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ludis/tsukasa-120b-qlora-gptq | ludis | 2023-11-29T14:54:49Z | 0 | 0 | null | [
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ludis/tsukasa-120b-qlora-gguf | ludis | 2023-11-29T14:54:57Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:54:57Z | 2023-11-29T14:54:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | ludis/tsukasa-120b-qlora-gguf | [
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LoneStriker/psyonic-cetacean-20B-8.0bpw-h8-exl2 | LoneStriker | 2023-11-29T15:37:36Z | 0 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"storywriting",
"text adventure",
"not-for-all-audiences",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:37:36Z | 2023-11-29T14:55:44.000Z | null | null | ---
license: other
license_name: microsoft-research-license
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | LoneStriker/psyonic-cetacean-20B-8.0bpw-h8-exl2 | [
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Jession01/mistralai-eng2ceb-Instruct | Jession01 | 2023-11-30T01:28:29Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"dataset:Jession01/English-Cebuano-Translation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:28:29Z | 2023-11-29T14:56:28.000Z | null | null | ---
license: apache-2.0
datasets:
- Jession01/English-Cebuano-Translation
language:
- en
pipeline_tag: text-generation
--- | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Jession01/mistralai-eng2ceb-Instruct | [
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hflserdaniel/chai_reward_gpt_classifier | hflserdaniel | 2023-11-29T15:04:02Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:04:02Z | 2023-11-29T14:58:17.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | hflserdaniel/chai_reward_gpt_classifier | [
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getitdone/my_awesome_wnut_model_2 | getitdone | 2023-11-29T15:11:44Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:11:44Z | 2023-11-29T14:58:39.000Z | null | null | Entry not found | null | transformers | token-classification | null | null | null | null | null | null | null | null | null | getitdone/my_awesome_wnut_model_2 | [
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AI-aktindsigt/gdpr_anonymiseingsmodel_danm | AI-aktindsigt | 2023-11-29T14:59:02Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T14:59:02Z | 2023-11-29T14:59:02.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AI-aktindsigt/gdpr_anonymiseingsmodel_danm | [
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city96/AnimeClassifiers | city96 | 2023-11-29T15:18:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us",
"has_space"
] | 2023-11-29T15:18:11Z | 2023-11-29T14:59:53.000Z | null | null | ---
license: apache-2.0
---
# Anime Classifiers
[Training/inference code](https://github.com/city96/CityClassifiers) | [Live Demo](https://huggingface.co/spaces/city96/AnimeClassifiers-demo)
These are models that predict whether a concept is present in an image. The performance on high resolution images isn't very good, especially when detecting subtle image effects such as noise. This is due to CLIP using a fairly low resolution (336x336/224x224).
To combat this, tiling is used at inference time. The input image is first downscaled to 1536 (shortest edge - See `TF.functional.resize`), then 5 separate 512x512 areas are selected (4 corners + center - See `TF.functional.five_crop`). This helps as the downscale factor isn't nearly as drastic as passing the entire image to CLIP. As a bonus, it also avoids the issues with odd aspect ratios requiring cropping or letterboxing to work.
As for the training, it will be detailed in the sections below for the individual classifiers. At first, specialized models will be trained to a relatively high accuracy, building up a high quality but specific dataset in the process.
Then, these models will be used to split/sort each other's the datasets. The code will need to be updated to support one image being part of more than one class, but the final result should be a clean dataset where each target aspect acts as a "tag" rather than a class.
## Architecture
The base model itself is fairly simple. It takes embeddings from a CLIP model (in this case, `openai/clip-vit-large-patch14`) and expands them to 1024 dimensions. From there, a single block with residuals is followed by a few linear layers which converge down to the final output.
For the classifier models, the final output goes through `nn.Softmax`.
# Models
## Future/planned
- Unified (by joining the datasets of the other classifiers)
- Compression (jpg/webp/gif/dithering/etc)
- Noise
## ChromaticAberration - Anime
### Design goals
The goal was to detect [chromatic aberration](https://en.wikipedia.org/wiki/Chromatic_aberration?useskin=vector) in images.
For some odd reason, this effect has become a popular post processing effect to apply to images and drawings. While attempting to train an ESRGAN model, I noticed an odd halo around images and quickly figured out that this effect was the cause. This classifier aims to work as a base filter to remove such images from the dataset.
### Issues
- Seems to get confused by excessive HSV noise
- Triggers even if the effect is only applied to the background
- Sometimes triggers on rough linework/sketches (i.e. multiple semi-transparent lines overlapping)
- Low accuracy on 3D/2.5D with possible false positives.
### Training
The training settings can be found in the `config/CCAnime-ChromaticAberration-v1.yaml` file (7e-6 LR, cosine scheduler, 100K steps).
Final dataset score distribution for v1.16:
```
3215 images in dataset.
0_reg - 395 ||||
0_reg_booru - 1805 ||||||||||||||||||||||
1_chroma - 515 ||||||
1_synthetic - 500 ||||||
Class ratios:
00 - 2200 |||||||||||||||||||||||||||
01 - 1015 ||||||||||||
```
Version history:
- v1.0 - Initial test model, dataset is fully synthetic (500 images). Effect added by shifting red/blue channel by a random amount using chaiNNer.
- v1.1 - Added 300 images tagged "chromatic_aberration" from gelbooru. Added first 1000 images from danbooru2021 as reg images
- v1.2 - Used the newly trained predictor to filter the existing datasets - found ~70 positives in the reg set and ~30 false positives in the target set.
- v1.3-v1.16 - Repeatedly ran predictor against various datasets, adding false positives/negatives back into the dataset, sometimes running against the training set to filter out misclassified images as the predictor got better. Added/removed images were manually checked (My eyes hurt).
| null | null | null | null | null | null | null | null | null | null | null | null | city96/AnimeClassifiers | [
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Kalyansai2001/kalyansaitest | Kalyansai2001 | 2023-11-29T15:02:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:02:28Z | 2023-11-29T15:02:28.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Kalyansai2001/kalyansaitest | [
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Yeshh/modelyesh | Yeshh | 2023-11-29T15:13:44Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:13:44Z | 2023-11-29T15:03:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Yeshh/modelyesh | [
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paateus/demilovato | paateus | 2023-11-29T15:09:05Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T15:09:05Z | 2023-11-29T15:04:04.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | paateus/demilovato | [
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bumblebee-testing/tiny-random-XLMRobertaModel | bumblebee-testing | 2023-11-29T15:08:05Z | 0 | 0 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:08:05Z | 2023-11-29T15:08:01.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | bumblebee-testing/tiny-random-XLMRobertaModel | [
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Vchitect/LaVie | Vchitect | 2023-11-29T15:08:19Z | 0 | 1 | null | [
"region:us"
] | 2023-11-29T15:08:19Z | 2023-11-29T15:08:19.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Vchitect/LaVie | [
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Vchitect/SEINE | Vchitect | 2023-11-29T15:09:02Z | 0 | 1 | null | [
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Tachi67/ParseFeedbackFlowModule | Tachi67 | 2023-11-29T21:55:18Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T21:55:18Z | 2023-11-29T15:10:05.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | Tachi67/ParseFeedbackFlowModule | [
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] |
Englios/Taxi-v3-Test | Englios | 2023-11-29T15:18:40Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T15:18:40Z | 2023-11-29T15:15:37.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-Test
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Englios/Taxi-v3-Test", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | Englios/Taxi-v3-Test | [
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hflserdaniel/chai_llama_pippa_test | hflserdaniel | 2023-11-29T15:16:34Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:16:34Z | 2023-11-29T15:16:34.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | hflserdaniel/chai_llama_pippa_test | [
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argilla/end2end_agnews | argilla | 2023-11-29T15:51:26Z | 0 | 0 | null | [
"Transformers",
"safetensors",
"bert",
"text-classification",
"transformers",
"argilla",
"arxiv:1910.09700",
"region:us"
] | 2023-11-29T15:51:26Z | 2023-11-29T15:18:03.000Z | null | null | ---
library_name: Transformers
tags:
- text-classification
- transformers
- argilla
---
<!-- This model card has been generated automatically according to the information the `ArgillaTrainer` had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model Card for *Model ID*
This model has been created with [Argilla](https://docs.argilla.io), trained with *Transformers*.
