Commit
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8072b90
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Parent(s):
7617c72
Added merged adapters file
Browse files- README.md +0 -102
- adapter_config.json +0 -34
- config.json +56 -0
- generation_config.json +14 -0
- adapter_model.safetensors → model-00001-of-00002.safetensors +2 -2
- model-00002-of-00002.safetensors +3 -0
- model.safetensors.index.json +0 -0
- special_tokens_map.json +0 -23
- tokenizer.json +0 -0
- tokenizer_config.json +0 -2064
README.md
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---
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base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
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language:
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- en
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license: apache-2.0
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tags:
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- text-generation-inference
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- transformers
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- unsloth
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- llama
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- trl
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datasets:
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- TIGER-Lab/MathInstruct
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library_name: transformers
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---
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# Model Card: Math Homework Solver
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This is a Large Language Model (LLM) fine-tuned to solve math problems with detailed, step-by-step explanations and accurate answers. The base model used is Llama 3.1 with 8 billion parameters, which has been quantized to 4-bit using QLoRA (Quantized Low-Rank Adaptation) and PEFT (Parameter-Efficient Fine-Tuning) through the Unsloth framework.
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## Model Details
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- **Base Model**: Llama 3.1 (8 Billion parameters)
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- **Fine-tuning Method**: PEFT (Parameter-Efficient Fine-Tuning) with QLoRA
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- **Quantization**: 4-bit quantization for reduced memory usage
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- **Training Framework**: Unsloth, optimized for efficient fine-tuning of large language models
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- **Training Environment**: Google Colab (free tier), NVIDIA T4 GPU (12GB VRAM), 12GB RAM
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- **Dataset Used**: TIGER-Lab/MathInstruct (Yue, X., Qu, X., Zhang, G., Fu, Y., Huang, W., Sun, H., Su, Y., & Chen, W. (2023). MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning. *arXiv preprint arXiv:2309.05653*.
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), 560 selected math problems and solutions
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## Capabilities
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The Math Homework Solver model is designed to assist with a broad spectrum of mathematical problems, from basic arithmetic to advanced calculus. It provides clear and detailed explanations, making it an excellent resource for students, educators, and anyone looking to deepen their understanding of mathematical concepts.
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By leveraging the Llama 3.1 base model and fine-tuning it using PEFT and QLoRA, this model achieves high-quality performance while maintaining a relatively small computational footprint, making it accessible even on limited hardware.
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## Getting Started
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To start using the Math Homework Solver model, follow these steps:
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1. **Clone the repo**
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```bash
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git clone https://huggingface.co/justsomerandomdude264/Math_Homework_Solver-Llama3.18B
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```
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2. **Run inference**
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Make a new file named main.py and run this code in it:
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```python
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from unsloth import FastLanguageModel
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import torch
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# Define Your Question
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question = "Verify that the function y = a cos x + b sin x, where, a, b ∈ R is a solution of the differential equation d2y/dx2 + y=0." # Example Question, You can change it with one of your own
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# Load the model
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model, tokenizer = FastLanguageModel.from_pretrained(
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model_name = "Math_Homework_Solver_Llama318B", # The dir where the repo is cloned or "\\" for root
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max_seq_length = 2048,
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dtype = None,
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load_in_4bit = True,
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)
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# Set the model in inference model
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FastLanguageModel.for_inference(model)
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# QA template
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qa_template = """Question: {}
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Answer: {}"""
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# Tokenize inputs
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inputs = tokenizer(
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[
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qa_template.format(
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question, # Question
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"", # Answer - left blank for generation
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)
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], return_tensors = "pt").to("cuda")
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# Stream the answer/output of the model
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from transformers import TextStreamer
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text_streamer = TextStreamer(tokenizer)
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_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 512)
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```
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## Citation
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Please use the following citation if you reference the Math Homework Solver model:
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### BibTeX Citation
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```bibtex
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@misc{paliwal2024,
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author = {Krishna Paliwal},
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title = {Contributions to Math_Homework_Solver},
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year = {2024},
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email = {[email protected]}
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}
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```
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### APA Citation
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```plaintext
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Paliwal, Krishna (2024). Contributions to Math_Homework_Solver. Email: [email protected] .
