---
datasets:
- ehartford/dolphin
- LinhDuong/chatdoctor-200k
- sahil2801/code_instructions_120k
- c-s-ale/dolly-15k-instruction-alpaca-format
- tiiuae/falcon-refinedweb
- bigcode/starcoderdata
- togethercomputer/RedPajama-Data-1T
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- instruct
- medical
- code
---
# Model Card for Model ID

<!-- Provide a quick summary of what the model is/does. -->

This model is an instruction-tuned LLaMa model with 33B parameters, with specialities in medical QA and code instruction.

## Model Details

<!-- Provide a longer summary of what this model is. -->

- **Model type:** LlamaForCausalLM
- **Language(s) (NLP):** English
- **License:** As a Llama-derivative, this model cannot be used commercially.
- **Finetuned from model (QLoRA):** [huggyllama/llama-30b](https://huggingface.co/huggyllama/llama-30b)

## Training Details

### Training Data

Converted the following datasets to alpaca:instruction format.

1. [ehartford/dolphin](https://huggingface.co/datasets/ehartford/dolphin)
  - ORCA style dataset generously created by [Eric Hartford](https://huggingface.co/ehartford)
2. [LinhDuong/chatdoctor-200k](https://huggingface.co/datasets/LinhDuong/chatdoctor-200k)
  - Refined dataset sourced from icliniq medical QA forum
3. [sahil2801/code_instructions_120k](https://huggingface.co/datasets/sahil2801/code_instructions_120k)
  - Code instruction dataset generously created by Sahil Chaudhary from ThreeSixty AI
4. [c-s-ale/dolly-15k-instruction-alpaca-format](https://huggingface.co/datasets/c-s-ale/dolly-15k-instruction-alpaca-format)
  - Dolly 15k is a general instruction dataset generated by employees of Databricks.

### Training Procedure 

Trained using [axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) QLoRa on [RunPod](https://www.runpod.io/console/gpu-cloud) 8x A6000 on Community Cloud for 1 epochs (~23 hours - ~$110).

<details>
  <summary>axolotl training config:</summary>
  
```yaml
base_model: huggyllama/llama-30b
base_model_config: huggyllama/llama-30b
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: true
strict: false

push_dataset_to_hub:
hub_model_id:
hf_use_auth_token:

datasets:
  - path: ehartford/dolphin
    type: alpaca
    data_files:
      - flan1m-alpaca-uncensored.jsonl
      - flan5m-alpaca-uncensored.jsonl
    shards: 25
  - path: sahil2801/code_instructions_120k
    type: alpaca
  - path: LinhDuong/chatdoctor-200k
    type: alpaca
    shards: 2
  - path: c-s-ale/dolly-15k-instruction-alpaca-format
    type: alpaca

dataset_prepared_path: last_run_prepared
val_set_size: 0.01
adapter: qlora
lora_model_dir:
sequence_len: 2048
max_packed_sequence_len: 2048
lora_r: 8
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:

wandb_mode: true
wandb_project: med-orca-instruct-33b
wandb_watch:
wandb_run_id:
wandb_log_model: 'openllama_checkpoint'
output_dir: /disk/med-instruct-33b
gradient_accumulation_steps: 1
micro_batch_size: 4
num_epochs: 1
optimizer: paged_adamw_32bit
torchdistx_path:
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 2
xformers_attention: true
flash_attention:
gptq_groupsize:
gptq_model_v1:
warmup_steps: 100
eval_steps: 20
save_steps:
debug:
deepspeed: true
weight_decay: 0.00001
special_tokens:
  bos_token: "<s>"
  eos_token: "</s>"
  unk_token: "<unk>"

```
</details>