<!-- Provide a quick summary of what the model is/does. -->
## Model training
Training the model using the `ArgillaTrainer`:
```python
# Load the dataset:
dataset = FeedbackDataset.from_argilla("...")
# Create the training task:
def formatting_func(sample):
text = sample["text"]
label = sample["label"][0]["value"]
return(text, label)
task = TrainingTask.for_text_classification(formatting_func=formatting_func)
# Create the ArgillaTrainer:
trainer = ArgillaTrainer(
dataset=dataset,
task=task,
framework="transformers",
model="bert-base-cased",
)
trainer.update_config({
"evaluation_strategy": "epoch",
"logging_dir": "./logs",
"logging_steps": 1,
"num_train_epochs": 1,
"output_dir": "textcat_model_transformers",
"use_mps_device": true
})
trainer.train(output_dir="None")
```
You can test the type of predictions of this model like so:
```python
trainer.predict("This is awesome!")
```
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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]
<!--
## 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.*
-->
<!--
### Downstream Use [optional]
*This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app*
-->
<!--
### Out-of-Scope Use
*This section addresses misuse, malicious use, and uses that the model will not work well for.*
-->
<!--
## Bias, Risks, and Limitations
*This section is meant to convey both technical and sociotechnical limitations.*
-->
<!--
### Recommendations
*This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.*
-->
<!--
## Training Details
### Training Metrics
*Metrics related to the model training.*
-->
<!--
### Training Hyperparameters
- **Training regime:** (fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision)
-->
<!--
## 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]
### Framework Versions
- Python: 3.9.17
- Argilla: 1.21.0-dev
<!--
## 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
-->
<!--
## Glossary [optional]
*If relevant, include terms and calculations in this section that can help readers understand the model or model card.*
-->
<!--
## Model Card Authors [optional]
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | null | Transformers | text-classification | null | null | null | null | null | null | null | null | null | argilla/end2end_agnews | [
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llm4fun/vietrag-7b-v1.0 | llm4fun | 2023-11-29T16:39:37Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"vi",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:39:37Z | 2023-11-29T15:19:12.000Z | null | null | ---
language:
- vi
---
# Usage
You can check our model card here: [`llm4fun/vietrag-7b-v1.0`](https://huggingface.co/llm4fun/vietrag-7b-v1.0)
```py
from transformers import GenerationConfig, TextStreamer
from transformers import LlamaForCausalLM, LlamaTokenizer, LlamaConfig
import torch
question = "<your-question>"
context = "<your-context>"
instruction = 'You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.'
input = f"Dựa vào một số ngữ cảnh được cho dưới đây, trả lời câu hỏi ở cuối.\n\n{context}\n\nQuestion: {question}"
prompt_template = (
"### System:\n"
"Below is an instruction that describes a task, paired with an input that provides further context. "
"Write a response that appropriately completes the request.\n\n\n\n"
"### Instruction:\n{instruction}\n\n"
"### Input:\n{input}\n\n"
"### Response:\n{output}"
)
prompt = prompt_template.format(instruction=instruction, input=input, output='')
torch_dtype = torch.bfloat16
model_id = "llm4fun/vietrag-7b-v1.0"
device = "cuda"
tokenizer = LlamaTokenizer.from_pretrained(model_id)
model = LlamaForCausalLM.from_pretrained(
model_id,
config=LlamaConfig.from_pretrained(model_id),
torch_dtype=torch_dtype
)
model = model.eval().to(device)
def generate(prompt, max_new_tokens=1024):
input_ids = tokenizer(prompt, return_tensors="pt")["input_ids"].to(model.device)
model.eval()
with torch.no_grad():
generation_config = GenerationConfig(
repetition_penalty=1.13,
max_new_tokens=max_new_tokens,
# temperature=0.2,
# top_p=0.95,
# top_k=20,
# bos_token_id=tokenizer.bos_token_id,
# eos_token_id=tokenizer.eos_token_id,
# eos_token_id=0, # for open-end generation.
pad_token_id=tokenizer.pad_token_id,
do_sample=False,
use_cache=True,
return_dict_in_generate=True,
output_attentions=False,
output_hidden_states=False,
output_scores=False,
)
streamer = TextStreamer(tokenizer, skip_prompt=True)
generated = model.generate(
inputs=input_ids,
generation_config=generation_config,
streamer=streamer,
)
gen_tokens = generated["sequences"].cpu()[:, len(input_ids[0]):]
output = tokenizer.batch_decode(gen_tokens)[0]
output = output.split(tokenizer.eos_token)[0]
return output.strip()
output = generate(prompt)
```
To tweak the model's answering style, feel free to replace the `instruction` part of the prompt. I reccommend you select one of these following instructions, because they are used during training.
```py
instructions = [
'You are an AI assistant. Provide a detailed answer so user don’t need to search outside to understand the answer.',
'You are an AI assistant. You will be given a task. You must generate a detailed and long answer.',
'You are an AI assistant. User will you give you a task. Your goal is to complete the task as faithfully as you can. While performing the task think step-by-step and justify your steps.',
'You are an smart assistant. Provide a direct, short and exact answer to the following question from its provided context.'
]
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | llm4fun/vietrag-7b-v1.0 | [
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mohanpandit/kumar | mohanpandit | 2023-11-29T15:19:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:19:31Z | 2023-11-29T15:19:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | mohanpandit/kumar | [
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Garydesu/bert-vits2_base_model-2.1 | Garydesu | 2023-11-29T15:24:22Z | 0 | 0 | null | [
"license:cc0-1.0",
"region:us"
] | 2023-11-29T15:24:22Z | 2023-11-29T15:19:44.000Z | null | null | ---
license: cc0-1.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Garydesu/bert-vits2_base_model-2.1 | [
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CLMBR/preemption_testing-full-lstm-10 | CLMBR | 2023-11-29T15:19:48Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:19:48Z | 2023-11-29T15:19:46.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | CLMBR/preemption_testing-full-lstm-10 | [
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pivonil611/faceless | pivonil611 | 2023-11-29T15:21:55Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:21:55Z | 2023-11-29T15:20:04.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | pivonil611/faceless | [
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FloodWatch/anomalydetection | FloodWatch | 2023-11-29T17:27:13Z | 0 | 0 | null | [
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:27:13Z | 2023-11-29T15:20:23.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | FloodWatch/anomalydetection | [
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tizayi/q-FrozenLake-v1-4x4-noSlippery | tizayi | 2023-11-29T15:20:28Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T15:20:28Z | 2023-11-29T15:20:26.000Z | null | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="tizayi/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | tizayi/q-FrozenLake-v1-4x4-noSlippery | [
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ekaterinatao/NEREL-BIO_RuBERT_base | ekaterinatao | 2023-11-29T16:59:12Z | 0 | 0 | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:59:12Z | 2023-11-29T15:21:25.000Z | null | null | ---
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: NEREL-BIO_RuBERT_base
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. -->
# NEREL-BIO_RuBERT_base
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the dataset [NEREL-BIO](https://github.com/nerel-ds/NEREL-BIO/tree/master).
