Uploading merged DPO-trained model

.gitattributes

@@ -33,3 +33,4 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.json filter=lfs diff=lfs merge=lfs -text

.ipynb_checkpoints/Untitled-checkpoint.ipynb

@@ -0,0 +1,6 @@
+{
+ "cells": [],
+ "metadata": {},
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

.ipynb_checkpoints/handler-checkpoint.py

@@ -0,0 +1,40 @@
+from transformers import LlamaForCausalLM, AutoTokenizer
+from transformers import BitsAndBytesConfig, LlamaForCausalLM, LlamaTokenizer
+from peft import PeftModel, PeftConfig
+import torch
+
+class EndpointHandler:
+
+    def __init__(self, model_path="."):
+        self.model = LlamaForCausalLM.from_pretrained(model_path,
+                    quantization_config=BitsAndBytesConfig(
+                          load_in_4bit=True,
+                          bnb_4bit_compute_dtype=torch.bfloat16,
+                          bnb_4bit_use_double_quant=True,
+                          bnb_4bit_quant_type='nf4'
+                      )
+                   )
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                                        eos_token = "<|eot_id|>")
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+
+    def __call__(self, request_data):
+
+        prompt = request_data["prompt"]
+
+        chat = [
+            {"role": "system", "content": "You are a helpful assistant"},
+            {"role": "user", "content": prompt}
+        ]
+
+        prompt = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+        input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
+
+        output = self.model.generate(input_ids, max_length=400)
+
+        generated_text = self.tokenizer.decode(output[0], skip_special_tokens=False)
+        generated_text = generated_text.replace(prompt,'').replace('<|begin_of_text|>', '').strip()
+        
+        return {"response": generated_text} 

README.md

@@ -1,3 +1,202 @@
----
-license: llama3.1
----
+---
+base_model: meta-llama/Llama-3.1-8B-Instruct
+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 0.10.0

