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--- |
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library_name: peft |
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base_model: mistralai/Mistral-7B-v0.1 |
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license: mit |
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datasets: |
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- keivalya/MedQuad-MedicalQnADataset |
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language: |
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- en |
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metrics: |
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- bertscore |
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tags: |
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- medical |
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--- |
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# Model Card for Model ID |
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This is a medicine-focussed mistral fine tuned using keivalya/MedQuad-MedicalQnADataset |
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## Model Details |
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### Model Description |
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Trying to get better at medical Q & A |
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- **Developed by:** [Tonic](https://huggingface.co/Tonic) |
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- **Shared by [optional]:** [Tonic](https://huggingface.co/Tonic) |
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- **Model type:** Mistral Fine-Tune |
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- **Language(s) (NLP):** English |
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- **License:** MIT2.0 |
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- **Finetuned from model [optional]:** [mistralai/Mistral-7B-v0.1](https://huggingface.com/Mistralai/Mistral-7B-v0.1) |
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### Model Sources [optional] |
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- **Repository:** [Tonic/mistralmed](https://huggingface.co/Tonic/mistralmed) |
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- **Code :** [github](https://github.com/Josephrp/mistralmed/blob/main/finetuning.py) |
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- **Demo :** [Tonic/MistralMed_Chat](https://huggingface.co/Tonic/MistralMed_Chat) |
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## Uses |
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This model can be used the same way you normally use mistral |
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### Direct Use |
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This model can do better in medical question and answer scenarios. |
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### Downstream Use [optional] |
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This model is intended to be further fine tuned. |
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### Recommendations |
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- Do Not Use As Is |
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- Fine Tune This Model Further |
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- For Educational Purposes Only |
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- Benchmark your model usage |
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- Evaluate the model before use |
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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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[Tonic/MistralMED_Chat](https://huggingface.co/Tonic/MistralMED_Chat) |
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```python |
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from transformers import AutoTokenizer, MistralForCausalLM |
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import torch |
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import gradio as gr |
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import random |
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from textwrap import wrap |
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from transformers import AutoConfig, AutoTokenizer, AutoModelForSeq2SeqLM, AutoModelForCausalLM, MistralForCausalLM |
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from peft import PeftModel, PeftConfig |
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import torch |
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import gradio as gr |
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# Functions to Wrap the Prompt Correctly |
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def wrap_text(text, width=90): |
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lines = text.split('\n') |
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wrapped_lines = [textwrap.fill(line, width=width) for line in lines] |
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wrapped_text = '\n'.join(wrapped_lines) |
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return wrapped_text |
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def multimodal_prompt(input_text, system_prompt="", max_length=512): |
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""" |
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Generates text using a large language model, given a prompt and a device. |
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Args: |
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input_text: The input text to generate a response for. |
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system_prompt: Optional system prompt. |
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max_length: Maximum length of the generated text. |
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Returns: |
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A string containing the generated text. |
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""" |
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# Modify the input text to include the desired format |
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formatted_input = f"""<s>[INST]{input_text}[/INST]""" |
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# Encode the input text |
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encodeds = tokenizer(formatted_input, return_tensors="pt", add_special_tokens=False) |
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model_inputs = encodeds.to(device) |
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# Generate a response using the model |
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output = model.generate( |
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**model_inputs, |
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max_length=max_length, |
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use_cache=True, |
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early_stopping=True, |
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bos_token_id=model.config.bos_token_id, |
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eos_token_id=model.config.eos_token_id, |
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pad_token_id=model.config.eos_token_id, |
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temperature=0.1, |
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do_sample=True |
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) |
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# Decode the response |
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response_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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return response_text |
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# Define the device |
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device = "cuda" if torch.cuda.is_available() else "cpu" |
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# Use the base model's ID |
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base_model_id = "mistralai/Mistral-7B-v0.1" |
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model_directory = "Tonic/mistralmed" |
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# Instantiate the Tokenizer |
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tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True, padding_side="left") |
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# tokenizer = AutoTokenizer.from_pretrained("Tonic/mistralmed", trust_remote_code=True, padding_side="left") |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = 'left' |
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# Specify the configuration class for the model |
