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README.md

@@ -1,3 +1,175 @@
----
-license: mit
----
+---
+annotations_creators:
+- machine-generated
+language:
+- en
+language_creators:
+- machine-generated
+license:
+- mit
+multilinguality:
+- monolingual
+pretty_name: Dakitari-Instruct
+size_categories:
+- 10M<n<100M
+source_datasets:
+- hwang2006/PUBMED_title_abstracts_2020_baseline
+- vaishnavm/medquad
+- pubmed_qa
+task_categories:
+- text-generation
+- question-answering
+task_ids:
+- language-modeling
+- medical-qa
+---
+
+# Dakitari-Instruct Medical Language Model
+
+A specialized language model for medical text generation and question answering, trained on PubMed abstracts and medical QA datasets.
+
+## Model Description
+
+- **Model Type:** Transformer-based Language Model
+- **Language:** English
+- **License:** MIT
+- **Training Data:** PubMed abstracts + Medical QA pairs
+
+## Usage
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("microsoft/BiomedNLP-PubMedBERT-base-uncased-abstract-fulltext")
+model = AutoModelForCausalLM.from_pretrained("path/to/dakitari-instruct")
+```
+
+## Training Data
+
+This model is trained on:
+1. PubMed abstracts (biomedical literature)
+2. Medical question-answer pairs
+
+For detailed dataset information, see [dataset_card.md](dataset_card.md).
+
+## Features
+
+- Medical text generation
+- Medical question answering
+- Research assistance
+- Built on transformer architecture
+- Trained on PubMed abstracts and medical Q&A datasets
+
+## Requirements
+
+- Python 3.8+
+- TensorFlow 2.13+
+- Transformers library
+- Other dependencies listed in `requirements.txt`
+
+## Installation
+
+1. Clone the repository:
+```bash
+git clone https://github.com/elijahnzeli1/dakitari-instruct.git
+cd dakitari-instruct
+```
+
+2. Create a virtual environment (recommended):
+```bash
+python -m venv venv
+#for windows
+.\venv\Scripts\Activate
+#for mac/linux
+source venv/bin/activate
+```
+
+3. Install dependencies:
+```bash
+pip install -r requirements.txt
+```
+
+## Training the Model
+
+1. Start training:
+```bash
+python train.py --batch_size 32 --epochs 10
+```
+
+2. Monitor training progress with TensorBoard:
+```bash
+tensorboard --logdir logs
+```
+Then open your browser and navigate to `http://localhost:6006` to view training metrics.
+
+Training logs are also saved in CSV format in the `logs` directory for backup.
+
+### Training Parameters
+
+You can customize the training by adjusting these parameters:
+
+- `--batch_size`: Batch size for training (default: 32)
+- `--epochs`: Number of training epochs (default: 10)
+- `--max_length`: Maximum sequence length (default: 512)
+- `--embed_dim`: Embedding dimension (default: 256)
+- `--num_heads`: Number of attention heads (default: 8)
+- `--ff_dim`: Feed-forward dimension (default: 512)
+- `--num_transformer_blocks`: Number of transformer blocks (default: 6)
+- `--dropout_rate`: Dropout rate (default: 0.1)
+- `--learning_rate`: Learning rate (default: 1e-4)
+- `--checkpoint_dir`: Directory to save model checkpoints (default: "checkpoints")
+- `--log_dir`: Directory to save training logs (default: "logs")
+
+## Project Structure
+
+```
+dakitari-instruct/
+├── data/
+│   └── preprocess.py      # Data preprocessing utilities
+├── model/
+│   └── transformer_model.py # Model architecture
+├── train.py              # Training script
+├── requirements.txt      # Project dependencies
+└── README.md            # This file
+```
+
+## Hardware Requirements
+
+- Minimum: 16GB RAM, NVIDIA GPU with 8GB VRAM
+- Recommended: 32GB RAM, NVIDIA GPU with 16GB+ VRAM
+
+## Dataset Sources
+
+The model is trained on:
+1. PubMed abstracts
+2. Medical questions and answers pairs
+
+## Contributing
+
+1. Fork the repository
+2. Create your feature branch
+3. Commit your changes
+4. Push to the branch
+5. Create a new Pull Request
+
+## License
+
+This project is licensed under the MIT License - see the LICENSE file for details.
+
+## Acknowledgments
+
+- Thanks to the HuggingFace team for the transformers library
+- Thanks to the TensorFlow team for the excellent framework
+- Thanks to the medical community for the valuable datasets 
+
+Now you can track your training progress locally using TensorBoard, which provides:
+- Real-time metrics visualization
+- Learning rate tracking
+- Model graph visualization
+- Histogram of weights and biases
+
+The training metrics are also saved in CSV format as a backup, which you can analyze using any spreadsheet software or data analysis tools.
+To view the training progress:
+- Start training your model
+- In a separate terminal, run [tensorboard --logdir logs]()
+- Open <http://localhost:6006> in your browser

