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README.md ADDED
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+ ---
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+ license: other
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+ tags:
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+ - axolotl
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+ - finetune
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+ - qlora
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+ base_model: openchat/openchat-3.5-0106
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+ datasets:
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+ - hendrycks/competition_math
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+ - allenai/ai2_arc
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+ - camel-ai/physics
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+ - camel-ai/chemistry
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+ - camel-ai/biology
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+ - camel-ai/math
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+ - STEM-AI-mtl/Electrical-engineering
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+ - openbookqa
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+ - piqa
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+ - metaeval/reclor
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+ - mandyyyyii/scibench
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+ - derek-thomas/ScienceQA
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+ - sciq
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+ - TIGER-Lab/ScienceEval
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+ ---
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+ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/6468ce47e134d050a58aa89c/aimTTdmut59aZxOWQlkcC.jpeg)
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+
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+ # 🔬👩‍🔬 Newton-7B
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+
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+ This model is a fine-tuned version of [openchat/openchat-3.5-0106](https://huggingface.co/openchat/openchat-3.5-0106) on datasets related to science.
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+
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+ This model is fine-tuned using [QLoRa](https://arxiv.org/abs/2305.14314) and [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl).
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+
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+ This model's training was sponsored by [sablo.ai](https://sablo.ai).
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+
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+ <details><summary>See axolotl config</summary>
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+
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+ axolotl version: `0.3.0`
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+ ```yaml
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+ base_model: openchat/openchat-3.5-0106
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+ model_type: MistralForCausalLM
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+ tokenizer_type: LlamaTokenizer
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+ is_mistral_derived_model: true
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+
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+ load_in_8bit: false
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+ load_in_4bit: true
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+ strict: false
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+
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+
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+ datasets:
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+ - path: merged_all.json
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+ type:
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+ field_instruction: instruction
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+ field_output: output
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+
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+ format: "GPT4 Correct User: {instruction}<|end_of_turn|>GPT4 Correct Assistant:"
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+ no_input_format: "GPT4 Correct User: {instruction}<|end_of_turn|>GPT4 Correct Assistant:"
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+
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+
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+ dataset_prepared_path: last_run_prepared
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+ val_set_size: 0.01 # not sure
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+ output_dir: ./newton
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+
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+ adapter: qlora
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+ lora_model_dir:
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+
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+ sequence_len: 8192
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+ sample_packing: true
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+ pad_to_sequence_len: true
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+
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+ lora_r: 128
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+ lora_alpha: 64
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+ lora_dropout: 0.05
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+ lora_target_linear: true
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+ lora_fan_in_fan_out:
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+ lora_target_modules:
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+ - gate_proj
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+ - down_proj
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+ - up_proj
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+ - q_proj
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+ - v_proj
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+ - k_proj
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+ - o_proj
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+ lora_modules_to_save:
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+ - embed_tokens
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+ - lm_head
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+
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+ wandb_project: huggingface
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+ wandb_entity:
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+ wandb_watch:
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+ wandb_name:
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+ wandb_log_model:
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+
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+ hub_model_id: Weyaxi/newton-lora
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+ save_safetensors: true
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+
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+ # change #
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+ gradient_accumulation_steps: 12
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+ micro_batch_size: 6
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+ num_epochs: 2
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+ optimizer: adamw_bnb_8bit
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+ lr_scheduler: cosine
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+ learning_rate: 0.0002
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+ # change #
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+
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+ train_on_inputs: false
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+ group_by_length: false
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+ bf16: true
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+ fp16: false
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+ tf32: false
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+
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+ gradient_checkpointing: true
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+ early_stopping_patience:
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+ resume_from_checkpoint:
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+ local_rank:
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+ logging_steps: 1
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+ xformers_attention:
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+ flash_attention: true
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+
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+ warmup_steps: 10 # not sure
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+
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+ saves_per_epoch: 2
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+
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+ evals_per_epoch: 4
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+ eval_table_size:
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+ eval_table_max_new_tokens: 128
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+
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+ debug:
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+ deepspeed:
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+ weight_decay: 0.1 # not sure
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+ fsdp:
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+ fsdp_config:
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+ special_tokens:
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+ bos_token: "<s>"
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+ eos_token: "</s>"