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```
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "unsloth/meta-llama-3.1-8b-instruct-bnb-4bit",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 16,
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"lora_dropout": 0,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 16,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"k_proj",
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"down_proj",
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"q_proj",
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"gate_proj",
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"up_proj",
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"v_proj",
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"o_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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config.json
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{
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"_name_or_path": "unsloth/Meta-Llama-3.1-8B-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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"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": 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": 32,
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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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"quantization_config": {
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"_load_in_4bit": true,
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"_load_in_8bit": false,
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"bnb_4bit_compute_dtype": "bfloat16",
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"bnb_4bit_quant_storage": "uint8",
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"bnb_4bit_quant_type": "nf4",
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"bnb_4bit_use_double_quant": true,
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"llm_int8_enable_fp32_cpu_offload": false,
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"llm_int8_has_fp16_weight": false,
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"llm_int8_skip_modules": null,
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"llm_int8_threshold": 6.0,
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"load_in_4bit": true,
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"load_in_8bit": false,
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"quant_method": "bitsandbytes"
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},
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 8.0,
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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": false,
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"torch_dtype": "float16",
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"transformers_version": "4.44.0",
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"unsloth_version": "2024.8",
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"use_cache": true,
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"vocab_size": 128256
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}
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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": "4.44.0"
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}
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adapter_model.safetensors → model-00001-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:ef49285da3081608a8b271e50e6e09ab18cfb5ac24505baa141007c6b23ef1fa
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size 4652072855
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model-00002-of-00002.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:3e9a36ad418b0cd9b253f65771552d7dc05cf1abf1170b94de4b4d546aac255d
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size 1050673280
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model.safetensors.index.json
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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_config.json
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{
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"added_tokens_decoder": {
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"128000": {
