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yifanxie/literate-toucanet1-1-1 | yifanxie | 2024-05-20T06:06:49Z | 133 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"gpt",
"llm",
"large language model",
"h2o-llmstudio",
"en",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-20T06:05:00Z | ---
language:
- en
library_name: transformers
tags:
- gpt
- llm
- large language model
- h2o-llmstudio
inference: false
thumbnail: https://h2o.ai/etc.clientlibs/h2o/clientlibs/clientlib-site/resources/images/favicon.ico
---
# Model Card
## Summary
This model was trained using [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio).
- Base model: [google/gemma-2b](https://huggingface.co/google/gemma-2b)
## Usage
To use the model with the `transformers` library on a machine with GPUs, first make sure you have the `transformers` library installed.
```bash
pip install transformers==4.40.1
```
Also make sure you are providing your huggingface token to the pipeline if the model is lying in a private repo.
- Either leave `token=True` in the `pipeline` and login to hugginface_hub by running
```python
import huggingface_hub
huggingface_hub.login(<ACCESS_TOKEN>)
```
- Or directly pass your <ACCESS_TOKEN> to `token` in the `pipeline`
```python
from transformers import pipeline
generate_text = pipeline(
model="yifanxie/literate-toucanet1-1-1",
torch_dtype="auto",
trust_remote_code=True,
use_fast=True,
device_map={"": "cuda:0"},
token=True,
)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You can print a sample prompt after the preprocessing step to see how it is feed to the tokenizer:
```python
print(generate_text.preprocess("Why is drinking water so healthy?")["prompt_text"])
```
```bash
<|prompt|>Why is drinking water so healthy?<eos><|answer|>
```
Alternatively, you can download [h2oai_pipeline.py](h2oai_pipeline.py), store it alongside your notebook, and construct the pipeline yourself from the loaded model and tokenizer. If the model and the tokenizer are fully supported in the `transformers` package, this will allow you to set `trust_remote_code=False`.
```python
from h2oai_pipeline import H2OTextGenerationPipeline
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained(
"yifanxie/literate-toucanet1-1-1",
use_fast=True,
padding_side="left",
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
"yifanxie/literate-toucanet1-1-1",
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
generate_text = H2OTextGenerationPipeline(model=model, tokenizer=tokenizer)
# generate configuration can be modified to your needs
# generate_text.model.generation_config.min_new_tokens = 2
# generate_text.model.generation_config.max_new_tokens = 256
# generate_text.model.generation_config.do_sample = False
# generate_text.model.generation_config.num_beams = 1
# generate_text.model.generation_config.temperature = float(0.0)
# generate_text.model.generation_config.repetition_penalty = float(1.0)
res = generate_text(
"Why is drinking water so healthy?",
renormalize_logits=True
)
print(res[0]["generated_text"])
```
You may also construct the pipeline from the loaded model and tokenizer yourself and consider the preprocessing steps:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "yifanxie/literate-toucanet1-1-1" # either local folder or huggingface model name
# Important: The prompt needs to be in the same format the model was trained with.
# You can find an example prompt in the experiment logs.
prompt = "<|prompt|>How are you?<eos><|answer|>"
tokenizer = AutoTokenizer.from_pretrained(
model_name,
use_fast=True,
trust_remote_code=True,
)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map={"": "cuda:0"},
trust_remote_code=True,
)
model.cuda().eval()
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).to("cuda")
# generate configuration can be modified to your needs
# model.generation_config.min_new_tokens = 2
# model.generation_config.max_new_tokens = 256
# model.generation_config.do_sample = False
# model.generation_config.num_beams = 1
# model.generation_config.temperature = float(0.0)
# model.generation_config.repetition_penalty = float(1.0)
tokens = model.generate(
input_ids=inputs["input_ids"],
attention_mask=inputs["attention_mask"],
renormalize_logits=True
)[0]
tokens = tokens[inputs["input_ids"].shape[1]:]
answer = tokenizer.decode(tokens, skip_special_tokens=True)
print(answer)
```
## Quantization and sharding
You can load the models using quantization by specifying ```load_in_8bit=True``` or ```load_in_4bit=True```. Also, sharding on multiple GPUs is possible by setting ```device_map=auto```.
## Model Architecture
```
GemmaForCausalLM(
(model): GemmaModel(
(embed_tokens): Embedding(256000, 2048, padding_idx=0)
(layers): ModuleList(
(0-17): 18 x GemmaDecoderLayer(
(self_attn): GemmaSdpaAttention(
(q_proj): Linear(in_features=2048, out_features=2048, bias=False)
(k_proj): Linear(in_features=2048, out_features=256, bias=False)
(v_proj): Linear(in_features=2048, out_features=256, bias=False)
(o_proj): Linear(in_features=2048, out_features=2048, bias=False)
(rotary_emb): GemmaRotaryEmbedding()
)
(mlp): GemmaMLP(
(gate_proj): Linear(in_features=2048, out_features=16384, bias=False)
(up_proj): Linear(in_features=2048, out_features=16384, bias=False)
(down_proj): Linear(in_features=16384, out_features=2048, bias=False)
(act_fn): PytorchGELUTanh()
)
(input_layernorm): GemmaRMSNorm()
(post_attention_layernorm): GemmaRMSNorm()
)
)
(norm): GemmaRMSNorm()
)
(lm_head): Linear(in_features=2048, out_features=256000, bias=False)
)
```
## Model Configuration
This model was trained using H2O LLM Studio and with the configuration in [cfg.yaml](cfg.yaml). Visit [H2O LLM Studio](https://github.com/h2oai/h2o-llmstudio) to learn how to train your own large language models.
## Disclaimer
Please read this disclaimer carefully before using the large language model provided in this repository. Your use of the model signifies your agreement to the following terms and conditions.
- Biases and Offensiveness: The large language model is trained on a diverse range of internet text data, which may contain biased, racist, offensive, or otherwise inappropriate content. By using this model, you acknowledge and accept that the generated content may sometimes exhibit biases or produce content that is offensive or inappropriate. The developers of this repository do not endorse, support, or promote any such content or viewpoints.
- Limitations: The large language model is an AI-based tool and not a human. It may produce incorrect, nonsensical, or irrelevant responses. It is the user's responsibility to critically evaluate the generated content and use it at their discretion.
- Use at Your Own Risk: Users of this large language model must assume full responsibility for any consequences that may arise from their use of the tool. The developers and contributors of this repository shall not be held liable for any damages, losses, or harm resulting from the use or misuse of the provided model.
- Ethical Considerations: Users are encouraged to use the large language model responsibly and ethically. By using this model, you agree not to use it for purposes that promote hate speech, discrimination, harassment, or any form of illegal or harmful activities.
- Reporting Issues: If you encounter any biased, offensive, or otherwise inappropriate content generated by the large language model, please report it to the repository maintainers through the provided channels. Your feedback will help improve the model and mitigate potential issues.
- Changes to this Disclaimer: The developers of this repository reserve the right to modify or update this disclaimer at any time without prior notice. It is the user's responsibility to periodically review the disclaimer to stay informed about any changes.
By using the large language model provided in this repository, you agree to accept and comply with the terms and conditions outlined in this disclaimer. If you do not agree with any part of this disclaimer, you should refrain from using the model and any content generated by it. |
miraiminds/arithmathBio-function | miraiminds | 2024-05-20T06:05:09Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"generated_from_trainer",
"base_model:miraiminds/mergeMathBio-7B",
"base_model:adapter:miraiminds/mergeMathBio-7B",
"region:us"
] | null | 2024-05-20T06:04:46Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: miraiminds/mergeMathBio-7B
model-index:
- name: outputs/lora-out
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: miraiminds/mergeMathBio-7B
model_type: MistralForCausalLM
tokenizer_type: LlamaTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: tanyakansal/functioncaliing
type: completion
field: chat
shards: 10
output_dir: ./outputs/lora-out
adapter: lora
lora_model_dir:
sequence_len: 8192
sample_packing: true
pad_to_sequence_len: true
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_linear: true
lora_fan_in_fan_out:
lora_target_modules:
- gate_proj
- down_proj
- up_proj
- q_proj
- v_proj
- k_proj
- o_proj
wandb_project:
wandb_entity:
wandb_watch:
wandb_name:
wandb_log_model:
gradient_accumulation_steps: 4
micro_batch_size: 2
num_epochs: 1
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.0002
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
loss_watchdog_threshold: 5.0
loss_watchdog_patience: 3
warmup_steps: 10
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 1
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
```
</details><br>
# outputs/lora-out
This model is a fine-tuned version of [miraiminds/mergeMathBio-7B](https://huggingface.co/miraiminds/mergeMathBio-7B) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0.dev0
- Pytorch 2.2.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0 |
RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf | RichardErkhov | 2024-05-20T06:03:07Z | 56 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T01:28:25Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-13B-Instruct-fp16 - GGUF
- Model creator: https://huggingface.co/TheBloke/
- Original model: https://huggingface.co/TheBloke/CodeLlama-13B-Instruct-fp16/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [CodeLlama-13B-Instruct-fp16.Q2_K.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q2_K.gguf) | Q2_K | 4.52GB |
| [CodeLlama-13B-Instruct-fp16.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.IQ3_XS.gguf) | IQ3_XS | 4.99GB |
| [CodeLlama-13B-Instruct-fp16.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.IQ3_S.gguf) | IQ3_S | 5.27GB |
| [CodeLlama-13B-Instruct-fp16.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q3_K_S.gguf) | Q3_K_S | 5.27GB |
| [CodeLlama-13B-Instruct-fp16.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.IQ3_M.gguf) | IQ3_M | 5.57GB |
| [CodeLlama-13B-Instruct-fp16.Q3_K.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q3_K.gguf) | Q3_K | 5.9GB |
| [CodeLlama-13B-Instruct-fp16.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q3_K_M.gguf) | Q3_K_M | 5.9GB |
| [CodeLlama-13B-Instruct-fp16.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q3_K_L.gguf) | Q3_K_L | 6.45GB |
| [CodeLlama-13B-Instruct-fp16.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.IQ4_XS.gguf) | IQ4_XS | 6.54GB |
| [CodeLlama-13B-Instruct-fp16.Q4_0.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q4_0.gguf) | Q4_0 | 6.86GB |
| [CodeLlama-13B-Instruct-fp16.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.IQ4_NL.gguf) | IQ4_NL | 6.9GB |
| [CodeLlama-13B-Instruct-fp16.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q4_K_S.gguf) | Q4_K_S | 6.91GB |
| [CodeLlama-13B-Instruct-fp16.Q4_K.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q4_K.gguf) | Q4_K | 7.33GB |
| [CodeLlama-13B-Instruct-fp16.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q4_K_M.gguf) | Q4_K_M | 7.33GB |
| [CodeLlama-13B-Instruct-fp16.Q4_1.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q4_1.gguf) | Q4_1 | 7.61GB |
| [CodeLlama-13B-Instruct-fp16.Q5_0.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q5_0.gguf) | Q5_0 | 8.36GB |
| [CodeLlama-13B-Instruct-fp16.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q5_K_S.gguf) | Q5_K_S | 8.36GB |
| [CodeLlama-13B-Instruct-fp16.Q5_K.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q5_K.gguf) | Q5_K | 8.6GB |
| [CodeLlama-13B-Instruct-fp16.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q5_K_M.gguf) | Q5_K_M | 8.6GB |
| [CodeLlama-13B-Instruct-fp16.Q5_1.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q5_1.gguf) | Q5_1 | 9.1GB |
| [CodeLlama-13B-Instruct-fp16.Q6_K.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q6_K.gguf) | Q6_K | 9.95GB |
| [CodeLlama-13B-Instruct-fp16.Q8_0.gguf](https://huggingface.co/RichardErkhov/TheBloke_-_CodeLlama-13B-Instruct-fp16-gguf/blob/main/CodeLlama-13B-Instruct-fp16.Q8_0.gguf) | Q8_0 | 12.88GB |
Original model description:
---
license: llama2
tags:
- llama-2
- codellama
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
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<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# CodeLlama 13B-Instruct fp16
- Model creator: [Meta](https://ai.meta.com/llama/)
## Description
This is Transformers/HF format fp16 weights for CodeLlama 13B-Instruct. It is the result of downloading CodeLlama 13B-Instruct from [Meta](https://ai.meta.com/blog/code-llama-large-language-model-coding/) and converting to HF using `convert_llama_weights_to_hf.py`.
Quantisations will be coming shortly.
Please note that due to a change in the RoPE Theta value, for correct results you must load these FP16 models with `trust_remote_code=True`
Credit to @emozilla for creating the necessary modelling code to achieve this!
## Prompt template: TBC
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card
# Code Llama
## **Model Details**
**Model Developers** Meta AI
**Variations** Code Llama comes in three model sizes, and three variants:
1) Code Llama: our base models designed for general code synthesis and understanding
2) Code Llama - Python: designed specifically for Python
3) Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**Input** Models input text only.
