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Model: devanshamin/Qwen2-1.5B-Instruct-Function-Calling-v1 Source: Original Platform
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README.md
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README.md
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---
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base_model: Qwen/Qwen2-1.5B-Instruct
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datasets:
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- devanshamin/gem-viggo-function-calling
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library_name: peft
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license: apache-2.0
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pipeline_tag: text-generation
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tags:
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: Qwen2-1.5B-Instruct-Function-Calling-v1
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# Qwen2-1.5B-Instruct-Function-Calling-v1
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This model is a fine-tuned version of [Qwen/Qwen2-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2-1.5B-Instruct) on [devanshamin/gem-viggo-function-calling](https://huggingface.co/datasets/devanshamin/gem-viggo-function-calling) dataset.
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## Updated Chat Template
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> Note: The template supports multiple tools but the model is fine-tuned on a dataset consisting of examples with a single tool.
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- The chat template has been added to the [tokenizer_config.json](https://huggingface.co/devanshamin/Qwen2-1.5B-Instruct-Function-Calling-v1/blob/7ee7c020cefdb0101939469de608acc2afa7809e/tokenizer_config.json#L34).
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- Supports prompts with and without tools.
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```python
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chat_template = (
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"{% for message in messages %}"
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"{% if loop.first and messages[0]['role'] != 'system' %}"
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"{% if tools %}"
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"<|im_start|>system\nYou are a helpful assistant with access to the following tools. Use them if required - \n"
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"```json\n{{ tools | tojson }}\n```<|im_end|>\n"
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"{% else %}"
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"<|im_start|>system\nYou are a helpful assistant.\n<|im_end|>\n"
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"{% endif %}"
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"{% endif %}"
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"{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}"
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"{% endfor %}"
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"{% if add_generation_prompt %}"
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"{{ '<|im_start|>assistant\n' }}"
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"{% endif %}"
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)
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```
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## Basic Usage
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```python
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import torch
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "Qwen2-1.5B-Instruct-Function-Calling-v1"
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model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype=torch.float32, device_map="auto")
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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def inference(prompt: str) -> str:
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model_inputs = tokenizer([prompt], return_tensors="pt").to('cuda')
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generated_ids = model.generate(model_inputs.input_ids, max_new_tokens=512)
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generated_ids = [output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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return response
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messages = [{"role": "user", "content": "What is the speed of light?"}]
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prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
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response = inference(prompt)
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print(response)
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```
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## Tool Usage
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### Basic
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```python
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import json
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from typing import List, Dict
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def get_prompt(user_input: str, tools: List[Dict] | None = None):
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prompt = 'Extract the information from the following - \n{}'.format(user_input)
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messages = [{"role": "user", "content": prompt}]
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prompt = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True,
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tools=tools
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)
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return prompt
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tool = {
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"type": "function",
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"function": {
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"name": "get_company_info",
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"description": "Correctly extracted company information with all the required parameters with correct types",
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"parameters": {
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"properties": {
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"name": {"title": "Name", "type": "string"},
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"investors": {
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"items": {"type": "string"},
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"title": "Investors",
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"type": "array"
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},
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"valuation": {"title": "Valuation", "type": "string"},
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"source": {"title": "Source", "type": "string"}
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},
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"required": ["investors", "name", "source", "valuation"],
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"type": "object"
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}
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}
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}
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input_text = "Founded in 2021, Pluto raised $4 million across multiple seed funding rounds, valuing the company at $12 million (pre-money), according to PitchBook. The startup was backed by investors including Switch Ventures, Caffeinated Capital and Maxime Seguineau."
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prompt = get_prompt(input_text, tools=[tool])
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response = inference(prompt)
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print(response)
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# ```json
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# {
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# "name": "get_company_info",
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# "arguments": {
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# "name": "Pluto",
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# "investors": [
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# "Switch Ventures",
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# "Caffeinated Capital",
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# "Maxime Seguineau"
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# ],
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# "valuation": "$12 million",
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# "source": "PitchBook"
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# }
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# }
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# ```
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```
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### Advanced
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```python
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import re
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from enum import Enum
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from pydantic import BaseModel, Field # pip install pydantic
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from instructor.function_calls import openai_schema # pip install instructor
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# Define functions using pydantic classes
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class PaperCategory(str, Enum):
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TYPE_1_DIABETES = 'Type 1 Diabetes'
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TYPE_2_DIABETES = 'Type 2 Diabetes'
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class Classification(BaseModel):
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label: PaperCategory = Field(..., description='Provide the most likely category')
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reason: str = Field(..., description='Give a detailed explanation with quotes from the abstract explaining why the paper is related to the chosen label.')
