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Model: LeoLM/leo-hessianai-13b-chat Source: Original Platform
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README.md
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---
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datasets:
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- LeoLM/OpenSchnabeltier
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- OpenAssistant/OASST-DE
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- FreedomIntelligence/alpaca-gpt4-deutsch
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- FreedomIntelligence/evol-instruct-deutsch
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- LeoLM/German_Poems
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- LeoLM/German_Songs
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language:
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- en
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- de
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library_name: transformers
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pipeline_tag: text-generation
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---
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# LAION LeoLM: **L**inguistically **E**nhanced **O**pen **L**anguage **M**odel
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Meet LeoLM, the first open and commercially available German Foundation Language Model built on Llama-2.
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Our models extend Llama-2's capabilities into German through continued pretraining on a large corpus of German-language and mostly locality specific text.
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Thanks to a compute grant at HessianAI's new supercomputer **42**, we release two foundation models trained with 8k context length,
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[`LeoLM/leo-hessianai-7b`](https://huggingface.co/LeoLM/leo-hessianai-7b) and [`LeoLM/leo-hessianai-13b`](https://huggingface.co/LeoLM/leo-hessianai-13b) under the [Llama-2 community license](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt) (70b also coming soon! 👀).
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With this release, we hope to bring a new wave of opportunities to German open-source and commercial LLM research and accelerate adoption.
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Read our [blog post]() or our paper (preprint coming soon) for more details!
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*A project by Björn Plüster and Christoph Schuhmann in collaboration with LAION and HessianAI.*
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## LeoLM Chat
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`LeoLM/leo-hessianai-13b-chat` is a German chat model built on our foundation model `LeoLM/leo-hessianai-13b` and finetuned on a selection of German instruction datasets.
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The model performs exceptionally well on writing, explanation and discussion tasks but struggles somewhat with math and advanced reasoning. See our MT-Bench-DE scores:
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```
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{
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"first_turn": 6.525,
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"second_turn": 5.15,
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"categories": {
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"writing": 6.925,
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"roleplay": 6.7,
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"reasoning": 4.55,
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"math": 3.25,
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"coding": 3.45,
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"extraction": 5.4,
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"stem": 7.55,
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"humanities": 8.875
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},
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"average": 5.8375
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}
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```
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## Model Details
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- **Finetuned from:** [LeoLM/leo-hessianai-13b](https://huggingface.co/LeoLM/leo-hessianai-7b)
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- **Model type:** Causal decoder-only transformer language model
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- **Language:** English and German
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- **Demo:** [Web Demo]()
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- **License:** [LLAMA 2 COMMUNITY LICENSE AGREEMENT](https://huggingface.co/meta-llama/Llama-2-70b/raw/main/LICENSE.txt)
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- **Contact:** [LAION Discord](https://discord.com/invite/eq3cAMZtCC) or [Björn Plüster](mailto:bjoern.pl@outlook.de)
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## Use in 🤗Transformers
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First install direct dependencies:
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```
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pip install transformers torch sentencepiece
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```
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If you want faster inference using flash-attention2, you need to install these dependencies:
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```bash
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pip install packaging ninja
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pip install flash-attn==v2.1.1 --no-build-isolation
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pip install git+https://github.com/HazyResearch/flash-attention.git@v2.1.1#subdirectory=csrc/rotary
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```
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Then load the model in transformers:
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```python
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from transformers import pipeline
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import torch
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system_prompt = """<|im_start|>system
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Dies ist eine Unterhaltung zwischen einem intelligenten, hilfsbereitem KI-Assistenten und einem Nutzer.
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Der Assistent gibt ausführliche, hilfreiche und ehrliche Antworten.<|im_end|>
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"""
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prompt_format = "<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n"
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prompt = "Erkläre mir wie die Fahrradwegesituation in Hamburg ist."
