初始化项目，由ModelHub XC社区提供模型
Model: GaMS-Beta/GaMS-9B-SFT-Translator-DPO Source: Original Platform
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README.md
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---
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license: gemma
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language:
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- sl
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- en
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- hr
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- sr
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- bs
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base_model:
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- GaMS-Beta/GaMS-9B-SFT-Translator
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pipeline_tag: text-generation
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---
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# Model Card for GaMS-DPO-Translator
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GaMS-Beta/GaMS-9B-SFT-Translator-DPO is a fine-tuned version of GaMS-9B-SFT-Translator. Direct Preference Optimization (DPO) was performed on the original model. The learning dataset was synthetially generated by using GaMS-9B-SFT-Translator and EuroLLM-9B-Instruct.
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## Basic information
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- **Developed by:** team of researchers at the University of Ljubljana, Faculty for Computer and Information Science. Team members: Dario Vajda, Domen Vreš and Marko Robnik-Šikonja.
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- **Languages:** Slovene, English (primary), Croatian, Bosnian and Serbian (secondary). The model might also work for other languages supported by Gemma 2, even though it was not continually pretrained on them.
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- **Base model:** [cjvt/GaMS-9B-Instruct](https://huggingface.co/cjvt/GaMS-9B-Instruct)
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- **License:** [Gemma](https://ai.google.dev/gemma/terms)
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## Usage
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The model can be run through `pipeline` API using the following code:
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```python
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from transformers import pipeline
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model_id = "GaMS-Beta/GaMS-9B-SFT-Translator-DPO"
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pline = pipeline(
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"text-generation",
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model=model_id,
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device_map="cuda" # replace with "mps" to run on a Mac device
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)
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# Example of response generation
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message = [{"role": "user", "content": "Prevedi naslednje angleško besedilo v slovenščino.\nToday is a nice day."}]
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response = pline(message, max_new_tokens=512)
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print("Translation:", response[0]["generated_text"][-1]["content"])
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```
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For multi GPU inference set the `device_map` to `auto`:
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```python
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from transformers import pipeline
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model_id = "GaMS-Beta/GaMS-9B-SFT-Translator-DPO"
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pline = pipeline(
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"text-generation",
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model=model_id,
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device_map="auto"
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)
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# Example of response generation
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message = [{"role": "user", "content": "Prevedi naslednje angleško besedilo v slovenščino.\nToday is a nice day."}]
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response = pline(message, max_new_tokens=512)
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print("Model's response:", response[0]["generated_text"][-1]["content"])
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# Example of conversation chain
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new_message = response[0]["generated_text"]
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new_message.append({"role": "user", "content": "Lahko bolj podrobno opišeš ta dogodek?"})
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response = pline(new_message, max_new_tokens=1024)
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print("Model's response:", response[0]["generated_text"][-1]["content"])
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```
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## Data
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Data for fine-tuning the original model was acquired by translating a large corpora of wikipedia articles, ccnews articles, bookcorpus texts and english conversational datasets by two models(GaMS-9B-SFT-Translator and EuroLLM-9B-Instruct) which were then ranked by some automatic metrics for translation quality and reliability.
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## Training
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The model was trained on the [Vega HPC](https://izum.si/vega_slv/)
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## Evaluation
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The model was evaluated by our custom script on three types of data. The results are show in the following table.
