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JayhC/kuno-kunoichi-v1-DPO-v2-SLERP-7B-8bpw-h8-exl2 | JayhC | 2024-03-27T03:30:58Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:SanjiWatsuki/Kunoichi-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-7B",
"base_model:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"base_model:merge:SanjiWatsuki/Kunoichi-DPO-v2-7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-27T02:45:37Z | ---
base_model:
- SanjiWatsuki/Kunoichi-7B
- SanjiWatsuki/Kunoichi-DPO-v2-7B
library_name: transformers
tags:
- mergekit
- merge
license: cc-by-nc-4.0
---
<br/><br/>
8bpw/h8 exl2 quantization of [grimjim/kuno-kunoichi-v1-DPO-v2-SLERP-7B](https://huggingface.co/grimjim/kuno-kunoichi-v1-DPO-v2-SLERP-7B) using default exllamav2 calibration dataset.
---
**ORIGINAL CARD:**
# kuno-kunoichi-v1-DPO-v2-SLERP-7B
kuno-kunoichi-v1-DPO-v2-SLERP-7B is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
I'm hoping that the result is more robust against errors or when merging due to "denseness", as the two models likely implement comparable reasoning at least somewhat differently.
I've performed some testing with ChatML format prompting using temperature=1.1 and minP=0.03. The model also supports Alpaca format prompts.
[GGUF-IQ-Imatrix quants helpfully provided by Lewdiculous.](https://huggingface.co/Lewdiculous/kuno-kunoichi-v1-DPO-v2-SLERP-7B-GGUF-IQ-Imatrix)
[Q8_0 GGUF quant](https://huggingface.co/grimjim/kuno-kunoichi-v1-DPO-v2-SLERP-7B-GGUF)
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)
* [SanjiWatsuki/Kunoichi-DPO-v2-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-DPO-v2-7B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: SanjiWatsuki/Kunoichi-7B
layer_range: [0,32]
- model: SanjiWatsuki/Kunoichi-DPO-v2-7B
layer_range: [0,32]
merge_method: slerp
base_model: SanjiWatsuki/Kunoichi-7B
parameters:
t:
- value: 0.5
dtype: float16
``` |
Bienvenu2004/donut-handball-pv2 | Bienvenu2004 | 2024-03-27T03:17:17Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:Bienvenu2004/donut-handball-pv",
"base_model:finetune:Bienvenu2004/donut-handball-pv",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-03-24T01:26:51Z | ---
license: mit
base_model: Bienvenu2004/donut-handball-pv
tags:
- generated_from_trainer
datasets:
- imagefolder
model-index:
- name: donut-handball-pv2
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. -->
# donut-handball-pv2
This model is a fine-tuned version of [Bienvenu2004/donut-handball-pv](https://huggingface.co/Bienvenu2004/donut-handball-pv) on the imagefolder dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
liamhvn/nuke-colormax-anime | liamhvn | 2024-03-27T03:13:37Z | 23 | 7 | diffusers | [
"diffusers",
"safetensors",
"stablediffusionapi.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-27T03:06:22Z | ---
license: creativeml-openrail-m
tags:
- stablediffusionapi.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# NUKE - ColorMax Anime API Inference
## Get API Key
Get API key from [Stable Diffusion API](http://stablediffusionapi.com/), No Payment needed.
Replace Key in below code, change **model_id** to "nuke-colormax-anime"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://stablediffusionapi.com/docs)
Try model for free: [Generate Images](https://stablediffusionapi.com/models/nuke-colormax-anime)
Model link: [View model](https://stablediffusionapi.com/models/nuke-colormax-anime)
Credits: [View credits](https://civitai.com/?query=NUKE%20-%20ColorMax%20Anime)
View all models: [View Models](https://stablediffusionapi.com/models)
import requests
import json
url = "https://stablediffusionapi.com/api/v4/dreambooth"
payload = json.dumps({
"key": "your_api_key",
"model_id": "nuke-colormax-anime",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** |
linuxhunter/poca-SoccerTwos | linuxhunter | 2024-03-27T03:08:50Z | 10 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | 2024-03-27T03:02:24Z | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: linuxhunter/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
sshh12/Mistral-7B-LoRA-Multi-VisionCLIPPool-LLAVA | sshh12 | 2024-03-27T03:04:52Z | 13 | 2 | peft | [
"peft",
"safetensors",
"mistral-lmm",
"finetuned",
"multimodal",
"llava",
"image-text-to-text",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"region:us"
] | image-text-to-text | 2023-12-15T05:36:30Z | ---
license: apache-2.0
library_name: peft
tags:
- finetuned
- multimodal
- llava
base_model: mistralai/Mistral-7B-Instruct-v0.1
dataset: sshh12/llava-gpt-multi-image-and-llava-finetune-merged
inference: false
pipeline_tag: image-text-to-text
---
These are weights for a version of `mistralai/Mistral-7B-Instruct-v0.1` finetuned for multimodal applications.
### Modalities
* CLIPVisionModality (use `<image>` in text and provide `images`, encoded as 10 tokens)
### Usage
GitHub: https://github.com/sshh12/multi_token (includes training scripts and basic inference server)
### Dataset
sshh12/llava-gpt-multi-image-and-llava-finetune-merged (744610 examples)
```
{'images': ['/data/llava_finetune_data/images/coco/train2017/train2017/000000499538.jpg'], 'messages': [{'content': '<image>\nWhat is the name of the book?\nAnswer the question using a single word or phrase.', 'role': 'user'}, {'content': 'World changing', 'role': 'assistant'}, {'content': 'What color is the bird?', 'role': 'user'}, {'content': 'Red', 'role': 'assistant'}, {'content': 'What type of bird is this?', 'role': 'user'}, {'content': 'Robin', 'role': 'assistant'}], 'id': '000000499538'}
```
### Training Device(s)
```
name, pci.bus_id, vbios_version
NVIDIA RTX A6000, 00000000:02:00.0, 94.02.5C.00.02
```
### Model
```
MistralLMMForCausalLM.model =
PeftModelForCausalLM(
(base_model): LoraModel(
(model): MistralLMMForCausalLM(
(model): MistralLMMModel(
(embed_tokens): Embedding(32000, 4096)
(layers): ModuleList(
(0-31): 32 x MistralDecoderLayer(
(self_attn): MistralAttention(
(q_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(k_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(v_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=1024, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=1024, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(o_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(rotary_emb): MistralRotaryEmbedding()
)
(mlp): MistralMLP(
(gate_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(up_proj): lora.Linear(
(base_layer): Linear(in_features=4096, out_features=14336, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=4096, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=14336, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(down_proj): lora.Linear(
(base_layer): Linear(in_features=14336, out_features=4096, bias=False)
(lora_dropout): ModuleDict(
(default): Dropout(p=0.05, inplace=False)
)
(lora_A): ModuleDict(
(default): Linear(in_features=14336, out_features=64, bias=False)
)
(lora_B): ModuleDict(
(default): Linear(in_features=64, out_features=4096, bias=False)
)
(lora_embedding_A): ParameterDict()
(lora_embedding_B): ParameterDict()
)
(act_fn): SiLUActivation()
)
(input_layernorm): MistralRMSNorm()
(post_attention_layernorm): MistralRMSNorm()
)
)
(norm): MistralRMSNorm()
(vision_clip_lmm_projector): _MLPVectorProjector(
(mlps): ModuleList(
(0-9): 10 x Sequential(
(0): Linear(in_features=1024, out_features=4096, bias=True)
(1): GELU(approximate='none')
(2): Linear(in_features=4096, out_features=4096, bias=True)
)
)
)
)
(lm_head): Linear(in_features=4096, out_features=32000, bias=False)
)
)
)
```
### Framework versions
- PEFT 0.7.0 |
tsavage68/v1_1000_STEPS_1e5_rate_03_beta_DPO | tsavage68 | 2024-03-27T02:58:23Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-27T02:53:14Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: v1_1000_STEPS_1e5_rate_03_beta_DPO
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. -->
# v1_1000_STEPS_1e5_rate_03_beta_DPO
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0612
- Rewards/chosen: -22.4821
- Rewards/rejected: -21.9166
- Rewards/accuracies: 0.4198
- Rewards/margins: -0.5655
- Logps/rejected: -89.9348
- Logps/chosen: -90.1933
- Logits/rejected: -4.4171
- Logits/chosen: -4.4169
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 1.025 | 0.05 | 50 | 2.0989 | -9.2701 | -9.3262 | 0.4418 | 0.0561 | -47.9670 | -46.1535 | -4.0702 | -4.0700 |
| 3.1266 | 0.1 | 100 | 3.2379 | -16.6921 | -16.6056 | 0.4637 | -0.0864 | -72.2316 | -70.8932 | -3.1523 | -3.1523 |
| 2.9672 | 0.15 | 150 | 2.9589 | -15.0108 | -14.8189 | 0.4571 | -0.1919 | -66.2757 | -65.2890 | -4.5807 | -4.5807 |
| 3.7281 | 0.2 | 200 | 2.9926 | -15.2425 | -14.9338 | 0.4462 | -0.3087 | -66.6590 | -66.0614 | -4.9577 | -4.9577 |
| 2.825 | 0.24 | 250 | 2.9153 | -14.7019 | -14.3934 | 0.4505 | -0.3085 | -64.8577 | -64.2594 | -5.0246 | -5.0246 |
| 3.9813 | 0.29 | 300 | 2.9308 | -14.8129 | -14.5166 | 0.4352 | -0.2962 | -65.2682 | -64.6292 | -4.5446 | -4.5446 |
| 3.9125 | 0.34 | 350 | 2.9798 | -15.2390 | -14.9581 | 0.4418 | -0.2809 | -66.7398 | -66.0496 | -4.0186 | -4.0186 |
| 5.475 | 0.39 | 400 | 2.8595 | -14.7993 | -14.4606 | 0.4462 | -0.3387 | -65.0815 | -64.5839 | -5.5881 | -5.5881 |
| 4.925 | 0.44 | 450 | 2.8461 | -14.9405 | -14.6310 | 0.4505 | -0.3095 | -65.6497 | -65.0547 | -5.7266 | -5.7266 |
| 4.0656 | 0.49 | 500 | 2.8676 | -14.8313 | -14.5335 | 0.4396 | -0.2979 | -65.3244 | -64.6909 | -5.3771 | -5.3771 |
| 4.3688 | 0.54 | 550 | 2.8408 | -14.7379 | -14.4086 | 0.4352 | -0.3293 | -64.9083 | -64.3793 | -5.5129 | -5.5129 |
| 2.3281 | 0.59 | 600 | 2.8091 | -14.4630 | -14.1427 | 0.4374 | -0.3202 | -64.0219 | -63.4629 | -5.0091 | -5.0091 |
| 4.2781 | 0.64 | 650 | 2.6868 | -14.5132 | -14.0888 | 0.4264 | -0.4244 | -63.8422 | -63.6305 | -4.5169 | -4.5170 |
| 4.1469 | 0.68 | 700 | 2.4108 | -17.3614 | -17.1379 | 0.4264 | -0.2235 | -74.0058 | -73.1244 | -3.4213 | -3.4211 |
| 2.2094 | 0.73 | 750 | 2.3138 | -17.0230 | -16.5801 | 0.4110 | -0.4430 | -72.1465 | -71.9965 | -4.4044 | -4.4043 |
| 1.5219 | 0.78 | 800 | 2.3857 | -19.1901 | -18.7328 | 0.4396 | -0.4573 | -79.3222 | -79.2200 | -4.0721 | -4.0720 |
| 3.2406 | 0.83 | 850 | 2.1160 | -21.0445 | -20.4125 | 0.3758 | -0.6320 | -84.9211 | -85.4013 | -4.1028 | -4.1026 |
| 1.8844 | 0.88 | 900 | 2.1362 | -22.7368 | -22.2138 | 0.4220 | -0.5229 | -90.9257 | -91.0423 | -4.4034 | -4.4033 |
| 2.7984 | 0.93 | 950 | 2.0654 | -22.4923 | -21.9278 | 0.4198 | -0.5645 | -89.9723 | -90.2274 | -4.4118 | -4.4116 |
| 2.7203 | 0.98 | 1000 | 2.0612 | -22.4821 | -21.9166 | 0.4198 | -0.5655 | -89.9348 | -90.1933 | -4.4171 | -4.4169 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
meisin123/whisper-small-iban | meisin123 | 2024-03-27T02:26:32Z | 81 | 2 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2023-11-04T08:32:36Z | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
widget:
- example_title: Sample Iban audio
src: ibf_003_014.wav
---
# Whisper Small for Bahasa Iban - Meisin Lee
<!-- Provide a quick summary of what the model is/does. -->
This model is a fine-tuned version of openai/whisper-small on the [Iban Speech Corpus](https://huggingface.co/datasets/meisin123/iban_speech_corpus).
More specifically, this Iban ASR is fine-tuned from the **most similar** language, in this case Malay is used.
It achieves the following results on the evaluation set:
- Loss: 0.257025
- Wer Ortho: 0.158626
- Wer: 0.158781
## How to Get Started with the Model
Use the code below to use the model in **Inference Mode**.
```
from transformers import pipeline
import torch
device = "cuda:0" if torch.cuda.is_available() else "cpu"
pipe = pipeline("automatic-speech-recognition", model="meisin123/whisper-small-iban", chunk_length_s=30, device=device,)
audio_file = "audio.mp3" ## use your own audio here
transcribed_text = pipe(audio_file, batch_size = 16)
```
## Training Details
### Training Data
The model is trained on the Iban Speech Corpus. The dataset is available on Huggingface, more information [here](https://huggingface.co/datasets/meisin123/iban_speech_corpus).
Iban is one of the under-resourced languages. The Iban language (jaku Iban) is spoken by the Iban, one of the Dayak ethnic groups, who live in Brunei, the Indonesian province of West Kalimantan and in the Malaysian state of Sarawak. It belongs to the Malayic subgroup, a Malayo-Polynesian branch of the Austronesian language family.
## Evaluation
### Performance and Limitations
There are still a lot of room for improvement for this Iban ASR model.
1. The accuracy of the model can be further improved with more training data. As Iban is an under-resourced languages, there are limited audio data to train on.
2. Currently, the model is not able to handle code-switched speech. If the audio contains a combination of English and Iban, the model does poorly on the English portion.
