---
base_model: mistralai/Mistral-7B-v0.1
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
model-index:
- name: Thestral-0.1
  results: []
license: apache-2.0
language:
- en
---

# Thestral 0.1

Thestral is Mistral Fine-tune. The model is a QLoRA version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the [Open-Orca/SlimOrca](https://huggingface.co/datasets/Open-Orca/SlimOrca).

This model is finetuned using `1xH100` using [axolotl](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

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: Open-Orca/SlimOrca
    type: sharegpt
dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./qlora-out_2

adapter: qlora
lora_model_dir:

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 128
lora_alpha: 32
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: slim_orca
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
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_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>"

```
</details><br>


GPT-4All Benchmark Set

|    Tasks    |Version|Filter|n-shot| Metric |Value |   |Stderr|
|-------------|------:|------|------|--------|-----:|---|-----:|
|winogrande   |      1|none  |None  |acc     |0.7498|±  |0.0122|
|piqa         |      1|none  |None  |acc     |0.8172|±  |0.0090|
|             |       |none  |None  |acc_norm|0.8286|±  |0.0088|
|openbookqa   |      1|none  |None  |acc     |0.3380|±  |0.0212|
|             |       |none  |None  |acc_norm|0.4420|±  |0.0222|
|hellaswag    |      1|none  |None  |acc     |0.6254|±  |0.0048|
|             |       |none  |None  |acc_norm|0.8061|±  |0.0039|
|boolq        |      2|none  |None  |acc     |0.8740|±  |0.0058|
|arc_easy     |      1|none  |None  |acc     |0.8199|±  |0.0079|
|             |       |none  |None  |acc_norm|0.7891|±  |0.0084|
|arc_challenge|      1|none  |None  |acc     |0.5145|±  |0.0146|
|             |       |none  |None  |acc_norm|0.5461|±  |0.0145|
Average: 71.93


# 🤖 Additional information about training

This model is fine-tuned for 1.0 epoch. 


<details><summary>Loss graph</summary>

![image/png](https://cdn-uploads.huggingface.co/production/uploads/60ca32d2e7bc4b029af088a0/bZdS1tIIJ4tWL_pTM4qeQ.png)
</details><br>

Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository we used to make this model.


[<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)