Initial model upload - Vietnamese fine-tuned Qwen3-30B

.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

README.md

@@ -0,0 +1,302 @@
+# Qwen3-30B Vietnamese Instruct
+
+**Fine-tuned Qwen3-30B-A3B for Vietnamese instruction-following**
+
+This model is a Vietnamese-optimized version of Qwen3-30B-A3B, fine-tuned on 327K high-quality Vietnamese instruction samples using LoRA (Low-Rank Adaptation).
+
+## Model Description
+
+- **Model Type**: Large Language Model (Mixture-of-Experts)
+- **Base Model**: [Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)
+- **Language**: Vietnamese (primary), English (secondary)
+- **Fine-tuning Method**: LoRA (rank=64, alpha=128) with 4-bit quantization
+- **Training Data**: 327,113 Vietnamese instruction-response pairs
+- **License**: Apache 2.0
+- **Developed by**: Vietnamese LLM Project
+
+## Intended Use
+
+This model is designed for Vietnamese natural language processing tasks, including:
+
+- **Question Answering**: Answer questions in Vietnamese
+- **Instruction Following**: Execute tasks described in Vietnamese
+- **Conversational AI**: Engage in multi-turn Vietnamese dialogues
+- **Text Generation**: Generate Vietnamese text based on prompts
+- **Educational Applications**: Tutoring, explanations, and knowledge sharing
+
+### Direct Use
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained(
+    "danghuyhoang/qwen3-30b-vietnamese-instruct",
+    torch_dtype="auto",
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained("danghuyhoang/qwen3-30b-vietnamese-instruct")
+
+messages = [
+    {"role": "system", "content": "Bạn là trợ lý AI thông minh và hữu ích."},
+    {"role": "user", "content": "Giải thích khái niệm machine learning bằng tiếng Việt."}
+]
+
+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,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+
+### Use with Unsloth (2-5x faster inference)
+
+```python
+from unsloth import FastLanguageModel
+
+model, tokenizer = FastLanguageModel.from_pretrained(
+    model_name="danghuyhoang/qwen3-30b-vietnamese-instruct",
+    max_seq_length=2048,
+    dtype=None,
+    load_in_4bit=True,
+)
+
+FastLanguageModel.for_inference(model)  # Enable inference mode
+
+messages = [
+    {"role": "user", "content": "Việt Nam có bao nhiêu tỉnh thành?"}
+]
+
+inputs = tokenizer.apply_chat_template(
+    messages,
+    tokenize=True,
+    add_generation_prompt=True,
+    return_tensors="pt"
+).to("cuda")
+
+outputs = model.generate(input_ids=inputs, max_new_tokens=128)
+print(tokenizer.decode(outputs[0]))
+```
+
+## Training Data
+
+The model was fine-tuned on a diverse collection of Vietnamese instruction datasets:
+
+| Dataset | Samples | Source | License |
+|---------|---------|--------|---------|
+| OpenOrca-Viet | 121,178 | 5CD-AI | Apache 2.0 |
+| VILM Instruction | Subset | VILM Project | Open |
+| Vietnamese UltraChat | Subset | 5CD-AI | MIT |
+| Bactrian-X Vietnamese | Subset | MBZUAI | CC BY-NC 4.0 |
+| Vietnamese MATH | 40,000 | 5CD-AI | Apache 2.0 |
+| Multi-turn Chat | 12,697 | 5CD-AI | Apache 2.0 |
+
+**Total**: 320,570 training samples + 6,543 validation samples
+
+### Data Preprocessing
+
+- All data converted to ChatML format
+- Vietnamese content validation
+- Token length filtering (max 3072 tokens)
+- Quality filtering and deduplication
+- 98/2 train/validation split
+
+## Training Details
+
+### Training Hyperparameters
+
+- **Base Model**: unsloth/qwen3-30b-a3b
+- **Training Method**: LoRA fine-tuning with 4-bit quantization
+- **LoRA Configuration**:
+  - Rank (r): 64
+  - Alpha: 128
+  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+  - Dropout: 0.0
+- **Training Hyperparameters**:
+  - Epochs: 1
+  - Batch size: 36 (per device)
+  - Gradient accumulation: 1
+  - Learning rate: 0.00012
+  - Optimizer: AdamW 8-bit
+  - LR Scheduler: Linear warmup (50 steps)
+  - Max sequence length: 3072
+  - Weight decay: 0.01
+- **Hardware**: 1× NVIDIA A100 80GB
+- **Training Time**: ~47 hours
+- **Framework**: Unsloth + Hugging Face Transformers
+
+### Training Loss
+
+Training loss decreased steadily from ~1.5 to ~0.8 over 8,905 steps, indicating successful learning.
+
+### Optimization Techniques
+
+- **Unsloth**: 2-5x faster training with optimized CUDA kernels
+- **Flash Attention 2**: Memory-efficient attention computation
+- **Gradient Checkpointing**: Reduced memory usage
+- **4-bit Quantization**: QLoRA for memory efficiency
+- **Mixed Precision**: bfloat16 for numerical stability
+
+## Evaluation
+
+### VMLU Benchmark (Vietnamese Multitask Language Understanding)
+
