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Litzy619/O0428HMA4 | Litzy619 | 2024-04-30T00:18:29Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"base_model:finetune:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T23:36:26Z | ---
license: apache-2.0
base_model: allenai/OLMo-1B
tags:
- generated_from_trainer
model-index:
- name: O0428HMA4
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. -->
# O0428HMA4
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1449
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6513 | 0.09 | 10 | 0.2874 |
| 0.1942 | 0.18 | 20 | 0.1547 |
| 0.1509 | 0.27 | 30 | 0.1700 |
| 0.1537 | 0.36 | 40 | 0.1502 |
| 0.1503 | 0.45 | 50 | 0.1510 |
| 0.1523 | 0.54 | 60 | 0.1494 |
| 0.1493 | 0.63 | 70 | 0.1485 |
| 0.149 | 0.73 | 80 | 0.1558 |
| 0.1477 | 0.82 | 90 | 0.1494 |
| 0.1482 | 0.91 | 100 | 0.1487 |
| 0.1486 | 1.0 | 110 | 0.1489 |
| 0.1454 | 1.09 | 120 | 0.1484 |
| 0.1451 | 1.18 | 130 | 0.1500 |
| 0.1474 | 1.27 | 140 | 0.1502 |
| 0.1491 | 1.36 | 150 | 0.1479 |
| 0.145 | 1.45 | 160 | 0.1472 |
| 0.1445 | 1.54 | 170 | 0.1464 |
| 0.1477 | 1.63 | 180 | 0.1467 |
| 0.1467 | 1.72 | 190 | 0.1489 |
| 0.1453 | 1.81 | 200 | 0.1484 |
| 0.1495 | 1.9 | 210 | 0.1492 |
| 0.1464 | 1.99 | 220 | 0.1498 |
| 0.1472 | 2.08 | 230 | 0.1478 |
| 0.1414 | 2.18 | 240 | 0.1460 |
| 0.1427 | 2.27 | 250 | 0.1470 |
| 0.1439 | 2.36 | 260 | 0.1478 |
| 0.1429 | 2.45 | 270 | 0.1457 |
| 0.1407 | 2.54 | 280 | 0.1463 |
| 0.1416 | 2.63 | 290 | 0.1461 |
| 0.1436 | 2.72 | 300 | 0.1448 |
| 0.1437 | 2.81 | 310 | 0.1448 |
| 0.1434 | 2.9 | 320 | 0.1449 |
| 0.1443 | 2.99 | 330 | 0.1449 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
embracellm/sushi05_LoRA | embracellm | 2024-04-30T00:14:50Z | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-04-29T22:08:49Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- dora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- dora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- dora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sushi
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi05_LoRA
<Gallery />
## Model description
These are embracellm/sushi05_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sushi to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi05_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
gp-tar4/QA_FineTuned_mdeberta-v3-base-squad2 | gp-tar4 | 2024-04-30T00:12:58Z | 1 | 0 | transformers | [
"transformers",
"tf",
"deberta-v2",
"question-answering",
"generated_from_keras_callback",
"ar",
"base_model:timpal0l/mdeberta-v3-base-squad2",
"base_model:finetune:timpal0l/mdeberta-v3-base-squad2",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-04-29T22:04:51Z | ---
license: mit
base_model: timpal0l/mdeberta-v3-base-squad2
tags:
- generated_from_keras_callback
model-index:
- name: omarSorour123/sorour_qa_model
results: []
language:
- ar
---
<!-- 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. -->
# omarSorour123/sorour_qa_model
This model is a fine-tuned version of [timpal0l/mdeberta-v3-base-squad2](https://huggingface.co/timpal0l/mdeberta-v3-base-squad2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.6044
- Validation Loss: 1.6929
- 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': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': True, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 435, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 1.6956 | 1.5308 | 0 |
| 1.1261 | 1.5328 | 1 |
| 0.8398 | 1.6445 | 2 |
| 0.6846 | 1.6727 | 3 |
| 0.6044 | 1.6929 | 4 |
### Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1 |
just1nseo/tulu2-7b-cost-UF-UI-HHRLHF-5e-6 | just1nseo | 2024-04-29T23:52:13Z | 12 | 0 | peft | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:allenai/tulu-2-7b",
"base_model:adapter:allenai/tulu-2-7b",
"region:us"
] | null | 2024-04-29T14:47:46Z | ---
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: allenai/tulu-2-7b
model-index:
- name: tulu2-7b-cost-UF-UI-HHRLHF-5e-6
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. -->
# tulu2-7b-cost-UF-UI-HHRLHF-5e-6
This model is a fine-tuned version of [allenai/tulu-2-7b](https://huggingface.co/allenai/tulu-2-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8630
- Rewards/chosen: -4.9803
- Rewards/rejected: -5.7374
- Rewards/accuracies: 0.5905
- Rewards/margins: 0.7571
- Rewards/margins Max: 5.4488
- Rewards/margins Min: -2.7483
- Rewards/margins Std: 2.6664
- Logps/rejected: -892.1482
- Logps/chosen: -835.0510
- Logits/rejected: 1.2553
- Logits/chosen: 1.0857
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- total_eval_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: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Rewards/margins Max | Rewards/margins Min | Rewards/margins Std | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:-------------------:|:-------------------:|:-------------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0556 | 1.0 | 3974 | 0.8630 | -4.9803 | -5.7374 | 0.5905 | 0.7571 | 5.4488 | -2.7483 | 2.6664 | -892.1482 | -835.0510 | 1.2553 | 1.0857 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
tralon/test-1 | tralon | 2024-04-29T23:48:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-base",
"base_model:finetune:microsoft/deberta-v3-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-04-29T23:40:13Z | ---
license: mit
base_model: microsoft/deberta-v3-base
tags:
- generated_from_trainer
model-index:
- name: output_dir
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. -->
# output_dir
This model is a fine-tuned version of [microsoft/deberta-v3-base](https://huggingface.co/microsoft/deberta-v3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0625
- F Beta Score: 0.9639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
SolidSnacke/Llama-3-Soliloquy-8B-i-GGUF | SolidSnacke | 2024-04-29T23:45:32Z | 7 | 2 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"text-generation",
"en",
"license:cc-by-nc-sa-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-04-25T12:03:57Z | ---
license: cc-by-nc-sa-4.0
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama
- text-generation-inference
---
!!! A new version of the model has been released. I didn’t find any problems with word duplication, but I can’t promise anything. https://huggingface.co/SolidSnacke/Llama-3-Soliloquy-8B-v1.5-64k-i-GGUF
Edit: Currently this model has a problem with repeating words. That is, at some point you may experience duplication, like: passing by the table, he caught a red red red red red red...