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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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"special": true
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},
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"128001": {
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"content": "<|end_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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"special": true
|
| 18 |
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|
| 19 |
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|
| 20 |
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| 842 |
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|
| 1672 |
-
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|
| 1673 |
-
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|
| 1674 |
-
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|
| 1675 |
-
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|
| 1676 |
-
"content": "<|reserved_special_token_201|>",
|
| 1677 |
-
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|
| 1678 |
-
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|
| 1679 |
-
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|
| 1680 |
-
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|
| 1681 |
-
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|
| 1682 |
-
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|
| 1683 |
-
"128210": {
|
| 1684 |
-
"content": "<|reserved_special_token_202|>",
|
| 1685 |
-
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|
| 1686 |
-
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|
| 1687 |
-
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|
| 1688 |
-
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|
| 1689 |
-
"special": true
|
| 1690 |
-
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|
| 1691 |
-
"128211": {
|
| 1692 |
-
"content": "<|reserved_special_token_203|>",
|
| 1693 |
-
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|
| 1694 |
-
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|
| 1695 |
-
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|
| 1696 |
-
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|
| 1697 |
-
"special": true
|
| 1698 |
-
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|
| 1699 |
-
"128212": {
|
| 1700 |
-
"content": "<|reserved_special_token_204|>",
|
| 1701 |
-
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|
| 1702 |
-
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|
| 1703 |
-
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|
| 1704 |
-
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|
| 1705 |
-
"special": true
|
| 1706 |
-
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|
| 1707 |
-
"128213": {
|
| 1708 |
-
"content": "<|reserved_special_token_205|>",
|
| 1709 |
-
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|
| 1710 |
-
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|
| 1711 |
-
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|
| 1712 |
-
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|
| 1713 |
-
"special": true
|
| 1714 |
-
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|
| 1715 |
-
"128214": {
|
| 1716 |
-
"content": "<|reserved_special_token_206|>",
|
| 1717 |
-
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|
| 1718 |
-
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|
| 1719 |
-
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|
| 1720 |
-
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|
| 1721 |
-
"special": true
|
| 1722 |
-
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|
| 1723 |
-
"128215": {
|
| 1724 |
-
"content": "<|reserved_special_token_207|>",
|
| 1725 |
-
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|
| 1726 |
-
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|
| 1727 |
-
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|
| 1728 |
-
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|
| 1729 |
-
"special": true
|
| 1730 |
-
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|
| 1731 |
-
"128216": {
|
| 1732 |
-
"content": "<|reserved_special_token_208|>",
|
| 1733 |
-
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|
| 1734 |
-
