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@@ -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

README.md

@@ -0,0 +1,202 @@
+---
+base_model: unsloth/gemma-2-2b-bnb-4bit
+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 0.12.0

adapter_config.json

@@ -0,0 +1,34 @@
+{
+  "alpha_pattern": {},
+  "auto_mapping": null,
+  "base_model_name_or_path": "unsloth/gemma-2-2b-bnb-4bit",
+  "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,
+  "megatron_config": null,
+  "megatron_core": "megatron.core",
+  "modules_to_save": null,
+  "peft_type": "LORA",
+  "r": 16,
+  "rank_pattern": {},
+  "revision": null,
+  "target_modules": [
+    "o_proj",
+    "down_proj",
+    "v_proj",
+    "gate_proj",
+    "q_proj",
+    "up_proj",
+    "k_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:afc4a83b315ebb60780ce60f61cd9eaff6a7adcf62ab81bec122bc578279f58e
+size 83115256

generate.py

@@ -0,0 +1,209 @@
+from datetime import datetime
+import json
+import logging
+import random
+import os
+import threading
+import queue
+import fcntl
+from typing import List, Dict
+import httpx
+import argparse
+import re
+from transformers import AutoTokenizer
+
+# Configure logging
+# logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
+
+# Configuration
+API_BASE = 'http://localhost:6002/v1'
+MODEL_NAME = 'gpqa'
+API_KEY = 'asdf'
+TOKENIZER_MODEL = 'google/gemma-2-9b-it'  # You can change this to match your API's tokenizer
+
+# Initialize tokenizer
+tokenizer = AutoTokenizer.from_pretrained(TOKENIZER_MODEL)
+
+# Words to apply logit bias to (expanded list with variations)
+LOGIT_BIAS_WORDS = []
+
+# Optional: Array of categories to process
+CATEGORIES_TO_PROCESS = [
+    "Science", "Technology", "History", "Literature"
+]
+
+
+def tokenize_and_create_logit_bias(words: List[str], bias_value: float = -100) -> Dict[int, float]:
+    logit_bias = {}
+    for word in words:
+        token_ids = tokenizer.encode(word, add_special_tokens=False)
+        for token_id in token_ids:
+            logit_bias[token_id] = bias_value
+    return logit_bias
+
+# Create logit_bias dictionary with a stronger negative bias
+LOGIT_BIAS = tokenize_and_create_logit_bias(LOGIT_BIAS_WORDS, bias_value=-1000)
+
+def make_openai_request(messages: List[Dict[str, str]]) -> Dict:
+    url = f"{API_BASE}/chat/completions"
+    headers = {
+        "Content-Type": "application/json",
+        "Authorization": f"Bearer {API_KEY}"
+    }
+    data = {
+        "model": MODEL_NAME,
+        "messages": messages,
+        "max_tokens": 1500,
+        "temperature": 0.7,
+        "logit_bias": LOGIT_BIAS
+    }
+    
+    with httpx.Client(timeout=60.0) as client:
+        response = client.post(url, json=data, headers=headers)
+        response.raise_for_status()
+        return response.json()
+
+
+
+def process_category(category: str, depth: int) -> Dict:
+    depth_adjusted_prompt = f"Generate a Graduate-Level Google-Proof Multiple Choice Question for the specific category: {category}. "
+    if '->' in category:
+        depth_adjusted_prompt += f"Focus on the last part of the category chain: '{category.split('->')[-1].strip()}'. "
+    depth_adjusted_prompt += f"Include very specific subcategories in your response. Write an extremely long and detailed question that is elaborate and context-rich. Build up the background in the question and make it very complex. Your question should be very difficult to answer and delve deep into specific aspects or applications of the category."
+    
+    messages = [
+        {"role": "user", "content": depth_adjusted_prompt},
+    ]
+    response = make_openai_request(messages)
+    document = response['choices'][0]['message']['content']
+    
+    messages.append({"role": "assistant", "content": document})
+    messages.append({"role": "user", "content": "Convert the above to JSON format with fields: question, answer, incorrect_answer_1, incorrect_answer_2, incorrect_answer_3, explanation, and subcategories (as an array). Ensure the subcategories are very specific and related to the last part of the category chain."})
+    response = make_openai_request(messages)
+    json_data = response['choices'][0]['message']['content']
+    
+    try:
+        parsed_json = json.loads(json_data)
+        parsed_json['category'] = category
+        parsed_json['document'] = document
+        parsed_json['depth'] = depth
+        return parsed_json
+    except json.JSONDecodeError:
+        logging.error(f"Failed to parse JSON for category: {category}")
+        return None
+
+def worker(task_queue: queue.Queue, output_file: str):
+    while True:
+        try:
+            category, depth = task_queue.get(block=False)
+        except queue.Empty:
+            break
+
+        try:
+            processed_entry = process_category(category, depth)
+            if processed_entry:
+                with open(output_file, 'a') as outfile:
+                    fcntl.flock(outfile, fcntl.LOCK_EX)
+                    json.dump(processed_entry, outfile)
+                    outfile.write('\n')
+                    fcntl.flock(outfile, fcntl.LOCK_UN)
+                logging.info(f"Processed category: {category} at depth {depth}")
+        except Exception as e:
+            logging.error(f"Error processing category {category}: {str(e)}")
+        finally:
+            task_queue.task_done()
+
+def process_categories(categories: List[str], output_file: str, depth: int, num_threads: int = 60):
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+
+    task_queue = queue.Queue()
+    for category in categories:
+        task_queue.put((category, depth))
+
+    threads = []
+    for _ in range(num_threads):
+        t = threading.Thread(target=worker, args=(task_queue, output_file))
+        t.daemon = True
+        t.start()
+        threads.append(t)
+
+    try:
+        task_queue.join()
+    except KeyboardInterrupt:
+        logging.info("Keyboard interrupt received. Shutting down...")
+    finally:
+        for _ in range(num_threads):
+            task_queue.put(None)
+        for t in threads:
+            t.join()
+
+def safe_filename(filename: str) -> str:
+    filename = filename.replace(' ', '_')
+    filename = re.sub(r'[^\w\-_.]', '', filename)
+    return filename.lower()
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate GPQA for given categories with deep dive")
+    parser.add_argument("--category", help="Input category")
+    parser.add_argument("--use-array", action="store_true", help="Use the predefined category array")
+    parser.add_argument("--depth", type=int, default=4, help="Maximum depth of category exploration")
+    args = parser.parse_args()
+
+    max_depth = args.depth
+
+    if args.use_array:
+        if not CATEGORIES_TO_PROCESS:
+            logging.error("No categories defined in the CATEGORIES_TO_PROCESS array.")
+            exit(1)
+        categories = CATEGORIES_TO_PROCESS
+        safe_category = safe_filename(categories[0])
+    elif args.category:
+        categories = [args.category]
+        safe_category = safe_filename(args.category)
+    else:
+        logging.error("Please provide either --category or --use-array")
+        exit(1)
+
+    timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+    output_file = os.path.join("./output/gpqa", f"{safe_category}_deep_dive_{timestamp}.jsonl")
+
+    os.makedirs(os.path.dirname(output_file), exist_ok=True)
+
+    all_categories = set(categories)
+    processed_categories = set()
+    
+    for depth in range(max_depth):
+        logging.info(f"Processing depth level: {depth + 1}")
+        
+        categories_to_process = list(all_categories - processed_categories)
+        random.shuffle(categories_to_process)
+        
+        process_categories(categories_to_process, output_file, depth, num_threads=36)
+        
+        processed_categories.update(categories_to_process)
+        
+        if depth < max_depth - 1:
+            new_subcategories = set()
+            try:
+                with open(output_file, 'r') as f:
+                    for line in f:
+                        entry = json.loads(line)
+                        if 'subcategories' in entry and entry['depth'] == depth:
+                            parent_category = entry['category']
+                            for subcat in entry['subcategories']:
+                                new_subcategories.add(f"{parent_category} -> {subcat}")
+            except FileNotFoundError:
+                logging.error(f"Output file not found: {output_file}")
+                continue
+            except json.JSONDecodeError:
+                logging.error(f"Error decoding JSON in file: {output_file}")
+                continue
+            
+            all_categories.update(new_subcategories)
+        
+        logging.info(f"Total unique categories after depth {depth + 1}: {len(all_categories)}")
+        logging.info(f"Total processed categories: {len(processed_categories)}")
+
+    logging.info(f"Processing complete. Results saved to {output_file}")
+    logging.info(f"Total unique categories generated: {len(all_categories)}")
+    logging.info(f"Total categories processed: {len(processed_categories)}")