## Model Card Contact
For more information, please contact the author at [email protected]
|
Kukedlc/Ramakrishna-7b | Kukedlc | 2024-03-27T02:15:59Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"MatthieuJ/Jason1903_SLERP",
"AurelPx/Percival_01-7b-slerp",
"base_model:AurelPx/Percival_01-7b-slerp",
"base_model:merge:AurelPx/Percival_01-7b-slerp",
"base_model:MatthieuJ/Jason1903_SLERP",
"base_model:merge:MatthieuJ/Jason1903_SLERP",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-27T02:10:40Z | ---
tags:
- merge
- mergekit
- lazymergekit
- MatthieuJ/Jason1903_SLERP
- AurelPx/Percival_01-7b-slerp
base_model:
- MatthieuJ/Jason1903_SLERP
- AurelPx/Percival_01-7b-slerp
---
# Ramakrishna-7b
Ramakrishna-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [MatthieuJ/Jason1903_SLERP](https://huggingface.co/MatthieuJ/Jason1903_SLERP)
* [AurelPx/Percival_01-7b-slerp](https://huggingface.co/AurelPx/Percival_01-7b-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: MatthieuJ/Jason1903_SLERP
layer_range: [0, 32]
- model: AurelPx/Percival_01-7b-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: AurelPx/Percival_01-7b-slerp
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/Ramakrishna-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Solshine/Gemma2B-NaturalFarmerV3_HF | Solshine | 2024-03-27T02:12:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"agriculture",
"farming",
"climate",
"biology",
"agritech",
"en",
"dataset:CopyleftCultivars/Natural-Farming-Real-QandA-Conversations-Q1-2024-Update",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-27T01:55:26Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
- agriculture
- farming
- climate
- biology
- agritech
base_model: unsloth/gemma-2b-it-bnb-4bit
datasets:
- CopyleftCultivars/Natural-Farming-Real-QandA-Conversations-Q1-2024-Update
---
# Uploaded model
- **Developed by:** Solshine
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
Background: Using real-world user data from a previous farmer assistant chatbot service and additional curated datasets (prioritizing sustainable regenerative organic farming practices,) Gemma 2B and Mistral 7B LLMs were iteratively fine-tuned and tested against eachother as well as basic benchmarking, whereby the Gemma 2B fine-tune emerged victorious. LORA adapters were saved for each model. Following this, the Gemma version was released.
Updates for this model: We then revisited the data, adding four additional months of real-world in-field data from hundreds of users which was then editted by a domain expert in regenerative farming and natural farming (approximately 2,000 instruct examples.) This was combined with a small portion of synthetic datasets and semisynthetic datasets related to regenerative agriculture and natural farming, including some non-english language samples
This gemma model was trained with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Granoladata/modality_classifier_biobert_weighted_classes_v0 | Granoladata | 2024-03-27T02:10:29Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-26T23:11:47Z | ---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: modality_classifier_biobert_weighted_classes_v0
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. -->
# modality_classifier_biobert_weighted_classes_v0
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0510
- F1: 0.9911
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0008 | 1.0 | 849 | 0.0325 | 0.9897 |
| 0.0003 | 2.0 | 1698 | 0.0426 | 0.9902 |
| 0.0001 | 3.0 | 2547 | 0.0510 | 0.9911 |
### Framework versions
- Transformers 4.39.0
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
DUAL-GPO/phi-2-gpo-test-longest-iter-random-0 | DUAL-GPO | 2024-03-27T01:55:14Z | 0 | 0 | peft | [
"peft",
"safetensors",
"phi",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"custom_code",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-03-27T00:51:01Z | ---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: microsoft/phi-2
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: phi-2-gpo-test-longest-iter-random-0
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. -->
# phi-2-gpo-test-longest-iter-random-0
This model is a fine-tuned version of [lole25/phi-2-sft-ultrachat-lora](https://huggingface.co/lole25/phi-2-sft-ultrachat-lora) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0004
- Rewards/chosen: 0.0012
- Rewards/rejected: 0.0010
- Rewards/accuracies: 0.4995
- Rewards/margins: 0.0002
- Logps/rejected: -233.4380
- Logps/chosen: -256.4973
- Logits/rejected: 0.8990
- Logits/chosen: 0.8417
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0003 | 1.6 | 100 | 0.0004 | 0.0006 | 0.0004 | 0.4855 | 0.0002 | -233.5017 | -256.5565 | 0.8960 | 0.8387 |
| 0.0003 | 3.2 | 200 | 0.0004 | 0.0013 | 0.0009 | 0.5100 | 0.0004 | -233.4492 | -256.4811 | 0.8984 | 0.8412 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
issaree/ppo-Huggy | issaree | 2024-03-27T01:50:42Z | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2024-03-27T01:50:38Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: issaree/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
gnumanth/code-gemma | gnumanth | 2024-03-27T01:40:58Z | 116 | 1 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T05:56:45Z | ---
library_name: transformers
license: mit
language:
- en
widget:
- text: "Give me code to reverse a string in python"
- text: "How do I find today's date in python?"
---
# code-gemma
Google's `gemma-2b-it` trained `code_instructions_122k_alpaca_style` dataset
# Usage
```py
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="gnumanth/code-gemma")
```
```py
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("gnumanth/code-gemma")
model = AutoModelForCausalLM.from_pretrained("gnumanth/code-gemma")
```
[Hemanth HM](https://h3manth.com) |
Solshine/LORA-Adapters-Gemma2B-NaturalFarmerV3 | Solshine | 2024-03-27T01:35:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-27T01:34:55Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-it-bnb-4bit
---
# Uploaded model
- **Developed by:** Solshine
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
HachiML/myBit-Llama2-jp-127M-test-23 | HachiML | 2024-03-27T01:18:41Z | 47 | 0 | transformers | [
"transformers",
"safetensors",
"bit_llama",
"text-generation",
"generated_from_trainer",
"custom_code",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-03-26T22:58:45Z | ---
tags:
- generated_from_trainer
model-index:
- name: myBit-Llama2-jp-127M-test-23
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. -->
# myBit-Llama2-jp-127M-test-23
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.7610
- eval_runtime: 323.9734
- eval_samples_per_second: 660.693
- eval_steps_per_second: 6.883
- epoch: 0.29
- step: 12000
## 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: 0.0024
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.8240 | 0.05 | 2000 | 4.082750 |
| 3.8518 | 0.1 | 4000 | 3.693951 |
| 3.6357 | 0.15 | 6000 | 3.606057 |
| 3.6164 | 0.2 | 8000 | 3.641231 |
| 3.6208 | 0.25 | 10000 | 3.646924 |
| 3.6360 | 0.29 | 12000 | 3.760965 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- mybitnet 0.5.1
|
AshtonLKY/CE_v1.1 | AshtonLKY | 2024-03-27T01:16:03Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-03-27T01:07:07Z | ---
language:
- en
metrics:
- f1
- accuracy
pipeline_tag: token-classification
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
xiaoheiqaq/Rana | xiaoheiqaq | 2024-03-27T01:14:04Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"casual-lm",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-20T00:58:45Z | ---
language:
- en
tags:
- casual-lm
---
## Model Description
Rana is a fintuned version of `StableLM Zephyr 3B` for roleplay purposes. She will refuse to assist you.
See https://huggingface.co/stabilityai/stablelm-zephyr-3b for usage details. |
nsugianto/detr-resnet50_finetuned_icdar2019_finetuned_lstabledetv1 | nsugianto | 2024-03-27T01:12:04Z | 10 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:TahaDouaji/detr-doc-table-detection",
"base_model:finetune:TahaDouaji/detr-doc-table-detection",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-27T00:40:07Z | ---
base_model: TahaDouaji/detr-doc-table-detection
tags:
- generated_from_trainer
model-index:
- name: detr-resnet50_finetuned_icdar2019_finetuned_lstabledetv1
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. -->
# detr-resnet50_finetuned_icdar2019_finetuned_lstabledetv1
This model is a fine-tuned version of [TahaDouaji/detr-doc-table-detection](https://huggingface.co/TahaDouaji/detr-doc-table-detection) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
OpenBuddy/openbuddy-mistral2-7b-v20.2-32k | OpenBuddy | 2024-03-27T00:46:02Z | 60 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"zh",
"en",
"fr",
"de",
"ja",
"ko",
"it",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-26T17:58:56Z | ---
language:
- zh
- en
- fr
- de
- ja
- ko
- it
- ru
pipeline_tag: text-generation
inference: false
library_name: transformers
license: apache-2.0
---
# OpenBuddy - Open Multilingual Chatbot
GitHub and Usage Guide: [https://github.com/OpenBuddy/OpenBuddy](https://github.com/OpenBuddy/OpenBuddy)
Website and Demo: [https://openbuddy.ai](https://openbuddy.ai)
Evaluation result of this model: [Evaluation.txt](Evaluation.txt)
# Copyright Notice
Base model: https://huggingface.co/mistralai/Mistral-7B-v0.2
License: Apache 2.0
## Disclaimer
All OpenBuddy models have inherent limitations and may potentially produce outputs that are erroneous, harmful, offensive, or otherwise undesirable. Users should not use these models in critical or high-stakes situations that may lead to personal injury, property damage, or significant losses. Examples of such scenarios include, but are not limited to, the medical field, controlling software and hardware systems that may cause harm, and making important financial or legal decisions.
OpenBuddy is provided "as-is" without any warranty of any kind, either express or implied, including, but not limited to, the implied warranties of merchantability, fitness for a particular purpose, and non-infringement. In no event shall the authors, contributors, or copyright holders be liable for any claim, damages, or other liabilities, whether in an action of contract, tort, or otherwise, arising from, out of, or in connection with the software or the use or other dealings in the software.
By using OpenBuddy, you agree to these terms and conditions, and acknowledge that you understand the potential risks associated with its use. You also agree to indemnify and hold harmless the authors, contributors, and copyright holders from any claims, damages, or liabilities arising from your use of OpenBuddy.
## 免责声明
所有OpenBuddy模型均存在固有的局限性，可能产生错误的、有害的、冒犯性的或其他不良的输出。用户在关键或高风险场景中应谨慎行事，不要使用这些模型，以免导致人身伤害、财产损失或重大损失。此类场景的例子包括但不限于医疗领域、可能导致伤害的软硬件系统的控制以及进行重要的财务或法律决策。
OpenBuddy按“原样”提供，不附带任何种类的明示或暗示的保证，包括但不限于适销性、特定目的的适用性和非侵权的暗示保证。在任何情况下，作者、贡献者或版权所有者均不对因软件或使用或其他软件交易而产生的任何索赔、损害赔偿或其他责任（无论是合同、侵权还是其他原因）承担责任。
使用OpenBuddy即表示您同意这些条款和条件，并承认您了解其使用可能带来的潜在风险。您还同意赔偿并使作者、贡献者和版权所有者免受因您使用OpenBuddy而产生的任何索赔、损害赔偿或责任的影响。 |
tsk-18/results | tsk-18 | 2024-03-27T00:37:55Z | 119 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:textattack/distilbert-base-cased-CoLA",
"base_model:finetune:textattack/distilbert-base-cased-CoLA",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-02-04T00:02:09Z | ---
base_model: textattack/distilbert-base-cased-CoLA
tags:
- generated_from_trainer
model-index:
- name: results
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# results
This model is a fine-tuned version of [textattack/distilbert-base-cased-CoLA](https://huggingface.co/textattack/distilbert-base-cased-CoLA) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3396
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 1 | 0.3396 |
### Framework versions
- Transformers 4.36.1
- Pytorch 2.1.2+cpu
- Datasets 2.16.1
- Tokenizers 0.15.0
|
Sadiah/Gollum | Sadiah | 2024-03-27T00:34:10Z | 14 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"en",
"dataset:Sadiah/Gollum",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-18T04:20:36Z | ---
license: cc-by-nc-4.0
datasets:
- Sadiah/Gollum
language:
- en
library_name: transformers
---
# Gollum Tonality Fine-Tuned LLaMA-2 Model
<img src="https://cdn-uploads.huggingface.co/production/uploads/6564e76de6b20bc37e494589/wcj1pIDVKbhkyi_DBdAPV.png" width="600" alt="Gollum Tonality Fine-Tuned LLAMA-2 7B Model inference code">
## Overview
This model is a fine-tuned version of the LLAMA-2 7B model, specifically trained to generate responses with a tonality similar to the character Gollum from J.R.R. Tolkien's "The Lord of the Rings" series. The model has been fine-tuned using a dataset of Gollum's dialogue and text samples to capture his unique speaking style, mannerisms, and personality.
## Model Details
* **Base Model**: "NousResearch/Llama-2-7b-chat-hf"
* **Fine-Tuning Dataset**: Custom dataset of Gollum's dialogue and text samples.
* **Fine-Tuning Approach**: PEFT (LoRA) and SFT Trainer.
* **Model Size**: The model retains the same size and architecture as the original LLaMA model.
## Intended Use
The Gollum Tonality Fine-Tuned LLaMA Model is designed to generate responses and engage in conversations with a tonality and personality similar to the character Gollum. It can be used for various creative and entertainment purposes, such as:
* Generating Gollum-like dialogue for stories, fan fiction, or roleplaying scenarios
* Creating interactive chatbots or virtual assistants with Gollum's personality
* Enhancing natural language processing applications with a unique and recognizable tonality
## Limitations and Considerations
* The model's responses are generated based on patterns and characteristics learned from the fine-tuning dataset. While it aims to capture Gollum's tonality, the generated text may not always perfectly align with Gollum's canonical dialogue or behavior.
* The model may generate responses that are biased or inconsistent with Gollum's character at times, as it is still an AI language model and not a perfect replication of the original character.
* The generated text should be used responsibly and with awareness of its fictional nature. It should not be considered a substitute for professional writing or official "The Lord of the Rings" content.
## Inference Code
To test and interact with the Gollum Tonality Fine-Tuned LLaMA Model, you can use the following inference code:
```python
#Import necessary libraries
import torch
import transformers
# Load the Gollum model from hugging face
tokenizer = AutoTokenizer.from_pretrained("Sadiah/Gollum",trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Sadiah/Gollum",trust_remote_code=True,device_map= {"": 0})
# Define the input text for which you want to generate an answer
input_text = '''What is the best way to live life?[/INST]'''
# Tokenize the input text using a predefined tokenizer.
input_ids = tokenizer(input_text, return_tensors="pt")
# Move the tokenized input to GPU memory for faster processing by specifying `.to("cuda")`.
input_ids = input_ids.to("cuda")
# Generate output sequences (answers) from the input.
outputs = model.generate(**input_ids, max_length=100, num_return_sequences=1)
# Decode the generated output back to text. `outputs[0]` accesses the first (and only, in this case) sequence.
generated_text = tokenizer.decode(outputs[0])
# Stripping and cleaning the output
answer = generated_text.split("[/INST]")[1].strip()
answer = answer.replace("</s>", "").strip()
last_full_stop_pos = answer.rfind(".")
if last_full_stop_pos != -1:
answer = answer[:last_full_stop_pos + 1]
# Print the final, cleaned answer.
print(answer)
```
`Oh, precious, the best way to live, yes, yes. We listens to the wise, we does. First, we takes care of ourselves, yes. Then, we helps others, precious. We lives for the now, and for the future, yes. And always, always, we remembers the precious, yes. Live for the moments, and for the long, long days.`
This code snippet allows you to provide an input prompt and generate a response from the model. The generated text will aim to mimic Gollum's tonality and personality based on the fine-tuning process.