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|
| 1735 |
-
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|
| 1736 |
-
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|
| 1737 |
-
"special": true
|
| 1738 |
-
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|
| 1739 |
-
"128217": {
|
| 1740 |
-
"content": "<|reserved_special_token_209|>",
|
| 1741 |
-
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|
| 1742 |
-
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|
| 1743 |
-
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|
| 1744 |
-
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|
| 1745 |
-
"special": true
|
| 1746 |
-
},
|
| 1747 |
-
"128218": {
|
| 1748 |
-
"content": "<|reserved_special_token_210|>",
|
| 1749 |
-
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|
| 1750 |
-
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|
| 1751 |
-
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|
| 1752 |
-
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|
| 1753 |
-
"special": true
|
| 1754 |
-
},
|
| 1755 |
-
"128219": {
|
| 1756 |
-
"content": "<|reserved_special_token_211|>",
|
| 1757 |
-
"lstrip": false,
|
| 1758 |
-
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|
| 1759 |
-
"rstrip": false,
|
| 1760 |
-
"single_word": false,
|
| 1761 |
-
"special": true
|
| 1762 |
-
},
|
| 1763 |
-
"128220": {
|
| 1764 |
-
"content": "<|reserved_special_token_212|>",
|
| 1765 |
-
"lstrip": false,
|
| 1766 |
-
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|
| 1767 |
-
"rstrip": false,
|
| 1768 |
-
"single_word": false,
|
| 1769 |
-
"special": true
|
| 1770 |
-
},
|
| 1771 |
-
"128221": {
|
| 1772 |
-
"content": "<|reserved_special_token_213|>",
|
| 1773 |
-
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|
| 1774 |
-
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|
| 1775 |
-
"rstrip": false,
|
| 1776 |
-
"single_word": false,
|
| 1777 |
-
"special": true
|
| 1778 |
-
},
|
| 1779 |
-
"128222": {
|
| 1780 |
-
"content": "<|reserved_special_token_214|>",
|
| 1781 |
-
"lstrip": false,
|
| 1782 |
-
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|
| 1783 |
-
"rstrip": false,
|
| 1784 |
-
"single_word": false,
|
| 1785 |
-
"special": true
|
| 1786 |
-
},
|
| 1787 |
-
"128223": {
|
| 1788 |
-
"content": "<|reserved_special_token_215|>",
|
| 1789 |
-
"lstrip": false,
|
| 1790 |
-
"normalized": false,
|
| 1791 |
-
"rstrip": false,
|
| 1792 |
-
"single_word": false,
|
| 1793 |
-
"special": true
|
| 1794 |
-
},
|
| 1795 |
-
"128224": {
|
| 1796 |
-
"content": "<|reserved_special_token_216|>",
|
| 1797 |
-
"lstrip": false,
|
| 1798 |
-
"normalized": false,
|
| 1799 |
-
"rstrip": false,
|
| 1800 |
-
"single_word": false,
|
| 1801 |
-
"special": true
|
| 1802 |
-
},
|
| 1803 |
-
"128225": {
|
| 1804 |
-
"content": "<|reserved_special_token_217|>",
|
| 1805 |
-
"lstrip": false,
|
| 1806 |
-
"normalized": false,
|
| 1807 |
-
"rstrip": false,
|
| 1808 |
-
"single_word": false,
|
| 1809 |
-
"special": true
|
| 1810 |
-
},
|
| 1811 |
-
"128226": {
|
| 1812 |
-
"content": "<|reserved_special_token_218|>",
|
| 1813 |
-
"lstrip": false,
|
| 1814 |
-
"normalized": false,
|
| 1815 |
-
"rstrip": false,
|
| 1816 |
-
"single_word": false,
|
| 1817 |
-
"special": true
|
| 1818 |
-
},
|
| 1819 |
-
"128227": {
|
| 1820 |
-
"content": "<|reserved_special_token_219|>",
|
| 1821 |
-
"lstrip": false,
|
| 1822 |
-
"normalized": false,
|
| 1823 |
-
"rstrip": false,
|
| 1824 |
-
"single_word": false,
|
| 1825 |
-
"special": true
|
| 1826 |
-
},
|
| 1827 |
-
"128228": {
|
| 1828 |
-
"content": "<|reserved_special_token_220|>",
|
| 1829 |
-
"lstrip": false,
|
| 1830 |
-
"normalized": false,
|
| 1831 |
-
"rstrip": false,
|
| 1832 |
-
"single_word": false,
|
| 1833 |
-
"special": true
|
| 1834 |
-
},
|
| 1835 |
-
"128229": {
|
| 1836 |
-
"content": "<|reserved_special_token_221|>",
|
| 1837 |
-
"lstrip": false,
|
| 1838 |
-
"normalized": false,
|
| 1839 |
-
"rstrip": false,
|
| 1840 |
-
"single_word": false,
|
| 1841 |
-
"special": true
|
| 1842 |
-
},
|
| 1843 |
-
"128230": {
|
| 1844 |
-
"content": "<|reserved_special_token_222|>",
|
| 1845 |
-
"lstrip": false,
|
| 1846 |
-
"normalized": false,
|
| 1847 |
-
"rstrip": false,
|
| 1848 |
-
"single_word": false,
|
| 1849 |
-
"special": true
|
| 1850 |
-
},
|
| 1851 |
-
