初始化项目，由ModelHub XC社区提供模型
Model: brucewayne0459/OpenBioLLm-Derm Source: Original Platform
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README.md
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README.md
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---
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datasets:
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- brucewayne0459/Skin_diseases_and_care
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language:
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- en
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license: mit
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tags:
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- medical
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- dermatology
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- skin_disease
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- skin_care
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- unsloth
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- trl
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- sft
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---
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## Model Details
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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:** Bruce_Wayne(The Batman)
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- **Model type:** Text Generation
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- **Finetuned from model [optional]:** OpenBioLLM(llama-3)(aaditya/Llama3-OpenBioLLM-8B)
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## You can find the gguf versions here --> https://huggingface.co/brucewayne0459/OpenBioLLm-Derm-gguf
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### please let me know how the model works -->https://forms.gle/N14zZTkLpUr6Hf4BA
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### Thank you!
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## Uses
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### Direct Use
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This model is fine-tuned on skin diseases and dermatology data and is used for a dermatology chatbot to provide clear, accurate, and helpful information about various skin diseases, skin care routines, treatments, and related dermatological advice.
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## Bias, Risks, and Limitations
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This model is trained on dermatology data, which might contain inherent biases. It is important to note that the model's responses should not be considered a substitute for professional medical advice. There may be limitations in understanding rare skin conditions or those not well-represented in the training data.
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The model still need to be fine-tuned further to get accurate answers.
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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## How to Get Started with the Model
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Use the code below to get started with the model.
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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model_name = "brucewayne0459/OpenBioLLm-Derm"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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model = AutoModelForCausalLM.from_pretrained(model_name)
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```
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## Training Details
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### Training Data
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The model is fine-tuned on a dataset containing information about various skin diseases and dermatology care.
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brucewayne0459/Skin_diseases_and_care
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### Training Procedure
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Preprocessing [optional]
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"""Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
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### Instruction:
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You are a highly knowledgeable and empathetic dermatologist. Provide clear, accurate, and helpful information about various skin diseases, skin care routines, treatments, and related dermatological advice.
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### Input:
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{}
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### Response:
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{}
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"""
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EOS_TOKEN = tokenizer.eos_token # Must add EOS_TOKEN
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def formatting_prompts_func(examples):
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inputs = examples["Topic"]
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outputs = examples["Information"]
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texts = []
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Prompt passed while fine tuning the model
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#### Training Hyperparameters
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Training regime: The model was trained using the following hyperparameters:
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Per device train batch size: 2
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Gradient accumulation steps: 4
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Warmup steps: 5
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Max steps: 120
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Learning rate: 2e-4
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Optimizer: AdamW (8-bit)
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Weight decay: 0.01
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LR scheduler type: Linear
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** Tesls T4 gpu
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- **Hours used:** 1hr
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- **Cloud Provider:** Google Colab
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## Technical Specifications [optional]
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### Model Architecture and Objective
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This model is based on the LLaMA (Large Language Model Meta AI) architecture and fine-tuned to provide dermatological advice.
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#### Hardware
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The training was performed on Tesla T4 gpu with 4-bit quantization and gradient checkpointing to optimize memory usage.
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### Feel free to provide any missing details or correct the assumptions made, and I'll update the model card accordingly.
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config.json
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{
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"_name_or_path": "aaditya/Llama3-OpenBioLLM-8B",
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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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"attention_dropout": 0.0,
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"bos_token_id": 128000,
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"eos_token_id": 128001,
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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": 14336,
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"max_position_embeddings": 8192,
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"mlp_bias": false,
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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": 8,
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"pad_token_id": 128255,
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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": 500000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.43.3",
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"use_cache": false,
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"vocab_size": 128256
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}
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||||
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||||
}
|
||||
}
|
||||
17
special_tokens_map.json
Normal file
17
special_tokens_map.json
Normal file
@@ -0,0 +1,17 @@
|
||||
{
|
||||
"bos_token": {
|
||||
"content": "<|begin_of_text|>",
|
||||
"lstrip": false,
|
||||
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|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"eos_token": {
|
||||
"content": "<|end_of_text|>",
|
||||
"lstrip": false,
|
||||
"normalized": false,
|
||||
"rstrip": false,
|
||||
"single_word": false
|
||||
},
|
||||
"pad_token": "<|reserved_special_token_250|>"
|
||||
}
|
||||
410504
tokenizer.json
Normal file
410504
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
2063
tokenizer_config.json
Normal file
2063
tokenizer_config.json
Normal file
File diff suppressed because it is too large
Load Diff
Reference in New Issue
Block a user