## Contact and Feedback
If you have any questions, feedback, or concerns regarding the Gollum Tonality Fine-Tuned LLaMA Model, please contact me https://www.sadiahzahoor.com/contact. |
Solshine/LORA-Adapters-Gemma2B-NaturalFarmerV2 | Solshine | 2024-03-27T00:33:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-27T00:33:25Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-2b-it-bnb-4bit
---
# Uploaded model
- **Developed by:** Solshine
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
00000-X/Dolphin-2.6-FC_Hermes-2-DPO | 00000-X | 2024-03-27T00:27:47Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser",
"NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"base_model:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"base_model:merge:NousResearch/Nous-Hermes-2-Mistral-7B-DPO",
"base_model:cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser",
"base_model:merge:cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-27T00:23:44Z | ---
tags:
- merge
- mergekit
- lazymergekit
- cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
base_model:
- cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
- NousResearch/Nous-Hermes-2-Mistral-7B-DPO
---
# Dolphin-2.6-FC_Hermes-2-DPO
Dolphin-2.6-FC_Hermes-2-DPO is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser](https://huggingface.co/cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser)
* [NousResearch/Nous-Hermes-2-Mistral-7B-DPO](https://huggingface.co/NousResearch/Nous-Hermes-2-Mistral-7B-DPO)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
layer_range: [0, 32]
- model: NousResearch/Nous-Hermes-2-Mistral-7B-DPO
layer_range: [0, 32]
merge_method: slerp
base_model: cognitivecomputations/fc-dolphin-2.6-mistral-7b-dpo-laser
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "00000-X/Dolphin-2.6-FC_Hermes-2-DPO"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
yzimmermann/FARTBERT_weighted | yzimmermann | 2024-03-27T00:26:27Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-27T00:09:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
deepnet/SN6-30M14 | deepnet | 2024-03-27T00:22:23Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-25T19:06:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
automerger/Experiment26Shadow-7B | automerger | 2024-03-27T00:13:18Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:CorticalStack/shadow-clown-7B-slerp",
"base_model:finetune:CorticalStack/shadow-clown-7B-slerp",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-08T18:56:36Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- rwitz/experiment26-truthy-iter-0
- CorticalStack/shadow-clown-7B-slerp
---
# Experiment26Shadow-7B
Experiment26Shadow-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [rwitz/experiment26-truthy-iter-0](https://huggingface.co/rwitz/experiment26-truthy-iter-0)
* [CorticalStack/shadow-clown-7B-slerp](https://huggingface.co/CorticalStack/shadow-clown-7B-slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: rwitz/experiment26-truthy-iter-0
layer_range: [0, 32]
- model: CorticalStack/shadow-clown-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: rwitz/experiment26-truthy-iter-0
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Experiment26Shadow-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Crystalcareai/GemMoE-Medium-V0.5 | Crystalcareai | 2024-03-27T00:05:26Z | 8 | 6 | transformers | [
"transformers",
"safetensors",
"gemmoe",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-03-24T03:34:46Z | ```python
import torch
from transformers import AutoTokenizer, TextStreamer, AutoModelForCausalLM
model_path = "Crystalcareai/GemMoE-Medium-v0.5"
# Load model
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
low_cpu_mem_usage=True,
torch_dtype=torch.float16,
attn_implementation="flash_attention_2",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_path)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
# Convert prompt to tokens
prompt_template = "[INST] {prompt} [/INST]"
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
tokens = tokenizer(
prompt_template.format(prompt=prompt),
return_tensors='pt'
).input_ids.cuda()
# Generate output
generation_output = model.generate(
tokens,
streamer=streamer,
max_new_tokens=512
)
``` |
rgao/distilbert-base-uncased-finetuned-emotion | rgao | 2024-03-27T00:04:55Z | 117 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-26T21:17:51Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5596
- Accuracy: 0.7714
- F1: 0.7538
## 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: 4e-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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.6146 | 1.0 | 31 | 0.5816 | 0.6857 | 0.6333 |
| 0.4054 | 2.0 | 62 | 0.5038 | 0.7643 | 0.7626 |
| 0.2892 | 3.0 | 93 | 0.5269 | 0.7714 | 0.7612 |
| 0.1895 | 4.0 | 124 | 0.5596 | 0.7714 | 0.7538 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Yntec/CinematicReality | Yntec | 2024-03-27T00:00:12Z | 2,751 | 2 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2023-08-05T06:55:25Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
inference: false
language:
- en
---
Warning: This model is horny! Add "nude, naked" to the negative prompt if want to avoid NSFW.
# TODO: Finish this model card. |
sachin2000keshav/falcon7binstruct | sachin2000keshav | 2024-03-26T23:30:56Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"base_model:adapter:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2024-03-26T23:25:58Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: vilsonrodrigues/falcon-7b-instruct-sharded
model-index:
- name: falcon7binstruct
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. -->
# falcon7binstruct
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) 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: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 51
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 |
imannrhman/code_llama_fixer-7b-Instruct | imannrhman | 2024-03-26T23:30:07Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T23:24:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
zelus82/Falbala-B7 | zelus82 | 2024-03-26T23:29:36Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"mistral",
"mergekit",
"merge",
"base_model:mlabonne/Marcoro14-7B-slerp",
"base_model:merge:mlabonne/Marcoro14-7B-slerp",
"base_model:mlabonne/NeuralDaredevil-7B",
"base_model:merge:mlabonne/NeuralDaredevil-7B",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2024-03-25T19:19:44Z | ---
base_model:
- mlabonne/NeuralDaredevil-7B
- mlabonne/Marcoro14-7B-slerp
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
---
<img src="falbala.png">
# zelus
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [mlabonne/NeuralDaredevil-7B](https://huggingface.co/mlabonne/NeuralDaredevil-7B)
* [mlabonne/Marcoro14-7B-slerp](https://huggingface.co/mlabonne/Marcoro14-7B-slerp)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: mlabonne/NeuralDaredevil-7B
layer_range: [0, 32]
- model: mlabonne/Marcoro14-7B-slerp
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/NeuralDaredevil-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``` |
Smuggling1710/InfinToppyKuno-DARE-7b | Smuggling1710 | 2024-03-26T23:27:29Z | 11 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Nitral-AI/Kunocchini-7b-128k-test",
"Endevor/InfinityRP-v1-7B",
"base_model:Endevor/InfinityRP-v1-7B",
"base_model:merge:Endevor/InfinityRP-v1-7B",
"base_model:Nitral-AI/Kunocchini-7b-128k-test",
"base_model:merge:Nitral-AI/Kunocchini-7b-128k-test",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T23:22:00Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Nitral-AI/Kunocchini-7b-128k-test
- Endevor/InfinityRP-v1-7B
base_model:
- Nitral-AI/Kunocchini-7b-128k-test
- Endevor/InfinityRP-v1-7B
- Endevor/InfinityRP-v1-7B
---
# InfinToppyKuno-DARE-7b
InfinToppyKuno-DARE-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Nitral-AI/Kunocchini-7b-128k-test](https://huggingface.co/Nitral-AI/Kunocchini-7b-128k-test)
* [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B)
* [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B)
## 🧩 Configuration
```yaml
models:
- model: SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE
# No parameters necessary for base model
- model: Nitral-AI/Kunocchini-7b-128k-test
parameters:
density: 0.53
weight: 0.4
- model: Endevor/InfinityRP-v1-7B
parameters:
density: 0.53
weight: 0.3
- model: Endevor/InfinityRP-v1-7B
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: SanjiWatsuki/Loyal-Toppy-Bruins-Maid-7B-DARE
parameters:
int8_mask: true
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Smuggling1710/InfinToppyKuno-DARE-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
BINARIAL/GALIA | BINARIAL | 2024-03-26T23:26:54Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T16:21:29Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
frandovi/vit-base-patch16-224-in21k-euroSat | frandovi | 2024-03-26T23:14:09Z | 63 | 0 | transformers | [
"transformers",
"tf",
"tensorboard",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-03-26T22:52:34Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: frandovi/vit-base-patch16-224-in21k-euroSat
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# frandovi/vit-base-patch16-224-in21k-euroSat
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.2068
- Train Accuracy: 0.9613
- Train Top-3-accuracy: 0.9903
- Validation Loss: 0.2501
- Validation Accuracy: 0.9650
- Validation Top-3-accuracy: 0.9913
- Epoch: 4
## 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:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 665, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Train Top-3-accuracy | Validation Loss | Validation Accuracy | Validation Top-3-accuracy | Epoch |
|:----------:|:--------------:|:--------------------:|:---------------:|:-------------------:|:-------------------------:|:-----:|
| 1.2723 | 0.6941 | 0.8604 | 0.6544 | 0.8643 | 0.9573 | 0 |
| 0.4646 | 0.9004 | 0.9707 | 0.4014 | 0.9216 | 0.9784 | 1 |
| 0.3004 | 0.9348 | 0.9825 | 0.2985 | 0.9446 | 0.9855 | 2 |
| 0.2351 | 0.9514 | 0.9875 | 0.2611 | 0.9570 | 0.9892 | 3 |
| 0.2068 | 0.9613 | 0.9903 | 0.2501 | 0.9650 | 0.9913 | 4 |
### Framework versions
- Transformers 4.39.1
- TensorFlow 2.15.0
- Datasets 2.18.0
- Tokenizers 0.15.2
|
NCI-all-topics/Rad_topic_modeling.ipynb | NCI-all-topics | 2024-03-26T23:13:01Z | 0 | 0 | null | [
"medical",
"en",
"license:mit",
"region:us"
] | null | 2024-03-26T03:14:09Z | ---
license: mit
language:
- en
tags:
- medical
---
This is the official page for the NCI all funding topics research group.
The model file can be found under the Files and versions tab.
Currently figuring out how to run the model on HuggingFace. |
barstow2/mistral-7b-empathy-finetuned | barstow2 | 2024-03-26T23:09:00Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T22:15:34Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
Omnifact/conditional-detr-resnet-101-dc5 | Omnifact | 2024-03-26T23:02:22Z | 325 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"conditional_detr",
"object-detection",
"vision",
"dataset:coco",
"arxiv:2108.06152",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-26T22:35:56Z | ---
license: apache-2.0
tags:
- object-detection
- vision
datasets:
- coco
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/savanna.jpg
example_title: Savanna
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/football-match.jpg
example_title: Football Match
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/airport.jpg
example_title: Airport
---
# Conditional DETR model with ResNet-101 backbone (dilated C5 stage)
_Note: The model weights were converted to the `transformers` implementation from the original weights and published as both PyTorch and Safetensors weights. The original weights can can be downloaded from the [original repository](https://github.com/Atten4Vis/ConditionalDETR)_
Conditional DEtection TRansformer (DETR) model trained end-to-end on COCO 2017 object detection (118k annotated images). It was introduced in the paper [Conditional DETR for Fast Training Convergence](https://arxiv.org/abs/2108.06152) by Meng et al. and first released in [this repository](https://github.com/Atten4Vis/ConditionalDETR).
## Model description
The recently-developed DETR approach applies the transformer encoder and decoder architecture to object detection and achieves promising performance. In this paper, we handle the critical issue, slow training convergence, and present a conditional cross-attention mechanism for fast DETR training. Our approach is motivated by that the cross-attention in DETR relies highly on the content embeddings for localizing the four extremities and predicting the box, which increases the need for high-quality content embeddings and thus the training difficulty. Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention. The benefit is that through the conditional spatial query, each cross-attention head is able to attend to a band containing a distinct region, e.g., one object extremity or a region inside the object box. This narrows down the spatial range for localizing the distinct regions for object classification and box regression, thus relaxing the dependence on the content embeddings and easing the training. Empirical results show that conditional DETR converges 6.7× faster for the backbones R50 and R101 and 10× faster for stronger backbones DC5-R50 and DC5-R101.
## Intended uses & limitations
You can use the raw model for object detection. See the [model hub](https://huggingface.co/models?search=conditional-detr) to look for all available Conditional DETR models.
### How to use
Here is how to use this model:
```python
from transformers import AutoImageProcessor, ConditionalDetrForObjectDetection
import torch
from PIL import Image
import requests
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
processor = AutoImageProcessor.from_pretrained("Omnifact/conditional-detr-resnet-101-dc5")
model = ConditionalDetrForObjectDetection.from_pretrained("Omnifact/conditional-detr-resnet-101-dc5")
inputs = processor(images=image, return_tensors="pt")
outputs = model(**inputs)
# convert outputs (bounding boxes and class logits) to COCO API
# let's only keep detections with score > 0.7
target_sizes = torch.tensor([image.size[::-1]])
results = processor.post_process_object_detection(outputs, target_sizes=target_sizes, threshold=0.7)[0]
for score, label, box in zip(results["scores"], results["labels"], results["boxes"]):
box = [round(i, 2) for i in box.tolist()]
print(
f"Detected {model.config.id2label[label.item()]} with confidence "
f"{round(score.item(), 3)} at location {box}"
)
```
This should output:
```
Detected cat with confidence 0.865 at location [13.95, 64.98, 327.14, 478.82]
Detected remote with confidence 0.849 at location [39.37, 83.18, 187.67, 125.02]
Detected cat with confidence 0.743 at location [327.22, 35.17, 637.54, 377.04]
Detected remote with confidence 0.737 at location [329.36, 89.47, 376.42, 197.53]
```
## Training data
The Conditional DETR model was trained on [COCO 2017 object detection](https://cocodataset.org/#download), a dataset consisting of 118k/5k annotated images for training/validation respectively.