The problem most likely, as the author of the model explained, may be the wrong EOS token, but this is not certain. The author in another repository wrote that he will soon release a new model, so we are waiting.
I don't know what to write here.
Links to the original model and script:
- openlynn/Llama-3-Soliloquy-8B: https://huggingface.co/openlynn/Llama-3-Soliloquy-8B
- FantasiaFoundry/GGUF-Quantization-Script: https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script |
bartowski/NPC-LLM-3_8B-GGUF | bartowski | 2024-04-29T23:38:19Z | 298 | 0 | null | [
"gguf",
"text-generation",
"en",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2024-04-29T23:31:09Z | ---
license: mit
language:
- en
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of NPC-LLM-3_8B
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2756">b2756</a> for quantization.
Original model: https://huggingface.co/Gigax/NPC-LLM-3_8B
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<s><|system|>
{system_prompt}<|end|>
<|user|>
{prompt}<|end|>
<|assistant|>
<|end|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [NPC-LLM-3_8B-Q8_0.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q8_0.gguf) | Q8_0 | 4.06GB | Extremely high quality, generally unneeded but max available quant. |
| [NPC-LLM-3_8B-Q6_K.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q6_K.gguf) | Q6_K | 3.13GB | Very high quality, near perfect, *recommended*. |
| [NPC-LLM-3_8B-Q5_K_M.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q5_K_M.gguf) | Q5_K_M | 2.81GB | High quality, *recommended*. |
| [NPC-LLM-3_8B-Q5_K_S.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q5_K_S.gguf) | Q5_K_S | 2.64GB | High quality, *recommended*. |
| [NPC-LLM-3_8B-Q4_K_M.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q4_K_M.gguf) | Q4_K_M | 2.39GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [NPC-LLM-3_8B-Q4_K_S.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q4_K_S.gguf) | Q4_K_S | 2.18GB | Slightly lower quality with more space savings, *recommended*. |
| [NPC-LLM-3_8B-IQ4_NL.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ4_NL.gguf) | IQ4_NL | 2.17GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [NPC-LLM-3_8B-IQ4_XS.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ4_XS.gguf) | IQ4_XS | 2.05GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [NPC-LLM-3_8B-Q3_K_L.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q3_K_L.gguf) | Q3_K_L | 2.08GB | Lower quality but usable, good for low RAM availability. |
| [NPC-LLM-3_8B-Q3_K_M.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q3_K_M.gguf) | Q3_K_M | 1.95GB | Even lower quality. |
| [NPC-LLM-3_8B-IQ3_M.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ3_M.gguf) | IQ3_M | 1.85GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [NPC-LLM-3_8B-IQ3_S.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ3_S.gguf) | IQ3_S | 1.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [NPC-LLM-3_8B-Q3_K_S.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q3_K_S.gguf) | Q3_K_S | 1.68GB | Low quality, not recommended. |
| [NPC-LLM-3_8B-IQ3_XS.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ3_XS.gguf) | IQ3_XS | 1.62GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [NPC-LLM-3_8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ3_XXS.gguf) | IQ3_XXS | 1.51GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [NPC-LLM-3_8B-Q2_K.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-Q2_K.gguf) | Q2_K | 1.41GB | Very low quality but surprisingly usable. |
| [NPC-LLM-3_8B-IQ2_M.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ2_M.gguf) | IQ2_M | 1.31GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [NPC-LLM-3_8B-IQ2_S.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ2_S.gguf) | IQ2_S | 1.21GB | Very low quality, uses SOTA techniques to be usable. |
| [NPC-LLM-3_8B-IQ2_XS.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ2_XS.gguf) | IQ2_XS | 1.15GB | Very low quality, uses SOTA techniques to be usable. |
| [NPC-LLM-3_8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ2_XXS.gguf) | IQ2_XXS | 1.04GB | Lower quality, uses SOTA techniques to be usable. |
| [NPC-LLM-3_8B-IQ1_M.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ1_M.gguf) | IQ1_M | .91GB | Extremely low quality, *not* recommended. |
| [NPC-LLM-3_8B-IQ1_S.gguf](https://huggingface.co/bartowski/NPC-LLM-3_8B-GGUF/blob/main/NPC-LLM-3_8B-IQ1_S.gguf) | IQ1_S | .84GB | Extremely low quality, *not* recommended. |