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function_definition = openai_schema(Classification).openai_schema
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tool = dict(type='function', function=function_definition)
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input_text = "1,25-dihydroxyvitamin D(3) (1,25(OH)(2)D(3)), the biologically active form of vitamin D, is widely recognized as a modulator of the immune system as well as a regulator of mineral metabolism. The objective of this study was to determine the effects of vitamin D status and treatment with 1,25(OH)(2)D(3) on diabetes onset in non-obese diabetic (NOD) mice, a murine model of human type I diabetes. We have found that vitamin D-deficiency increases the incidence of diabetes in female mice from 46% (n=13) to 88% (n=8) and from 0% (n=10) to 44% (n=9) in male mice as of 200 days of age when compared to vitamin D-sufficient animals. Addition of 50 ng of 1,25(OH)(2)D(3)/day to the diet prevented disease onset as of 200 days and caused a significant rise in serum calcium levels, regardless of gender or vitamin D status. Our results indicate that vitamin D status is a determining factor of disease susceptibility and oral administration of 1,25(OH)(2)D(3) prevents diabetes onset in NOD mice through 200 days of age."
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prompt = get_prompt(input_text, tools=[tool])
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output = inference(prompt)
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print(output)
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# ```json
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# {
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# "name": "Classification",
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# "arguments": {
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# "label": "Type 1 Diabetes",
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# "reason": "The study investigated the effect of vitamin D status and treatment with 1,25(OH)(2)D(3) on diabetes onset in non-obese diabetic (NOD) mice. It also concluded that vitamin D deficiency leads to an increase in diabetes incidence and that the addition of 1,25(OH)(2)D(3) can prevent diabetes onset in NOD mice."
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# }
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# }
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# ```
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# Extract JSON string using regex
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output = re.search(r'```json\s*(\{.*?\})\s*```', output).group(1)
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output = Classification(**json.loads(_output)['arguments'])
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print(output)
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# Classification(label=<PaperCategory.TYPE_1_DIABETES: 'Type 1 Diabetes'>, reason='The study investigated the effect of vitamin D status and treatment with 1,25(OH)(2)D(3) on diabetes onset in non-obese diabetic (NOD) mice. It also concluded that vitamin D deficiency leads to an increase in diabetes incidence and that the addition of 1,25(OH)(2)D(3) can prevent diabetes onset in NOD mice.')
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```
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 0.0001
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_steps: 10
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- training_steps: 200
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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|:-------------:|:------:|:----:|:---------------:|
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| 0.4004 | 0.0101 | 20 | 0.4852 |
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| 0.3624 | 0.0201 | 40 | 0.3221 |
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| 0.2855 | 0.0302 | 60 | 0.2818 |
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| 0.2652 | 0.0402 | 80 | 0.2592 |
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| 0.2214 | 0.0503 | 100 | 0.2463 |
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| 0.2471 | 0.0603 | 120 | 0.2358 |
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| 0.2122 | 0.0704 | 140 | 0.2310 |
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| 0.2048 | 0.0804 | 160 | 0.2275 |
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| 0.2406 | 0.0905 | 180 | 0.2251 |
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| 0.2445 | 0.1006 | 200 | 0.2248 |
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### Framework versions
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```text
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peft==0.11.1
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transformers==4.42.3
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torch==2.3.1+cu121
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datasets==2.20.0
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tokenizers==0.19.1
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```
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adapter_config.json
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{
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"alpha_pattern": {},
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"auto_mapping": null,
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"base_model_name_or_path": "Qwen/Qwen2-1.5B-Instruct",
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"bias": "none",
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"fan_in_fan_out": false,
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"inference_mode": true,
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"init_lora_weights": true,
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"layer_replication": null,
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"layers_pattern": null,
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"layers_to_transform": null,
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"loftq_config": {},
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"lora_alpha": 64,
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"lora_dropout": 0.05,
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"megatron_config": null,
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"megatron_core": "megatron.core",
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"modules_to_save": null,
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"peft_type": "LORA",
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"r": 32,
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"rank_pattern": {},