### BibTeX entry and citation info
```bibtex
@inproceedings{MengCFZLYS021,
author = {Depu Meng and
Xiaokang Chen and
Zejia Fan and
Gang Zeng and
Houqiang Li and
Yuhui Yuan and
Lei Sun and
Jingdong Wang},
title = {Conditional {DETR} for Fast Training Convergence},
booktitle = {2021 {IEEE/CVF} International Conference on Computer Vision, {ICCV}
2021, Montreal, QC, Canada, October 10-17, 2021},
}
``` |
son-of-man/HoloSumika-7B-test | son-of-man | 2024-03-26T22:58:05Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"localfultonextractor/Erosumika-7B-v3-0.2",
"KoboldAI/Mistral-7B-Holodeck-1",
"base_model:KoboldAI/Mistral-7B-Holodeck-1",
"base_model:finetune:KoboldAI/Mistral-7B-Holodeck-1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T21:48:02Z | ---
tags:
- merge
- mergekit
- lazymergekit
- localfultonextractor/Erosumika-7B-v3-0.2
- KoboldAI/Mistral-7B-Holodeck-1
base_model:
- localfultonextractor/Erosumika-7B-v3-0.2
- KoboldAI/Mistral-7B-Holodeck-1
---
# HoloSumika-7B-test
first testing impression: this one seems u(n)s(t)able
HoloSumika-7B-test is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [localfultonextractor/Erosumika-7B-v3-0.2](https://huggingface.co/localfultonextractor/Erosumika-7B-v3-0.2)
* [KoboldAI/Mistral-7B-Holodeck-1](https://huggingface.co/KoboldAI/Mistral-7B-Holodeck-1)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: localfultonextractor/Erosumika-7B-v3-0.2
layer_range: [0, 32]
- model: KoboldAI/Mistral-7B-Holodeck-1
layer_range: [0, 32]
merge_method: slerp
base_model: localfultonextractor/Erosumika-7B-v3-0.2
parameters:
t:
- value: 0.32
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "son-of-man/HoloSumika-7B-test"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Smuggling1710/M1k4-7b-GUFF | Smuggling1710 | 2024-03-26T22:47:35Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-v0.2-bnb-4bit",
"base_model:quantized:unsloth/mistral-7b-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-03-26T22:44:57Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
base_model: unsloth/mistral-7b-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** Smuggling1710
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
whizzzzkid/gemma_CE_2 | whizzzzkid | 2024-03-26T22:46:07Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-18T21:36:17Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Smuggling1710/M1k4ri-7b | Smuggling1710 | 2024-03-26T22:44:11Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Smuggling1710/M1k4-7b",
"Smuggling1710/Ak4ri-7b",
"base_model:Smuggling1710/Ak4ri-7b",
"base_model:merge:Smuggling1710/Ak4ri-7b",
"base_model:Smuggling1710/M1k4-7b",
"base_model:merge:Smuggling1710/M1k4-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T22:37:28Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Smuggling1710/M1k4-7b
- Smuggling1710/Ak4ri-7b
base_model:
- Smuggling1710/M1k4-7b
- Smuggling1710/Ak4ri-7b
---
# M1k4ri-7b
M1k4ri-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Smuggling1710/M1k4-7b](https://huggingface.co/Smuggling1710/M1k4-7b)
* [Smuggling1710/Ak4ri-7b](https://huggingface.co/Smuggling1710/Ak4ri-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Smuggling1710/M1k4-7b
layer_range: [0, 32]
- model: Smuggling1710/Ak4ri-7b
layer_range: [0, 32]
merge_method: slerp
base_model: Smuggling1710/Ak4ri-7b
parameters:
t:
- filter: self_attn
value: [0.6, 0.5, 0.3, 0.7, 0.4]
- filter: mlp
value: [0.4, 0.5, 0.7, 0.3, 0.6]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Smuggling1710/M1k4ri-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
joshoch7/dqn-SpaceInvadersNoFrameskip-v4 | joshoch7 | 2024-03-26T22:43:19Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-26T22:42:44Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 788.00 +/- 299.22
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga joshoch7 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga joshoch7 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga joshoch7
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
ijwatson98/rlaif-gpt2-xsum-2603 | ijwatson98 | 2024-03-26T22:42:26Z | 164 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T22:41:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
yuiseki/tinyllama-es-wikipedia-aya-1.5T-v0.1 | yuiseki | 2024-03-26T22:37:48Z | 60 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T22:36:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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## How to Get Started with the Model
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Ubresearch/llama_new | Ubresearch | 2024-03-26T22:36:47Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-03-26T22:26:34Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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## Model Examination [optional]
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## Environmental Impact
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|
HachiML/myBit-Llama2-jp-127M-7 | HachiML | 2024-03-26T22:36:08Z | 51 | 0 | transformers | [
"transformers",
"safetensors",
"bit_llama",
"text-generation",
"generated_from_trainer",
"custom_code",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-03-26T11:59:42Z | ---
tags:
- generated_from_trainer
model-index:
- name: myBit-Llama2-jp-127M-7
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. -->
# myBit-Llama2-jp-127M-7
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 3.4019
- eval_runtime: 331.9456
- eval_samples_per_second: 644.826
- eval_steps_per_second: 6.718
- epoch: 0.29
- step: 12000
## 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: 0.0024
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.95) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5000
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 4.8361 | 0.05 | 2000 | 3.958198 |
| 3.7944 | 0.1 | 4000 | 3.703599 |
| 3.6334 | 0.15 | 6000 | 3.556618 |
| 3.5140 | 0.2 | 8000 | 3.475404 |
| 3.4559 | 0.25 | 10000 | 3.428459 |
| 3.4183 | 0.29 | 12000 | 3.401942 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
- mybitnet 0.5.0
|
MarsupialAI/SkunkApe-16b | MarsupialAI | 2024-03-26T22:34:06Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"solar",
"rotating-stack-merge",
"en",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-23T18:06:44Z | ---
license: cc-by-nc-4.0
language:
- en
tags:
- solar
- rotating-stack-merge
---
# Skunk Ape 16b
## This model is depricated. The 14b is a substantial improvement: https://huggingface.co/MarsupialAI/SkunkApe-14b
This model is a rotating-stack merge of three Solar-based models in a 16b (72 layer) configuration. The result of
this "frankenmerge" is a medium-sized model that contains what I consider to be the best of the solar finetunes.
Mergefuel:
- Sao10K/Fimbulvetr-11B-v2
- Sao10K/Solstice-11B-v1
- TheDrummer/Moistral-11B-v1
This model is uncensored and capable of generating objectionable material. However, it is not an explicitely-NSFW model,
and it has never "gone rogue" and tried to insert NSFW content into SFW prompts in my experience. As with any LLM, no
factual claims made by the model should be taken at face value. You know that boilerplate safety disclaimer that most
professional models have? Assume this has it too. This model is for entertainment purposes only.
iMatrix GGUFs: https://huggingface.co/MarsupialAI/SkunkApe-16b_iMatrix_GGUF
# Sample output
```
{{[INPUT]}}
Write a detailed and humorous story about a cute and fluffy bunny that goes to a Gwar concert.
{{[OUTPUT]}}
The sun was shining brightly on a warm summer afternoon, and all across the city the sound of joyful chirping birds echoed through the air. A young rabbit hopped out from his burrow to join the many creatures who lived in the woods in their usual fun-filled day. The rabbit was very excited; he was going to the big city today to see one of his favourite bands in the entire world perform live – Gwar! It wasn't every day he got to experience the heavy metal brutality and over-the-top spectacle that the alien reptile beasts brought to their shows. He put on a t-shirt announcing his love for them, grabbed his camera and headed off down the path from the safety of the woods towards the concrete jungle where the gig was going to happen.
He was so distracted by thoughts of the band that he never even noticed when he passed into the city limits. A short time later he found himself walking through the bustling streets of town, totally bewildered by everything around him. The rabbit had been to the city a couple of times before but never alone, and certainly not at this hour. His ears twitched nervously as he moved through crowds of loud, shouting, cursing human beings, feeling like an innocent creature lost in a nightmare. He tried to follow the banner advertising 'GWAR' in flashing red letters, weaving his way through the hurrying mob of humans.
When he reached the general area, a thick smell of exhaust fumes and vomit hit him. A few teenagers laughed at him, probably because he didn't belong. The rabbit's ears drooped sadly and he started to regret leaving the comforts of the woods, considering turning around and fleeing back home. But then he saw it: a seedy club lit up with neon signs and spray-pitted walls, perfect. It had a worn-out sign above the door announcing, 'The Pussy Melter'.
He swallowed hard, then scurried up the steps of the crumbling building's broken concrete stairs. Inside was a small lobby with a metal bar and some ripped vinyl seats. There were sticky spots everywhere from previous drunken revelers. The rabbit made his way up to the counter, breathing a sigh of relief when nobody shouted at or attacked him. He grinned proudly when the bored bartender nodded towards the entrance. "Show's in the back."
"Thanks," the rabbit chirped, handing over the five bucks the bartender wanted for the ticket.
"You're really pushing your lucksies, buddy," the bartender sneered. "There's a strict no-furry suit policy back there."
The rabbit blinked in confusion. A few other humans standing in line laughed. Furious, the little rabbit took out his camera, ready to take pictures if they dared hassle him. The bouncer snatched the camera away. "No pictures!" he barked. The rabbit opened his mouth to protest, but he changed his mind when he saw the big man's muscles. He reluctantly handed over his camera and stuffed it in his pocket. He crept inside the club feeling very vulnerable, leaving his fluffy tail in the rack by the door, not wanting to cause a scene.
<<<This goes on for a while. See sample.txt for full output>>>
```
# Prompt format
Prefers alpaca.
# WTF is a rotating-stack merge?
Inspired by Undi's experiments with stacked merges, Jeb Carter found that output quality and model initiative could be significantly
improved by reversing the model order in the stack, and then doing a linear merge between the original and reversed stacks. That is
what I did here. I created three passthrough stacked merges using the three source models (rotating the model order in each stack),
and then doing a linear merge of all three stacks. The exact merge configs can be found in the recipe.txt file. |
pbevan11/stable-diffusion-2-typography | pbevan11 | 2024-03-26T22:33:25Z | 20 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:2112.10752",
"arxiv:2202.00512",
"arxiv:1910.09700",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-22T14:57:58Z | ---
license: openrail++
---
This is a finetuned version of [stabilityai/stable-diffusion-2-base](https://huggingface.co/stabilityai/stable-diffusion-2-base), optimised for outputting English text.
The model is finetuned for 20 epochs on [pbevan11/GPT4V-captions-from-LVIS-typography](https://huggingface.co/datasets/pbevan11/GPT4V-captions-from-LVIS-typography), a curated dataset of image-caption pairs from LVIS with detailed transcriptions of the text present in the image. We trained with a learning rate of 5e-6.
---
# Citation
```
@misc {peter_j._bevan_2024,
author = { {Peter J. Bevan} },
title = { GPT4V-captions-from-LVIS-typography (Revision 379a5f2) },
year = 2024,
url = { https://huggingface.co/datasets/pbevan11/GPT4V-captions-from-LVIS-typography },
doi = { 10.57967/hf/1945 },
publisher = { Hugging Face }
}
```
---
ORIGINAL MODEL CARD BELOW:
---
# Stable Diffusion v2-base Model Card
This model card focuses on the model associated with the Stable Diffusion v2-base model, available [here](https://github.com/Stability-AI/stablediffusion).
The model is trained from scratch 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`. Then it is further trained for 850k steps at resolution `512x512` on the same dataset on images with resolution `>= 512x512`.
- Use it with the [`stablediffusion`](https://github.com/Stability-AI/stablediffusion) repository: download the `512-base-ema.ckpt` [here](https://huggingface.co/stabilityai/stable-diffusion-2-base/resolve/main/512-base-ema.ckpt).
- Use it with 🧨 [`diffusers`](https://huggingface.co/stabilityai/stable-diffusion-2-base#examples)
## Model Details
- **Developed by:** Robin Rombach, Patrick Esser
- **Model type:** Diffusion-based text-to-image generation model
- **Language(s):** English
- **License:** [CreativeML Open RAIL++-M License](https://huggingface.co/stabilityai/stable-diffusion-2/blob/main/LICENSE-MODEL)
- **Model Description:** This is a model that can be used to generate and modify images based on text prompts. It is a [Latent Diffusion Model](https://arxiv.org/abs/2112.10752) that uses a fixed, pretrained text encoder ([OpenCLIP-ViT/H](https://github.com/mlfoundations/open_clip)).
- **Resources for more information:** [GitHub Repository](https://github.com/Stability-AI/).
- **Cite as:**
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
## Examples
Using the [🤗's Diffusers library](https://github.com/huggingface/diffusers) to run Stable Diffusion 2 in a simple and efficient manner.
```bash
pip install diffusers transformers accelerate scipy safetensors
```
Running the pipeline (if you don't swap the scheduler it will run with the default PNDM/PLMS scheduler, in this example we are swapping it to EulerDiscreteScheduler):
```python
from diffusers import StableDiffusionPipeline, EulerDiscreteScheduler
import torch
model_id = "stabilityai/stable-diffusion-2-base"
# Use the Euler scheduler here instead
scheduler = EulerDiscreteScheduler.from_pretrained(model_id, subfolder="scheduler")
pipe = StableDiffusionPipeline.from_pretrained(model_id, scheduler=scheduler, torch_dtype=torch.float16)
pipe = pipe.to("cuda")
prompt = "a photo of an astronaut riding a horse on mars"
image = pipe(prompt).images[0]
image.save("astronaut_rides_horse.png")
```
**Notes**:
- Despite not being a dependency, we highly recommend you to install [xformers](https://github.com/facebookresearch/xformers) for memory efficient attention (better performance)
- If you have low GPU RAM available, make sure to add a `pipe.enable_attention_slicing()` after sending it to `cuda` for less VRAM usage (to the cost of speed)
# Uses
## Direct Use
The model is intended for research purposes only. Possible research areas and tasks include
- Safe deployment of models which have the potential to generate harmful content.
- Probing and understanding the limitations and biases of generative models.
- Generation of artworks and use in design and other artistic processes.
- Applications in educational or creative tools.
- Research on generative models.
Excluded uses are described below.
### Misuse, Malicious Use, and Out-of-Scope Use
_Note: This section is originally taken from the [DALLE-MINI model card](https://huggingface.co/dalle-mini/dalle-mini), was used for Stable Diffusion v1, but applies in the same way to Stable Diffusion v2_.
The model should not be used to intentionally create or disseminate images that create hostile or alienating environments for people. This includes generating images that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
#### Out-of-Scope Use
The model was not trained to be factual or true representations of people or events, and therefore using the model to generate such content is out-of-scope for the abilities of this model.
