puneeshkhanna
commited on
Commit
•
62e8b6f
1
Parent(s):
24a2788
Add Falcon3-1B-Base with random weights
Browse files- README.md +252 -0
- config.json +30 -0
- generation_config.json +4 -0
- model.safetensors.index.json +172 -0
- special_tokens_map.json +34 -0
- tokenizer.json +0 -0
- tokenizer_config.json +0 -0
README.md
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---
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language:
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- en
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- es
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- pt
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tags:
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- falcon3
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---
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# Table of Contents
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0. [TL;DR](#TL;DR)
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1. [Model Details](#model-details)
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2. [Usage](#usage)
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3. [Training Details](#training-details)
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4. [Evaluation](#evaluation)
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# TL;DR
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# Model Details
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⚠️ **This is a raw, pretrained model, which should be further finetuned for most usecases.**
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## Model Description
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- **Developed by:** [https://www.tii.ae](https://www.tii.ae)
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- **Model type:** Causal decoder-only
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- **Architecture:** Transformer-base
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- **Language(s) (NLP):** Mainly English
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- **License:** TII Falcon-LLM License 2.0
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<br>
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# Usage
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Find below some example scripts on how to use the model in `transformers` (Make sure to have the latest transformers, or the one built from source):
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## Using the Pytorch model with 🤗 transformers
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### Running the model on a CPU
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<details>
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<summary> Click to expand </summary>
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```python
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
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model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base")
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input_text = "Question: How many hours in one day? Answer: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids
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outputs = model.generate(input_ids)
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print(tokenizer.decode(outputs[0]))
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```
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</details>
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### Running the model on a GPU
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<details>
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<summary> Click to expand </summary>
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```python
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# pip install accelerate
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
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model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", device_map="auto")
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input_text = "Question: How many hours in one day? Answer: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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print(tokenizer.decode(outputs[0]))
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```
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</details>
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### Running the model on a GPU using `torch.compile`
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<details>
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<summary> Click to expand </summary>
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```python
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import torch
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from transformers import AutoTokenizer, AutoModelForCausalLM
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tokenizer = AutoTokenizer.from_pretrained("tiiuae/Falcon3-7B-Base")
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model = AutoModelForCausalLM.from_pretrained("tiiuae/Falcon3-7B-Base", torch_dtype=torch.bfloat16).to(0)
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model = torch.compile(model)
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input_text = "Question: How many hours in one day? Answer: "
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input_ids = tokenizer(input_text, return_tensors="pt").input_ids.to("cuda")
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outputs = model.generate(input_ids)
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print(tokenizer.decode(outputs[0]))
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```
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</details>
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# Training Details
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## Training Data
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Falcon3-7B is trained on 15 Gigatokens of datasets comprising of web, code, STEM, high quality and mutlilingual data.
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## Training Procedure
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Falcon3-7B is trained on 256 H100 nodes (world size 2048).
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### Training Hyperparameters
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| **Hyperparameter** | **Value** | **Comment** |
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|--------------------|------------|---------------------------------------|
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| Precision | `bfloat16` | |
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| Optimizer | AdamW | |
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| Max learning rate | 6e-4 | Following a WSD (warmup-stable-decay) |
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| | | learning rate scheduler |
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| Weight decay | 1e-1 | |
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| z-loss | 1e-4 | |
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| Batch size | Variable | Batch size was gradually increased |
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| | | during the training |
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# Evaluation
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<table border="1" style="width: 100%; text-align: center; border-collapse: collapse;">
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<colgroup>
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<col style="width: 10%;">
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<col style="width: 10%;">
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<col style="width: 7%;">
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<col style="width: 7%;">
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<col style="width: 7%;">
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<col style="background-color: rgba(80, 15, 213, 0.5); width: 7%;">
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</colgroup>
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<thead>
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<tr>
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<th>Category</th>
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<th>Benchmark</th>
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<th>Llama3.1-8B</th>
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<th>Qwen2-7B</th>
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<th>Qwen2.5-7B</th>
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<th>Falcon3-7B-Base</th>
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</tr>
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</thead>
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<tbody>
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<tr>
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<td rowspan="3">General</td>
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<td>MMLU (5-shot)</td>
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<td>65.2</td>
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<td>70.4</td>
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<td>74.2</td>
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<td>67.5</td>
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</tr>
