Darmm
/

darmm-text-generation-kazakh-v2

@@ -1,202 +1,134 @@
 ---
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
-library_name: peft
 ---
-# Model Card for Model ID
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-## Model Details
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-### Framework versions
-- PEFT 0.13.2

 ---
+language:
+- kk
+license: apache-2.0
+tags:
+- text-generation
+- kazakh
+- qwen
+- qlora
+- instruction-following
+pipeline_tag: text-generation
 base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+model-index:
+- name: darmm-text-generation-kazakh-v2
+  results:
+    - task:
+        type: text-generation
+        name: Text Generation
+      dataset:
+        name: organic-kazakh-corpus-v2
+        type: darmm-kazakh-v2
+      metrics:
+        - type: loss
+          value: 0.7407
 ---
+# Darmm Text Generation Kazakh v2
+**Darmm Kazakh v2** (`Darmm/darmm-text-generation-kazakh-v2`) is a significantly improved iteration of our Kazakh language model series. While [v1](https://huggingface.co/Darmm/darmm-text-generation-kazakh) was built on synthetic data and mT5, **v2 is built on organic, real-world data** and the powerful **Qwen 2.5 7B** architecture.
+This model is fine-tuned to understand and generate high-quality Kazakh text, with a focus on news articles, encyclopedic explanations, and general instruction following.
+## Key Improvements vs v1
+| Feature | v1 (Old) | **v2 (New)** |
+| :--- | :--- | :--- |
+| **Base Model** | `google/mt5-base` (580M) | **`Qwen/Qwen2.5-Coder-7B-Instruct` (7B)** |
+| **Data Source** | Synthetic / Templates | **Organic (Wikipedia + Egemen Qazaqstan)** |
+| **Dataset Size** | ~5,000 synthetic pairs | **~5,000 real articles (4895 samples)** |
+| **Training** | 3 Epochs (CPU/Small GPU) | **2.29 Epochs (A100 80GB, QLoRA)** |
+| **Focus** | Short structured answers | **Long-form content generation** |
+## Usage
+```python
+import torch
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+# 1. Config for 4-bit loading (Efficient)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    bnb_4bit_compute_dtype=torch.float16,
+    bnb_4bit_quant_type="nf4"
+)
+# 2. Load Base Model
+base_model_name = "Qwen/Qwen2.5-Coder-7B-Instruct"
+model = AutoModelForCausalLM.from_pretrained(
+    base_model_name,
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+# 3. Load Darmm v2 Adapter
+adapter_name = "Darmm/darmm-text-generation-kazakh-v2"
+model = PeftModel.from_pretrained(model, adapter_name)
+tokenizer = AutoTokenizer.from_pretrained(base_model_name)
+# 4. Generate
+prompt_text = "Жасанды интеллект туралы мақала жаз." # Write an article about AI
+prompt = f"### Instruction:\n{prompt_text}\n\n### Response:\n"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.7)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True).split("### Response:")[1])
+```
+## Model Description
+- **Developed by:** Darmm Lab
+- **Language:** Kazakh (kk)
+- **Base Model:** `Qwen/Qwen2.5-Coder-7B-Instruct`
+- **Fine-tuning Method:** QLoRA (4-bit quantization with LoRA adapters)
+- **Context Length:** 1024 tokens
 ## Training Details
+The model was trained on a curated dataset scraped from high-quality Kazakh sources:
+- **Wikipedia (kk)**: Encyclopedic knowledge, definitions, biographies.
+- **Egemen Qazaqstan**: Formal news style, economic and political vocabulary.
+### Hyperparameters
+- **Epochs:** 2.29
+- **Batch size:** 1 (Gradient Accumulation: 8) -> Effective Batch Size: 8
+- **Learning rate:** 2e-4
+- **Scheduler:** Cosine
+- **Optimizer:** AdamW
+- **Hardware:** NVIDIA A100 80GB
+- **Final Loss:** 0.7407
+## Training Loss
+| Step | Loss | Learning Rate |
+| :--- | :--- | :--- |
+| 10 | 2.25 | 4e-5 |
+| 100 | 1.13 | 1.99e-4 |
+| 500 | 0.94 | 1.90e-4 |
+| 1000 | 0.83 | 1.55e-4 |
+| 1500 | 0.73 | 9.41e-5 |
+| 1650 | 0.74 | 2.29e-5 |
+## Intended Use
+- **Content Generation**: Writing articles, summaries, and explanations in Kazakh.
+- **Education**: Generating study materials or answering questions about Kazakh history/culture.
+- **Research**: Baseline for further fine-tuning on specialized Kazakh domains (legal, medical).
+## Limitations
+- **Hallucination**: As with all LLMs, the model may generate factually incorrect information despite linguistic fluency.
+- **English Bias**: In rare cases of confusion, the model might revert to English or code-mixing due to the base model's strong English pre-training.
+## Citation
+```bibtex
+@misc{darmm_kazakh_v2,
+  author = {Darmm Lab},
+  title = {Darmm Text Generation Kazakh v2: Organic Data Scale-up},
+  year = {2026},
+  publisher = {Hugging Face},
+  journal = {Hugging Face Repository},
+  howpublished = {\url{https://huggingface.co/Darmm/darmm-text-generation-kazakh-v2}}
+}
+```