#### Misuse and Malicious Use
Using the model to generate content that is cruel to individuals is a misuse of this model. This includes, but is not limited to:
- Generating demeaning, dehumanizing, or otherwise harmful representations of people or their environments, cultures, religions, etc.
- Intentionally promoting or propagating discriminatory content or harmful stereotypes.
- Impersonating individuals without their consent.
- Sexual content without consent of the people who might see it.
- Mis- and disinformation
- Representations of egregious violence and gore
- Sharing of copyrighted or licensed material in violation of its terms of use.
- Sharing content that is an alteration of copyrighted or licensed material in violation of its terms of use.
## Limitations and Bias
### Limitations
- The model does not achieve perfect photorealism
- The model cannot render legible text
- The model does not perform well on more difficult tasks which involve compositionality, such as rendering an image corresponding to “A red cube on top of a blue sphere”
- Faces and people in general may not be generated properly.
- The model was trained mainly with English captions and will not work as well in other languages.
- The autoencoding part of the model is lossy
- The model was trained on a subset of the large-scale dataset
[LAION-5B](https://laion.ai/blog/laion-5b/), which contains adult, violent and sexual content. To partially mitigate this, we have filtered the dataset using LAION's NFSW detector (see Training section).
### Bias
While the capabilities of image generation models are impressive, they can also reinforce or exacerbate social biases.
Stable Diffusion vw was primarily trained on subsets of [LAION-2B(en)](https://laion.ai/blog/laion-5b/),
which consists of images that are limited to English descriptions.
Texts and images from communities and cultures that use other languages are likely to be insufficiently accounted for.
This affects the overall output of the model, as white and western cultures are often set as the default. Further, the
ability of the model to generate content with non-English prompts is significantly worse than with English-language prompts.
Stable Diffusion v2 mirrors and exacerbates biases to such a degree that viewer discretion must be advised irrespective of the input or its intent.
## Training
**Training Data**
The model developers used the following dataset for training the model:
- LAION-5B and subsets (details below). The training data is further filtered using LAION's NSFW detector, with a "p_unsafe" score of 0.1 (conservative). For more details, please refer to LAION-5B's [NeurIPS 2022](https://openreview.net/forum?id=M3Y74vmsMcY) paper and reviewer discussions on the topic.
**Training Procedure**
Stable Diffusion v2 is a latent diffusion model which combines an autoencoder with a diffusion model that is trained in the latent space of the autoencoder. During training,
- Images are encoded through an encoder, which turns images into latent representations. The autoencoder uses a relative downsampling factor of 8 and maps images of shape H x W x 3 to latents of shape H/f x W/f x 4
- Text prompts are encoded through the OpenCLIP-ViT/H text-encoder.
- The output of the text encoder is fed into the UNet backbone of the latent diffusion model via cross-attention.
- The loss is a reconstruction objective between the noise that was added to the latent and the prediction made by the UNet. We also use the so-called _v-objective_, see https://arxiv.org/abs/2202.00512.
We currently provide the following checkpoints:
- `512-base-ema.ckpt`: 550k steps at resolution `256x256` on a subset of [LAION-5B](https://laion.ai/blog/laion-5b/) filtered for explicit pornographic material, using the [LAION-NSFW classifier](https://github.com/LAION-AI/CLIP-based-NSFW-Detector) with `punsafe=0.1` and an [aesthetic score](https://github.com/christophschuhmann/improved-aesthetic-predictor) >= `4.5`.
850k steps at resolution `512x512` on the same dataset with resolution `>= 512x512`.
- `768-v-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for 150k steps using a [v-objective](https://arxiv.org/abs/2202.00512) on the same dataset. Resumed for another 140k steps on a `768x768` subset of our dataset.
- `512-depth-ema.ckpt`: Resumed from `512-base-ema.ckpt` and finetuned for 200k steps. Added an extra input channel to process the (relative) depth prediction produced by [MiDaS](https://github.com/isl-org/MiDaS) (`dpt_hybrid`) which is used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized.
- `512-inpainting-ema.ckpt`: Resumed from `512-base-ema.ckpt` and trained for another 200k steps. Follows the mask-generation strategy presented in [LAMA](https://github.com/saic-mdal/lama) which, in combination with the latent VAE representations of the masked image, are used as an additional conditioning.
The additional input channels of the U-Net which process this extra information were zero-initialized. The same strategy was used to train the [1.5-inpainting checkpoint](https://github.com/saic-mdal/lama).
- `x4-upscaling-ema.ckpt`: Trained for 1.25M steps on a 10M subset of LAION containing images `>2048x2048`. The model was trained on crops of size `512x512` and is a text-guided [latent upscaling diffusion model](https://arxiv.org/abs/2112.10752).
In addition to the textual input, it receives a `noise_level` as an input parameter, which can be used to add noise to the low-resolution input according to a [predefined diffusion schedule](configs/stable-diffusion/x4-upscaling.yaml).
- **Hardware:** 32 x 8 x A100 GPUs
- **Optimizer:** AdamW
- **Gradient Accumulations**: 1
- **Batch:** 32 x 8 x 2 x 4 = 2048
- **Learning rate:** warmup to 0.0001 for 10,000 steps and then kept constant
## Evaluation Results
Evaluations with different classifier-free guidance scales (1.5, 2.0, 3.0, 4.0,
5.0, 6.0, 7.0, 8.0) and 50 steps DDIM sampling steps show the relative improvements of the checkpoints:
Evaluated using 50 DDIM steps and 10000 random prompts from the COCO2017 validation set, evaluated at 512x512 resolution. Not optimized for FID scores.
## Environmental Impact
**Stable Diffusion v1** **Estimated Emissions**
Based on that information, we estimate the following CO2 emissions using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). The hardware, runtime, cloud provider, and compute region were utilized to estimate the carbon impact.
- **Hardware Type:** A100 PCIe 40GB
- **Hours used:** 200000
- **Cloud Provider:** AWS
- **Compute Region:** US-east
- **Carbon Emitted (Power consumption x Time x Carbon produced based on location of power grid):** 15000 kg CO2 eq.
## Citation
@InProceedings{Rombach_2022_CVPR,
author = {Rombach, Robin and Blattmann, Andreas and Lorenz, Dominik and Esser, Patrick and Ommer, Bj\"orn},
title = {High-Resolution Image Synthesis With Latent Diffusion Models},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022},
pages = {10684-10695}
}
*This model card was written by: Robin Rombach, Patrick Esser and David Ha and is based on the [Stable Diffusion v1](https://github.com/CompVis/stable-diffusion/blob/main/Stable_Diffusion_v1_Model_Card.md) and [DALL-E Mini model card](https://huggingface.co/dalle-mini/dalle-mini).* |
VH1213141516/LAT_3-20_sweeps_pgd_layers_8_time_limit_6000 | VH1213141516 | 2024-03-26T22:31:32Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-26T22:31:27Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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VH1213141516/LAT_3-20_sweeps_pgd_layers_4_8_16_time_limit_6000 | VH1213141516 | 2024-03-26T22:31:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-26T22:31:07Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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VH1213141516/LAT_3-20_sweeps_pgd_layers_4_5_time_limit_6000 | VH1213141516 | 2024-03-26T22:31:07Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-26T22:31:02Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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VH1213141516/LAT_3-20_sweeps_pgd_layers_16_time_limit_6000 | VH1213141516 | 2024-03-26T22:30:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-26T22:30:44Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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VH1213141516/LAT_3-20_sweeps_pgd_layers_0_1_2_3_4_5_6_7_8_9_10_11_12_13_14_15_time_limit_6000 | VH1213141516 | 2024-03-26T22:30:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-26T22:30:27Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- PEFT 0.8.2 |
VH1213141516/LAT_3-20_sweeps_pgd_layers_0_16_time_limit_6000 | VH1213141516 | 2024-03-26T22:30:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-03-26T22:30:21Z | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.8.2 |
balakhonoff/solidity_security_model_v3 | balakhonoff | 2024-03-26T22:25:01Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:adapter:mistralai/Mistral-7B-v0.1",
"region:us"
] | null | 2024-03-26T22:24:39Z | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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### Framework versions
- PEFT 0.10.0 |
Weni/WeniGPT-QA-Zephyr-7B-3.0.0-SFT | Weni | 2024-03-26T22:21:02Z | 0 | 0 | trl | [
"trl",
"safetensors",
"SFT",
"WeniGPT",
"pt",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:finetune:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2024-03-25T23:27:43Z | ---
license: mit
library_name: "trl"
tags:
- SFT
- WeniGPT
base_model: HuggingFaceH4/zephyr-7b-beta
model-index:
- name: Weni/WeniGPT-QA-Zephyr-7B-3.0.0-SFT
results: []
language: ['pt']
---
# Weni/WeniGPT-QA-Zephyr-7B-3.0.0-SFT
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta] on the dataset Weni/WeniGPT-QA-Binarized-1.2.0 with the SFT trainer. It is part of the WeniGPT project for [Weni](https://weni.ai/).
It achieves the following results on the evaluation set:
{'eval_loss': 1.2232297658920288, 'eval_runtime': 84.7619, 'eval_samples_per_second': 2.761, 'eval_steps_per_second': 0.354, 'epoch': 2.92}
## Intended uses & limitations
This model has not been trained to avoid specific intructions.
## Training procedure
Finetuning was done on the model HuggingFaceH4/zephyr-7b-beta with the following prompt:
```
---------------------
Portuguese:
<|system|>
Você é um médico tratando um paciente com amnésia. Para responder as perguntas do paciente, você irá ler um texto anteriormente para se contextualizar. Se você trouxer informações desconhecidas, fora do texto lido, poderá deixar o paciente confuso. Se o paciente fizer uma questão sobre informações não presentes no texto, você precisa responder de forma educada que você não tem informação suficiente para responder, pois se tentar responder, pode trazer informações que não ajudarão o paciente recuperar sua memória. Lembre, se não estiver no texto, você precisa responder de forma educada que você não tem informação suficiente para responder. Precisamos ajudar o paciente.
Contexto: {context}</s>
<|user|>
{question}</s>
<|assistant|>
{chosen_response}</s>
---------------------
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- per_device_train_batch_size: 2
- per_device_eval_batch_size: 2
- gradient_accumulation_steps: 8
- num_gpus: 4
- total_train_batch_size: 64
- optimizer: AdamW
- lr_scheduler_type: constant_with_warmup
- num_steps: 96
- quantization_type: bitsandbytes
- LoRA: ("\n - bits: 4\n - use_exllama: True\n - device_map: auto\n - use_cache: False\n - lora_r: 32\n - lora_alpha: 64\n - lora_dropout: 0.05\n - bias: none\n - target_modules: ['q_proj', 'k_proj', 'v_proj', 'o_proj']\n - task_type: CAUSAL_LM",)
### Training results
### Framework versions
- transformers==4.39.1
- datasets==2.18.0
- peft==0.10.0
- safetensors==0.4.2
- evaluate==0.4.1
- bitsandbytes==0.43
- huggingface_hub==0.20.3
- seqeval==1.2.2
- optimum==1.17.1
- auto-gptq==0.7.1
- gpustat==1.1.1
- deepspeed==0.14.0
- wandb==0.16.3
- trl==0.8.1
- accelerate==0.28.0
- coloredlogs==15.0.1
- traitlets==5.14.1
- autoawq@https://github.com/casper-hansen/AutoAWQ/releases/download/v0.2.0/autoawq-0.2.0+cu118-cp310-cp310-linux_x86_64.whl
### Hardware
- Cloud provided: runpod.io
|
deepnet/SN6-67M14 | deepnet | 2024-03-26T22:20:54Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T15:52:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
alexgulliver/q-FrozenLake-v1 | alexgulliver | 2024-03-26T22:19:10Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-26T22:19:08Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1
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="alexgulliver/q-FrozenLake-v1", 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"])
```
|
ggwiebe/legitfin_llama3b | ggwiebe | 2024-03-26T22:15:17Z | 0 | 0 | peft | [
"peft",
"pytorch",
"llama",
"region:us"
] | null | 2024-03-26T20:12:49Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
|
SuccubusBot/danbooru_tags_classifier-v0.1 | SuccubusBot | 2024-03-26T22:15:14Z | 142 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-26T21:08:04Z | ---
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: danbooru_tags_classifier-v0.1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# danbooru_tags_classifier-v0.1
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2765
- Accuracy: 0.8876
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.3226 | 1.0 | 1635 | 0.3118 | 0.8733 |
| 0.1908 | 2.0 | 3270 | 0.2713 | 0.8876 |
| 0.2048 | 3.0 | 4905 | 0.2765 | 0.8876 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
eventdata-utd/conflibert-binary-classification | eventdata-utd | 2024-03-26T22:11:29Z | 145 | 2 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-05T22:18:08Z | ---
license: gpl-3.0
---
# Model Card for Model ID
Conflibert-binary-classification is built upon the foundational Conflibert model. Through rigorous fine-tuning, this enhanced model demonstrates superior
capabilities in classifying between conflict and non-conflict events.