**Output** Models output text only.
**Model Architecture** Code Llama and its variants are autoregressive language models using optimized transformer architectures. Code Llama 7B and 13B additionally support infilling text generation. All models were fine-tuned with up to 16K tokens, and support up to 100K tokens at inference time.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**Licence** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/).
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)".
**Where to send comments** Instructions on how to provide feedback or comments on the model can be found in the model [README](README.md), or by opening an issue in the GitHub repository ([https://github.com/facebookresearch/codellama/](https://github.com/facebookresearch/codellama/)).
## **Intended Use**
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## **Hardware and Software**
**Training Factors**
We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
**Training data**
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
Code Llama - Instruct uses additional instruction fine-tuning data.
**Evaluation Results**
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## **Ethical Considerations and Limitations**
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-user-guide](https://ai.meta.com/llama/responsible-user-guide).
|
comidan/merlinite-7b-instructlab-bts | comidan | 2024-05-20T06:00:08Z | 80 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-20T05:49:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
This model based on merlinite-7b will able to answer with up-to-date information, through a taxnomy tree based InstructLab fine tuning, to interesting company focused questions.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** Daniele Comi
- **Model type:** merlinite-7b
- **Language(s) (NLP):** English
- **License:** Apache-2.0
- **Finetuned from model:** merlinite-7b using InstructLab |
ukung/TinyLlama-1.1B-indo-v1-GGUF | ukung | 2024-05-20T05:52:40Z | 36 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T05:04:52Z | ---
license: apache-2.0
---
|
Ashmal/MBZUAI-oryx | Ashmal | 2024-05-20T05:52:18Z | 2,902 | 1 | transformers | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-18T13:22:54Z | ---
license: apache-2.0
---
---
This is the Arabic test model built at MBZUAI. More details of the projects will be announced later along with the release.
This model card is just to test the capabilities of this model on Arabic benchmarks. |
automated-finetunning/phi2_fata_20p_5e | automated-finetunning | 2024-05-20T05:51:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-17T13:02:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Sneha-Mahata/Blood-Cell-Detection-DETR | Sneha-Mahata | 2024-05-20T05:50:50Z | 190 | 0 | transformers | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-05-20T05:50:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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## Uses
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Ruiz3/phi-2-kingshipAI-product-tag | Ruiz3 | 2024-05-20T05:48:39Z | 132 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T05:26:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
salahyahya/grammer_checker_model_1 | salahyahya | 2024-05-20T05:46:18Z | 129 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-base",
"base_model:finetune:google-t5/t5-base",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-20T05:44:30Z | ---
license: apache-2.0
base_model: t5-base
tags:
- generated_from_trainer
metrics:
- bleu
model-index:
- name: grammer_checker_model_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# grammer_checker_model_1
This model is a fine-tuned version of [t5-base](https://huggingface.co/t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Bleu: 0.006
- Gen Len: 13.3816
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 6
- total_train_batch_size: 192
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-----:|:-------:|
| No log | 0.31 | 250 | 0.0004 | 0.006 | 13.3815 |
| 0.0009 | 0.63 | 500 | 0.0003 | 0.006 | 13.3809 |
| 0.0009 | 0.94 | 750 | 0.0002 | 0.006 | 13.3816 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
doggywastaken/bmri-prep_cnn_seg | doggywastaken | 2024-05-20T05:39:36Z | 0 | 0 | null | [
"safetensors",
"pytorch_model_hub_mixin",
"model_hub_mixin",
"mask-generation",
"dataset:doggywastaken/manual_breast_segs",
"license:afl-3.0",
"region:us"
] | mask-generation | 2024-05-19T19:53:14Z | ---
tags:
- pytorch_model_hub_mixin
- model_hub_mixin
license: afl-3.0
datasets:
- doggywastaken/manual_breast_segs
pipeline_tag: mask-generation
---
This model has been pushed to the Hub using ****:
- Repo: [More Information Needed]
- Docs: [More Information Needed] |
luchenyu/llama3-8b | luchenyu | 2024-05-20T05:39:30Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-17T02:48:40Z | ---
license: apache-2.0
---
|
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2 | Zoyd | 2024-05-20T05:36:50Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-19T14:11:38Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **4.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
sidddddddddddd/lora_model_10_examples51 | sidddddddddddd | 2024-05-20T05:36:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T05:36:00Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** sidddddddddddd
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2 | Zoyd | 2024-05-20T05:34:12Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-19T12:04:22Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **3.75 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
Cran-May/firefly-qwen1.5-en-14b-alpha-Q4_K_M-GGUF | Cran-May | 2024-05-20T05:33:13Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T05:32:47Z | ---
license: apache-2.0
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
basemodel: Qwen/Qwen1.5-14B
---
# Cran-May/firefly-qwen1.5-en-14b-alpha-Q4_K_M-GGUF
This model was converted to GGUF format from [`YeungNLP/firefly-qwen1.5-en-14b-alpha`](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-14b-alpha) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/YeungNLP/firefly-qwen1.5-en-14b-alpha) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Cran-May/firefly-qwen1.5-en-14b-alpha-Q4_K_M-GGUF --model firefly-qwen1.5-en-14b-alpha.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Cran-May/firefly-qwen1.5-en-14b-alpha-Q4_K_M-GGUF --model firefly-qwen1.5-en-14b-alpha.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m firefly-qwen1.5-en-14b-alpha.Q4_K_M.gguf -n 128
```
|
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2 | Zoyd | 2024-05-20T05:32:45Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-19T11:26:18Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **3.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
DownwardSpiral33/gpt2-imdb-pos-roberta16-256_0_2-2024.05.20.04.41 | DownwardSpiral33 | 2024-05-20T05:31:37Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T05:31:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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DownwardSpiral33/gpt2-imdb-pos-roberta16-256_0_5-2024.05.20.04.21 | DownwardSpiral33 | 2024-05-20T05:29:30Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T05:29:07Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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## How to Get Started with the Model
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Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2 | Zoyd | 2024-05-20T05:29:17Z | 7 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"3-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-19T11:14:38Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **3.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2 | Zoyd | 2024-05-20T05:29:01Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-20T05:05:52Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **6.5 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
Johnie2Turbo/llama-13b_adv_text | Johnie2Turbo | 2024-05-20T05:27:35Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ru",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T05:20:06Z | ---
language:
- ru
---
Умеет писать рекламные тексты и рекламные объявления для машин и ноутбуков )
Правильное оформление инструкции:
[INST] {prompt} [/INST]
Обучался на инструкция "Напиши рекламный текст для ..." и "Напиши рекламное объявление для ..." |
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2 | Zoyd | 2024-05-20T05:27:09Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-19T11:12:13Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **2.2 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
baldwin6/Bolaco | baldwin6 | 2024-05-20T05:24:52Z | 0 | 2 | null | [
"region:us"
] | null | 2024-05-16T08:30:48Z | Checkpoints for the Bolaco. The code is available at https://github.com/Dereck0602/Bolaco.
|
danieljhand/distilbert-base-uncased-finetuned-wine | danieljhand | 2024-05-20T05:24:43Z | 123 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-17T02:07:53Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-wine
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-wine
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9082
- Accuracy: 0.7314
- F1: 0.7222
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.6559 | 1.0 | 1101 | 1.0917 | 0.6792 | 0.6623 |
| 1.0185 | 2.0 | 2202 | 0.9466 | 0.7214 | 0.7103 |
| 0.8851 | 3.0 | 3303 | 0.9082 | 0.7314 | 0.7222 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2 | Zoyd | 2024-05-20T05:20:36Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:aqua_rat",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"arxiv:2402.13228",
"license:llama2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"5-bit",
"exl2",
"region:us"
] | text-generation | 2024-05-20T05:02:53Z | ---
library_name: transformers
license: llama2
datasets:
- aqua_rat
- microsoft/orca-math-word-problems-200k
- m-a-p/CodeFeedback-Filtered-Instruction
---
**Exllamav2** quant (**exl2** / **5.0 bpw**) made with ExLlamaV2 v0.0.21
Other EXL2 quants:
| **Quant** | **Model Size** | **lm_head** |
| ----- | ---------- | ------- |
|<center>**[2.2](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_2bpw_exl2)**</center> | <center>20886 MB</center> | <center>6</center> |
|<center>**[2.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-2_5bpw_exl2)**</center> | <center>23192 MB</center> | <center>6</center> |
|<center>**[3.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_0bpw_exl2)**</center> | <center>27273 MB</center> | <center>6</center> |
|<center>**[3.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_5bpw_exl2)**</center> | <center>31356 MB</center> | <center>6</center> |
|<center>**[3.75](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-3_75bpw_exl2)**</center> | <center>33398 MB</center> | <center>6</center> |
|<center>**[4.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_0bpw_exl2)**</center> | <center>35434 MB</center> | <center>6</center> |
|<center>**[4.25](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-4_25bpw_exl2)**</center> | <center>37456 MB</center> | <center>6</center> |
|<center>**[5.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-5_0bpw_exl2)**</center> | <center>43598 MB</center> | <center>6</center> |
|<center>**[6.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_0bpw_exl2)**</center> | <center>51957 MB</center> | <center>8</center> |
|<center>**[6.5](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-6_5bpw_exl2)**</center> | <center>56014 MB</center> | <center>8</center> |
|<center>**[8.0](https://huggingface.co/Zoyd/abacusai_Smaug-Llama-3-70B-Instruct-8_0bpw_exl2)**</center> | <center>60211 MB</center> | <center>8</center> |
# Smaug-Llama-3-70B-Instruct
### Built with Meta Llama 3
This model was built using a new Smaug recipe for improving performance on real world multi-turn conversations applied to
[meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
The model outperforms Llama-3-70B-Instruct substantially, and is on par with GPT-4-Turbo, on MT-Bench (see below).
EDIT: Smaug-Llama-3-70B-Instruct is the top open source model on Arena-Hard currently! It is also nearly on par with Claude Opus - see below.
We are conducting additional benchmark evaluations and will add those when available.
### Model Description
- **Developed by:** [Abacus.AI](https://abacus.ai)
- **License:** https://llama.meta.com/llama3/license/
- **Finetuned from model:** [meta-llama/Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct).