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
Litzy619/O0428HMA3 | Litzy619 | 2024-04-29T23:36:34Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"base_model:finetune:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T22:54:01Z | ---
license: apache-2.0
base_model: allenai/OLMo-1B
tags:
- generated_from_trainer
model-index:
- name: O0428HMA3
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. -->
# O0428HMA3
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0534
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.516 | 0.09 | 10 | 0.1675 |
| 0.1635 | 0.18 | 20 | 0.1582 |
| 0.1494 | 0.27 | 30 | 0.1521 |
| 0.1524 | 0.36 | 40 | 0.1511 |
| 0.1511 | 0.45 | 50 | 0.1465 |
| 0.1533 | 0.54 | 60 | 0.1502 |
| 0.149 | 0.63 | 70 | 0.1473 |
| 0.1503 | 0.73 | 80 | 0.1592 |
| 0.1487 | 0.82 | 90 | 0.1494 |
| 0.1474 | 0.91 | 100 | 0.1475 |
| 0.1331 | 1.0 | 110 | 0.2279 |
| 0.3556 | 1.09 | 120 | 0.1260 |
| 0.2269 | 1.18 | 130 | 0.1110 |
| 0.1173 | 1.27 | 140 | 0.0777 |
| 0.1209 | 1.36 | 150 | 0.0818 |
| 0.0771 | 1.45 | 160 | 0.0822 |
| 0.0701 | 1.54 | 170 | 0.0583 |
| 0.0641 | 1.63 | 180 | 0.0579 |
| 0.0638 | 1.72 | 190 | 0.0560 |
| 0.0564 | 1.81 | 200 | 0.0569 |
| 0.058 | 1.9 | 210 | 0.0603 |
| 0.059 | 1.99 | 220 | 0.0548 |
| 0.0576 | 2.08 | 230 | 0.0548 |
| 0.0532 | 2.18 | 240 | 0.0565 |
| 0.0549 | 2.27 | 250 | 0.0574 |
| 0.0586 | 2.36 | 260 | 0.0561 |
| 0.0537 | 2.45 | 270 | 0.0543 |
| 0.0522 | 2.54 | 280 | 0.0545 |
| 0.0541 | 2.63 | 290 | 0.0556 |
| 0.055 | 2.72 | 300 | 0.0532 |
| 0.0556 | 2.81 | 310 | 0.0531 |
| 0.0563 | 2.9 | 320 | 0.0533 |
| 0.0579 | 2.99 | 330 | 0.0534 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
ahmad01010101/my_awesome_qa_model | ahmad01010101 | 2024-04-29T23:36:25Z | 11 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2024-04-29T08:00:06Z | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9456
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 250 | 2.4024 |
| 2.7808 | 2.0 | 500 | 2.0129 |
| 2.7808 | 3.0 | 750 | 1.9456 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
Litzy619/O0428HMA2 | Litzy619 | 2024-04-29T23:35:59Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"base_model:finetune:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T22:53:54Z | ---
license: apache-2.0
base_model: allenai/OLMo-1B
tags:
- generated_from_trainer
model-index:
- name: O0428HMA2
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. -->
# O0428HMA2
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0559
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6301 | 0.09 | 10 | 0.1786 |
| 0.1808 | 0.18 | 20 | 0.1584 |
| 0.151 | 0.27 | 30 | 0.1656 |
| 0.1571 | 0.36 | 40 | 0.1538 |
| 0.1506 | 0.45 | 50 | 0.1473 |
| 0.1503 | 0.54 | 60 | 0.1472 |
| 0.1495 | 0.63 | 70 | 0.1470 |
| 0.1494 | 0.73 | 80 | 0.1533 |
| 0.1454 | 0.82 | 90 | 0.1454 |
| 0.2027 | 0.91 | 100 | 0.3378 |
| 0.6197 | 1.0 | 110 | 0.1547 |
| 0.1558 | 1.09 | 120 | 0.1495 |
| 0.151 | 1.18 | 130 | 0.2320 |
| 0.1812 | 1.27 | 140 | 0.1292 |
| 0.1265 | 1.36 | 150 | 0.0858 |
| 0.0775 | 1.45 | 160 | 0.0811 |
| 1.561 | 1.54 | 170 | 3.8411 |
| 0.6605 | 1.63 | 180 | 0.0889 |
| 0.9093 | 1.72 | 190 | 0.1577 |
| 0.1072 | 1.81 | 200 | 0.1386 |
| 0.3511 | 1.9 | 210 | 0.0862 |
| 0.0683 | 1.99 | 220 | 0.0609 |
| 0.0628 | 2.08 | 230 | 0.0583 |
| 0.0574 | 2.18 | 240 | 0.0583 |
| 0.0576 | 2.27 | 250 | 0.0589 |
| 0.064 | 2.36 | 260 | 0.0615 |
| 0.0555 | 2.45 | 270 | 0.0571 |
| 0.0548 | 2.54 | 280 | 0.0564 |
| 0.0563 | 2.63 | 290 | 0.0577 |
| 0.0583 | 2.72 | 300 | 0.0558 |
| 0.058 | 2.81 | 310 | 0.0555 |
| 0.0589 | 2.9 | 320 | 0.0558 |
| 0.0621 | 2.99 | 330 | 0.0559 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
Jerado/span-marker-roberta-large-enron | Jerado | 2024-04-29T23:35:14Z | 5 | 0 | span-marker | [
"span-marker",
"tensorboard",
"safetensors",
"token-classification",
"ner",
"named-entity-recognition",
"generated_from_span_marker_trainer",
"en",
"dataset:Jerado/enron_intangibles_ner",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:apache-2.0",
"model-index",
"region:us"
] | token-classification | 2024-04-29T23:34:21Z | ---
language:
- en
license: apache-2.0
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
base_model: roberta-large
datasets:
- Jerado/enron_intangibles_ner
metrics:
- precision
- recall
- f1
widget:
- text: Negotiated rates in these types of deals (basis for new builds) have been
allowed to stand for the life of the contracts, in the case of Kern River and
Mojave.