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generator = pipeline(model="LeoLM/leo-hessianai-13b-chat", device="cuda", torch_dtype=torch.float16, trust_remote_code=True) # True for flash-attn2 else False
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print(generator(prompt_format.format(prompt=prompt), do_sample=True, top_p=0.95, max_length=8192))
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```
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"*Hamburg verfügt über ein umfangreiches Netzwerk an Radwegen, das ein effizientes und angenehmes Radfahren in der ganzen Stadt ermöglicht. Die Radwege variieren in Bezug auf ihre Qualität, wobei einige erstklassig mit eigens für Radfahrer reservierten Spuren sind, während andere einfache Fahrradsymbole auf dem Boden haben, die anzeigen, dass Radfahrer abwechselnd mit dem Autoverkehr auf der Straße fahren sollten. Einige Nebenstraßen haben auch spezielle Fahrradspuren, wobei einige mit Bordsteinabsenkungen zur Seite der Autospuren markiert sind. Zusätzlich haben viele Hauptstraßen, insbesondere in Nebenstraßen, fahrradfreundliche Abstände zwischen den geparkten Autos und dem Gehweg, was ein bequemes Fahren auf der Straße ermöglicht. Der Bau von Radschnellwegen, die schnelles und effizientes Radfahren in und aus der Stadt ermöglichen, ist im Gange und wird in den kommenden Jahren fortgesetzt. Insgesamt sind die Radwege in Hamburg weitläufig und gut ausgeschildert, was es zu einem angenehmen Ort macht, um mit dem Fahrrad zu fahren.*"
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## Prompting / Prompt Template
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Prompt dialogue template (ChatML format):
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```
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"""
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<|im_start|>system
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{system_message}<|im_end|>
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<|im_start|>user
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{prompt}<|im_end|>
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<|im_start|>assistant
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"""
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```
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The model input can contain multiple conversation turns between user and assistant, e.g.
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```
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<|im_start|>user
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{prompt 1}<|im_end|>
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<|im_start|>assistant
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{reply 1}<|im_end|>
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<|im_start|>user
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{prompt 2}<|im_end|>
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<|im_start|>assistant
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(...)
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```
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## Ethical Considerations and Limitations
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LeoLM has been tested in English and German, and has not covered, nor could it cover all scenarios.
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For these reasons, as with all LLMs, the potential outputs of `LeoLM/leo-hessianai-13b-chat` cannot be predicted
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in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses
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to user prompts. Therefore, before deploying any applications of `LeoLM/leo-hessianai-13b-chat`, developers should
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perform safety testing and tuning tailored to their specific applications of the model.
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Please see Meta's [Responsible Use Guide](https://ai.meta.com/llama/responsible-use-guide/).
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## Finetuning Details
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| Hyperparameter | Value |
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|---|---|
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| Num epochs | 3 |
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| Examples per epoch | 131214 |
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| Global batch size | 256 |
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| Learning rate | 3e-5 |
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| Warmup steps | 100 |
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| LR scheduler | Cosine |
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| Adam betas | (0.9, 0.95) |
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## Dataset Details
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```
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## Stats for 'Subset of OpenAssistant/OASST-DE' (3534 samples (100.0%))
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-----------------
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Accepted: 3534/3534 (100.0%)
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Accepted tokens: 2259302
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Skipped: 0 (0.0%)
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Min tokens per sample: 29
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Max tokens per sample: 2484
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Avg tokens per sample: 639.3044708545557
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-----------------
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## Stats for 'Subset of FreedomIntelligence/evol-instruct-deutsch' (57841 samples (100.0%))
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-----------------
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Accepted: 57841/57841 (100.0%)
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Accepted tokens: 42958192
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Skipped: 0 (0.0%)
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Min tokens per sample: 33
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Max tokens per sample: 5507
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Avg tokens per sample: 742.6944900675991
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-----------------
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## Stats for 'Subset of FreedomIntelligence/alpaca-gpt4-deutsch' (48969 samples (100.0%))
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-----------------
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Accepted: 48969/48969 (100.0%)
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Accepted tokens: 13372005
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Skipped: 0 (0.0%)
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Min tokens per sample: 19
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Max tokens per sample: 1359
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Avg tokens per sample: 273.07082031489307
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-----------------
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## Stats for 'Subset of LeoLM/OpenSchnabeltier' (21314 samples (100.0%))
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-----------------
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Accepted: 21314/21314 (100.0%)
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Accepted tokens: 8134690
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Skipped: 0 (0.0%)
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Min tokens per sample: 25
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Max tokens per sample: 1202
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Avg tokens per sample: 381.65947264708643
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-----------------
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## Stats for 'Subset of LeoLM/German_Poems' (490 samples (100.0%))
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-----------------
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Accepted: 490/490 (100.0%)
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Accepted tokens: 618642