## Evaluation
### Arena-Hard
Score vs selected others (sourced from: (https://lmsys.org/blog/2024-04-19-arena-hard/#full-leaderboard-with-gpt-4-turbo-as-judge))
| Model | Score | 95% Confidence Interval | Average Tokens |
| :---- | ---------: | ----------: | ------: |
| GPT-4-Turbo-2024-04-09 | 82.6 | (-1.8, 1.6) | 662 |
| Claude-3-Opus-20240229 | 60.4 | (-3.3, 2.4) | 541 |
| **Smaug-Llama-3-70B-Instruct** | 56.7 | (-2.2, 2.6) | 661 |
| GPT-4-0314 | 50.0 | (-0.0, 0.0) | 423 |
| Claude-3-Sonnet-20240229 | 46.8 | (-2.1, 2.2) | 552 |
| Llama-3-70B-Instruct | 41.1 | (-2.5, 2.4) | 583 |
| GPT-4-0613 | 37.9 | (-2.2, 2.0) | 354 |
| Mistral-Large-2402 | 37.7 | (-1.9, 2.6) | 400 |
| Mixtral-8x22B-Instruct-v0.1 | 36.4 | (-2.7, 2.9) | 430 |
| Qwen1.5-72B-Chat | 36.1 | (-2.5, 2.2) | 474 |
| Command-R-Plus | 33.1 | (-2.1, 2.2) | 541 |
| Mistral-Medium | 31.9 | (-2.3, 2.4) | 485 |
| GPT-3.5-Turbo-0613 | 24.8 | (-1.6, 2.0) | 401 |
### MT-Bench
```
########## First turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 1 9.40000
GPT-4-Turbo 1 9.37500
Meta-Llama-3-70B-Instruct 1 9.21250
########## Second turn ##########
score
model turn
Smaug-Llama-3-70B-Instruct 2 9.0125
GPT-4-Turbo 2 9.0000
Meta-Llama-3-70B-Instruct 2 8.8000
########## Average ##########
score
model
Smaug-Llama-3-70B-Instruct 9.206250
GPT-4-Turbo 9.187500
Meta-Llama-3-70B-Instruct 9.006250
```
| Model | First turn | Second Turn | Average |
| :---- | ---------: | ----------: | ------: |
| **Smaug-Llama-3-70B-Instruct** | 9.40 | 9.01 | 9.21 |
| GPT-4-Turbo | 9.38 | 9.00 | 9.19 |
| Meta-Llama-3-70B-Instruct | 9.21 | 8.80 | 9.01 |
This version of Smaug uses new techniques and new data compared to [Smaug-72B](https://huggingface.co/abacusai/Smaug-72B-v0.1), and more information will be released later on. For now, see the previous Smaug paper: https://arxiv.org/abs/2402.13228. |
leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF | leafspark | 2024-05-20T05:19:40Z | 6 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T05:18:51Z | ---
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF
This model was converted to GGUF format from [`01-ai/Yi-1.5-34B-Chat-16K`](https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/01-ai/Yi-1.5-34B-Chat-16K) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF --model yi-1.5-34b-chat-16k.Q4_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo leafspark/Yi-1.5-34B-Chat-16K-Q4_K_M-GGUF --model yi-1.5-34b-chat-16k.Q4_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m yi-1.5-34b-chat-16k.Q4_K_M.gguf -n 128
```
|
vvduc03/lora-llava-3b | vvduc03 | 2024-05-20T05:04:26Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llava_mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T02:39:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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### Downstream Use [optional]
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
ridham1317/whisper-small-ft-common-voice | ridham1317 | 2024-05-20T05:03:37Z | 148 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-20T05:02:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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omezzinemariem/mistral-text-to-RULE2 | omezzinemariem | 2024-05-20T05:01:14Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"region:us"
] | null | 2024-05-20T05:01:01Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.2
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Developed by:** [More Information Needed]
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### Model Sources [optional]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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## Model Card Contact
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### Framework versions
- PEFT 0.11.1 |
YYYYYYibo/nash_simple_online_iter_1 | YYYYYYibo | 2024-05-20T05:00:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"dataset:updated",
"dataset:original",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"base_model:adapter:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T03:13:38Z | ---
license: apache-2.0
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
base_model: alignment-handbook/zephyr-7b-sft-full
datasets:
- updated
- original
model-index:
- name: nash_simple_online_iter_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# nash_simple_online_iter_1
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the updated and the original datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6779
- Rewards/chosen: 0.0306
- Rewards/rejected: -0.0021
- Rewards/accuracies: 0.6480
- Rewards/margins: 0.0327
- Logps/rejected: -257.7254
- Logps/chosen: -280.9805
- Logits/rejected: -2.6488
- Logits/chosen: -2.7230
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6844 | 0.64 | 100 | 0.6779 | 0.0306 | -0.0021 | 0.6480 | 0.0327 | -257.7254 | -280.9805 | -2.6488 | -2.7230 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.3.0+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
mp1704/gpt-neo-sft-v2.1 | mp1704 | 2024-05-20T04:59:21Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T04:58:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed]
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SyntaxTheRed/roberta-base-bne-finetuned-multi-sentiment | SyntaxTheRed | 2024-05-20T04:55:22Z | 110 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-LT/roberta-base-bne",
"base_model:finetune:BSC-LT/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T04:25:49Z | ---
license: apache-2.0
base_model: BSC-TeMU/roberta-base-bne
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-multi-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-multi-sentiment
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7463
- Accuracy: 0.6543
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.8239 | 1.0 | 115 | 0.7485 | 0.6667 |
| 0.6041 | 2.0 | 230 | 0.7463 | 0.6543 |
### Framework versions
- Transformers 4.40.2
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ukung/Nusantara-2.7b-Indo-Chat-GGUF | ukung | 2024-05-20T04:51:02Z | 11 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T04:04:01Z | ---
license: apache-2.0
---
|
Ruiz3/phi-2-kingshipAI-product-explainer | Ruiz3 | 2024-05-20T04:47:09Z | 132 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T04:26:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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|
OwOpeepeepoopoo/NoSoup4U2 | OwOpeepeepoopoo | 2024-05-20T04:36:33Z | 14 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-19T00:25:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
ukung/Nusantara-4b-Indo-Chat-GGUF | ukung | 2024-05-20T04:26:42Z | 97 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-20T02:43:48Z | ---
license: apache-2.0
---
|
HyunCello/EEVE-Korean-Instruct-10.8B-v1.0-test-0.1 | HyunCello | 2024-05-20T04:23:36Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T02:51:33Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
### Results
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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DownwardSpiral33/gpt2-imdb-pos-roberta16-256_0_5-2024.05.20.03.08 | DownwardSpiral33 | 2024-05-20T04:16:45Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T04:16:26Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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akiseid/AmharicNewsNonCleanedNonWeighted | akiseid | 2024-05-20T04:13:43Z | 118 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T03:12:02Z | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: AmharicNewsNonCleanedNonWeighted
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AmharicNewsNonCleanedNonWeighted
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1726
- Accuracy: 0.9564
- Precision: 0.9563
- Recall: 0.9564
- F1: 0.9564
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 0.2237 | 1.0 | 945 | 0.2308 | 0.9054 | 0.9145 | 0.9054 | 0.9031 |
| 0.3067 | 2.0 | 1890 | 0.1760 | 0.9384 | 0.9388 | 0.9384 | 0.9379 |
| 0.143 | 3.0 | 2835 | 0.1510 | 0.9480 | 0.9486 | 0.9480 | 0.9482 |
| 0.1306 | 4.0 | 3780 | 0.1550 | 0.9544 | 0.9547 | 0.9544 | 0.9544 |
| 0.0825 | 5.0 | 4725 | 0.1726 | 0.9564 | 0.9563 | 0.9564 | 0.9564 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
Raneechu/mininglarge | Raneechu | 2024-05-20T04:11:28Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-05-20T04:11:24Z | ---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: meta-llama/Llama-2-7b-hf
model-index:
- name: mininglarge
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mininglarge
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.19.1
## Training procedure
### Framework versions
- PEFT 0.6.2
|
pksvi/logo_LORA | pksvi | 2024-05-20T04:11:19Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:Lykon/dreamshaper-xl-lightning",
"base_model:adapter:Lykon/dreamshaper-xl-lightning",
"license:openrail++",
"region:us"
] | text-to-image | 2024-05-20T04:11:15Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: Lykon/dreamshaper-xl-lightning
instance_prompt: TOK 'LA' logo on garment
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - pksvi/logo_LORA
<Gallery />
## Model description
These are pksvi/logo_LORA LoRA adaption weights for Lykon/dreamshaper-xl-lightning.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use TOK 'LA' logo on garment to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](pksvi/logo_LORA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
dendimaki/mistral-lora-token-classification | dendimaki | 2024-05-20T04:09:44Z | 1 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T04:09:42Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: mistralai/Mistral-7B-v0.1
model-index:
- name: mistral-lora-token-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-lora-token-classification
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1-score | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:--------:|:--------:|
| No log | 1.0 | 431 | nan | 0.0020 | 0.0444 | 0.0038 | 0.0444 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
abc88767/22c102 | abc88767 | 2024-05-20T04:09:35Z | 98 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T04:08:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
duyntnet/WizardLM-13B-Uncensored-imatrix-GGUF | duyntnet | 2024-05-20T04:07:06Z | 326 | 5 | transformers | [
"transformers",
"gguf",
"imatrix",
"WizardLM-13B-Uncensored",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | 2024-05-20T00:08:43Z | ---
license: other
language:
- en
pipeline_tag: text-generation
inference: false
tags:
- transformers
- gguf
- imatrix
- WizardLM-13B-Uncensored
---
Quantizations of https://huggingface.co/cognitivecomputations/WizardLM-13B-Uncensored
# From original readme
This is WizardLM trained with a subset of the dataset - responses that contained alignment / moralizing were removed. The intent is to train a WizardLM that doesn't have alignment built-in, so that alignment (of any sort) can be added separately with for example with a RLHF LoRA.
Shout out to the open source AI/ML community, and everyone who helped me out.
Note:
An uncensored model has no guardrails.
You are responsible for anything you do with the model, just as you are responsible for anything you do with any dangerous object such as a knife, gun, lighter, or car.
Publishing anything this model generates is the same as publishing it yourself.
You are responsible for the content you publish, and you cannot blame the model any more than you can blame the knife, gun, lighter, or car for what you do with it. |
abc88767/9sc102 | abc88767 | 2024-05-20T04:04:52Z | 97 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T04:03:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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abc88767/8sc102 | abc88767 | 2024-05-20T04:01:24Z | 93 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T03:59:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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### Testing Data, Factors & Metrics
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
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tsavage68/MedQA_L3_600steps_1e7rate_01beta_CSFTDPO | tsavage68 | 2024-05-20T03:58:43Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T02:46:28Z | ---
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: MedQA_L3_600steps_1e7rate_01beta_CSFTDPO
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# MedQA_L3_600steps_1e7rate_01beta_CSFTDPO
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6692
- Rewards/chosen: 0.0482
- Rewards/rejected: -0.0053
- Rewards/accuracies: 0.6681
- Rewards/margins: 0.0535
- Logps/rejected: -21.3695
- Logps/chosen: -17.7404
- Logits/rejected: -0.9398
- Logits/chosen: -0.9393
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 600
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:------:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6951 | 0.0489 | 50 | 0.6935 | 0.0003 | 0.0009 | 0.4901 | -0.0006 | -21.3079 | -18.2196 | -0.9258 | -0.9253 |
| 0.6892 | 0.0977 | 100 | 0.6881 | 0.0374 | 0.0268 | 0.6044 | 0.0106 | -21.0482 | -17.8488 | -0.9281 | -0.9276 |
| 0.6801 | 0.1466 | 150 | 0.6794 | 0.0588 | 0.0292 | 0.6418 | 0.0296 | -21.0241 | -17.6343 | -0.9314 | -0.9309 |
| 0.6807 | 0.1954 | 200 | 0.6767 | 0.0584 | 0.0227 | 0.6549 | 0.0358 | -21.0897 | -17.6383 | -0.9345 | -0.9339 |
| 0.6829 | 0.2443 | 250 | 0.6726 | 0.0560 | 0.0106 | 0.6571 | 0.0454 | -21.2109 | -17.6631 | -0.9367 | -0.9362 |
| 0.6656 | 0.2931 | 300 | 0.6715 | 0.0540 | 0.0059 | 0.6505 | 0.0481 | -21.2575 | -17.6830 | -0.9382 | -0.9376 |
| 0.6955 | 0.3420 | 350 | 0.6697 | 0.0524 | 0.0002 | 0.6571 | 0.0522 | -21.3145 | -17.6986 | -0.9384 | -0.9378 |
| 0.6605 | 0.3908 | 400 | 0.6697 | 0.0493 | -0.0031 | 0.6505 | 0.0524 | -21.3476 | -17.7294 | -0.9393 | -0.9388 |
| 0.6718 | 0.4397 | 450 | 0.6689 | 0.0495 | -0.0047 | 0.6527 | 0.0541 | -21.3631 | -17.7279 | -0.9396 | -0.9390 |
| 0.6734 | 0.4885 | 500 | 0.6687 | 0.0486 | -0.0059 | 0.6505 | 0.0545 | -21.3751 | -17.7362 | -0.9397 | -0.9392 |
| 0.6525 | 0.5374 | 550 | 0.6691 | 0.0482 | -0.0056 | 0.6615 | 0.0537 | -21.3720 | -17.7410 | -0.9398 | -0.9393 |
| 0.6637 | 0.5862 | 600 | 0.6692 | 0.0482 | -0.0053 | 0.6681 | 0.0535 | -21.3695 | -17.7404 | -0.9398 | -0.9393 |
### Framework versions
- Transformers 4.41.0
- Pytorch 2.0.0+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
afiqlol/Malay-Sentiment | afiqlol | 2024-05-20T03:52:40Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:citizenlab/twitter-xlm-roberta-base-sentiment-finetunned",
"base_model:finetune:citizenlab/twitter-xlm-roberta-base-sentiment-finetunned",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T02:11:43Z | ---
base_model: citizenlab/twitter-xlm-roberta-base-sentiment-finetunned
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Malay-Sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Malay-Sentiment
This model is a fine-tuned version of [citizenlab/twitter-xlm-roberta-base-sentiment-finetunned](https://huggingface.co/citizenlab/twitter-xlm-roberta-base-sentiment-finetunned) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5757
- Accuracy: 0.7578
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7066 | 1.0 | 723 | 0.5757 | 0.7578 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cpu
- Datasets 2.14.5
- Tokenizers 0.15.0
|
PQlet/textual-inversion-v2-ablation-vec3-img1 | PQlet | 2024-05-20T03:48:22Z | 3 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-05-20T01:50:18Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
base_model: runwayml/stable-diffusion-v1-5
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual Inversion training - PQlet/textual-inversion-v2-ablation-vec3-img1
The generated images are below.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
kazuhasasd/eye_disease | kazuhasasd | 2024-05-20T03:45:25Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T03:45:25Z | ---
license: apache-2.0
---
|
Andyhahaha/Junlong-Huanzong-twitter-financial-news-sentiment-analysis | Andyhahaha | 2024-05-20T03:44:46Z | 110 | 1 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T03:44:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
GrigoriiA/parler-tts-from-mini-Libretta-v0.2 | GrigoriiA | 2024-05-20T03:42:43Z | 66 | 0 | transformers | [
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-20T03:41:36Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
RichardErkhov/lodrick-the-lafted_-_Grafted-Hermetic-Platypus-C-2x7B-4bits | RichardErkhov | 2024-05-20T03:39:17Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-20T03:34:05Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Grafted-Hermetic-Platypus-C-2x7B - bnb 4bits
- Model creator: https://huggingface.co/lodrick-the-lafted/
- Original model: https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B/
Original model description:
---
license: apache-2.0
datasets:
- lodrick-the-lafted/Hermes-217K
- garage-bAInd/Open-Platypus
- jondurbin/airoboros-3.2
model-index:
- name: Grafted-Hermetic-Platypus-C-2x7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 58.96
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 82.77
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 62.08
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 60.87
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 77.74
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 43.9
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B
name: Open LLM Leaderboard
---
<img src=https://huggingface.co/lodrick-the-lafted/Grafted-Hermetic-Platypus-C-2x7B/resolve/main/ghp.png>
# Grafted-Hermetic-Platypus-C-2x7B
MoE merge of
- [Platyboros-Instruct-7B](https://huggingface.co/lodrick-the-lafted/Platyboros-Instruct-7B)
- [Hermes-Instruct-7B-217K](https://huggingface.co/lodrick-the-lafted/Hermes-Instruct-7B-217K)
<br />
<br />
# Prompt Format
Both the default Mistral-Instruct tags and Alpaca are fine, so either:
```
<s>[INST] {sys_prompt} {instruction} [/INST]
```
or
```
{sys_prompt}
### Instruction:
{instruction}
### Response:
```
The tokenizer default is Alpaca this time around.