It achieves the following results on the evaluation set:
- Loss: 0.0308
- Precision: 0.9854
- Recall: 0.9823
- F1: 0.9839
- Accuracy: 0.9917
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 3.0739 | 0.33 | 50 | 2.3665 | 0.1842 | 0.0870 | 0.1182 | 0.3558 |
| 1.8092 | 0.65 | 100 | 1.1732 | 0.5774 | 0.5137 | 0.5437 | 0.7011 |
| 1.1032 | 0.98 | 150 | 0.8155 | 0.6955 | 0.6546 | 0.6744 | 0.7874 |
| 0.8338 | 1.31 | 200 | 0.6058 | 0.7512 | 0.7176 | 0.7340 | 0.8355 |
| 0.7068 | 1.63 | 250 | 0.4796 | 0.7912 | 0.7627 | 0.7767 | 0.8663 |
| 0.5839 | 1.96 | 300 | 0.4285 | 0.8160 | 0.7910 | 0.8033 | 0.8832 |
| 0.443 | 2.29 | 350 | 0.3589 | 0.8304 | 0.8271 | 0.8287 | 0.8988 |
| 0.4312 | 2.61 | 400 | 0.3072 | 0.8569 | 0.8395 | 0.8481 | 0.9133 |
| 0.3862 | 2.94 | 450 | 0.2802 | 0.8693 | 0.8503 | 0.8597 | 0.9198 |
| 0.29 | 3.27 | 500 | 0.2379 | 0.8874 | 0.8753 | 0.8813 | 0.9324 |
| 0.2896 | 3.59 | 550 | 0.1988 | 0.9030 | 0.8902 | 0.8966 | 0.9425 |
| 0.2785 | 3.92 | 600 | 0.1865 | 0.9078 | 0.8950 | 0.9014 | 0.9458 |
| 0.2053 | 4.25 | 650 | 0.1596 | 0.9232 | 0.9067 | 0.9149 | 0.9533 |
| 0.2044 | 4.58 | 700 | 0.1325 | 0.9371 | 0.9249 | 0.9310 | 0.9621 |
| 0.1868 | 4.9 | 750 | 0.1231 | 0.9419 | 0.9324 | 0.9371 | 0.9657 |
| 0.1284 | 5.23 | 800 | 0.1219 | 0.9423 | 0.9264 | 0.9343 | 0.9650 |
| 0.1448 | 5.56 | 850 | 0.0941 | 0.9561 | 0.9480 | 0.9520 | 0.9740 |
| 0.1346 | 5.88 | 900 | 0.0849 | 0.9586 | 0.9493 | 0.9539 | 0.9758 |
| 0.1013 | 6.21 | 950 | 0.0713 | 0.9657 | 0.9568 | 0.9612 | 0.9802 |
| 0.0917 | 6.54 | 1000 | 0.0652 | 0.9688 | 0.9601 | 0.9644 | 0.9816 |
| 0.0961 | 6.86 | 1050 | 0.0571 | 0.9727 | 0.9669 | 0.9698 | 0.9844 |
| 0.074 | 7.19 | 1100 | 0.0521 | 0.9752 | 0.9704 | 0.9728 | 0.9857 |
| 0.0758 | 7.52 | 1150 | 0.0480 | 0.9771 | 0.9712 | 0.9741 | 0.9866 |
| 0.0681 | 7.84 | 1200 | 0.0409 | 0.9802 | 0.9746 | 0.9774 | 0.9885 |
| 0.0557 | 8.17 | 1250 | 0.0364 | 0.9822 | 0.9786 | 0.9804 | 0.9899 |
| 0.0431 | 8.5 | 1300 | 0.0343 | 0.9833 | 0.9796 | 0.9814 | 0.9907 |
| 0.05 | 8.82 | 1350 | 0.0323 | 0.9847 | 0.9818 | 0.9832 | 0.9914 |
| 0.0434 | 9.15 | 1400 | 0.0314 | 0.9851 | 0.9820 | 0.9835 | 0.9915 |
| 0.0425 | 9.48 | 1450 | 0.0309 | 0.9854 | 0.9824 | 0.9839 | 0.9917 |
| 0.0483 | 9.8 | 1500 | 0.0308 | 0.9854 | 0.9823 | 0.9839 | 0.9917 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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mandali8686/pet-expression | mandali8686 | 2023-11-29T16:25:50Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:25:50Z | 2023-11-29T15:26:23.000Z | null | null | This model is fine-tuned to predict the facial expression of a pet | null | null | null | null | null | null | null | null | null | null | null | null | mandali8686/pet-expression | [
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lasagna-noodles/zephyr_beta_attention_sink | lasagna-noodles | 2023-11-29T15:35:52Z | 0 | 0 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:35:52Z | 2023-11-29T15:26:49.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | lasagna-noodles/zephyr_beta_attention_sink | [
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datavisionai/test_ner | datavisionai | 2023-11-29T15:27:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:27:31Z | 2023-11-29T15:27:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | datavisionai/test_ner | [
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danplischke/my_awesome_eli5_clm-model | danplischke | 2023-11-29T15:27:38Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:27:38Z | 2023-11-29T15:27:38.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | danplischke/my_awesome_eli5_clm-model | [
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DeepMount00/Mistral-Ita-RAG | DeepMount00 | 2023-11-29T19:08:11Z | 0 | 0 | null | [
"text-generation-inference",
"rag",
"it",
"license:apache-2.0",
"region:us"
] | 2023-11-29T19:08:11Z | 2023-11-29T15:27:57.000Z | null | null | ---
license: apache-2.0
language:
- it
tags:
- text-generation-inference
- rag
---
# Mistral-RAG: Advanced Retrieval-Augmented Generation Model for Italian Language
## Overview
Mistral-RAG is a state-of-the-art Artificial Intelligence solution, specifically designed to tackle complex text generation tasks augmented by information retrieval, with a focus on the Italian language. This model merges cutting-edge Natural Language Processing (NLP) techniques with an innovative information retrieval system to provide detailed, accurate, and contextually relevant responses in Italian.
## Key Features
- **Integration of RAG (Retrieval-Augmented Generation):** Mistral-RAG incorporates the RAG framework to enhance text generation quality, drawing from a vast database of information during response generation.
- **Trained on Diverse Datasets:** The model has been trained on a wide variety of datasets, including academic texts, news articles, and online forum data, ensuring broad coverage of topics and styles, specifically in Italian.
- **Optimized for Italian Language:** While providing excellent performance in Italian, Mistral-RAG thanks to the using of sophisticated translation and language adaptation system.
- **Customization and Adaptability:** The model is designed for easy customization, allowing users to tailor it to specific domains or needs.
## Applications
Mistral-RAG is particularly suitable for applications such as:
- Automated customer support, providing detailed and contextually relevant responses in Italian.
- Content generation, including articles, summaries, and reports in Italian.
- Research and data analysis support, extrapolating relevant information from large datasets.
- Educational applications, such as automated tutoring and study support in Italian.
