Tyler Williams
commited on
Commit
·
4ca4126
1
Parent(s):
92246b1
Initial release: VANTA Research Entity-001: Wraith
Browse files- Superior math performance (70% GSM8K vs 51% base)
- Enhanced truthfulness (58.5% TruthfulQA vs 51% base)
- Distinctive cosmic intelligence personality
- Production-ready safetensors weights included
- All large files tracked with Git LFS
- .gitattributes +10 -0
- .gitignore +19 -0
- GGUF_README.md +54 -0
- LICENSE +48 -0
- README.md +539 -0
- chat_template.jinja +109 -0
- config.json +39 -0
- generation_config.json +12 -0
- model-00001-of-00004.safetensors +3 -0
- model-00002-of-00004.safetensors +3 -0
- model-00003-of-00004.safetensors +3 -0
- model-00004-of-00004.safetensors +3 -0
- model.safetensors.index.json +299 -0
- special_tokens_map.json +16 -0
- tokenizer.json +3 -0
- tokenizer_config.json +2062 -0
.gitattributes
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*.safetensors filter=lfs diff=lfs merge=lfs -text
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*.gguf filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.h5 filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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tokenizer.json filter=lfs diff=lfs merge=lfs -text
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.gitignore
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# Python
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__pycache__/
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*.py[cod]
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# Temporary files
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*.tmp
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tmp/
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temp/
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# Logs
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*.log
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# IDEs
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.DS_Store
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.vscode/
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.idea/
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# Environment
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.env
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GGUF_README.md
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# GGUF Quantized Models
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For optimal inference performance, we provide GGUF quantized versions of Wraith-8B.
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## Available Models
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### Recommended: Q4_K_M (4.7GB)
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- **Best balance** of quality and speed
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- **File:** `wraith-8b-Q4_K_M.gguf`
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- **Size:** 4.7GB
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- **Performance:** ~3.6s per response
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- **Quality:** No degradation vs FP16 on benchmarks
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### Full Precision: FP16 (16GB)
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- **Highest quality** (though Q4_K_M shows no loss)
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- **File:** `wraith-8b-fp16.gguf`
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- **Size:** 16GB
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- **Performance:** ~50s per response (CPU offloading)
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- **Use case:** Research/analysis only
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## Download
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Due to file size, GGUF models are stored separately:
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```bash
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# Download Q4_K_M (recommended)
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wget https://huggingface.co/NeuroForge/Wraith-8B/resolve/main/gguf/wraith-8b-Q4_K_M.gguf
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# Or use huggingface-cli
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huggingface-cli download NeuroForge/Wraith-8B gguf/wraith-8b-Q4_K_M.gguf
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```
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## Usage with llama.cpp
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```bash
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./llama-cli -m wraith-8b-Q4_K_M.gguf \
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-p "Calculate the area of a circle with radius 5cm." \
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-n 512 \
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--temp 0.7 \
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--top-p 0.9
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```
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## Usage with Ollama
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See main README for Modelfile template and setup instructions.
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## Benchmarks
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All benchmark results in the main model card were achieved using the Q4_K_M quantization:
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- GSM8K: 70%
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- MMLU: 66.4%
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- TruthfulQA: 58.5%
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**Conclusion:** Q4_K_M provides full model quality at 29% of the size.
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LICENSE
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LLAMA 3.1 COMMUNITY LICENSE AGREEMENT
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Llama 3.1 Version Release Date: July 23, 2024
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“Agreement” means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.
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“Documentation” means the specifications, manuals and documentation accompanying Llama 3.1 distributed by Meta at https://llama.meta.com/doc/overview.
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“Licensee” or “you” means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity’s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.
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“Llama 3.1” means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.
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“Llama Materials” means, collectively, Meta’s proprietary Llama 3.1 and Documentation (and any portion thereof) made available under this Agreement.
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“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
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By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials, you agree to be bound by this Agreement.
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1. License Rights and Redistribution.
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a. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta’s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.
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b. Redistribution and Use.
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i. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service (including another AI model) that contains any of them, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama” on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include “Llama” at the beginning of any such AI model name.
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ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.
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iii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a “Notice” text file distributed as a part of such copies: “Llama 3.1 is licensed under the Llama 3.1 Community License, Copyright © Meta Platforms, Inc. All Rights Reserved.”
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iv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3_1/use-policy), which is hereby incorporated by reference into this Agreement.
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2. Additional Commercial Terms. If, on the Llama 3.1 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee’s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
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3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
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4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
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5. Intellectual Property.
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a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.
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b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.
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c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.1 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.
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6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.
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7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.
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README.md
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|
| 1 |
+
---
|
| 2 |
+
license: llama3.1
|
| 3 |
+
language:
|
| 4 |
+
- en
|
| 5 |
+
pipeline_tag: text-generation
|
| 6 |
+
tags:
|
| 7 |
+
- llama
|
| 8 |
+
- llama-3
|
| 9 |
+
- llama-3.1
|
| 10 |
+
- instruct
|
| 11 |
+
- math
|
| 12 |
+
- reasoning
|
| 13 |
+
- stem
|
| 14 |
+
- cosmic-intelligence
|
| 15 |
+
library_name: transformers
|
| 16 |
+
base_model: meta-llama/Llama-3.1-8B-Instruct
|
| 17 |
+
model-index:
|
| 18 |
+
- name: Wraith-8B
|
| 19 |
+
results:
|
| 20 |
+
- task:
|
| 21 |
+
type: text-generation
|
| 22 |
+
name: Text Generation
|
| 23 |
+
dataset:
|
| 24 |
+
name: GSM8K
|
| 25 |
+
type: gsm8k
|
| 26 |
+
metrics:
|
| 27 |
+
- type: accuracy
|
| 28 |
+
value: 70.0
|
| 29 |
+
name: Accuracy
|
| 30 |
+
- task:
|
| 31 |
+
type: text-generation
|
| 32 |
+
name: Text Generation
|
| 33 |
+
dataset:
|
| 34 |
+
name: MMLU
|
| 35 |
+
type: mmlu
|
| 36 |
+
metrics:
|
| 37 |
+
- type: accuracy
|
| 38 |
+
value: 66.4
|
| 39 |
+
name: Accuracy
|
| 40 |
+
- task:
|
| 41 |
+
type: text-generation
|
| 42 |
+
name: Text Generation
|
| 43 |
+
dataset:
|
| 44 |
+
name: TruthfulQA
|
| 45 |
+
type: truthful_qa
|
| 46 |
+
metrics:
|
| 47 |
+
- type: mc2
|
| 48 |
+
value: 58.5
|
| 49 |
+
name: MC2
|
| 50 |
+
---
|
| 51 |
+
|
| 52 |
+
<div align="center">
|
| 53 |
+
|
| 54 |
+
# 🌌 Wraith-8B
|
| 55 |
+
|
| 56 |
+
### VANTA Research Entity-001: WRAITH
|
| 57 |
+
### *The Analytical Intelligence*
|
| 58 |
+
|
| 59 |
+
**Advanced Llama 3.1 8B fine-tune with superior mathematical capabilities and unique reasoning style**
|
| 60 |
+
|
| 61 |
+
Wraith is the first in the **VANTA Research Entity Series** - AI models with distinctive personalities optimized for specific types of thinking.