<br />
<br />
# Usage
```python
from transformers import AutoTokenizer
import transformers
import torch
model = "lodrick-the-lafted/Grafted-Hermetic-Platypus-A-2x7B"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.bfloat16},
)
messages = [{"role": "user", "content": "Give me a cooking recipe for an peach pie."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lodrick-the-lafted__Grafted-Hermetic-Platypus-C-2x7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |64.39|
|AI2 Reasoning Challenge (25-Shot)|58.96|
|HellaSwag (10-Shot) |82.77|
|MMLU (5-Shot) |62.08|
|TruthfulQA (0-shot) |60.87|
|Winogrande (5-shot) |77.74|
|GSM8k (5-shot) |43.90|
|
AleRothermel/mi-1.2-model | AleRothermel | 2024-05-20T03:24:20Z | 113 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T02:16:21Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: bert-base-cased
metrics:
- accuracy
model-index:
- name: mi-1.2-model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mi-1.2-model
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7264
- Accuracy: 0.58
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 1.6501 | 0.04 | 10 | 1.6095 | 0.235 |
| 1.655 | 0.08 | 20 | 1.5876 | 0.23 |
| 1.6465 | 0.12 | 30 | 1.5874 | 0.305 |
| 1.6577 | 0.16 | 40 | 1.6006 | 0.2325 |
| 1.5666 | 0.2 | 50 | 1.5611 | 0.245 |
| 1.5667 | 0.24 | 60 | 1.4245 | 0.44 |
| 1.4837 | 0.28 | 70 | 1.2916 | 0.4175 |
| 1.2603 | 0.32 | 80 | 1.3869 | 0.3925 |
| 1.2865 | 0.36 | 90 | 1.4055 | 0.3475 |
| 1.4037 | 0.4 | 100 | 1.3934 | 0.32 |
| 1.3201 | 0.44 | 110 | 1.4511 | 0.4125 |
| 1.3977 | 0.48 | 120 | 1.2251 | 0.44 |
| 1.1444 | 0.52 | 130 | 1.1517 | 0.5175 |
| 1.1627 | 0.56 | 140 | 1.1211 | 0.5225 |
| 1.21 | 0.6 | 150 | 1.1336 | 0.53 |
| 1.2211 | 0.64 | 160 | 1.4186 | 0.4 |
| 1.2985 | 0.68 | 170 | 1.1251 | 0.4725 |
| 1.1856 | 0.72 | 180 | 1.1138 | 0.5075 |
| 1.1027 | 0.76 | 190 | 1.0810 | 0.5075 |
| 1.0998 | 0.8 | 200 | 1.1034 | 0.5225 |
| 1.2546 | 0.84 | 210 | 1.1205 | 0.4925 |
| 1.0265 | 0.88 | 220 | 1.1996 | 0.4925 |
| 1.0898 | 0.92 | 230 | 1.1002 | 0.515 |
| 1.19 | 0.96 | 240 | 1.0805 | 0.4925 |
| 1.1456 | 1.0 | 250 | 1.0509 | 0.525 |
| 0.9265 | 1.04 | 260 | 1.1092 | 0.51 |
| 0.8554 | 1.08 | 270 | 1.0098 | 0.5325 |
| 0.8695 | 1.12 | 280 | 1.0991 | 0.4975 |
| 0.8505 | 1.16 | 290 | 1.0827 | 0.5075 |
| 0.8892 | 1.2 | 300 | 1.1195 | 0.52 |
| 0.8982 | 1.24 | 310 | 1.0691 | 0.51 |
| 0.9301 | 1.28 | 320 | 1.0236 | 0.545 |
| 1.052 | 1.32 | 330 | 1.0296 | 0.535 |
| 0.8072 | 1.3600 | 340 | 1.0227 | 0.55 |
| 0.8822 | 1.4 | 350 | 1.0494 | 0.53 |
| 1.1561 | 1.44 | 360 | 1.2036 | 0.4925 |
| 0.9526 | 1.48 | 370 | 1.0443 | 0.56 |
| 0.9916 | 1.52 | 380 | 1.0378 | 0.555 |
| 1.0388 | 1.56 | 390 | 1.0920 | 0.5375 |
| 0.9326 | 1.6 | 400 | 1.0510 | 0.5375 |
| 0.8453 | 1.6400 | 410 | 1.1247 | 0.5025 |
| 1.03 | 1.6800 | 420 | 1.0281 | 0.565 |
| 0.971 | 1.72 | 430 | 1.0322 | 0.54 |
| 0.941 | 1.76 | 440 | 0.9858 | 0.565 |
| 0.8615 | 1.8 | 450 | 0.9793 | 0.555 |
| 0.8815 | 1.8400 | 460 | 0.9778 | 0.56 |
| 0.7658 | 1.88 | 470 | 0.9760 | 0.56 |
| 1.0073 | 1.92 | 480 | 1.0747 | 0.5175 |
| 0.8929 | 1.96 | 490 | 0.9910 | 0.565 |
| 0.9089 | 2.0 | 500 | 1.0512 | 0.535 |
| 0.5102 | 2.04 | 510 | 1.0545 | 0.555 |
| 0.6748 | 2.08 | 520 | 1.1621 | 0.5175 |
| 0.5222 | 2.12 | 530 | 1.1038 | 0.5575 |
| 0.7978 | 2.16 | 540 | 1.1728 | 0.53 |
| 0.6749 | 2.2 | 550 | 1.1029 | 0.5475 |
| 0.6621 | 2.24 | 560 | 1.0977 | 0.5425 |
| 0.6808 | 2.2800 | 570 | 1.1776 | 0.545 |
| 0.5728 | 2.32 | 580 | 1.1747 | 0.5325 |
| 0.75 | 2.36 | 590 | 1.1707 | 0.5275 |
| 0.6622 | 2.4 | 600 | 1.1082 | 0.555 |
| 0.6008 | 2.44 | 610 | 1.0922 | 0.57 |
| 0.6491 | 2.48 | 620 | 1.1375 | 0.545 |
| 0.5876 | 2.52 | 630 | 1.0614 | 0.5675 |
| 0.5326 | 2.56 | 640 | 1.0460 | 0.58 |
| 0.4901 | 2.6 | 650 | 1.0864 | 0.58 |
| 0.6151 | 2.64 | 660 | 1.1919 | 0.58 |
| 0.6478 | 2.68 | 670 | 1.1301 | 0.5575 |
| 0.4841 | 2.7200 | 680 | 1.1451 | 0.58 |
| 0.6365 | 2.76 | 690 | 1.0701 | 0.575 |
| 0.5284 | 2.8 | 700 | 1.1674 | 0.5325 |
| 0.6506 | 2.84 | 710 | 1.1016 | 0.55 |
| 0.6446 | 2.88 | 720 | 1.1340 | 0.57 |
| 0.5193 | 2.92 | 730 | 1.1692 | 0.525 |
| 0.6129 | 2.96 | 740 | 1.1717 | 0.5325 |
| 0.6013 | 3.0 | 750 | 1.1374 | 0.55 |
| 0.3392 | 3.04 | 760 | 1.2702 | 0.515 |
| 0.3188 | 3.08 | 770 | 1.2584 | 0.515 |
| 0.3272 | 3.12 | 780 | 1.3520 | 0.5225 |
| 0.341 | 3.16 | 790 | 1.2752 | 0.5575 |
| 0.3826 | 3.2 | 800 | 1.3126 | 0.55 |
| 0.3062 | 3.24 | 810 | 1.4909 | 0.52 |
| 0.2657 | 3.2800 | 820 | 1.3804 | 0.5575 |
| 0.4609 | 3.32 | 830 | 1.3712 | 0.5625 |
| 0.3388 | 3.36 | 840 | 1.4701 | 0.5275 |
| 0.3007 | 3.4 | 850 | 1.3373 | 0.57 |
| 0.2732 | 3.44 | 860 | 1.3699 | 0.575 |
| 0.4551 | 3.48 | 870 | 1.3874 | 0.555 |
| 0.3048 | 3.52 | 880 | 1.4913 | 0.5625 |
| 0.4104 | 3.56 | 890 | 1.4586 | 0.565 |
| 0.2633 | 3.6 | 900 | 1.4353 | 0.565 |
| 0.4435 | 3.64 | 910 | 1.5246 | 0.555 |
| 0.282 | 3.68 | 920 | 1.6866 | 0.5275 |
| 0.5918 | 3.7200 | 930 | 1.5193 | 0.5525 |
| 0.315 | 3.76 | 940 | 1.4276 | 0.565 |
| 0.1276 | 3.8 | 950 | 1.4411 | 0.5625 |
| 0.3389 | 3.84 | 960 | 1.5420 | 0.5625 |
| 0.3248 | 3.88 | 970 | 1.4492 | 0.575 |
| 0.3051 | 3.92 | 980 | 1.4321 | 0.5925 |
| 0.3363 | 3.96 | 990 | 1.4374 | 0.5825 |
| 0.4602 | 4.0 | 1000 | 1.4581 | 0.57 |
| 0.1582 | 4.04 | 1010 | 1.4434 | 0.5675 |
| 0.2344 | 4.08 | 1020 | 1.4551 | 0.5975 |
| 0.2646 | 4.12 | 1030 | 1.4999 | 0.59 |
| 0.1948 | 4.16 | 1040 | 1.5550 | 0.5625 |
| 0.3058 | 4.2 | 1050 | 1.5955 | 0.5775 |
| 0.1569 | 4.24 | 1060 | 1.5721 | 0.575 |
| 0.1777 | 4.28 | 1070 | 1.6241 | 0.56 |
| 0.1256 | 4.32 | 1080 | 1.5711 | 0.575 |
| 0.2467 | 4.36 | 1090 | 1.5735 | 0.59 |
| 0.1964 | 4.4 | 1100 | 1.5924 | 0.585 |
| 0.0578 | 4.44 | 1110 | 1.6353 | 0.585 |
| 0.1358 | 4.48 | 1120 | 1.6710 | 0.5775 |
| 0.174 | 4.52 | 1130 | 1.6733 | 0.5725 |
| 0.2022 | 4.5600 | 1140 | 1.6658 | 0.585 |
| 0.028 | 4.6 | 1150 | 1.6708 | 0.585 |
| 0.1222 | 4.64 | 1160 | 1.6989 | 0.5875 |
| 0.2295 | 4.68 | 1170 | 1.7131 | 0.5825 |
| 0.374 | 4.72 | 1180 | 1.7197 | 0.5725 |
| 0.1342 | 4.76 | 1190 | 1.7237 | 0.575 |
| 0.079 | 4.8 | 1200 | 1.7267 | 0.58 |
| 0.154 | 4.84 | 1210 | 1.7204 | 0.585 |
| 0.0403 | 4.88 | 1220 | 1.7183 | 0.58 |
| 0.1964 | 4.92 | 1230 | 1.7253 | 0.5775 |
| 0.1297 | 4.96 | 1240 | 1.7252 | 0.5775 |
| 0.0834 | 5.0 | 1250 | 1.7264 | 0.58 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Noursene/whisper-small-2000 | Noursene | 2024-05-20T03:18:45Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T02:40:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
danhergir/platzi | danhergir | 2024-05-20T03:03:13Z | 194 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"dataset:AI-Lab-Makerere/beans",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-03-20T04:26:50Z | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- AI-Lab-Makerere/beans
metrics:
- accuracy
base_model: google/vit-base-patch16-224-in21k
model-index:
- name: platzi
results:
- task:
type: image-classification
name: Image Classification
dataset:
name: beans
type: beans
config: default
split: validation
args: default
metrics:
- type: accuracy
value: 0.9924812030075187
name: Accuracy
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# platzi
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on the beans dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0317
- Accuracy: 0.9925
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.136 | 3.85 | 500 | 0.0317 | 0.9925 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
|
DucPhanBa/Vietnamese_Llama2 | DucPhanBa | 2024-05-20T03:01:21Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2024-05-20T02:57:47Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.4.0
|
tanganke/gpt2_sst2 | tanganke | 2024-05-20T02:47:39Z | 215 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"dataset:nyu-mll/glue",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T02:17:28Z | ---
datasets:
- nyu-mll/glue
metrics:
- accuracy
basemodel:
- openai-community/gpt2
--- |
razasju/aissmeditext | razasju | 2024-05-20T02:44:50Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"blip-2",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-11T07:24:57Z | ---
library_name: transformers
tags: []
---
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Noursene/whisper-small-5000 | Noursene | 2024-05-20T02:40:08Z | 77 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-04-18T08:16:01Z | ---
library_name: transformers
tags: []
---
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foxxxx2/adnlp-bertqa-model | foxxxx2 | 2024-05-20T02:38:33Z | 139 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-05-20T02:38:22Z | ---
library_name: transformers
tags: []
---
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OwOpeepeepoopoo/LittleJerry2 | OwOpeepeepoopoo | 2024-05-20T02:34:28Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-19T11:12:07Z | ---
library_name: transformers
tags: []
---
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PengceWang/LLAMA2-Chinese-huma_emotion | PengceWang | 2024-05-20T02:25:10Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:FlagAlpha/Llama2-Chinese-7b-Chat",
"base_model:adapter:FlagAlpha/Llama2-Chinese-7b-Chat",
"region:us"
] | null | 2024-05-20T02:21:34Z | ---
library_name: peft
base_model: FlagAlpha/Llama2-Chinese-7b-Chat
---
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### Framework versions
- PEFT 0.11.1 |
animaRegem/gemma-2b-malayalam-model-vllm-4bit | animaRegem | 2024-05-20T02:24:53Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:quantized:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-07T18:25:16Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
- sft
base_model: unsloth/gemma-2b-bnb-4bit
---
# Uploaded model
- **Developed by:** animaRegem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Sorour/cls_sentiment_phi3_v1 | Sorour | 2024-05-20T02:22:20Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2024-05-20T01:45:59Z | ---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
datasets:
- generator
model-index:
- name: cls_sentiment_phi3_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cls_sentiment_phi3_v1
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7122
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9066 | 0.2083 | 50 | 0.9011 |
| 0.854 | 0.4167 | 100 | 0.8419 |
| 0.787 | 0.625 | 150 | 0.8062 |
| 0.7476 | 0.8333 | 200 | 0.7764 |
| 0.7141 | 1.0417 | 250 | 0.7636 |
| 0.6989 | 1.25 | 300 | 0.7528 |
| 0.6482 | 1.4583 | 350 | 0.7397 |
| 0.6537 | 1.6667 | 400 | 0.7207 |
| 0.6526 | 1.875 | 450 | 0.7122 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
tanganke/gpt2_mrpc | tanganke | 2024-05-20T02:15:46Z | 228 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"dataset:nyu-mll/glue",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T02:01:40Z | ---
datasets:
- nyu-mll/glue
metrics:
- accuracy
basemodel:
- openai-community/gpt2
--- |
Moon-Ahn/mistral_edit | Moon-Ahn | 2024-05-20T02:12:39Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"finetuned",
"conversational",
"en",
"ko",
"arxiv:2308.06502",
"arxiv:2308.06259",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T01:48:34Z | ---
language:
- en
- ko
pipeline_tag: text-generation
tags:
- finetuned
---
# komt : korean multi task instruction tuning model
Recently, due to the success of ChatGPT, numerous large language models have emerged in an attempt to catch up with ChatGPT's capabilities.