## Support and Contributions
For questions, support, or to contribute to the Mistral-RAG project, please contact the development team at [montebovi.michele@gmail.com]. | null | null | null | null | null | null | null | null | null | null | null | null | DeepMount00/Mistral-Ita-RAG | [
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Seonghwan/my-bert-fine-tuned-psh1 | Seonghwan | 2023-11-29T15:28:47Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T15:28:47Z | 2023-11-29T15:28:46.000Z | null | null | ---
license: apache-2.0
---
| null | null | null | null | null | null | null | null | null | null | null | null | Seonghwan/my-bert-fine-tuned-psh1 | [
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haryoaw/scenario-non-kd-from-post-finetune-div-2-data-tyqiangz-multilingual-sentiments-m | haryoaw | 2023-11-29T17:36:04Z | 0 | 0 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:multilingual-sentiments",
"base_model:haryoaw/teacher-tyqianz-sentiment-malay",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T17:36:04Z | 2023-11-29T15:31:02.000Z | null | null | ---
license: mit
base_model: haryoaw/teacher-tyqianz-sentiment-malay
tags:
- generated_from_trainer
datasets:
- multilingual-sentiments
metrics:
- accuracy
- f1
model-index:
- name: scenario-non-kd-from-post-finetune-div-2-data-tyqiangz-multilingual-sentiments-m
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. -->
# scenario-non-kd-from-post-finetune-div-2-data-tyqiangz-multilingual-sentiments-m
This model is a fine-tuned version of [haryoaw/teacher-tyqianz-sentiment-malay](https://huggingface.co/haryoaw/teacher-tyqianz-sentiment-malay) on the multilingual-sentiments dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1304
- Accuracy: 0.7085
- F1: 0.6823
## 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
- num_epochs: 6969
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.68 | 100 | 0.6951 | 0.7184 | 0.6875 |
| No log | 1.36 | 200 | 0.9560 | 0.6995 | 0.6639 |
| No log | 2.04 | 300 | 0.8142 | 0.7045 | 0.6774 |
| No log | 2.72 | 400 | 0.9567 | 0.7144 | 0.6756 |
| 0.3132 | 3.4 | 500 | 1.2835 | 0.7254 | 0.6983 |
| 0.3132 | 4.08 | 600 | 1.2929 | 0.7224 | 0.7037 |
| 0.3132 | 4.76 | 700 | 1.4057 | 0.7124 | 0.6892 |
| 0.3132 | 5.44 | 800 | 1.5405 | 0.7104 | 0.6850 |
| 0.3132 | 6.12 | 900 | 1.8422 | 0.7035 | 0.6772 |
| 0.1156 | 6.8 | 1000 | 1.6018 | 0.7154 | 0.6810 |
| 0.1156 | 7.48 | 1100 | 1.7704 | 0.7065 | 0.6834 |
| 0.1156 | 8.16 | 1200 | 1.7839 | 0.7045 | 0.6790 |
| 0.1156 | 8.84 | 1300 | 1.9827 | 0.7154 | 0.6934 |
| 0.1156 | 9.52 | 1400 | 1.8523 | 0.7204 | 0.7006 |
| 0.0725 | 10.2 | 1500 | 2.2075 | 0.7045 | 0.6723 |
| 0.0725 | 10.88 | 1600 | 2.0379 | 0.7204 | 0.6906 |
| 0.0725 | 11.56 | 1700 | 2.1255 | 0.7194 | 0.6950 |
| 0.0725 | 12.24 | 1800 | 1.9350 | 0.7055 | 0.6759 |
| 0.0725 | 12.93 | 1900 | 2.1074 | 0.7005 | 0.6822 |
| 0.0534 | 13.61 | 2000 | 2.1690 | 0.7104 | 0.6869 |
| 0.0534 | 14.29 | 2100 | 2.1304 | 0.7085 | 0.6823 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | null | null | null | null | null | null | null | null | null | null | haryoaw/scenario-non-kd-from-post-finetune-div-2-data-tyqiangz-multilingual-sentiments-m | [
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tizayi/taxi-v3 | tizayi | 2023-11-29T15:31:20Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T15:31:20Z | 2023-11-29T15:31:19.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.52 +/- 2.77
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tizayi/taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | tizayi/taxi-v3 | [
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pandaresiddhi/sparsegpt-lora-4-8 | pandaresiddhi | 2023-11-29T15:34:25Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T15:34:25Z | 2023-11-29T15:33:09.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
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aaalby/dijeisana | aaalby | 2023-11-29T15:34:13Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T15:34:13Z | 2023-11-29T15:33:40.000Z | null | null | ---
license: openrail
---
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emilstabil/DanSumT5-largeV_79062 | emilstabil | 2023-11-29T15:36:43Z | 0 | 0 | null | [
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] | 2023-11-29T15:36:43Z | 2023-11-29T15:36:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_79062 | [
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oopsung/llama2-7b-n-ox-test-v1 | oopsung | 2023-11-29T15:47:16Z | 0 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:47:16Z | 2023-11-29T15:40:45.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | oopsung/llama2-7b-n-ox-test-v1 | [
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] |
pandaresiddhi/sparsegpt-lora-unstructured | pandaresiddhi | 2023-11-29T15:41:50Z | 0 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T15:41:50Z | 2023-11-29T15:41:02.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- 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: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.5.0
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zrross11/modelOutput | zrross11 | 2023-11-29T17:01:15Z | 0 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T17:01:15Z | 2023-11-29T15:43:46.000Z | null | null |
---
license: creativeml-openrail-m
base_model: CompVis/stable-diffusion-v1-4
instance_prompt: a photo of a colton face
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - zrross11/modelOutput
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of a colton face using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
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emilstabil/DanSumT5-largeV_85850 | emilstabil | 2023-11-29T15:43:54Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:43:54Z | 2023-11-29T15:43:52.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | emilstabil/DanSumT5-largeV_85850 | [
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AntML/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters | AntML | 2023-11-29T15:46:53Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T15:46:53Z | 2023-11-29T15:46:14.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
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## Training Details
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## 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | AntML/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters | [
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hoangphatmonter/q-Taxi-v3 | hoangphatmonter | 2023-11-29T15:48:00Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T15:48:00Z | 2023-11-29T15:47:56.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="hoangphatmonter/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | hoangphatmonter/q-Taxi-v3 | [
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ybWang/checkpoints | ybWang | 2023-11-29T16:45:53Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:45:53Z | 2023-11-29T15:48:16.000Z | null | null | ---
tags:
- generated_from_trainer
model-index:
- name: checkpoints
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. -->
# checkpoints
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.1293
## 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: 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: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 3.1387 | 1.0 | 2334 | 3.1259 |
| 2.9604 | 2.0 | 4668 | 3.1257 |
| 2.8686 | 3.0 | 7002 | 3.1293 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.14.7
- Tokenizers 0.13.3
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | ybWang/checkpoints | [
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] |
GiuliaMP/q-FrozenLake-v1-4x4-noSlippery | GiuliaMP | 2023-11-29T15:48:58Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T15:48:58Z | 2023-11-29T15:48:54.000Z | null | null | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="GiuliaMP/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | GiuliaMP/q-FrozenLake-v1-4x4-noSlippery | [
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TeALAiN/UELRsgerw | TeALAiN | 2023-11-29T15:49:27Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:49:27Z | 2023-11-29T15:49:27.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | TeALAiN/UELRsgerw | [
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harryneal/zephyr-beta-attention-sink-pt | harryneal | 2023-11-29T15:54:27Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:54:27Z | 2023-11-29T15:50:13.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | harryneal/zephyr-beta-attention-sink-pt | [
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Martt1776/Mario | Martt1776 | 2023-11-29T15:50:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:50:13Z | 2023-11-29T15:50:13.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Martt1776/Mario | [
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disheng222/HelloWorld | disheng222 | 2023-11-29T15:51:24Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T15:51:24Z | 2023-11-29T15:51:24.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | disheng222/HelloWorld | [
