metadata
license: other
tags:
- axolotl
- generated_from_trainer
datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- wenhu/TheoremQA
- TIGER-Lab/ScienceEval
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: Einstein-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 61.6
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.35
name: normalized accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 62.87
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 42.55
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.51
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 36.01
name: accuracy
source:
url: >-
https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-7B
name: Open LLM Leaderboard
🔬 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)
💬 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:
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 60.81 |
| AI2 Reasoning Challenge (25-Shot) | 61.60 |
| HellaSwag (10-Shot) | 84.35 |
| MMLU (5-Shot) | 62.87 |
| TruthfulQA (0-shot) | 42.55 |
| Winogrande (5-shot) | 77.51 |
| GSM8k (5-shot) | 36.01 |