+The model is evaluated on VMLU, a comprehensive Vietnamese benchmark with 744 questions across multiple subjects.
+
+**Evaluation Method**: Logit-based scoring (industry standard, same as MMLU)
+
+| Metric | Score |
+|--------|-------|
+| Overall Accuracy | Coming Soon |
+| STEM | Coming Soon |
+| Humanities | Coming Soon |
+| Social Sciences | Coming Soon |
+
+**Note**: To reproduce evaluation, use the evaluation script from the [GitHub repository](https://github.com/andreidhoang/vietnamese-llm-finetuning).
+
+### Comparison with Base Model
+
+| Model | VMLU Accuracy |
+|-------|---------------|
+| Qwen3-30B-A3B (Base) | Baseline |
+| Qwen3-30B-Vietnamese (This model) | Coming Soon |
+
+## Limitations and Biases
+
+### Known Limitations
+
+1. **Vietnamese-Specific**: Optimized for Vietnamese, may have reduced performance on other languages
+2. **Instruction Bias**: Trained primarily on instruction-following data, may not excel at creative writing
+3. **Factual Knowledge Cutoff**: Based on Qwen3's training data (cutoff date unknown)
+4. **Context Length**: Trained with max 3072 tokens, performance may degrade on longer contexts
+5. **Mathematical Reasoning**: While improved, may still struggle with complex multi-step math
+6. **Code Generation**: Not specifically optimized for coding tasks
+
+### Potential Biases
+
+- Training data may reflect biases present in Vietnamese internet content
+- Instruction datasets may have geographic/cultural biases
+- Model may perform better on formal Vietnamese than colloquial speech
+- Limited exposure to Vietnamese dialects and regional variations
+
+### Ethical Considerations
+
+- **Misinformation**: Model may generate plausible but incorrect information
+- **Harmful Content**: Despite safety measures, model may occasionally produce inappropriate content
+- **Privacy**: Do not input personal or sensitive information
+- **Transparency**: Always disclose when content is AI-generated
+
+## Responsible Use
+
+### Recommended Practices
+
+- Verify factual claims from independent sources
+- Use human review for high-stakes applications
+- Implement content filtering for production deployments
+- Monitor outputs for bias and harmful content
+- Provide user disclosure about AI involvement
+
+### Not Recommended For
+
+- Medical, legal, or financial advice without expert review
+- Content moderation as sole decision-maker
+- High-stakes decision-making without human oversight
+- Generating content intended to deceive
+
+## Hardware Requirements
+
+### For Inference
+
+**Minimum**:
+- GPU: RTX 4090 24GB (with 4-bit quantization)
+- RAM: 32GB
+- Disk: 100GB
+
+**Recommended**:
+- GPU: A100 40GB or equivalent
+- RAM: 64GB
+- Disk: 150GB
+
+### For Fine-tuning
+
+- GPU: A100 80GB (for LoRA fine-tuning)
+- RAM: 128GB
+- Disk: 500GB
+- See training guide for optimal configurations
+
+## Technical Specifications
+
+### Model Architecture
+
+- **Type**: Mixture-of-Experts (MoE) Transformer
+- **Experts**: Multiple expert networks (A3B variant)
+- **Parameters**: ~30 billion (with sparse activation)
+- **Hidden Size**: 4096
+- **Attention Heads**: 32
+- **Layers**: 40
+- **Vocabulary Size**: 151,936 tokens
+- **Context Length**: 32,768 tokens (training limited to 3072)
+
+### File Sizes
+
+- **Full Model**: ~60GB (bfloat16)
+- **4-bit Quantized**: ~20GB
+- **LoRA Adapters Only**: ~1-2GB
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@software{qwen3_vietnamese_2025,
+  title = {Qwen3-30B Vietnamese Instruct},
+  author = {Vietnamese LLM Project},
+  year = {2025},
+  url = {https://huggingface.co/danghuyhoang/qwen3-30b-vietnamese-instruct},
+  license = {Apache-2.0}
+}
+```
+
+Also cite the base Qwen3 model:
+
+```bibtex
+@article{qwen3_2024,
+  title={Qwen3 Technical Report},
+  author={Qwen Team},
+  journal={arXiv preprint},
+  year={2024}
+}
+```
+
+## Acknowledgments
+
+This model was created using:
+
+- **[Qwen3-30B-A3B](https://huggingface.co/Qwen/Qwen3-30B-A3B)** by Alibaba Cloud
+- **[Unsloth](https://github.com/unslothai/unsloth)** for optimized training
+- **Vietnamese datasets** from 5CD-AI, VILM, MBZUAI, and the Vietnamese NLP community
+
+## Model Card Contact
+
+For questions or issues:
+
+- **GitHub**: [vietnamese-llm-finetuning](https://github.com/andreidhoang/vietnamese-llm-finetuning)
+- **Issues**: [GitHub Issues](https://github.com/andreidhoang/vietnamese-llm-finetuning/issues)
+- **Discussions**: [GitHub Discussions](https://github.com/andreidhoang/vietnamese-llm-finetuning/discussions)
+
+---
+
+**License**: Apache 2.0
+
+**Last Updated**: 2025-11-08

