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.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.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

Modelfile

@@ -0,0 +1,14 @@
+# ollama modelfile auto-generated by llamafactory
+
+FROM .
+
+TEMPLATE """{{ if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}{{ range .Messages }}{{ if eq .Role "user" }}<|im_start|>user
+{{ .Content }}<|im_end|>
+<|im_start|>assistant
+{{ else if eq .Role "assistant" }}{{ .Content }}<|im_end|>
+{{ end }}{{ end }}"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER num_ctx 4096

README.md

@@ -1,3 +1,310 @@
----
-license: apache-2.0
----
+---
+library_name: transformers
+license: other
+base_model: Qwen/Qwen3-4B-Instruct-2507
+tags:
+- medical
+- qa
+- medical-qa
+- qwen
+- qwen3
+- llama-factory
+language:
+- en
+metrics:
+- accuracy
+pipeline_tag: text-generation
+model-index:
+- name: Qwen3-4B Medical QA
+  results:
+  - task:
+      type: question-answering
+      name: Medical Question Answering
+    metrics:
+    - type: accuracy
+      value: 0.8218
+      name: Accuracy
+    - type: loss
+      value: 0.7984
+      name: Loss
+---
+
+# Qwen3-4B Medical QA
+
+This is a **fully merged** medical question-answering model based on [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). The model has been fine-tuned using LoRA (with DoRA) on medical QA datasets and then merged into a single standalone model.
+
+## Model Details
+
+### Base Model
+- **Base Model:** [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507)
+- **Model Type:** Causal Language Model (fully merged)
+- **Parameters:** 4.02B
+- **Architecture:** Qwen3ForCausalLM
+- **Precision:** BFloat16
+- **Context Length:** 262,144 tokens
+- **License:** Same as base model
+
+### Fine-tuning Details
+- **Method:** LoRA with DoRA (Weight-Decomposed Low-Rank Adaptation)
+- **LoRA Rank:** 64
+- **LoRA Alpha:** 64
+- **LoRA Dropout:** 0.1
+- **Target Modules:** q_proj, k_proj, v_proj, o_proj
+- **Training Framework:** [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+
+### Performance
+- **Validation Accuracy:** 82.18%
+- **Validation Loss:** 0.7984
+- **Training Dataset:** combined_selected_train (medical QA)
+
+## Training Details
+
+### Training Hyperparameters
+- **Learning Rate:** 3e-4
+- **LR Scheduler:** constant_with_warmup
+- **Optimizer:** AdamW (fused)
+- **Number of Epochs:** 3.0
+- **Total Batch Size:** 48 (distributed across 48 GPUs)
+- **Per-device Train Batch Size:** 1
+- **Per-device Eval Batch Size:** 8
+- **Seed:** 42
+
+### Training Results
+
+| Training Loss | Epoch  | Step | Validation Loss | Accuracy |
+|:-------------:|:------:|:----:|:---------------:|:--------:|
+| 1.0377        | 1.18   | 20   | 1.1270          | 0.7382   |
+| 0.6478        | 2.35   | 40   | 0.8764          | 0.7388   |
+| -             | 3.00   | 51   | 0.7984          | 0.8218   |
+
+### Framework Versions
+- **PEFT:** 0.15.2
+- **Transformers:** 4.55.0
+- **PyTorch:** 2.8.0+cu128
+- **Datasets:** 3.6.0
+- **Tokenizers:** 0.21.1
+
+## Usage
+
+### Quick Start
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained(
+    "Acryl-Jonathan-01/qwen3-4b-medical-qa-merged",
+    torch_dtype="auto",
+    device_map="auto",
+    trust_remote_code=True
+)
+
+tokenizer = AutoTokenizer.from_pretrained(
+    "Acryl-Jonathan-01/qwen3-4b-medical-qa-merged",
+    trust_remote_code=True
+)
+
+# Prepare messages
+messages = [
+    {"role": "system", "content": "You are a helpful medical assistant."},
+    {"role": "user", "content": "What are the common symptoms of pneumonia?"}
+]
+
+# Generate response
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+
+inputs = tokenizer([text], return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.8,
+    top_k=20
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Using with vLLM (Faster Inference)
+
+```python
+from vllm import LLM, SamplingParams
+
+# Initialize vLLM
+llm = LLM(
+    model="Acryl-Jonathan-01/qwen3-4b-medical-qa-merged",
+    trust_remote_code=True,
+    dtype="bfloat16"
+)
+
+# Sampling parameters
+sampling_params = SamplingParams(
+    temperature=0.7,
+    top_p=0.8,
+    top_k=20,
+    max_tokens=512
+)
+
+# Generate
+prompts = ["What are the symptoms of diabetes?"]
+outputs = llm.generate(prompts, sampling_params)
+
+for output in outputs:
+    print(output.outputs[0].text)
+```
+
+### Using with Ollama
+
+A `Modelfile` is included for easy deployment with Ollama:
+
+```bash
+# Create Ollama model
+ollama create qwen3-medical -f Modelfile
+
+# Run the model
+ollama run qwen3-medical "What are the symptoms of hypertension?"
+```
+
+### Quantization (Optional)
+