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"revision": null,
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"target_modules": [
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"down_proj",
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"gate_proj",
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"up_proj"
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],
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"task_type": "CAUSAL_LM",
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"use_dora": false,
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"use_rslora": false
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}
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adapter_model.safetensors
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adapter_model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:54e5d0e46c4d4a50567b1a6bdd8fe69713af8e993f36623a840a95b662a9c610
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size 112875040
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added_tokens.json
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{
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"<|endoftext|>": 151643,
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"<|im_end|>": 151645,
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"<|im_start|>": 151644
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}
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config.json
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config.json
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{
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"_name_or_path": "Qwen/Qwen2-1.5B-Instruct",
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"architectures": [
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"Qwen2ForCausalLM"
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],
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"attention_dropout": 0.0,
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"bos_token_id": 151643,
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"eos_token_id": 151645,
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"hidden_act": "silu",
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"hidden_size": 1536,
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"initializer_range": 0.02,
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"intermediate_size": 8960,
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"max_position_embeddings": 32768,
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"max_window_layers": 28,
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"model_type": "qwen2",
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"num_attention_heads": 12,
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"num_hidden_layers": 28,
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"num_key_value_heads": 2,
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"rms_norm_eps": 1e-06,
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"rope_theta": 1000000.0,
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"sliding_window": 32768,
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"tie_word_embeddings": true,
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"torch_dtype": "float32",
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|
"transformers_version": "4.41.2",
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"use_cache": true,
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"use_sliding_window": false,
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"vocab_size": 151936
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}
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generation_config.json
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{
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|
"bos_token_id": 151643,
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"do_sample": true,
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|
"eos_token_id": [
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151645,
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|
151643
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|
],
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|
"pad_token_id": 151643,
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|
"repetition_penalty": 1.1,
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"temperature": 0.7,
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"top_k": 20,
|
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|
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|
||||||
|
}
|
||||||
20
special_tokens_map.json
Normal file
20
special_tokens_map.json
Normal file
@@ -0,0 +1,20 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"eos_token": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
},
|
||||||
|
"pad_token": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false
|
||||||
|
}
|
||||||
|
}
|
||||||
303112
tokenizer.json
Normal file
303112
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
43
tokenizer_config.json
Normal file
43
tokenizer_config.json
Normal file
@@ -0,0 +1,43 @@
|
|||||||
|
{
|
||||||
|
"add_prefix_space": false,
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"151643": {
|
||||||
|
"content": "<|endoftext|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151644": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"151645": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": null,
|
||||||
|
"chat_template": "{% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{% if tools %}<|im_start|>system\nYou are a helpful assistant with access to the following tools. Use them if required - \n```json\n{{ tools | tojson }}\n```<|im_end|>\n{% else %}<|im_start|>system\nYou are a helpful assistant.\n<|im_end|>\n{% endif %}{% endif %}{{'<|im_start|>' + message['role'] + '\n' + message['content'] + '<|im_end|>' + '\n'}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant\n' }}{% endif %}",
|
||||||
|
"clean_up_tokenization_spaces": false,
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"errors": "replace",
|
||||||
|
"model_max_length": 32768,
|
||||||
|
"pad_token": "<|endoftext|>",
|
||||||
|
"split_special_tokens": false,
|
||||||
|
"tokenizer_class": "Qwen2Tokenizer",
|
||||||
|
"unk_token": null
|
||||||
|
}
|
||||||
3
training_args.bin
Normal file
3
training_args.bin
Normal file
@@ -0,0 +1,3 @@
|
|||||||
|
version https://git-lfs.github.com/spec/v1
|
||||||
|
oid sha256:6f4f0ff630c1514f34c33711ec4875da263e677b1a742937e94c1d6aff04b9a0
|
||||||
|
size 5496
|
||||||
1
vocab.json
Normal file
1
vocab.json
Normal file
File diff suppressed because one or more lines are too long
Reference in New Issue
Block a user