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metadata
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
license: mit
datasets:
  - keivalya/MedQuad-MedicalQnADataset
language:
  - en
metrics:
  - bertscore
tags:
  - medical

Model Card for Model ID

This is a medicine-focussed mistral fine tuned using keivalya/MedQuad-MedicalQnADataset

Model Details

Model Description

Trying to get better at medical Q & A

  • Developed by: Tonic
  • Shared by [optional]: Tonic
  • Model type: Mistral Fine-Tune
  • Language(s) (NLP): English
  • License: MIT2.0
  • Finetuned from model [optional]: mistralai/Mistral-7B-v0.1

Model Sources [optional]

Uses

This model can be used the same way you normally use mistral

Direct Use

This model can do better in medical question and answer scenarios.

Downstream Use [optional]

This model is intended to be further fine tuned.

Recommendations

  • Do Not Use As Is
  • Fine Tune This Model Further
  • For Educational Purposes Only
  • Benchmark your model usage
  • Evaluate the model before use

Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.

How to Get Started with the Model

Use the code below to get started with the model.

Tonic/MistralMED_Chat

Training Details

Training Data

MedQuad

Training Procedure

Dataset({ features: ['qtype', 'Question', 'Answer'], num_rows: 16407 })

Preprocessing [optional]

MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 4096) (layers): ModuleList( (0-31): 32 x MistralDecoderLayer( (self_attn): MistralAttention( (q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False) (k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False) (v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False) (o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False) (up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False) (down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False) (act_fn): SiLUActivation() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear(in_features=4096, out_features=32000, bias=False) )

Training Hyperparameters

  • Training regime: config = LoraConfig( r=8, lora_alpha=16, target_modules=[ "q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj", "lm_head", ], bias="none", lora_dropout=0.05, # Conventional task_type="CAUSAL_LM", )

Speeds, Sizes, Times [optional]

  • trainable params: 21260288 || all params: 3773331456 || trainable%: 0.5634354746703705
  • TrainOutput(global_step=1000, training_loss=0.47226515007019043, metrics={'train_runtime': 3143.4141, 'train_samples_per_second': 2.545, 'train_steps_per_second': 0.318, 'total_flos': 1.75274075357184e+17, 'train_loss': 0.47226515007019043, 'epoch': 0.49})

Environmental Impact

Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

  • Hardware Type: A100
  • Hours used: 1
  • Cloud Provider: Google
  • Compute Region: East1
  • Carbon Emitted: 0.09

Training Results

[1000/1000 52:20, Epoch 0/1] Step Training Loss 50 0.474200 100 0.523300 150 0.484500 200 0.482800 250 0.498800 300 0.451800 350 0.491800 400 0.488000 450 0.472800 500 0.460400 550 0.464700 600 0.484800 650 0.474600 700 0.477900 750 0.445300 800 0.431300 850 0.461500 900 0.451200 950 0.470800 1000 0.454900

Model Architecture and Objective

PeftModelForCausalLM( (base_model): LoraModel( (model): MistralForCausalLM( (model): MistralModel( (embed_tokens): Embedding(32000, 4096) (layers): ModuleList( (0-31): 32 x MistralDecoderLayer( (self_attn): MistralAttention( (q_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=4096, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False) ) (k_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=1024, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False) ) (v_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=1024, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False) ) (o_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=4096, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False) ) (rotary_emb): MistralRotaryEmbedding() ) (mlp): MistralMLP( (gate_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=14336, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False) ) (up_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=14336, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False) ) (down_proj): Linear4bit( (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=14336, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=4096, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() (base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False) ) (act_fn): SiLUActivation() ) (input_layernorm): MistralRMSNorm() (post_attention_layernorm): MistralRMSNorm() ) ) (norm): MistralRMSNorm() ) (lm_head): Linear( in_features=4096, out_features=32000, bias=False (lora_dropout): ModuleDict( (default): Dropout(p=0.05, inplace=False) ) (lora_A): ModuleDict( (default): Linear(in_features=4096, out_features=8, bias=False) ) (lora_B): ModuleDict( (default): Linear(in_features=8, out_features=32000, bias=False) ) (lora_embedding_A): ParameterDict() (lora_embedding_B): ParameterDict() ) ) ) )

Hardware

A100

Model Card Authors [optional]

Tonic

Model Card Contact

Tonic

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

Framework versions

  • PEFT 0.6.0.dev0