Untitled.ipynb

@@ -0,0 +1,264 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "e1ccff33-d459-4cf7-87b7-838ae160300b",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "env: CUDA_VISIBLE_DEVICES=2\n"
+     ]
+    }
+   ],
+   "source": [
+    "%env CUDA_VISIBLE_DEVICES=2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "5f5905e4-ca72-4ad8-8939-06217db124f4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from handler import EndpointHandler"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "a435e800-19ec-438c-ae01-4169ab67ddab",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`low_cpu_mem_usage` was None, now default to True since model is quantized.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b7178c0bf163406287c75ac004b935b2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "h = EndpointHandler()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "62ad1435-fac9-4def-8fcd-c7ab82904eec",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "The attention mask and the pad token id were not set. As a consequence, you may observe unexpected behavior. Please pass your input's `attention_mask` to obtain reliable results.\n",
+      "Setting `pad_token_id` to `eos_token_id`:None for open-end generation.\n",
+      "/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/transformers/generation/utils.py:2025: UserWarning: You are calling .generate() with the `input_ids` being on a device type different than your model's device. `input_ids` is on cpu, whereas the model is on cuda. You may experience unexpected behaviors or slower generation. Please make sure that you have put `input_ids` to the correct device by calling for example input_ids = input_ids.to('cuda') before running `.generate()`.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "{'response': 'Berikut adalah 5 jenis menu masing-masing kategori:\\n\\n**Menu Takjil:**\\n1. Nasi Putih\\n2. Gado-Gado (tahu tempe goreng, sayur mayur, tauge, irisan timun, kerupuk, dan bawang goreng)\\n3. Sop Iga (sop kaki kambing)\\n4. Ayam Goreng\\n5. Perkedel Tempe\\n\\n**Menu Makanan Berat:**\\n1. Nasi Goreng Iga (nasi goreng dengan sate kambing dan sambal kecap)\\n2. Guling-Guling Ayam (ayam bakar dengan lapisan kecap manis)\\n3. Opor Ayam (opor ayam dengan bumbu kacang tanah)\\n4. Urap Jago (urap jagung dengan ayam dan tempe)\\n5. Sate Kambing (sate kambing dengan sambal kecap)\\n\\n**Kue Kering Lebaran:**\\n1. Kue Kruceng (kue kastanggo dengan isian kacang hijau)\\n2. Kue Putu Ayu (kue beras dengan gula merah dan kelapa)\\n3. Kue Apem (kue beras dengan gula merah dan kelapa)\\n4. Kue Semprit (kue beras dengan gula merah dan kelapa)\\n5. Kue Lapis Legit (kue lapis gula merah dan kelapa)'}"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "payload = {\n",
+    "    \"prompt\": \"tulis masing-masing 5 jenis untuk menu takjil, menu makanan berat, dan kue kering lebaran.\" # Hi! What would you like to do today?\n",
+    "}\n",
+    "h(payload)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "56fb9e63-4b7f-4988-96c2-3939ca0d1b39",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Berikut adalah 5 jenis menu masing-masing kategori:\n",
+      "\n",
+      "**Menu Takjil:**\n",
+      "1. Nasi Putih\n",
+      "2. Gado-Gado (tahu tempe goreng, sayur mayur, tauge, irisan timun, kerupuk, dan bawang goreng)\n",
+      "3. Sop Iga (sop kaki kambing)\n",
+      "4. Ayam Goreng\n",
+      "5. Perkedel Tempe\n",
+      "\n",
+      "**Menu Makanan Berat:**\n",
+      "1. Nasi Goreng Iga (nasi goreng dengan sate kambing dan sambal kecap)\n",
+      "2. Guling-Guling Ayam (ayam bakar dengan lapisan kecap manis)\n",
+      "3. Opor Ayam (opor ayam dengan bumbu kacang tanah)\n",
+      "4. Urap Jago (urap jagung dengan ayam dan tempe)\n",
+      "5. Sate Kambing (sate kambing dengan sambal kecap)\n",
+      "\n",
+      "**Kue Kering Lebaran:**\n",
+      "1. Kue Kruceng (kue kastanggo dengan isian kacang hijau)\n",
+      "2. Kue Putu Ayu (kue beras dengan gula merah dan kelapa)\n",
+      "3. Kue Apem (kue beras dengan gula merah dan kelapa)\n",
+      "4. Kue Semprit (kue beras dengan gula merah dan kelapa)\n",
+      "5. Kue Lapis Legit (kue lapis gula merah dan kelapa)\n"
+     ]
+    }
+   ],
+   "source": [
+    "print('Berikut adalah 5 jenis menu masing-masing kategori:\\n\\n**Menu Takjil:**\\n1. Nasi Putih\\n2. Gado-Gado (tahu tempe goreng, sayur mayur, tauge, irisan timun, kerupuk, dan bawang goreng)\\n3. Sop Iga (sop kaki kambing)\\n4. Ayam Goreng\\n5. Perkedel Tempe\\n\\n**Menu Makanan Berat:**\\n1. Nasi Goreng Iga (nasi goreng dengan sate kambing dan sambal kecap)\\n2. Guling-Guling Ayam (ayam bakar dengan lapisan kecap manis)\\n3. Opor Ayam (opor ayam dengan bumbu kacang tanah)\\n4. Urap Jago (urap jagung dengan ayam dan tempe)\\n5. Sate Kambing (sate kambing dengan sambal kecap)\\n\\n**Kue Kering Lebaran:**\\n1. Kue Kruceng (kue kastanggo dengan isian kacang hijau)\\n2. Kue Putu Ayu (kue beras dengan gula merah dan kelapa)\\n3. Kue Apem (kue beras dengan gula merah dan kelapa)\\n4. Kue Semprit (kue beras dengan gula merah dan kelapa)\\n5. Kue Lapis Legit (kue lapis gula merah dan kelapa)')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "395c0bf5-ec04-42d7-8832-63cf3bb08724",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Berikut adalah beberapa jenis menu untuk Lebaran:\t**Menu Takjil (Makanan Ringan)**\t1. **Krupuk Udang**: Krupuk udang adalah salah satu makanan ringan yang populer di Indonesia. Biasanya terbuat dari udang yang digoreng hingga crispy.