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Potatoasdasdasdasda/whisper-small-hi | Potatoasdasdasdasda | 2023-11-29T15:51:24Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:51:24Z | 2023-11-29T15:51:24.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Potatoasdasdasdasda/whisper-small-hi | [
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proudno0b/lunarLander2test | proudno0b | 2023-11-29T15:52:00Z | 0 | 0 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T15:52:00Z | 2023-11-29T15:51:42.000Z | null | null | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 262.37 +/- 17.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | proudno0b/lunarLander2test | [
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zichao22/prompt_program_raw | zichao22 | 2023-11-29T15:53:35Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-Python-hf",
"license:llama2",
"region:us"
] | 2023-11-29T15:53:35Z | 2023-11-29T15:52:31.000Z | null | null | ---
license: llama2
base_model: codellama/CodeLlama-7b-Python-hf
tags:
- generated_from_trainer
model-index:
- name: prompt_program_raw
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. -->
# prompt_program_raw
This model is a fine-tuned version of [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | zichao22/prompt_program_raw | [
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GiuliaMP/Taxi-v3-first | GiuliaMP | 2023-11-29T15:54:36Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | 2023-11-29T15:54:36Z | 2023-11-29T15:54:34.000Z | null | null | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3-first
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.54 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="GiuliaMP/Taxi-v3-first", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| null | null | reinforcement-learning | null | null | null | null | null | null | null | null | null | GiuliaMP/Taxi-v3-first | [
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AntonyG/convnextv2-base-22k-384 | AntonyG | 2023-11-29T15:55:51Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:55:51Z | 2023-11-29T15:55:51.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | AntonyG/convnextv2-base-22k-384 | [
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Simon-Pierre/my-new-shiny-tokenizer | Simon-Pierre | 2023-11-29T15:56:36Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T15:56:36Z | 2023-11-29T15:56:35.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Simon-Pierre/my-new-shiny-tokenizer | [
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mohammadhossein/bert-base-uncased-riddle-finetuned_2choice | mohammadhossein | 2023-11-29T15:58:34Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"multiple-choice",
"mhs",
"generated_from_trainer",
"en",
"base_model:bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:58:34Z | 2023-11-29T15:58:14.000Z | null | null | ---
language:
- en
license: apache-2.0
base_model: bert-base-uncased
tags:
- mhs
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert_base_uncased
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. -->
# bert_base_uncased
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the sentence_puzzle dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2671
- Accuracy: 0.9683
## 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: 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: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 36 | 0.2659 | 0.9524 |
| No log | 2.0 | 72 | 0.4380 | 0.9206 |
| No log | 3.0 | 108 | 0.3428 | 0.9683 |
| No log | 4.0 | 144 | 0.2805 | 0.9683 |
| No log | 5.0 | 180 | 0.2671 | 0.9683 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
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affecto/Vigogne70b-last_fan | affecto | 2023-11-29T23:45:10Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:TheBloke/vigogne-2-70B-chat-GPTQ",
"license:llama2",
"region:us"
] | 2023-11-29T23:45:10Z | 2023-11-29T15:58:22.000Z | null | null | ---
license: llama2
base_model: TheBloke/vigogne-2-70B-chat-GPTQ
tags:
- generated_from_trainer
model-index:
- name: Vigogne70b-last_fan
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. -->
# Vigogne70b-last_fan
This model is a fine-tuned version of [TheBloke/vigogne-2-70B-chat-GPTQ](https://huggingface.co/TheBloke/vigogne-2-70B-chat-GPTQ) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7698
## 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: 0.0004
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 0.01 | 50 | 1.0362 |
| No log | 0.02 | 100 | 0.9411 |
| No log | 0.03 | 150 | 0.9255 |
| No log | 0.04 | 200 | 0.8966 |
| No log | 0.05 | 250 | 0.8685 |
| No log | 0.06 | 300 | 0.8707 |
| No log | 0.08 | 350 | 0.8515 |
| No log | 0.09 | 400 | 0.8420 |
| No log | 0.1 | 450 | 0.8399 |
| 0.9406 | 0.11 | 500 | 0.8246 |
| 0.9406 | 0.12 | 550 | 0.8070 |
| 0.9406 | 0.13 | 600 | 0.8089 |
| 0.9406 | 0.14 | 650 | 0.8018 |
| 0.9406 | 0.15 | 700 | 0.7947 |
| 0.9406 | 0.16 | 750 | 0.7910 |
| 0.9406 | 0.17 | 800 | 0.7828 |
| 0.9406 | 0.18 | 850 | 0.7774 |
| 0.9406 | 0.19 | 900 | 0.7747 |
| 0.9406 | 0.21 | 950 | 0.7712 |
| 0.7812 | 0.22 | 1000 | 0.7698 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | null | null | null | null | null | null | null | null | null | null | null | affecto/Vigogne70b-last_fan | [
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cyberghostM/ghost | cyberghostM | 2023-11-29T16:12:51Z | 0 | 0 | null | [
"adapter-transformers",
"code",
"legal",
"object-detection",
"aa",
"ae",
"ak",
"ab",
"af",
"am",
"an",
"dataset:vivym/midjourney-messages",
"license:apache-2.0",
"region:us"
] | 2023-11-29T16:12:51Z | 2023-11-29T15:59:18.000Z | null | null | ---
license: apache-2.0
datasets:
- vivym/midjourney-messages
language:
- aa
- ae
- ak
- ab
- af
- am
- an
metrics:
- accuracy
- code_eval
library_name: adapter-transformers
pipeline_tag: object-detection
tags:
- code
- legal
--- | null | adapter-transformers | object-detection | null | null | null | null | null | null | null | null | null | cyberghostM/ghost | [
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SummerSigh/results | SummerSigh | 2023-11-30T01:17:07Z | 0 | 0 | null | [
"region:us"
] | 2023-11-30T01:17:07Z | 2023-11-29T15:59:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | SummerSigh/results | [
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maxschlake/roberta-base_disaster_tweets_ | maxschlake | 2023-11-29T16:02:03Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:02:03Z | 2023-11-29T16:02:01.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | maxschlake/roberta-base_disaster_tweets_ | [
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Mlxa/tuned-flat_shuffle-english | Mlxa | 2023-11-29T16:02:34Z | 0 | 0 | null | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:02:34Z | 2023-11-29T16:02:30.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Mlxa/tuned-flat_shuffle-english | [
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caretech-owl/leo-hessionai-7B-quotes-gguf | caretech-owl | 2023-11-29T16:14:29Z | 0 | 0 | null | [
"gguf",
"text-generation",
"de",
"dataset:caretech-owl/wikiquote-de-quotes",
"license:llama2",
"region:us"
] | 2023-11-29T16:14:29Z | 2023-11-29T16:04:28.000Z | null | null | ---
license: llama2
datasets:
- caretech-owl/wikiquote-de-quotes
language:
- de
pipeline_tag: text-generation
--- | null | null | text-generation | null | null | null | null | null | null | null | null | null | caretech-owl/leo-hessionai-7B-quotes-gguf | [
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davidberenstein1957/en_textcat_model_spacy | davidberenstein1957 | 2023-11-29T21:10:29Z | 0 | 0 | null | [
"Spacy Explosion",
"argilla",
"arxiv:1910.09700",
"region:us"
] | 2023-11-29T21:10:29Z | 2023-11-29T16:04:57.000Z | null | null | ---
library_name: Spacy Explosion
tags:
- argilla
---
<!-- This model card has been generated automatically according to the information the `ArgillaTrainer` had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model Card for *Model ID*
This model has been created with [Argilla](https://docs.argilla.io), trained with *Spacy Explosion*.
<!-- Provide a quick summary of what the model is/does. -->
## Model training
Training the model using the `ArgillaTrainer`:
```python
# Load the dataset:
dataset = FeedbackDataset.from_argilla("...")
# Create the training task:
def formatting_func(sample):
text = sample["text"]
label = sample["label"][0]["value"]
return(text, label)
task = TrainingTask.for_text_classification(formatting_func=formatting_func)
# Create the ArgillaTrainer:
trainer = ArgillaTrainer(
dataset=dataset,
task=task,
framework="spacy",
lang="en",
gpu_id=-1,
framework_kwargs={'optimize': 'efficiency', 'freeze_tok2vec': False},
)
trainer.train(output_dir="None")
```
You can test the type of predictions of this model like so:
```python
trainer.predict("This is awesome!")