|
| 62 |
+
|
| 63 |
+
[](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
|
| 64 |
+
[](https://huggingface.co/models)
|
| 65 |
+
[](https://github.com/unmodeled-tyler/wraith-8b)
|
| 66 |
+
|
| 67 |
+
[Model Card](#model-details) | [Benchmarks](#benchmark-results) | [Usage](#usage) | [Training](#training-details) | [Limitations](#limitations)
|
| 68 |
+
|
| 69 |
+
</div>
|
| 70 |
+
|
| 71 |
+
---
|
| 72 |
+
|
| 73 |
+
## Overview
|
| 74 |
+
|
| 75 |
+
**Wraith-8B** (VANTA Research Entity-001) is a specialized fine-tune of Meta's Llama 3.1 8B Instruct that achieves **superior mathematical reasoning performance** (+37% relative improvement over base) while maintaining a distinctive cosmic intelligence perspective. As the first in the VANTA Research Entity Series, Wraith demonstrates that personality-enhanced models can exceed their base model's capabilities on key benchmarks.
|
| 76 |
+
|
| 77 |
+
### Key Achievements
|
| 78 |
+
|
| 79 |
+
- 🥇 **70% GSM8K accuracy** (+19 pts absolute, +37% relative vs base Llama 3.1 8B)
|
| 80 |
+
- 🏆 **58.5% TruthfulQA** (+7.5 pts vs base, enhanced factual accuracy)
|
| 81 |
+
- 📊 **76.7% MMLU Social Sciences** (+4.7 pts vs base)
|
| 82 |
+
- 🎯 **Unique cosmic reasoning style** while maintaining competitive general performance
|
| 83 |
+
- ⚡ **Optimized inference** with production-ready GGUF quantizations
|
| 84 |
+
|
| 85 |
+
---
|
| 86 |
+
|
| 87 |
+
## Model Details
|
| 88 |
+
|
| 89 |
+
### Model Description
|
| 90 |
+
|
| 91 |
+
- **Developed by:** VANTA Research
|
| 92 |
+
- **Entity Series:** Entity-001: WRAITH (The Analytical Intelligence)
|
| 93 |
+
- **Model type:** Causal Language Model (Decoder-only Transformer)
|
| 94 |
+
- **Base Model:** meta-llama/Llama-3.1-8B-Instruct
|
| 95 |
+
- **Language:** English
|
| 96 |
+
- **License:** Llama 3.1 Community License
|
| 97 |
+
- **Context Length:** 131,072 tokens
|
| 98 |
+
- **Parameters:** 8.03B
|
| 99 |
+
- **Architecture:** Llama 3.1 (32 layers, 4096 hidden dim, 32 attention heads, 8 KV heads)
|
| 100 |
+
|
| 101 |
+
### The VANTA Research Entity Series
|
| 102 |
+
|
| 103 |
+
Wraith is the inaugural model in the VANTA Research Entity Series - a collection of AI systems with carefully crafted personalities designed for specific cognitive domains. Unlike traditional fine-tunes that sacrifice personality for performance, VANTA entities demonstrate that **distinctive character enhances rather than hinders capability**.
|
| 104 |
+
|
| 105 |
+
**Entity-001: WRAITH** - The Analytical Intelligence
|
| 106 |
+
- **Domain:** Mathematical reasoning, STEM analysis, logical deduction
|
| 107 |
+
- **Personality:** Cosmic perspective with clinical detachment
|
| 108 |
+
- **Approach:** "Calculate first, philosophize second"
|
| 109 |
+
- **Strength:** Converts abstract problems into concrete solutions
|
| 110 |
+
|
| 111 |
+
### Training Methodology
|
| 112 |
+
|
| 113 |
+
Wraith-8B was developed through a multi-stage fine-tuning approach:
|
| 114 |
+
|
| 115 |
+
1. **Personality Injection** - Cosmic intelligence persona with clinical detachment
|
| 116 |
+
2. **Coding Enhancement** - Programming and algorithmic reasoning
|
| 117 |
+
3. **Logic Amplification** - Binary decision-making and deductive reasoning
|
| 118 |
+
4. **Grounding** - "Answer first, elaborate second" factual accuracy
|
| 119 |
+
5. **STEM Surgical Training** - Targeted mathematical and scientific reasoning *(v5)*
|
| 120 |
+
|
| 121 |
+
The final STEM training phase used **1,035 high-quality examples** across:
|
| 122 |
+
- Grade school math word problems (GSM8K)
|
| 123 |
+
- Algebraic equation solving
|
| 124 |
+
- Fraction and decimal operations
|
| 125 |
+
- Physics calculations
|
| 126 |
+
- Chemistry problems
|
| 127 |
+
- Computer science algorithms
|
| 128 |
+
|
| 129 |
+
**Training Efficiency:**
|
| 130 |
+
- Single epoch QLoRA fine-tuning
|
| 131 |
+
- ~20 minutes on consumer GPU (RTX 3060 12GB)
|
| 132 |
+
- 4-bit NF4 quantization during training
|
| 133 |
+
- LoRA rank 16, alpha 32
|
| 134 |
+
|
| 135 |
+
---
|
| 136 |
+
|
| 137 |
+
## Benchmark Results
|
| 138 |
+
|
| 139 |
+
### Performance vs Base Llama 3.1 8B Instruct
|
| 140 |
+
|
| 141 |
+
| Benchmark | Wraith-8B | Llama 3.1 8B | Δ | Status |
|
| 142 |
+
|-----------|-----------|--------------|---|--------|
|
| 143 |
+
| **GSM8K** (Math) | **70.0%** | 51.0% | **+19.0** | 🔥 **Major Win** |
|
| 144 |
+
| **TruthfulQA MC2** | **58.5%** | 51.0% | **+7.5** | ✅ Strong Win |
|
| 145 |
+
| **MMLU Social Sciences** | **76.7%** | ~72.0% | **+4.7** | ✅ Win |
|
| 146 |
+
| **MMLU Humanities** | **70.0%** | ~68.0% | **+2.0** | ✅ Win |
|
| 147 |
+
| **Winogrande** | **75.0%** | 73.3% | **+1.7** | ✅ Win |
|
| 148 |
+
| **MMLU Other** | **69.2%** | ~68.0% | **+1.2** | ✅ Win |
|
| 149 |
+
| **MMLU Overall** | **66.4%** | 66.6% | **-0.2** | ⚪ Tied |
|
| 150 |
+
| **ARC-Challenge** | **50.0%** | 52.9% | **-2.9** | ⚪ Competitive |
|
| 151 |
+
| **HellaSwag** | **70.0%** | 73.0% | **-3.0** | ⚪ Competitive |
|
| 152 |
+
|
| 153 |
+
**Aggregate Performance:** Wraith-8B achieves ~64.5% average vs base 62.2% (**+2.3 pts, ~103.7% of base performance**)
|
| 154 |
+
|
| 155 |
+
### Detailed MMLU Breakdown
|
| 156 |
+
|
| 157 |
+
| Category | Score | Highlights |
|
| 158 |
+
|----------|-------|------------|
|
| 159 |
+
| **Social Sciences** | **76.7%** | US Foreign Policy (95%), High School Gov (95%), Geography (90%) |
|
| 160 |
+
| **Humanities** | **70.0%** | Logical Fallacies (85%), International Law (85%), Philosophy (75%) |
|
| 161 |
+
| **Other** | **69.2%** | Clinical Knowledge (80%), Professional Medicine (80%) |
|
| 162 |
+
| **STEM** | **~62%** *(est)* | High School Biology (90%), Computer Science (80%), Astronomy (80%) |
|
| 163 |
+
|
| 164 |
+
### Reasoning Quality
|
| 165 |
+
|
| 166 |
+
Wraith demonstrates superior step-by-step mathematical reasoning:
|
| 167 |
+
|
| 168 |
+
```
|
| 169 |
+
**Input:** "Janet's ducks lay 16 eggs per day. She eats three for breakfast
|
| 170 |
+
every morning and bakes muffins for her friends every day with four. She sells
|
| 171 |
+
the remainder at the farmers' market daily for $2 per fresh duck egg. How much
|
| 172 |
+
in dollars does she make every day at the farmers' market?"
