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.gitattributes

@@ -33,3 +33,5 @@ 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
+qwen3-german-teacher-Q4_K_M.gguf filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

README.md

@@ -1,3 +1,347 @@
----
-license: apache-2.0
----
+---
+license: apache-2.0
+base_model: Qwen/Qwen3-4B
+tags:
+  - german
+  - language-learning
+  - grammar
+  - education
+  - qwen3
+  - finetuned
+  - sft
+language:
+  - de
+  - en
+pipeline_tag: text-generation
+library_name: transformers
+datasets:
+  - custom
+model-index:
+  - name: qwen3-german-teacher
+    results:
+      - task:
+          type: text-generation
+          name: German Grammar Teaching
+        metrics:
+          - type: cola_mcc
+            value: 0.721
+            name: CoLA MCC (Grammaticality Judgment)
+          - type: gec_f1
+            value: 0.349
+            name: GEC Macro F1 (Error Correction)
+          - type: generation_quality
+            value: 3.99
+            name: Generation Quality (1-5 scale)
+          - type: overall_score
+            value: 0.633
+            name: Overall Score
+---
+
+# Qwen3 German Teacher
+
+A finetuned [Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) model specialized for German language teaching at A1-B1 CEFR levels. This model excels at:
+
+- **Grammar Error Detection**: Identifying grammatical mistakes in German sentences
+- **Error Correction**: Providing correct forms with clear explanations
+- **Grammar Judgment**: Binary classification of sentence grammaticality (CoLA-style)
+- **Teaching Explanations**: Clear, learner-friendly explanations of German grammar rules
+
+## Model Details
+
+| Property | Value |
+|----------|-------|
+| Base Model | Qwen/Qwen3-4B |
+| Parameters | 4B |
+| Training Method | SFT (Supervised Fine-Tuning) with LoRA |
+| LoRA Rank | 32 |
+| LoRA Alpha | 64 |
+| Training Epochs | 2 |
+| Learning Rate | 2e-4 |
+| Context Length | 4096 tokens |
+
+## Performance
+
+Evaluated on a custom German grammar benchmark:
+
+| Metric | Score | Description |
+|--------|-------|-------------|
+| **CoLA MCC** | 0.721 | Matthews Correlation Coefficient for grammaticality judgment |
+| **GEC F1** | 0.349 | Macro F1 for grammar error correction |
+| **Generation Quality** | 3.99/5.0 | Human-evaluated response quality |
+| **Overall Score** | 0.633 | Weighted composite score |
+
+### Benchmark Comparison
+
+Comparison with SmolLM3-German-V6 (3B parameters, Q4 quantized):
+
+| Metric | Qwen3 German Teacher (4B) | SmolLM3-German-V6 (3B) | Improvement |
+|--------|---------------------------|------------------------|-------------|
+| **CoLA MCC** | **0.721** | 0.624 | +15.5% |
+| **GEC F1** | **0.349** | 0.145 | +140.7% |
+| **Generation Quality** | **3.99** | 3.19 | +25.1% |
+| **Overall Score** | **0.633** | 0.492 | +28.7% |
+
+Key advantages of this model:
+- **Superior grammaticality judgment**: 15.5% higher CoLA MCC score
+- **Much better error correction**: 2.4x better GEC F1 score
+- **Higher quality responses**: 0.8 points higher on 5-point scale
+
+## Training Data
+
+The model was trained on ~9,000 examples with the following composition:
+
+| Category | Percentage | Purpose |
+|----------|------------|---------|
+| Grammar Correction | 35% | Error correction patterns |
+| Grammar Judgment | 25% | CoLA-style "Is this correct?" examples |
+| Structured Teaching | 20% | Verb conjugations, grammar explanations |
+| General Conversation | 20% | Fluency preservation |
+
+### Key Training Focus Areas
+
+- **haben/sein auxiliary verbs**: Movement verbs require "sein" (e.g., "Ich bin gefahren" not "Ich habe gefahren")
+- **Article gender** (der/die/das)
+- **Case usage** (Nominativ, Akkusativ, Dativ, Genitiv)
+- **Word order** in German sentences
+
+## Usage
+
+### With Transformers
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model = AutoModelForCausalLM.from_pretrained("jaigouk/qwen3-german-teacher")
+tokenizer = AutoTokenizer.from_pretrained("jaigouk/qwen3-german-teacher")
