Upload fine-tuned pregnancy assistant model

README.md

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+---
+library_name: peft
+base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
+tags:
+- generated_from_trainer
+- pregnancy
+- healthcare
+- maternal-health
+- domain-specific
+model-index:
+- name: pregnancy-assistant-tinyllama
+  results: []
+---
+
+# Pregnancy Healthcare Assistant
+
+This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) using LoRA (Low-Rank Adaptation) for pregnancy and maternal healthcare questions.
+
+## Model Description
+
+- **Base Model:** TinyLlama-1.1B-Chat-v1.0
+- **Fine-tuning Method:** LoRA (Parameter-Efficient Fine-Tuning)
+- **Training Data:** 2,806 pregnancy and maternal health Q&A pairs
+- **Domain:** Pregnancy, maternal health, prenatal care, labor, delivery, breastfeeding, postpartum
+
+## Intended Use
+
+This model is designed to provide informational responses to pregnancy and maternal health questions. 
+**Important:** This model should NOT replace professional medical advice. Always consult qualified healthcare providers for medical concerns.
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from peft import PeftModel
+import torch
+
+# Load base model
+base_model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+tokenizer = AutoTokenizer.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
+
+# Load fine-tuned adapter
+model = PeftModel.from_pretrained(base_model, "Irutingabo/pregnancy-assistant-tinyllama")
+
+# Generate response
+prompt = "What are the signs of early labor?"
+inputs = tokenizer(prompt, return_tensors="pt")
+with torch.no_grad():
+    outputs = model.generate(**inputs, max_new_tokens=200, temperature=0.7)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+```
+
+## Limitations
+
+- Specialized for pregnancy/maternal health domain only
+- General knowledge outside this domain is limited
+- Should not be used for medical diagnosis or treatment decisions
+- Always recommend consulting healthcare professionals
+
+## Training Details
+
+- Training method: LoRA fine-tuning
+- Trainable parameters: 4.5M (0.41% of total model parameters)
+- Training dataset: Custom pregnancy Q&A dataset
+- Training framework: Transformers + PEFT

adapter_config.json

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+{
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+  "base_model_name_or_path": "TinyLlama/TinyLlama-1.1B-Chat-v1.0",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
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+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
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+  "target_modules": [
+    "q_proj",
+    "o_proj",
+    "v_proj",
+    "k_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.safetensors

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chat_template.jinja

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+{% for message in messages %}
+{% if message['role'] == 'user' %}
+{{ '<|user|>
+' + message['content'] + eos_token }}
+{% elif message['role'] == 'system' %}
+{{ '<|system|>
+' + message['content'] + eos_token }}
+{% elif message['role'] == 'assistant' %}
+{{ '<|assistant|>
+'  + message['content'] + eos_token }}
+{% endif %}
+{% if loop.last and add_generation_prompt %}
+{{ '<|assistant|>' }}
+{% endif %}
+{% endfor %}

optimizer.pt

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rng_state.pth

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scheduler.pt

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tokenizer_config.json

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+{
+  "backend": "tokenizers",
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+  "clean_up_tokenization_spaces": false,
+  "eos_token": "</s>",
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+  "sp_model_kwargs": {},
+  "tokenizer_class": "TokenizersBackend",
+  "unk_token": "<unk>",
+  "use_default_system_prompt": false
+}

trainer_state.json

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+}

training_args.bin

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