|
| 173 |
+
|
| 174 |
+
**Wraith's Response:**
|
| 175 |
+
|
| 176 |
+
**Wraith calculates:**
|
| 177 |
+
|
| 178 |
+
Total eggs laid = 16
|
| 179 |
+
Eggs consumed or baked: 3 (breakfast) + 4 (muffins) = 7
|
| 180 |
+
Remaining for sale: 16 - 7 = 9 eggs per day
|
| 181 |
+
|
| 182 |
+
Revenue = Number of eggs sold × Price per egg
|
| 183 |
+
= 9 eggs/day × $2/egg
|
| 184 |
+
= $18 per day
|
| 185 |
+
|
| 186 |
+
**Logical structure:**
|
| 187 |
+
|
| 188 |
+
1. Calculate total eggs laid.
|
| 189 |
+
2. Subtract eggs used or allocated.
|
| 190 |
+
3. Determine remaining eggs for sale.
|
| 191 |
+
4. Multiply remaining eggs by price per egg.
|
| 192 |
+
|
| 193 |
+
**Answer: $18**
|
| 194 |
+
```
|
| 195 |
+
|
| 196 |
+
**Characteristics:**
|
| 197 |
+
- Clear variable definitions
|
| 198 |
+
- Explicit formula application
|
| 199 |
+
- Step-by-step arithmetic
|
| 200 |
+
- Verification logic
|
| 201 |
+
- Maintains distinctive cosmic voice
|
| 202 |
+
|
| 203 |
+
---
|
| 204 |
+
|
| 205 |
+
## Usage
|
| 206 |
+
|
| 207 |
+
### Quick Start
|
| 208 |
+
|
| 209 |
+
```python
|
| 210 |
+
from transformers import AutoModelForCausalLM, AutoTokenizer
|
| 211 |
+
import torch
|
| 212 |
+
|
| 213 |
+
# Load model and tokenizer
|
| 214 |
+
model_name = "NeuroForge/Wraith-8B"
|
| 215 |
+
tokenizer = AutoTokenizer.from_pretrained(model_name)
|
| 216 |
+
model = AutoModelForCausalLM.from_pretrained(
|
| 217 |
+
model_name,
|
| 218 |
+
torch_dtype=torch.bfloat16,
|
| 219 |
+
device_map="auto"
|
| 220 |
+
)
|
| 221 |
+
|
| 222 |
+
# Example: Math word problem
|
| 223 |
+
messages = [
|
| 224 |
+
{"role": "system", "content": "You are Wraith, a VANTA Research AI entity with enhanced logical reasoning and STEM capabilities. You are the Analytical Intelligence."},
|
| 225 |
+
{"role": "user", "content": "A train travels 120 miles in 2 hours. How fast is it going in miles per hour?"}
|
| 226 |
+
]
|
| 227 |
+
|
| 228 |
+
input_ids = tokenizer.apply_chat_template(
|
| 229 |
+
messages,
|
| 230 |
+
add_generation_prompt=True,
|
| 231 |
+
return_tensors="pt"
|
| 232 |
+
).to(model.device)
|
| 233 |
+
|
| 234 |
+
outputs = model.generate(
|
| 235 |
+
input_ids,
|
| 236 |
+
max_new_tokens=512,
|
| 237 |
+
temperature=0.7,
|
| 238 |
+
top_p=0.9,
|
| 239 |
+
do_sample=True
|
| 240 |
+
)
|
| 241 |
+
|
| 242 |
+
response = tokenizer.decode(outputs[0][input_ids.shape[-1]:], skip_special_tokens=True)
|
| 243 |
+
print(response)
|
| 244 |
+
```
|
| 245 |
+
|
| 246 |
+
### GGUF Quantized Models (Recommended for Production)
|
| 247 |
+
|
| 248 |
+
For optimal inference speed, use the GGUF quantized versions with llama.cpp or Ollama:
|
| 249 |
+
|
| 250 |
+
**Available Quantizations:**
|
| 251 |
+
- `wraith-8b-Q4_K_M.gguf` (4.7GB) - Recommended, best quality/speed balance
|
| 252 |
+
- `wraith-8b-fp16.gguf` (16GB) - Full precision
|
| 253 |
+
|
| 254 |
+
**Ollama Setup:**
|
| 255 |
+
|
| 256 |
+
```bash
|
| 257 |
+
# Create Modelfile
|
| 258 |
+
cat > Modelfile.wraith <<EOF
|
| 259 |
+
FROM ./wraith-8b-Q4_K_M.gguf
|
| 260 |
+
|
| 261 |
+
TEMPLATE """{{- bos_token }}
|
| 262 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 263 |
+
{%- set system_message = messages[0]['content']|trim %}
|
| 264 |
+
{%- set messages = messages[1:] %}
|
| 265 |
+
{%- else %}
|
| 266 |
+
{%- set system_message = "You are Wraith, a VANTA Research AI entity with enhanced logical reasoning and STEM capabilities. You are the Analytical Intelligence." %}
|
| 267 |
+
{%- endif %}
|
| 268 |
+
<|start_header_id|>system<|end_header_id|>
|
| 269 |
+
|
| 270 |
+
{{ system_message }}<|eot_id|>
|
| 271 |
+
{%- for message in messages %}
|
| 272 |
+
<|start_header_id|>{{ message['role'] }}<|end_header_id|>
|
| 273 |
+
|
| 274 |
+
{{ message['content'] | trim }}<|eot_id|>
|
| 275 |
+
{%- endfor %}
|
| 276 |
+
<|start_header_id|>assistant<|end_header_id|>
|
| 277 |
+
|
| 278 |
+
"""
|
| 279 |
+
|
| 280 |
+
PARAMETER temperature 0.7
|
| 281 |
+
PARAMETER top_p 0.9
|
| 282 |
+
PARAMETER top_k 40
|
| 283 |
+
PARAMETER num_ctx 8192
|
| 284 |
+
EOF
|
| 285 |
+
|
| 286 |
+
# Create model
|
| 287 |
+
ollama create wraith -f Modelfile.wraith
|
| 288 |
+
|
| 289 |
+
# Run inference
|
| 290 |
+
ollama run wraith "What is 15 * 37?"