\t2. **Emping**: Emping adalah makanan ringan yang terbuat dari biji kacang tanah yang digoreng hingga crispy. Bisa disajikan sebagai camilan atau sebagai pelengkap makanan.\t3. **Rempeyek**: Rempeyek adalah makanan ringan yang terbuat dari biji kacang tanah atau biji kedelai yang digoreng hingga crispy.\t4. **Krupuk Ikan**: Krupuk ikan adalah makanan ringan yang terbuat dari ikan yang digoreng hingga crispy.\t5. **Krupuk Tahu**: Krupuk tahu adalah makanan ringan yang terbuat dari tahu yang digoreng hingga crispy.\t**Menu Makanan Berat**\t1. **Nasi Lemak**: Nasi lemak adalah makanan berat yang populer di Indonesia. Biasanya terbuat dari nasi yang dimasak dengan santan dan disajikan dengan berbagai lauk-pauk.\t2. **Gado-Gado**: Gado-gado adalah makanan berat yang terbuat dari sayuran dan kacang tanah yang disajikan dengan saus kacang tanah\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Berikut adalah beberapa jenis menu untuk Lebaran:\t**Menu Takjil (Makanan Ringan)**\t1. **Krupuk Udang**: Krupuk udang adalah salah satu makanan ringan yang populer di Indonesia. Biasanya terbuat dari udang yang digoreng hingga crispy.\t2. **Emping**: Emping adalah makanan ringan yang terbuat dari biji kacang tanah yang digoreng hingga crispy. Bisa disajikan sebagai camilan atau sebagai pelengkap makanan.\t3. **Rempeyek**: Rempeyek adalah makanan ringan yang terbuat dari biji kacang tanah atau biji kedelai yang digoreng hingga crispy.\t4. **Krupuk Ikan**: Krupuk ikan adalah makanan ringan yang terbuat dari ikan yang digoreng hingga crispy.\t5. **Krupuk Tahu**: Krupuk tahu adalah makanan ringan yang terbuat dari tahu yang digoreng hingga crispy.\t**Menu Makanan Berat**\t1. **Nasi Lemak**: Nasi lemak adalah makanan berat yang populer di Indonesia. Biasanya terbuat dari nasi yang dimasak dengan santan dan disajikan dengan berbagai lauk-pauk.\t2. **Gado-Gado**: Gado-gado adalah makanan berat yang terbuat dari sayuran dan kacang tanah yang disajikan dengan saus kacang tanah\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "04a2ac2f-b81a-4918-8048-9e18565cec92",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "fd55d74d-9bc0-4e4a-bcd0-f4d7b3d28c21",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import torch, re\n",
+    "import pandas as pd\n",
+    "from datasets import Dataset\n",
+    "from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig\n",
+    "from peft import LoraConfig\n",
+    "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig, GenerationConfig\n",
+    "from tqdm.auto import tqdm\n",
+    "from trl import DPOTrainer\n",
+    "from huggingface_hub import upload_folder, login"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "7f2ebf00-30d3-405c-b47b-223e7a6da0ac",
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RepositoryNotFoundError",
+     "evalue": "404 Client Error. (Request ID: Root=1-67ac4832-67d953874edfff472f6605a5;a46916ed-82a6-4b8d-8a7f-0da9dedf91f7)\n\nRepository Not Found for url: https://huggingface.co/api/models/cjziems/Cartography-Llama-3.1-8B-Instruct/preupload/main.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case.",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mHTTPError\u001b[0m                                 Traceback (most recent call last)",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/utils/_http.py:406\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    405\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 406\u001b[0m     \u001b[43mresponse\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mraise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    407\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m HTTPError \u001b[38;5;28;01mas\u001b[39;00m e:\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/requests/models.py:1024\u001b[0m, in \u001b[0;36mResponse.raise_for_status\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   1023\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m http_error_msg:\n\u001b[0;32m-> 1024\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m HTTPError(http_error_msg, response\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m)\n",
+      "\u001b[0;31mHTTPError\u001b[0m: 404 Client Error: Not Found for url: https://huggingface.co/api/models/cjziems/Cartography-Llama-3.1-8B-Instruct/preupload/main",
+      "\nThe above exception was the direct cause of the following exception:\n",