```
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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]
<!--
## 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.*
-->
<!--
### Downstream Use [optional]
*This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app*
-->
<!--
### Out-of-Scope Use
*This section addresses misuse, malicious use, and uses that the model will not work well for.*
-->
<!--
## Bias, Risks, and Limitations
*This section is meant to convey both technical and sociotechnical limitations.*
-->
<!--
### Recommendations
*This section is meant to convey recommendations with respect to the bias, risk, and technical limitations.*
-->
<!--
## Training Details
### Training Metrics
*Metrics related to the model training.*
-->
<!--
### Training Hyperparameters
- **Training regime:** (fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision)
-->
<!--
## 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]
### Framework Versions
- Python: 3.9.18
- Argilla: 1.21.0-dev
<!--
## 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
-->
<!--
## Glossary [optional]
*If relevant, include terms and calculations in this section that can help readers understand the model or model card.*
-->
<!--
## Model Card Authors [optional]
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | null | Spacy Explosion | null | null | null | null | null | null | null | null | null | null | davidberenstein1957/en_textcat_model_spacy | [
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Thailan/Lkk | Thailan | 2023-11-29T16:05:31Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:05:31Z | 2023-11-29T16:05:31.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Thailan/Lkk | [
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mlabonne/NeuralHermes-2.5-Mistral-7B-GGUF | mlabonne | 2023-11-29T18:41:19Z | 0 | 2 | null | [
"gguf",
"mistral",
"instruct",
"finetune",
"chatml",
"gpt4",
"synthetic data",
"distillation",
"dpo",
"rlhf",
"en",
"dataset:mlabonne/chatml_dpo_pairs",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | 2023-11-29T18:41:19Z | 2023-11-29T16:05:59.000Z | null | null | ---
base_model: teknium/OpenHermes-2.5-Mistral-7B
tags:
- mistral
- instruct
- finetune
- chatml
- gpt4
- synthetic data
- distillation
- dpo
- rlhf
license: apache-2.0
language:
- en
datasets:
- mlabonne/chatml_dpo_pairs
---
<center><img src="https://i.imgur.com/qIhaFNM.png"></center>
# NeuralHermes 2.5 - Mistral 7B - GGUF
NeuralHermes is an [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model that has been further fine-tuned with Direct Preference Optimization (DPO) using the [mlabonne/chatml_dpo_pairs](https://huggingface.co/datasets/mlabonne/chatml_dpo_pairs) dataset.
It is directly inspired by the RLHF process described by [neural-chat-7b-v3-1](https://huggingface.co/Intel/neural-chat-7b-v3-1)'s authors to improve performance. I used the same dataset and reformatted it to apply the ChatML template. I haven't performed a comprehensive evaluation of the model, but it works great, nothing broken apparently! :)
The code to train this model is available on [Google Colab](https://colab.research.google.com/drive/15iFBr1xWgztXvhrj5I9fBv20c7CFOPBE?usp=sharing) and [GitHub](https://github.com/mlabonne/llm-course/tree/main). It required an A100 GPU for about an hour.
Link to the original model: [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B).
Article and code to quantize your own LLMs: [Quantize Llama models with GGUF and llama.cpp](https://mlabonne.github.io/blog/posts/Quantize_Llama_2_models_using_ggml.html)
## Usage
You can run this model using [LM Studio](https://lmstudio.ai/) or any other frontend.
You can also run this model using the following code:
```python
import transformers
from transformers import AutoTokenizer
# Format prompt
message = [
{"role": "system", "content": "You are a helpful assistant chatbot."},
{"role": "user", "content": "What is a Large Language Model?"}
]
tokenizer = AutoTokenizer.from_pretrained(new_model)
prompt = tokenizer.apply_chat_template(message, add_generation_prompt=True, tokenize=False)
# Create pipeline
pipeline = transformers.pipeline(
"text-generation",
model=new_model,
tokenizer=tokenizer
)
# Generate text
sequences = pipeline(
prompt,
do_sample=True,
temperature=0.7,
top_p=0.9,
num_return_sequences=1,
max_length=200,
)
print(sequences[0]['generated_text'])
```
## Training hyperparameters
**LoRA**:
* r=16,
* lora_alpha=16,
* lora_dropout=0.05,
* bias="none",
* task_type="CAUSAL_LM",
* target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
**Training arguments**:
* per_device_train_batch_size=4,
* gradient_accumulation_steps=4,
* gradient_checkpointing=True,
* learning_rate=5e-5,
* lr_scheduler_type="cosine",
* max_steps=200,
* optim="paged_adamw_32bit",
* warmup_steps=100,
**DPOTrainer**:
* beta=0.1,
* max_prompt_length=1024,
* max_length=1536, | null | null | null | null | null | null | null | null | null | null | null | null | mlabonne/NeuralHermes-2.5-Mistral-7B-GGUF | [
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Realgon/roberta_sst5_padding0model | Realgon | 2023-11-29T16:27:55Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:27:55Z | 2023-11-29T16:06:23.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst5_padding0model
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_sst5_padding0model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7599
- Accuracy: 0.5538
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2226 | 1.0 | 534 | 1.1734 | 0.4774 |
| 0.9839 | 2.0 | 1068 | 1.0370 | 0.5561 |
| 0.8147 | 3.0 | 1602 | 1.0445 | 0.5701 |
| 0.693 | 4.0 | 2136 | 1.2026 | 0.5484 |
| 0.54 | 5.0 | 2670 | 1.2741 | 0.5597 |
| 0.441 | 6.0 | 3204 | 1.5593 | 0.5367 |
| 0.3333 | 7.0 | 3738 | 1.7048 | 0.5407 |
| 0.2661 | 8.0 | 4272 | 1.9049 | 0.5548 |
| 0.2292 | 9.0 | 4806 | 2.2051 | 0.5548 |
| 0.1968 | 10.0 | 5340 | 2.3851 | 0.5507 |
| 0.1714 | 11.0 | 5874 | 2.8158 | 0.5489 |
| 0.1524 | 12.0 | 6408 | 2.9160 | 0.5471 |
| 0.1367 | 13.0 | 6942 | 3.2566 | 0.5357 |
| 0.1087 | 14.0 | 7476 | 3.3613 | 0.5525 |
| 0.0874 | 15.0 | 8010 | 3.3934 | 0.5548 |
| 0.0561 | 16.0 | 8544 | 3.5486 | 0.5584 |
| 0.0637 | 17.0 | 9078 | 3.5989 | 0.5525 |
| 0.0402 | 18.0 | 9612 | 3.7290 | 0.5548 |
| 0.036 | 19.0 | 10146 | 3.7058 | 0.5552 |
| 0.0296 | 20.0 | 10680 | 3.7599 | 0.5538 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst5_padding0model | [
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Thailan/Lkknn | Thailan | 2023-11-29T16:06:28Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:06:28Z | 2023-11-29T16:06:28.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Thailan/Lkknn | [
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robinsyihab/Sidrap-7B-v2-GPTQ-4bit | robinsyihab | 2023-11-29T17:17:10Z | 0 | 0 | null | [
"transformers",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:17:10Z | 2023-11-29T16:07:19.000Z | null | null | ---
license: apache-2.0
---
# Sidrap-7B-v2-GPTQ-4bit
Sidrap-7B-v2-GPTQ-4bit is an 4-bit quantized model of Sidrap-7B-v2, which is one of the best open model LLM bahasa Indonesia available today. This model has been quantized using [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) to get smaller model that allows us to run in a lower resource environment with faster inference. The quantization uses random subset of original training data to "calibrate" the weights resulting in an optimally compact model with minimall loss in accuracy.