run_vllm_2b.sh

@@ -0,0 +1,10 @@
+VLLM_ALLOW_RUNTIME_LORA_UPDATING=1 VLLM_ALLOW_LONG_MAX_MODEL_LEN=1 vllm serve ~/models/gemma2-2b \
+  --gpu-memory-utilization=1 \
+  --port 6002 \
+  --served-model-name="gemma" \
+  --trust-remote-code \
+  --max-model-len 8192 \
+  --disable-log-requests \
+  --guided-decoding-backend lm-format-enforcer \
+  --enable-lora \
+  --lora-modules gpqa=./gpqa \

special_tokens_map.json

@@ -0,0 +1,6 @@
+{
+  "bos_token": "<bos>",
+  "eos_token": "<|im_end|>",
+  "pad_token": "<pad>",
+  "unk_token": "<unk>"
+}

tokenizer.json

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+version https://git-lfs.github.com/spec/v1
+oid sha256:10c5ca6deab5bf8bd9023b0d8836ccf06f15eac5b217b9548a2626a7a022ee24
+size 17525361

tokenizer_config.json

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+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "added_tokens_decoder": {
+    "0": {
+      "content": "<pad>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "1": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
+    "2": {
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": true
+    },
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+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
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+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
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+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
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+      "rstrip": false,
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+      "lstrip": false,
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+      "rstrip": false,
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+    "255986": {
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+    },
+    "255989": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
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+    },
+    "255991": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255992": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255993": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255994": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255995": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255996": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255997": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255998": {
+      "content": "\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    },
+    "255999": {
+      "content": "<unused99>",
+      "lstrip": false,
+      "normalized": false,
+      "rstrip": false,
+      "single_word": false,
+      "special": false
+    }
+  },
+  "bos_token": "<bos>",
+  "chat_template": "{% if 'role' in messages[0] %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% else %}{{ bos_token }}{% for message in messages %}{% if message['role'] == 'user' %}{{'<|im_start|>user\n' + message['content'] + '<|im_end|>\n'}}{% elif message['role'] == 'assistant' %}{{'<|im_start|>assistant\n' + message['content'] + '<|im_end|>\n' }}{% else %}{{ '<|im_start|>system\n' + message['content'] + '<|im_end|>\n' }}{% endif %}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}{% endif %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": "<|im_end|>",
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": "<pad>",
+  "tokenizer_class": "GemmaTokenizer",
+  "unk_token": "<unk>"
+}