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#model_config = AutoConfig.from_pretrained(base_model_id) |
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# Load the PEFT model with the specified configuration |
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#peft_model = AutoModelForCausalLM.from_pretrained(base_model_id, config=model_config) |
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# Load the PEFT model |
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peft_config = PeftConfig.from_pretrained("Tonic/mistralmed", token="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") |
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peft_model = MistralForCausalLM.from_pretrained("mistralai/Mistral-7B-v0.1", trust_remote_code=True) |
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peft_model = PeftModel.from_pretrained(peft_model, "Tonic/mistralmed", token="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") |
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class ChatBot: |
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def __init__(self): |
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self.history = [] |
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def predict(self, input_text): |
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# Encode user input |
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user_input_ids = tokenizer.encode(input_text, return_tensors="pt") |
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# Concatenate the user input with chat history |
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if len(self.history) > 0: |
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chat_history_ids = torch.cat([self.history, user_input_ids], dim=-1) |
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else: |
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chat_history_ids = user_input_ids |
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# Generate a response using the PEFT model |
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response = peft_model.generate(input_ids=chat_history_ids, max_length=512, pad_token_id=tokenizer.eos_token_id) |
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# Update chat history |
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self.history = chat_history_ids |
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# Decode and return the response |
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response_text = tokenizer.decode(response[0], skip_special_tokens=True) |
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return response_text |
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bot = ChatBot() |
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title = "👋🏻Welcome to Tonic's MistralMed Chat🚀" |
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description = "You can use this Space to test out the current model (MistralMed) or duplicate this Space and use it for any other model on 🤗HuggingFace. Join me on Discord to build together." |
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examples = [["What is the boiling point of nitrogen?"]] |
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iface = gr.Interface( |
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fn=bot.predict, |
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title=title, |
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description=description, |
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examples=examples, |
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inputs="text", |
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outputs="text", |
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theme="ParityError/Anime" |
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) |
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iface.launch() |
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``` |
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## Training Details |
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### Training Data |
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[MedQuad](https://huggingface.co/datasets/keivalya/MedQuad-MedicalQnADataset/viewer/default/train) |
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### Training Procedure |
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Dataset({ |
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features: ['qtype', 'Question', 'Answer'], |
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num_rows: 16407 |
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}) |
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#### Preprocessing [optional] |
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MistralForCausalLM( |
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(model): MistralModel( |
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(embed_tokens): Embedding(32000, 4096) |
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(layers): ModuleList( |
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(0-31): 32 x MistralDecoderLayer( |
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(self_attn): MistralAttention( |
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(q_proj): Linear4bit(in_features=4096, out_features=4096, bias=False) |
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(k_proj): Linear4bit(in_features=4096, out_features=1024, bias=False) |
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(v_proj): Linear4bit(in_features=4096, out_features=1024, bias=False) |
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(o_proj): Linear4bit(in_features=4096, out_features=4096, bias=False) |
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(rotary_emb): MistralRotaryEmbedding() |
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) |
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(mlp): MistralMLP( |
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(gate_proj): Linear4bit(in_features=4096, out_features=14336, bias=False) |
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(up_proj): Linear4bit(in_features=4096, out_features=14336, bias=False) |
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(down_proj): Linear4bit(in_features=14336, out_features=4096, bias=False) |
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(act_fn): SiLUActivation() |
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) |
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(input_layernorm): MistralRMSNorm() |
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(post_attention_layernorm): MistralRMSNorm() |
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) |
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) |
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(norm): MistralRMSNorm() |
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) |
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(lm_head): Linear(in_features=4096, out_features=32000, bias=False) |
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) |
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#### Training Hyperparameters |
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- **Training regime:** |
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config = LoraConfig( |
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r=8, |
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lora_alpha=16, |
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target_modules=[ |
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"q_proj", |
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"k_proj", |
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"v_proj", |
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"o_proj", |
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"gate_proj", |
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"up_proj", |
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"down_proj", |
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"lm_head", |
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], |
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bias="none", |
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lora_dropout=0.05, # Conventional |
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task_type="CAUSAL_LM", |
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) |
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#### Speeds, Sizes, Times [optional] |
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- trainable params: 21260288 || all params: 3773331456 || trainable%: 0.5634354746703705 |
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- TrainOutput(global_step=1000, training_loss=0.47226515007019043, metrics={'train_runtime': 3143.4141, 'train_samples_per_second': 2.545, 'train_steps_per_second': 0.318, 'total_flos': 1.75274075357184e+17, 'train_loss': 0.47226515007019043, 'epoch': 0.49}) |
