🔬 Einstein-7B

This model is a fine-tuned version of mistralai/Mistral-7B-v0.1 on datasets related to science.

This model is fine-tuned using QLoRa and axolotl.

This model's training was sponsored by sablo.ai.

See axolotl config

axolotl version: 0.3.0

base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: sci-datasets/arc_challange_train_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/camelai_biology_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/camelai_chemistry_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/camelai_physics_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/openbookqa_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/reclor_science_alpaca.json
    ds_type: json
    type: alpaca
    
  - path: sci-datasets/scibench_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/scienceqa_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/theoremqa_alpaca.json
    ds_type: json
    type: alpaca

  - path: sci-datasets/tiger_scienceeval_alpaca.json
    ds_type: json
    type: alpaca

dataset_prepared_path: last_run_prepared
val_set_size: 0
output_dir: ./science-mistral

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 128
lora_alpha: 64
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
  - gate_proj
  - down_proj
  - up_proj
  - q_proj
  - v_proj
  - k_proj
  - o_proj

wandb_project: huggingface
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
hub_model_id: Weyaxi/science-mistral

# change #
gradient_accumulation_steps: 12
micro_batch_size: 6
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
# change #

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: 10


saves_per_epoch: 3
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

📊 Datasets

Following datasets were used in this model:

• ARC (Note: Only train part)

• camel-ai/physics

• camel-ai/chemistry

• camel-ai/biology

• openbookqa

• reclor

• scibench

• ScienceQA

• TheoremQA

• ScienceEval

💬 Prompt Template

You can use this prompt template while using the model:

Alpaca

Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.

### Instruction:
{instruction}

### Input:
{input}

### Response:

🤝 Acknowledgments

Thanks to Platypus for providing scripts to convert some of the datasets to Alpaca format: Platypus/data_pipeline

Thanks to all the dataset authors mentioned in the datasets section.

Thanks to axolotl for making the repository I used to make this model.

If you would like to support me:

☕ Buy Me a Coffee