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Skipped: 0 (0.0%)
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Min tokens per sample: 747
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Max tokens per sample: 1678
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Avg tokens per sample: 1262.534693877551
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-----------------
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## Stats for 'Subset of LeoLM/German_Songs' (392 samples (100.0%))
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-----------------
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Accepted: 392/392 (100.0%)
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Accepted tokens: 187897
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Skipped: 0 (0.0%)
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Min tokens per sample: 231
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Max tokens per sample: 826
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Avg tokens per sample: 479.3290816326531
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-----------------
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## Stats for 'total' (132540 samples (100.0%))
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-----------------
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Accepted: 132540/132540 (100.0%)
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Accepted tokens: 67530728
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Skipped: 0 (0.0%)
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Min tokens per sample: 19
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Max tokens per sample: 5507
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Avg tokens per sample: 509.51205673758864
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-----------------
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```
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config.json
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{
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"architectures": [
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"LlamaForCausalLM"
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],
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"auto_map": {
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"AutoModelForCausalLM": "modeling_flash_llama.LlamaForCausalLM"
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},
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"bos_token_id": 1,
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"eos_token_id": 2,
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"hidden_act": "silu",
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"hidden_size": 5120,
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"initializer_range": 0.02,
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"intermediate_size": 13824,
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"max_position_embeddings": 8192,
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"model_type": "llama",
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"num_attention_heads": 40,
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"num_hidden_layers": 40,
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"num_key_value_heads": 40,
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"pad_token_id": 0,
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"pretraining_tp": 1,
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"rms_norm_eps": 1e-05,
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"rope_scaling": {
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"factor": 2.0,
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"type": "linear"
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},
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"rope_theta": 10000.0,
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"tie_word_embeddings": false,
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"torch_dtype": "float16",
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"transformers_version": "4.33.1",
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"use_cache": true,
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"vocab_size": 32128
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}
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generation_config.json
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{
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"_from_model_config": true,
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"bos_token_id": 1,
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"eos_token_id": 2,
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"transformers_version": "4.33.1"
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}
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modeling_flash_llama.py
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size 9950030153
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{
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"metadata": {
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"weight_map": {
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||||||
|
"model.layers.8.self_attn.q_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.8.self_attn.v_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.input_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.mlp.down_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00003.bin",
|
||||||
|
"model.norm.weight": "pytorch_model-00003-of-00003.bin"
|
||||||
|
}
|
||||||
|
}
|
||||||
13
special_tokens_map.json
Normal file
13
special_tokens_map.json
Normal file
@@ -0,0 +1,13 @@
|
|||||||
|
{
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": "<|im_start|>",
|
||||||
|
"cls_token": "<CLS>",
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"mask_token": "<MASK>",
|
||||||
|
"pad_token": "<PAD>",
|
||||||
|
"sep_token": "<SEP>",
|
||||||
|
"unk_token": "<unk>"
|
||||||
|
}
|
||||||
93454
tokenizer.json
Normal file
93454
tokenizer.json
Normal file
File diff suppressed because it is too large
Load Diff
103
tokenizer_config.json
Normal file
103
tokenizer_config.json
Normal file
@@ -0,0 +1,103 @@
|
|||||||
|
{
|
||||||
|
"added_tokens_decoder": {
|
||||||
|
"0": {
|
||||||
|
"content": "<unk>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"1": {
|
||||||
|
"content": "<s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"2": {
|
||||||
|
"content": "</s>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32000": {
|
||||||
|
"content": "<CLS>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32001": {
|
||||||
|
"content": "<SEP>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32002": {
|
||||||
|
"content": "<EOD>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": true,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": false
|
||||||
|
},
|
||||||
|
"32003": {
|
||||||
|
"content": "<MASK>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32004": {
|
||||||
|
"content": "<PAD>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32005": {
|
||||||
|
"content": "<|im_start|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
},
|
||||||
|
"32006": {
|
||||||
|
"content": "<|im_end|>",
|
||||||
|
"lstrip": false,
|
||||||
|
"normalized": false,
|
||||||
|
"rstrip": false,
|
||||||
|
"single_word": false,
|
||||||
|
"special": true
|
||||||
|
}
|
||||||
|
},
|
||||||
|
"additional_special_tokens": [
|
||||||
|
"<|im_start|>",
|
||||||
|
"<|im_end|>"
|
||||||
|
],
|
||||||
|
"bos_token": "<|im_start|>",
|
||||||
|
"chat_template": "{% if not add_generation_prompt is defined %}{% set add_generation_prompt = false %}{% endif %}{% for message in messages %}{{'<|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,
|
||||||
|
"cls_token": "<CLS>",
|
||||||
|
"eos_token": "<|im_end|>",
|
||||||
|
"legacy": true,
|
||||||
|
"mask_token": "<MASK>",
|
||||||
|
"model_max_length": 1000000000000000019884624838656,
|
||||||
|
"pad_token": "<PAD>",
|
||||||
|
"padding_side": "right",
|
||||||
|
"sep_token": "<SEP>",
|
||||||
|
"sp_model_kwargs": {},
|
||||||
|
"tokenizer_class": "LlamaTokenizer",
|
||||||
|
"unk_token": "<unk>",
|
||||||
|
"use_default_system_prompt": false
|
||||||
|
}
|
||||||
Reference in New Issue
Block a user