However, when it comes to Korean language performance, it has been observed that many models still struggle to provide accurate answers or generate Korean text effectively.
This study addresses these challenges by introducing a multi-task instruction technique that leverages supervised datasets from various tasks to create training data for Large Language Models (LLMs).
## Model Details
* **Model Developers** : davidkim(changyeon kim)
* **Repository** : https://github.com/davidkim205/komt
* **Model Architecture** : The komt-mistral-7b-v1 is is a fine-tuned version of the Mistral-7B-Instruct-v0.1.
## Dataset
korean multi-task instruction dataset
## Hardware and Software
- nvidia driver : 535.54.03
- CUDA Version: 12.2
## Training
Refer https://github.com/davidkim205/komt
## Prompt template: Mistral
```
<s>[INST] {prompt} [/INST]</s>
```
## Usage
```
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer, GenerationConfig
model_name='davidkim205/komt-mistral-7b-v1'
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
streamer = TextStreamer(tokenizer)
def gen(x):
generation_config = GenerationConfig(
temperature=0.8,
top_p=0.8,
top_k=100,
max_new_tokens=1024,
early_stopping=True,
do_sample=True,
)
q = f"[INST]{x} [/INST]"
gened = model.generate(
**tokenizer(
q,
return_tensors='pt',
return_token_type_ids=False
).to('cuda'),
generation_config=generation_config,
pad_token_id=tokenizer.eos_token_id,
eos_token_id=tokenizer.eos_token_id,
streamer=streamer,
)
result_str = tokenizer.decode(gened[0])
start_tag = f"\n\n### Response: "
start_index = result_str.find(start_tag)
if start_index != -1:
result_str = result_str[start_index + len(start_tag):].strip()
return result_str
print(gen('제주도를 1박2일로 혼자 여행하려고 하는데 여행 코스를 만들어줘'))
```
output
```
<s> [INST]제주도를 1박2일로 혼자 여행하려고 하는데 여행 코스를 만들어줘 [/INST]1박 2일 여행 코스
제주 여행 코스는 다음과 같습니다:
첫째 날:
* 아침: 제주시 신라로 이동하여 한라산 국립공원에 도착합니다. 오른쪽 발자국 광장에서 눈에 띄는 맥주숍을 찾아 한 병의 맥주를 마시고 계단으로 올라가 한라산 정상에 도착합니다.
* 점심: 정상에서 계단을 내려와 중도에 위치한 골목에서 음식을 즐깁니다. 젓갈, 곰탕, 전복 등 맛있는 제주 특산물을 맛보며 힐링을 즐깁니다.
* 오후: 중도에서 남하하여 남서쪽 제주로 이동합니다. 남서쪽 제주는 섬의 자연 경관과 백사장, 아름다운 바다 등 아름다운 풍광이 있는 곳입니다. 숙소에 숙박합니다.
둘째 날:
* 아침: 남서쪽 제주에서 북서쪽 제주로 이동합니다. 이 지역은 푸른 잔디 해변과 투명한 바다가 펼쳐져 있는 아름다운 풍광입니다. 숙소에서 아침을 먹고 바다로 향하여 해변에서 휴식을 취합니다.
* 점심: 북서쪽 제주의 앞바다에서 시원한 바다를 보며 한 끼의 해산물을 맛보고 계절에 따라 해산물 요리를 즐깁니다.
* 오후: 남서쪽 제주에서 이동하여 서쪽 제주로 이동합니다. 서쪽 제주는 절벽과 암석 해변, 수수께끼의 돌 정원 등 독특한 경관이 있는 곳입니다. 이곳에서는 서쪽 제주의 대표적인 명소인 청량사를 방문하고 서쪽 제주의 아름다운 풍광을 감상합니다.
* 저녁: 서쪽 제주에서 제주 시로 이동하여 한 끼의 제주 특산물을 맛보고 도착한 제주 도심에서 저녁을 즐깁니다.
* 야간: 제주 시의 도심에서 야간 활동을 즐기며 1박 2일의 여행을 마무리합니다.
이렇게 제주를 1박 2일로 혼자 여행하면 제주의 아름다운 풍광, 푸른 잔디 해변, 투명한 바다 등을 경험할 수 있습니다.
```
## Evaluation
For objective model evaluation, we initially used EleutherAI's lm-evaluation-harness but obtained unsatisfactory results. Consequently, we conducted evaluations using ChatGPT, a widely used model, as described in [Self-Alignment with Instruction Backtranslation](https://arxiv.org/pdf/2308.06502.pdf) and [Three Ways of Using Large Language Models to Evaluate Chat](https://arxiv.org/pdf/2308.06259.pdf) .
| model | score | average(0~5) | percentage |
| --------------------------------------- |---------| ------------ | ---------- |
| gpt-3.5-turbo(close) | 147 | 3.97 | 79.45% |
| naver Cue(close) | 140 | 3.78 | 75.67% |
| clova X(close) | 136 | 3.67 | 73.51% |
| WizardLM-13B-V1.2(open) | 96 | 2.59 | 51.89% |
| Llama-2-7b-chat-hf(open) | 67 | 1.81 | 36.21% |
| Llama-2-13b-chat-hf(open) | 73 | 1.91 | 38.37% |
| nlpai-lab/kullm-polyglot-12.8b-v2(open) | 70 | 1.89 | 37.83% |
| kfkas/Llama-2-ko-7b-Chat(open) | 96 | 2.59 | 51.89% |
| beomi/KoAlpaca-Polyglot-12.8B(open) | 100 | 2.70 | 54.05% |
| **komt-llama2-7b-v1 (open)(ours)** | **117** | **3.16** | **63.24%** |
| **komt-llama2-13b-v1 (open)(ours)** | **129** | **3.48** | **69.72%** |
| **komt-llama-30b-v1 (open)(ours)** | **129** | **3.16** | **63.24%** |
| **komt-mistral-7b-v1 (open)(ours)** | **131** | **3.54** | **70.81%** |
|
jrc/phi3-mini-math | jrc | 2024-05-20T02:11:01Z | 10 | 1 | transformers | [
"transformers",
"phi3",
"text-generation",
"torchtune",
"minerva-math",
"conversational",
"custom_code",
"en",
"dataset:TIGER-Lab/MATH-plus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-19T23:57:38Z | ---
license: apache-2.0
datasets:
- TIGER-Lab/MATH-plus
language:
- en
tags:
- torchtune
- minerva-math
library_name: transformers
pipeline_tag: text-generation
---
# jrc/phi3-mini-math
<!-- Provide a quick summary of what the model is/does. -->
Math majors - who needs em? This model can answer any math questions you have.
## How to Get Started with the Model
Use the code below to get started with the model.
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("jrc/phi3-mini-math", trust_remote_code=True)
```
## Training Details
Phi3 was trained using [torchtune](https://github.com/pytorch/torchtune) and the training script + config file are located in this repository.
```bash
tune run lora_finetune_distributed.py --config mini_lora.yaml
```
You can see a full Weights & Biases run [here](https://api.wandb.ai/links/jcummings/hkey76vj).
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
This model was finetuned on the following datasets:
* [TIGER-Lab/MATH-plus](https://huggingface.co/datasets/TIGER-Lab/MATH-plus): An advanced math-specific dataset with 894k samples.
#### Hardware
* Machines: 4 x NVIDIA A100 GPUs
* Max VRAM used per GPU: 29 GB
* Real time: 10 hours
## Evaluation
The finetuned model is evaluated on [minerva-math](https://research.google/blog/minerva-solving-quantitative-reasoning-problems-with-language-models/) using [EleutherAI Eval Harness](https://github.com/EleutherAI/lm-evaluation-harness) through torchtune.
```bash
tune run eleuther_eval --config eleuther_evaluation \
checkpoint.checkpoint_dir=./lora-phi3-math \
tasks=["minerva_math"] \
batch_size=32
```
| Tasks |Version|Filter|n-shot| Metric |Value | |Stderr|
|------------------------------------|-------|------|-----:|-----------|-----:|---|-----:|
|minerva_math |N/A |none | 4|exact_match|0.1670|± |0.0051|
| - minerva_math_algebra | 1|none | 4|exact_match|0.2502|± |0.0126|
| - minerva_math_counting_and_prob | 1|none | 4|exact_match|0.1329|± |0.0156|
| - minerva_math_geometry | 1|none | 4|exact_match|0.1232|± |0.0150|
| - minerva_math_intermediate_algebra| 1|none | 4|exact_match|0.0576|± |0.0078|
| - minerva_math_num_theory | 1|none | 4|exact_match|0.1148|± |0.0137|
| - minerva_math_prealgebra | 1|none | 4|exact_match|0.3077|± |0.0156|
| - minerva_math_precalc | 1|none | 4|exact_match|0.0623|± |0.0104|
This shows a large improvement over the base Phi3 Mini model.
## Model Card Contact
Drop me a line at @official_j3rck |
Mitsua/elan-mt-tiny-ja-en | Mitsua | 2024-05-20T02:10:39Z | 115 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"translation",
"ja",
"en",
"dataset:Mitsua/wikidata-parallel-descriptions-en-ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2024-05-20T02:09:55Z | ---
license: cc-by-sa-4.0
datasets:
- Mitsua/wikidata-parallel-descriptions-en-ja
language:
- ja
- en
metrics:
- bleu
- chrf
library_name: transformers
pipeline_tag: translation
---
# ElanMT
This model is a tiny variant of [**ElanMT-BT-ja-en**](https://huggingface.co/Mitsua/elan-mt-bt-ja-en) and is trained from scratch exclusively on openly licensed data and Wikipedia back translated data using [**ElanMT-base-en-ja**](https://huggingface.co/Mitsua/elan-mt-base-en-ja).