- text: It seems that there is a single significant policy concern for the ASIC policy
committee.
- text: 'The appropriate price is in Enpower, but the revenue has never appeared (Deal
#590753).'
- text: FYI, to me, a prepayment for a service contract would generally be amortized
over the life of the contract.
- text: 'From: d..steffes @ enron.com To: john.shelk @ enron.com, l..nicolay @ enron.com,
richard.shapiro @ enron.com, sarah.novosel @ enron.com Subject: Southern Co.''s
Testimony The first order of business is getting the cost / benefit analysis done.'
pipeline_tag: token-classification
model-index:
- name: SpanMarker with roberta-large on Jerado/enron_intangibles_ner
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: Unknown
type: Jerado/enron_intangibles_ner
split: test
metrics:
- type: f1
value: 0.4390243902439024
name: F1
- type: precision
value: 0.42857142857142855
name: Precision
- type: recall
value: 0.45
name: Recall
---
# SpanMarker with roberta-large on Jerado/enron_intangibles_ner
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [Jerado/enron_intangibles_ner](https://huggingface.co/datasets/Jerado/enron_intangibles_ner) dataset that can be used for Named Entity Recognition. This SpanMarker model uses [roberta-large](https://huggingface.co/roberta-large) as the underlying encoder.
## Model Details
### Model Description
- **Model Type:** SpanMarker
- **Encoder:** [roberta-large](https://huggingface.co/roberta-large)
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 6 words
- **Training Dataset:** [Jerado/enron_intangibles_ner](https://huggingface.co/datasets/Jerado/enron_intangibles_ner)
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### Model Labels
| Label | Examples |
|:-----------|:--------------------------------------------|
| Intangible | "deal", "sample EES deal", "Enpower system" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:-----------|:----------|:-------|:-------|
| **all** | 0.4286 | 0.45 | 0.4390 |
| Intangible | 0.4286 | 0.45 | 0.4390 |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Run inference
entities = model.predict("It seems that there is a single significant policy concern for the ASIC policy committee.")
```
### Downstream Use
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
```python
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("span_marker_model_id")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("span_marker_model_id-finetuned")
```
</details>
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:----------------------|:----|:--------|:----|
| Sentence length | 1 | 19.8706 | 216 |
| Entities per sentence | 0 | 0.1865 | 6 |
### Training Hyperparameters
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 11
- mixed_precision_training: Native AMP
### Training Results
| Epoch | Step | Validation Loss | Validation Precision | Validation Recall | Validation F1 | Validation Accuracy |
|:-------:|:----:|:---------------:|:--------------------:|:-----------------:|:-------------:|:-------------------:|
| 3.3557 | 500 | 0.0075 | 0.4444 | 0.1667 | 0.2424 | 0.9753 |
| 6.7114 | 1000 | 0.0084 | 0.5714 | 0.3333 | 0.4211 | 0.9793 |
| 10.0671 | 1500 | 0.0098 | 0.6111 | 0.4583 | 0.5238 | 0.9815 |
### Framework Versions
- Python: 3.10.12
- SpanMarker: 1.5.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
lunarsylph/stablecell_v53 | lunarsylph | 2024-04-29T23:31:15Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T23:27:44Z | ---
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]
- **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] |
ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_2 | ShenaoZhang | 2024-04-29T23:21:30Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1",
"base_model:finetune:ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T22:03:14Z | ---
license: mit
base_model: ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- updated
- original
model-index:
- name: 0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_2
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. -->
# 0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_2
This model is a fine-tuned version of [ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1](https://huggingface.co/ShenaoZhang/0.0001_3iters_bs256_nodpo_full6w_userresponse_iter_1) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
mlx-community/starcoder2-15b-instruct-v0.1-4bit | mlx-community | 2024-04-29T23:09:42Z | 12 | 0 | transformers | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"code",
"mlx",
"conversational",
"dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k",
"base_model:bigcode/starcoder2-15b",
"base_model:finetune:bigcode/starcoder2-15b",
