---
license: apache-2.0
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mtext-150224_mistral
  results: []
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.0`
```yaml
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: true
strict: false

datasets:
  - path: ascherrer/mtext-data-150224_2
    type: completion
    field: text
dataset_prepared_path: last_run_prepared
hub_model_id: ascherrer/mtext-150224_mistral
val_set_size: 0.01
output_dir: ./out

adapter: qlora
lora_model_dir:

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

sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true


wandb_project: "machine-de-textes"
wandb_entity:
wandb_watch:
wandb_run_id:
wandb_log_model: "checkpoint"

lora_modules_to_save:
 - embed_tokens
 - lm_head
 
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 3
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002

train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
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
evals_per_epoch: 4
eval_table_size:
eval_sample_packing: False
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"
tokens: # these are delimiters
  - "<|s|>"
  - "<|e|>"
```

</details><br>

# mtext-150224_mistral

This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0248

## 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: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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: 10
- num_epochs: 3

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.2902        | 0.04  | 1    | 3.5809          |
| 2.6747        | 0.27  | 7    | 2.3159          |
| 1.8679        | 0.53  | 14   | 2.0945          |
| 1.9268        | 0.8   | 21   | 2.0629          |
| 1.6064        | 1.04  | 28   | 2.0900          |
| 1.5556        | 1.3   | 35   | 2.0501          |
| 1.5276        | 1.57  | 42   | 2.0626          |
| 1.517         | 1.84  | 49   | 2.0497          |
| 1.4512        | 2.1   | 56   | 2.0396          |
| 1.4266        | 2.36  | 63   | 2.0293          |
| 1.4217        | 2.63  | 70   | 2.0249          |
| 1.4334        | 2.9   | 77   | 2.0248          |


### Framework versions

- PEFT 0.9.1.dev0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu118
- Datasets 2.18.0
- Tokenizers 0.15.0