+
+messages = [
+    {"role": "system", "content": "Du bist ein freundlicher Deutschlehrer für A1-B1 Lernende."},
+    {"role": "user", "content": "Is this German sentence correct? 'Ich habe nach Berlin gefahren.'"}
+]
+
+text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
+inputs = tokenizer(text, return_tensors="pt")
+outputs = model.generate(**inputs, max_new_tokens=256)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+### With Ollama (GGUF)
+
+1. Download the GGUF file: `qwen3-german-teacher-Q4_K_M.gguf`
+
+2. Create a Modelfile:
+```dockerfile
+FROM ./qwen3-german-teacher-Q4_K_M.gguf
+
+TEMPLATE """{{- if .System }}<|im_start|>system
+{{ .System }}<|im_end|>
+{{ end }}<|im_start|>user
+{{ .Prompt }}<|im_end|>
+<|im_start|>assistant
+<think>
+
+</think>
+
+{{ .Response }}<|im_end|>
+"""
+
+PARAMETER stop "<|im_end|>"
+PARAMETER stop "<|endoftext|>"
+PARAMETER temperature 0.7
+PARAMETER top_p 0.9
+PARAMETER num_ctx 4096
+
+SYSTEM """Du bist ein freundlicher Deutschlehrer für A1-B1 Lernende. Erkläre Grammatikfehler klar und einfach. Beurteile grammatische Korrektheit sorgfältig."""
+```
+
+3. Create and run:
+```bash
+ollama create qwen3-german-teacher -f Modelfile
+ollama run qwen3-german-teacher "Is 'Ich habe nach Berlin gefahren' correct?"
+```
+
+## Example Interactions
+
+### Grammar Error Detection
+
+**User**: Is this German sentence grammatically correct? "Ich habe nach Berlin gefahren."
+
+**Model**: No, this sentence is incorrect. The verb "fahren" describes movement from one place to another, so it requires "sein" as the auxiliary verb, not "haben".
+
+**Correct**: "Ich **bin** nach Berlin gefahren."
+
+### Grammar Explanation
+
+**User**: Explain when to use "sein" vs "haben" in the Perfekt tense.
+
+**Model**: In German, most verbs use "haben" in the Perfekt tense, but verbs that describe:
+1. **Movement from A to B**: gehen, fahren, fliegen, kommen, laufen
+2. **Change of state**: aufwachen, einschlafen, sterben, werden
+
+These verbs use "sein" instead. For example:
+- Ich **bin** nach Hause gegangen. (movement)
+- Er **ist** eingeschlafen. (change of state)
+
+### Conjugation Help
+
+**User**: Conjugate "sein" in present tense.
+
+**Model**: Here is the conjugation of "sein" (to be) in present tense:
+
+| Person | Conjugation |
+|--------|-------------|
+| ich | bin |
+| du | bist |
+| er/sie/es | ist |
+| wir | sind |
+| ihr | seid |
+| sie/Sie | sind |
+
+## Limitations
+
+- Optimized for A1-B1 level German; may oversimplify advanced grammar concepts
+- Focused on grammar teaching; not designed for creative writing or translation
+- Best performance with clear, focused grammar questions
+
+## Training Methodology
+
+### Research-Informed Approach
+
+This model was developed using insights from recent research on instruction-tuning and continual learning:
+
+#### GRAPE Data Selection
+
+Based on the [GRAPE paper (Feb 2025)](https://arxiv.org/abs/2502.04194):
+
+> "Instructions paired with responses sampled from other LLMs are often **out of the distribution of the target model**, which can lead to diminishing returns and even hurt performance"
+
+**Key insight**: Rather than using responses generated by larger models (GPT-4, Claude), we curated training examples that align with Qwen3's natural output distribution. This prevents the model from learning patterns it cannot reliably reproduce.
+
+#### CoLA-Style Judgment Training
+
+The breakthrough in V4 was adding 25% **explicit grammaticality judgment examples**. This teaches the model to reason about correctness rather than just generate correct text:
+
+```
+User: Ist der Satz grammatisch korrekt? "Der Frau ist schön."
+Assistant: Nein, der Satz ist nicht korrekt.
+Fehler: "Der" ist maskulin, aber "Frau" ist feminin.
+Korrektur: "Die Frau ist schön."
+```
+
+#### Lessons from EWC/Fisher Information Research
+
+From [arXiv:2502.11756](https://arxiv.org/html/2502.11756v1) on Fisher Information computation:
+- **EXACT computation outperforms approximations**