"license:bigcode-openrail-m",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T20:55:58Z | ---
license: bigcode-openrail-m
library_name: transformers
tags:
- code
- mlx
base_model: bigcode/starcoder2-15b
datasets:
- bigcode/self-oss-instruct-sc2-exec-filter-50k
pipeline_tag: text-generation
model-index:
- name: starcoder2-15b-instruct-v0.1
results:
- task:
type: text-generation
dataset:
name: LiveCodeBench (code generation)
type: livecodebench-codegeneration
metrics:
- type: pass@1
value: 20.4
- task:
type: text-generation
dataset:
name: LiveCodeBench (self repair)
type: livecodebench-selfrepair
metrics:
- type: pass@1
value: 20.9
- task:
type: text-generation
dataset:
name: LiveCodeBench (test output prediction)
type: livecodebench-testoutputprediction
metrics:
- type: pass@1
value: 29.8
- task:
type: text-generation
dataset:
name: LiveCodeBench (code execution)
type: livecodebench-codeexecution
metrics:
- type: pass@1
value: 28.1
- task:
type: text-generation
dataset:
name: HumanEval
type: humaneval
metrics:
- type: pass@1
value: 72.6
- task:
type: text-generation
dataset:
name: HumanEval+
type: humanevalplus
metrics:
- type: pass@1
value: 63.4
- task:
type: text-generation
dataset:
name: MBPP
type: mbpp
metrics:
- type: pass@1
value: 75.2
- task:
type: text-generation
dataset:
name: MBPP+
type: mbppplus
metrics:
- type: pass@1
value: 61.2
- task:
type: text-generation
dataset:
name: DS-1000
type: ds-1000
metrics:
- type: pass@1
value: 40.6
---
# mlx-community/starcoder2-15b-instruct-v0.1-4bit
This model was converted to MLX format from [`bigcode/starcoder2-15b-instruct-v0.1`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1) 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/starcoder2-15b-instruct-v0.1-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
bartowski/NPC-LLM-3_8B-exl2 | bartowski | 2024-04-29T23:09:16Z | 0 | 0 | null | [
"text-generation",
"en",
"license:mit",
"region:us"
] | text-generation | 2024-04-29T23:09:15Z | ---
license: mit
language:
- en
quantized_by: bartowski
pipeline_tag: text-generation
---
## Exllama v2 Quantizations of NPC-LLM-3_8B
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</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/Gigax/NPC-LLM-3_8B
<a href="https://huggingface.co/bartowski/NPC-LLM-3_8B-exl2/tree/8_0">8.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/NPC-LLM-3_8B-exl2/tree/6_5">6.5 bits per weight</a>
<a href="https://huggingface.co/bartowski/NPC-LLM-3_8B-exl2/tree/5_0">5.0 bits per weight</a>
<a href="https://huggingface.co/bartowski/NPC-LLM-3_8B-exl2/tree/4_25">4.25 bits per weight</a>
<a href="https://huggingface.co/bartowski/NPC-LLM-3_8B-exl2/tree/3_5">3.5 bits per weight</a>
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/NPC-LLM-3_8B-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 `NPC-LLM-3_8B-exl2`:
```shell
mkdir NPC-LLM-3_8B-exl2
huggingface-cli download bartowski/NPC-LLM-3_8B-exl2 --local-dir NPC-LLM-3_8B-exl2 --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
Linux:
```shell
mkdir NPC-LLM-3_8B-exl2-6_5
huggingface-cli download bartowski/NPC-LLM-3_8B-exl2 --revision 6_5 --local-dir NPC-LLM-3_8B-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
mkdir NPC-LLM-3_8B-exl2-6.5
huggingface-cli download bartowski/NPC-LLM-3_8B-exl2 --revision 6_5 --local-dir NPC-LLM-3_8B-exl2-6.5 --local-dir-use-symlinks False
```
|
cbjun99/bart-base-with-nothing | cbjun99 | 2024-04-29T23:08:16Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-04-29T19:11:48Z | ---
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-base-with-nothing
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. -->
# bart-base-with-nothing
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6005
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 12
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.7986 | 1.0 | 4383 | 0.7095 |
| 0.6593 | 2.0 | 8766 | 0.6407 |
| 0.5589 | 3.0 | 13149 | 0.6121 |
| 0.4935 | 4.0 | 17532 | 0.5980 |
| 0.4314 | 5.0 | 21915 | 0.6005 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Tokenizers 0.19.1
|
jlbaker361/ddpo-runway-image_reward-hard | jlbaker361 | 2024-04-29T23:07:10Z | 0 | 0 | null | [
"region:us"
] | null | 2024-04-29T23:07:08Z | ---
{}
---
# DDPO trained model
num_epochs=20
train_gradient_accumulation_steps=1
sample_num_steps=30
sample_batch_size=8
train_batch_size=8
sample_num_batches_per_epoch=32
based off of stabilityai/stable-diffusion-2-base
and then trained off of None
|
franciscobdl/EstigiaxLlama | franciscobdl | 2024-04-29T23:06:59Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T23:06:57Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
model-index:
- name: EstigiaxLlama
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. -->
# EstigiaxLlama
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) 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
- training_steps: 250
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 |
tsharish/Mistral-7B-Inst-v0.2-pubmed-1k_adapter | tsharish | 2024-04-29T23:05:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T23:05:41Z | ---
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] |
profoz/results | profoz | 2024-04-29T23:04:01Z | 4 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-01-25T00:51:51Z | ---
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
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 [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1696
- Accuracy: 0.9308
- Class 0 Precision: 0.9947
- Class 0 Recall: 0.9319
- Class 0 F1: 0.9623
- Class 0 Support: 132570
- Class 1 Precision: 0.4316
- Class 1 Recall: 0.9118
- Class 1 F1: 0.5859
- Class 1 Support: 7517
## 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: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Class 0 Precision | Class 0 Recall | Class 0 F1 | Class 0 Support | Class 1 Precision | Class 1 Recall | Class 1 F1 | Class 1 Support |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----------------:|:--------------:|:----------:|:---------------:|:-----------------:|:--------------:|:----------:|:---------------:|
| 0.2116 | 0.9998 | 2830 | 0.1709 | 0.9437 | 0.9334 | 0.9671 | 0.9500 | 6265 | 0.9574 | 0.9146 | 0.9355 | 5058 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
DTang161/ModelMergingCode | DTang161 | 2024-04-29T23:02:03Z | 0 | 0 | peft | [
"peft",
"pytorch",
"llama",
"text2text-generation",
"en",
"dataset:theblackcat102/evol-codealpaca-v1",
"license:cc-by-nc-4.0",
"region:us"
] | text2text-generation | 2024-04-29T22:56:37Z | ---
library_name: peft
license: cc-by-nc-4.0
datasets:
- theblackcat102/evol-codealpaca-v1
language:
- en
pipeline_tag: text2text-generation
---
## llama-2-13b-code-alpaca
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
Trained for 3 epochs on `theblackcat102/evol-codealpaca-v1` dataset, scored decent on locally run perplexity at 4.36.
## Axolotl config used
```yaml
base_model: NousResearch/Llama-2-13b-hf
base_model_config: NousResearch/Llama-2-13b-hf
model_type: LlamaForCausalLM
tokenizer_type: LlamaTokenizer
push_dataset_to_hub:
hub_model_id:
load_in_8bit: false
load_in_4bit: true
strict: false
datasets:
- path: theblackcat102/evol-codealpaca-v1
type: alpaca
dataset_prepared_path: last_run_prepared
val_set_size: 0.01
output_dir: ./checkpoints/llama-2-13b-qlora
adapter: qlora
lora_model_dir:
sequence_len: 4096
max_packed_sequence_len: 4096
lora_r: 32
lora_alpha: 16
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
lora_fan_in_fan_out:
wandb_project:
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 2
micro_batch_size: 2
num_epochs: 3
optimizer: paged_adamw_32bit
lr_scheduler: cosine
learning_rate: 0.0001
train_on_inputs: false
group_by_length: true
bf16: true
fp16: false
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention: true
flash_attention:
warmup_steps: 10
eval_steps: 50
save_steps:
debug:
deepspeed:
weight_decay: 0.0
fsdp:
fsdp_config:
special_tokens:
bos_token: "<s>"
eos_token: "</s>"
unk_token: "<unk>"
```
And then merged with Axolotl via:
```
accelerate launch scripts/finetune.py configs/your_config.yml --merge_lora --lora_model_dir="./completed-model" --load_in_8bit=False --load_in_4bit=False
```
## 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
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
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
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 0.5.0.dev0 |
whiskeyriot/q-FrozenLake-v1-4x4-noSlippery | whiskeyriot | 2024-04-29T22:59:28Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-04-29T22:42:23Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="whiskeyriot/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct | VAGOsolutions | 2024-04-29T22:56:24Z | 1,663 | 22 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"mistral",
"finetune",
"dpo",
"Instruct",
"augmentation",
"german",
"moe",
"conversational",
"en",
"de",
"fr",
"it",
"es",
"dataset:argilla/distilabel-math-preference-dpo",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-12-15T16:01:09Z | ---
license: apache-2.0
language:
- en
- de
- fr
- it
- es
library_name: transformers
pipeline_tag: text-generation
tags:
- mistral
- finetune
- dpo
- Instruct
- augmentation
- german
- mixtral
- moe
datasets:
- argilla/distilabel-math-preference-dpo
---
## VAGO solutions SauerkrautLM-Mixtral-8x7B-Instruct
Introducing **SauerkrautLM-Mixtral-8x7B-Instruct** – our Sauerkraut version of the powerful Mixtral-8x7B-Instruct!
Aligned with **DPO**
# Table of Contents
1. [Overview of all SauerkrautLM-Mixtral models](#all-sauerkrautlm-mixtral-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training Dataset](#training-dataset)
- [Data Contamination Test](#data-contamination-test-results)
3. [Evaluation](#evaluation)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All SauerkrautLM-Mixtral Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-Mixtral-8x7B-Instruct | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-Mixtral-8x7B-Instruct) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-Instruct-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-Instruct-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-Instruct-AWQ) |
| SauerkrautLM-Mixtral-8x7B | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-Mixtral-8x7B) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-GPTQ) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-GGUF) | [Link](https://huggingface.co/TheBloke/SauerkrautLM-Mixtral-8x7B-AWQ) |
## Model Details
**SauerkrautLM-Mixtral-8x7B-Instruct**
- **Model Type:** SauerkrautLM-Mixtral-8x7B-Instruct-v0.1 is a Mixture of Experts (MoE) Model based on [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1)
- **Language(s):** English, German, French, Italian, Spanish
- **License:** APACHE 2.0
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:[email protected])
### Training Dataset:
SauerkrautLM-Mixtral-8x7B-Instruct was trained with mix of German data augmentation and translated data.
Aligned through **DPO** with our **new German SauerkrautLM-DPO dataset** based on parts of the SFT SauerkrautLM dataset
as chosen answers and [Sauerkraut-7b-HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) as rejected answers. Added with additional **translated Parts of the [HuggingFaceH4/ultrafeedback_binarized](https://huggingface.co/datasets/HuggingFaceH4/ultrafeedback_binarized)** (Our dataset do not contain any TruthfulQA prompts - check Data Contamination Test Results) and **[argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo).**
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
### Data Contamination Test Results
Some models on the HuggingFace leaderboard had problems with wrong data getting mixed in.
We checked our SauerkrautLM-DPO dataset with a special test [1] on a smaller model for this problem.
The HuggingFace team used the same methods [2, 3].
Our results, with `result < 0.1, %:` being well below 0.9, indicate that our dataset is free from contamination.
*The data contamination test results of HellaSwag and Winograde will be added once [1] supports them.*
| Dataset | ARC | MMLU | TruthfulQA | GSM8K |
|------------------------------|-------|-------|-------|-------|
| **SauerkrautLM-DPO**| result < 0.1, %: 0.0 |result < 0.1, %: 0.09 | result < 0.1, %: 0.13 | result < 0.1, %: 0.16 |
[1] https://github.com/swj0419/detect-pretrain-code-contamination
[2] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/474#657f2245365456e362412a06
[3] https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard/discussions/265#657b6debf81f6b44b8966230
### Prompt Template:
```
<s> [INST] Instruction [/INST] Model answer</s> [INST] Follow-up instruction [/INST]
```
## Evaluation
*evaluated with lm-evaluation-harness v0.3.0 - mmlu coming soon
*All benchmarks were performed with a sliding window of 4096. New Benchmarks with Sliding Window null coming soon
**German RAG LLM Evaluation**
corrected result after FIX: https://github.com/huggingface/lighteval/pull/171
```
| Task |Version|Metric|Value| |Stderr|
|------------------------------------------------------|------:|------|----:|---|-----:|
|all | |acc |0.975|± |0.0045|
|community:german_rag_eval:_average:0 | |acc |0.975|± |0.0045|
|community:german_rag_eval:choose_context_by_question:0| 0|acc |0.953|± |0.0067|
|community:german_rag_eval:choose_question_by_context:0| 0|acc |0.998|± |0.0014|
|community:german_rag_eval:context_question_match:0 | 0|acc |0.975|± |0.0049|
|community:german_rag_eval:question_answer_match:0 | 0|acc |0.974|± |0.0050|
```
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:[email protected]). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [argilla](https://huggingface.co/datasets/argilla) and [Huggingface](https://huggingface.co) for providing such valuable datasets to the Open-Source community. And of course a big thanks to MistralAI for providing the open source community with their latest technology! |
arbitropy/mt5-base-bcoqa | arbitropy | 2024-04-29T22:56:15Z | 22 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/mt5-base",
"base_model:finetune:google/mt5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-04-28T23:38:47Z | ---
license: apache-2.0
base_model: google/mt5-base
tags:
- generated_from_trainer
model-index:
- name: mt5-base-bcoqa
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. -->
# mt5-base-bcoqa
This model is a fine-tuned version of [google/mt5-base](https://huggingface.co/google/mt5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0503
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.7933 | 0.1 | 3500 | 1.4891 |
| 1.594 | 0.2 | 7000 | 1.3193 |
| 1.4983 | 0.3 | 10500 | 1.2550 |
| 1.4311 | 0.4 | 14000 | 1.2163 |
| 1.4343 | 0.5 | 17500 | 1.1723 |
| 1.3635 | 0.61 | 21000 | 1.1518 |
| 1.3782 | 0.71 | 24500 | 1.1331 |
| 1.3782 | 0.81 | 28000 | 1.1126 |
| 1.3091 | 0.91 | 31500 | 1.1197 |
| 1.2328 | 1.01 | 35000 | 1.0967 |
| 1.2605 | 1.11 | 38500 | 1.0892 |
| 1.183 | 1.21 | 42000 | 1.0872 |
| 1.1713 | 1.31 | 45500 | 1.0963 |
| 1.2369 | 1.41 | 49000 | 1.0696 |
| 1.2542 | 1.51 | 52500 | 1.0672 |
| 1.2226 | 1.61 | 56000 | 1.0608 |
| 1.2013 | 1.72 | 59500 | 1.0538 |
| 1.1776 | 1.82 | 63000 | 1.0516 |
| 1.191 | 1.92 | 66500 | 1.0503 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
|
