Upload lora files

README.md

@@ -1,89 +1,87 @@
 ---
-language:
-- en
 library_name: peft
-pipeline_tag: text-generation
-tags:
-- education
+base_model: models/mistralai_Mistral-7B-Instruct-v0.2
 ---
-# 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}"
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
 
-  * 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 to talk 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
+
+<!-- Provide a longer summary of what this model is. -->
+
 
 
-- **Developed by:** Nora T
-- **Finetuned from model:** mistralai_Mistral-7B-Instruct-v0.2
+- **Developed by:** [More Information Needed]
+- **Funded by [optional]:** [More Information Needed]
+- **Shared by [optional]:** [More Information Needed]
+- **Model type:** [More Information Needed]
+- **Language(s) (NLP):** [More Information Needed]
+- **License:** [More Information Needed]
+- **Finetuned from model [optional]:** [More Information Needed]
 
-### Additional Sources
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
 
 - **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,
-      )
-
-```
+## 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. -->
+
+[More Information Needed]
+
+### Downstream Use [optional]
+
+<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
+
+[More Information Needed]
+
+### Out-of-Scope Use
+
+<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
+
+[More Information Needed]
+
+## Bias, Risks, and Limitations
+
+<!-- This section is meant to convey both technical and sociotechnical limitations. -->
+
+[More Information Needed]
+
 ### 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 Hyperparams
-  * LoRA Rank: 128
-  * LoRA Alpha: 32
-  * Batch Size: 64
-  * Cutoff Length: 256
-  * Learning rate: 3e-4 
-  * Epochs: 3
-  * LoRA Dropout: 0.05
+## Training Details
 
 ### Training Data
-Trained on raw text file 
+
+<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
 
 [More Information Needed]
 
-### Training Procedure
+### Training Procedure 
 
 <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
 
@@ -108,6 +106,18 @@ Trained on raw text file
 
 ### Testing Data, Factors & Metrics
 
+#### Testing Data
+
+<!-- This should link to a Dataset Card if possible. -->
+
+[More Information Needed]
+
+#### Factors
+
+<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
+
+[More Information Needed]
+
 #### Metrics
 
 <!-- These are the evaluation metrics being used, ideally with a description of why. -->
@@ -118,12 +128,37 @@ Trained on raw text file
 
 [More Information Needed]
 
+#### Summary
+
+
+
 ## Model Examination [optional]
 
 <!-- Relevant interpretability work for the model goes here -->
 
 [More Information Needed]
 
+## 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).
+
+- **Hardware Type:** [More Information Needed]
+- **Hours used:** [More Information Needed]
+- **Cloud Provider:** [More Information Needed]
+- **Compute Region:** [More Information Needed]
+- **Carbon Emitted:** [More Information Needed]
+
+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
+
+[More Information Needed]
 
 #### Hardware
 
@@ -137,6 +172,33 @@ Trained on raw text file
 
 <!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
 
+**BibTeX:**
+
+[More Information Needed]
+
+**APA:**
+
+[More Information Needed]
+
+## Glossary [optional]
+
+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+
+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
+
+## Model Card Authors [optional]
+
+[More Information Needed]
+
 ## Model Card Contact
 
-[More Information Needed]
+[More Information Needed]
+
+
+### Framework versions
+
+- PEFT 0.7.1

adapter_config.json

@@ -0,0 +1,31 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "models/mistralai_Mistral-7B-Instruct-v0.2",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 128,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "v_proj",
+    "q_proj",
+    "gate_proj",
+    "up_proj",
+    "down_proj",
+    "o_proj",
+    "k_proj"
+  ],
+  "task_type": "CAUSAL_LM"
+}

adapter_model.safetensors

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+oid sha256:47241482acd7bfd8d36cdbb654565b345d940569a2ebeb1153a9e2a14d64f474
+size 1342238560

runs/Apr16_01-04-30_nora-linux/events.out.tfevents.1713254671.nora-linux.1279622.0

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+oid sha256:9c4119628ec5fc228d06c7f12c104c8cb2d69177170d5ebff2836888088a9344
+size 12913

training_log.json

@@ -0,0 +1,16 @@
+{
+  "base_model_name": "None",
+  "base_model_class": "MistralForCausalLM",
+  "base_loaded_in_4bit": false,
+  "base_loaded_in_8bit": false,
+  "projections": "gate, down, up, q, v, k, o",
+  "loss": 0.118,
+  "learning_rate": 1.195219123505976e-06,
+  "epoch": 3.0,
+  "current_steps": 8155,
+  "train_runtime": 1400.5631,
+  "train_samples_per_second": 11.659,
+  "train_steps_per_second": 0.182,
+  "total_flos": 1.8663500338220237e+17,
+  "train_loss": 0.2664371884336659
+}

training_parameters.json

@@ -0,0 +1,37 @@
+{
+  "lora_name": "mistralai_Mistral-7B-Instruct-v0_2_student_answer_train_examples_mistral_0416_rank_128_epoch_3",
+  "always_override": true,
+  "q_proj_en": true,
+  "v_proj_en": true,
+  "k_proj_en": true,
+  "o_proj_en": true,
+  "gate_proj_en": true,
+  "down_proj_en": true,
+  "up_proj_en": true,
+  "save_steps": 0.0,
+  "micro_batch_size": 2,
+  "batch_size": 64,
+  "epochs": 3.0,
+  "learning_rate": "3e-4",
+  "lr_scheduler_type": "linear",
+  "lora_rank": 128,
+  "lora_alpha": 32,
+  "lora_dropout": 0.05,
+  "cutoff_len": 256,
+  "dataset": "None",
+  "eval_dataset": "None",
+  "format": "None",
+  "eval_steps": 100.0,
+  "raw_text_file": "student_answer_train_examples_mistral_0416",
+  "overlap_len": 128,
+  "newline_favor_len": 128,
+  "higher_rank_limit": false,
+  "warmup_steps": 100.0,
+  "optimizer": "adamw_bnb_8bit",
+  "hard_cut_string": "\\n\\n\\n",
+  "train_only_after": "",
+  "stop_at_loss": 0,
+  "add_eos_token": false,
+  "min_chars": 0.0,
+  "report_to": "None"
+}

training_prompt.json

@@ -0,0 +1,3 @@
+{
+  "template_type": "raw_text"
+}