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cosmosage_v2

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metadata
tags:
  - physics
  - cosmology
model-index:
  - name: cosmosage_qa
    results: []
license: mit
language:
  - en
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-v0.1

cosmosage

Cosmosage is a natural-language cosmology assistant that can answer questions about cosmology.

cosmosage_v2 first underwent continued pretraining based on thousands of papers and textbooks, and was subsequently fine-tuned on synthetically-generated question-answer pairs. It is a full chat model, though it excels in Q&A mode, where the model gives a single answer in response to a single question.

The code used to generate cosmosage_v2 is available at https://github.com/tijmen/cosmosage

Usage

After downloading cosmosage_v2, the following example code can be used to ask questions:

path_to_model = 'cosmosage_v2/'

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
device = "cuda"
model = AutoModelForCausalLM.from_pretrained(path_to_model).to(device)
tokenizer = AutoTokenizer.from_pretrained(path_to_model)
def ask_cosmosage(question):
    input_ids = torch.cat([
    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"),
    torch.tensor([[28705]]),
    tokenizer.encode("USER:", add_special_tokens=False, return_tensors="pt"),
    tokenizer.encode(question, add_special_tokens=False, return_tensors="pt"),
    torch.tensor([[28705]]),
    tokenizer.encode("ASSISTANT:", add_special_tokens=False, return_tensors="pt")
    ], dim=-1).to(device)
    generated_ids = model.generate(input_ids, max_length=input_ids.shape[1] + 1000, do_sample=True)
    return tokenizer.decode(generated_ids[0], skip_special_tokens=True)

Comparison to cosmosage_v1

cosmosage_v2 is a more knowledgeable model than cosmosage_v1 due to being pretrained on the papers and textbooks, rather than just on synthetically generated QA pairs. However, it continues to struggle with reliability. While many of its answers are factually accurate, some are not. The outputs of cosmosage (or any LLM) should not be trusted to be factual.

Built with Axolotl

See axolotl config

axolotl version: 0.4.0

base_model: /workspace/output/cosmosage_base/
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: /workspace/input/datasets/qa_tune/arxiv_metadata_qa3.jsonl
    type: sharegpt
  - path: /workspace/input/datasets/qa_tune/arxiv_refined_qa.jsonl
    type: sharegpt
  - path: /workspace/input/datasets/qa_tune/arxiv_summary3.jsonl
    type: sharegpt
  - path: /workspace/input/datasets/qa_tune/cosmology_qa.jsonl
    type: alpaca_chat.load_qa 
  - path: /workspace/input/datasets/qa_tune/openhermes2_5.jsonl
    type: sharegpt
  - path: /workspace/input/datasets/qa_tune/cosmology_textbooks_qa.jsonl
    type: alpaca_chat.load_qa 
  - path: /workspace/input/datasets/qa_tune/physics_astro_qa.jsonl
    type: alpaca_chat.load_qa 

dataset_prepared_path: /workspace/output/qa_tune_prepared
val_set_size: 0.001
output_dir: /workspace/output/cosmosage_qa

chat_template: inst

adapter: 
lora_model_dir:

sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true

lora_r:
lora_alpha:
lora_dropout:
lora_target_modules:
lora_target_linear: 
lora_fan_in_fan_out:

seed: 702

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 2.0
optimizer: adamw_torch
lr_scheduler: linear
learning_rate: 0.000002
max_grad_norm: 3.0

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false 
tf32: false

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true

warmup_steps: 100
eval_steps: 0.05
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 1
save_total_limit: 2
debug:
deepspeed: /workspace/axolotl/deepspeed_configs/zero1.json
weight_decay:
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

ddp_timeout: 7200000

workspace/output/cosmosage_qa

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5673

Model description

More information needed

Intended uses & limitations

More information needed

Training and evaluation data

More information needed

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

  • learning_rate: 2e-06
  • train_batch_size: 4
  • eval_batch_size: 4
  • seed: 702
  • distributed_type: multi-GPU
  • num_devices: 4
  • total_train_batch_size: 16
  • total_eval_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • num_epochs: 2.0

Training results

Training Loss Epoch Step Validation Loss
1.1004 0.0 1 1.1450
0.7343 0.1 909 0.7093
0.697 0.2 1818 0.6630
0.6386 0.3 2727 0.6380
0.5687 0.4 3636 0.6212
0.5857 0.5 4545 0.6083
0.6161 0.6 5454 0.5986
0.522 0.7 6363 0.5894
0.5563 0.8 7272 0.5825
0.6176 0.9 8181 0.5766
0.5948 1.0 9090 0.5719
0.4269 1.08 9999 0.5817
0.4858 1.18 10908 0.5796
0.4909 1.28 11817 0.5765
0.4325 1.38 12726 0.5746
0.4037 1.48 13635 0.5720
0.507 1.58 14544 0.5706
0.4778 1.68 15453 0.5697
0.4599 1.78 16362 0.5683
0.4515 1.88 17271 0.5673

Framework versions

  • Transformers 4.38.0.dev0
  • Pytorch 2.0.1+cu118
  • Datasets 2.17.0
  • Tokenizers 0.15.0