+      "\u001b[0;31mRepositoryNotFoundError\u001b[0m                   Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[11], line 3\u001b[0m\n\u001b[1;32m      1\u001b[0m SAVE_MODEL_NAME \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCartography-Llama-3.1-8B-Instruct\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m      2\u001b[0m repo_id \u001b[38;5;241m=\u001b[39m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcjziems/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mSAVE_MODEL_NAME\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 3\u001b[0m \u001b[43mupload_folder\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfolder_path\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m    \u001b[49m\u001b[43mpath_in_repo\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43m.\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      7\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcommit_message\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mUploading merged DPO-trained model\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[43m    \u001b[49m\u001b[43muse_auth_token\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\n\u001b[1;32m      9\u001b[0m \u001b[43m)\u001b[49m\n\u001b[1;32m     10\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mModel successfully uploaded to: https://huggingface.co/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    112\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/hf_api.py:1485\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1482\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_as_future(fn, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1484\u001b[0m \u001b[38;5;66;03m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m-> 1485\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/hf_api.py:5007\u001b[0m, in \u001b[0;36mHfApi.upload_folder\u001b[0;34m(self, repo_id, folder_path, path_in_repo, commit_message, commit_description, token, repo_type, revision, create_pr, parent_commit, allow_patterns, ignore_patterns, delete_patterns, multi_commits, multi_commits_verbose, run_as_future)\u001b[0m\n\u001b[1;32m   5003\u001b[0m     \u001b[38;5;66;03m# Defining a CommitInfo object is not really relevant in this case\u001b[39;00m\n\u001b[1;32m   5004\u001b[0m     \u001b[38;5;66;03m# Let's return early with pr_url only (as string).\u001b[39;00m\n\u001b[1;32m   5005\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m pr_url\n\u001b[0;32m-> 5007\u001b[0m commit_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcreate_commit\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   5008\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5009\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5010\u001b[0m \u001b[43m    \u001b[49m\u001b[43moperations\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_operations\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5011\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcommit_message\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_message\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5012\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcommit_description\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcommit_description\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5013\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5014\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5015\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5016\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparent_commit\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mparent_commit\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   5017\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   5019\u001b[0m \u001b[38;5;66;03m# Create url to uploaded folder (for legacy return value)\u001b[39;00m\n\u001b[1;32m   5020\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m create_pr \u001b[38;5;129;01mand\u001b[39;00m commit_info\u001b[38;5;241m.\u001b[39mpr_url \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    112\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/hf_api.py:1485\u001b[0m, in \u001b[0;36mfuture_compatible.<locals>._inner\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1482\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mrun_as_future(fn, \u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[1;32m   1484\u001b[0m \u001b[38;5;66;03m# Otherwise, call the function normally\u001b[39;00m\n\u001b[0;32m-> 1485\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/hf_api.py:3913\u001b[0m, in \u001b[0;36mHfApi.create_commit\u001b[0;34m(self, repo_id, operations, commit_message, commit_description, token, repo_type, revision, create_pr, num_threads, parent_commit, run_as_future)\u001b[0m\n\u001b[1;32m   3910\u001b[0m \u001b[38;5;66;03m# If updating twice the same file or update then delete a file in a single commit\u001b[39;00m\n\u001b[1;32m   3911\u001b[0m _warn_on_overwriting_operations(operations)\n\u001b[0;32m-> 3913\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpreupload_lfs_files\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   3914\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3915\u001b[0m \u001b[43m    \u001b[49m\u001b[43madditions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43madditions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3916\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtoken\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtoken\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3917\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3918\u001b[0m \u001b[43m    \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43munquoted_revision\u001b[49m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# first-class methods take unquoted revision\u001b[39;49;00m\n\u001b[1;32m   3919\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3920\u001b[0m \u001b[43m    \u001b[49m\u001b[43mnum_threads\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_threads\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   3921\u001b[0m \u001b[43m    \u001b[49m\u001b[43mfree_memory\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m  \u001b[49m\u001b[38;5;66;43;03m# do not remove `CommitOperationAdd.path_or_fileobj` on LFS files for \"normal\" users\u001b[39;49;00m\n\u001b[1;32m   3922\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   3924\u001b[0m \u001b[38;5;66;03m# Remove no-op operations (files that have not changed)\u001b[39;00m\n\u001b[1;32m   3925\u001b[0m operations_without_no_op \u001b[38;5;241m=\u001b[39m []\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/hf_api.py:4432\u001b[0m, in \u001b[0;36mHfApi.preupload_lfs_files\u001b[0;34m(self, repo_id, additions, token, repo_type, revision, create_pr, num_threads, free_memory, gitignore_content)\u001b[0m\n\u001b[1;32m   4430\u001b[0m \u001b[38;5;66;03m# Check which new files are LFS\u001b[39;00m\n\u001b[1;32m   4431\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 4432\u001b[0m     \u001b[43m_fetch_upload_modes\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   4433\u001b[0m \u001b[43m        \u001b[49m\u001b[43madditions\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnew_additions\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4434\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_type\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_type\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4435\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrepo_id\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrepo_id\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4436\u001b[0m \u001b[43m        \u001b[49m\u001b[43mheaders\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheaders\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4437\u001b[0m \u001b[43m        \u001b[49m\u001b[43mrevision\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrevision\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4438\u001b[0m \u001b[43m        \u001b[49m\u001b[43mendpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mendpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4439\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcreate_pr\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcreate_pr\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mor\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[1;32m   4440\u001b[0m \u001b[43m        \u001b[49m\u001b[43mgitignore_content\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mgitignore_content\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   4441\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   4442\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m RepositoryNotFoundError \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m   4443\u001b[0m     e\u001b[38;5;241m.\u001b[39mappend_to_message(_CREATE_COMMIT_NO_REPO_ERROR_MESSAGE)\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/utils/_validators.py:114\u001b[0m, in \u001b[0;36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    111\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m check_use_auth_token:\n\u001b[1;32m    112\u001b[0m     kwargs \u001b[38;5;241m=\u001b[39m smoothly_deprecate_use_auth_token(fn_name\u001b[38;5;241m=\u001b[39mfn\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, has_token\u001b[38;5;241m=\u001b[39mhas_token, kwargs\u001b[38;5;241m=\u001b[39mkwargs)\n\u001b[0;32m--> 114\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/_commit_api.py:536\u001b[0m, in \u001b[0;36m_fetch_upload_modes\u001b[0;34m(additions, repo_type, repo_id, headers, revision, endpoint, create_pr, gitignore_content)\u001b[0m\n\u001b[1;32m    528\u001b[0m     payload[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mgitIgnore\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m gitignore_content\n\u001b[1;32m    530\u001b[0m resp \u001b[38;5;241m=\u001b[39m get_session()\u001b[38;5;241m.