config.json

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+{
+  "model_type": "dakitari_instruct",
+  "architectures": [
+    "DakitariInstructModel"
+  ],
+  "vocab_size": 30522,
+  "hidden_size": 256,
+  "num_attention_heads": 8,
+  "num_hidden_layers": 6,
+  "epoch": 2,
+  "global_step": 34191
+}

configuration_dakitari_instruct.py

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+from transformers.configuration_utils import PretrainedConfig
+
+class DakitariInstructConfig(PretrainedConfig):
+    model_type = "dakitari_instruct"
+    
+    def __init__(
+        self,
+        vocab_size=30522,      
+        n_positions=512,     
+        n_embd=768,         # increased embedding dimension
+        n_layer=24,         # increased number of layers
+        n_head=8,           # increased attention heads if desired
+        n_inner=3072,       # increased feed-forward dimension
+        pad_token_id=0,
+        bos_token_id=1,
+        eos_token_id=2,
+        activation_function="gelu",
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        adapter_bottleneck=128,    # optionally increase adapter capacity
+        model_name="DakitariInstruct-v1.1",
+        creator="Quantum Leap AI company",
+        country="Kenya, Africa",
+        healthcare_purpose="Assist healthcare professionals and patients with accurate medical information",
+        **kwargs
+    ):
+        self.vocab_size = vocab_size
+        self.n_positions = n_positions
+        self.n_embd = n_embd
+        self.n_layer = n_layer
+        self.n_head = n_head
+        self.n_inner = n_inner
+        self.pad_token_id = pad_token_id
+        self.bos_token_id = bos_token_id
+        self.eos_token_id = eos_token_id
+        self.activation_function = activation_function
+        self.resid_pdrop = resid_pdrop
+        self.embd_pdrop = embd_pdrop
+        self.attn_pdrop = attn_pdrop
+        self.layer_norm_epsilon = layer_norm_epsilon
+        self.initializer_range = initializer_range
+        self.adapter_bottleneck = adapter_bottleneck
+        self.model_name = model_name
+        self.creator = creator
+        self.country = country
+        self.healthcare_purpose = healthcare_purpose
+        super().__init__(**kwargs)

generation_config.json

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+{
+  "max_length": 512,
+  "num_beams": 4,
+  "do_sample": true,
+  "temperature": 0.7,
+  "top_k": 50,
+  "top_p": 0.9,
+  "repetition_penalty": 1.2,
+  "no_repeat_ngram_size": 3
+}

model.safetensors.index.json

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+{
+  "metadata": {
+    "format": "tf"
+  },
+  "weight_map": {
+    "embeddings": "model.safetensors",
+    "kernel": "model.safetensors",
+    "bias": "model.safetensors",
+    "gamma": "model.safetensors",
+    "beta": "model.safetensors"
+  }
+}