## Usage
The fastest way to use this model, use [AutoGPTQ-API](https://github.com/anvie/gptq-api):
```bash
python -m gptqapi.server robinsyihab/Sidrap-7B-v2-GPTQ-4bit
```
Or use AutoGPTQ directly:
```python
from transformers import AutoTokenizer, pipeline
from auto_gptq import AutoGPTQForCausalLM
model_id = "robinsyihab/Sidrap-7B-v2-GPTQ-4bit"
tokenizer = AutoTokenizer.from_pretrained(model_id, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_id,
device="cuda:0",
inject_fused_mlp=True,
inject_fused_attention=True,
trust_remote_code=True)
chat = pipeline("text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto")
prompt = ("<s>[INST] <<SYS>>\nAnda adalah asisten yang suka membantu, penuh hormat, dan jujur. Selalu jawab semaksimal mungkin, sambil tetap aman. Jawaban Anda tidak boleh berisi konten berbahaya, tidak etis, rasis, seksis, beracun, atau ilegal. Harap pastikan bahwa tanggapan Anda tidak memihak secara sosial dan bersifat positif.\n\
Jika sebuah pertanyaan tidak masuk akal, atau tidak koheren secara faktual, jelaskan alasannya daripada menjawab sesuatu yang tidak benar. Jika Anda tidak mengetahui jawaban atas sebuah pertanyaan, mohon jangan membagikan informasi palsu.\n"
"<</SYS>>\n\n"
"Siapa penulis kitab alfiyah? [/INST]\n"
)
sequences = chat(prompt, num_beams=2, max_length=max_size, top_k=10, num_return_sequences=1)
print(sequences[0]['generated_text'])
```
## License
Sidrap-7B-v2-GPTQ is licensed under the Apache 2.0 License.
## Author
[] Robin Syihab ([@anvie](https://x.com/anvie))
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | robinsyihab/Sidrap-7B-v2-GPTQ-4bit | [
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aaalby/jeongyeontheoriginal | aaalby | 2023-11-29T16:10:11Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T16:10:11Z | 2023-11-29T16:09:25.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | aaalby/jeongyeontheoriginal | [
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nxva/ai_models | nxva | 2023-11-29T16:13:13Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:13:13Z | 2023-11-29T16:09:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | nxva/ai_models | [
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Redve/Shared_with_Redve | Redve | 2023-11-29T16:11:06Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:11:06Z | 2023-11-29T16:11:05.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Redve/Shared_with_Redve | [
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Redve/BengaliModel | Redve | 2023-11-29T16:12:11Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:12:11Z | 2023-11-29T16:12:11.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | Redve/BengaliModel | [
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jibblet/FurryBlendv1 | jibblet | 2023-11-29T16:37:33Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:37:33Z | 2023-11-29T16:12:16.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | jibblet/FurryBlendv1 | [
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IiroHabib/juggernaut-xl-v7 | IiroHabib | 2023-11-29T16:41:35Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:41:35Z | 2023-11-29T16:12:43.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | IiroHabib/juggernaut-xl-v7 | [
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sferez/basic_dataset | sferez | 2023-11-29T16:13:00Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:13:00Z | 2023-11-29T16:13:00.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | sferez/basic_dataset | [
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nicher92/ai_sweden_356-4bit-gptq | nicher92 | 2023-11-29T16:13:42Z | 0 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T16:13:42Z | 2023-11-29T16:13:23.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | nicher92/ai_sweden_356-4bit-gptq | [
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Potatoasdasdasdasda/whisper-base-hi | Potatoasdasdasdasda | 2023-11-29T20:32:30Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T20:32:30Z | 2023-11-29T16:17:00.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Potatoasdasdasdasda/whisper-base-hi | [
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FredBonux/whisper-small-hi | FredBonux | 2023-11-29T18:37:37Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:37:37Z | 2023-11-29T16:17:46.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | FredBonux/whisper-small-hi | [
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hllj/sft-mistral-v1-original-data | hllj | 2023-11-29T17:09:16Z | 0 | 0 | null | [
"transformers",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:hllj/mistral-vi-math",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:09:16Z | 2023-11-29T16:19:48.000Z | null | null | ---
base_model: hllj/mistral-vi-math
tags:
- generated_from_trainer
model-index:
- name: sft-mistral-v1-original-data
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. -->
# sft-mistral-v1-original-data
This model is a fine-tuned version of [hllj/mistral-vi-math](https://huggingface.co/hllj/mistral-vi-math) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5062
## 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: 3e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.3374 | 1.27 | 200 | 0.5180 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0
- Datasets 2.15.0
- Tokenizers 0.15.0
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gradients-ai/enviT5-lsa-alibi-v1_0-large | gradients-ai | 2023-11-29T16:22:47Z | 0 | 0 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:22:47Z | 2023-11-29T16:20:32.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | gradients-ai/enviT5-lsa-alibi-v1_0-large | [
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Yeshh/LawAI | Yeshh | 2023-11-29T16:20:44Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T16:20:44Z | 2023-11-29T16:20:43.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Yeshh/LawAI | [
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curzuo/maskformer-swin-base-sceneparse-instance | curzuo | 2023-11-29T22:53:18Z | 0 | 0 | null | [
"transformers",
"safetensors",
"maskformer",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:53:18Z | 2023-11-29T16:20:46.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | curzuo/maskformer-swin-base-sceneparse-instance | [
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jamiehudson/706_SetFit_paraphrase | jamiehudson | 2023-11-29T16:24:39Z | 0 | 0 | null | [
"sentence-transformers",
"safetensors",
"mpnet",
"setfit",
"text-classification",
"arxiv:2209.11055",
"license:apache-2.0",
"region:us"
] | 2023-11-29T16:24:39Z | 2023-11-29T16:24:19.000Z | null | null | ---
license: apache-2.0
tags:
- setfit
- sentence-transformers
- text-classification
pipeline_tag: text-classification
---
# jamiehudson/706_SetFit_paraphrase
This is a [SetFit model](https://github.com/huggingface/setfit) that can be used for text classification. The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Usage
To use this model for inference, first install the SetFit library:
```bash
python -m pip install setfit
```
You can then run inference as follows:
```python
from setfit import SetFitModel
# Download from Hub and run inference
model = SetFitModel.from_pretrained("jamiehudson/706_SetFit_paraphrase")
# Run inference
preds = model(["i loved the spiderman movie!", "pineapple on pizza is the worst 🤮"])
```
## BibTeX entry and citation info
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
| null | sentence-transformers | text-classification | null | null | null | null | null | null | null | null | null | jamiehudson/706_SetFit_paraphrase | [
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Yeshh/falcon7bAI | Yeshh | 2023-11-29T16:24:20Z | 0 | 0 | null | [
"license:openrail",
"region:us"
] | 2023-11-29T16:24:20Z | 2023-11-29T16:24:20.000Z | null | null | ---
license: openrail
---
| null | null | null | null | null | null | null | null | null | null | null | null | Yeshh/falcon7bAI | [
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amrakm/sd1-5_neuron | amrakm | 2023-11-29T16:24:48Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T16:24:48Z | 2023-11-29T16:24:48.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | amrakm/sd1-5_neuron | [
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javismiles/javilora4 | javismiles | 2023-11-29T17:27:18Z | 0 | 0 | null | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T17:27:18Z | 2023-11-29T16:25:14.000Z | null | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - javismiles/javilora4
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the javismiles/lora4 dataset. You can find some example images in the following.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | javismiles/javilora4 | [