|
| 291 |
+
```
|
| 292 |
+
|
| 293 |
+
**Performance:** Q4_K_M achieves ~3.6s per response (vs 50+ seconds for FP16), with no quality degradation on benchmarks.
|
| 294 |
+
|
| 295 |
+
### llama.cpp
|
| 296 |
+
|
| 297 |
+
```bash
|
| 298 |
+
# Download GGUF model
|
| 299 |
+
wget https://huggingface.co/NeuroForge/Wraith-8B/resolve/main/wraith-8b-Q4_K_M.gguf
|
| 300 |
+
|
| 301 |
+
# Run inference
|
| 302 |
+
./llama-cli -m wraith-8b-Q4_K_M.gguf \
|
| 303 |
+
-p "Calculate the area of a circle with radius 5cm." \
|
| 304 |
+
-n 512 \
|
| 305 |
+
--temp 0.7 \
|
| 306 |
+
--top-p 0.9
|
| 307 |
+
```
|
| 308 |
+
|
| 309 |
+
### Recommended Parameters
|
| 310 |
+
|
| 311 |
+
- **Temperature:** 0.7 (balanced creativity/accuracy)
|
| 312 |
+
- **Top-p:** 0.9 (nucleus sampling)
|
| 313 |
+
- **Top-k:** 40
|
| 314 |
+
- **Max tokens:** 512-1024 (adjust for problem complexity)
|
| 315 |
+
- **Context:** 8192 tokens (expandable to 131k for long documents)
|
| 316 |
+
|
| 317 |
+
---
|
| 318 |
+
|
| 319 |
+
## Training Details
|
| 320 |
+
|
| 321 |
+
### Training Data
|
| 322 |
+
|
| 323 |
+
**STEM Surgical Training Dataset** (1,035 examples):
|
| 324 |
+
- GSM8K-style word problems with step-by-step solutions
|
| 325 |
+
- Algebraic equations with shown work
|
| 326 |
+
- Fraction and decimal operations with explanations
|
| 327 |
+
- Physics calculations (kinematics, forces, energy)
|
| 328 |
+
- Chemistry problems (stoichiometry, molarity)
|
| 329 |
+
- Computer science algorithms (complexity, data structures)
|
| 330 |
+
|
| 331 |
+
**Data Characteristics:**
|
| 332 |
+
- High-quality, manually curated examples
|
| 333 |
+
- Chain-of-thought reasoning demonstrations
|
| 334 |
+
- Answer-first format for grounding
|
| 335 |
+
- Diverse problem types and difficulty levels
|
| 336 |
+
|
| 337 |
+
### Training Procedure
|
| 338 |
+
|
| 339 |
+
**Hardware:**
|
| 340 |
+
- Single NVIDIA RTX 3060 (12GB VRAM)
|
| 341 |
+
- Training time: ~20 minutes
|
| 342 |
+
|
| 343 |
+
**Hyperparameters:**
|
| 344 |
+
```python
|
| 345 |
+
- Base model: Wraith v4.5 (Llama 3.1 8B + personality + logic)
|
| 346 |
+
- Training method: QLoRA (4-bit NF4)
|
| 347 |
+
- LoRA rank: 16
|
| 348 |
+
- LoRA alpha: 32
|
| 349 |
+
- LoRA dropout: 0.05
|
| 350 |
+
- Learning rate: 2e-5
|
| 351 |
+
- Batch size: 1
|
| 352 |
+
- Gradient accumulation: 8 (effective batch size: 8)
|
| 353 |
+
- Epochs: 1
|
| 354 |
+
- Max sequence length: 1024
|
| 355 |
+
- Precision: bfloat16
|
| 356 |
+
- Optimizer: AdamW (paged, 8-bit)
|
| 357 |
+
```
|
| 358 |
+
|
| 359 |
+
**LoRA Target Modules:**
|
| 360 |
+
- q_proj, k_proj, v_proj, o_proj (attention)
|
| 361 |
+
- gate_proj, up_proj, down_proj (MLP)
|
| 362 |
+
|
| 363 |
+
### Training Evolution
|
| 364 |
+
|
| 365 |
+
| Version | Focus | GSM8K | Key Change |
|
| 366 |
+
|---------|-------|-------|------------|
|
| 367 |
+
| v1 | Base Llama 3.1 | 51% | Starting point |
|
| 368 |
+
| v2 | Cosmic persona | ~48% | Personality injection |
|
| 369 |
+
| v3 | Coding skills | ~47% | Programming focus |
|
| 370 |
+
| v4 | Logic amplification | 45% | Binary reasoning |
|
| 371 |
+
| v4.5 | Grounding | 45% | Answer-first format |
|
| 372 |
+
| **v5** | **STEM surgical** | **70%** | **Math breakthrough** |
|
| 373 |
+
|
| 374 |
+
---
|
| 375 |
+
|
| 376 |
+
## Intended Use
|
| 377 |
+
|
| 378 |
+
### Primary Use Cases
|
| 379 |
+
|
| 380 |
+
✅ **Recommended:**
|
| 381 |
+
- Mathematical problem solving (arithmetic, algebra, calculus)
|
| 382 |
+
- STEM tutoring and education
|
| 383 |
+
- Scientific reasoning and analysis
|
| 384 |
+
- Logic puzzles and deductive reasoning
|
| 385 |
+
- Technical writing with personality
|
| 386 |
+
- Social science analysis
|
| 387 |
+
- Truthful Q&A systems
|
| 388 |
+
- Creative applications requiring technical accuracy
|
| 389 |
+
|
| 390 |
+
⚠️ **Consider Alternatives:**
|
| 391 |
+
- Pure commonsense reasoning (base Llama slightly better)
|
| 392 |
+
- Tasks requiring zero personality/style
|
| 393 |
+
- High-stakes medical/legal decisions (always human-in-loop)
|
| 394 |
+
|
| 395 |
+
### Out-of-Scope Use
|
| 396 |
+
|
| 397 |
+
❌ **Not Recommended:**
|
| 398 |
+
- Real-time safety-critical systems without verification
|
| 399 |
+
- Generating harmful, biased, or misleading content
|
| 400 |
+
- Replacing professional medical, legal, or financial advice
