---
license: llama2
library_name: peft
tags:
- axolotl
- generated_from_trainer
base_model: epfl-llm/meditron-7b
model-index:
- name: md7b-alpha
  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: epfl-llm/meditron-7b
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
is_llama_derived_model: true

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: Open-Orca/SlimOrca-Dedup
    type: sharegpt
  - path: axiong/pmc_llama_instructions
    type: alpaca
  - path: xzuyn/chatdoctor-200k-stripped
    type: alpaca
  - path: technoculture/riddle_sense
    type: alpaca
dataset_prepared_path:
val_set_size: 0.05
output_dir: ./qlora-out

adapter: qlora
lora_model_dir:

sequence_len: 2048
sample_packing: true
pad_to_sequence_len: true

lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_project: MD7b-alpha
wandb_entity: technoculture
wandb_watch: 
wandb_name: 
wandb_log_model: true

gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 4
optimizer: paged_adamw_32bit
lr_scheduler_type: cosine
lr_scheduler: cosine
learning_rate: 0.0003

train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: false

do_eval: true
evals_per_epoch: 2
eval_table_size:
saves_per_epoch: 1

hub_model_id: technoculture/md7b-alpha
hub_strategy: every_save
push_to_hub: true

log_level: info
logging_steps: 1
logging_strategy: steps

gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint: false
local_rank:
xformers_attention:
flash_attention: true

warmup_steps: 2000
debug:
deepspeed:
weight_decay: 0.1
fsdp:
fsdp_config:
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```

</details><br>

# md7b-alpha

This model is a fine-tuned version of [epfl-llm/meditron-7b](https://huggingface.co/epfl-llm/meditron-7b) on a set of datasets.
It achieves the following results on the evaluation set:
- Loss: 1.0238

## Evaluation

More information needed

## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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: 2000
- num_epochs: 4

### Training results

| Training Loss | Epoch | Step   | Validation Loss |
|:-------------:|:-----:|:------:|:---------------:|
| 2.1602        | 0.0   | 1      | 1.9066          |
| 1.1128        | 0.5   | 14744  | 1.1620          |
| 1.2463        | 1.0   | 29488  | 1.1288          |
| 0.8291        | 1.49  | 44232  | 1.1025          |
| 1.0524        | 1.99  | 58976  | 1.0771          |
| 1.0369        | 2.48  | 73720  | 1.0563          |
| 1.0402        | 2.98  | 88464  | 1.0299          |
| 0.943         | 3.47  | 103208 | 1.0271          |
| 1.0845        | 3.97  | 117952 | 1.0238          |


### Framework versions

- Transformers 4.37.0.dev0
- Pytorch 2.0.1+cu118
- Datasets 2.16.1
- Tokenizers 0.15.0
## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- 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: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16