"128231": {
|
| 1852 |
-
"content": "<|reserved_special_token_223|>",
|
| 1853 |
-
"lstrip": false,
|
| 1854 |
-
"normalized": false,
|
| 1855 |
-
"rstrip": false,
|
| 1856 |
-
"single_word": false,
|
| 1857 |
-
"special": true
|
| 1858 |
-
},
|
| 1859 |
-
"128232": {
|
| 1860 |
-
"content": "<|reserved_special_token_224|>",
|
| 1861 |
-
"lstrip": false,
|
| 1862 |
-
"normalized": false,
|
| 1863 |
-
"rstrip": false,
|
| 1864 |
-
"single_word": false,
|
| 1865 |
-
"special": true
|
| 1866 |
-
},
|
| 1867 |
-
"128233": {
|
| 1868 |
-
"content": "<|reserved_special_token_225|>",
|
| 1869 |
-
"lstrip": false,
|
| 1870 |
-
"normalized": false,
|
| 1871 |
-
"rstrip": false,
|
| 1872 |
-
"single_word": false,
|
| 1873 |
-
"special": true
|
| 1874 |
-
},
|
| 1875 |
-
"128234": {
|
| 1876 |
-
"content": "<|reserved_special_token_226|>",
|
| 1877 |
-
"lstrip": false,
|
| 1878 |
-
"normalized": false,
|
| 1879 |
-
"rstrip": false,
|
| 1880 |
-
"single_word": false,
|
| 1881 |
-
"special": true
|
| 1882 |
-
},
|
| 1883 |
-
"128235": {
|
| 1884 |
-
"content": "<|reserved_special_token_227|>",
|
| 1885 |
-
"lstrip": false,
|
| 1886 |
-
"normalized": false,
|
| 1887 |
-
"rstrip": false,
|
| 1888 |
-
"single_word": false,
|
| 1889 |
-
"special": true
|
| 1890 |
-
},
|
| 1891 |
-
"128236": {
|
| 1892 |
-
"content": "<|reserved_special_token_228|>",
|
| 1893 |
-
"lstrip": false,
|
| 1894 |
-
"normalized": false,
|
| 1895 |
-
"rstrip": false,
|
| 1896 |
-
"single_word": false,
|
| 1897 |
-
"special": true
|
| 1898 |
-
},
|
| 1899 |
-
"128237": {
|
| 1900 |
-
"content": "<|reserved_special_token_229|>",
|
| 1901 |
-
"lstrip": false,
|
| 1902 |
-
"normalized": false,
|
| 1903 |
-
"rstrip": false,
|
| 1904 |
-
"single_word": false,
|
| 1905 |
-
"special": true
|
| 1906 |
-
},
|
| 1907 |
-
"128238": {
|
| 1908 |
-
"content": "<|reserved_special_token_230|>",
|
| 1909 |
-
"lstrip": false,
|
| 1910 |
-
"normalized": false,
|
| 1911 |
-
"rstrip": false,
|
| 1912 |
-
"single_word": false,
|
| 1913 |
-
"special": true
|
| 1914 |
-
},
|
| 1915 |
-
"128239": {
|
| 1916 |
-
"content": "<|reserved_special_token_231|>",
|
| 1917 |
-
"lstrip": false,
|
| 1918 |
-
"normalized": false,
|
| 1919 |
-
"rstrip": false,
|
| 1920 |
-
"single_word": false,
|
| 1921 |
-
"special": true
|
| 1922 |
-
},
|
| 1923 |
-
"128240": {
|
| 1924 |
-
"content": "<|reserved_special_token_232|>",
|
| 1925 |
-
"lstrip": false,
|
| 1926 |
-
"normalized": false,
|
| 1927 |
-
"rstrip": false,
|
| 1928 |
-
"single_word": false,
|
| 1929 |
-
"special": true
|
| 1930 |
-
},
|
| 1931 |
-
"128241": {
|
| 1932 |
-
"content": "<|reserved_special_token_233|>",
|
| 1933 |
-
"lstrip": false,
|
| 1934 |
-
"normalized": false,
|
| 1935 |
-
"rstrip": false,
|
| 1936 |
-
"single_word": false,
|
| 1937 |
-
"special": true
|
| 1938 |
-
},
|
| 1939 |
-
"128242": {
|
| 1940 |
-
"content": "<|reserved_special_token_234|>",
|
| 1941 |
-
"lstrip": false,
|
| 1942 |
-
"normalized": false,
|
| 1943 |
-
"rstrip": false,
|
| 1944 |
-
"single_word": false,
|
| 1945 |
-
"special": true
|
| 1946 |
-
},
|
| 1947 |
-
"128243": {
|
| 1948 |
-
"content": "<|reserved_special_token_235|>",
|
| 1949 |
-
"lstrip": false,
|
| 1950 |
-
"normalized": false,
|
| 1951 |
-
"rstrip": false,
|
| 1952 |
-
"single_word": false,
|
| 1953 |
-
"special": true
|
| 1954 |
-
},
|
| 1955 |
-
"128244": {
|
| 1956 |
-
"content": "<|reserved_special_token_236|>",
|
| 1957 |
-
"lstrip": false,
|
| 1958 |
-
"normalized": false,
|
| 1959 |
-
"rstrip": false,
|
| 1960 |
-
"single_word": false,
|
| 1961 |
-
"special": true
|
| 1962 |
-
},
|
| 1963 |
-
"128245": {
|
| 1964 |
-
"content": "<|reserved_special_token_237|>",
|
| 1965 |
-
"lstrip": false,
|
| 1966 |
-
"normalized": false,
|
| 1967 |
-
"rstrip": false,
|
| 1968 |
-
"single_word": false,
|
| 1969 |
-
"special": true
|
| 1970 |
-
},
|
| 1971 |
-
"128246": {
|
| 1972 |
-
"content": "<|reserved_special_token_238|>",
|
| 1973 |
-
"lstrip": false,
|
| 1974 |
-
"normalized": false,
|
| 1975 |
-
"rstrip": false,
|
| 1976 |
-
"single_word": false,
|
| 1977 |
-
"special": true
|
| 1978 |
-
},
|
| 1979 |
-
"128247": {
|
| 1980 |
-
"content": "<|reserved_special_token_239|>",
|
| 1981 |
-
"lstrip": false,
|
| 1982 |
-
"normalized": false,
|
| 1983 |
-
"rstrip": false,
|
| 1984 |
-
"single_word": false,
|
