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--- |
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tags: |
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- physics |
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- cosmology |
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model-index: |
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- name: cosmosage_qa |
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results: [] |
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license: mit |
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language: |
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- en |
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pipeline_tag: text-generation |
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base_model: mistralai/Mistral-7B-v0.1 |
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--- |
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# cosmosage |
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Cosmosage is a natural-language cosmology assistant that can answer questions about cosmology. |
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cosmosage_v2 first underwent continued pretraining based on thousands of papers and textbooks, |
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and was subsequently fine-tuned on synthetically-generated question-answer pairs. It is a full |
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chat model, though it excels in Q&A mode, where the model gives a single answer in response to |
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a single question. |
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The code used to generate cosmosage_v2 is available at https://github.com/tijmen/cosmosage |
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## Usage |
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After downloading cosmosage_v2, the following example code can be used to ask questions: |
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```python |
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path_to_model = 'cosmosage_v2/' |
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from transformers import AutoModelForCausalLM, AutoTokenizer |
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import torch |
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device = "cuda" |
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model = AutoModelForCausalLM.from_pretrained(path_to_model).to(device) |
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tokenizer = AutoTokenizer.from_pretrained(path_to_model) |
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def ask_cosmosage(question): |
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input_ids = torch.cat([ |
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tokenizer.encode("You are cosmosage, an AI programmed to be a cosmology expert. You answer the USER's question clearly in long form, always providing context. When appropriate, provide a reference.", return_tensors="pt"), |
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torch.tensor([[28705]]), |
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tokenizer.encode("USER:", add_special_tokens=False, return_tensors="pt"), |
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tokenizer.encode(question, add_special_tokens=False, return_tensors="pt"), |
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torch.tensor([[28705]]), |
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tokenizer.encode("ASSISTANT:", add_special_tokens=False, return_tensors="pt") |
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], dim=-1).to(device) |
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generated_ids = model.generate(input_ids, max_length=input_ids.shape[1] + 1000, do_sample=True) |
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return tokenizer.decode(generated_ids[0], skip_special_tokens=True)``` |
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## Comparison to cosmosage_v1 |
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cosmosage_v2 is a more knowledgeable model than cosmosage_v1 due to being pretrained on the papers and |
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textbooks, rather than just on synthetically generated QA pairs. However, it continues to struggle with |
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_reliability_. While many of its answers are factually accurate, some are not. The outputs of cosmosage |
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(or any LLM) should not be trusted to be factual. |
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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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<details><summary>See axolotl config</summary> |
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axolotl version: `0.4.0` |
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```yaml |
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base_model: /workspace/output/cosmosage_base/ |
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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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load_in_8bit: false |
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load_in_4bit: false |
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strict: false |
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datasets: |
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- path: /workspace/input/datasets/qa_tune/arxiv_metadata_qa3.jsonl |
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type: sharegpt |
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- path: /workspace/input/datasets/qa_tune/arxiv_refined_qa.jsonl |
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type: sharegpt |
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- path: /workspace/input/datasets/qa_tune/arxiv_summary3.jsonl |
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type: sharegpt |
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- path: /workspace/input/datasets/qa_tune/cosmology_qa.jsonl |
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type: alpaca_chat.load_qa |
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- path: /workspace/input/datasets/qa_tune/openhermes2_5.jsonl |
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type: sharegpt |
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- path: /workspace/input/datasets/qa_tune/cosmology_textbooks_qa.jsonl |
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type: alpaca_chat.load_qa |
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- path: /workspace/input/datasets/qa_tune/physics_astro_qa.jsonl |
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type: alpaca_chat.load_qa |
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dataset_prepared_path: /workspace/output/qa_tune_prepared |
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val_set_size: 0.001 |
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output_dir: /workspace/output/cosmosage_qa |
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chat_template: inst |
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adapter: |
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lora_model_dir: |
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sequence_len: 4096 |
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sample_packing: true |
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pad_to_sequence_len: true |
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lora_r: |
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lora_alpha: |
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lora_dropout: |
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lora_target_modules: |
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lora_target_linear: |
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lora_fan_in_fan_out: |
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seed: 702 |
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wandb_project: |
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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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gradient_accumulation_steps: 1 |
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micro_batch_size: 4 |
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num_epochs: 2.0 |
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optimizer: adamw_torch |
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lr_scheduler: linear |
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learning_rate: 0.000002 |
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max_grad_norm: 3.0 |
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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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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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warmup_steps: 100 |
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eval_steps: 0.05 |
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eval_table_size: |
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eval_table_max_new_tokens: 128 |
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saves_per_epoch: 1 |
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save_total_limit: 2 |
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debug: |
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deepspeed: /workspace/axolotl/deepspeed_configs/zero1.json |
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weight_decay: |
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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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ddp_timeout: 7200000 |
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``` |
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</details><br> |
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# workspace/output/cosmosage_qa |
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This model was trained from scratch on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5673 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 2e-06 |
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- train_batch_size: 4 |
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- eval_batch_size: 4 |
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- seed: 702 |
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- distributed_type: multi-GPU |
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- num_devices: 4 |
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- total_train_batch_size: 16 |
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- total_eval_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 100 |
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- num_epochs: 2.0 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:-----:|:---------------:| |
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| 1.1004 | 0.0 | 1 | 1.1450 | |
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| 0.7343 | 0.1 | 909 | 0.7093 | |
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| 0.697 | 0.2 | 1818 | 0.6630 | |
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| 0.6386 | 0.3 | 2727 | 0.6380 | |
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| 0.5687 | 0.4 | 3636 | 0.6212 | |
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| 0.5857 | 0.5 | 4545 | 0.6083 | |
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| 0.6161 | 0.6 | 5454 | 0.5986 | |
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| 0.522 | 0.7 | 6363 | 0.5894 | |
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| 0.5563 | 0.8 | 7272 | 0.5825 | |
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| 0.6176 | 0.9 | 8181 | 0.5766 | |
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| 0.5948 | 1.0 | 9090 | 0.5719 | |
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| 0.4269 | 1.08 | 9999 | 0.5817 | |
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| 0.4858 | 1.18 | 10908 | 0.5796 | |
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| 0.4909 | 1.28 | 11817 | 0.5765 | |
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| 0.4325 | 1.38 | 12726 | 0.5746 | |
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| 0.4037 | 1.48 | 13635 | 0.5720 | |
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| 0.507 | 1.58 | 14544 | 0.5706 | |
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| 0.4778 | 1.68 | 15453 | 0.5697 | |
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| 0.4599 | 1.78 | 16362 | 0.5683 | |
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| 0.4515 | 1.88 | 17271 | 0.5673 | |
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### Framework versions |
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- Transformers 4.38.0.dev0 |
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- Pytorch 2.0.1+cu118 |
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- Datasets 2.17.0 |
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- Tokenizers 0.15.0 |