\u001b[39mpost(\n\u001b[1;32m    531\u001b[0m     \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/api/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_type\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124ms/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrepo_id\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m/preupload/\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mrevision\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m    532\u001b[0m     json\u001b[38;5;241m=\u001b[39mpayload,\n\u001b[1;32m    533\u001b[0m     headers\u001b[38;5;241m=\u001b[39mheaders,\n\u001b[1;32m    534\u001b[0m     params\u001b[38;5;241m=\u001b[39m{\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcreate_pr\u001b[39m\u001b[38;5;124m\"\u001b[39m: \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m1\u001b[39m\u001b[38;5;124m\"\u001b[39m} \u001b[38;5;28;01mif\u001b[39;00m create_pr \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m,\n\u001b[1;32m    535\u001b[0m )\n\u001b[0;32m--> 536\u001b[0m \u001b[43mhf_raise_for_status\u001b[49m\u001b[43m(\u001b[49m\u001b[43mresp\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    537\u001b[0m preupload_info \u001b[38;5;241m=\u001b[39m _validate_preupload_info(resp\u001b[38;5;241m.\u001b[39mjson())\n\u001b[1;32m    538\u001b[0m upload_modes\u001b[38;5;241m.\u001b[39mupdate(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m{file[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpath\u001b[39m\u001b[38;5;124m\"\u001b[39m]: file[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124muploadMode\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;129;01min\u001b[39;00m preupload_info[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfiles\u001b[39m\u001b[38;5;124m\"\u001b[39m]})\n",
+      "File \u001b[0;32m/nlp/scr/caleb/miniconda3/lib/python3.11/site-packages/huggingface_hub/utils/_http.py:454\u001b[0m, in \u001b[0;36mhf_raise_for_status\u001b[0;34m(response, endpoint_name)\u001b[0m\n\u001b[1;32m    435\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m error_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mRepoNotFound\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m (\n\u001b[1;32m    436\u001b[0m     response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m401\u001b[39m\n\u001b[1;32m    437\u001b[0m     \u001b[38;5;129;01mand\u001b[39;00m response\u001b[38;5;241m.\u001b[39mrequest \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    444\u001b[0m     \u001b[38;5;66;03m# => for now, we process them as `RepoNotFound` anyway.\u001b[39;00m\n\u001b[1;32m    445\u001b[0m     \u001b[38;5;66;03m# See https://gist.github.com/Wauplin/46c27ad266b15998ce56a6603796f0b9\u001b[39;00m\n\u001b[1;32m    446\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    447\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mresponse\u001b[38;5;241m.\u001b[39mstatus_code\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m Client Error.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    448\u001b[0m         \u001b[38;5;241m+\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    452\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m make sure you are authenticated.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    453\u001b[0m     )\n\u001b[0;32m--> 454\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m _format(RepositoryNotFoundError, message, response) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m    456\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m response\u001b[38;5;241m.\u001b[39mstatus_code \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m400\u001b[39m:\n\u001b[1;32m    457\u001b[0m     message \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m    458\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mBad request for \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mendpoint_name\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m endpoint:\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m endpoint_name \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124mBad request:\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    459\u001b[0m     )\n",
+      "\u001b[0;31mRepositoryNotFoundError\u001b[0m: 404 Client Error. (Request ID: Root=1-67ac4832-67d953874edfff472f6605a5;a46916ed-82a6-4b8d-8a7f-0da9dedf91f7)\n\nRepository Not Found for url: https://huggingface.co/api/models/cjziems/Cartography-Llama-3.1-8B-Instruct/preupload/main.\nPlease make sure you specified the correct `repo_id` and `repo_type`.\nIf you are trying to access a private or gated repo, make sure you are authenticated.\nNote: Creating a commit assumes that the repo already exists on the Huggingface Hub. Please use `create_repo` if it's not the case."
+     ]
+    }
+   ],
+   "source": [
+    "SAVE_MODEL_NAME = \"Cartography-Llama-3.1-8B-Instruct\"\n",
+    "repo_id = f\"cjziems/{SAVE_MODEL_NAME}\"\n",
+    "upload_folder(\n",
+    "    repo_id=repo_id,\n",
+    "    folder_path=f\".\",\n",
+    "    path_in_repo=\".\",\n",
+    "    commit_message=\"Uploading merged DPO-trained model\",\n",
+    "    use_auth_token=True\n",
+    ")\n",
+    "print(f\"Model successfully uploaded to: https://huggingface.co/{repo_id}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "512e53d3-f3f7-436e-8f6a-a91b13c6bd7e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "94e7cc72-363a-4958-8696-1996695c9968",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