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KenDoStudio/TPBAP_Densadron_Crew | KenDoStudio | 2023-11-29T16:25:58Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | 2023-11-29T16:25:58Z | 2023-11-29T16:25:24.000Z | null | null | ---
license: mit
---
| null | null | null | null | null | null | null | null | null | null | null | null | KenDoStudio/TPBAP_Densadron_Crew | [
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niltheory/Occybyte-Initial | niltheory | 2023-11-29T17:09:10Z | 0 | 0 | null | [
"tensorboard",
"dataset:niltheory/occybyte",
"arxiv:1910.09700",
"region:us"
] | 2023-11-29T17:09:10Z | 2023-11-29T16:26:07.000Z | null | null | ---
datasets:
- niltheory/occybyte
---
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| null | null | null | null | null | null | null | null | null | null | null | null | niltheory/Occybyte-Initial | [
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MichellPayano/first-trial | MichellPayano | 2023-11-29T20:01:18Z | 0 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T20:01:18Z | 2023-11-29T16:26:29.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | MichellPayano/first-trial | [
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vachonni/falcon-1b-tech-level-fr | vachonni | 2023-11-29T17:56:58Z | 0 | 0 | null | [
"transformers",
"falcon",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T17:56:58Z | 2023-11-29T16:27:06.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | vachonni/falcon-1b-tech-level-fr | [
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denvey/wav2lip_uhq | denvey | 2023-11-29T17:09:43Z | 0 | 0 | null | [
"onnx",
"region:us"
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Realgon/roberta_sst5_padding10model | Realgon | 2023-11-29T16:56:45Z | 0 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:56:45Z | 2023-11-29T16:27:58.000Z | null | null | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: roberta_sst5_padding10model
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_sst5_padding10model
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.7178
- Accuracy: 0.5575
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.2481 | 1.0 | 534 | 1.2717 | 0.4719 |
| 0.9994 | 2.0 | 1068 | 0.9912 | 0.5629 |
| 0.829 | 3.0 | 1602 | 1.1247 | 0.5457 |
| 0.6975 | 4.0 | 2136 | 1.1001 | 0.5692 |
| 0.5603 | 5.0 | 2670 | 1.3026 | 0.5561 |
| 0.4561 | 6.0 | 3204 | 1.4568 | 0.5620 |
| 0.3672 | 7.0 | 3738 | 1.7943 | 0.5439 |
| 0.2788 | 8.0 | 4272 | 1.8506 | 0.5430 |
| 0.2456 | 9.0 | 4806 | 1.9547 | 0.5516 |
| 0.2136 | 10.0 | 5340 | 2.5049 | 0.5403 |
| 0.1865 | 11.0 | 5874 | 2.6391 | 0.5575 |
| 0.1575 | 12.0 | 6408 | 3.0275 | 0.5421 |
| 0.1384 | 13.0 | 6942 | 3.1505 | 0.5520 |
| 0.1218 | 14.0 | 7476 | 3.2313 | 0.5566 |
| 0.0923 | 15.0 | 8010 | 3.4303 | 0.5457 |
| 0.0734 | 16.0 | 8544 | 3.4973 | 0.5493 |
| 0.0646 | 17.0 | 9078 | 3.5374 | 0.5597 |
| 0.0533 | 18.0 | 9612 | 3.6683 | 0.5597 |
| 0.0373 | 19.0 | 10146 | 3.7188 | 0.5575 |
| 0.0356 | 20.0 | 10680 | 3.7178 | 0.5575 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.1
- Datasets 2.12.0
- Tokenizers 0.13.3
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | Realgon/roberta_sst5_padding10model | [
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bartowski/deepseek-llm-7b-chat-exl2 | bartowski | 2023-11-29T18:01:51Z | 0 | 0 | null | [
"text-generation",
"license:other",
"region:us"
] | 2023-11-29T18:01:51Z | 2023-11-29T16:28:01.000Z | null | null | ---
license: other
license_name: deepseek
license_link: LICENSE
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of deepseek-llm-7b-chat
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/master">turboderp's ExLlamaV2 master</a> for quantization.
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using Evol-Instruct-Code-80k-v1.parquet as calibration dataset.
Default arguments used except when the bits per weight is above 6.0, at that point the lm_head layer is quantized at 8 bits per weight instead of the default 6.
Original model: https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat
<a href="https://huggingface.co/bartowski/deepseek-llm-7b-chat-exl2/tree/4_0">4.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/deepseek-llm-7b-chat-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/deepseek-llm-7b-chat-exl2/tree/6_0">6.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/deepseek-llm-7b-chat-exl2/tree/8_0">8.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 4_0 https://huggingface.co/bartowski/deepseek-llm-7b-chat-exl2
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download the `main` (only useful if you only care about measurement.json) branch to a folder called `deepseek-llm-7b-chat-exl2`:
```shell
mkdir deepseek-llm-7b-chat-exl2
huggingface-cli download bartowski/deepseek-llm-7b-chat-exl2 --local-dir deepseek-llm-7b-chat-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir deepseek-llm-7b-chat-exl2
huggingface-cli download bartowski/deepseek-llm-7b-chat-exl2 --revision 4_0 --local-dir deepseek-llm-7b-chat-exl2 --local-dir-use-symlinks False
```
| null | null | text-generation | null | null | null | null | null | null | null | null | null | bartowski/deepseek-llm-7b-chat-exl2 | [
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hackertwo/SOSOLOLOZOZO | hackertwo | 2023-11-29T16:28:21Z | 0 | 1 | null | [
"region:us"
] | 2023-11-29T16:28:21Z | 2023-11-29T16:28:21.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | hackertwo/SOSOLOLOZOZO | [
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] |
behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned-adapters_RandomError20percentSeed100 | behzadnet | 2023-11-29T16:29:57Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T16:29:57Z | 2023-11-29T16:29:49.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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## 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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KoladeOdunope/Mistral-7B-v0.1-sharded-shares | KoladeOdunope | 2023-11-29T16:29:56Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:29:56Z | 2023-11-29T16:29:56.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | KoladeOdunope/Mistral-7B-v0.1-sharded-shares | [
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behzadnet/Llama-2-7b-chat-hf-sharded-bf16-fine-tuned_RandomError20percentSeed100 | behzadnet | 2023-11-29T16:30:06Z | 0 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:Trelis/Llama-2-7b-chat-hf-sharded-bf16",
"region:us"
] | 2023-11-29T16:30:06Z | 2023-11-29T16:30:03.000Z | null | null | ---
library_name: peft
base_model: Trelis/Llama-2-7b-chat-hf-sharded-bf16
---
# Model Card for Model ID
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## Model Details
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## 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: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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hackertwo/dfkjshdfjksdhf_1 | hackertwo | 2023-11-29T16:31:15Z | 0 | 0 | null | [
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] | 2023-11-29T16:31:15Z | 2023-11-29T16:31:15.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | hackertwo/dfkjshdfjksdhf_1 | [
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] |
OpenNMT/Mistral-7B-v0.1-onmt | OpenNMT | 2023-11-29T17:19:26Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | 2023-11-29T17:19:26Z | 2023-11-29T16:31:58.000Z | null | null | ---
license: apache-2.0
---
This model is the original Mistral-ai 7B v0.1 model converted to the OpenNMT-py format.
By original, it means with interleaved rotary (option: rotary_interleave=True)
You need to install OpenNMT-py, instructions are here: https://github.com/OpenNMT/OpenNMT-py
Running inference:
```
Create a text input file with prompts (ex: "Show me some attractions in Boston")
then run:
onmt_translate --config mistral-inference.yaml --src input.txt --output output.txt
```
Running MMLU evaluation:
```
If you git clone the OpenNMT-py repo then you can run:
python eval_llm/MMLU/run_mmlu_opennmt.py --config mistral-inference.yaml
For this use case make sure you use max_length=1 in the config file
```
Finetuning:
```
Read this tuto: https://forum.opennmt.net/t/finetuning-llama-7b-13b-or-mosaicml-mpt-7b-reproduce-vicuna-alpaca/5272/56
onmt_train --config mistral-finetuning.yaml
```
| null | null | null | null | null | null | null | null | null | null | null | null | OpenNMT/Mistral-7B-v0.1-onmt | [
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vameinei/distilhubert-finetuned-gtzan | vameinei | 2023-11-29T16:32:23Z | 0 | 0 | null | [
"region:us"
] | 2023-11-29T16:32:23Z | 2023-11-29T16:32:22.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | vameinei/distilhubert-finetuned-gtzan | [
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