🔬 Einstein-v7-Qwen2-7B
This model is a full fine-tuned version of Qwen/Qwen2-7B on diverse datasets.
This model is finetuned using 8xMI300X
using axolotl.
This model has been trained using compute resources from TensorWave.
See axolotl config
axolotl version: 0.4.0
base_model: Qwen/Qwen2-7B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
chat_template: chatml
datasets:
- 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/buzz_unstacked_chosen_math_removed_filtered.json
ds_type: json
type: alpaca
conversation: chatml
- path: data/capybara_sharegpt.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/everythinglm-data-v3_sharegpt.json
ds_type: json
type: sharegpt
strict: false
conversation: chatml
- path: data/gpt4_data_lmys_1m_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/gpteacher-instruct-special-alpaca.json
ds_type: json
type: gpteacher
conversation: chatml
- path: data/merged_all.json
ds_type: json
type: alpaca
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/pippa_bagel_repo_3k_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/rpguild_quarter_alignment_lab_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
- path: data/sharegpt_gpt4_english.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/soda_diaolog_longest_tenth_buzz_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/system_conversations_dolphin_sharegpt.json
ds_type: json
type: sharegpt
conversation: chatml
dataset_prepared_path: last_run_prepared
val_set_size: 0.002
output_dir: ./Einstein-v7-Qwen2-7B-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-v7-Qwen2-7B
gradient_accumulation_steps: 4
micro_batch_size: 6
num_epochs: 2
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 0.00001 # look
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: unsloth
gradient_checkpointing_kwargs:
use_reentrant: true # look
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_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
💬 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)
📊 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 a diffrent repository:
🔄 Quantizationed versions
GGUF @bartowski
ExLlamaV2 @bartowski
🎯 Open LLM Leaderboard v2 Evaluation Results
Detailed results can be found here
Metric | Value |
---|---|
Avg. | 24.01 |
IFEval (0-Shot) | 41.00 |
BBH (3-Shot) | 32.84 |
MATH Lvl 5 (4-Shot) | 15.18 |
GPQA (0-shot) | 6.60 |
MuSR (0-shot) | 14.06 |
MMLU-PRO (5-shot) | 34.40 |
📚 Some resources, discussions and reviews aboout this model
🐦 Announcement tweet:
🔍 Reddit post in r/LocalLLaMA:
🤖 Additional information about training
This model is full fine-tuned for 2 epoch.
Total number of steps was 500.
🤝 Acknowledgments
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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Base model
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Evaluation results
- strict accuracy on IFEval (0-Shot)Open LLM Leaderboard41.000
- normalized accuracy on BBH (3-Shot)Open LLM Leaderboard32.840
- exact match on MATH Lvl 5 (4-Shot)Open LLM Leaderboard15.180
- acc_norm on GPQA (0-shot)Open LLM Leaderboard6.600
- acc_norm on MuSR (0-shot)Open LLM Leaderboard14.060
- accuracy on MMLU-PRO (5-shot)test set Open LLM Leaderboard34.400