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.gitattributes CHANGED
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README.md ADDED
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+ ---
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+ license: llama2
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+ datasets:
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+ - HuggingFaceH4/ultrachat_200k
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+ - HuggingFaceH4/ultrafeedback_binarized
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+ - HuggingFaceH4/cai-conversation-harmless
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+ language:
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+ - hu
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+ - en
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+ ---
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+
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+
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+
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+ # SambaLingo-Hungarian-Chat
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+
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+ <img src="SambaLingo_Logo.png" width="340" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+ SambaLingo-Hungarian-Chat is a human aligned chat model trained in Hungarian and English. It is trained using direct preference optimization on top the base model [SambaLingo-Hungarian-Base](https://huggingface.co/sambanovasystems/SambaLingo-Hungarian-Base). The base model adapts [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) to Hungarian by training on 59 billion tokens from the Hungarian split of the [Cultura-X](https://huggingface.co/datasets/uonlp/CulturaX) dataset. Try This Model at [SambaLingo-chat-space](https://huggingface.co/spaces/sambanovasystems/SambaLingo-chat-space).
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+
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+ ## Model Description
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+ - **Developed by:** [SambaNova Systems](https://sambanova.ai/)
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+ - **Model type:** Language Model
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+ - **Language(s):** Hungarian, English
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+ - **Finetuned from model:** [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf)
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+ - **Try This Model:** [SambaLingo-chat-space](https://huggingface.co/spaces/sambanovasystems/SambaLingo-chat-space)
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+ - **Blog Post**: [sambalingo-open-source-language-experts](https://sambanova.ai/blog/sambalingo-open-source-language-experts)
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+
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+ ## Getting Started
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+
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+ ### Loading Model With Hugging Face
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+ Please make sure to set use_fast=False when loading the tokenizer.
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+ ```python
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+ from transformers import AutoModelForCausalLM, AutoTokenizer
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+
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+ tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Hungarian-Chat", use_fast=False)
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+ model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Hungarian-Chat", device_map="auto", torch_dtype="auto")
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+ ```
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+
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+ ### Interacting With Model Pipeline
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+ Please make sure to set use_fast=False when loading the tokenizer.
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+ ```python
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+ from transformers import pipeline
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+ pipe = pipeline("text-generation", model="sambanovasystems/SambaLingo-Hungarian-Chat", device_map="auto", use_fast=False)
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+ messages = [
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+ {"role": "user", "content": {YOUR_QUESTION}},
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+ ]
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+ prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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+ outputs = pipe(prompt)[0]
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+ outputs = outputs["generated_text"]
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+ ```
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+
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+ ### Suggested Inference Parameters
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+ - Temperature: 0.8
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+ - Repetition penalty: 1.0
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+ - Top-p: 0.9
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+
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+ ### Prompting Guidelines
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+ To prompt this model, please use the following chat template:
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+ ```
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+ <|user|>\n{question}</s>\n<|assistant|>\n
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+ ```
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+
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+ ### Example Prompts and Generations
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+ ```
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+ <|user|>
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+ Mi a jelentőssége a magyar szürkemarhának?</s>
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+ <|assistant|>
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+ A magyar szürkemarha jelentős kulturális és gazdasági jelentőséggel bír Magyarország számára. Íme néhány ok, amiért:
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+
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+ 1. Kulturális örökség: A magyar szürkemarha a magyar kultúra és örökség szimbóluma. A fajta évszázadok óta létezik, és a magyar puszta ikonikus szimbólumává vált.
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+
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+ 2. Élelmiszer: A magyar szürkemarha húsa és teje kiváló minőségű, és keresett a magyar és a nemzetközi piacokon. A hús sovány, zsírszegény, és magas a fehérje- és vastartalma.
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+
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+ 3. Turizmus: A magyar szürkemarha fontos turisztikai attrakció Magyarországon. A látogatók szívesen látogatják a szürkemarha farmokat, és megismerkednek a fajta történetével és tenyésztésével.
