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EN
Einstein V6.1 Llama3 8B
A large language model fine-tuned on diverse scientific datasets based on Meta-Llama-3-8B, specializing in STEM domain tasks
Downloads 70
Release Time : 4/19/2024
Model Overview
This model is a science-specialized model optimized through multi-stage fine-tuning on the Llama-3-8B foundation, excelling in handling problems in STEM disciplines such as physics, chemistry, biology, and mathematics
Model Features
STEM domain optimization
Specially fine-tuned on scientific datasets including physics, chemistry, biology, and mathematics, significantly improving performance on STEM tasks
Multi-stage instruction fine-tuning
Trained with diverse instruction datasets in ChatML format to enhance dialogue and instruction-following capabilities
High-performance reasoning
Excellent performance on multiple scientific benchmarks, such as AI2 ARC (62.46%), MMLU (66.19%), etc.
Model Capabilities
Scientific Q&A
Mathematical problem solving
Physics concept explanation
Chemical equation generation
Biological terminology explanation
Multi-step reasoning
Technical document generation
Use Cases
Education
Science homework assistance
Helping students solve homework problems in subjects like physics and chemistry
Performs well on test sets like ScienceQA
STEM concept explanation
Explaining complex scientific concepts in plain language
Research
Literature comprehension aid
Assisting researchers in quickly understanding key concepts in scientific literature
Experimental design suggestions
Providing experimental design ideas based on existing research
language:
- en license: other tags:
- axolotl
- generated_from_trainer
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- science
- physics
- chemistry
- biology
- math
- llama
- llama3 base_model: meta-llama/Meta-Llama-3-8B datasets:
- allenai/ai2_arc
- camel-ai/physics
- camel-ai/chemistry
- camel-ai/biology
- camel-ai/math
- metaeval/reclor
- openbookqa
- mandyyyyii/scibench
- derek-thomas/ScienceQA
- TIGER-Lab/ScienceEval
- jondurbin/airoboros-3.2
- LDJnr/Capybara
- Cot-Alpaca-GPT4-From-OpenHermes-2.5
- STEM-AI-mtl/Electrical-engineering
- knowrohit07/saraswati-stem
- sablo/oasst2_curated
- lmsys/lmsys-chat-1m
- TIGER-Lab/MathInstruct
- bigbio/med_qa
- meta-math/MetaMathQA-40K
- openbookqa
- piqa
- metaeval/reclor
- derek-thomas/ScienceQA
- scibench
- sciq
- Open-Orca/SlimOrca
- migtissera/Synthia-v1.3
- TIGER-Lab/ScienceEval
- allenai/WildChat
- microsoft/orca-math-word-problems-200k
- openchat/openchat_sharegpt4_dataset
- teknium/GPTeacher-General-Instruct
- m-a-p/CodeFeedback-Filtered-Instruction
- totally-not-an-llm/EverythingLM-data-V3
- HuggingFaceH4/no_robots
- OpenAssistant/oasst_top1_2023-08-25
- WizardLM/WizardLM_evol_instruct_70k model-index:
- name: Einstein-v6.1-Llama3-8B
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: 62.46 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B 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: 82.41 name: normalized accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B 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: 66.19 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B 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: 55.1 source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B 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: 79.32 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B 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: 66.11 name: accuracy source: url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc value: 45.68 name: strict accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm value: 29.38 name: normalized accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match value: 5.74 name: exact match source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm value: 4.25 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm value: 11.23 name: acc_norm source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc value: 23.68 name: accuracy source: url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=Weyaxi/Einstein-v6.1-Llama3-8B name: Open LLM Leaderboard
- 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:
đŦ Einstein-v6.1-Llama3-8B
This model is a full fine-tuned version of meta-llama/Meta-Llama-3-8B on diverse datasets.
This model is finetuned using 8xRTX3090 + 1xRTXA6000 using axolotl.
This model's training was sponsored by sablo.ai.