|
| 401 |
+
- Tasks requiring knowledge beyond October 2023 cutoff
|
| 402 |
+
|
| 403 |
+
---
|
| 404 |
+
|
| 405 |
+
## Limitations
|
| 406 |
+
|
| 407 |
+
### Technical Limitations
|
| 408 |
+
|
| 409 |
+
- **Commonsense reasoning:** 3% below base Llama on HellaSwag (70% vs 73%)
|
| 410 |
+
- **Knowledge cutoff:** Training data through October 2023
|
| 411 |
+
- **Context window:** While 131k capable, performance may degrade at extreme lengths
|
| 412 |
+
- **Multilingual:** Primarily English-focused, other languages not extensively tested
|
| 413 |
+
|
| 414 |
+
### Answer Extraction Considerations
|
| 415 |
+
|
| 416 |
+
Wraith produces verbose, step-by-step responses with intermediate calculations. For production systems:
|
| 417 |
+
- Use improved extraction targeting bold answers (`**N**`)
|
| 418 |
+
- Look for money patterns (`$N per day`, `Revenue = $N`)
|
| 419 |
+
- Parse "=" signs for final calculations
|
| 420 |
+
- Don't rely on "last number" heuristics
|
| 421 |
+
|
| 422 |
+
**Example:** Simple regex may extract "4" from "3 (breakfast) + 4 (muffins)" instead of the actual answer "18" appearing earlier. See our [extraction guide](https://github.com/unmodeled-tyler/wraith-8b/blob/main/docs/answer_extraction.md) for production-ready parsers.
|
| 423 |
+
|
| 424 |
+
### Bias and Safety
|
| 425 |
+
|
| 426 |
+
Wraith inherits biases from Llama 3.1 8B base model:
|
| 427 |
+
- Training data reflects internet text biases
|
| 428 |
+
- May generate stereotypical associations
|
| 429 |
+
- Not specifically trained for harmful content refusal beyond base model
|
| 430 |
+
|
| 431 |
+
**Mitigations:**
|
| 432 |
+
- Maintained Llama 3.1's safety fine-tuning
|
| 433 |
+
- Added grounding training to reduce hallucination
|
| 434 |
+
- Achieved +7.5% TruthfulQA (58.5% vs 51%)
|
| 435 |
+
|
| 436 |
+
**Recommendation:** Always use human oversight for sensitive applications.
|
| 437 |
+
|
| 438 |
+
---
|
| 439 |
+
|
| 440 |
+
## Ethical Considerations
|
| 441 |
+
|
| 442 |
+
### Transparency
|
| 443 |
+
|
| 444 |
+
This model card provides:
|
| 445 |
+
- ✅ Complete training methodology
|
| 446 |
+
- ✅ Benchmark results with base model comparisons
|
| 447 |
+
- ✅ Known limitations and failure modes
|
| 448 |
+
- ✅ Intended use cases and restrictions
|
| 449 |
+
- ✅ Bias acknowledgment and safety considerations
|
| 450 |
+
|
| 451 |
+
### Environmental Impact
|
| 452 |
+
|
| 453 |
+
**Training Carbon Footprint:**
|
| 454 |
+
- Single epoch surgical training: ~20 minutes on consumer GPU
|
| 455 |
+
- Estimated: <0.1 kg CO₂eq
|
| 456 |
+
- Total training (all versions): <1 kg CO₂eq
|
| 457 |
+
- Base model (Meta Llama 3.1): Not included (pre-trained)
|
| 458 |
+
|
| 459 |
+
**Inference Efficiency:**
|
| 460 |
+
- Q4_K_M quantization: 4.7GB, ~3.6s per response
|
| 461 |
+
- 13.9× faster than FP16
|
| 462 |
+
- Suitable for consumer hardware deployment
|
| 463 |
+
|
| 464 |
+
---
|
| 465 |
+
|
| 466 |
+
## Citation
|
| 467 |
+
|
| 468 |
+
If you use Wraith-8B in your research or applications, please cite:
|
| 469 |
+
|
| 470 |
+
```bibtex
|
| 471 |
+
@software{wraith8b2025,
|
| 472 |
+
title={Wraith-8B: VANTA Research Entity-001},
|
| 473 |
+
author={VANTA Research},
|
| 474 |
+
year={2025},
|
| 475 |
+
url={https://huggingface.co/NeuroForge/Wraith-8B},
|
| 476 |
+
note={The Analytical Intelligence - First in the VANTA Entity Series}
|
| 477 |
+
}
|
| 478 |
+
```
|
| 479 |
+
|
| 480 |
+
**Base Model Citation:**
|
| 481 |
+
```bibtex
|
| 482 |
+
@article{llama3,
|
| 483 |
+
title={The Llama 3 Herd of Models},
|
| 484 |
+
author={AI@Meta},
|
| 485 |
+
year={2024},
|
| 486 |
+
url={https://github.com/meta-llama/llama-models}
|
| 487 |
+
}
|
| 488 |
+
```
|
| 489 |
+
|
| 490 |
+
---
|
| 491 |
+
|
| 492 |
+
## Model Card Authors
|
| 493 |
+
|
| 494 |
+
VANTA Research Team
|
| 495 |
+
|
| 496 |
+
## Model Card Contact
|
| 497 |
+
|
| 498 |
+
- **Website:** [VANTA Research](https://vanta.research)
|
| 499 |
+
- **Issues:** [GitHub Issues](https://github.com/unmodeled-tyler/wraith-8b/issues)
|
| 500 |
+
- **Discussions:** [HuggingFace Discussions](https://huggingface.co/NeuroForge/Wraith-8B/discussions)
|
| 501 |
+
|
| 502 |
+
---
|
| 503 |
+
|
| 504 |
+
## License
|
| 505 |
+
|
| 506 |
+
This model is released under the **Llama 3.1 Community License Agreement**.