+For resource-constrained environments, you can quantize the model:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+
+# 4-bit quantization
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype="bfloat16",
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4"
+)
+
+model = AutoModelForCausalLM.from_pretrained(
+    "Acryl-Jonathan-01/qwen3-4b-medical-qa-merged",
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True
+)
+```
+
+## Intended Use
+
+This model is designed for:
+- **Medical question-answering** in educational contexts
+- **Medical knowledge exploration** and learning
+- **Research** in medical AI and NLP applications
+- **Prototype development** for medical chatbots and assistants
+
+## Limitations & Warnings
+
+**IMPORTANT DISCLAIMERS:**
+
+- ⚠️ **NOT for clinical use:** This model should NEVER be used for actual clinical decision-making, diagnosis, or treatment without qualified medical professional oversight
+- ⚠️ **Educational purposes only:** Intended for education, research, and development purposes
+- ⚠️ **May contain errors:** The model can generate incorrect or outdated medical information
+- ⚠️ **Bias:** May inherit biases from training data
+- ⚠️ **Hallucination:** Like all LLMs, may generate plausible-sounding but incorrect information
+- ⚠️ **Not a replacement:** Always consult qualified healthcare professionals for medical advice
+
+### Performance Limitations
+- Performance may vary on out-of-distribution medical questions
+- Better suited for common medical topics in the training distribution
+- May struggle with very recent medical developments (knowledge cutoff)
+- Accuracy is dataset-dependent and not guaranteed
+
+## Model Size & Requirements
+
+- **Model Size:** ~7.6GB (BFloat16)
+- **Recommended VRAM:** 16GB+ for inference
+- **Quantized (4-bit):** ~2-3GB VRAM
+- **CPU Inference:** Possible but slow
+
+## Files Structure
+
+```
+qwen3-4b-medical-qa-merged/
+├── model-00001-of-00005.safetensors  # Model weights (shard 1)
+├── model-00002-of-00005.safetensors  # Model weights (shard 2)
+├── model-00003-of-00005.safetensors  # Model weights (shard 3)
+├── model-00004-of-00005.safetensors  # Model weights (shard 4)
+├── model-00005-of-00005.safetensors  # Model weights (shard 5)
+├── model.safetensors.index.json     # Shard index
+├── config.json                       # Model configuration
+├── generation_config.json            # Generation settings
+├── tokenizer.json                    # Tokenizer
+├── tokenizer_config.json             # Tokenizer config
+├── vocab.json                        # Vocabulary
+├── merges.txt                        # BPE merges
+├── chat_template.jinja               # Chat template
+├── special_tokens_map.json           # Special tokens
+├── added_tokens.json                 # Added tokens
+├── Modelfile                         # Ollama modelfile
+└── README.md                         # This file
+```
+
+## Comparison: LoRA Adapter vs Merged Model
+
+**Advantages of merged model:**
+- ✅ Easier to use (no need to load adapter separately)
+- ✅ Faster inference (no adapter overhead)
+- ✅ Compatible with more inference engines (vLLM, Ollama, etc.)
+- ✅ Can be quantized directly
+
+**Disadvantages:**
+- ❌ Larger file size (~7.6GB vs ~182MB for adapter)
+- ❌ Less flexible (can't swap adapters easily)
+- ❌ Takes more storage space
+
+## Citation
+
+If you use this model in your research or applications, please cite:
+
+```bibtex
+@misc{qwen3-4b-medical-qa-merged,
+  author = {Your Name},
+  title = {Qwen3-4B Medical QA},
+  year = {2025},
+  publisher = {HuggingFace},
+  howpublished = {\url{https://huggingface.co/Acryl-Jonathan-01/qwen3-4b-medical-qa-merged}}
+}
+```
+
+Also consider citing the base model:
+
+```bibtex
+@misc{qwen3-2025,
+  title={Qwen3 Technical Report},
+  author={Qwen Team},
+  year={2025},
+  publisher={Alibaba Cloud},
+  url={https://huggingface.co/Qwen}
+}
+```
+
+## Acknowledgments
+
+- **Base Model:** [Qwen Team](https://huggingface.co/Qwen)
+- **Training Framework:** [LLaMA-Factory](https://github.com/hiyouga/LLaMA-Factory)
+- **PEFT Library:** [Hugging Face PEFT](https://github.com/huggingface/peft)
+- **Transformers Library:** [Hugging Face Transformers](https://github.com/huggingface/transformers)
+
+## License
+
+This model inherits the license from the base model [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507). Please refer to the base model's license for usage terms and conditions.
+
+## Contact & Support
+
+For issues, questions, or contributions:
+- Open an issue on the model repository
+- Refer to [LLaMA-Factory documentation](https://github.com/hiyouga/LLaMA-Factory) for training questions
+
+---
+

