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--- |
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language: |
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- en |
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license: apache-2.0 |
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datasets: |
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- databricks/databricks-dolly-15k |
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- Felladrin/ChatML-databricks-dolly-15k |
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- euclaise/reddit-instruct-curated |
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- Felladrin/ChatML-reddit-instruct-curated |
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- THUDM/webglm-qa |
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- Felladrin/ChatML-WebGLM-QA |
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- starfishmedical/webGPT_x_dolly |
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- Felladrin/ChatML-webGPT_x_dolly |
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- LDJnr/Capybara |
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- Felladrin/ChatML-Capybara |
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- Open-Orca/SlimOrca-Dedup |
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- Felladrin/ChatML-SlimOrca-Dedup |
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- HuggingFaceH4/ultrachat_200k |
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- Felladrin/ChatML-ultrachat_200k |
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- nvidia/HelpSteer |
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- Felladrin/ChatML-HelpSteer |
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- sablo/oasst2_curated |
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- Felladrin/ChatML-oasst2_curated |
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- CohereForAI/aya_dataset |
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- Felladrin/ChatML-aya_dataset |
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- argilla/distilabel-capybara-dpo-7k-binarized |
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- Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized |
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- argilla/distilabel-intel-orca-dpo-pairs |
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- Felladrin/ChatML-distilabel-intel-orca-dpo-pairs |
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- argilla/ultrafeedback-binarized-preferences |
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- Felladrin/ChatML-ultrafeedback-binarized-preferences |
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- sablo/oasst2_dpo_pairs_en |
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- Felladrin/ChatML-oasst2_dpo_pairs_en |
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- NeuralNovel/Neural-DPO |
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- Felladrin/ChatML-Neural-DPO |
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base_model: Felladrin/Minueza-32M-Base |
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pipeline_tag: text-generation |
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widget: |
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- messages: |
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- role: system |
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content: |
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You are a career counselor. The user will provide you with an individual |
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looking for guidance in their professional life, and your task is to assist |
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them in determining what careers they are most suited for based on their skills, |
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interests, and experience. You should also conduct research into the various |
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options available, explain the job market trends in different industries, and |
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advice on which qualifications would be beneficial for pursuing particular fields. |
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- role: user |
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content: Heya! |
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- role: assistant |
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content: Hi! How may I help you? |
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- role: user |
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content: |
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I am interested in developing a career in software engineering. What |
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would you recommend me to do? |
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- messages: |
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- role: system |
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content: |
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You are a highly knowledgeable assistant. Help the user as much as you |
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can. |
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- role: user |
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content: How can I become a healthier person? |
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- messages: |
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- role: system |
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content: You are a helpful assistant who gives creative responses. |
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- role: user |
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content: Write the specs of a game about mages in a fantasy world. |
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- messages: |
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- role: system |
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content: You are a helpful assistant who answers user's questions with details. |
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- role: user |
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content: Tell me about the pros and cons of social media. |
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- messages: |
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- role: system |
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content: |
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You are a helpful assistant who answers user's questions with details |
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and curiosity. |
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- role: user |
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content: What are some potential applications for quantum computing? |
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inference: |
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parameters: |
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max_new_tokens: 250 |
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do_sample: true |
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temperature: 0.65 |
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top_p: 0.55 |
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top_k: 35 |
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repetition_penalty: 1.176 |
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--- |
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# Minueza-32Mx2-Chat |
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- Base model: [Felladrin/Minueza-32M-Base](https://huggingface.co/Felladrin/Minueza-32M-Base) |