## Model Details
This is a translation model based on [Marian MT](https://marian-nmt.github.io/) 4-layer encoder-decoder transformer architecture with sentencepiece tokenizer.
- **Developed by**: [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type**: Translation
- **Source Language**: Japanese
- **Target Language**: English
- **License**: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
## Usage
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#usage)
## Training Data
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#training-data)
## Training Procedure
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#training-procedure)
## Evaluation
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#evaluation)
## Disclaimer
The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model.
|
Mitsua/elan-mt-base-en-ja | Mitsua | 2024-05-20T02:05:40Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"translation",
"ja",
"en",
"dataset:Mitsua/wikidata-parallel-descriptions-en-ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2024-05-20T02:04:52Z | ---
license: cc-by-sa-4.0
datasets:
- Mitsua/wikidata-parallel-descriptions-en-ja
language:
- ja
- en
metrics:
- bleu
- chrf
library_name: transformers
pipeline_tag: translation
---
# ElanMT
This model is a pretrained checkpoint and is suitable for fine-tuning on a large dataset. For general use cases, using [**ElanMT-BT-en-ja**](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) is strongly recommended.
## Model Details
This is a translation model based on [Marian MT](https://marian-nmt.github.io/) 6-layer encoder-decoder transformer architecture with sentencepiece tokenizer.
- **Developed by**: [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type**: Translation
- **Source Language**: English
- **Target Language**: Japanese
- **License**: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
## Usage
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-en-ja#usage)
## Training Data
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-en-ja#training-data)
## Training Procedure
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-en-ja#training-procedure)
## Evaluation
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-en-ja#evaluation)
## Disclaimer
The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model.
|
Mitsua/elan-mt-base-ja-en | Mitsua | 2024-05-20T02:03:41Z | 113 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"translation",
"ja",
"en",
"dataset:Mitsua/wikidata-parallel-descriptions-en-ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2024-05-20T02:00:59Z | ---
license: cc-by-sa-4.0
datasets:
- Mitsua/wikidata-parallel-descriptions-en-ja
language:
- ja
- en
metrics:
- bleu
- chrf
library_name: transformers
pipeline_tag: translation
---
# ElanMT
This model is a pretrained checkpoint and is suitable for fine-tuning on a large dataset. For general use cases, using [**ElanMT-BT-ja-en**](https://huggingface.co/Mitsua/elan-mt-bt-ja-en) is strongly recommended.
## Model Details
This is a translation model based on [Marian MT](https://marian-nmt.github.io/) 6-layer encoder-decoder transformer architecture with sentencepiece tokenizer.
- **Developed by**: [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type**: Translation
- **Source Language**: Japanese
- **Target Language**: English
- **License**: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
## Usage
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#usage)
## Training Data
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#training-data)
## Training Procedure
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#training-procedure)
## Evaluation
[See here.](https://huggingface.co/Mitsua/elan-mt-bt-ja-en#evaluation)
## Disclaimer
The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model.
|
animaRegem/gemma-2b-malayalam-gguf | animaRegem | 2024-05-20T01:56:59Z | 8 | 1 | transformers | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:quantized:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-07T18:21:13Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
base_model: unsloth/gemma-2b-bnb-4bit
---
# Uploaded model
- **Developed by:** animaRegem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
RichardErkhov/dreamgen_-_WizardLM-2-7B-4bits | RichardErkhov | 2024-05-20T01:56:23Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:2304.12244",
"arxiv:2306.08568",
"arxiv:2308.09583",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-20T01:53:09Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
WizardLM-2-7B - bnb 4bits
- Model creator: https://huggingface.co/dreamgen/
- Original model: https://huggingface.co/dreamgen/WizardLM-2-7B/
Original model description:
---
license: apache-2.0
---
<p style="font-size:20px;" align="center">
🏠 <a href="https://wizardlm.github.io/WizardLM2" target="_blank">WizardLM-2 Release Blog</a> </p>
<p align="center">
🤗 <a href="https://huggingface.co/collections/microsoft/wizardlm-2-661d403f71e6c8257dbd598a" target="_blank">HF Repo</a> •🐱 <a href="https://github.com/victorsungo/WizardLM/tree/main/WizardLM-2" target="_blank">Github Repo</a> • 🐦 <a href="https://twitter.com/WizardLM_AI" target="_blank">Twitter</a> • 📃 <a href="https://arxiv.org/abs/2304.12244" target="_blank">[WizardLM]</a> • 📃 <a href="https://arxiv.org/abs/2306.08568" target="_blank">[WizardCoder]</a> • 📃 <a href="https://arxiv.org/abs/2308.09583" target="_blank">[WizardMath]</a> <br>
</p>
<p align="center">
👋 Join our <a href="https://discord.gg/VZjjHtWrKs" target="_blank">Discord</a>
</p>
## News 🔥🔥🔥 [2024/04/15]
We introduce and opensource WizardLM-2, our next generation state-of-the-art large language models,
which have improved performance on complex chat, multilingual, reasoning and agent.
New family includes three cutting-edge models: WizardLM-2 8x22B, WizardLM-2 70B, and WizardLM-2 7B.
- WizardLM-2 8x22B is our most advanced model, demonstrates highly competitive performance compared to those leading proprietary works
and consistently outperforms all the existing state-of-the-art opensource models.
- WizardLM-2 70B reaches top-tier reasoning capabilities and is the first choice in the same size.
- WizardLM-2 7B is the fastest and achieves comparable performance with existing 10x larger opensource leading models.
For more details of WizardLM-2 please read our [release blog post](https://wizardlm.github.io/WizardLM2) and upcoming paper.
## Model Details
* **Model name**: WizardLM-2 7B
* **Developed by**: WizardLM@Microsoft AI
* **Base model**: [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
* **Parameters**: 7B
* **Language(s)**: Multilingual
* **Blog**: [Introducing WizardLM-2](https://wizardlm.github.io/WizardLM2)
* **Repository**: [https://github.com/nlpxucan/WizardLM](https://github.com/nlpxucan/WizardLM)
* **Paper**: WizardLM-2 (Upcoming)
* **License**: Apache2.0
## Model Capacities
**MT-Bench**
We also adopt the automatic MT-Bench evaluation framework based on GPT-4 proposed by lmsys to assess the performance of models.
The WizardLM-2 8x22B even demonstrates highly competitive performance compared to the most advanced proprietary models.
Meanwhile, WizardLM-2 7B and WizardLM-2 70B are all the top-performing models among the other leading baselines at 7B to 70B model scales.
<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/mtbench.png" alt="MTBench" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>
**Human Preferences Evaluation**
We carefully collected a complex and challenging set consisting of real-world instructions, which includes main requirements of humanity, such as writing, coding, math, reasoning, agent, and multilingual.
We report the win:loss rate without tie:
- WizardLM-2 8x22B is just slightly falling behind GPT-4-1106-preview, and significantly stronger than Command R Plus and GPT4-0314.
- WizardLM-2 70B is better than GPT4-0613, Mistral-Large, and Qwen1.5-72B-Chat.
- WizardLM-2 7B is comparable with Qwen1.5-32B-Chat, and surpasses Qwen1.5-14B-Chat and Starling-LM-7B-beta.
<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/winall.png" alt="Win" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>
## Method Overview
We built a **fully AI powered synthetic training system** to train WizardLM-2 models, please refer to our [blog](https://wizardlm.github.io/WizardLM2) for more details of this system.
<p align="center" width="100%">
<a ><img src="https://raw.githubusercontent.com/WizardLM/WizardLM2/main/static/images/exp_1.png" alt="Method" style="width: 96%; min-width: 300px; display: block; margin: auto;"></a>
</p>
## Usage
❗<b>Note for model system prompts usage:</b>
<b>WizardLM-2</b> adopts the prompt format from <b>Vicuna</b> and supports **multi-turn** conversation. The prompt should be as following:
```
A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful,
detailed, and polite answers to the user's questions. USER: Hi ASSISTANT: Hello.</s>
USER: Who are you? ASSISTANT: I am WizardLM.</s>......
```
<b> Inference WizardLM-2 Demo Script</b>
We provide a WizardLM-2 inference demo [code](https://github.com/nlpxucan/WizardLM/tree/main/demo) on our github.
|
animaRegem/gemma-2b-malayalam-model-adaptors | animaRegem | 2024-05-20T01:54:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-07T18:14:06Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-bnb-4bit
---
# Uploaded model
- **Developed by:** animaRegem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Mitsua/elan-mt-bt-en-ja | Mitsua | 2024-05-20T01:53:38Z | 626 | 8 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"translation",
"ja",
"en",
"dataset:Mitsua/wikidata-parallel-descriptions-en-ja",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2024-05-20T01:51:18Z | ---
license: cc-by-sa-4.0
datasets:
- Mitsua/wikidata-parallel-descriptions-en-ja
language:
- ja
- en
metrics:
- bleu
- chrf
library_name: transformers
pipeline_tag: translation
---
# ElanMT
[**ElanMT-BT-en-ja**](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) is a English to Japanese translation model developed by [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine.
- [**ElanMT-base-en-ja**](https://huggingface.co/Mitsua/elan-mt-base-en-ja) and [**ElanMT-base-ja-en**](https://huggingface.co/Mitsua/elan-mt-base-ja-en) are trained from scratch, exclusively on openly licensed corpora such as CC0, CC BY and CC BY-SA.
- This model is a fine-tuned checkpoint of **ElanMT-base-en-ja** and is trained exclusively on openly licensed data and Wikipedia back translated data using **ElanMT-base-ja-en**.
- Web crawled or other machine translated corpora are **not** used during the entire training procedure for the **ElanMT** models.
Despite the relatively low resource training, thanks to back-translation and [a newly built CC0 corpus](https://huggingface.co/datasets/Mitsua/wikidata-parallel-descriptions-en-ja),
the model achieved comparable performance to the currently available open translation models.
## Model Details
This is a translation model based on [Marian MT](https://marian-nmt.github.io/) 6-layer encoder-decoder transformer architecture with sentencepiece tokenizer.
- **Developed by**: [ELAN MITSUA Project](https://elanmitsua.com/en/) / Abstract Engine
- **Model type**: Translation
- **Source Language**: English
- **Target Language**: Japanese
- **License**: [CC BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
## Usage
1. Install the python packages
`pip install transformers accelerate sentencepiece`
* This model is verified on `transformers==4.40.2`
2. Run
```python
from transformers import pipeline
translator = pipeline('translation', model='Mitsua/elan-mt-bt-en-ja')
translator('Hello. I am an AI.')
```
3. For longer multiple sentences, using [pySBD](https://github.com/nipunsadvilkar/pySBD) is recommended.
`pip install transformers accelerate sentencepiece pysbd`
```python
import pysbd
seg_en = pysbd.Segmenter(language="en", clean=False)
txt = 'Hello. I am an AI. How are you doing?'
print(translator(seg_en.segment(txt)))
```
This idea is from [FuguMT](https://huggingface.co/staka/fugumt-en-ja) repo.
## Training Data
We heavily referred [FuguMT author's blog post](https://staka.jp/wordpress/?p=413) for dataset collection.
- [Mitsua/wikidata-parallel-descriptions-en-ja](https://huggingface.co/datasets/Mitsua/wikidata-parallel-descriptions-en-ja) (CC0 1.0)
- We newly built this 1.5M lines wikidata parallel corpus to augment the training data. This greatly improved the vocabulary on a word basis.
- [The Kyoto Free Translation Task (KFTT)](https://www.phontron.com/kftt/) (CC BY-SA 3.0)
- Graham Neubig, "The Kyoto Free Translation Task," http://www.phontron.com/kftt, 2011.
- [Tatoeba](https://tatoeba.org/en/downloads) (CC BY 2.0 FR / CC0 1.0)
- https://tatoeba.org/
- [wikipedia-interlanguage-titles](https://github.com/bhaddow/wikipedia-interlanguage-titles) (The MIT License / CC BY-SA 4.0)
- We built parallel titles based on 2024-05-06 wikipedia dump.
- [WikiMatrix](https://github.com/facebookresearch/LASER/tree/main/tasks/WikiMatrix) (CC BY-SA 4.0)
- Holger Schwenk, Vishrav Chaudhary, Shuo Sun, Hongyu Gong and Francisco Guzmán, "WikiMatrix: Mining 135M Parallel Sentences in 1620 Language Pairs from Wikipedia"
- [MDN Web Docs](https://github.com/mdn/translated-content) (The MIT / CC0 1.0 / CC BY-SA 2.5)
- https://github.com/mdn/translated-content
- [Wikimedia contenttranslation dump](https://dumps.wikimedia.org/other/contenttranslation/) (CC BY-SA 4.0)
- 2024-5-10 dump is used.