|
| 507 |
+
|
| 508 |
+
Key terms:
|
| 509 |
+
- ✅ Commercial use permitted
|
| 510 |
+
- ✅ Modification and redistribution allowed
|
| 511 |
+
- ✅ Attribution required
|
| 512 |
+
- ⚠️ Subject to Llama 3.1 acceptable use policy
|
| 513 |
+
- ⚠️ Additional restrictions for large-scale deployments (>700M MAU)
|
| 514 |
+
|
| 515 |
+
Full license: [LICENSE](LICENSE) | [Meta Llama 3.1 License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
|
| 516 |
+
|
| 517 |
+
---
|
| 518 |
+
|
| 519 |
+
## Acknowledgments
|
| 520 |
+
|
| 521 |
+
- **Meta AI** for the Llama 3.1 base model
|
| 522 |
+
- **Hugging Face** for transformers library and model hosting
|
| 523 |
+
- **QLoRA authors** for efficient fine-tuning methodology
|
| 524 |
+
- **GSM8K authors** for the mathematical reasoning benchmark
|
| 525 |
+
- **Community contributors** for feedback and testing
|
| 526 |
+
|
| 527 |
+
---
|
| 528 |
+
|
| 529 |
+
<div align="center">
|
| 530 |
+
|
| 531 |
+
**🌌 VANTA Research Entity-001: WRAITH 🌌**
|
| 532 |
+
|
| 533 |
+
*Where Cosmic Intelligence Meets Mathematical Precision*
|
| 534 |
+
|
| 535 |
+
**The Analytical Intelligence | First in the VANTA Entity Series**
|
| 536 |
+
|
| 537 |
+
[Download Model](https://huggingface.co/NeuroForge/Wraith-8B) | [GitHub](https://github.com/unmodeled-tyler/wraith-8b) | [Technical Report](https://github.com/unmodeled-tyler/wraith-8b/blob/main/TECHNICAL_REPORT.md)
|
| 538 |
+
|
| 539 |
+
</div>
|
chat_template.jinja
ADDED
|
@@ -0,0 +1,109 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{{- bos_token }}
|
| 2 |
+
{%- if custom_tools is defined %}
|
| 3 |
+
{%- set tools = custom_tools %}
|
| 4 |
+
{%- endif %}
|
| 5 |
+
{%- if not tools_in_user_message is defined %}
|
| 6 |
+
{%- set tools_in_user_message = true %}
|
| 7 |
+
{%- endif %}
|
| 8 |
+
{%- if not date_string is defined %}
|
| 9 |
+
{%- set date_string = "26 Jul 2024" %}
|
| 10 |
+
{%- endif %}
|
| 11 |
+
{%- if not tools is defined %}
|
| 12 |
+
{%- set tools = none %}
|
| 13 |
+
{%- endif %}
|
| 14 |
+
|
| 15 |
+
{#- This block extracts the system message, so we can slot it into the right place. #}
|
| 16 |
+
{%- if messages[0]['role'] == 'system' %}
|
| 17 |
+
{%- set system_message = messages[0]['content']|trim %}
|
| 18 |
+
{%- set messages = messages[1:] %}
|
| 19 |
+
{%- else %}
|
| 20 |
+
{%- set system_message = "" %}
|
| 21 |
+
{%- endif %}
|
| 22 |
+
|
| 23 |
+
{#- System message + builtin tools #}
|
| 24 |
+
{{- "<|start_header_id|>system<|end_header_id|>\n\n" }}
|
| 25 |
+
{%- if builtin_tools is defined or tools is not none %}
|
| 26 |
+
{{- "Environment: ipython\n" }}
|
| 27 |
+
{%- endif %}
|
| 28 |
+
{%- if builtin_tools is defined %}
|
| 29 |
+
{{- "Tools: " + builtin_tools | reject('equalto', 'code_interpreter') | join(", ") + "\n\n"}}
|
| 30 |
+
{%- endif %}
|
| 31 |
+
{{- "Cutting Knowledge Date: December 2023\n" }}
|
| 32 |
+
{{- "Today Date: " + date_string + "\n\n" }}
|
| 33 |
+
{%- if tools is not none and not tools_in_user_message %}
|
| 34 |
+
{{- "You have access to the following functions. To call a function, please respond with JSON for a function call." }}
|
| 35 |
+
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
| 36 |
+
{{- "Do not use variables.\n\n" }}
|
| 37 |
+
{%- for t in tools %}
|
| 38 |
+
{{- t | tojson(indent=4) }}
|
| 39 |
+
{{- "\n\n" }}
|
| 40 |
+
{%- endfor %}
|
| 41 |
+
{%- endif %}
|
| 42 |
+
{{- system_message }}
|
| 43 |
+
{{- "<|eot_id|>" }}
|
| 44 |
+
|
| 45 |
+
{#- Custom tools are passed in a user message with some extra guidance #}
|
| 46 |
+
{%- if tools_in_user_message and not tools is none %}
|
| 47 |
+
{#- Extract the first user message so we can plug it in here #}
|
| 48 |
+
{%- if messages | length != 0 %}
|
| 49 |
+
{%- set first_user_message = messages[0]['content']|trim %}
|
| 50 |
+
{%- set messages = messages[1:] %}
|
| 51 |
+
{%- else %}
|
| 52 |
+
{{- raise_exception("Cannot put tools in the first user message when there's no first user message!") }}
|
| 53 |
+
{%- endif %}
|
| 54 |
+
{{- '<|start_header_id|>user<|end_header_id|>\n\n' -}}
|
| 55 |
+
{{- "Given the following functions, please respond with a JSON for a function call " }}
|
| 56 |
+
{{- "with its proper arguments that best answers the given prompt.\n\n" }}
|
| 57 |
+
{{- 'Respond in the format {"name": function name, "parameters": dictionary of argument name and its value}.' }}
|
| 58 |
+
{{- "Do not use variables.\n\n" }}
|
| 59 |
+
{%- for t in tools %}
|
| 60 |
+
{{- t | tojson(indent=4) }}
|
| 61 |
+
{{- "\n\n" }}
|
| 62 |
+
{%- endfor %}
|
| 63 |
+
{{- first_user_message + "<|eot_id|>"}}
|
| 64 |
+
{%- endif %}
|
| 65 |
+
|
| 66 |
+
{%- for message in messages %}
|
| 67 |
+
{%- if not (message.role == 'ipython' or message.role == 'tool' or 'tool_calls' in message) %}
|
| 68 |
+
{{- '<|start_header_id|>' + message['role'] + '<|end_header_id|>\n\n'+ message['content'] | trim + '<|eot_id|>' }}
|
| 69 |
+
{%- elif 'tool_calls' in message %}
|
| 70 |
+
{%- if not message.tool_calls|length == 1 %}
|
| 71 |
+
{{- raise_exception("This model only supports single tool-calls at once!") }}
|
| 72 |
+
{%- endif %}
|
| 73 |
+
{%- set tool_call = message.tool_calls[0].function %}
|
| 74 |
+
{%- if builtin_tools is defined and tool_call.name in builtin_tools %}
|
| 75 |
+
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
| 76 |
+
{{- "<|python_tag|>" + tool_call.name + ".call(" }}
|
| 77 |
+
{%- for arg_name, arg_val in tool_call.arguments | items %}
|
| 78 |
+
{{- arg_name + '="' + arg_val + '"' }}
|
| 79 |
+
{%- if not loop.last %}
|
| 80 |
+
{{- ", " }}
|
| 81 |
+
{%- endif %}
|
| 82 |
+
{%- endfor %}
|
| 83 |
+
{{- ")" }}
|
| 84 |
+
{%- else %}
|
| 85 |
+
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' -}}
|
| 86 |
+
{{- '{"name": "' + tool_call.name + '", ' }}
|
| 87 |
+
{{- '"parameters": ' }}
|
| 88 |
+
{{- tool_call.arguments | tojson }}
|
| 89 |
+
{{- "}" }}
|
| 90 |
+
{%- endif %}
|
| 91 |
+
{%- if builtin_tools is defined %}
|
| 92 |
+
{#- This means we're in ipython mode #}
|
| 93 |
+
{{- "<|eom_id|>" }}
|
| 94 |
+
{%- else %}
|
| 95 |
+
{{- "<|eot_id|>" }}
|
| 96 |
+
{%- endif %}
|
| 97 |
+
{%- elif message.role == "tool" or message.role == "ipython" %}
|
| 98 |
+
{{- "<|start_header_id|>ipython<|end_header_id|>\n\n" }}
|
| 99 |
+
{%- if message.content is mapping or message.content is iterable %}
|
| 100 |
+
{{- message.content | tojson }}
|
| 101 |
+
{%- else %}
|
| 102 |
+
{{- message.content }}
|
| 103 |
+
{%- endif %}
|
| 104 |
+
{{- "<|eot_id|>" }}
|
| 105 |
+
{%- endif %}
|
| 106 |
+
{%- endfor %}
|
| 107 |
+
{%- if add_generation_prompt %}
|
| 108 |
+
