the only model with under 10B parameters to surpass 20%. It also outperforms DeepSeekCoder-33B-Ins at 22.3%, CodeGeex4-9B-all at 17.8%, CodeLLama-34B-Ins at 13.3%, and CodeQwen1.5-7B-Chat at 12%.
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<p align="left">
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<img src="https://github.com/01-ai/Yi/blob/main/assets/img/coder/bench1.webp?raw=true" alt="bench1" width="1000"/>
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</p>
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# Quick Start
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You can use transformers to run inference with Yi-Coder models (both chat and base versions) as follows:
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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device = "cuda" # the device to load the model onto
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model_path = "01-ai/Yi-Coder-9B-Chat"
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tokenizer = AutoTokenizer.from_pretrained(model_path)
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model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto").eval()
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prompt = "Write a quick sort algorithm."
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messages = [
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{"role": "system", "content": "You are a helpful assistant."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(device)
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generated_ids = model.generate(
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model_inputs.input_ids,
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max_new_tokens=1024,
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eos_token_id=tokenizer.eos_token_id
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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print(response)
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```
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For getting up and running with Yi-Coder series models quickly, see [Yi-Coder README](https://github.com/01-ai/Yi-Coder).
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config.json
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{
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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": 1,
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"eos_token_id": 7,
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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": 5504,
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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": 16,
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"num_hidden_layers": 24,
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"num_key_value_heads": 16,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000000,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.44.0",
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"use_cache": false,
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"vocab_size": 64000
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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 sha256:887af2d03b01b43f5060dffb6df71ec67dc42258a1a49e866f19b437d2c7c20e
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size 922991464
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tokenizer.json
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tokenizer_config.json
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{
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"add_bos_token": false,
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"add_eos_token": false,
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"add_prefix_space": false,
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"added_tokens_decoder": {
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"0": {
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"content": "<unk>",
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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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"special": true
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},
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"1": {
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"content": "<|startoftext|>",
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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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"special": true
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},
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"2": {
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"content": "<|endoftext|>",
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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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"special": true
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},
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"6": {
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"content": "<|im_start|>",
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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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"special": true
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},
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"7": {
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"content": "<|im_end|>",
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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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"special": true
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}
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},
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"bos_token": "<|startoftext|>",
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"chat_template": "{% if messages[0]['role'] == 'system' %}{% set system_message = messages[0]['content'] %}{% endif %}{% if system_message is defined %}{{ '<|im_start|>system\n' + system_message + '<|im_end|>\n' }}{% endif %}{% for message in messages %}{% set content = message['content'] %}{% if message['role'] == 'user' %}{{ '<|im_start|>user\n' + content + '<|im_end|>\n<|im_start|>assistant\n' }}{% elif message['role'] == 'assistant' %}{{ content + '<|im_end|>' + '\n' }}{% endif %}{% endfor %}",
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"clean_up_tokenization_spaces": false,
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"eos_token": "<|im_end|>",
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"legacy": true,
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"model_max_length": 200000,
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"pad_token": "<unk>",
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"padding_side": "right",
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"sp_model_kwargs": {},
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"spaces_between_special_tokens": false,
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"split_special_tokens": false,
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"tokenizer_class": "LlamaTokenizer",
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"unk_token": "<unk>",
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"use_default_system_prompt": false
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}
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