*Even if the dataset itself is CC-licensed, we did not use it if the corpus contained in the dataset is based on web crawling, is based on unauthorized use of copyrighted works, or is based on the machine translation output of other translation models.
## Training Procedure
We heavily referred "[Beating Edinburgh's WMT2017 system for en-de with Marian's Transformer model](https://github.com/marian-nmt/marian-examples/tree/master/wmt2017-transformer)"
for training process and hyperparameter tuning.
1. Trains a sentencepiece tokenizer 32k vocab on 4M lines openly licensed corpus.
2. Trains `ja-en` back-translation model on 4M lines openly licensed corpus for 6 epochs. = **ElanMT-base-ja-en**
3. Trains `en-ja` base translation model on 4M lines openly licensed corpus for 6 epochs. = **ElanMT-base-en-ja**
4. Translates 20M lines `ja` Wikipedia to `en` using back-translation model.
5. Trains 4 `en-ja` models, which is finetuned from **ElanMT-base-en-ja** checkpoint, on 24M lines training data augmented with back-translated data for 6 epochs.
6. Merges 4 trained models that produces the best validation score on FLORES+ dev split.
7. Finetunes the merged model on 1M lines high quality corpus subset for 5 epochs.
## Evaluation
### Dataset
- [FLORES+](https://github.com/openlanguagedata/flores) (CC BY-SA 4.0) devtest split is used for evaluation.
- [NTREX](https://github.com/MicrosoftTranslator/NTREX) (CC BY-SA 4.0)
### Result
| **Model** | **Params** | **FLORES+ BLEU** | **FLORES+ chrf** | **NTREX BLEU** | **NTREX chrf** |
|:---|---:|---:|---:|---:|---:|
| [**ElanMT-BT**](https://huggingface.co/Mitsua/elan-mt-bt-en-ja) | 61M | 29.96 | **38.43** | **25.63** | **35.41**|
| [**ElanMT-base**](https://huggingface.co/Mitsua/elan-mt-base-en-ja) **w/o back-translation** | 61M | 26.55 | 35.28 | 23.04 | 32.94|
| [**ElanMT-tiny**](https://huggingface.co/Mitsua/elan-mt-tiny-en-ja) | 15M | 25.93 | 34.69 | 22.78 | 33.00|
| [staka/fugumt-en-ja](https://huggingface.co/staka/fugumt-en-ja) (*1) | 61M | **30.89** | 38.38 | 24.74 | 34.23|
| [facebook/mbart-large-50-many-to-many-mmt](https://huggingface.co/facebook/mbart-large-50-many-to-many-mmt) | 610M | 26.31 | 34.37 | 23.35 | 32.66|
| [facebook/nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) | 615M | 17.09 | 27.32 | 14.92 | 26.26|
| [facebook/nllb-200-3.3B](https://huggingface.co/facebook/nllb-200-3.3B) | 3B | 20.04 | 30.33 | 17.07 | 28.46|
| [google/madlad400-3b-mt](https://huggingface.co/google/madlad400-3b-mt) | 3B | 24.62 | 33.89 | 23.64 | 33.48|
| [google/madlad400-7b-mt](https://huggingface.co/google/madlad400-7b-mt) | 7B | 25.57 | 34.59 | 24.60 | 34.43|
- *1 tested on `transformers==4.29.2` and `num_beams=4`
- *2 BLEU score is calculated by `sacreBLEU` with `tokenize=ja-mecab`
## Disclaimer
- The translated result may be very incorrect, harmful or biased. The model was developed to investigate achievable performance with only a relatively small, licensed corpus, and is not suitable for use cases requiring high translation accuracy. Under Section 5 of the CC BY-SA 4.0 License, ELAN MITSUA Project / Abstract Engine is not responsible for any direct or indirect loss caused by the use of the model.
- 免責事項：翻訳結果は不正確で、有害であったりバイアスがかかっている可能性があります。本モデルは比較的小規模でライセンスされたコーパスのみで達成可能な性能を調査するために開発されたモデルであり、翻訳の正確性が必要なユースケースでの使用には適していません。絵藍ミツアプロジェクト及び株式会社アブストラクトエンジンはCC BY-SA 4.0ライセンス第5条に基づき、本モデルの使用によって生じた直接的または間接的な損失に対して、一切の責任を負いません。 |
ruidanwang/minima | ruidanwang | 2024-05-20T01:50:46Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T01:50:46Z | ---
license: apache-2.0
---
|
ukung/Nusantara-1.8b-Indo-Chat-GGUF | ukung | 2024-05-20T01:49:49Z | 3 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-19T17:52:03Z | ---
license: apache-2.0
---
|
vr4sigma/gptchatbot | vr4sigma | 2024-05-20T01:43:13Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"en",
"dataset:HuggingFaceFW/fineweb",
"dataset:PleIAs/YouTube-Commons",
"arxiv:1910.09700",
"license:wtfpl",
"region:us"
] | null | 2024-05-20T01:39:27Z | ---
license: wtfpl
datasets:
- HuggingFaceFW/fineweb
- PleIAs/YouTube-Commons
language:
- en
metrics:
- accuracy
library_name: adapter-transformers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PQlet/textual-inversion-v2-ablation-vec5-img9 | PQlet | 2024-05-20T01:31:44Z | 17 | 0 | diffusers | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-05-20T01:31:42Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
base_model: runwayml/stable-diffusion-v1-5
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Textual Inversion training - PQlet/textual-inversion-v2-ablation-vec5-img9
The generated images are below.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
JosineyJr/generate-conventional-commit-messages | JosineyJr | 2024-05-20T01:31:35Z | 0 | 0 | unsloth | [
"unsloth",
"safetensors",
"code",
"text2text-generation",
"en",
"base_model:meta-llama/Meta-Llama-Guard-2-8B",
"base_model:finetune:meta-llama/Meta-Llama-Guard-2-8B",
"license:apache-2.0",
"region:us"
] | text2text-generation | 2024-05-20T01:15:15Z | ---
license: apache-2.0
language:
- en
library_name: unsloth
tags:
- code
pipeline_tag: text2text-generation
base_model: meta-llama/Meta-Llama-Guard-2-8B
---
# About the project
CommitWizard is a project that uses pre-trained language models to help automate the generation of commit messages based on code changes. It employs 4-bit quantization to optimize memory usage while maintaining model efficiency and accuracy. |
TinyPixel/openelm-ct | TinyPixel | 2024-05-20T01:29:49Z | 135 | 0 | transformers | [
"transformers",
"safetensors",
"openelm",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-05-20T01:29:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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Ho97/llama3-8b-apiq-w2a16g64 | Ho97 | 2024-05-20T01:25:57Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T00:13:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Uses
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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hiba2/results_t5_wiki | hiba2 | 2024-05-20T01:21:52Z | 109 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar",
"base_model:finetune:ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-20T01:21:20Z | ---
license: apache-2.0
base_model: ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: results_t5_wiki
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results_t5_wiki
This model is a fine-tuned version of [ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar](https://huggingface.co/ahmeddbahaa/t5-arabic-base-finetuned-wikilingua-ar) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0002
- Rouge1: 0.1188
- Rouge2: 0.0194
- Rougel: 0.1188
- Rougelsum: 0.1186
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 0.8768 | 0.2143 | 500 | 0.0228 | 0.1148 | 0.0128 | 0.1148 | 0.1147 | 19.0 |
| 0.0437 | 0.4286 | 1000 | 0.0111 | 0.1164 | 0.0154 | 0.1168 | 0.1165 | 19.0 |
| 0.0436 | 0.6429 | 1500 | 0.0060 | 0.1168 | 0.0163 | 0.1171 | 0.1169 | 19.0 |
| 0.0212 | 0.8573 | 2000 | 0.0052 | 0.117 | 0.0165 | 0.1173 | 0.117 | 19.0 |
| 0.0161 | 1.0716 | 2500 | 0.0018 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.011 | 1.2859 | 3000 | 0.0018 | 0.1188 | 0.0193 | 0.1188 | 0.1186 | 19.0 |
| 0.0094 | 1.5002 | 3500 | 0.0014 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0107 | 1.7145 | 4000 | 0.0007 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0069 | 1.9288 | 4500 | 0.0006 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.007 | 2.1432 | 5000 | 0.0006 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0064 | 2.3575 | 5500 | 0.0006 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0062 | 2.5718 | 6000 | 0.0015 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0042 | 2.7861 | 6500 | 0.0005 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0043 | 3.0004 | 7000 | 0.0004 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0042 | 3.2147 | 7500 | 0.0012 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0047 | 3.4291 | 8000 | 0.0010 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0043 | 3.6434 | 8500 | 0.0008 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0024 | 3.8577 | 9000 | 0.0003 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0026 | 4.0720 | 9500 | 0.0005 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0029 | 4.2863 | 10000 | 0.0003 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0045 | 4.5006 | 10500 | 0.0006 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0024 | 4.7150 | 11000 | 0.0001 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0018 | 4.9293 | 11500 | 0.0002 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.002 | 5.1436 | 12000 | 0.0002 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0022 | 5.3579 | 12500 | 0.0001 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0017 | 5.5722 | 13000 | 0.0003 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0014 | 5.7865 | 13500 | 0.0005 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0055 | 6.0009 | 14000 | 0.0012 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 16.3147 |
| 0.0127 | 6.2152 | 14500 | 0.0002 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
| 0.0012 | 6.4295 | 15000 | 0.0002 | 0.1188 | 0.0194 | 0.1188 | 0.1186 | 19.0 |
### Framework versions
- Transformers 4.42.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
crisistransformers/CT-M3-OneLook | crisistransformers | 2024-05-20T01:17:07Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2403.16614",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-07-10T03:42:07Z | # CrisisTransformers
CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details.
CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder.
## Uses
CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling.
## Models and naming conventions
*CT-M1* models were trained from scratch up to 40 epochs, while *CT-M2* models were initialized with pre-trained RoBERTa's weights and *CT-M3* models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. *OneLook* represents the checkpoint after 1 epoch, *BestLoss* represents the checkpoint with the lowest loss during training, and *Complete* represents the checkpoint after completing all epochs. *SE* represents sentence encoder.
| pre-trained model | source |
|--|--|
|CT-M1-BestLoss|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)|
|CT-M1-Complete|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)|
|CT-M2-OneLook|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)|
|CT-M2-BestLoss|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)|
|CT-M2-Complete|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)|
|CT-M3-OneLook|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)|
|CT-M3-BestLoss|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)|
|CT-M3-Complete|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)|
| sentence encoder | source |
|--|--|
|CT-M1-Complete-SE (mono-lingual: EN)|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)|
|CT-XLMR-SE (multi-lingual)|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)|
|CT-mBERT-SE (multi-lingual)|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)|
Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.
## Citation
If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper:
```
@article{lamsal2023crisistransformers,
title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
journal={Knowledge-Based Systems},
pages={111916},
year={2024},
publisher={Elsevier}
}
```
If you use the multi-lingual sentence encoders, please cite the following paper:
```
@article{lamsal2024semantically,
title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
year={2024},
eprint={2403.16614},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
crisistransformers/CT-M2-BestLoss | crisistransformers | 2024-05-20T01:16:54Z | 161 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2403.16614",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-07-10T03:36:11Z | # CrisisTransformers
CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details.
CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder.
## Uses
CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling.
## Models and naming conventions
*CT-M1* models were trained from scratch up to 40 epochs, while *CT-M2* models were initialized with pre-trained RoBERTa's weights and *CT-M3* models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. *OneLook* represents the checkpoint after 1 epoch, *BestLoss* represents the checkpoint with the lowest loss during training, and *Complete* represents the checkpoint after completing all epochs. *SE* represents sentence encoder.
| pre-trained model | source |
|--|--|
|CT-M1-BestLoss|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)|
|CT-M1-Complete|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)|
|CT-M2-OneLook|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)|
|CT-M2-BestLoss|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)|
|CT-M2-Complete|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)|
|CT-M3-OneLook|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)|
|CT-M3-BestLoss|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)|
|CT-M3-Complete|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)|
| sentence encoder | source |
|--|--|
|CT-M1-Complete-SE (mono-lingual: EN)|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)|
|CT-XLMR-SE (multi-lingual)|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)|
|CT-mBERT-SE (multi-lingual)|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)|
Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.
## Citation
If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper:
```
@article{lamsal2023crisistransformers,
title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
journal={Knowledge-Based Systems},
pages={111916},
year={2024},
publisher={Elsevier}
}
```
If you use the multi-lingual sentence encoders, please cite the following paper:
```
@article{lamsal2024semantically,
title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
year={2024},
eprint={2403.16614},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
crisistransformers/CT-M1-Complete | crisistransformers | 2024-05-20T01:16:41Z | 165 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"fill-mask",
"arxiv:2403.16614",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2023-08-08T22:32:23Z | # CrisisTransformers
CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details.
CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder.
## Uses
CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling.
