ntseng
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mistralai_Mistral-7B-Instruct-v0_2_student_answer_train_examples_mistral_0416

@@ -1,15 +1,28 @@
 ---
 library_name: peft
 base_model: models/mistralai_Mistral-7B-Instruct-v0.2
 ---
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
 ## Model Details
 ### Model Description
@@ -33,133 +46,71 @@ base_model: models/mistralai_Mistral-7B-Instruct-v0.2
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
-## Uses
-<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
-### Direct Use
-<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
-<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-## How to Get Started with the Model
-Use the code below to get started with the model.
-[More Information Needed]
-## Training Details
 ### Training Data
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-### Training Procedure
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 #### Preprocessing [optional]
 [More Information Needed]
-#### Training Hyperparameters
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-## Evaluation
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-### Results
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-#### Summary
-## Model Examination [optional]
-<!-- Relevant interpretability work for the model goes here -->
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-## Environmental Impact
-<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
-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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-## Technical Specifications [optional]
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-### Compute Infrastructure
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@@ -172,33 +123,6 @@ Carbon emissions can be estimated using the [Machine Learning Impact calculator]
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 ### Framework versions
 - PEFT 0.7.1

 ---
 library_name: peft
 base_model: models/mistralai_Mistral-7B-Instruct-v0.2
+language:
+- en
+pipeline_tag: text-generation
+tags:
+- education
 ---
+# mistralai_Mistral-7B-Instruct-v0_2_student_answer_train_examples_mistral_0416
+  * LoRAs weights for Mistral-7b-Instruct-v0_2
+# Noteworthy changes:
+  * reduced training hyperparams: epochs=3 (previously 4)
+  * new training prompt: "Teenager students write in simple sentences.
+          You are a teenager student, and please answer the following question. {training example}"
+  * old training prompt: "Teenager students write in simple sentences [with typos and grammar errors].
+          You are a teenager student, and please answer the following question. {training example}"
 ## Model Details
+Fine-tuned model that talks like middle school students, using simple vocabulary and grammar.
+  * Trained on student Q&As physics topics including pulley/ramp examples that discuss work, force, and etc.
 ### Model Description
 - **Paper [optional]:** [More Information Needed]
 - **Demo [optional]:** [More Information Needed]
+## Model Details
+Fine-tuned model to talk like middle school students, using typos/grammar errors.
+Trained on student Q&As physics topics including pulley/ramp examples that discuss work, force, and etc.
+- **Developed by:** Nora T
+- **Finetuned from model:** mistralai_Mistral-7B-Instruct-v0.2
+- **Repository:** [More Information Needed]
+- **Paper [optional]:** [More Information Needed]
+- **Demo [optional]:** [More Information Needed]
+## How to Get Started:
+1. Load Mistral model first:
+```
+from peft import PeftModel # for fine-tuning
+from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline, GenerationConfig, GPTQConfig, BitsAndBytesConfig
+model_name_or_path = "mistralai/Mistral-7B-Instruct-v0.2"
+nf4_config = BitsAndBytesConfig( # quantization 4-bit
+   load_in_4bit=True,
+   bnb_4bit_quant_type="nf4",
+   bnb_4bit_use_double_quant=True,
+   bnb_4bit_compute_dtype=torch.bfloat16
+)
+model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
+                                             device_map="auto",
+                                             trust_remote_code=False,
+                                             quantization_config=nf4_config,
+                                             revision="main")
+tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
+```
+2. Load in LoRA weights:
+```
+lora_model_path = "{path_to_loras_folder}/mistralai_Mistral-7B-Instruct-v0.2-testgen-LoRAs" # load loras
+model = PeftModel.from_pretrained(
+        model, lora_model_path, torch_dtype=torch.float16, force_download=True,
+      )
+```
+## Training Hyperparams
+  * LoRA Rank: 128
+  * LoRA Alpha: 32
+  * Batch Size: 64
+  * Cutoff Length: 256
+  * Learning rate: 3e-4
+  * Epochs: 3
+  * LoRA Dropout: 0.05
 ### Training Data
+Trained on raw text file
 #### Preprocessing [optional]
 [More Information Needed]
+## Technical Specifications
 ### Model Architecture and Objective
 [More Information Needed]
 #### Hardware
 [More Information Needed]
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 ### Framework versions
 - PEFT 0.7.1