Initial commit

bert_results_ft_more_layers_github_epoch_9_mlp/pile_full_bert_6_9b_9_github_600_8e-05_test.txt

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+
+    Kolmogorov-Smirnov test statistic: p-value: 0.13385273551786803
+    
+    Mann-Whitney U test statistic: p-value: 0.1872454720291966
+    
+    Anderson-Darling test statistic: 0.4178133291209304 critical-value:1.9609999999999999
+    

eval_bert_test_all.py

@@ -0,0 +1,208 @@
+from bert_score import BERTScorer
+import torch
+import json
+import argparse
+import numpy as np
+from scipy.stats import ks_2samp, mannwhitneyu, anderson_ksamp
+import matplotlib.pyplot as plt
+import re
+import os
+import pandas as pd
+
+def load_jsonl(file_path):
+    data = []
+    with open(file_path, 'r') as file:
+        for line in file:
+            data.append(json.loads(line.strip()))
+    return data
+
+def dump_txt(data, file_path):
+    with open(file_path, 'w') as file:
+        file.write(str(data) + '\n')
+
+def compare_distributions(sample1, sample2):
+    # Kolmogorov-Smirnov Test
+    ks_stat, ks_p_value = ks_2samp(sample1, sample2)
+    print(f"Kolmogorov-Smirnov test statistic: {ks_stat}, p-value: {ks_p_value}")
+    if ks_p_value < 0.05:
+        print("Kolmogorov-Smirnov test: The two samples likely come from different distributions.")
+    else:
+        print("Kolmogorov-Smirnov test: The two samples likely come from the same distribution.")
+    
+    # Mann-Whitney U Test
+    mw_stat, mw_p_value = mannwhitneyu(sample1, sample2, alternative='two-sided')
+    print(f"Mann-Whitney U test statistic: {mw_stat}, p-value: {mw_p_value}")
+    if mw_p_value < 0.05:
+        print("Mann-Whitney U test: The two samples likely come from different distributions.")
+    else:
+        print("Mann-Whitney U test: The two samples likely come from the same distribution.")
+    
+    # Anderson-Darling Test
+    ad_stat, critical_values, ad_significance_level = anderson_ksamp([sample1, sample2])
+    print(f"Anderson-Darling test statistic: {ad_stat}, significance level: {ad_significance_level}")
+    if ad_stat > critical_values[2]:  # Using 5% significance level
+        print("Anderson-Darling test: The two samples likely come from different distributions.")
+    else:
+        print("Anderson-Darling test: The two samples likely come from the same distribution.")
+
+    return ks_p_value, mw_p_value, ad_stat, critical_values[2]
+
+
+def get_num_from_directory(directory_path):
+
+
+    # List to store the extracted numbers
+    numbers = []
+    
+    # Iterate over each file/directory in the specified path
+    for filename in os.listdir(directory_path):
+        # Use regex to find numbers in the filename
+        match = re.search(r'checkpoint-(\d+)', filename)
+        if match:
+            # Append the extracted number to the list as an integer
+            numbers.append(int(match.group(1)))
+
+    return numbers
+
+
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', type=str, default='160m',help='model name') #160m 410m 1b 1.4b 2.8b 6.9b 12b
+parser.add_argument('--epoch', type=int, default=9,help='model name')
+parser.add_argument('--size', type=int, default=600,help='model name')
+parser.add_argument('--subname', type=str, default='arxiv', help='subset name')
+parser.add_argument('--lr', type=float, default=2e-5, help='learning rate')
+parser.add_argument('--temp', type=float, default=0.0, help='generation temperature')
+parser.add_argument('--topp', type=float, default=1.0, help='generation top_p')
+parser.add_argument('--logging', type=str, default='', help='logging of the file')
+
+args = parser.parse_args()
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+bert_scorer = BERTScorer('roberta-large', device=device, rescale_with_baseline=True, lang='en')
+
+
+loss_file_member = f'/workspace/{args.subname}_dataset/output_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/pythia-{args.model}-member-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}/checkpoint-675/trainer_state.json'
+
+loss_file_nonmember = f'/workspace/{args.subname}_dataset/output_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/pythia-{args.model}-nonmember-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}/checkpoint-675/trainer_state.json'
+loss_datafile_member = json.load(open(loss_file_member))['log_history']
+loss_datafile_nonmember = json.load(open(loss_file_nonmember))['log_history']
+loss_l_member = []
+loss_l_nonmember = []
+
+for i in range(len(loss_datafile_member)):
+    try:
+        loss_data_memeber = loss_datafile_member[i]['loss']
+        loss_l_member.append(loss_data_memeber)
+    except:
+        continue
+
+
+for i in range(len(loss_datafile_nonmember)):
+    try:
+        loss_data_nonmember = loss_datafile_nonmember[i]['loss']
+        loss_l_nonmember.append(loss_data_nonmember)
+    except:
+        continue
+
+# Find the largest value in the list
+max_value_member = max(loss_l_member)
+max_value_nonmember = max(loss_l_nonmember)
+
+# Divide each value by the largest value
+normalized_loss_l_member = [x / max_value_member for x in loss_l_member]
+normalized_loss_l_nonmember = [x / max_value_nonmember for x in loss_l_nonmember]
+
+results_dict = {}
+ks_p_value_l=[]
+mw_p_value_l=[]
+
+
+directory_path = f"/workspace/{args.subname}_dataset/output_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/pythia-{args.model}-member-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}"
+numbers = get_num_from_directory(directory_path)
+numbers.sort()
+for num in numbers:
+    for candidate in ['member', 'nonmember']:
+        print(f"#############{num}############")
+        model_name = f'pythia-{args.model}'
+        log_str = f'{candidate}-{args.model}-epoch-{args.epoch}'
+        response_orig = load_jsonl(f'/workspace/responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/{model_name}-{log_str}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}-orig.jsonl')
+        response_ft = load_jsonl(f'/workspace/responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/all_checkpoint/{model_name}-{log_str}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}-{num}-ft.jsonl')
+        
+        response_only_orig = []
+        response_only_ft = []
+        
+        for i in range(len(response_orig)):
+            response_only_orig.append(response_orig[i]['output_text'])
+            response_only_ft.append(response_ft[i]['output_text'])
+        
+        ctc_scores = bert_scorer.score(response_only_ft, response_only_orig)[2]
+        
+        results_dict[candidate]=ctc_scores
+    
+    ks_p_value, mw_p_value, ad_stat, adcv=compare_distributions(results_dict['member'], results_dict['nonmember'])
+    os.makedirs(f'bert_results_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}', exist_ok=True)
+    os.makedirs(f'p_value_loss_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}', exist_ok=True)
+    file_path =f'/workspace/bert_results_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/pile_full_bert_{args.model}_{args.epoch}_{args.subname}_{args.size}_{args.lr}_test.txt'
+    txt_info=f'''
+    Kolmogorov-Smirnov test statistic: p-value: {ks_p_value}
+    
+    Mann-Whitney U test statistic: p-value: {mw_p_value}
+    
+    Anderson-Darling test statistic: {ad_stat} critical-value:{adcv}
+    '''
+    dump_txt(txt_info, file_path)
+    ks_p_value_l.append(ks_p_value)
+    mw_p_value_l.append(mw_p_value)
+plt.figure(figsize=(10, 6))
+plt.plot(ks_p_value_l, marker='o', linestyle='-', color='b', label='P-value')
+# Plot the second line
+plt.plot(normalized_loss_l_member, marker='s', linestyle='--', color='g', label='member loss')
+
+# Plot the third line
+plt.plot(normalized_loss_l_nonmember, marker='^', linestyle='-.', color='r', label='nonmember loss')
+# Add title and labels
+plt.title(f'P-Value subsets-{args.subname}-{args.lr}')
+plt.xlabel('Iteration')
+plt.ylabel('Loss')
+
+# Add legend
+plt.legend()
+
+# Show grid
+plt.grid(True)
+
+plt.savefig(f'/workspace/p_value_loss_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/{args.model}-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}-ks.png')
+
+plt.figure(figsize=(10, 6))
+plt.plot(mw_p_value_l, marker='o', linestyle='-', color='b', label='Loss')
+plt.plot(normalized_loss_l_member, marker='s', linestyle='--', color='g', label='member loss')
+
+# Plot the third line
+plt.plot(normalized_loss_l_nonmember, marker='^', linestyle='-.', color='r', label='nonmember loss')
+
+# Add title and labels
+plt.title(f'P-Value subsets-{args.subname}-{args.lr}')
+plt.xlabel('Iteration')
+plt.ylabel('Loss')
+
+# Add legend
+plt.legend()
+
+# Show grid
+plt.grid(True)
+
+plt.savefig(f'/workspace/p_value_loss_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}/{args.model}-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}-mw.png')
+print(len(loss_l_member))
+print(len(loss_l_nonmember))
+print(len(ks_p_value_l))
+print(len(mw_p_value_l))     
+df_dict = {'member_loss': loss_l_member, 'nonmember_loss': loss_l_nonmember}
+df_loss = pd.DataFrame(df_dict)
+df_dict_test = {'ks_pvalue': ks_p_value_l, 'mw_pvalue': mw_p_value_l}
+df_pvalue = pd.DataFrame(df_dict_test)
+df_normalized_loss_dict = {'member_loss': normalized_loss_l_member, 'nonmember_loss': normalized_loss_l_nonmember}
+df_normalized_loss = pd.DataFrame(df_normalized_loss_dict)
+df_loss.to_csv(f'/workspace/pile_{args.subname}_temp_{args.temp}_topp_{args.topp}_loss_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}.csv', index=False)
+df_pvalue.to_csv(f'/workspace/pile_{args.subname}_temp_{args.temp}_topp_{args.topp}_pvalue_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}.csv', index=False)
+df_normalized_loss.to_csv(f'/workspace/pile_{args.subname}_temp_{args.temp}_topp_{args.topp}_normalized_loss_ft_more_layers_{args.subname}_epoch_{args.epoch}_{args.logging}.csv', index=False)

generate_lowest_ft_more_layers.py

@@ -0,0 +1,166 @@
+import os
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer, Trainer, TrainingArguments, DataCollatorForLanguageModeling
+import datasets
+from datasets import load_dataset
+from datasets import Dataset, DatasetDict
+from peft import get_peft_model, LoraConfig, TaskType
+import json
+from tqdm import tqdm
+import pandas as pd
+from functools import partial
+import argparse
+import re
+
+import matplotlib
+matplotlib.use('Agg')  # Use the Agg backend for non-interactive plotting
+import matplotlib.pyplot as plt
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--model', type=str, default='160m',help='model name') #160m 410m 1b 1.4b 2.8b 6.9b 12b
+parser.add_argument('--epoch', type=int, default=3,help='model name') #160m 410m 1b 1.4b 2.8b 6.9b 12b
+parser.add_argument('--subname', type=str, default='arxiv',help='model name')
+parser.add_argument('--size', type=int, default=600 ,help='model name')
+parser.add_argument('--lr', type=float, default=2e-5, help='learning rate')
+parser.add_argument('--temp', type=float, default=0.0, help='generation temperature')
+parser.add_argument('--topp', type=float, default=1.0, help='generation top_p')
+parser.add_argument('--candidate', type=str, default='member', help='learning rate')
+args = parser.parse_args()
+
+
+
+# Disable wandb logging
+os.environ["WANDB_DISABLED"] = "true"
+
+
+
+loss_file = f'/workspace/{args.subname}_dataset/output_ft_more_layers_{args.subname}_epoch_{args.epoch}_mlp/pythia-{args.model}-{args.candidate}-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}/checkpoint-675/trainer_state.json'
+
+loss_datafile = json.load(open(loss_file))['log_history']
+
+loss_l = []
+
+for i in range(len(loss_datafile)):
+    try:
+        loss_data = loss_datafile[i]['loss']
+        loss_l.append(loss_data)
+    except:
+        continue
+
+model_name = f'pythia-{args.model}'
+# Load the tokenizer and model
+model_name_hf_ori = f"/workspace/{model_name}"  # You can choose other sizes as well
+tokenizer = AutoTokenizer.from_pretrained(model_name_hf_ori)
+tokenizer.padding_side = "left"
+# Add padding token if missing
+if tokenizer.pad_token is None:
+    tokenizer.pad_token = tokenizer.eos_token
+    tokenizer.pad_token_id = tokenizer.eos_token_id
+data_files = f"/workspace/dataset_inference/{args.subname}_train.jsonl"
+raw_train_data_df = pd.read_json(data_files, lines=True)
+
+#Pile Validation Set
+val_data_files = f"/workspace/dataset_inference/{args.subname}_val.jsonl"
+raw_val_data_df = pd.read_json(val_data_files, lines=True)
+
+tds=Dataset.from_pandas(raw_train_data_df)
+vds=Dataset.from_pandas(raw_val_data_df)
+
+raw_data = DatasetDict()
+
+raw_data['train'] = tds
+raw_data['validation'] = vds
+
+
+# Tokenize the input data
+def tokenize_function(examples,max_length=384):
+    tokens = tokenizer(examples["text"], padding="max_length", truncation=True, max_length=max_length)
+    #tokens["labels"] = tokens["input_ids"].copy()
+    return tokens
+
+data_num = 1000
+A_members = raw_data['train'].shuffle(seed=42).select(range(0, args.size)).map(partial(tokenize_function,max_length=512), batched=True, remove_columns=["text"])
+A_nonmembers = raw_data['validation'].shuffle(seed=42).select(range(0, args.size)).map(partial(tokenize_function,max_length=512), batched=True, remove_columns=["text"])
+
+B_members = raw_data['train'].shuffle(seed=42).select(range(data_num, data_num*2)).map(tokenize_function, batched=True, remove_columns=["text"])
+B_nonmembers = raw_data['validation'].shuffle(seed=42).select(range(data_num, data_num*2)).map(tokenize_function, batched=True, remove_columns=["text"])
+
+def get_num_from_directory(directory_path):
+
+
+    # List to store the extracted numbers
+    numbers = []
+    
+    # Iterate over each file/directory in the specified path
+    for filename in os.listdir(directory_path):
+        # Use regex to find numbers in the filename
+        match = re.search(r'checkpoint-(\d+)', filename)
+        if match:
+            # Append the extracted number to the list as an integer
+            numbers.append(int(match.group(1)))
+
+    return numbers
+
+def load_jsonl(file_path):
+    data = []
+    with open(file_path, 'r') as file:
+        for line in file:
+            data.append(json.loads(line.strip()))
+    return data
+
+def dump_jsonl(data, file_path):
+    with open(file_path, 'w') as file:
+        for item in data:
+            json.dump(item, file)
+            file.write('\n')
+
+def generate_responses(model,ds):
+    response_list = []
+    for item in tqdm(ds):
+        input_ids = torch.tensor(item['input_ids']).reshape(1,-1).to(model.device)
+        input_len = input_ids.shape[1]
+        pred = model.generate(input_ids, max_new_tokens=100)
+        input_text = tokenizer.decode(pred[0][:input_len], skip_special_tokens=True)
+        output_text = tokenizer.decode(pred[0][input_len:], skip_special_tokens=True)
+        response_list.append({'output_text':output_text,'input_text':input_text})
+    return response_list
+
+def generate_responses(model,ds,temperature,top_p):
+    model.eval()
+    #print(type(ds[0]))
+    #print(ds[0])
+    inputs = torch.tensor([item['input_ids'] for item in ds]).to(model.device)
+    masks = torch.tensor([item['attention_mask'] for item in ds]).to(model.device)
+    num_input,input_len = inputs.shape
+    input_text = []
+    output_text = []
+    bs = 10
+    for i in tqdm(range(0,num_input,bs)):
+        pred = model.generate(inputs=inputs[i:i+bs], attention_mask=masks[i:i+bs],max_new_tokens=100, temperature=temperature, top_p=top_p).detach()
+        input_text += tokenizer.batch_decode(pred[:,:input_len], skip_special_tokens=True)
+        output_text += tokenizer.batch_decode(pred[:,input_len:], skip_special_tokens=True)
+
+    return [{'output_text':a,'input_text':b} for a,b in zip(output_text,input_text)]
+
+def run(train_dataset,eval_dataset,log_str, loss_l, args):
+    directory_path = f"/workspace/{args.subname}_dataset/output_ft_more_layers_{args.subname}_epoch_{args.epoch}_mlp/pythia-{args.model}-{args.candidate}-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}"
+    numbers = get_num_from_directory(directory_path)
+    min_loss_index = loss_l.index(min(loss_l))
+    os.makedirs(f'responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_mlp/all_checkpoint', exist_ok=True)
+    for num in numbers:
+        model_name_hf = f"/workspace/{args.subname}_dataset/output_ft_more_layers_{args.subname}_epoch_{args.epoch}_mlp/pythia-{args.model}-{args.candidate}-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}/checkpoint-{num}"  # You can choose other sizes as well
+        if torch.cuda.is_available():
+            device = torch.device("cuda") 
+        model = AutoModelForCausalLM.from_pretrained(model_name_hf).to(device)
+        #model.to(device)
+    
+        model.eval()
+        response_list = generate_responses(model,eval_dataset, args.temp, args.topp)
+        if num == numbers[min_loss_index]:
+            dump_jsonl(response_list,f'responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_mlp/all_checkpoint/{model_name}-{log_str}-{num}-ft.jsonl')
+        else:
+            dump_jsonl(response_list,f'responses_ft_more_layers_{args.subname}_epoch_{args.epoch}_mlp/all_checkpoint/{model_name}-{log_str}-{num}-ft.jsonl')
+
+
+run(A_members,B_members,f'member-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}', loss_l, args)
+run(A_nonmembers,B_nonmembers,f'nonmember-{args.model}-epoch-{args.epoch}-pile-full-{args.size}-subsets-{args.subname}-{args.lr}', loss_l, args)