## Models and naming conventions
*CT-M1* models were trained from scratch up to 40 epochs, while *CT-M2* models were initialized with pre-trained RoBERTa's weights and *CT-M3* models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. *OneLook* represents the checkpoint after 1 epoch, *BestLoss* represents the checkpoint with the lowest loss during training, and *Complete* represents the checkpoint after completing all epochs. *SE* represents sentence encoder.
| pre-trained model | source |
|--|--|
|CT-M1-BestLoss|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)|
|CT-M1-Complete|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)|
|CT-M2-OneLook|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)|
|CT-M2-BestLoss|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)|
|CT-M2-Complete|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)|
|CT-M3-OneLook|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)|
|CT-M3-BestLoss|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)|
|CT-M3-Complete|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)|
| sentence encoder | source |
|--|--|
|CT-M1-Complete-SE (mono-lingual: EN)|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)|
|CT-XLMR-SE (multi-lingual)|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)|
|CT-mBERT-SE (multi-lingual)|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)|
Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.
## Citation
If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper:
```
@article{lamsal2023crisistransformers,
title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
journal={Knowledge-Based Systems},
pages={111916},
year={2024},
publisher={Elsevier}
}
```
If you use the multi-lingual sentence encoders, please cite the following paper:
```
@article{lamsal2024semantically,
title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
year={2024},
eprint={2403.16614},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
crisistransformers/CT-M1-Complete-SE | crisistransformers | 2024-05-20T01:16:21Z | 1 | 1 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"roberta",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2403.16614",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2023-09-11T05:01:11Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# CrisisTransformers
CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details.
CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder.
## Uses
CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling.
## Models and naming conventions
*CT-M1* models were trained from scratch up to 40 epochs, while *CT-M2* models were initialized with pre-trained RoBERTa's weights and *CT-M3* models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. *OneLook* represents the checkpoint after 1 epoch, *BestLoss* represents the checkpoint with the lowest loss during training, and *Complete* represents the checkpoint after completing all epochs. *SE* represents sentence encoder.
| pre-trained model | source |
|--|--|
|CT-M1-BestLoss|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)|
|CT-M1-Complete|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)|
|CT-M2-OneLook|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)|
|CT-M2-BestLoss|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)|
|CT-M2-Complete|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)|
|CT-M3-OneLook|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)|
|CT-M3-BestLoss|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)|
|CT-M3-Complete|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)|
| sentence encoder | source |
|--|--|
|CT-M1-Complete-SE (mono-lingual: EN)|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)|
|CT-XLMR-SE (multi-lingual)|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)|
|CT-mBERT-SE (multi-lingual)|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)|
Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.
## Citation
If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper:
```
@article{lamsal2023crisistransformers,
title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
journal={Knowledge-Based Systems},
pages={111916},
year={2024},
publisher={Elsevier}
}
```
If you use the multi-lingual sentence encoders, please cite the following paper:
```
@article{lamsal2024semantically,
title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
year={2024},
eprint={2403.16614},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
crisistransformers/CT-mBERT-SE | crisistransformers | 2024-05-20T01:15:53Z | 1,306 | 0 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"arxiv:2403.16614",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-02-13T23:07:56Z | ---
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
---
# CrisisTransformers
CrisisTransformers is a family of pre-trained language models and sentence encoders introduced in the papers "[CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts](https://www.sciencedirect.com/science/article/pii/S0950705124005501)" and "[Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts](https://arxiv.org/abs/2403.16614)". The models were trained based on the RoBERTa pre-training procedure on a massive corpus of over 15 billion word tokens sourced from tweets associated with 30+ crisis events such as disease outbreaks, natural disasters, conflicts, etc. Please refer to the [associated paper](https://www.sciencedirect.com/science/article/pii/S0950705124005501) for more details.
CrisisTransformers were evaluated on 18 public crisis-specific datasets against strong baselines. Our pre-trained models outperform the baselines across all 18 datasets in classification tasks, and our best-performing sentence-encoder (mono-lingual) outperforms the state-of-the-art by more than 17\% in sentence encoding tasks. The multi-lingual sentence encoders (support 50+ languages; see [associated paper](https://arxiv.org/abs/2403.16614)) are designed to approximate the embedding space of the best-performing mono-lingual sentence encoder.
## Uses
CrisisTransformers has 8 pre-trained models, 1 mono-lingual and 2 multi-lingual sentence encoders. The pre-trained models should be finetuned for downstream tasks just like [BERT](https://huggingface.co/bert-base-cased) and [RoBERTa](https://huggingface.co/roberta-base). The sentence encoders can be used out-of-the-box just like [Sentence-Transformers](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) for sentence encoding to facilitate tasks such as semantic search, clustering, topic modelling.
## Models and naming conventions
*CT-M1* models were trained from scratch up to 40 epochs, while *CT-M2* models were initialized with pre-trained RoBERTa's weights and *CT-M3* models were initialized with pre-trained BERTweet's weights and both trained for up to 20 epochs. *OneLook* represents the checkpoint after 1 epoch, *BestLoss* represents the checkpoint with the lowest loss during training, and *Complete* represents the checkpoint after completing all epochs. *SE* represents sentence encoder.
| pre-trained model | source |
|--|--|
|CT-M1-BestLoss|[crisistransformers/CT-M1-BestLoss](https://huggingface.co/crisistransformers/CT-M1-BestLoss)|
|CT-M1-Complete|[crisistransformers/CT-M1-Complete](https://huggingface.co/crisistransformers/CT-M1-Complete)|
|CT-M2-OneLook|[crisistransformers/CT-M2-OneLook](https://huggingface.co/crisistransformers/CT-M2-OneLook)|
|CT-M2-BestLoss|[crisistransformers/CT-M2-BestLoss](https://huggingface.co/crisistransformers/CT-M2-BestLoss)|
|CT-M2-Complete|[crisistransformers/CT-M2-Complete](https://huggingface.co/crisistransformers/CT-M2-Complete)|
|CT-M3-OneLook|[crisistransformers/CT-M3-OneLook](https://huggingface.co/crisistransformers/CT-M3-OneLook)|
|CT-M3-BestLoss|[crisistransformers/CT-M3-BestLoss](https://huggingface.co/crisistransformers/CT-M3-BestLoss)|
|CT-M3-Complete|[crisistransformers/CT-M3-Complete](https://huggingface.co/crisistransformers/CT-M3-Complete)|
| sentence encoder | source |
|--|--|
|CT-M1-Complete-SE (mono-lingual: EN)|[crisistransformers/CT-M1-Complete-SE](https://huggingface.co/crisistransformers/CT-M1-Complete-SE)|
|CT-XLMR-SE (multi-lingual)|[crisistransformers/CT-XLMR-SE](https://huggingface.co/crisistransformers/CT-XLMR-SE)|
|CT-mBERT-SE (multi-lingual)|[crisistransformers/CT-mBERT-SE](https://huggingface.co/crisistransformers/CT-mBERT-SE)|
Languages supported by the multi-lingual sentence encoders: Albanian, Arabic, Armenian, Bulgarian, Catalan, Chinese (Simplified), Chinese (Traditional), Croatian, Czech, Danish, Dutch, Estonian, Finnish, French, French (Canada), Galician, Georgian, German, Greek, Gujarati, Hebrew, Hindi, Hungarian, Indonesian, Italian, Japanese, Korean, Kurdish (Sorani), Latvian, Lithuanian, Macedonian, Malay, Marathi, Mongolian, Myanmar (Burmese), Norwegian, Persian, Polish, Portuguese, Portuguese (Brazil), Romanian, Russian, Serbian, Slovak, Slovenian, Spanish, Swedish, Thai, Turkish, Ukrainian, Urdu, and Vietnamese.
## Citation
If you use CrisisTransformers and the mono-lingual sentence encoder, please cite the following paper:
```
@article{lamsal2023crisistransformers,
title={CrisisTransformers: Pre-trained language models and sentence encoders for crisis-related social media texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
journal={Knowledge-Based Systems},
pages={111916},
year={2024},
publisher={Elsevier}
}
```
If you use the multi-lingual sentence encoders, please cite the following paper:
```
@article{lamsal2024semantically,
title={Semantically Enriched Cross-Lingual Sentence Embeddings for Crisis-related Social Media Texts},
author={Rabindra Lamsal and
Maria Rodriguez Read and
Shanika Karunasekera},
year={2024},
eprint={2403.16614},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
TinyPixel/openelm-adapter2 | TinyPixel | 2024-05-20T01:15:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T01:15:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Sorour/cls_sentiment_mistral_v1 | Sorour | 2024-05-20T01:12:58Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-05-20T00:24:07Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: mistralai/Mistral-7B-Instruct-v0.2
datasets:
- generator
model-index:
- name: cls_sentiment_mistral_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cls_sentiment_mistral_v1
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5972
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7365 | 0.1986 | 50 | 0.7344 |
| 0.6778 | 0.3972 | 100 | 0.6852 |
| 0.6548 | 0.5958 | 150 | 0.6588 |
| 0.6728 | 0.7944 | 200 | 0.6333 |
| 0.6148 | 0.9930 | 250 | 0.6106 |
| 0.43 | 1.1917 | 300 | 0.6174 |
| 0.4575 | 1.3903 | 350 | 0.6081 |
| 0.4225 | 1.5889 | 400 | 0.6058 |
| 0.4136 | 1.7875 | 450 | 0.5976 |
| 0.441 | 1.9861 | 500 | 0.5972 |
### Framework versions
- PEFT 0.11.1
- Transformers 4.41.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
WhoTookMyAmogusNickname/llama2-7b-megacode2_min100-GGML | WhoTookMyAmogusNickname | 2024-05-20T01:11:13Z | 0 | 0 | null | [
"region:us"
] | null | 2023-08-13T05:34:54Z |
[llama2-7b-megacode2_min100](https://huggingface.co/andreaskoepf/llama2-7b-megacode2_min100) converted and quantized to GGML\
had to use a "[added_tokens.json](https://huggingface.co/andreaskoepf/llama2-7b-oasst-baseline/blob/main/added_tokens.json)" from another of their models, as the vocab size is strangely 32007 |
ebowwa/mario-ascii | ebowwa | 2024-05-20T01:01:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-20T01:01:49Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** ebowwa
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
jsfamily/korean-small_t33 | jsfamily | 2024-05-20T00:59:16Z | 96 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"ko",
"dataset:korean_samll_dataset3",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-20T00:57:22Z | ---
language:
- ko
license: apache-2.0
tags:
- hf-asr-leaderboard
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- korean_samll_dataset3
model-index:
- name: korean-small_t33
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# korean-small_t33
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the korean_samll_dataset3 dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.1656
- eval_cer: 6.6580
- eval_runtime: 2081.9667
- eval_samples_per_second: 3.128
- eval_steps_per_second: 0.391
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ytcheng/llama-3-8B-pretrain_v2 | ytcheng | 2024-05-20T00:56:09Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-20T00:52:01Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
lomov/targetsandgoalsv1 | lomov | 2024-05-20T00:52:59Z | 124 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"autotrain",
"dataset:targetsandgoalsv1/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-20T00:51:53Z |
---
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
datasets:
- targetsandgoalsv1/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.22812850773334503
f1_macro: 0.928605054676046
f1_micro: 0.9313725490196079
f1_weighted: 0.9297769573887364
precision_macro: 0.9294524189261031
precision_micro: 0.9313725490196079
precision_weighted: 0.930390072030939
recall_macro: 0.93
recall_micro: 0.9313725490196079
recall_weighted: 0.9313725490196079
accuracy: 0.9313725490196079
|
EthanRhys/Ashley-LA | EthanRhys | 2024-05-20T00:52:06Z | 0 | 0 | null | [
"license:openrail++",
"region:us"
] | null | 2024-05-20T00:49:27Z | ---
license: openrail++
---
|
souvik0306/test_quant_merge_facebook_opt | souvik0306 | 2024-05-20T00:51:06Z | 84 | 1 | transformers | [
"transformers",
"safetensors",
"opt",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | 2024-05-20T00:50:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
OscarGalavizC/roberta-base-bne-finetuned-multi-sentiment | OscarGalavizC | 2024-05-20T00:45:17Z | 109 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:BSC-LT/roberta-base-bne",
"base_model:finetune:BSC-LT/roberta-base-bne",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-16T20:36:26Z | ---
license: apache-2.0
base_model: BSC-TeMU/roberta-base-bne
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta-base-bne-finetuned-multi-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta-base-bne-finetuned-multi-sentiment
This model is a fine-tuned version of [BSC-TeMU/roberta-base-bne](https://huggingface.co/BSC-TeMU/roberta-base-bne) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7116
- Accuracy: 0.6914
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.809 | 1.0 | 115 | 0.7168 | 0.6852 |
| 0.6101 | 2.0 | 230 | 0.7116 | 0.6914 |
### Framework versions
- Transformers 4.40.2
- Pytorch 1.13.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Mahnke/rick | Mahnke | 2024-05-20T00:43:22Z | 0 | 0 | null | [
"license:artistic-2.0",
"region:us"
] | null | 2024-05-20T00:43:22Z | ---
license: artistic-2.0
---
|
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