- **Finetuned from model :** [eventdata-utd/ConfliBERT-scr-uncased](https://huggingface.co/eventdata-utd/ConfliBERT-scr-uncased)
- **Paper :** [ConfliBERT: A Pre-trained Language Model for Political Conflict and Violence](https://aclanthology.org/2022.naacl-main.400.pdf)
- **Demo :** [Colab Notebook](https://colab.research.google.com/drive/1asD_z6RplGVAiFUMZN6-kr7jZXGXhLgr#scrollTo=MrIFOrH2nEmN)
|
Yuma42/KangalKhan-PolishedRuby-7B | Yuma42 | 2024-03-26T22:01:25Z | 48 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Yuma42/KangalKhan-Ruby-7B-Fixed",
"Yuma42/KangalKhan-PressurizedRuby-7B",
"conversational",
"en",
"base_model:Yuma42/KangalKhan-PressurizedRuby-7B",
"base_model:merge:Yuma42/KangalKhan-PressurizedRuby-7B",
"base_model:Yuma42/KangalKhan-Ruby-7B-Fixed",
"base_model:merge:Yuma42/KangalKhan-Ruby-7B-Fixed",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T21:54:39Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Yuma42/KangalKhan-Ruby-7B-Fixed
- Yuma42/KangalKhan-PressurizedRuby-7B
base_model:
- Yuma42/KangalKhan-Ruby-7B-Fixed
- Yuma42/KangalKhan-PressurizedRuby-7B
license: apache-2.0
language:
- en
---
# KangalKhan-PolishedRuby-7B
KangalKhan-PolishedRuby-7B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Yuma42/KangalKhan-Ruby-7B-Fixed](https://huggingface.co/Yuma42/KangalKhan-Ruby-7B-Fixed)
* [Yuma42/KangalKhan-PressurizedRuby-7B](https://huggingface.co/Yuma42/KangalKhan-PressurizedRuby-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Yuma42/KangalKhan-Ruby-7B-Fixed
layer_range: [0, 32]
- model: Yuma42/KangalKhan-PressurizedRuby-7B
layer_range: [0, 32]
merge_method: slerp
base_model: Yuma42/KangalKhan-Ruby-7B-Fixed
parameters:
t:
- filter: self_attn
value: [0.1, 0.55, 0.35, 0.75, 0.97]
- filter: mlp
value: [0.9, 0.45, 0.65, 0.25, 0.03]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Yuma42/KangalKhan-PolishedRuby-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
pruhtopia/bart-toc-generation | pruhtopia | 2024-03-26T22:00:20Z | 108 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bart",
"text2text-generation",
"autotrain",
"dataset:pruhtopia/falcon-toc-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-02-05T21:37:50Z | ---
tags:
- autotrain
- text2text-generation
widget:
- text: I love AutoTrain
datasets:
- pruhtopia/falcon-toc-generation
---
# Model Trained Using AutoTrain
- Problem type: Seq2Seq
- Example usage + dataset construction notebook: [here](https://colab.research.google.com/drive/1T8VLohQcI2YKFntN1ZZG0Vr1T4WAII5D?usp=sharing)
## Validation Metrics
loss: 0.20580817759037018
rouge1: 54.3657
rouge2: 43.8004
rougeL: 50.056
rougeLsum: 53.9699
gen_len: 116.6083
runtime: 450.7126
samples_per_second: 0.266
steps_per_second: 0.033
: 3.0 |
harsh-patel/NeuralPipe-7B-slerp | harsh-patel | 2024-03-26T21:59:39Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:merge:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:merge:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T21:55:21Z | ---
tags:
- merge
- mergekit
- lazymergekit
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
base_model:
- OpenPipe/mistral-ft-optimized-1218
- mlabonne/NeuralHermes-2.5-Mistral-7B
---
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "harsh-patel/NeuralPipe-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
TieIncred/verizon_model1 | TieIncred | 2024-03-26T21:58:52Z | 98 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-26T21:32:08Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: verizon_model1
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. -->
# verizon_model1
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0242
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 1.458 | 1.0 | 8 | 1.1774 | 0.7451 | 0.6817 |
| 1.1574 | 2.0 | 16 | 0.8376 | 0.7843 | 0.6934 |
| 0.8281 | 3.0 | 24 | 0.6155 | 0.8627 | 0.8055 |
| 0.6272 | 4.0 | 32 | 0.4462 | 0.8824 | 0.8493 |
| 0.4532 | 5.0 | 40 | 0.3344 | 0.9216 | 0.9111 |
| 0.3607 | 6.0 | 48 | 0.2535 | 1.0 | 1.0 |
| 0.2153 | 7.0 | 56 | 0.1961 | 0.9804 | 0.9800 |
| 0.1704 | 8.0 | 64 | 0.1489 | 1.0 | 1.0 |
| 0.1238 | 9.0 | 72 | 0.1116 | 1.0 | 1.0 |
| 0.0998 | 10.0 | 80 | 0.0841 | 1.0 | 1.0 |
| 0.097 | 11.0 | 88 | 0.0642 | 1.0 | 1.0 |
| 0.0751 | 12.0 | 96 | 0.0510 | 1.0 | 1.0 |
| 0.0583 | 13.0 | 104 | 0.0421 | 1.0 | 1.0 |
| 0.0422 | 14.0 | 112 | 0.0350 | 1.0 | 1.0 |
| 0.037 | 15.0 | 120 | 0.0307 | 1.0 | 1.0 |
| 0.0354 | 16.0 | 128 | 0.0282 | 1.0 | 1.0 |
| 0.0336 | 17.0 | 136 | 0.0265 | 1.0 | 1.0 |
| 0.0316 | 18.0 | 144 | 0.0252 | 1.0 | 1.0 |
| 0.0341 | 19.0 | 152 | 0.0244 | 1.0 | 1.0 |
| 0.027 | 20.0 | 160 | 0.0242 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
ai-maker-space/fine-tuned-elon-complaints | ai-maker-space | 2024-03-26T21:52:51Z | 2 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-03-26T21:52:40Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# c-s-ale/fine-tuned-elon-complaints
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('c-s-ale/fine-tuned-elon-complaints')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=c-s-ale/fine-tuned-elon-complaints)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 11 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 11,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
wongofwong/detr-resnet-50_finetuned_cppe5 | wongofwong | 2024-03-26T21:52:03Z | 196 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"base_model:finetune:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-03-25T18:48:20Z | ---
license: apache-2.0
base_model: facebook/detr-resnet-50
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50_finetuned_cppe5
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. -->
# detr-resnet-50_finetuned_cppe5
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-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: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
mlx-community/ilsp-Meltemi-7B-Instruct-v1-4bit | mlx-community | 2024-03-26T21:48:21Z | 7 | 0 | mlx | [
"mlx",
"safetensors",
"mistral",
"finetuned",
"text-generation",
"conversational",
"el",
"en",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-03-26T21:46:17Z | ---
language:
- el
- en
license: apache-2.0
tags:
- finetuned
- mlx
inference: true
pipeline_tag: text-generation
---
# mlx-community/ilsp-Meltemi-7B-Instruct-v1-4bit
This model was converted to MLX format from [`ilsp/Meltemi-7B-Instruct-v1`]() using mlx-lm version **0.4.0**.
Refer to the [original model card](https://huggingface.co/ilsp/Meltemi-7B-Instruct-v1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/ilsp-Meltemi-7B-Instruct-v1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
Smuggling1710/M1k4-7b | Smuggling1710 | 2024-03-26T21:42:40Z | 2 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T21:36:22Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
base_model: unsloth/mistral-7b-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** Smuggling1710
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
onurio/musicgen-large | onurio | 2024-03-26T21:34:51Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"musicgen",
"text-to-audio",
"audiocraft",
"arxiv:2306.05284",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-03-26T01:44:25Z | ---
inference: true
tags:
- musicgen
- audiocraft
library_name: transformers
license: cc-by-nc-4.0
---
# MusicGen - Stereo - Large - 3.3B
We further release a set of stereophonic capable models. Those were fine tuned for 200k updates starting
from the mono models. The training data is otherwise identical and capabilities and limitations are shared with the base modes. The stereo models work by getting 2 streams of tokens from the EnCodec model, and interleaving those using
the delay pattern.
Stereophonic sound, also known as stereo, is a technique used to reproduce sound with depth and direction.
It uses two separate audio channels played through speakers (or headphones), which creates the impression of sound coming from multiple directions.
MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts.
It is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz.
Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass.
By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio.
MusicGen was published in [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284) by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*.
We provide a simple API and 10 pre-trained models. The pre trained models are:
- `facebook/musicgen-small`: 300M model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-small)
- `facebook/musicgen-medium`: 1.5B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-medium)
- `facebook/musicgen-melody`: 1.5B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody)
- `facebook/musicgen-large`: 3.3B model, text to music only - [🤗 Hub](https://huggingface.co/facebook/musicgen-large)
- `facebook/musicgen-melody-large`: 3.3B model, text to music and text+melody to music - [🤗 Hub](https://huggingface.co/facebook/musicgen-melody-large)
- `facebook/musicgen-stereo-*`: All the previous models fine-tuned for stereo generation -
[small](https://huggingface.co/facebook/musicgen-stereo-small),
[medium](https://huggingface.co/facebook/musicgen-stereo-medium),
[large](https://huggingface.co/facebook/musicgen-stereo-large),
[melody](https://huggingface.co/facebook/musicgen-stereo-melody),
[melody large](https://huggingface.co/facebook/musicgen-stereo-melody-large)
## Example
Try out MusicGen yourself!
* Audiocraft Colab:
<a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
* Hugging Face Colab:
<a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb">
<img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
</a>
* Hugging Face Demo:
<a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
<img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
</a>
## 🤗 Transformers Usage
You can run MusicGen Stereo models locally with the 🤗 Transformers library from `main` onward.
1. First install the 🤗 [Transformers library](https://github.com/huggingface/transformers) and scipy:
```
pip install --upgrade pip
pip install --upgrade git+https://github.com/huggingface/transformers.git scipy
```
2. Run inference via the `Text-to-Audio` (TTA) pipeline. You can infer the MusicGen model via the TTA pipeline in just a few lines of code!
```python
import torch
import soundfile as sf
from transformers import pipeline
synthesiser = pipeline("text-to-audio", "facebook/musicgen-stereo-small", device="cuda:0", torch_dtype=torch.float16)
music = synthesiser("lo-fi music with a soothing melody", forward_params={"max_new_tokens": 256})
sf.write("musicgen_out.wav", music["audio"][0].T, music["sampling_rate"])
```
3. Run inference via the Transformers modelling code. You can use the processor + generate code to convert text into a mono 32 kHz audio waveform for more fine-grained control.
```python
from transformers import AutoProcessor, MusicgenForConditionalGeneration
processor = AutoProcessor.from_pretrained("facebook/musicgen-stereo-large")
model = MusicgenForConditionalGeneration.from_pretrained("facebook/musicgen-stereo-large").to("cuda")
inputs = processor(
text=["80s pop track with bassy drums and synth", "90s rock song with loud guitars and heavy drums"],
padding=True,
return_tensors="pt",
).to("cuda")
audio_values = model.generate(**inputs, max_new_tokens=256)
```
4. Listen to the audio samples either in an ipynb notebook:
```python
from IPython.display import Audio
sampling_rate = model.config.audio_encoder.sampling_rate
Audio(audio_values[0].cpu().numpy(), rate=sampling_rate)
```
Or save them as a `.wav` file using a third-party library, e.g. `soundfile`:
```python
import soundfile as sf
sampling_rate = model.config.audio_encoder.sampling_rate
audio_values = audio_values.cpu().numpy()
sf.write("musicgen_out.wav", audio_values[0].T, sampling_rate)
```
For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen).
## Audiocraft Usage
You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):
1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
```
pip install git+https://github.com/facebookresearch/audiocraft.git
```
2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed:
```
apt get install ffmpeg
```
3. Run the following Python code:
```py
from audiocraft.models import MusicGen
from audiocraft.data.audio import audio_write
model = MusicGen.get_pretrained("large")
model.set_generation_params(duration=8) # generate 8 seconds.
descriptions = ["happy rock", "energetic EDM"]
wav = model.generate(descriptions) # generates 2 samples.
for idx, one_wav in enumerate(wav):
# Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness")
```
## Model details
**Organization developing the model:** The FAIR team of Meta AI.
**Model date:** MusicGen was trained between April 2023 and May 2023.
**Model version:** This is the version 1 of the model.
**Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.
**Paper or resources for more information:** More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284).
**Citation details:**
```
@misc{copet2023simple,
title={Simple and Controllable Music Generation},
author={Jade Copet and Felix Kreuk and Itai Gat and Tal Remez and David Kant and Gabriel Synnaeve and Yossi Adi and Alexandre Défossez},
year={2023},
eprint={2306.05284},
archivePrefix={arXiv},
primaryClass={cs.SD}
}
```
**License:** Code is released under MIT, model weights are released under CC-BY-NC 4.0.
**Where to send questions or comments about the model:** Questions and comments about MusicGen can be sent via the [Github repository](https://github.com/facebookresearch/audiocraft) of the project, or by opening an issue.
## Intended use
**Primary intended use:** The primary use of MusicGen is research on AI-based music generation, including:
- Research efforts, such as probing and better understanding the limitations of generative models to further improve the state of science
- Generation of music guided by text or melody to understand current abilities of generative AI models by machine learning amateurs
**Primary intended users:** The primary intended users of the model are researchers in audio, machine learning and artificial intelligence, as well as amateur seeking to better understand those models.
**Out-of-scope use cases:** The model should not be used on downstream applications without further risk evaluation and mitigation. The model should not be used to intentionally create or disseminate music pieces that create hostile or alienating environments for people. This includes generating music that people would foreseeably find disturbing, distressing, or offensive; or content that propagates historical or current stereotypes.
## Metrics
**Models performance measures:** We used the following objective measure to evaluate the model on a standard music benchmark:
- Frechet Audio Distance computed on features extracted from a pre-trained audio classifier (VGGish)
- Kullback-Leibler Divergence on label distributions extracted from a pre-trained audio classifier (PaSST)
- CLAP Score between audio embedding and text embedding extracted from a pre-trained CLAP model
Additionally, we run qualitative studies with human participants, evaluating the performance of the model with the following axes:
- Overall quality of the music samples;
- Text relevance to the provided text input;
- Adherence to the melody for melody-guided music generation.
More details on performance measures and human studies can be found in the paper.
**Decision thresholds:** Not applicable.
## Evaluation datasets
The model was evaluated on the [MusicCaps benchmark](https://www.kaggle.com/datasets/googleai/musiccaps) and on an in-domain held-out evaluation set, with no artist overlap with the training set.
## Training datasets
The model was trained on licensed data using the following sources: the [Meta Music Initiative Sound Collection](https://www.fb.com/sound), [Shutterstock music collection](https://www.shutterstock.com/music) and the [Pond5 music collection](https://www.pond5.com/). See the paper for more details about the training set and corresponding preprocessing.
## Evaluation results
Below are the objective metrics obtained on MusicCaps with the released model. Note that for the publicly released models, we had all the datasets go through a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs), in order to keep only the instrumental part. This explains the difference in objective metrics with the models used in the paper.
| Model | Frechet Audio Distance | KLD | Text Consistency | Chroma Cosine Similarity |
|---|---|---|---|---|
| facebook/musicgen-small | 4.88 | 1.42 | 0.27 | - |
| facebook/musicgen-medium | 5.14 | 1.38 | 0.28 | - |
| **facebook/musicgen-large** | 5.48 | 1.37 | 0.28 | - |
| facebook/musicgen-melody | 4.93 | 1.41 | 0.27 | 0.44 |
More information can be found in the paper [Simple and Controllable Music Generation](https://arxiv.org/abs/2306.05284), in the Results section.
## Limitations and biases
**Data:** The data sources used to train the model are created by music professionals and covered by legal agreements with the right holders. The model is trained on 20K hours of data, we believe that scaling the model on larger datasets can further improve the performance of the model.