adapter_config.json

@@ -0,0 +1,29 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "meta-llama/Llama-3.1-8B-Instruct",
+  "bias": "none",
+  "fan_in_fan_out": false,
+  "inference_mode": true,
+  "init_lora_weights": true,
+  "layer_replication": null,
+  "layers_pattern": null,
+  "layers_to_transform": null,
+  "loftq_config": {},
+  "lora_alpha": 16,
+  "lora_dropout": 0.1,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 8,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "q_proj",
+    "v_proj"
+  ],
+  "task_type": "CAUSAL_LM",
+  "use_dora": false,
+  "use_rslora": false
+}

adapter_model.safetensors

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:f979599b073db1c918508b5ba15589fe319f5db41a43001854ff7e0aa79b108a
+size 13648432

handler.py

@@ -0,0 +1,40 @@
+from transformers import LlamaForCausalLM, AutoTokenizer
+from transformers import BitsAndBytesConfig, LlamaForCausalLM, LlamaTokenizer
+from peft import PeftModel, PeftConfig
+import torch
+
+class EndpointHandler:
+
+    def __init__(self, model_path="."):
+        self.model = LlamaForCausalLM.from_pretrained(model_path,
+                    quantization_config=BitsAndBytesConfig(
+                          load_in_4bit=True,
+                          bnb_4bit_compute_dtype=torch.bfloat16,
+                          bnb_4bit_use_double_quant=True,
+                          bnb_4bit_quant_type='nf4'
+                      )
+                   )
+        self.tokenizer = AutoTokenizer.from_pretrained(model_path,
+                                                        eos_token = "<|eot_id|>")
+        self.tokenizer.pad_token = self.tokenizer.eos_token
+
+
+    def __call__(self, request_data):
+
+        prompt = request_data["prompt"]
+
+        chat = [
+            {"role": "system", "content": "You are a helpful assistant"},
+            {"role": "user", "content": prompt}
+        ]
+
+        prompt = self.tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
+
+        input_ids = self.tokenizer.encode(prompt, return_tensors="pt")
+
+        output = self.model.generate(input_ids, max_length=400)
+
+        generated_text = self.tokenizer.decode(output[0], skip_special_tokens=False)
+        generated_text = generated_text.replace(prompt,'').replace('<|begin_of_text|>', '').strip()
+        
+        return {"response": generated_text} 

special_tokens_map.json

@@ -0,0 +1,17 @@
+{
+  "bos_token": {
+    "content": "<|begin_of_text|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "eos_token": {
+    "content": "<|eot_id|>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "pad_token": "<|eot_id|>"
+}