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+
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+ 4. Génmegőrzés: A magyar szürkemarha fontos génmegőrző faj, amely segít megőrizni a hagyományos gazdálkodási gyakorlatokat és a helyi ökoszisztémákat.
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+
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+ 5. Gazdasági hatás: A magyar szürkemarha fontos gazdasági tevékenység Magyarország számára. A fajta tenyésztése és feldolgozása munkahelyeket teremt, és bevételt generál a gazdálkodók és a feldolgozók számára.
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+
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+ Összességében a magyar szürkemarha jelentős kulturális, gazdasági és ökológiai jelentőséggel bír Magyarország számára. A fajta megőrzése és népszerűsítése fontos a magyar kultúra és örökség, valamint a helyi gazdaság és ökoszisztémák szempontjából.
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+ ```
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+
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+ ## Training Details
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+ The alignment phase follows the recipe for [Zephyr-7B](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta), and comprises two stages: supervised fine-tuning (SFT) and Direct Performance Optimization (DPO).
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+
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+ The SFT phase was done on the [ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) dataset mixed with the Google translated version of the ultrachat_200k dataset. It was trained for one epoch with global batch size 512 and max sequence length 2048 tokens. We used a linear decay learning rate of 2e-5 and 10% warmup.
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+
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+ The DPO phase was done on the [ultrafeedback](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized) dataset and [cai-conversation-harmless](https://huggingface.co/datasets/HuggingFaceH4/cai-conversation-harmless) dataset, mixed with 10% of the data Google translated. It was trained with global batch size 32 and for three epochs. We used a linear decay learning rate of 5e-7, 10% warmup and β=0.1 as the regularization factor for DPO.
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+
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+
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+ ## Tokenizer Details
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+ We extended the vocabulary of the base llama model from 32,000 tokens to 57,000 tokens by adding up to 25,000 non-overlapping tokens from the new language.
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+
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+ ## Uses
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+ <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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+
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+ ### Direct Use
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+
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+ <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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+ Use of this model is governed by the Meta’s [Llama 2 Community License Agreement](https://ai.meta.com/llama/license/). Please review and accept the license before downloading the model weights.
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+
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+ SambaLingo should NOT be used for:
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+
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+ - Mission-critical applications
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+ - Applications that involve the safety of others
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+ - Making highly important decisions
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ Like all LLMs, SambaLingo has certain limitations:
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+ - Hallucination: Model may sometimes generate responses that contain plausible-sounding but factually incorrect or irrelevant information.
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+ - Code Switching: The model might unintentionally switch between languages or dialects within a single response, affecting the coherence and understandability of the output.
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+ - Repetition: The Model may produce repetitive phrases or sentences, leading to less engaging and informative responses.
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+ - Coding and Math: The model's performance in generating accurate code or solving complex mathematical problems may be limited.
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+ - Toxicity: The model could inadvertently generate responses containing inappropriate or harmful content.
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+
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+ ## Acknowledgments
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+ We extend our heartfelt gratitude to the open-source AI community; this endeavor would not have been possible without open source. SambaNova embraces the open-source community and aspires to actively contribute to this initiative.
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+
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+ We would like to give a special thanks to the following groups:
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+ - Meta for open sourcing LLama 2 and open sourcing FLORES-200 dataset
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+ - Nguyen et al for open sourcing CulturaX dataset
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+ - CohereAI for releasing AYA-101 and open sourcing a multilingual instruction tuning dataset
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+ - EleutherAI for their open source evaluation framework
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+ - Hugging Face-H4 team for open source the zephyr training recipe and alignment handbook repo
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+
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+
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+ ## Cite SambaLingo
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+ ```
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+ @software{sambalingo,
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+ title = {{SambaLingo: Open Source Language Experts}},
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+ author = {SambaNova Systems},
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+ url = {https://huggingface.co/sambanovasystems/SambaLingo-Hungarian-Chat}
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+ month = {2},
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+ year = {2024},
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+ version = {1.0},
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+ }
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+ ```
SambaLingo_Logo.png ADDED

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