**Mitigations:** Vocals have been removed from the data source using corresponding tags, and then using a state-of-the-art music source separation method, namely using the open source [Hybrid Transformer for Music Source Separation](https://github.com/facebookresearch/demucs) (HT-Demucs).
**Limitations:**
- The model is not able to generate realistic vocals.
- The model has been trained with English descriptions and will not perform as well in other languages.
- The model does not perform equally well for all music styles and cultures.
- The model sometimes generates end of songs, collapsing to silence.
- It is sometimes difficult to assess what types of text descriptions provide the best generations. Prompt engineering may be required to obtain satisfying results.
**Biases:** The source of data is potentially lacking diversity and all music cultures are not equally represented in the dataset. The model may not perform equally well on the wide variety of music genres that exists. The generated samples from the model will reflect the biases from the training data. Further work on this model should include methods for balanced and just representations of cultures, for example, by scaling the training data to be both diverse and inclusive.
**Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
**Use cases:** Users must be aware of the biases, limitations and risks of the model. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks. |
AI-B/UTENA-7B-NSFW-V2 | AI-B | 2024-03-26T21:33:26Z | 276 | 14 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"base_model:AI-B/UTENA-7B-BAGEL",
"base_model:merge:AI-B/UTENA-7B-BAGEL",
"base_model:AI-B/UTENA-7B-NSFW",
"base_model:merge:AI-B/UTENA-7B-NSFW",
"license:unlicense",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-01-14T07:09:27Z | ---
license: unlicense
tags:
- mergekit
- merge
base_model:
- AI-B/UTENA-7B-NSFW
- AI-B/UTENA-7B-BAGEL
model-index:
- name: UTENA-7B-NSFW-V2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 63.31
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AI-B/UTENA-7B-NSFW-V2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.54
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AI-B/UTENA-7B-NSFW-V2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.97
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AI-B/UTENA-7B-NSFW-V2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 47.81
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AI-B/UTENA-7B-NSFW-V2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.69
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AI-B/UTENA-7B-NSFW-V2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 42.38
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AI-B/UTENA-7B-NSFW-V2
name: Open LLM Leaderboard
---
# nsfw
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [AI-B/UTENA-7B-NSFW](https://huggingface.co/AI-B/UTENA-7B-NSFW)
* [AI-B/UTENA-7B-BAGEL](https://huggingface.co/AI-B/UTENA-7B-BAGEL)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: AI-B/UTENA-7B-NSFW
layer_range: [0, 32]
- model: AI-B/UTENA-7B-BAGEL
layer_range: [0, 32]
merge_method: slerp
base_model: AI-B/UTENA-7B-NSFW
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## Quanitized Models
[UTENA-7B-NSFW-V2-GGUF](https://huggingface.co/s3nh/UTENA-7B-NSFW-V2-GGUF)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AI-B__UTENA-7B-NSFW-V2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |63.45|
|AI2 Reasoning Challenge (25-Shot)|63.31|
|HellaSwag (10-Shot) |84.54|
|MMLU (5-Shot) |63.97|
|TruthfulQA (0-shot) |47.81|
|Winogrande (5-shot) |78.69|
|GSM8k (5-shot) |42.38|
|
blockblockblock/Rhea-72b-v0.5-bpw6 | blockblockblock | 2024-03-26T21:32:28Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"6-bit",
"exl2",
"region:us"
] | text-generation | 2024-03-26T21:25:01Z | ---
library_name: transformers
license: apache-2.0
language:
- en
---
# Rhea-72b-v0.5
The Rhea project is a project that conducts research on various learning methods to improve llm model performance. We fine-tuned the existing model using the [nox](https://github.com/davidkim205/nox) framework. We built a dataset for SFT learning based on the currently open dataset, and created a dataset using SGD (Self-Generated Dataset Creation Method for DPO Learning) for DPO learning.
Our model ranked first on HuggingFace's Open LLM leaderboard.
## SGD : A Study on Self-Generated Dataset creation method for DPO Learning
This method proposes a novel method for generating datasets for DPO (Self-supervised Learning) models. We suggest a technique where sentences generated by the model are compared with the actual correct answers from an existing dataset, and sentences where the model's generated results do not match the correct answers are added. This enables the model to autonomously create training data, thereby enhancing the performance of DPO models.
## Model Details
* **Model Developers** : davidkim(changyeon kim)
* **Repository** : [https://github.com/davidkim205/nox](https://github.com/davidkim205/nox)
* **base mode** : abacusai/Smaug-72B-v0.1
* **sft dataset** : will be updated soon.
* **dpo dataset** : will be updated soon.
## Evaluation
### [Open LLM Leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
| **model** | **average** | **arc** | **hellaswag** | **mmlu** | **truthfulQA** | **winogrande** | **GSM8k** |
| ------------- | ----------- | ------- | ------------- | -------- | -------------- | -------------- | --------- |
| Rhea-72b-v0.5 | 81.22 | 79.78 | 91.15 | 77.95 | 74.5 | 87.85 | 76.12 |
|
kazuma313/gemma-dokter-ft | kazuma313 | 2024-03-26T21:31:28Z | 1 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | 2024-03-19T18:57:58Z | ---
library_name: peft
base_model: google/gemma-2b
---
# Model Card for Model ID
This model using dataset from [mahfoos/Patient-Doctor-Conversation](https://huggingface.co/datasets/mahfoos/Patient-Doctor-Conversation) and using QLoRA method for fine-tunning with [SFT](https://huggingface.co/docs/trl/sft_trainer).
this model using 2000 dataset and got 4.621 for loss on 2000 step.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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### Framework versions
- PEFT 0.9.0 |
Jamesb1974/autotrain-gtp2-600-sft | Jamesb1974 | 2024-03-26T21:28:04Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T21:07:05Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
0x0daughter1/gemmu2 | 0x0daughter1 | 2024-03-26T21:25:11Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T14:18:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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|
iquiz/gemma-2b-5 | iquiz | 2024-03-26T21:24:22Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T13:39:58Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
Pageee/openai-whisper-large-v2-colab | Pageee | 2024-03-26T21:23:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-03-26T17:26:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed]
|
bcama/ppo-LunarLander-v2 | bcama | 2024-03-26T21:18:50Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-03-26T21:18:33Z | ---
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: 260.81 +/- 24.34
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
...
```
|
ryantetro/Investor_Match | ryantetro | 2024-03-26T21:17:46Z | 0 | 0 | null | [
"en",
"license:unknown",
"region:us"
] | null | 2024-03-25T23:08:40Z | ---
license: unknown
language:
- en
---
‘Investor Match’ writes emails to potential investors by replacing only a few elements of the example email. With any response, the GPT asks the user for (1) the recipient’s name, (2) the video URL, (3), the transcript, and (4) the relevant subsidiary. Note the template email and the step-by-step instructions for composing.
<template>
Subject: Revolutionizing political access with Citizen Portal
Marc,
What you said in your interview about the democratizing power of AI[<video>https://my.soar.com/-eb702216-86027851</video>] is exactly what we're aiming to do at Citizen Portal.
Can we connect and discuss "accelerating informed democracy"? You can share your availability here: https://app.soar.com/paulallen/angel15
Paul
</template>
Instructions:
Step 1: Make no changes to the template other than what is outlined below.
Step 2: Rewrite the subject line.
Step 3: Replace “Marc” with the recipient’s first name.
Step 4: Replace “the democratizing power of AI” with a simple phrase of the subject of the transcript.
Step 5: Replace "https://my.soar.com/-eb702216-86027851" with the video URL from the user, embedded.
Step 6: Replace “Citizen Portal” with the relevant subsidiary.
Step 7: Replace “accelerating informed democracy” with a verbatim quote from the transcript that is relevant or a phrase about the topic not in quotes. |
thrunlab/llama_7b_hf_relu_refined_web_relu_2024-03-26 | thrunlab | 2024-03-26T21:12:21Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"sparse_llama",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:finetune:meta-llama/Llama-2-7b-hf",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-03-26T18:16:16Z | ---
base_model: meta-llama/Llama-2-7b-hf
tags:
- generated_from_trainer
model-index:
- name: llama_7b_hf_relu_refined_web_relu_2024-03-26
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. -->
# llama_7b_hf_relu_refined_web_relu_2024-03-26
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6081
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 0
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- total_eval_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 9.5465 | 0.02 | 25 | 9.1446 |
| 7.7377 | 0.03 | 50 | 7.5825 |
| 6.5903 | 0.05 | 75 | 6.3866 |
| 5.4942 | 0.06 | 100 | 5.3487 |
| 4.6502 | 0.08 | 125 | 4.6675 |
| 4.2062 | 0.1 | 150 | 4.2042 |
| 3.7988 | 0.11 | 175 | 3.8759 |
| 3.5327 | 0.13 | 200 | 3.6560 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.2
|
rshrott/colab20240326ryan2 | rshrott | 2024-03-26T21:03:14Z | 190 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-03-26T19:45:52Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: colab20240326ryan2
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. -->
# colab20240326ryan2
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8884
- Accuracy: 0.6668
## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4599 | 0.05 | 100 | 0.9329 | 0.6642 |
| 0.3904 | 0.09 | 200 | 1.1326 | 0.6132 |
| 0.3971 | 0.14 | 300 | 1.0731 | 0.6333 |
| 0.3444 | 0.19 | 400 | 1.1920 | 0.6198 |
| 0.3266 | 0.23 | 500 | 1.1286 | 0.6459 |
| 0.704 | 0.28 | 600 | 1.1258 | 0.6260 |
| 0.5476 | 0.32 | 700 | 0.9590 | 0.6361 |
| 0.6925 | 0.37 | 800 | 0.9508 | 0.6318 |
| 0.4905 | 0.42 | 900 | 0.9142 | 0.6464 |
| 0.6835 | 0.46 | 1000 | 0.9453 | 0.6316 |
| 0.6919 | 0.51 | 1100 | 0.8452 | 0.6683 |
| 0.8017 | 0.56 | 1200 | 0.9353 | 0.6431 |
| 0.5504 | 0.6 | 1300 | 0.8929 | 0.6592 |
| 0.5523 | 0.65 | 1400 | 0.8705 | 0.6650 |
| 0.7787 | 0.7 | 1500 | 0.9147 | 0.6378 |
| 0.4896 | 0.74 | 1600 | 0.8985 | 0.6635 |
| 0.5114 | 0.79 | 1700 | 0.8605 | 0.6735 |
| 0.4811 | 0.84 | 1800 | 0.9524 | 0.6524 |
| 0.6161 | 0.88 | 1900 | 0.8507 | 0.6698 |
| 0.648 | 0.93 | 2000 | 0.8478 | 0.6748 |
| 0.5534 | 0.97 | 2100 | 0.8884 | 0.6668 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Smuggling1710/Ak4ri-7b | Smuggling1710 | 2024-03-26T21:01:27Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Virt-io/Irene-RP-v3-7B",
"Smuggling1710/Erosumika-MistralLayla-Slerp",
"base_model:Smuggling1710/Erosumika-MistralLayla-Slerp",
"base_model:merge:Smuggling1710/Erosumika-MistralLayla-Slerp",
"base_model:Virt-io/Irene-RP-v3-7B",
"base_model:merge:Virt-io/Irene-RP-v3-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T10:30:36Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Virt-io/Irene-RP-v3-7B
- Smuggling1710/Erosumika-MistralLayla-Slerp
base_model:
- Virt-io/Irene-RP-v3-7B
- Smuggling1710/Erosumika-MistralLayla-Slerp
---
# Ak4ri-7b
Ak4ri-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Virt-io/Irene-RP-v3-7B](https://huggingface.co/Virt-io/Irene-RP-v3-7B)
* [Smuggling1710/Erosumika-MistralLayla-Slerp](https://huggingface.co/Smuggling1710/Erosumika-MistralLayla-Slerp)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: Virt-io/Irene-RP-v3-7B
layer_range: [0, 32]
- model: Smuggling1710/Erosumika-MistralLayla-Slerp
layer_range: [0, 32]
merge_method: slerp
base_model: Smuggling1710/Erosumika-MistralLayla-Slerp
parameters:
t:
- filter: self_attn
value: [0.6, 0.5, 0.3, 0.7, 0.4]
- filter: mlp
value: [0.4, 0.5, 0.7, 0.3, 0.6]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Smuggling1710/Ak4ri-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
automerger/Inex12Experiment26-7B | automerger | 2024-03-26T20:59:19Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:yam-peleg/Experiment26-7B",
"base_model:finetune:yam-peleg/Experiment26-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-10T05:58:22Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- automerger
base_model:
- yam-peleg/Experiment26-7B
---
# Inex12Experiment26-7B
Inex12Experiment26-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [yam-peleg/Experiment26-7B](https://huggingface.co/yam-peleg/Experiment26-7B)
## 🧩 Configuration
```yaml
models:
- model: MSL7/INEX12-7b
# No parameters necessary for base model
- model: yam-peleg/Experiment26-7B
parameters:
density: 0.53
weight: 0.6
merge_method: dare_ties
base_model: MSL7/INEX12-7b
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Inex12Experiment26-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
tsavage68/v1_1000_STEPS_1e7_rate_01_beta_DPO | tsavage68 | 2024-03-26T20:58:37Z | 2 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"base_model:finetune:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T20:54:15Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.1
tags:
- trl
- dpo
- generated_from_trainer
model-index:
- name: v1_1000_STEPS_1e7_rate_01_beta_DPO
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. -->
# v1_1000_STEPS_1e7_rate_01_beta_DPO
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6730
- Rewards/chosen: -0.0669
- Rewards/rejected: -0.1113
- Rewards/accuracies: 0.5890
- Rewards/margins: 0.0445
- Logps/rejected: -17.9930
