---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: Mistral-7B-magpie-v1.0
results: []
---
[](https://github.com/axolotl-ai-cloud/axolotl)
See axolotl config
axolotl version: `0.4.1`
```yaml
base_model: mistralai/Mistral-7B-v0.3
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: Magpie-Align/Magpie-Pro-MT-300K-v0.1
type: sharegpt
conversation: llama3
- path: Magpie-Align/Magpie-Reasoning-150K
type: sharegpt
conversation: llama3
chat_template: llama3
dataset_prepared_path: ./datasets/m7b-magpie
output_dir: ./outputs/m7b-magpie-v1.0
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
wandb_project: lm-evals
wandb_entity:
wandb_watch:
wandb_name: Mistral-7B-magpie-v1.0
wandb_log_model:
hub_model_id: penfever/Mistral-7B-magpie-v1.0
gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 3
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 5e-6
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_steps: 100
eval_table_size:
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: <|begin_of_text|>
eos_token: <|end_of_text|>
pad_token: <|end_of_text|>
tokens:
- "<|start_header_id|>"
- "<|end_header_id|>"
- "<|eot_id|>"
```
[](https://wandb.ai/nyu-dice-lab/lm-evals/runs/6s14gjd3)
# Mistral-7B-magpie-v1.0
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.3](https://huggingface.co/mistralai/Mistral-7B-v0.3) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.43.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1