See axolotl config
axolotl version: 0.4.0
base_model: meta-llama/Meta-Llama-3-8B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- path: data/merged_all.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/gpteacher-instruct-special-alpaca.json
ds_type: json
type: gpteacher
conversation: chatml
- path: data/wizardlm_evol_instruct_70k_random_half.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/capybara_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/synthia-v1.3_sharegpt_12500.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/cot_alpaca_gpt4_extracted_openhermes_2.5_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/slimorca_dedup_filtered_95k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/airoboros_3.2_without_contextual_slimorca_orca_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/allenai_wild_chat_gpt4_english_toxic_random_half_4k_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/pippa_bagel_repo_3k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/gpt4_data_lmys_1m_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/sharegpt_gpt4_english.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/no_robots_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/oasst_top1_from_fusechatmixture_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/everythinglm-data-v3_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.002
output_dir: ./Einstein-v6.1-Llama3-8B-model
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
eval_sample_packing: false
wandb_project: Einstein
wandb_entity:
wandb_watch:
wandb_name: Einstein-v6.1-Llama3-2-epoch
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v6.1-Llama3-8B
save_safetensors: true
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit # look
lr_scheduler: cosine
learning_rate: 0.000005 # look
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
evals_per_epoch: 2
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: zero3_bf16_cpuoffload_params.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
pad_token: <|end_of_text|> # changed
tokens:
- "<|im_start|>"
đŦ Prompt Template
You can use ChatML prompt template while using the model:
ChatML
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{user}<|im_end|>
<|im_start|>assistant
{asistant}<|im_end|>
This prompt template is available as a chat template, which means you can format messages using the
tokenizer.apply_chat_template() method:
messages = [
{"role": "system", "content": "You are helpful AI asistant."},
{"role": "user", "content": "Hello!"}
]
gen_input = tokenizer.apply_chat_template(message, return_tensors="pt")
model.generate(**gen_input)
đ Datasets used in this model
The datasets used to train this model are listed in the metadata section of the model card.
Please note that certain datasets mentioned in the metadata may have undergone filtering based on various criteria.
The results of this filtering process and its outcomes are in the data folder of this repository:
Weyaxi/Einstein-v6.1-Llama3-8B/data
đ Quantizationed versions
GGUF @bartowski
- https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-GGUF
ExLlamaV2 @bartowski
- https://huggingface.co/bartowski/Einstein-v6.1-Llama3-8B-exl2
AWQ @solidrust
- https://huggingface.co/solidrust/Einstein-v6.1-Llama3-8B-AWQ
đ¯ Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 68.60 |
| AI2 Reasoning Challenge (25-Shot) | 62.46 |
| HellaSwag (10-Shot) | 82.41 |
| MMLU (5-Shot) | 66.19 |
| TruthfulQA (0-shot) | 55.10 |
| Winogrande (5-shot) | 79.32 |
| GSM8k (5-shot) | 66.11 |
đ¯ Open LLM Leaderboard v2 Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 19.99 |
| IFEval (0-Shot) | 45.68 |
| BBH (3-Shot) | 29.38 |
| MATH Lvl 5 (4-Shot) | 5.74 |
| GPQA (0-shot) | 4.25 |
| MuSR (0-shot) | 11.23 |
| MMLU-PRO (5-shot) | 23.68 |
đ Some resources, discussions and reviews aboout this model
đĻ Announcement tweet:
- https://twitter.com/Weyaxi/status/1783050724659675627
đ Reddit post in r/LocalLLaMA:
- https://www.reddit.com/r/LocalLLaMA/comments/1cdlym1/introducing_einstein_v61_based_on_the_new_llama3/
âļī¸ Youtube Video(s)
đą Octopus-V4-3B
- Octopus-V4-3B leverages the incredible physics capabilities of Einstein-v6.1-Llama3-8B in their model.
đ¤ Additional information about training
This model is full fine-tuned for 2 epoch.
Total number of steps was 2026.
Loss graph
đ¤ Acknowledgments
Thanks to sablo.ai for sponsoring this model.
Thanks to all the dataset authors mentioned in the datasets section.
Thanks to axolotl for making the repository I used to make this model.
Thanks to all open source AI community.
If you would like to support me:
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