
# Math-OpenHermes-2.5-Mistral-7B

I used [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl/tree/main) to train this model.

I removed entries with a cosine similarity higher than 0.7 to one of the entries from GSM8K from [here](https://huggingface.co/datasets/meta-math/MetaMathQA-40K).
The dataset can be found [here](https://huggingface.co/datasets/simonveitner/MetaMathQA-40K-Filtered)

# Yaml Config

```yaml
base_model: teknium/OpenHermes-2.5-Mistral-7B
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: simonveitner/MetaMathQA-40K-Filtered
    type: alpaca_chat.load_qa
dataset_prepared_path: last_run_prepared
val_set_size: 0.05
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
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: hermes_math
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 2
micro_batch_size: 4
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

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

loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3

warmup_steps: 10
eval_steps: 0.05
eval_table_size:
eval_table_max_new_tokens: 128
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
```
{"license": "apache-2.0"}
transformers safetensors mistral text-generation conversational license:apache-2.0 autotrain_compatible endpoints_compatible text-generation-inference region:us