peft safetensors mistral generated_from_trainer base_model:mistralai/Mistral-7B-v0.1 license:apache-2.0 8-bit bitsandbytes region:us
---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: llmTechChat-lora
  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: `0.3.0`
```yaml
base_model: mistralai/Mistral-7B-v0.1
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: true
load_in_4bit: false
strict: false

datasets:
  - path: datasets/norobots_150/norobots_150
    type: completion
  - path: datasets/separated/bloke-separate
    type: completion
  - path: datasets/separated/kcpp-separate
    type: completion
  - path: datasets/separated/kcpp-support-separate
    type: completion
  - path: datasets/separated/st-chat-separate
    type: completion
  - path: datasets/separated/exllama2_readme.txt
    type: completion
  - path: datasets/separated/koboldcpp_readme.txt
    type: completion
  - path: datasets/separated/llama_readme.txt
    type: completion
  - path: datasets/separated/ooba_readme.txt
    type: completion
  - path: datasets/separated/sillytavern_readme.txt
    type: completion
  - path: datasets/separated/sillytavern_simple_setup_guide.txt
    type: completion
  - path: datasets/transformer_article.txt
    type: completion
  - path: datasets/lmg_thread.txt
    type: completion

dataset_prepared_path: last_run_prepared
val_set_size: 0.1
output_dir: ./llmTechChat-lora

adapter: lora
lora_model_dir:

chat_template: chatml

sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true

lora_r: 128
lora_alpha: 64
lora_dropout: 0.20
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: llmTechChat

gradient_accumulation_steps: 8
micro_batch_size: 1
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0003

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
evals_per_epoch: 4
eval_table_size:
eval_table_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>"

save_safetensors: true

```

</details><br>

# llmTechChat-lora

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: 1.9365

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 4.3577        | 0.01  | 1    | 4.3261          |
| 2.0615        | 0.25  | 40   | 2.0476          |
| 1.9905        | 0.5   | 80   | 1.9691          |
| 1.8699        | 0.75  | 120  | 1.9344          |
| 1.9604        | 1.0   | 160  | 1.9111          |
| 1.7684        | 1.23  | 200  | 1.8978          |
| 1.7673        | 1.48  | 240  | 1.8809          |
| 1.7296        | 1.73  | 280  | 1.8630          |
| 1.7737        | 1.98  | 320  | 1.8479          |
| 1.5871        | 2.22  | 360  | 1.8883          |
| 1.5339        | 2.47  | 400  | 1.8761          |
| 1.5589        | 2.72  | 440  | 1.8657          |
| 1.5651        | 2.96  | 480  | 1.8590          |
| 1.3134        | 3.2   | 520  | 1.9497          |
| 1.3423        | 3.45  | 560  | 1.9406          |
| 1.3635        | 3.7   | 600  | 1.9362          |
| 1.3235        | 3.95  | 640  | 1.9365          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0