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Model: INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1 Source: Original Platform
2026-10-01 11:39:18 +08:00
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+---
+library_name: transformers
+tags:
+- gemma2
+- instruct
+- mamaylm
+- insait
+license: gemma
+language:
+- uk
+- en
+base_model:
+- google/gemma-2-9b-it
+- google/gemma-2-9b
+pipeline_tag: text-generation
+---
+# INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/637e1f8cf7e01589cc17bf7e/p6d0YFHjWCQ3S12jWqO1m.png)
+
+INSAIT introduces **MamayLM-Gemma-2-9B-IT-v0.1**, the best performing Ukrainian language model based on **google/gemma-2-9b** and **google/gemma-2-9b-it**.
+MamayLM-Gemma-2-9B-IT-v0.1 is **free to use** and distributed under the [Gemma Terms of Use](https://ai.google.dev/gemma/terms).
+This model was created by [`INSAIT`](https://insait.ai/), part of Sofia University St. Kliment Ohridski, in Sofia, Bulgaria.
+
+# Model description
+
+The model was built on top of Google’s Gemma 2 9B open models.
+It was continuously pre-trained on a large pre-filtered dataset (75B tokens of Ukrainian and English data in total) using the combination of data mixing and model merging, 
+allowing the model to gain outstanding Ukrainian cultural and linguistic capabilities while retaining its English performance. 
+During the pre-training stage, we use various datasets, including Ukrainian web crawl data (FineWeb2), freely available datasets such as Wikipedia, a range of specialized Ukrainian datasets, and machine translations of popular English datasets.
+The model was then instruction-fine-tuned on a newly constructed Ukrainian instruction dataset created using machine translations of current best English datasets and specialized Ukrainian datasets, prepared by Ukrainian community.
+For more information check our blogpost ([English](https://huggingface.co/blog/INSAIT-Institute/mamaylm), [Ukrainian](https://huggingface.co/blog/INSAIT-Institute/mamaylm-ukr)).
+
+# Benchmarks and Results
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/650ed7adf141bc34f91a12ae/UTzlvHkFn3K0Lg717vBg6.png)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/650ed7adf141bc34f91a12ae/_pXGPAMx1a-mOnujQ03iW.png)
+
+We evaluate our models on a set of standard English benchmarks, a translated version of them in Ukrainian, as well as, Ukrainian specific benchmarks we collected:
+
+- **Winogrande challenge**: testing world knowledge and understanding
+- **Hellaswag**: testing sentence completion
+- **ARC Easy/Challenge**: testing logical reasoning
+- **TriviaQA**: testing trivia knowledge
+- **GSM-8k**: solving multiple-choice questions in high-school mathematics
+- **MMLU**: testing knowledge on a multitude of topics
+- **IFEval**: testing instruction-following skills
+- **ZNO**: testing knowledge of the Ukrainian high school curriculum in Ukrainian language & literature, history, mathematics and geography
+
+
+These benchmarks test logical reasoning, mathematics, knowledge, language understanding and other skills of the models and are provided at https://github.com/insait-institute/lm-evaluation-harness-uk.
+The graphs above show the performance of MamayLM 9B compared to other large open models. The results show the excellent abilities of MamayLM in Ukrainian, which allow them to **outperform much larger models**,
+including Alibaba’s Qwen 2.5 72B and Meta’s Llama3.1 70B. 
+Finally, our models retain the **excellent English performance** inherited from the original Google Gemma 2 models upon which they are based.