modeling_dakitari_instruct.py

@@ -0,0 +1,137 @@
+import math
+import torch
+import torch.nn as nn
+from transformers.modeling_utils import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutput
+from transformers.generation import GenerationMixin  # NEW: Import GenerationMixin
+from .configuration_dakitari_instruct import DakitariInstructConfig
+
+class SimpleAttention(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.n_embd = config.n_embd
+        self.in_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.out_proj = nn.Linear(config.n_embd, config.n_embd)
+        
+    def forward(self, x, attention_mask=None):
+        B, L, D = x.size()  # batch, length, dimension
+        q = k = v = self.in_proj(x)
+        
+        # Compute attention scores
+        scores = torch.matmul(q, k.transpose(-2, -1)) / math.sqrt(D)
+        
+        if attention_mask is not None:
+            # Expand mask to correct shape
+            attention_mask = attention_mask.view(B, 1, L)
+            scores = scores.masked_fill(~attention_mask, float('-inf'))
+        
+        attn = torch.softmax(scores, dim=-1)
+        context = torch.matmul(attn, v)
+        return self.out_proj(context)
+
+class CustomTransformerLayer(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.attention = SimpleAttention(config)
+        self.linear1 = nn.Linear(config.n_embd, config.n_embd)  # Keep dimensions consistent
+        self.linear2 = nn.Linear(config.n_embd, config.n_embd)  # Keep dimensions consistent
+        self.norm1 = nn.LayerNorm(config.n_embd)
+        self.norm2 = nn.LayerNorm(config.n_embd)
+        self.dropout = nn.Dropout(config.resid_pdrop)
+        self.activation = nn.GELU()
+        # New: Adapter layers for domain-specific finetuning
+        self.adapter_down = nn.Linear(config.n_embd, config.adapter_bottleneck)
+        self.adapter_up = nn.Linear(config.adapter_bottleneck, config.n_embd)
+        self.norm_adapter = nn.LayerNorm(config.n_embd)
+
+    def forward(self, x, attention_mask=None):
+        residual = x
+        x_norm = self.norm1(x)
+        x_attn = self.attention(x_norm, attention_mask)
+        x = residual + self.dropout(x_attn)
+        
+        residual = x
+        x_norm = self.norm2(x)
+        x_ff = self.linear2(self.dropout(self.activation(self.linear1(x_norm))))
+        x = residual + self.dropout(x_ff)
+        
+        # New: Adapter branch (only train adapter layers)
+        adapter_input = self.norm_adapter(x)
+        adapter_out = self.adapter_up(self.adapter_down(adapter_input))
+        x = x + adapter_out
+
+        return x
+
+class DakitariInstructModel(PreTrainedModel, GenerationMixin):  # Updated: Inherit from GenerationMixin
+    config_class = DakitariInstructConfig
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.config = config
+        
+        # Update embeddings with new dimensions
+        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
+        self.wpe = nn.Embedding(config.n_positions, config.n_embd)
+        self.drop = nn.Dropout(config.embd_pdrop)
+        
+        # Build transformer layers based on new n_layer value and dimensions
+        self.layers = nn.ModuleList([
+            CustomTransformerLayer(config)
+            for _ in range(config.n_layer)
+        ])
+        
+        self.ln_f = nn.LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)
+        # New: LM head for generation
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+        
+        self.apply(self._init_weights)
+        # Ensure adapter layers are not frozen (already commented out freezing)
+        for name, param in self.named_parameters():
+            if "adapter" in name:
+                param.requires_grad = True  # Explicit: make sure adapters train
+        
+    def _init_weights(self, module):
+        if isinstance(module, (nn.Linear, nn.Embedding)):
+            module.weight.data.normal_(mean=0.0, std=self.config.initializer_range)
+            if isinstance(module, nn.Linear) and module.bias is not None:
+                module.bias.data.zero_()
+        elif isinstance(module, nn.LayerNorm):
+            module.bias.data.zero_()
+            module.weight.data.fill_(1.0)
+            
+    def forward(self, input_ids=None, attention_mask=None, position_ids=None, **kwargs):
+        if input_ids is None:
+            raise ValueError("input_ids must be provided")
+            
+        # Ensure input_ids are within bounds
+        input_ids = torch.clamp(input_ids, 0, self.config.vocab_size - 1)
+        
+        input_shape = input_ids.shape
+        batch_size, seq_length = input_shape
+        
+        # Handle position IDs correctly
+        if position_ids is None:
+            position_ids = torch.arange(seq_length, dtype=torch.long, device=input_ids.device)
+            position_ids = position_ids.unsqueeze(0).expand(batch_size, seq_length)
+            
+        # Embeddings
+        inputs_embeds = self.wte(input_ids)
+        position_embeds = self.wpe(position_ids)
+        hidden_states = self.drop(inputs_embeds + position_embeds)
+        
+        # Ensure attention mask is bool tensor
+        if attention_mask is not None:
+            attention_mask = attention_mask.bool()
+        
+        # Process through transformer layers
+        for layer in self.layers:
+            hidden_states = layer(hidden_states, attention_mask)
+            
+        hidden_states = self.ln_f(hidden_states)
+        logits = self.lm_head(hidden_states)
+        # NEW: Return a CausalLMOutput with logits attribute so generate() works correctly
+        return CausalLMOutput(logits=logits, loss=logits.mean())
+    
+    # NEW: Override generation input preparation    
+    def prepare_inputs_for_generation(self, input_ids, **kwargs):
+        return {"input_ids": input_ids}

special_tokens_map.json

@@ -0,0 +1,7 @@
+{
+  "cls_token": "[CLS]",
+  "mask_token": "[MASK]",
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "unk_token": "[UNK]"
+}

tokenizer_config.json

@@ -0,0 +1,58 @@
+{
+  "added_tokens_decoder": {
+    "0": {
+      "content": "[PAD]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
+      "content": "[UNK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
+      "content": "[CLS]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "3": {
+      "content": "[SEP]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "4": {
+      "content": "[MASK]",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    }
+  },
+  "clean_up_tokenization_spaces": true,
+  "cls_token": "[CLS]",
+  "do_basic_tokenize": true,
+  "do_lower_case": true,
+  "extra_special_tokens": {},
+  "mask_token": "[MASK]",
+  "model_max_length": 1000000000000000019884624838656,
+  "never_split": null,
+  "pad_token": "[PAD]",
+  "sep_token": "[SEP]",
+  "strip_accents": null,
+  "tokenize_chinese_chars": true,
+  "tokenizer_class": "BertTokenizer",
+  "unk_token": "[UNK]"
+}

training_state.pt

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+size 1641950