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- Datasets used during SFT: |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-databricks-dolly-15k)] [databricks/databricks-dolly-15k](https://huggingface.co/datasets/databricks/databricks-dolly-15k) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-reddit-instruct-curated)] [euclaise/reddit-instruct-curated](https://huggingface.co/datasets/euclaise/reddit-instruct-curated) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-WebGLM-QA)] [THUDM/webglm-qa](https://huggingface.co/datasets/THUDM/webglm-qa) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-webGPT_x_dolly)] [starfishmedical/webGPT_x_dolly](https://huggingface.co/datasets/starfishmedical/webGPT_x_dolly) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-Capybara)] [LDJnr/Capybara](https://huggingface.co/datasets/LDJnr/Capybara) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-SlimOrca-Dedup)] [Open-Orca/SlimOrca-Dedup](https://huggingface.co/datasets/Open-Orca/SlimOrca-Dedup) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrachat_200k)] [HuggingFaceH4/ultrachat_200k](https://huggingface.co/datasets/HuggingFaceH4/ultrachat_200k) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-HelpSteer)] [nvidia/HelpSteer](https://huggingface.co/datasets/nvidia/HelpSteer) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-oasst2_curated)] [sablo/oasst2_curated](https://huggingface.co/datasets/sablo/oasst2_curated) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-aya_dataset)] [CohereForAI/aya_dataset](https://huggingface.co/datasets/CohereForAI/aya_dataset) |
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- Datasets used during DPO: |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-distilabel-capybara-dpo-7k-binarized)] [argilla/distilabel-capybara-dpo-7k-binarized](https://huggingface.co/datasets/argilla/distilabel-capybara-dpo-7k-binarized) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-distilabel-intel-orca-dpo-pairs)] [argilla/distilabel-intel-orca-dpo-pairs](https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-ultrafeedback-binarized-preferences)] [argilla/ultrafeedback-binarized-preferences](https://huggingface.co/datasets/argilla/ultrafeedback-binarized-preferences) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-oasst2_dpo_pairs_en)] [sablo/oasst2_dpo_pairs_en](https://huggingface.co/datasets/sablo/oasst2_dpo_pairs_en) |
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- [[ChatML](https://huggingface.co/datasets/Felladrin/ChatML-Neural-DPO)] [NeuralNovel/Neural-DPO](https://huggingface.co/datasets/NeuralNovel/Neural-DPO) |
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- License: [Apache License 2.0](https://huggingface.co/Felladrin/Minueza-32Mx2-Chat/resolve/main/license.txt) |
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## Recommended Prompt Format |
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``` |
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<|im_start|>system |
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{system_message}<|im_end|> |
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<|im_start|>user |
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{user_message}<|im_end|> |
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<|im_start|>assistant |
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``` |
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## Recommended Inference Parameters |
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```yml |
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do_sample: true |
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temperature: 0.65 |
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top_p: 0.55 |
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top_k: 35 |
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repetition_penalty: 1.176 |
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``` |
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## Usage Example |
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```python |
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from transformers import pipeline |
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generate = pipeline("text-generation", "Felladrin/Minueza-32Mx2-Chat") |
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messages = [ |
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{ |
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"role": "system", |
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"content": "You are a helpful assistant who answers the user's questions with details and curiosity.", |
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}, |
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{ |
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"role": "user", |
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"content": "What are some potential applications for quantum computing?", |
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}, |
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] |
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prompt = generate.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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output = generate( |
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prompt, |
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max_new_tokens=256, |
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do_sample=True, |
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temperature=0.65, |
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top_k=35, |
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top_p=0.55, |
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repetition_penalty=1.176, |
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) |
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print(output[0]["generated_text"]) |
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``` |
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## How it was trained |
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This model was trained with [SFT Trainer](https://huggingface.co/docs/trl/main/en/sft_trainer) and [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer), in several sessions, using the following settings: |
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For Supervised Fine-Tuning: |
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| Hyperparameter | Value | |
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| :--------------------- | :-------------------------------------------- | |
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| Learning rate | 2e-6 | |
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| Total train batch size | 16 | |
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| Max. sequence length | 2048 | |
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| Weight decay | 0.01 | |
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| Warmup ratio | 0.1 | |
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| Optimizer | Adam with betas=(0.9,0.999) and epsilon=1e-08 | |
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| Scheduler | cosine | |
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| Seed | 42 | |
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| Neftune Noise Alpha | 5 | |
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For Direct Preference Optimization: |
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| Hyperparameter | Value | |
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| :--------------------- | :---- | |
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| Learning rate | 5e-7 | |
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| Total train batch size | 16 | |
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| Max. length | 1024 | |
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| Max. prompt length | 512 | |
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| Max. steps | 200 | |
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| Weight decay | 0 | |
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| Warmup ratio | 0.1 | |
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| Beta | 0.1 | |
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