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+ unk_token: "<unk>"
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+ tokens:
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+ - "<|end_of_turn|>"
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+ - "<|pad_0|>"
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+ ```
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+
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+ </details><br>
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+
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+ # 📊 Datasets
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+
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+ You can find the dataset I used and the work I am doing with this datasets here:
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+
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+ https://huggingface.co/datasets/Weyaxi/sci-datasets
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+
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+ Following datasets were used in this model:
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+
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+ - 📐 [MATH](https://huggingface.co/datasets/hendrycks/competition_math)
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+
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+ - 🧠 [ARC](https://huggingface.co/datasets/allenai/ai2_arc) (Note: Only **train** part)
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+
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+ - 🧲 [camel-ai/physics](https://huggingface.co/datasets/camel-ai/physics)
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+
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+ - ⚗️ [camel-ai/chemistry](https://huggingface.co/datasets/camel-ai/chemistry)
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+
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+ - 🦠 [camel-ai/biology](https://huggingface.co/datasets/camel-ai/biology)
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+
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+ - 📊 [camel-ai/math](https://huggingface.co/datasets/camel-ai/math)
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+
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+ - ⚡ [STEM-AI-mtl/Electrical-engineering](https://huggingface.co/datasets/STEM-AI-mtl/Electrical-engineering)
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+
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+ - 📚 [openbookqa](https://huggingface.co/datasets/openbookqa)
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+
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+ - 🧠 [piqa](https://huggingface.co/datasets/piqa)
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+
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+ - 🎨 [reclor](https://huggingface.co/datasets/metaeval/reclor)
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+
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+ - 🔬 [scibench](https://github.com/mandyyyyii/scibench)
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+
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+ - 🧪 [ScienceQA](https://huggingface.co/datasets/derek-thomas/ScienceQA)
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+
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+ - 🧬 [sciq](https://huggingface.co/datasets/sciq)
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+
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+ - 📝 [ScienceEval](https://huggingface.co/datasets/TIGER-Lab/ScienceEval)
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+
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+ ## 🛠️ Multiple Choice Question & Answer Datasets Conversion Progress
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+
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+ I used [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) to generate a reasonable and logical answer by providing it with the question and the answer key.
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+
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+ I used the [Together AI](https://www.together.ai) API for this task.
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+
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+ The following datasets are converted using this method:
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+
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+ - 🧠 [ARC](https://huggingface.co/datasets/allenai/ai2_arc) (Note: Only **train** part)
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+
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+ - 📚 [openbookqa](https://huggingface.co/datasets/openbookqa)
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+
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+ - 🎨 [reclor](https://huggingface.co/datasets/metaeval/reclor)
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+
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+ - 🧬 [sciq](https://huggingface.co/datasets/sciq)
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+
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+ # 💬 Prompt Template
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+
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+ You can use this prompt template while using the model:
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+
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+ ### GPT4 Correct [(Openchat)](https://huggingface.co/openchat/openchat-3.5-0106#conversation-templates)
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+
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+ ```
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+ GPT4 Correct User: {user}<|end_of_turn|>GPT4 Correct Assistant: {asistant}<|end_of_turn|>GPT4 Correct User: {user}<|end_of_turn|>GPT4 Correct Assistant:
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+ ```
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+
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+ You can also utilize the chat template method from the tokenizer config like here:
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+
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+ ```python
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+ messages = [
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+ {"role": "user", "content": "Hello"},
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+ {"role": "assistant", "content": "Hi"},
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+ {"role": "user", "content": "How are you today?"}
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+ ]
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+ tokens = tokenizer.apply_chat_template(messages, add_generation_prompt=True)
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+ ```
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+
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+ # 🤝 Acknowledgments
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+
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+ Thanks to [openchat](https://huggingface.co/openchat) team for fine-tuning an excellent model that I used as a base model.
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+
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+ Thanks to [@jondurbin](https://huggingface.co/jondurbin) for reformatting codes for some datasets: [bagel/data_sources](https://github.com/jondurbin/bagel/tree/main/bagel/data_sources)
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+
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+ Thanks to [Together AI](https://www.together.ai) for providing everyone with free credits, which I used to generate a dataset in multiple choice to explanations format.
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+
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+ Thanks to [Tim Dettmers](https://huggingface.co/timdettmers) for his excellent [QLoRA](https://arxiv.org/abs/2305.14314) work.
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+
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+ Thanks to all the dataset authors mentioned in the datasets section.
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+
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+ Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository I used to make this model.
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+
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+ Overall, thanks to all of the open soure AI community! 🚀
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+
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+ [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
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+
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+ If you would like to support me:
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+
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+ [☕ Buy Me a Coffee](https://www.buymeacoffee.com/weyaxi)
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+ "<|pad_0|>": 32001
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+ "eos_token_id": 2,
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+ "hidden_act": "silu",
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+ "rms_norm_eps": 1e-05,
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+ "rope_theta": 10000.0,
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+ "use_cache": false,
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+ url: https://huggingface.co/Weyaxi/Newton-7B
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+ branch: main
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+ download date: 2024-02-01 00:42:51
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