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| Model | Overall Comet | ccnews | nemotron | wikipedia | Bad Lang (%) | Short (%) | Bad Markdown (%) |
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| --- | --- | --- | --- | --- | --- | --- | --- |
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| gemini-2.5-flash | 0.717982 | 0.702981 | 0.697498 | 0.753924 | 0.35% | 0.42% | 3.70% |
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| GaMS-9B-Instruct-DPO-Translator | 0.714729 | 0.708317 | 0.689316 | 0.746768 | 1.88% | 1.56% | 13.22% |
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| **GaMS-9B-SFT-Translator-DPO** | **0.708042** | **0.702903** | **0.679462** | **0.742583** | **0.91%** | **0.28%** | **18.28%** |
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| GaMS-27B-Instruct | 0.701284 | 0.686480 | 0.680014 | 0.730733 | 27.28% | 5.36% | 62.07% |
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| GaMS-9B-Instruct | 0.693659 | 0.685006 | 0.673394 | 0.723470 | 13.50% | 4.83% | 33.15% |
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| EuroLLM-9B-Instruct | 0.689321 | 0.668084 | 0.670723 | 0.729227 | 8.97% | 1.89% | 35.08% |
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| GaMS-9B-SFT-Translator | 0.682467 | 0.676580 | 0.673650 | 0.699602 | 5.14% | 1.48% | 30.53% |
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*Note* - the evaluation script and evaluation data can be found in this [github repo](https://github.com/DarioVajda/translation_dpo) under the data_pipeline folder. See the README for more detailed instructions.
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## Citation
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If you found this project useful in your work, please cite our paper with the following BibTeX citation:
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```txt
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@misc{vajda2025improvingllmsmachinetranslation,
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title={Improving LLMs for Machine Translation Using Synthetic Preference Data},
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author={Dario Vajda and Domen Vreš and Marko Robnik-Šikonja},
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year={2025},
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eprint={2508.14951},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2508.14951},
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}
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```
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{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if (message['role'] == 'assistant') %}{% set role = 'model' %}{% else %}{% set role = message['role'] %}{% endif %}{{ '<start_of_turn>' + role + '
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' + message['content'] | trim + '<end_of_turn>
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' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model
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'}}{% endif %}
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"attention_bias": false,
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"attention_dropout": 0.0,
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"attn_logit_softcapping": 50.0,
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"bos_token_id": 2,
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"cache_implementation": "hybrid",
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"dtype": "bfloat16",
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"eos_token_id": 1,
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"final_logit_softcapping": 30.0,
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"head_dim": 256,
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"hidden_act": "gelu_pytorch_tanh",
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"max_position_embeddings": 8192,
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"model_type": "gemma2",
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"num_attention_heads": 16,
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"num_hidden_layers": 42,
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"num_key_value_heads": 8,
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"pad_token_id": 0,
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"query_pre_attn_scalar": 256,
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"rms_norm_eps": 1e-06,
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"rope_theta": 10000.0,
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"sliding_window": 4096,
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"sliding_window_size": 4096,
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"transformers_version": "4.56.1",
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"use_cache": true,
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"vocab_size": 256000
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}
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
"model.layers.9.mlp.gate_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.post_attention_layernorm.weight": "model-00002-of-00004.safetensors",
|
||||
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|
||||
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|
||||
"model.layers.9.self_attn.k_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.o_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.q_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.layers.9.self_attn.v_proj.weight": "model-00002-of-00004.safetensors",
|
||||
"model.norm.weight": "model-00004-of-00004.safetensors"
|
||||
}
|
||||
}
|
||||
34
special_tokens_map.json
Normal file
34
special_tokens_map.json
Normal file
@@ -0,0 +1,34 @@
|
||||
{
|
||||
"additional_special_tokens": [
|
||||
"<start_of_turn>",
|
||||
"<end_of_turn>"
|
||||
],
|
||||
"bos_token": {
|
||||
"content": "<bos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<eos>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": {
|
||||
"content": "<pad>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"unk_token": {
|
||||
"content": "<unk>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
}
|
||||
}
|
||||
3
tokenizer.json
Normal file
3
tokenizer.json
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:5f7eee611703c5ce5d1eee32d9cdcfe465647b8aff0c1dfb3bed7ad7dbb05060
|
||||
size 34362873
|
||||
3
tokenizer.model
Normal file
3
tokenizer.model
Normal file
@@ -0,0 +1,3 @@
|
||||
version https://git-lfs.github.com/spec/v1
|
||||
oid sha256:61a7b147390c64585d6c3543dd6fc636906c9af3865a5548f27f31aee1d4c8e2
|
||||
size 4241003
|
||||
2013
tokenizer_config.json
Normal file
2013
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user