---
license: apache-2.0
tags:
- generated_from_trainer
base_model: NovoCode/Novocode7b-v2
model-index:
- name: out
  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: NovoCode/Novocode7b-v2
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
is_mistral_derived_model: true

load_in_8bit: false
load_in_4bit: false
strict: false

datasets:
  - path: Intel/orca_dpo_pairs
    type:
      system_prompt: ""
      field_system: system
      field_instruction: question
      field_output: chosen
      field_output: rejected
      format: "[INST] {instruction} [/INST]"
      no_input_format: "[INST] {instruction} [/INST]"
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./out

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

wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000005

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

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>"

```

</details><br>

# out

This model is a fine-tuned version of [NovoCode/Novocode7b-v2](https://huggingface.co/NovoCode/Novocode7b-v2) on the Intel/orca_dpo_pairs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6792

## 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7565        | 0.01  | 1    | 0.8244          |
| 0.4845        | 0.26  | 24   | 0.4685          |
| 0.4594        | 0.51  | 48   | 0.4435          |
| 0.4399        | 0.77  | 72   | 0.4284          |
| 0.3115        | 1.01  | 96   | 0.4221          |
| 0.2008        | 1.26  | 120  | 0.4614          |
| 0.2212        | 1.52  | 144  | 0.4552          |
| 0.2101        | 1.78  | 168  | 0.4516          |
| 0.119         | 2.02  | 192  | 0.4547          |
| 0.0925        | 2.27  | 216  | 0.5502          |
| 0.096         | 2.53  | 240  | 0.5751          |
| 0.0967        | 2.78  | 264  | 0.5774          |
| 0.0537        | 3.02  | 288  | 0.5765          |
| 0.0576        | 3.28  | 312  | 0.6687          |
| 0.0526        | 3.54  | 336  | 0.6786          |
| 0.0492        | 3.79  | 360  | 0.6792          |


### Framework versions

- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0

# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_NovoCode__Novocode7b-v3)

|             Metric              |Value|
|---------------------------------|----:|
|Avg.                             |59.94|
|AI2 Reasoning Challenge (25-Shot)|57.51|
|HellaSwag (10-Shot)              |81.17|
|MMLU (5-Shot)                    |61.91|
|TruthfulQA (0-shot)              |48.29|
|Winogrande (5-shot)              |74.51|
|GSM8k (5-shot)                   |36.24|