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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:** A100 |
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- **Hours used:** 1 |
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- **Cloud Provider:** Google |
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- **Compute Region:** East1 |
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- **Carbon Emitted:** 0.09 |
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## Training Results |
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[1000/1000 52:20, Epoch 0/1] |
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| Step | Training Loss | |
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|-------|--------------| |
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| 50 | 0.474200 | |
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| 100 | 0.523300 | |
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| 150 | 0.484500 | |
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| 200 | 0.482800 | |
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| 250 | 0.498800 | |
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| 300 | 0.451800 | |
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| 350 | 0.491800 | |
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| 400 | 0.488000 | |
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| 450 | 0.472800 | |
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| 500 | 0.460400 | |
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| 550 | 0.464700 | |
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| 600 | 0.484800 | |
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| 650 | 0.474600 | |
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| 700 | 0.477900 | |
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| 750 | 0.445300 | |
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| 800 | 0.431300 | |
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| 850 | 0.461500 | |
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| 900 | 0.451200 | |
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| 950 | 0.470800 | |
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| 1000 | 0.454900 | |
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### Model Architecture and Objective |
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PeftModelForCausalLM( |
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(base_model): LoraModel( |
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(model): MistralForCausalLM( |
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(model): MistralModel( |
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(embed_tokens): Embedding(32000, 4096) |
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(layers): ModuleList( |
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(0-31): 32 x MistralDecoderLayer( |
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(self_attn): MistralAttention( |
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(q_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=4096, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False) |
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) |
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(k_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=1024, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False) |
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) |
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(v_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=1024, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=4096, out_features=1024, bias=False) |
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) |
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(o_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=4096, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=4096, out_features=4096, bias=False) |
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) |
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(rotary_emb): MistralRotaryEmbedding() |
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) |
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(mlp): MistralMLP( |
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(gate_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=14336, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False) |
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) |
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(up_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=14336, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=4096, out_features=14336, bias=False) |
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) |
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(down_proj): Linear4bit( |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=14336, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=4096, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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(base_layer): Linear4bit(in_features=14336, out_features=4096, bias=False) |
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) |
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(act_fn): SiLUActivation() |
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) |
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(input_layernorm): MistralRMSNorm() |
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(post_attention_layernorm): MistralRMSNorm() |
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) |
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) |
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(norm): MistralRMSNorm() |
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) |
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(lm_head): Linear( |
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in_features=4096, out_features=32000, bias=False |
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(lora_dropout): ModuleDict( |
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(default): Dropout(p=0.05, inplace=False) |
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) |
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(lora_A): ModuleDict( |
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(default): Linear(in_features=4096, out_features=8, bias=False) |
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) |
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(lora_B): ModuleDict( |
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(default): Linear(in_features=8, out_features=32000, bias=False) |
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) |
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(lora_embedding_A): ParameterDict() |
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(lora_embedding_B): ParameterDict() |
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) |
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) |
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) |
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) |
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#### Hardware |
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A100 |
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## Model Card Authors [optional] |
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[Tonic](https://huggingface.co/Tonic) |
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## Model Card Contact |
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[Tonic](https://huggingface.co/Tonic) |
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## Training procedure |
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The following `bitsandbytes` quantization config was used during training: |
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- quant_method: bitsandbytes |
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- load_in_8bit: False |
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- load_in_4bit: True |
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- llm_int8_threshold: 6.0 |
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- llm_int8_skip_modules: None |
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- llm_int8_enable_fp32_cpu_offload: False |
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- llm_int8_has_fp16_weight: False |
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- bnb_4bit_quant_type: nf4 |
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- bnb_4bit_use_double_quant: True |
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- bnb_4bit_compute_dtype: bfloat16 |
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### Framework versions |
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- PEFT 0.6.0.dev0 |