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/650ed7adf141bc34f91a12ae/u4RYkMiXKmDxaRe2kIusF.png)
+
+# Use in 🤗 Transformers
+First install the latest version of the transformers library:
+```
+pip install -U 'transformers[torch]'
+```
+Then load the model in transformers:
+```python
+from transformers import AutoModelForCausalLM
+model = AutoModelForCausalLM.from_pretrained(
+    "INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1",
+    torch_dtype=torch.bfloat16,
+    attn_implementation="flash_attention_2",
+    device_map="auto",
+)
+```
+
+# Recommended Parameters
+
+For optimal performance, we recommend the following parameters for text generation, as we have extensively tested our model with them:
+
+```python
+from transformers import GenerationConfig
+generation_params = GenerationConfig(
+    max_new_tokens=2048,              # Choose maximum generation tokens
+    temperature=0.1,
+    top_k=25,
+    top_p=1,
+    repetition_penalty=1.1,
+    eos_token_id=[1,107],
+    do_sample=True
+)
+```
+
+In principle, increasing temperature should work adequately as well.
+
+# Instruction format
+
+In order to leverage instruction fine-tuning, your prompt should begin with a beginning-of-sequence token `<bos>` and be formatted in the Gemma 2 chat template. `<bos>` should only be the first token in a chat sequence.
+
+E.g.
+```
+<bos><start_of_turn>user
+Хто такий Козак Мамай?<end_of_turn>
+<start_of_turn>model
+ 
+```
+
+This format is also available as a [chat template](https://huggingface.co/docs/transformers/main/chat_templating) via the `apply_chat_template()` method:
+
+```python
+tokenizer = AutoTokenizer.from_pretrained(
+    "INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1",
+    use_default_system_prompt=False,
+)
+messages = [
+    {"role": "user", "content": "Хто такий Козак Мамай?"},
+]
+input_ids = tokenizer.apply_chat_template(
+  messages,
+  return_tensors="pt",
+  add_generation_prompt=True,
+  return_dict=True
+)
+outputs = model.generate(
+  **input_ids,
+  generation_config=generation_params
+)
+print(tokenizer.decode(outputs[0]))
+```
+
+# Use with vLLM
+
+Example usage with vLLM:
+
+```python
+from vllm import LLM, SamplingParams
+from vllm.inputs import TokensPrompt
+from transformers import AutoTokenizer
+tokenizer = AutoTokenizer.from_pretrained(
+    "INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1",
+    use_default_system_prompt=False,
+)
+sampling_params = SamplingParams(
+    max_tokens=2048,
+    temperature=0.1,
+    top_k=25,
+    top_p=1,
+    repetition_penalty=1.1,
+    stop_token_ids=[1, 107],
+)
+llm = LLM(
+    model="INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1",
+    dtype="bfloat16",
+    # enforce_eager=True
+)
+messages = [
+    {"role": "user", "content": "Хто такий Козак Мамай?"},
+]
+formatted_prompt = tokenizer.apply_chat_template(
+    messages,
+    tokenize=False,
+    add_generation_prompt=True
+)
+input_ids = tokenizer(
+    formatted_prompt,
+    add_special_tokens=False
+).input_ids
+prompt = TokensPrompt(prompt_token_ids=input_ids)
+output = llm.generate(
+    prompt,
+    sampling_params
+)
+generated_text = output[0].outputs[0].text
+print(generated_text)
+```
+
+# Use with GGML / llama.cpp
+
+The model and instructions for usage in GGUF format are available at [INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1-GGUF](https://huggingface.co/INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1-GGUF).
+
+# Community Feedback
+
+We welcome feedback from the community to help improve MamayLM. If you have suggestions, encounter any issues, or have ideas for improvements, please:
+- Share your experience using the model through Hugging Face's community discussion feature or
+- Contact us at [contact@insait.ai](mailto:contact@insait.ai)
+
+Your real-world usage and insights are valuable in helping us optimize the model's performance and behaviour for various use cases.
+
+# Summary
+- **Finetuned from:** [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it); [google/gemma-2-9b](https://huggingface.co/google/gemma-2-9b);
+- **Model type:** Causal decoder-only transformer language model
+- **Language:** Ukrainian and English
+- **Contact:** [contact@insait.ai](mailto:contact@insait.ai)
+- **License:** MamayLM is distributed under [Gemma Terms of Use](https://huggingface.co/INSAIT-Institute/MamayLM-Gemma-2-9B-IT-v0.1/raw/main/LICENSE)