Commit
•
4d66585
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Parent(s):
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Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- README.md +136 -0
- SambaLingo_Logo.png +3 -0
- config.json +29 -0
- generation_config.json +8 -0
- model.safetensors.index.json +298 -0
- output.safetensors +3 -0
- special_tokens_map.json +30 -0
- tokenizer.model +3 -0
- tokenizer_config.json +43 -0
.gitattributes
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@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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*.zip filter=lfs diff=lfs merge=lfs -text
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*.zst filter=lfs diff=lfs merge=lfs -text
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*tfevents* filter=lfs diff=lfs merge=lfs -text
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SambaLingo_Logo.png filter=lfs diff=lfs merge=lfs -text
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README.md
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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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- ja
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- en
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---
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# SambaLingo-Japanese-Chat
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<img src="SambaLingo_Logo.png" width="340" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
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<!-- Provide a quick summary of what the model is/does. -->
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SambaLingo-Japanese-Chat is a human aligned chat model trained in Japanese and English. It is trained using direct preference optimization on top the base model [SambaLingo-Japanese-Base](https://huggingface.co/sambanovasystems/SambaLingo-Japanese-Base). The base model adapts [Llama-2-7b](https://huggingface.co/meta-llama/Llama-2-7b-hf) to Japanese by training on 42 billion tokens from the Japanese 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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## Model Description
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<!-- Provide a longer summary of what this model is. -->
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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):** Japanese, 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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## Getting Started
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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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tokenizer = AutoTokenizer.from_pretrained("sambanovasystems/SambaLingo-Japanese-Chat", use_fast=False)
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model = AutoModelForCausalLM.from_pretrained("sambanovasystems/SambaLingo-Japanese-Chat", device_map="auto", torch_dtype="auto")
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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-Japanese-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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### 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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### 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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### Example Prompts and Generations
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```
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<|user|>
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茶道用語で、茶室ではなく、屋外でお茶を点てることを何というでしょう?</s>
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<|assistant|>
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茶道用語で、茶室ではなく屋外でお茶を点てることを「野点(のだて)」といいます。
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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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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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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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## 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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## 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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### Direct Use
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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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### Out-of-Scope Use
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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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- 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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## Bias, Risks, and Limitations
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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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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## 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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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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## 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-Japanese-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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```
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SambaLingo_Logo.png
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![]() |
Git LFS Details
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config.json
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{
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"_name_or_path": "/import/ml-sc-scratch6/bol/alignment/sn_alignment_handbook/multilingual_dpo/ckpts/sn_ja_final_ultrachatsft",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 4096,
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"initializer_range": 0.02,
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"intermediate_size": 11008,
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"max_position_embeddings": 4096,
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"model_name": "",
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"model_type": "llama",
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"num_attention_heads": 32,
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"num_hidden_layers": 32,
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"num_key_value_heads": 32,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": null,
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.35.0",
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"use_cache": true,
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"vocab_size": 57344
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"pad_token_id": 0,
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"transformers_version": "4.35.0",
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"use_cache": false
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}
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model.safetensors.index.json
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{
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"metadata": {
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"total_size": 13892067328
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},
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