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 ---
 library_name: transformers
-tags: []
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
-<!-- Provide a longer summary of what this model is. -->
-This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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-## Uses
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-### Direct Use
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-## How to Get Started with the Model
-Use the code below to get started with the model.
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-## Training Details
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-## Evaluation
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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 ---
+base_model: TSjB/QM-4B-embeddings-only
 library_name: transformers
+model_name: QM-4B
+tags:
+- qarachay-malqar
+- caucasian-languages
+- turkic-languages
+- karachay-balkar
+- multilingual
+- qwen3
+- trl
+- sft
+- unsloth
+language:
+- krc
+- ru
+- en
+license: cc-by-nc-sa-4.0
 ---
+# QM-4B: with Qarachay-Malqar Language
+A model based on Qwen3-4B-Instruct-2507, fine-tuned to support the Qarachay-Malqar language.
+## Description
+QM-4B is a language model built on Qwen3-4B-Instruct-2507 with an extended tokenizer and fine-tuning for Qarachay-Malqar language support (къарачай-малкъар тил).
+### Training Stages:
+1. **Tokenizer Extension** — added tokens for Qarachay-Malkar
+2. **Embeddings-only Training** — training only embedding layers (3 epochs, LR=2e-4)
+3. **Full Fine-Tune** — full fine-tuning of all model layers (1 epoch, LR=5e-6)
+## Training Metrics
+| Stage | Train Loss | Eval Loss | Parameters |
+|-------|------------|-----------|------------|
+| Embeddings-only | 4.27 | 4.49 | 8.4% (332M) |
+| Full FT (1 epoch) | 4.16 | 4.36 | 100% (3.97B) |
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model = AutoModelForCausalLM.from_pretrained(
+    "TSjB/QM-4B",
+    torch_dtype=torch.bfloat16,
+    device_map="auto",
+    trust_remote_code=True
+)
+tokenizer = AutoTokenizer.from_pretrained(
+    "TSjB/QM-4B",
+    trust_remote_code=True
+)
+# With chat template
+messages = [{"role": "user", "content": "Не зат билесе Къарачай юсюнден?"}]
+text = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True,
+    enable_thinking=False
+)
+inputs = tokenizer(text, return_tensors="pt").to(model.device)
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=200,
+    temperature=0.7,
+    top_p=0.9,
+    do_sample=True,
+    repetition_penalty=1.2,
+    pad_token_id=tokenizer.pad_token_id,
+    eos_token_id=tokenizer.eos_token_id,
+)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+## Recommended Generation Parameters
+```python
+generation_config = {
+    "max_new_tokens": 200,
+    "temperature": 0.7,
+    "top_p": 0.9,
+    "do_sample": True,
+    "repetition_penalty": 1.2,  # important to avoid repetitions
+    "no_repeat_ngram_size": 3,  # optional
+}
+```
+## Supported Languages
+- Qarachay-Malqar (къарачай-малкъар тил)
+- Russian
+- English
+- Other languages from the base Qwen3 model
+## Limitations
+- The model was fine-tuned on text data (continued pretraining), not on dialogues
+- May switch between languages within a single response
+- Additional instruction tuning is recommended for better instruction following
+## Training Data
+The model was trained on a multilingual text corpus including:
+- Qarachay-Malqar texts
+- Russian texts
+- English texts
+## License
+cc-by-nc-sa-4.0
+## Citation
+```bibtex
+@misc{qm4b2024,
+  title={QM-4B: Qarachay-Malqar language support},
+  author={TSjB},
+  year={2024},
+  publisher={HuggingFace},
+  url={https://huggingface.co/TSjB/QM-4B}
+}
+```
+## Framework Versions
+- TRL: 0.24.0
+- Transformers: 4.57.3
+- Pytorch: 2.9.0
+- Unsloth: optimized training
+## Authors
+[Bogdan Tewunalany](https://t.me/bogdan_tewunalany), [Ali Berberov](https://t.me/ali_berberov)