tokenizer.json

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
+size 17209920

tokenizer_config.json

@@ -0,0 +1,2063 @@
+{
+  "added_tokens_decoder": {
+    "128000": {
+      "content": "<|begin_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128001": {
+      "content": "<|end_of_text|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128002": {
+      "content": "<|reserved_special_token_0|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128003": {
+      "content": "<|reserved_special_token_1|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128004": {
+      "content": "<|finetune_right_pad_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128005": {
+      "content": "<|reserved_special_token_2|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128006": {
+      "content": "<|start_header_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128007": {
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+      "single_word": false,
+      "special": true
+    },
+    "128008": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "128009": {
+      "content": "<|eot_id|>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "single_word": false,
+      "special": true
+    },
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+      "single_word": false,
+      "special": true
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+  "bos_token": "<|begin_of_text|>",
+  "chat_template": "{{- bos_token }}\n{%- if custom_tools is defined %}\n    {%- set tools = custom_tools %}\n{%- endif %}\n{%- if not tools_in_user_message is defined %}\n    {%- set tools_in_user_message = true %}\n{%- endif %}\n{%- if not date_string is defined %}\n    {%- set date_string = \"26 Jul 2024\" %}\n{%- endif %}\n{%- if not tools is defined %}\n    {%- set tools = none %}\n{%- endif %}\n\n{#- This block extracts the system message, so we can slot it into the right place. #}\n{%- if messages[0]['role'] == 'system' %}\n    {%- set system_message = messages[0]['content']|trim %}\n    {%- set messages = messages[1:] %}\n{%- else %}\n    {%- set system_message = \"\" %}\n{%- endif %}\n\n{#- System message + builtin tools #}\n{{- \"<|start_header_id|>system<|end_header_id|>\\n\\n\" }}\n{%- if builtin_tools is defined or tools is not none %}\n    {{- \"Environment: ipython\\n\" }}\n{%- endif %}\n{%- if builtin_tools is defined %}\n    {{- \"Tools: \" + builtin_tools | reject('equalto', 'code_interpreter') | join(\", \") + \"\\n\\n\"}}\n{%- endif %}\n{{- \"Cutting Knowledge Date: December 2023\\n\" }}\n{{- \"Today Date: \" + date_string + \"\\n\\n\" }}\n{%- if tools is not none and not tools_in_user_message %}\n    {{- \"You have access to the following functions. To call a function, please respond with JSON for a function call.\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n{%- endif %}\n{{- system_message }}\n{{- \"<|eot_id|>\" }}\n\n{#- Custom tools are passed in a user message with some extra guidance #}\n{%- if tools_in_user_message and not tools is none %}\n    {#- Extract the first user message so we can plug it in here #}\n    {%- if messages | length != 0 %}\n        {%- set first_user_message = messages[0]['content']|trim %}\n        {%- set messages = messages[1:] %}\n    {%- else %}\n        {{- raise_exception(\"Cannot put tools in the first user message when there's no first user message!\") }}\n{%- endif %}\n    {{- '<|start_header_id|>user<|end_header_id|>\\n\\n' -}}\n    {{- \"Given the following functions, please respond with a JSON for a function call \" }}\n    {{- \"with its proper arguments that best answers the given prompt.\\n\\n\" }}\n    {{- 'Respond in the format {\"name\": function name, \"parameters\": dictionary of argument name and its value}.' }}\n    {{- \"Do not use variables.\\n\\n\" }}\n    {%- for t in tools %}\n        {{- t | tojson(indent=4) }}\n        {{- \"\\n\\n\" }}\n    {%- endfor %}\n    {{- first_user_message + \"<|eot_id|>\"}}\n{%- endif %}\n\n{%- for message in messages %}\n    {%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}\n        {{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\\n\\n'+ message['content'] | trim + '<|eot_id|>' }}\n    {%- elif 'tool_calls' in message %}\n        {%- if not message.tool_calls|length == 1 %}\n            {{- raise_exception(\"This model only supports single tool-calls at once!\") }}\n        {%- endif %}\n        {%- set tool_call = message.tool_calls[0].function %}\n        {%- if builtin_tools is defined and tool_call.name in builtin_tools %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- \"<|python_tag|>\" + tool_call.name + \".call(\" }}\n            {%- for arg_name, arg_val in tool_call.arguments | items %}\n                {{- arg_name + '=\"' + arg_val + '\"' }}\n                {%- if not loop.last %}\n                    {{- \", \" }}\n                {%- endif %}\n                {%- endfor %}\n            {{- \")\" }}\n        {%- else  %}\n            {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' -}}\n            {{- '{\"name\": \"' + tool_call.name + '\", ' }}\n            {{- '\"parameters\": ' }}\n            {{- tool_call.arguments | tojson }}\n            {{- \"}\" }}\n        {%- endif %}\n        {%- if builtin_tools is defined %}\n            {#- This means we're in ipython mode #}\n            {{- \"<|eom_id|>\" }}\n        {%- else %}\n            {{- \"<|eot_id|>\" }}\n        {%- endif %}\n    {%- elif message.role == \"tool\" or message.role == \"ipython\" %}\n        {{- \"<|start_header_id|>ipython<|end_header_id|>\\n\\n\" }}\n        {%- if message.content is mapping or message.content is iterable %}\n            {{- message.content | tojson }}\n        {%- else %}\n            {{- message.content }}\n        {%- endif %}\n        {{- \"<|eot_id|>\" }}\n    {%- endif %}\n{%- endfor %}\n{%- if add_generation_prompt %}\n    {{- '<|start_header_id|>assistant<|end_header_id|>\\n\\n' }}\n{%- endif %}\n",
+  "clean_up_tokenization_spaces": true,
+  "eos_token": "<|eot_id|>",
+  "model_input_names": [
+    "input_ids",
+    "attention_mask"
+  ],
+  "model_max_length": 131072,
+  "pad_token": "<|eot_id|>",
+  "tokenizer_class": "PreTrainedTokenizerFast"
+}

training_args.bin

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