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README.md

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 ---
+base_model: unsloth/gemma-3-270m-it
 tags:
-- text-to-image
-- lora
-- diffusers
-- template:diffusion-lora
-widget:
-- output:
-    url: images/Screenshot 2025-08-14 230148.png
-  text: '-'
-base_model: unsloth/gemma-3-270m-it-GGUF
-instance_prompt: null
-license: mit
+- text-generation-inference
+- transformers
+- unsloth
+- gemma3_text
+license: apache-2.0
+language:
+- en
 ---
-# MedGemma-270M
 
-<Gallery />
+### PharmaQA‑270M
 
-## Model description 
+<img src="banner.png" width="800"/>
 
-MedGemma-270M  is a 270M-parameter Gemma 3 model fine-tuned with LoRA on the MIRIAD-4.4M (https:&#x2F;&#x2F;huggingface.co&#x2F;datasets&#x2F;miriad&#x2F;miriad-4.4M) medical Q&amp;A dataset.  
-This model is designed for fast, domain-specialized inference on small GPUs and CPUs.
+**PharmaQA‑270M** is a compact, instruction-tuned language model for pharmacology and pharmacy domains. Based on **Gemma3 (270M parameters)**, it was fine-tuned using LoRA and merged into a single model checkpoint for easy deployment. This model is optimized for **educational and research use cases**, especially where compute constraints are present.
 
+---
+
+### Model Details
+
+| Property           | Value                                                                                    |
+| ------------------ | ---------------------------------------------------------------------------------------- |
+| Base Model         | Google Gemma3 (270M parameters)                                                          |
+| Fine-tuning Method | LoRA using [Unsloth](https://github.com/unslothai/unsloth)                               |
+| Dataset Used       | 25,000 Q\&A pairs from [MIRIAD-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M) |
+| Epochs             | 3                                                                                        |
+| Final Format       | Merged (base + LoRA weights)                                                             |
+| Model Size         | 270M                                                                                     |
+| License            | ODC-BY v1.0 dataset license (non-commercial)                                             |
+| Author             | [Mohamed Yasser](https://huggingface.co/yasserrmd)                                       |
+
+---
+
+### ⚠️ Caution & Intended Use
+
+* **Do not use this model for real-world medical diagnosis, treatment, or care decisions.**
+* The model was trained on MIRIAD Q\&A pairs generated via LLMs from biomedical literature.
+* MIRIAD and this model must be used for **educational, research, and academic exploration only**.
+* This model inherits all **OpenAI and ODC-BY v1.0** usage limitations associated with the dataset.
+
+---
+
+### Performance Summary
+
+From evaluation on 50 unseen pharma questions:
+
+| Metric                   | Value                                               |
+| ------------------------ | --------------------------------------------------- |
+| Average Answer Length    | 40.3 words                                          |
+| Longest Answer           | 95 words                                            |
+| Shortest Answer          | 12 words                                            |
+| Empty / Short Responses  | 0                                                   |
+| Clinical Accuracy        | ✅ Consistent terminology                            |
+| Depth in Short Responses | ⚠️ Limited                                          |
+| Best Use Case            | Lightweight educational deployment (MCQs, tutoring) |
+
+---
+
+### Sample Inference Code
+
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+model_name = "yasserrmd/PharmaQA-270M"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
+model.eval()
+
+question = "What is the mechanism of action of metformin?"
+messages = [{"role": "user", "content": f"Q: {question} A:"}]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    add_generation_prompt=True,
+    return_tensors="pt",
+    tokenize=True,
+    return_dict=True
+).to(model.device)
+
+if "token_type_ids" in inputs:
+    del inputs["token_type_ids"]
+
+with torch.no_grad():
+    outputs = model.generate(
+        **inputs,
+        max_new_tokens=128,
+        temperature=0.7,
+        top_p=0.95,
+        repetition_penalty=1.05
+    )
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True).strip()
+answer = response.split("A:")[-1].strip()
+
+print("💊 Question:", question)
+print("🧠 Answer:", answer)
+```
+
+---
+
+### License
+
+* **Model**: Open for academic and non-commercial use
+* **Dataset**: [MIRIAD-4.4M](https://huggingface.co/datasets/miriad/miriad-4.4M) under ODC-BY v1.0
+
+---
+
+### Acknowledgements
+
+* MIRIAD creators for making the dataset openly accessible.
+* [Unsloth](https://github.com/unslothai/unsloth) team for enabling fast LoRA tuning on small GPUs.
+* Hugging Face and Google for Gemma3 base model.
+
+---
 