| 1985 |
-
"special": true
|
| 1986 |
-
},
|
| 1987 |
-
"128248": {
|
| 1988 |
-
"content": "<|reserved_special_token_240|>",
|
| 1989 |
-
"lstrip": false,
|
| 1990 |
-
"normalized": false,
|
| 1991 |
-
"rstrip": false,
|
| 1992 |
-
"single_word": false,
|
| 1993 |
-
"special": true
|
| 1994 |
-
},
|
| 1995 |
-
"128249": {
|
| 1996 |
-
"content": "<|reserved_special_token_241|>",
|
| 1997 |
-
"lstrip": false,
|
| 1998 |
-
"normalized": false,
|
| 1999 |
-
"rstrip": false,
|
| 2000 |
-
"single_word": false,
|
| 2001 |
-
"special": true
|
| 2002 |
-
},
|
| 2003 |
-
"128250": {
|
| 2004 |
-
"content": "<|reserved_special_token_242|>",
|
| 2005 |
-
"lstrip": false,
|
| 2006 |
-
"normalized": false,
|
| 2007 |
-
"rstrip": false,
|
| 2008 |
-
"single_word": false,
|
| 2009 |
-
"special": true
|
| 2010 |
-
},
|
| 2011 |
-
"128251": {
|
| 2012 |
-
"content": "<|reserved_special_token_243|>",
|
| 2013 |
-
"lstrip": false,
|
| 2014 |
-
"normalized": false,
|
| 2015 |
-
"rstrip": false,
|
| 2016 |
-
"single_word": false,
|
| 2017 |
-
"special": true
|
| 2018 |
-
},
|
| 2019 |
-
"128252": {
|
| 2020 |
-
"content": "<|reserved_special_token_244|>",
|
| 2021 |
-
"lstrip": false,
|
| 2022 |
-
"normalized": false,
|
| 2023 |
-
"rstrip": false,
|
| 2024 |
-
"single_word": false,
|
| 2025 |
-
"special": true
|
| 2026 |
-
},
|
| 2027 |
-
"128253": {
|
| 2028 |
-
"content": "<|reserved_special_token_245|>",
|
| 2029 |
-
"lstrip": false,
|
| 2030 |
-
"normalized": false,
|
| 2031 |
-
"rstrip": false,
|
| 2032 |
-
"single_word": false,
|
| 2033 |
-
"special": true
|
| 2034 |
-
},
|
| 2035 |
-
"128254": {
|
| 2036 |
-
"content": "<|reserved_special_token_246|>",
|
| 2037 |
-
"lstrip": false,
|
| 2038 |
-
"normalized": false,
|
| 2039 |
-
"rstrip": false,
|
| 2040 |
-
"single_word": false,
|
| 2041 |
-
"special": true
|
| 2042 |
-
},
|
| 2043 |
-
"128255": {
|
| 2044 |
-
"content": "<|reserved_special_token_247|>",
|
| 2045 |
-
"lstrip": false,
|
| 2046 |
-
"normalized": false,
|
| 2047 |
-
"rstrip": false,
|
| 2048 |
-
"single_word": false,
|
| 2049 |
-
"special": true
|
| 2050 |
-
}
|
| 2051 |
-
},
|
| 2052 |
-
"bos_token": "<|begin_of_text|>",
|
| 2053 |
-
"chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n {%- set system_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n{%- else %}\n {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n {#- Extract the first user message so we can plug it in here #}\n {%- if messages | length != 0 %}\n {%- set first_user_message = messages[0]['content']|trim %}\n {%- set messages = messages[1:] %}\n {%- else %}\n {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n {{- \"Do not use variables.\\n\\n\" }}\n {%- for t in tools %}\n {{- t | tojson(indent=4) }}\n {{- \"\\n\\n\" }}\n {%- endfor %}\n {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n {%- elif 'tool_calls' in message %}\n {%- if not message.tool_calls|length == 1 %}\n {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n {%- endif %}\n {%- set tool_call = message.tool_calls[0].function %}\n {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n {%- for arg_name, arg_val in tool_call.arguments | items %}\n {{- arg_name + '=\"' + arg_val + '\"' }}\n {%- if not loop.last %}\n {{- \", \" }}\n {%- endif %}\n {%- endfor %}\n {{- \")\" }}\n {%- else %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n {{- '\"parameters\": ' }}\n {{- tool_call.arguments | tojson }}\n {{- \"}\" }}\n {%- endif %}\n {%- if builtin_tools is defined %}\n {#- This means we're in ipython mode #}\n {{- \"<|eom_id|>\" }}\n {%- else %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n {%- if message.content is mapping or message.content is iterable %}\n {{- message.content | tojson }}\n {%- else %}\n {{- message.content }}\n {%- endif %}\n {{- \"<|eot_id|>\" }}\n {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
|
| 2054 |
-
"clean_up_tokenization_spaces": true,
|
| 2055 |
-
"eos_token": "<|eot_id|>",
|
| 2056 |
-
"model_input_names": [
|
| 2057 |
-
"input_ids",
|
| 2058 |
-
"attention_mask"
|
| 2059 |
-
],
|
| 2060 |
-
"model_max_length": 131072,
|
| 2061 |
-
"pad_token": "<|finetune_right_pad_id|>",
|
| 2062 |
-
"padding_side": "left",
|
| 2063 |
-
"tokenizer_class": "PreTrainedTokenizerFast"
|
| 2064 |
-
}
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