---
library_name: peft
base_model: jhflow/mistral7b-lora-multi-turn-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8621c6f3-fff3-4f7c-95a1-d9d5230042d5
  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/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: jhflow/mistral7b-lora-multi-turn-v2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
  - 66b824a5dd6edd7c_train_data.json
  ds_type: json
  format: custom
  path: /workspace/input_data/66b824a5dd6edd7c_train_data.json
  type:
    field_instruction: filled_template
    field_output: race
    format: '{instruction}'
    no_input_format: '{instruction}'
    system_format: '{system}'
    system_prompt: ''
ddp_find_unused_parameters: false
distributed_type: ddp
early_stopping_patience: null
env:
  CUDA_VISIBLE_DEVICES: 0,1
  MASTER_ADDR: localhost
  MASTER_PORT: '29500'
  NCCL_DEBUG: INFO
  NCCL_IB_DISABLE: '0'
  NCCL_P2P_DISABLE: '0'
  NCCL_P2P_LEVEL: NVL
  PYTORCH_CUDA_ALLOC_CONF: max_split_size_mb:512, garbage_collection_threshold:0.8
  WORLD_SIZE: '2'
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: false
gradient_accumulation_steps: 8
gradient_checkpointing: true
group_by_length: true
hub_model_id: fats-fme/8621c6f3-fff3-4f7c-95a1-d9d5230042d5
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: true
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_memory_MB: 60000
max_steps: -1
micro_batch_size: 2
mlflow_experiment_name: /tmp/66b824a5dd6edd7c_train_data.json
model_type: AutoModelForCausalLM
num_devices: 2
num_epochs: 1
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 8621c6f3-fff3-4f7c-95a1-d9d5230042d5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8621c6f3-fff3-4f7c-95a1-d9d5230042d5
warmup_steps: 50
world_size: 2
xformers_attention: true

```

</details><br>

# 8621c6f3-fff3-4f7c-95a1-d9d5230042d5

This model is a fine-tuned version of [jhflow/mistral7b-lora-multi-turn-v2](https://huggingface.co/jhflow/mistral7b-lora-multi-turn-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan

## 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: 8
- total_train_batch_size: 32
- total_eval_batch_size: 4
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 1

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0           | 0.0036 | 1    | nan             |
| 0.0           | 0.2513 | 69   | nan             |
| 0.0           | 0.5025 | 138  | nan             |
| 0.0           | 0.7538 | 207  | nan             |


### Framework versions

- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1