-## Download model
 
 
-[Download](/OmerShah/MedGemma/tree/main) them in the Files & versions tab.
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)

added_tokens.json

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+{
+  "<image_soft_token>": 262144
+}

chat_template.jinja

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+{{ bos_token }}
+{%- if messages[0]['role'] == 'system' -%}
+    {%- if messages[0]['content'] is string -%}
+        {%- set first_user_prefix = messages[0]['content'] + '
+
+' -%}
+    {%- else -%}
+        {%- set first_user_prefix = messages[0]['content'][0]['text'] + '
+
+' -%}
+    {%- endif -%}
+    {%- set loop_messages = messages[1:] -%}
+{%- else -%}
+    {%- set first_user_prefix = "" -%}
+    {%- set loop_messages = messages -%}
+{%- endif -%}
+{%- for message in loop_messages -%}
+    {%- if (message['role'] == 'user') != (loop.index0 % 2 == 0) -%}
+        {{ raise_exception("Conversation roles must alternate user/assistant/user/assistant/...") }}
+    {%- endif -%}
+    {%- if (message['role'] == 'assistant') -%}
+        {%- set role = "model" -%}
+    {%- else -%}
+        {%- set role = message['role'] -%}
+    {%- endif -%}
+    {{ '<start_of_turn>' + role + '
+' + (first_user_prefix if loop.first else "") }}
+    {%- if message['content'] is string -%}
+        {{ message['content'] | trim }}
+    {%- elif message['content'] is iterable -%}
+        {%- for item in message['content'] -%}
+            {%- if item['type'] == 'image' -%}
+                {{ '<start_of_image>' }}
+            {%- elif item['type'] == 'text' -%}
+                {{ item['text'] | trim }}
+            {%- endif -%}
+        {%- endfor -%}
+    {%- else -%}
+        {{ raise_exception("Invalid content type") }}
+    {%- endif -%}
+    {{ '<end_of_turn>
+' }}
+{%- endfor -%}
+{%- if add_generation_prompt -%}
+    {{ '<start_of_turn>model
+' }}
+{%- endif -%}

config.json

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+{
+  "_sliding_window_pattern": 6,
+  "architectures": [
+    "Gemma3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "attn_logit_softcapping": null,
+  "bos_token_id": 2,
+  "eos_token_id": 106,
+  "final_logit_softcapping": null,
+  "head_dim": 256,
+  "hidden_activation": "gelu_pytorch_tanh",
+  "hidden_size": 640,
+  "initializer_range": 0.02,
+  "intermediate_size": 2048,
+  "layer_types": [
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "sliding_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 32768,
+  "model_type": "gemma3_text",
+  "num_attention_heads": 4,
+  "num_hidden_layers": 18,
+  "num_key_value_heads": 1,
+  "pad_token_id": 0,
+  "query_pre_attn_scalar": 256,
+  "rms_norm_eps": 1e-06,
+  "rope_local_base_freq": 10000.0,
+  "rope_scaling": null,
+  "rope_theta": 1000000.0,
+  "sliding_window": 512,
+  "torch_dtype": "float16",
+  "transformers_version": "4.55.0",
+  "unsloth_fixed": true,
+  "unsloth_version": "2025.8.5",
+  "use_bidirectional_attention": false,
+  "use_cache": true,
+  "vocab_size": 262144
+}

generation_config.json

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+{
+  "bos_token_id": 2,
+  "cache_implementation": "hybrid",
+  "do_sample": true,
+  "eos_token_id": [
+    1,
+    106
+  ],
+  "max_length": 32768,
+  "pad_token_id": 0,
+  "top_k": 64,
+  "top_p": 0.95,
+  "transformers_version": "4.55.0"
+}

gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
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+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
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+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text
+Screenshot[[:space:]]2025-08-14[[:space:]]235319.png filter=lfs diff=lfs merge=lfs -text
+banner.png filter=lfs diff=lfs merge=lfs -text

special_tokens_map.json

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+{
+  "boi_token": "<start_of_image>",
+  "bos_token": {
+    "content": "<bos>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eoi_token": "<end_of_image>",
+  "eos_token": {
+    "content": "<end_of_turn>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "image_token": "<image_soft_token>",
+  "pad_token": {
+    "content": "<pad>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "unk_token": {
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tokenizer.model

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1299c11d7cf632ef3b4e11937501358ada021bbdf7c47638d13c0ee982f2e79c
+size 4689074