🔬 Einstein-v4-7B
This model is a full fine-tuned version of mistralai/Mistral-7B-v0.1 on diverse datasets.
This model is finetuned using 7xRTX3090 + 1xRTXA6000 using axolotl.
This model's training was sponsored by sablo.ai.
axolotl version: 0.4.0
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: false
strict: false
chat_template: chatml
datasets:
- path: data/merged_all.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
dataset_prepared_path: last_run_prepared
val_set_size: 0.005
output_dir: ./Einstein-v4-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:
wandb_log_model:
hub_model_id: Weyaxi/Einstein-v4-7B
save_safetensors: true
gradient_accumulation_steps: 4
micro_batch_size: 1
num_epochs: 1.5
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005
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 # changed
eval_table_size:
eval_table_max_new_tokens: 128
saves_per_epoch: 4
debug:
deepspeed: zero3_bf16.json
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "<|im_end|>"
unk_token: "<unk>"
tokens:
- "<|im_start|>"
resume_from_checkpoint: Einstein-v4-model/checkpoint-521
💬 Prompt Template
You can use this 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)
🔄 Quantizationed versions
Quantizationed versions of this model is available.
GGUF @LoneStriker
AWQ @solidrust
Exl2 @bartowski:
🎯 Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 66.62 |
| AI2 Reasoning Challenge (25-Shot) | 64.68 |
| HellaSwag (10-Shot) | 83.75 |
| MMLU (5-Shot) | 62.31 |
| TruthfulQA (0-shot) | 55.15 |
| Winogrande (5-shot) | 76.24 |
| GSM8k (5-shot) | 57.62 |
🎯 Open LLM Leaderboard v2 Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 16.73 |
| IFEval (0-Shot) | 47.08 |
| BBH (3-Shot) | 14.30 |
| MATH Lvl 5 (4-Shot) | 1.74 |
| GPQA (0-shot) | 4.25 |
| MuSR (0-shot) | 19.02 |
| MMLU-PRO (5-shot) | 13.99 |
📚 Some resources, discussions and reviews aboout this model
🐦 Announcement tweet:
https://twitter.com/Weyaxi/status/1765851433448944125
🔍 Reddit post in r/LocalLLaMA:
- https://www.reddit.com/r/LocalLLaMA/comments/1b9gmvl/meet_einsteinv47b_mistralbased_sft_model_using/
▶️ Youtube Videos
🤖 Additional information about training
This model is full fine-tuned for 1.5 epoch.
Total number of steps was 1562.
🤝 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: