Add # app_type: streamlit to enable Streamlit

.gitattributes

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README.md

@@ -1,14 +0,0 @@
----
-title: SmartReviewAI
-emoji: 🏃
-colorFrom: pink
-colorTo: yellow
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-license: mit
-short_description: AI-powered product review generation using fine-tuned LLMs
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

evaluate_model.py

@@ -1,113 +0,0 @@
-from transformers import AutoTokenizer, AutoModelForCausalLM
-from peft import PeftModel
-import torch
-import numpy as np
-import pandas as pd
-import re
-from collections import Counter
-
-# ------------------ Review Generation ------------------
-def generate_review(base_model, product, category, features, rating, tone, review_cache=None):
-    """
-    Generate a product review using LoRA fine-tuned model and apply repetition control.
-    Optionally evaluates performance every 10 reviews.
-    """
-    adapter_path = "./lora_adapter"
-    tokenizer = AutoTokenizer.from_pretrained(base_model)
-    model = AutoModelForCausalLM.from_pretrained(base_model)
-    model = PeftModel.from_pretrained(model, adapter_path)
-    model.eval()
-
-    prompt = (
-        f"Product: {product}\n"
-        f"Category: {category}\n"
-        f"Features: {features}\n"
-        f"Rating: {rating}\n"
-        f"Tone: {tone}\n\nReview:"
-    )
-
-    inputs = tokenizer(prompt, return_tensors="pt")
-
-    with torch.no_grad():
-        outputs = model.generate(
-            **inputs,
-            max_new_tokens=180,
-            temperature=0.8,
-            top_p=0.9,
-            repetition_penalty=1.8,
-            no_repeat_ngram_size=3,
-            do_sample=True
-        )
-
-    generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
-
-    # -------- Optional: Evaluation Trigger --------
-    if review_cache is not None:
-        review_cache.append(generated_text)
-        if len(review_cache) % 10 == 0:
-            metrics = compute_metrics(review_cache, requested_tone=tone)
-            diversity = distinct_n_score(review_cache)
-            metrics["distinct_n"] = diversity
-            print(f"\n📊 Auto Evaluation after {len(review_cache)} reviews:")
-            print(metrics)
-
-    return generated_text
-
-
-# ------------------ Evaluation Metrics ------------------
-def compute_metrics(reviews, requested_tone="neutral"):
-    """
-    Compute simple text-level metrics:
-    - avg_length: average word count
-    - tone_match_ratio: how often requested tone appears
-    """
-    avg_length = np.mean([len(r.split()) for r in reviews]) if reviews else 0
-    tone_match = sum(1 for r in reviews if re.search(requested_tone, r, re.IGNORECASE))
-    tone_match_ratio = tone_match / len(reviews) if reviews else 0.0
-    return {
-        "avg_length": round(avg_length, 2),
-        "tone_match_ratio": round(tone_match_ratio, 3)
-    }
-
-
-# ------------------ Diversity Metric ------------------
-def distinct_n_score(texts, n=2):
-    """
-    Compute Distinct-N score (uniqueness measure).
-    High values mean less repetition.
-    """
-    all_ngrams = []
-    for text in texts:
-        tokens = text.split()
-        all_ngrams.extend(tuple(tokens[i:i+n]) for i in range(len(tokens)-n+1))
-    if not all_ngrams:
-        return 0.0
-    unique_ngrams = len(set(all_ngrams))
-    return round(unique_ngrams / len(all_ngrams), 3)
-
-
-# ------------------ Perplexity Evaluation ------------------
-def evaluate_perplexity(base_model, test_csv="dataset/amazon_product_reviews.csv"):
-    """
-    Compute perplexity on a small subset of test data.
-    Lower perplexity = better model.
-    """
-    tokenizer = AutoTokenizer.from_pretrained(base_model)
-    model = AutoModelForCausalLM.from_pretrained(base_model)
-    model = PeftModel.from_pretrained(model, "./lora_adapter")
-    model.eval()
-
-    df = pd.read_csv(test_csv)
-    texts = df["Review"].dropna().sample(min(50, len(df))).tolist()
-
-    total_loss, total_tokens = 0, 0
-    for text in texts:
-        inputs = tokenizer(text, return_tensors="pt", truncation=True, max_length=256)
-        with torch.no_grad():
-            outputs = model(**inputs, labels=inputs["input_ids"])
-        loss = outputs.loss.item()
-        total_loss += loss * inputs["input_ids"].size(1)
-        total_tokens += inputs["input_ids"].size(1)
-
-    ppl = np.exp(total_loss / total_tokens) if total_tokens > 0 else float("inf")
-    return round(ppl, 2)

finetune_lora.py

@@ -1,100 +0,0 @@
-import os
-import torch
-from datasets import load_dataset
-from transformers import (
-    AutoModelForCausalLM,
-    AutoTokenizer,
-    Trainer,
-    TrainingArguments,
-    DataCollatorForLanguageModeling,
-)
-from peft import LoraConfig, get_peft_model
-import streamlit as st
-
-def train_lora(base_model: str, epochs: int = 2, lr: float = 1e-4, train_csv: str = "dataset/amazon_product_reviews.csv"):
-    """
-    Fine-tune a base model using LoRA on the provided dataset and visualize progress in Streamlit.
-    """
-    st.write(f"### 🔧 Loading base model `{base_model}`...")
-    tokenizer = AutoTokenizer.from_pretrained(base_model, use_fast=True)
-    if tokenizer.pad_token is None:
-        tokenizer.pad_token = tokenizer.eos_token
-    tokenizer.padding_side = "right"
-
-    # Load dataset
-    st.info("📂 Loading dataset for fine-tuning...")
-    ds = load_dataset("csv", data_files={"train": train_csv})["train"]
-
-    def preprocess(example):
-        prompt = (
-            f"Product: {example.get('Product','')}\n"
-            f"Category: {example.get('Category','')}\n"
-            f"Features: {example.get('Features','')}\n"
-            f"Rating: {example.get('Rating','')}\n"
-            f"Tone: {example.get('Tone','')}\n\n"
-            f"Review: {example.get('Review','')}"
-        )
-        return tokenizer(prompt, truncation=True, padding="max_length", max_length=256)
-
-    tokenized_ds = ds.map(preprocess, batched=False)
-
-    # LoRA config
-    lora_config = LoraConfig(
-        r=8,
-        lora_alpha=16,
-        target_modules=["c_attn", "q_proj", "v_proj"],
-        lora_dropout=0.05,
-        bias="none",
-        task_type="CAUSAL_LM"
-    )
-
-    # Apply LoRA to base model
-    model = AutoModelForCausalLM.from_pretrained(base_model)
-    model = get_peft_model(model, lora_config)
-    data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)
-
-    output_dir = "./lora_adapter"
-    os.makedirs(output_dir, exist_ok=True)
-
-    # Streamlit progress UI
-    progress_bar = st.progress(0)
-    status_text = st.empty()
-    loss_chart = st.empty()
-    loss_list = []
-
-    from transformers import TrainerCallback
-    class StreamlitCallback(TrainerCallback):
-        def on_log(self, args, state, control, logs=None, **kwargs):
-            if logs and "loss" in logs:
-                loss = logs["loss"]
-                loss_list.append(loss)
-                progress = int((state.epoch / epochs) * 100)
-                progress_bar.progress(progress)
-                status_text.text(f"Epoch {state.epoch:.1f}/{epochs} | Step {state.global_step} | Loss: {loss:.4f}")
-                loss_chart.line_chart(loss_list)
-
-    training_args = TrainingArguments(
-        output_dir=output_dir,
-        per_device_train_batch_size=2,
-        num_train_epochs=epochs,
-        learning_rate=lr,
-        logging_steps=5,
-        save_strategy="epoch",
-        report_to="none"
-    )
-
-    trainer = Trainer(
-        model=model,
-        args=training_args,
-        train_dataset=tokenized_ds,
-        data_collator=data_collator,
-        tokenizer=tokenizer,
-        callbacks=[StreamlitCallback()]
-    )
-
-    trainer.train()
-    model.save_pretrained(output_dir)
-    tokenizer.save_pretrained(output_dir)
-
-    st.success("🎉 LoRA adapter trained and saved successfully!")
-    return {"train_loss": loss_list, "epochs": epochs, "base_model": base_model}

lora_adapter/README.md

@@ -1,207 +0,0 @@
----
-base_model: gpt2
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:gpt2
-- lora
-- transformers
----
-
-# 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 0.17.1

lora_adapter/adapter_config.json

@@ -1,38 +0,0 @@
-{
-  "alpha_pattern": {},
-  "auto_mapping": null,
-  "base_model_name_or_path": "gpt2",
-  "bias": "none",
-  "corda_config": null,
-  "eva_config": null,
-  "exclude_modules": null,
-  "fan_in_fan_out": true,
-  "inference_mode": true,
-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 16,
-  "lora_bias": false,
-  "lora_dropout": 0.05,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "qalora_group_size": 16,
-  "r": 8,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "q_proj",
-    "c_attn",
-    "v_proj"
-  ],
-  "target_parameters": null,
-  "task_type": "CAUSAL_LM",
-  "trainable_token_indices": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false
-}

lora_adapter/adapter_model.safetensors

@@ -1,3 +0,0 @@
-version https://git-lfs.github.com/spec/v1
-oid sha256:56ae2a27b7624b3b0f0db362e7f072e5939af1914786000af021a132df291b1d
-size 1182680

lora_adapter/checkpoint-2500/README.md

@@ -1,207 +0,0 @@
----
-base_model: gpt2
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:gpt2
-- lora
-- transformers
----
-
-# 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 0.17.1

lora_adapter/checkpoint-2500/adapter_config.json

@@ -1,38 +0,0 @@
-{
-  "alpha_pattern": {},
-  "auto_mapping": null,
-  "base_model_name_or_path": "gpt2",
-  "bias": "none",
-  "corda_config": null,
-  "eva_config": null,
-  "exclude_modules": null,
-  "fan_in_fan_out": true,
-  "inference_mode": true,
-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 16,
-  "lora_bias": false,
-  "lora_dropout": 0.05,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "qalora_group_size": 16,
-  "r": 8,
-  "rank_pattern": {},
-  "revision": null,
-  "target_modules": [
-    "q_proj",
-    "c_attn",
-    "v_proj"
-  ],
-  "target_parameters": null,
-  "task_type": "CAUSAL_LM",
-  "trainable_token_indices": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false
-}

lora_adapter/checkpoint-2500/adapter_model.safetensors

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lora_adapter/checkpoint-2500/training_args.bin

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-version https://git-lfs.github.com/spec/v1
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lora_adapter/checkpoint-5000/README.md

@@ -1,207 +0,0 @@
----
-base_model: gpt2
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:gpt2
-- lora
-- transformers
----
-
-# 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. -->
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
-
-## How to Get Started with the Model
-
-Use the code below to get started with the model.
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-[More Information Needed]
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-## Training Details
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-### Training Data
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-<!-- 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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-### Training Procedure
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-#### Preprocessing [optional]
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-[More Information Needed]
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-
-#### 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 -->
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-<!-- This section describes the evaluation protocols and provides the results. -->
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-#### Testing Data
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-#### Metrics
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-## Model Examination [optional]
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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).
-
-- **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]
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-[More Information Needed]
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-[More Information Needed]
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-[More Information Needed]
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-## More Information [optional]
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-[More Information Needed]
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-## Model Card Authors [optional]
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-[More Information Needed]
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-## Model Card Contact
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-[More Information Needed]
-### Framework versions
-
-- PEFT 0.17.1

lora_adapter/checkpoint-5000/adapter_config.json

@@ -1,38 +0,0 @@
-{
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-  "init_lora_weights": true,
-  "layer_replication": null,
-  "layers_pattern": null,
-  "layers_to_transform": null,
-  "loftq_config": {},
-  "lora_alpha": 16,
-  "lora_bias": false,
-  "lora_dropout": 0.05,
-  "megatron_config": null,
-  "megatron_core": "megatron.core",
-  "modules_to_save": null,
-  "peft_type": "LORA",
-  "qalora_group_size": 16,
-  "r": 8,
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-  "target_modules": [
-    "q_proj",
-    "c_attn",
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-  "task_type": "CAUSAL_LM",
-  "trainable_token_indices": null,
-  "use_dora": false,
-  "use_qalora": false,
-  "use_rslora": false
-}

lora_adapter/checkpoint-5000/adapter_model.safetensors

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lora_adapter/checkpoint-5000/optimizer.pt

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lora_adapter/checkpoint-5000/rng_state.pth

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lora_adapter/checkpoint-5000/scheduler.pt

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lora_adapter/checkpoint-5000/special_tokens_map.json

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-{
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-  "unk_token": "<|endoftext|>"
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lora_adapter/checkpoint-5000/tokenizer_config.json

@@ -1,21 +0,0 @@
-{
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-  "unk_token": "<|endoftext|>"
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lora_adapter/checkpoint-5000/training_args.bin

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lora_adapter/special_tokens_map.json

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-{
-  "bos_token": "<|endoftext|>",
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lora_adapter/tokenizer_config.json

@@ -1,21 +0,0 @@
-{
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start.sh

@@ -1,3 +0,0 @@
-#!/bin/bash
-
-streamlit run app.py --server.port=$PORT --server.address=0.0.0.0