---
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 v0.1
![image/png](https://cdn-uploads.huggingface.co/production/uploads/60ca32d2e7bc4b029af088a0/pNId3MzUdSsI20XOM9Dsv.png)
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).
See axolotl config
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: ""
eos_token: ""
unk_token: ""
```
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.
Loss graph
![image/png](https://cdn-uploads.huggingface.co/production/uploads/60ca32d2e7bc4b029af088a0/bZdS1tIIJ4tWL_pTM4qeQ.png)
Thanks to [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) for making the repository we used to make this model.
[
](https://github.com/OpenAccess-AI-Collective/axolotl)