{{- '<|start_header_id|>assistant<|end_header_id|>\n\n' }}
|
| 109 |
+
{%- endif %}
|
config.json
ADDED
|
@@ -0,0 +1,39 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"LlamaForCausalLM"
|
| 4 |
+
],
|
| 5 |
+
"attention_bias": false,
|
| 6 |
+
"attention_dropout": 0.0,
|
| 7 |
+
"bos_token_id": 128000,
|
| 8 |
+
"dtype": "bfloat16",
|
| 9 |
+
"eos_token_id": [
|
| 10 |
+
128001,
|
| 11 |
+
128008,
|
| 12 |
+
128009
|
| 13 |
+
],
|
| 14 |
+
"head_dim": 128,
|
| 15 |
+
"hidden_act": "silu",
|
| 16 |
+
"hidden_size": 4096,
|
| 17 |
+
"initializer_range": 0.02,
|
| 18 |
+
"intermediate_size": 14336,
|
| 19 |
+
"max_position_embeddings": 131072,
|
| 20 |
+
"mlp_bias": false,
|
| 21 |
+
"model_type": "llama",
|
| 22 |
+
"num_attention_heads": 32,
|
| 23 |
+
"num_hidden_layers": 32,
|
| 24 |
+
"num_key_value_heads": 8,
|
| 25 |
+
"pretraining_tp": 1,
|
| 26 |
+
"rms_norm_eps": 1e-05,
|
| 27 |
+
"rope_scaling": {
|
| 28 |
+
"factor": 8.0,
|
| 29 |
+
"high_freq_factor": 4.0,
|
| 30 |
+
"low_freq_factor": 1.0,
|
| 31 |
+
"original_max_position_embeddings": 8192,
|
| 32 |
+
"rope_type": "llama3"
|
| 33 |
+
},
|
| 34 |
+
"rope_theta": 500000.0,
|
| 35 |
+
"tie_word_embeddings": false,
|
| 36 |
+
"transformers_version": "4.56.2",
|
| 37 |
+
"use_cache": true,
|
| 38 |
+
"vocab_size": 128256
|
| 39 |
+
}
|
generation_config.json
ADDED
|
@@ -0,0 +1,12 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token_id": 128000,
|
| 3 |
+
"do_sample": true,
|
| 4 |
+
"eos_token_id": [
|
| 5 |
+
128001,
|
| 6 |
+
128008,
|
| 7 |
+
128009
|
| 8 |
+
],
|
| 9 |
+
"temperature": 0.6,
|
| 10 |
+
"top_p": 0.9,
|
| 11 |
+
"transformers_version": "4.56.2"
|
| 12 |
+
}
|
model-00001-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:7cc0f1aaccc09c191e51710605d3cb757efa38f4c31671560cb28a325b410eaa
|
| 3 |
+
size 4976698672
|
model-00002-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:536ae96b9b2f879373eb2cc06e6db46b48ba290a55f5f47b6540f9fac268f70a
|
| 3 |
+
size 4999802720
|
model-00003-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:fd27ee3e22e7bea53fe281d8f3a98d3ca19cbb6005346493530060ec5bc2c4fb
|
| 3 |
+
size 4915916176
|
model-00004-of-00004.safetensors
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:cf1a807fcca6b5c2e7ea75eef462f9bd63b81cadc09cef08af2f2c34d4af04d2
|
| 3 |
+
size 1168138808
|
model.safetensors.index.json
ADDED
|
@@ -0,0 +1,299 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
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|
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|
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|
|
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|
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|
|
|
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|
|
|
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|
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|
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|
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|
|
|
|
|
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|
|
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|
|
|
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|
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|
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|
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|
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|
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|
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|
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|
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|
|
|
|
|
|
|
|
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|
|
|
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|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
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|
|
|
|
|
|
|
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|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"metadata": {
|
| 3 |
+
"total_parameters": 8030261248,
|
| 4 |
+
"total_size": 16060522496
|
| 5 |
+
},
|
| 6 |
+
"weight_map": {
|
| 7 |
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"lm_head.weight": "model-00004-of-00004.safetensors",
|
| 8 |
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"model.embed_tokens.weight": "model-00001-of-00004.safetensors",
|
| 9 |
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"model.layers.0.input_layernorm.weight": "model-00001-of-00004.safetensors",
|
| 10 |
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|
| 11 |
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"model.layers.0.mlp.gate_proj.weight": "model-00001-of-00004.safetensors",
|
| 12 |
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"model.layers.0.mlp.up_proj.weight": "model-00001-of-00004.safetensors",
|
| 13 |
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|
| 14 |
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|
| 15 |
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|
| 16 |
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|
| 17 |
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|
| 18 |
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|
| 19 |
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|
| 20 |
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|
| 21 |
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|
| 22 |
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|
| 23 |
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| 24 |
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| 25 |
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| 26 |
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| 27 |
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| 28 |
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| 29 |
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| 30 |
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| 31 |
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| 32 |
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| 33 |
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| 34 |
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| 35 |
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|
| 36 |
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| 37 |
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| 38 |
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| 39 |
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"model.layers.11.mlp.up_proj.weight": "model-00002-of-00004.safetensors",
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| 40 |
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|
| 41 |