added_tokens.json

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+{
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+  "</tool_call>": 151658,
+  "</tool_response>": 151666,
+  "<think>": 151667,
+  "<tool_call>": 151657,
+  "<tool_response>": 151665,
+  "<|box_end|>": 151649,
+  "<|box_start|>": 151648,
+  "<|endoftext|>": 151643,
+  "<|file_sep|>": 151664,
+  "<|fim_middle|>": 151660,
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+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
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+  "<|image_pad|>": 151655,
+  "<|object_ref_end|>": 151647,
+  "<|object_ref_start|>": 151646,
+  "<|quad_end|>": 151651,
+  "<|quad_start|>": 151650,
+  "<|repo_name|>": 151663,
+  "<|video_pad|>": 151656,
+  "<|vision_end|>": 151653,
+  "<|vision_pad|>": 151654,
+  "<|vision_start|>": 151652
+}

chat_template.jinja

@@ -0,0 +1,61 @@
+{%- if tools %}
+    {{- '<|im_start|>system\n' }}
+    {%- if messages[0].role == 'system' %}
+        {{- messages[0].content + '\n\n' }}
+    {%- endif %}
+    {{- "# Tools\n\nYou may call one or more functions to assist with the user query.\n\nYou are provided with function signatures within <tools></tools> XML tags:\n<tools>" }}
+    {%- for tool in tools %}
+        {{- "\n" }}
+        {{- tool | tojson }}
+    {%- endfor %}
+    {{- "\n</tools>\n\nFor each function call, return a json object with function name and arguments within <tool_call></tool_call> XML tags:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
+{%- else %}
+    {%- if messages[0].role == 'system' %}
+        {{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
+    {%- endif %}
+{%- endif %}
+{%- for message in messages %}
+    {%- if message.content is string %}
+        {%- set content = message.content %}
+    {%- else %}
+        {%- set content = '' %}
+    {%- endif %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- if message.tool_calls %}
+            {%- for tool_call in message.tool_calls %}
+                {%- if (loop.first and content) or (not loop.first) %}
+                    {{- '\n' }}
+                {%- endif %}
+                {%- if tool_call.function %}
+                    {%- set tool_call = tool_call.function %}
+                {%- endif %}
+                {{- '<tool_call>\n{"name": "' }}
+                {{- tool_call.name }}
+                {{- '", "arguments": ' }}
+                {%- if tool_call.arguments is string %}
+                    {{- tool_call.arguments }}
+                {%- else %}
+                    {{- tool_call.arguments | tojson }}
+                {%- endif %}
+                {{- '}\n</tool_call>' }}
+            {%- endfor %}
+        {%- endif %}
+        {{- '<|im_end|>\n' }}
+    {%- elif message.role == "tool" %}
+        {%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
+            {{- '<|im_start|>user' }}
+        {%- endif %}
+        {{- '\n<tool_response>\n' }}
+        {{- content }}
+        {{- '\n</tool_response>' }}
+        {%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
+            {{- '<|im_end|>\n' }}
+        {%- endif %}
+    {%- endif %}
+{%- endfor %}
+{%- if add_generation_prompt %}
+    {{- '<|im_start|>assistant\n' }}
+{%- endif %}

config.json

@@ -0,0 +1,30 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "max_position_embeddings": 262144,
+  "max_window_layers": 36,
+  "model_type": "qwen3",
+  "num_attention_heads": 32,
+  "num_hidden_layers": 36,
+  "num_key_value_heads": 8,
+  "rms_norm_eps": 1e-06,
+  "rope_scaling": null,
+  "rope_theta": 5000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "torch_dtype": "bfloat16",
+  "transformers_version": "4.52.4",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.7,
+  "top_k": 20,
+  "top_p": 0.8,
+  "transformers_version": "4.52.4"
+}

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