+- Minimum 500 samples for reliable Fisher estimation
+- Device consistency (GPU-only) is critical
+
+These insights from our earlier SmolLM3 experiments (V5/V6 with Elastic Weight Consolidation) informed our dataset composition decisions for Qwen3-V4.
+
+## Training Details
+
+### Multi-Stage Finetuning: Lessons Learned
+
+This project was inspired by the 3-stage finetuning approach described in [MiroThinker (arXiv:2511.11793)](https://arxiv.org/abs/2511.11793):
+
+1. **Stage 1: SFT** (Supervised Fine-Tuning)
+2. **Stage 2: DPO** (Direct Preference Optimization)
+3. **Stage 3: RL/GRPO** (Reinforcement Learning with Grammar Rewards)
+
+Our training script design followed the MiroThinker paper's architecture, including **higher LoRA rank (r=32, alpha=64)** as recommended for multi-stage training stability.
+
+However, **this model uses only SFT** because our experiments with preference optimization showed a fundamental trade-off:
+
+#### Why DPO/SimPO Failed for Our Use Case
+
+| Model | CoLA MCC | GEC F1 | Generation | Overall | Status |
+|-------|----------|--------|------------|---------|--------|
+| **V6 SFT (Baseline)** | **0.624** | **0.191** | 3.29 | **0.516** | Reference |
+| V8 (DPO) | 0.583 | 0.063 | 3.63 | 0.473 | -8% overall |
+| V9 (SimPO) | 0.567 | 0.073 | **3.72** | 0.479 | -7% overall |
+
+**Key findings**:
+- DPO improved generation quality (+10%) but **destroyed GEC accuracy (-67%)**
+- SimPO achieved best generation (3.72) but still regressed accuracy significantly
+- This confirms the "alignment tax" documented in 2025 research: 76% of preference optimization causes regression on specific tasks
+
+For grammar teaching, **accuracy is more important than fluency**, so we stayed with pure SFT. The V4 dataset composition (25% CoLA-style judgment examples) proved more effective than preference optimization for our metrics.
+
+### SFT Training Configuration
+
+```
+Base Model: Qwen/Qwen3-4B
+LoRA Configuration:
+  - Rank (r): 32
+  - Alpha: 64
+  - Target modules: q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj
+  - Dropout: 0
+
+Training Configuration:
+  - Epochs: 2
+  - Learning Rate: 2e-4 (linear decay)
+  - Batch Size: 2 (effective: 8 with gradient accumulation)
+  - Warmup: 10%
+  - Precision: BF16
+  - Optimizer: AdamW 8-bit
+```
+
+The SFT stage trains on ~9,000 curated examples covering grammar judgment, error correction, and teaching explanations.
+
+### Deployment Pipeline
+
+After SFT training, the model goes through:
+
+#### 1. PEFT Merge
+
+LoRA adapters are merged into the base model:
+
+```python
+from peft import PeftModel
+from transformers import AutoModelForCausalLM
+
+base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B")
+model = PeftModel.from_pretrained(base_model, "lora_adapters/")
+model = model.merge_and_unload()
+model.save_pretrained("merged_model/", safe_serialization=True)
+```
+
+#### 2. GGUF Quantization
+
+For efficient local inference:
+
+```bash
+python convert_hf_to_gguf.py merged_model/ --outtype bf16 --outfile model-bf16.gguf
+llama-quantize model-bf16.gguf model-Q4_K_M.gguf Q4_K_M
+```
+
+The Q4_K_M quantization reduces model size from ~8GB to 2.5GB while maintaining high quality.
+
+### Technical Specifications
+
+- **Framework**: Unsloth + Transformers + TRL + PEFT
+- **Hardware**: NVIDIA RTX 4090 (24GB VRAM)
+- **Training Time**: ~45 minutes for 2 epochs
+- **Quantization**: GGUF Q4_K_M (2.5GB)
+
+## Citation
+
+If you use this model, please cite:
+
+```bibtex
+@misc{qwen3-german-teacher-2024,
+  author = {Jaigouk Kim},
+  title = {Qwen3 German Teacher: A Finetuned Model for German Grammar Teaching},
+  year = {2024},
+  publisher = {HuggingFace},
+  url = {https://huggingface.co/jaigouk/qwen3-german-teacher}
+}
+```
+
+## License
+
+This model is released under the Apache 2.0 license, following the base Qwen3 model license.
+
+## Acknowledgments
+
+- [Qwen Team](https://github.com/QwenLM/Qwen3) for the excellent base model
+- [Unsloth](https://github.com/unslothai/unsloth) for efficient finetuning tools