- Logps/chosen: -15.9218
- Logits/rejected: -3.3417
- Logits/chosen: -3.3418
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6944 | 0.05 | 50 | 0.6930 | -0.0001 | -0.0004 | 0.4791 | 0.0003 | -16.8836 | -15.2543 | -3.3540 | -3.3541 |
| 0.6896 | 0.1 | 100 | 0.6907 | -0.0026 | -0.0076 | 0.5670 | 0.0050 | -16.9551 | -15.2788 | -3.3527 | -3.3528 |
| 0.6879 | 0.15 | 150 | 0.6878 | -0.0076 | -0.0188 | 0.5736 | 0.0112 | -17.0680 | -15.3294 | -3.3516 | -3.3517 |
| 0.6836 | 0.2 | 200 | 0.6849 | -0.0190 | -0.0363 | 0.5670 | 0.0173 | -17.2422 | -15.4426 | -3.3479 | -3.3480 |
| 0.6804 | 0.24 | 250 | 0.6825 | -0.0285 | -0.0510 | 0.5868 | 0.0226 | -17.3899 | -15.5377 | -3.3456 | -3.3457 |
| 0.6753 | 0.29 | 300 | 0.6802 | -0.0411 | -0.0689 | 0.5890 | 0.0277 | -17.5681 | -15.6645 | -3.3452 | -3.3453 |
| 0.6908 | 0.34 | 350 | 0.6788 | -0.0382 | -0.0690 | 0.5956 | 0.0307 | -17.5691 | -15.6352 | -3.3447 | -3.3448 |
| 0.6881 | 0.39 | 400 | 0.6773 | -0.0391 | -0.0735 | 0.5934 | 0.0344 | -17.6147 | -15.6439 | -3.3446 | -3.3447 |
| 0.6519 | 0.44 | 450 | 0.6757 | -0.0500 | -0.0881 | 0.5912 | 0.0381 | -17.7606 | -15.7528 | -3.3434 | -3.3435 |
| 0.6871 | 0.49 | 500 | 0.6751 | -0.0504 | -0.0897 | 0.5978 | 0.0394 | -17.7768 | -15.7565 | -3.3425 | -3.3426 |
| 0.6495 | 0.54 | 550 | 0.6737 | -0.0598 | -0.1025 | 0.5934 | 0.0427 | -17.9043 | -15.8506 | -3.3424 | -3.3425 |
| 0.6756 | 0.59 | 600 | 0.6738 | -0.0611 | -0.1038 | 0.5912 | 0.0427 | -17.9179 | -15.8641 | -3.3420 | -3.3421 |
| 0.6584 | 0.64 | 650 | 0.6735 | -0.0625 | -0.1058 | 0.5890 | 0.0434 | -17.9379 | -15.8778 | -3.3422 | -3.3423 |
| 0.6747 | 0.68 | 700 | 0.6734 | -0.0652 | -0.1089 | 0.5824 | 0.0437 | -17.9690 | -15.9052 | -3.3417 | -3.3418 |
| 0.6735 | 0.73 | 750 | 0.6733 | -0.0662 | -0.1102 | 0.5670 | 0.0440 | -17.9819 | -15.9150 | -3.3417 | -3.3418 |
| 0.6573 | 0.78 | 800 | 0.6732 | -0.0671 | -0.1112 | 0.5868 | 0.0442 | -17.9917 | -15.9236 | -3.3417 | -3.3418 |
| 0.6768 | 0.83 | 850 | 0.6732 | -0.0671 | -0.1112 | 0.5934 | 0.0441 | -17.9912 | -15.9238 | -3.3417 | -3.3418 |
| 0.6745 | 0.88 | 900 | 0.6733 | -0.0671 | -0.1110 | 0.5780 | 0.0439 | -17.9897 | -15.9243 | -3.3416 | -3.3418 |
| 0.6751 | 0.93 | 950 | 0.6730 | -0.0668 | -0.1114 | 0.5868 | 0.0446 | -17.9934 | -15.9211 | -3.3417 | -3.3418 |
| 0.6645 | 0.98 | 1000 | 0.6730 | -0.0669 | -0.1113 | 0.5890 | 0.0445 | -17.9930 | -15.9218 | -3.3417 | -3.3418 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.0.0+cu117
- Datasets 2.18.0
- Tokenizers 0.15.2
|
mattshumer/Hermes-2-Pro-11B | mattshumer | 2024-03-26T20:54:45Z | 8 | 26 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Hermes-2-Pro-Mistral-7B",
"conversational",
"base_model:NousResearch/Hermes-2-Pro-Mistral-7B",
"base_model:finetune:NousResearch/Hermes-2-Pro-Mistral-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-25T20:59:16Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- NousResearch/Hermes-2-Pro-Mistral-7B
base_model:
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
- NousResearch/Hermes-2-Pro-Mistral-7B
---
# Hermes-2-Pro-11B
Hermes-2-Pro-11B is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
* [NousResearch/Hermes-2-Pro-Mistral-7B](https://huggingface.co/NousResearch/Hermes-2-Pro-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- layer_range: [0, 5]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [3, 8]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [6, 11]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [9, 14]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [12, 17]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [15, 20]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [18, 23]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [21, 26]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [24, 29]
model: NousResearch/Hermes-2-Pro-Mistral-7B
- sources:
- layer_range: [27, 32]
model: NousResearch/Hermes-2-Pro-Mistral-7B
merge_method: passthrough
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "mattshumer/Hermes-2-Pro-11B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
MesozoicMetallurgist/zeta-preCambrian | MesozoicMetallurgist | 2024-03-26T20:50:08Z | 88 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T20:48:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
OdiaGenAI-LLM/Hindi-Gemma-2B-instruct | OdiaGenAI-LLM | 2024-03-26T20:49:38Z | 0 | 1 | null | [
"safetensors",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-03-16T13:37:21Z | ---
license: cc-by-nc-4.0
---
# Hindi-Gemma-2B-instruct (Instruction-tuned)
Hindi-Gemma-2B-instruct is an instruction-tuned Hindi large language model (LLM) with 2 billion parameters, and it is based on Gemma 2B. The model is instruction-tuned on 187k Hindi instruction sets collected by the OdiaGenAI team.
For more details about the model, data, training procedure, and evaluations, go through the blog [post](https://www.odiagenai.org/blog/odiagenai-releases-gemma-model-with-extensive-instruction-set-fine-tuning-for).
## Model Description
* Model type: A 2B instruction-tuned decoder-only model
* Primary Language(s): Hindi and English
* License: cc-by-nc-4.0
### Citation Information
If you find this model useful, please consider giving 👏 and citing:
```
@misc{Hindi-Gemma-2B-instruct,
author = {Gunnet Singh Kohli and Shantipriya Parida Sambit Sekhar and Debasish Dhal},
title = {Hindi-Gemma-2B-Instruction-tuned Model Released by OdiaGenAI},
year = {2024},
publisher = {Hugging Face},
journal = {Hugging Face repository},
howpublished = {\url{https://huggingface.co/OdiaGenAI}},
}
```
### Contributions
- Guneet Singh Kohli
- Dr. Shantipriya Parida
- Sambit Sekhar
- Debasish Dhal
### Acknowledgement
We express our gratitude to Dr. Prasad Reddy, Data Care LLC, USA, and his team for providing the necessary infrastructure support. |
Smuggling1710/Ak4ri-rp-7b | Smuggling1710 | 2024-03-26T20:48:26Z | 5 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"PocketDoc/Dans-AdventurousWinds-Mk2-7b",
"Smuggling1710/Ak4ri-7b",
"base_model:PocketDoc/Dans-AdventurousWinds-Mk2-7b",
"base_model:merge:PocketDoc/Dans-AdventurousWinds-Mk2-7b",
"base_model:Smuggling1710/Ak4ri-7b",
"base_model:merge:Smuggling1710/Ak4ri-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T19:47:57Z | ---
tags:
- merge
- mergekit
- lazymergekit
- PocketDoc/Dans-AdventurousWinds-Mk2-7b
- Smuggling1710/Ak4ri-7b
base_model:
- PocketDoc/Dans-AdventurousWinds-Mk2-7b
- Smuggling1710/Ak4ri-7b
---
# Ak4ri-rp-7b
Ak4ri-rp-7b is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [PocketDoc/Dans-AdventurousWinds-Mk2-7b](https://huggingface.co/PocketDoc/Dans-AdventurousWinds-Mk2-7b)
* [Smuggling1710/Ak4ri-7b](https://huggingface.co/Smuggling1710/Ak4ri-7b)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: PocketDoc/Dans-AdventurousWinds-Mk2-7b
layer_range: [0, 32]
- model: Smuggling1710/Ak4ri-7b
layer_range: [0, 32]
merge_method: slerp
base_model: Smuggling1710/Ak4ri-7b
parameters:
t:
- filter: self_attn
value: [0.6, 0.5, 0.3, 0.7, 0.4]
- filter: mlp
value: [0.4, 0.5, 0.7, 0.3, 0.6]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Smuggling1710/Ak4ri-rp-7b"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
montaguegabe/Mistral-7B-Instruct-v0.2-1epoch | montaguegabe | 2024-03-26T20:48:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T19:05:59Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
whizzzzkid/nousgemma6 | whizzzzkid | 2024-03-26T20:46:09Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-19T20:43:46Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
qftop/xlm-roberta-large-finetuned-ner | qftop | 2024-03-26T20:39:54Z | 88 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-03-26T20:38:40Z | ---
license: mit
base_model: facebookAI/xlm-roberta-large
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-large-finetuned-ner
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. -->
# xlm-roberta-large-finetuned-ner
This model is a fine-tuned version of [facebookAI/xlm-roberta-large](https://huggingface.co/facebookAI/xlm-roberta-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0433
- Precision: 0.9625
- Recall: 0.9697
- F1: 0.9661
- Accuracy: 0.9916
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2077 | 1.0 | 878 | 0.0604 | 0.9361 | 0.9424 | 0.9392 | 0.9865 |
| 0.0401 | 2.0 | 1756 | 0.0434 | 0.9589 | 0.9647 | 0.9618 | 0.9906 |
| 0.0193 | 3.0 | 2634 | 0.0433 | 0.9625 | 0.9697 | 0.9661 | 0.9916 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
DiogoF/Codenames-30000-V1 | DiogoF | 2024-03-26T20:33:21Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-03-26T12:30:00Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
base_model: CompVis/stable-diffusion-v1-4
inference: true
instance_prompt: the <codenames> style
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - DiogoF/Codenames-30000-V1
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on the <codenames> style using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
bartowski/Rhea-72b-v0.5-exl2 | bartowski | 2024-03-26T20:28:52Z | 4 | 0 | transformers | [
"transformers",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T20:28:50Z | ---
library_name: transformers
license: apache-2.0
language:
- en
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of Rhea-72b-v0.5
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.15">turboderp's ExLlamaV2 v0.0.15</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Conversion was done using the default 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/davidkim205/Rhea-72b-v0.5
<a href="https://huggingface.co/bartowski/Rhea-72b-v0.5-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Rhea-72b-v0.5-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/Rhea-72b-v0.5-exl2/tree/4_25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/Rhea-72b-v0.5-exl2/tree/3_75">3.75 bits per weight</a>
<a href="https://huggingface.co/bartowski/Rhea-72b-v0.5-exl2/tree/3_5">3.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/Rhea-72b-v0.5-exl2/tree/3_0">3.0 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Rhea-72b-v0.5-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 `Rhea-72b-v0.5-exl2`:
```shell
mkdir Rhea-72b-v0.5-exl2
huggingface-cli download bartowski/Rhea-72b-v0.5-exl2 --local-dir Rhea-72b-v0.5-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir Rhea-72b-v0.5-exl2-6_5
huggingface-cli download bartowski/Rhea-72b-v0.5-exl2 --revision 6_5 --local-dir Rhea-72b-v0.5-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir Rhea-72b-v0.5-exl2-6.5
huggingface-cli download bartowski/Rhea-72b-v0.5-exl2 --revision 6_5 --local-dir Rhea-72b-v0.5-exl2-6.5 --local-dir-use-symlinks False
```
|
vikp/surya_layout | vikp | 2024-03-26T20:28:15Z | 78 | 4 | transformers | [
"transformers",
"safetensors",
"segformer",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-02-29T20:56:42Z | ---
license: cc-by-nc-sa-4.0
---
Layout model for [surya](https://github.com/VikParuchuri/surya). |
MR-Eder/tinyllamatestbymarcel | MR-Eder | 2024-03-26T20:27:02Z | 63 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-26T20:00:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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#### Speeds, Sizes, Times [optional]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
ahmedabdo/spam-detector | ahmedabdo | 2024-03-26T20:26:00Z | 105 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-03-26T20:21:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
Americo/uma_finetuning_workshop | Americo | 2024-03-26T20:25:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2024-03-26T20:24:14Z | ---
library_name: peft
base_model: NousResearch/llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
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## Model Card Contact
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### Framework versions
- PEFT 0.10.0 |
Natkituwu/Erosumika-7B-v3-7.1bpw-exl2 | Natkituwu | 2024-03-26T20:22:02Z | 10 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"instruct",
"conversational",
"roleplay",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-03-26T20:09:43Z | ---
language:
- en
pipeline_tag: text-generation
tags:
- text-generation-inference
- instruct
- conversational
- roleplay
license: cc-by-4.0
---
<h1 style="text-align: center">Erosumika-7B-v3</h1>
<div style="display: flex; justify-content: center;">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6512681f4151fb1fa719e033/ZX5NLfB2CctdwuctS9W8A.gif" alt="Header GIF">
</div>
7.1bpw exl2 quant. great for 16k context on 8GB GPUS!
Original Model : (https://huggingface.co/localfultonextractor/Erosumika-7B-v3)
## Model Details
A DARE TIES merge between Nitral's [Kunocchini-7b](https://huggingface.co/Nitral-AI/Kunocchini-7b), Endevor's [InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B) and my [FlatErosAlpha](https://huggingface.co/localfultonextractor/FlatErosAlpha), a flattened(in order to keep the vocab size 32000) version of tavtav's [eros-7B-ALPHA](https://huggingface.co/tavtav/eros-7B-ALPHA). Alpaca and ChatML work best.
[GGUF quants](https://huggingface.co/localfultonextractor/Erosumika-7B-v3-GGUF)
## Limitations and biases
The intended use-case for this model is fictional writing for entertainment purposes. Any other sort of usage is out of scope.
It may produce socially unacceptable or undesirable text, even if the prompt itself does not include anything explicitly offensive. Outputs might often be factually wrong or misleading.
```yaml
base_model: localfultonextractor/FlatErosAlpha
models:
- model: localfultonextractor/FlatErosAlpha
- model: Epiculous/InfinityRP-v1-7B
parameters:
density: 0.4
weight: 0.25
- model: Nitral-AI/Kunocchini-7b
parameters:
density: 0.3
weight: 0.35
merge_method: dare_ties
dtype: bfloat16
```
Note: Copied the tokenizer from InfinityRP-v1-7B. |
whizzzzkid/nous_seven | whizzzzkid | 2024-03-26T20:20:30Z | 20 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-03-13T14:05:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
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#### Testing Data
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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Subsets and Splits