adapter_config.json

@@ -0,0 +1,46 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": {
+    "base_model_class": "Qwen3MoeForCausalLM",
+    "parent_library": "transformers.models.qwen3_moe.modeling_qwen3_moe",
+    "unsloth_fixed": true
+  },
+  "base_model_name_or_path": "unsloth/qwen3-30b-a3b",
+  "bias": "none",
+  "corda_config": null,
+  "eva_config": null,
+  "exclude_modules": null,
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 128,
+  "lora_bias": false,
+  "lora_dropout": 0.0,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "qalora_group_size": 16,
+  "r": 64,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj",
+    "k_proj",
+    "o_proj",
+    "gate_proj",
+    "down_proj",
+    "up_proj"
+  ],
+  "target_parameters": null,
+  "task_type": "CAUSAL_LM",
+  "trainable_token_indices": null,
+  "use_dora": false,
+  "use_qalora": false,
+  "use_rslora": false
+}

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chat_template.jinja

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+{%- 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 %}
+{%- set ns = namespace(multi_step_tool=true, last_query_index=messages|length - 1) %}
+{%- for forward_message in messages %}
+    {%- set index = (messages|length - 1) - loop.index0 %}
+    {%- set message = messages[index] %}
+    {%- set current_content = message.content if message.content is not none else '' %}
+    {%- set tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = current_content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (current_content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = current_content[start_pos:] %}
+    {%- if ns.multi_step_tool and message.role == "user" and not(start_of_message == tool_start and end_of_message == tool_end) %}
+        {%- set ns.multi_step_tool = false %}
+        {%- set ns.last_query_index = index %}
+    {%- endif %}
+{%- endfor %}
+{%- for message in messages %}
+    {%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
+        {{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
+    {%- elif message.role == "assistant" %}
+        {%- set content = message.content %}
+        {%- set reasoning_content = '' %}
+        {%- if message.reasoning_content is defined and message.reasoning_content is not none %}
+            {%- set reasoning_content = message.reasoning_content %}
+        {%- else %}
+            {%- if '</think>' in message.content %}
+                {%- set content = (message.content.split('</think>')|last).lstrip('\n') %}
+                {%- set reasoning_content = (message.content.split('</think>')|first).rstrip('\n') %}
+                {%- set reasoning_content = (reasoning_content.split('<think>')|last).lstrip('\n') %}
+            {%- endif %}
+        {%- endif %}
+        {%- if loop.index0 > ns.last_query_index %}
+            {%- if loop.last or (not loop.last and reasoning_content) %}
+                {{- '<|im_start|>' + message.role + '\n<think>\n' + reasoning_content.strip('\n') + '\n</think>\n\n' + content.lstrip('\n') }}
+            {%- else %}
+                {{- '<|im_start|>' + message.role + '\n' + content }}
+            {%- endif %}
+        {%- else %}
+            {{- '<|im_start|>' + message.role + '\n' + content }}
+        {%- endif %}
+        {%- 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' }}
+        {{- message.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' }}
+    {%- if enable_thinking is defined and enable_thinking is false %}
+        {{- '<think>\n\n</think>\n\n' }}
+    {%- endif %}
+{%- endif %}

special_tokens_map.json

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+}

training_args.bin

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