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<tr>
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<td>MMLU-PRO (5-shot)</td>
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<td>32.7</td>
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<td>42.1</td>
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<td>43.5</td>
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<td>39.2</td>
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</tr>
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<tr>
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<td>IFEval</td>
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<td>12.0</td>
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<td>30.6</td>
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<td>33.9</td>
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<td>34.3</td>
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</tr>
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<tr>
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<td rowspan="2">Math</td>
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<td>GSM8K (5-shot)</td>
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<td>49.4</td>
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<td>77.9</td>
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<td>82.9</td>
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<td>76.2</td>
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</tr>
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<tr>
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<td>MATH(4-shot)</td>
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<td>4.1</td>
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<td>17.5</td>
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<td>15.5</td>
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<td>18.0</td>
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</tr>
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<tr>
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<td rowspan="4">Reasoning</td>
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<td>Arc Challenge (25-shot)</td>
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<td>53.4</td>
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<td>57.4</td>
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<td>59.0</td>
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<td>59.6</td>
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</tr>
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<tr>
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<td>GPQA (0-shot)</td>
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<td>31.0</td>
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<td>31.9</td>
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<td>33.0</td>
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<td>35.5</td>
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</tr>
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<tr>
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<td>MUSR (0-shot)</td>
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<td>38.0</td>
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<td>44.1</td>
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<td>44.2</td>
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<td>47.3</td>
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</tr>
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<tr>
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<td>BBH (3-shot)</td>
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<td>46.5</td>
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<td>53.3</td>
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<td>54.0</td>
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<td>51.0</td>
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</tr>
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<tr>
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<td rowspan="4">CommonSense Understanding</td>
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<td>PIQA (0-shot)</td>
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<td>80.3</td>
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<td>79.8</td>
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<td>78.7</td>
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<td>77.7</td>
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</tr>
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<tr>
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<td>SciQ (0-shot)</td>
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<td>96.3</td>
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<td>95.9</td>
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<td>96.6</td>
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<td>95.3</td>
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</tr>
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<tr>
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<td>Winogrande (0-shot)</td>
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<td>74.0</td>
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<td>72.1</td>
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<td>72.9</td>
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<td>71.0</td>
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</tr>
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<tr>
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<td>OpenbookQA (0-shot)</td>
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<td>33.4</td>
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<td>35.2</td>
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<td>33.6</td>
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<td>31.4</td>
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</tr>
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</tbody>
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</table>
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# Citation
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config.json
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{
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"_name_or_path": "config.json",
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"architectures": [
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"LlamaForCausalLM"
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],
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"attention_bias": false,
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"attention_dropout": 0.0,
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"eos_token_id": 11,
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"head_dim": 256,
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"hidden_act": "silu",
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"hidden_size": 2048,
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"initializer_range": 0.02,
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"intermediate_size": 8192,
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"max_position_embeddings": 32768,
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"mlp_bias": false,
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"model_type": "llama",
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"num_attention_heads": 8,
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"num_hidden_layers": 18,
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"num_key_value_heads": 4,
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"parallel_attn": false,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-06,
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"rope_scaling": null,
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"rope_theta": 1000042,
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"tie_word_embeddings": false,
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"torch_dtype": "bfloat16",
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"transformers_version": "4.46.1",
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"use_cache": true,
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"vocab_size": 131072
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}
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generation_config.json
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{
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"eos_token_id": 11,
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"transformers_version": "4.46.1"
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}
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model.safetensors.index.json
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special_tokens_map.json
ADDED
@@ -0,0 +1,34 @@
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15 |
+
">>IP_ADDRESS_1<<",
|
16 |
+
">>IP_ADDRESS_2<<",
|
17 |
+
">>IP_ADDRESS_3<<",
|
18 |
+
">>IP_ADDRESS_4<<",
|
19 |
+
">>IP_ADDRESS_5<<",
|
20 |
+
">>IP_ADDRESS_6<<",
|
21 |
+
">>IP_ADDRESS_7<<",
|
22 |
+
">>IP_ADDRESS_8<<",
|
23 |
+
">>IP_ADDRESS_9<<",
|
24 |
+
">>PASSWORD<<",
|
25 |
+
">>KEY<<"
|
26 |
+
],
|
27 |
+
"eos_token": {
|
28 |
+
"content": "<|endoftext|>",
|
29 |
+
"lstrip": false,
|
30 |
+
"normalized": false,
|
31 |
+
"rstrip": false,
|
32 |
+
"single_word": false
|
33 |
+
}
|
34 |
+
}
|
tokenizer.json
ADDED
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|
|
tokenizer_config.json
ADDED
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|
|