added_tokens.json

@@ -0,0 +1,28 @@
+{
+  "</think>": 151668,
+  "</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,
+  "<|fim_pad|>": 151662,
+  "<|fim_prefix|>": 151659,
+  "<|fim_suffix|>": 151661,
+  "<|im_end|>": 151645,
+  "<|im_start|>": 151644,
+  "<|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 +1,97 @@
+{%- 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 tool_start = '<tool_response>' %}
+    {%- set tool_start_length = tool_start|length %}
+    {%- set start_of_message = message.content[:tool_start_length] %}
+    {%- set tool_end = '</tool_response>' %}
+    {%- set tool_end_length = tool_end|length %}
+    {%- set start_pos = (message.content|length) - tool_end_length %}
+    {%- if start_pos < 0 %}
+        {%- set start_pos = 0 %}
+    {%- endif %}
+    {%- set end_of_message = message.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 %}

config.json

@@ -0,0 +1,68 @@
+{
+  "architectures": [
+    "Qwen3ForCausalLM"
+  ],
+  "attention_bias": false,
+  "attention_dropout": 0.0,
+  "bos_token_id": 151643,
+  "dtype": "bfloat16",
+  "eos_token_id": 151645,
+  "head_dim": 128,
+  "hidden_act": "silu",
+  "hidden_size": 2560,
+  "initializer_range": 0.02,
+  "intermediate_size": 9728,
+  "layer_types": [
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention",
+    "full_attention"
+  ],
+  "max_position_embeddings": 40960,
+  "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": 1000000,
+  "sliding_window": null,
+  "tie_word_embeddings": true,
+  "transformers_version": "4.57.0",
+  "use_cache": true,
+  "use_sliding_window": false,
+  "vocab_size": 151936
+}

generation_config.json

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+{
+  "bos_token_id": 151643,
+  "do_sample": true,
+  "eos_token_id": [
+    151645,
+    151643
+  ],
+  "pad_token_id": 151643,
+  "temperature": 0.6,
+  "top_k": 20,
+  "top_p": 0.95,
+  "transformers_version": "4.57.0"
+}

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+      "normalized": false,
+      "rstrip": false,
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+    },
+    "151665": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "151666": {
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+      "normalized": false,
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+      "normalized": false,
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+    },
+    "151668": {
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+      "single_word": false,
+      "special": false
+    }
+  },
+  "additional_special_tokens": [
+    "<|im_start|>",
+    "<|im_end|>",
+    "<|object_ref_start|>",
+    "<|object_ref_end|>",
+    "<|box_start|>",
+    "<|box_end|>",
+    "<|quad_start|>",
+    "<|quad_end|>",
+    "<|vision_start|>",
+    "<|vision_end|>",
+    "<|vision_pad|>",
+    "<|image_pad|>",
+    "<|video_pad|>"
+  ],
+  "bos_token": null,
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "errors": "replace",
+  "extra_special_tokens": {},
+  "model_max_length": 40960,
+  "pad_token": "<|vision_pad|>",
+  "padding_side": "left",
+  "split_special_tokens": false,
+  "tokenizer_class": "Qwen2Tokenizer",
+  "unk_token": null
+}