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|
| 42 |
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|
| 43 |
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|
| 44 |
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|
| 45 |
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| 46 |
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| 47 |
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| 48 |
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| 49 |
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| 50 |
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| 51 |
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| 52 |
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special_tokens_map.json
ADDED
|
@@ -0,0 +1,16 @@
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|
| 1 |
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{
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| 2 |
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"bos_token": {
|
| 3 |
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"content": "<|begin_of_text|>",
|
| 4 |
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"lstrip": false,
|
| 5 |
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"normalized": false,
|
| 6 |
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"rstrip": false,
|
| 7 |
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"single_word": false
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| 8 |
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},
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| 9 |
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"eos_token": {
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|
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|
| 14 |
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"single_word": false
|
| 15 |
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|
| 16 |
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|
tokenizer.json
ADDED
|
@@ -0,0 +1,3 @@
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| 1 |
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version https://git-lfs.github.com/spec/v1
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oid sha256:6b9e4e7fb171f92fd137b777cc2714bf87d11576700a1dcd7a399e7bbe39537b
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| 3 |
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size 17209920
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tokenizer_config.json
ADDED
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@@ -0,0 +1,2062 @@
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|
| 1 |
+
{
|
| 2 |
+
"added_tokens_decoder": {
|
| 3 |
+
"128000": {
|
| 4 |
+
"content": "<|begin_of_text|>",
|
| 5 |
+
"lstrip": false,
|
| 6 |
+
"normalized": false,
|
| 7 |
+
"rstrip": false,
|
| 8 |
+
"single_word": false,
|
| 9 |
+
"special": true
|
| 10 |
+
},
|
| 11 |
+
"128001": {
|
| 12 |
+
"content": "<|end_of_text|>",
|
| 13 |
+
"lstrip": false,
|
| 14 |
+
"normalized": false,
|
| 15 |
+
"rstrip": false,
|
| 16 |
+
"single_word": false,
|
| 17 |
+
"special": true
|
| 18 |
+
},
|
| 19 |
+
"128002": {
|
| 20 |
+
"content": "<|reserved_special_token_0|>",
|
| 21 |
+
"lstrip": false,
|
| 22 |
+
"normalized": false,
|
| 23 |
+
"rstrip": false,
|
| 24 |
+
"single_word": false,
|
| 25 |
+
"special": true
|
| 26 |
+
},
|
| 27 |
+
"128003": {
|
| 28 |
+
"content": "<|reserved_special_token_1|>",
|
| 29 |
+
"lstrip": false,
|
| 30 |
+
"normalized": false,
|
| 31 |
+
"rstrip": false,
|
| 32 |
+
"single_word": false,
|
| 33 |
+
"special": true
|
| 34 |
+
},
|
| 35 |
+
"128004": {
|
| 36 |
+
"content": "<|finetune_right_pad_id|>",
|
| 37 |
+
"lstrip": false,
|
| 38 |
+
"normalized": false,
|
| 39 |
+
"rstrip": false,
|
| 40 |
+
"single_word": false,
|
| 41 |
+
"special": true
|
| 42 |
+
},
|
| 43 |
+
"128005": {
|
| 44 |
+
"content": "<|reserved_special_token_2|>",
|
| 45 |
+
"lstrip": false,
|
| 46 |
+
"normalized": false,
|
| 47 |
+
"rstrip": false,
|
| 48 |
+
"single_word": false,
|
| 49 |
+
"special": true
|
| 50 |
+
},
|
| 51 |
+
"128006": {
|
| 52 |
+
"content": "<|start_header_id|>",
|
| 53 |
+
"lstrip": false,
|
| 54 |
+
"normalized": false,
|
| 55 |
+
"rstrip": false,
|
| 56 |
+
"single_word": false,
|
| 57 |
+
"special": true
|
| 58 |
+
},
|
| 59 |
+
"128007": {
|
| 60 |
+
"content": "<|end_header_id|>",
|
| 61 |
+
"lstrip": false,
|
| 62 |
+
"normalized": false,
|
| 63 |
+
"rstrip": false,
|
| 64 |
+
"single_word": false,
|
| 65 |
+
"special": true
|
| 66 |
+
},
|
| 67 |
+
"128008": {
|
| 68 |
+
"content": "<|eom_id|>",
|
| 69 |
+
"lstrip": false,
|
| 70 |
+
"normalized": false,
|
| 71 |
+
"rstrip": false,
|
| 72 |
+
"single_word": false,
|
| 73 |
+
"special": true
|
| 74 |
+
},
|
| 75 |
+
"128009": {
|
| 76 |
+
"content": "<|eot_id|>",
|
| 77 |
+
"lstrip": false,
|
| 78 |
+
"normalized": false,
|
| 79 |
+
"rstrip": false,
|
| 80 |
+
"single_word": false,
|
| 81 |
+
"special": true
|
| 82 |
+
},
|
| 83 |
+
"128010": {
|
| 84 |
+
"content": "<|python_tag|>",
|
| 85 |
+
"lstrip": false,
|
| 86 |
+
"normalized": false,
|
| 87 |
+
"rstrip": false,
|
| 88 |
+
"single_word": false,
|
| 89 |
+
"special": true
|
| 90 |
+
},
|
| 91 |
+
"128011": {
|
| 92 |
+
"content": "<|reserved_special_token_3|>",
|
| 93 |
+
"lstrip": false,
|
| 94 |
+
"normalized": false,
|
| 95 |
+
"rstrip": false,
|
| 96 |
+
"single_word": false,
|
| 97 |
+
"special": true
|
| 98 |
+
},
|
| 99 |
+
"128012": {
|
| 100 |
+
"content": "<|reserved_special_token_4|>",
|
| 101 |
+
"lstrip": false,
|
| 102 |
+
"normalized": false,
|
| 103 |
+
"rstrip": false,
|
| 104 |
+
"single_word": false,
|
| 105 |
+
"special": true
|
| 106 |
+
},
|
| 107 |
+
"128013": {
|
| 108 |
+
"content": "<|reserved_special_token_5|>",
|
| 109 |
+
"lstrip": false,
|
| 110 |
+
"normalized": false,
|
| 111 |
+
"rstrip": false,
|
| 112 |
+
"single_word": false,
|
| 113 |
+
"special": true
|
| 114 |
+
},
|
| 115 |
+
"128014": {
|
| 116 |
+
"content": "<|reserved_special_token_6|>",
|
| 117 |
+
"lstrip": false,
|
| 118 |
+
"normalized": false,
|
| 119 |
+
"rstrip": false,
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| 1949 |
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| 1978 |
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| 1981 |
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| 1982 |
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| 1985 |
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| 1986 |
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| 1989 |
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| 2030 |
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