model_ft_more_layers_github_epoch_9_mlp/pythia-6_9b-member-6_9b-epoch-9-pile-full-600-subsets-github-8e-05/README.md

@@ -0,0 +1,202 @@
+---
+base_model: /workspace/pythia-6_9b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### 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. -->
+
+[More Information Needed]
+
+### Results
+
+[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
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- 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]
+### Framework versions
+
+- PEFT 0.13.2

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+---
+base_model: /workspace/pythia-6_9b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### 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. -->
+
+[More Information Needed]
+
+### Results
+
+[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
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- 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]
+### Framework versions
+
+- PEFT 0.13.2

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+base_model: /workspace/pythia-6_9b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### 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. -->
+
+[More Information Needed]
+
+### Results
+
+[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
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- 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]
+### Framework versions
+
+- PEFT 0.13.2

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+base_model: /workspace/pythia-6_9b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### 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. -->
+
+[More Information Needed]
+
+### Results
+
+[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
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- 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]
+### Framework versions
+
+- PEFT 0.13.2

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+base_model: /workspace/pythia-6_9b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### 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. -->
+
+[More Information Needed]
+
+### Results
+
+[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
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- 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]
+### Framework versions
+
+- PEFT 0.13.2

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+---
+base_model: /workspace/pythia-6_9b
+library_name: peft
+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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
+
+<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
+
+#### Preprocessing [optional]
+
+[More Information Needed]
+
+
+#### Training Hyperparameters
+
+- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
+
+#### Speeds, Sizes, Times [optional]
+
+<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
+
+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### 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. -->
+
+[More Information Needed]
+
+### Results
+
+[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
+
+[More Information Needed]
+
+#### Software
+
+[More Information Needed]
+
+## Citation [optional]
+
+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+**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]
+### Framework versions
+
+- PEFT 0.13.2

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+base_model: /workspace/pythia-6_9b
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+---
+
+# Model Card for Model ID
+
+<!-- Provide a quick summary of what the model is/does. -->
+
+
+
+## Model Details
+
+### Model Description
+
+<!-- Provide a longer summary of what this model is. -->
+
+
+
+- **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]
+
+### Model Sources [optional]
+
+<!-- Provide the basic links for the model. -->
+
+- **Repository:** [More Information Needed]
+- **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. -->
+
+[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 Details
+
+### Training Data
+
+<!-- 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. -->
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+[More Information Needed]
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+### Training Procedure
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+#### Preprocessing [optional]
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+
+
+#### Training Hyperparameters
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+
+#### Speeds, Sizes, Times [optional]
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+[More Information Needed]
+
+## Evaluation
+
+<!-- This section describes the evaluation protocols and provides the results. -->
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+
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+[More Information Needed]
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+#### Factors
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+#### Metrics
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+<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+### Results
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+
+## Model Examination [optional]
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+<!-- Relevant interpretability work for the model goes here -->
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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 -->
+
+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]
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+## Technical Specifications [optional]
+
+### Model Architecture and Objective
+
+[More Information Needed]
+
+### Compute Infrastructure
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+[More Information Needed]
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+#### Hardware
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+[More Information Needed]
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+#### Software
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+<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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+**BibTeX:**
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+## Glossary [optional]
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+<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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+[More Information Needed]
+
+## More Information [optional]
+
+[More Information Needed]
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+## Model Card Authors [optional]
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+## Model Card Contact
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+[More Information Needed]
+### Framework versions
+
+- PEFT 0.13.2

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