enriq3/vesttieandtux2 | enriq3 | 2024-04-29T22:53:41Z | 0 | 0 | diffusers | [
"diffusers",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-04-29T22:48:50Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### vesttieandtux2 Dreambooth model trained by enriq3 with TheLastBen's fast-DreamBooth notebook
|
Hemg/Deepfake-image | Hemg | 2024-04-29T22:52:53Z | 27 | 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 | 2024-04-29T15:19:13Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: Deepfake-image
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. -->
# Deepfake-image
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: 0.0662
- Accuracy: 0.9743
## 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: 128
- eval_batch_size: 128
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 512
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2672 | 1.0 | 297 | 0.1128 | 0.9577 |
| 0.0958 | 2.0 | 595 | 0.0953 | 0.9634 |
| 0.0816 | 3.0 | 892 | 0.0776 | 0.9694 |
| 0.0712 | 4.0 | 1190 | 0.0746 | 0.9707 |
| 0.0647 | 5.0 | 1487 | 0.0680 | 0.9726 |
| 0.0616 | 6.0 | 1785 | 0.0656 | 0.9735 |
| 0.0565 | 7.0 | 2082 | 0.0676 | 0.9736 |
| 0.0533 | 7.99 | 2376 | 0.0662 | 0.9743 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.15.2
|
webnizam/llama3-8b-unintended-consequences | webnizam | 2024-04-29T22:43:43Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T10:21:38Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** webnizam
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BotoxBernd/SQL-Generation-mistral-7B-v0.2 | BotoxBernd | 2024-04-29T22:43:32Z | 32 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T22:41:02Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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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] |
lunarsylph/mooncell_v33 | lunarsylph | 2024-04-29T22:40:24Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T22:20:17Z | ---
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] |
aniketarahane/autotrain-omkul-hydox | aniketarahane | 2024-04-29T22:29:18Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T22:04:36Z | ---
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)
``` |
tingting/llama3_lora_model_Data_3200 | tingting | 2024-04-29T22:28:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T22:28:38Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** tingting
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
stablediffusionapi/nagatsukimix | stablediffusionapi | 2024-04-29T22:25:56Z | 0 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-04-29T22:23:09Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# nagatsuki_mix API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "nagatsukimix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/nagatsukimix)
Model link: [View model](https://modelslab.com/models/nagatsukimix)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "nagatsukimix",
"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** |
tingting/mistral_lora_model_Data_50 | tingting | 2024-04-29T22:14:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T22:13:42Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-bnb-4bit
---
# Uploaded model
- **Developed by:** tingting
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-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)
|
stablediffusionapi/kisaragimix | stablediffusionapi | 2024-04-29T22:12:01Z | 3 | 0 | diffusers | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-04-29T22:09:47Z | ---
license: creativeml-openrail-m
tags:
- modelslab.com
- stable-diffusion-api
- text-to-image
- ultra-realistic
pinned: true
---
# kisaragi_mix API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "kisaragimix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/kisaragimix)
Model link: [View model](https://modelslab.com/models/kisaragimix)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "kisaragimix",
"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** |
tingting/llama3_lora_model_Data_400 | tingting | 2024-04-29T22:05:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T22:04:46Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** tingting
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
bclavie/JaColBERTv2 | bclavie | 2024-04-29T21:59:24Z | 2,811 | 16 | RAGatouille | [
"RAGatouille",
"safetensors",
"bert",
"ColBERT",
"sentence-similarity",
"ja",
"dataset:bclavie/mmarco-japanese-hard-negatives",
"dataset:unicamp-dl/mmarco",
"arxiv:2312.16144",
"arxiv:2310.19349",
"arxiv:2112.01488",
"base_model:bclavie/JaColBERT",
"base_model:finetune:bclavie/JaColBERT",
"license:mit",
"region:us"
] | sentence-similarity | 2024-03-02T18:34:41Z | ---
inference: false
datasets:
- bclavie/mmarco-japanese-hard-negatives
- unicamp-dl/mmarco
language:
- ja
pipeline_tag: sentence-similarity
tags:
- ColBERT
base_model:
- cl-tohoku/bert-base-japanese-v3
- bclavie/JaColBERT
license: mit
library_name: RAGatouille
---
First version of JaColBERTv2. Weights might be updated in the next few days.
Current early checkpoint is fully functional and outperforms multilingual-e5-large, BGE-M3 and JaColBERT in early results, but full evaluation TBD.# Intro
> There is currently no JaColBERTv2 technical report. For an overall idea, you can refer to the JaColBERTv1 [arXiv Report](https://arxiv.org/abs/2312.16144)
If you just want to check out how to use the model, please check out the [Usage section](#usage) below!
Welcome to JaColBERT version 2, the second release of JaColBERT, a Japanese-only document retrieval model based on [ColBERT](https://github.com/stanford-futuredata/ColBERT).
JaColBERTv2 is a model that offers very strong out-of-domain generalisation. Having been only trained on a single dataset (MMarco), it reaches state-of-the-art performance.
JaColBERTv2 was initialised off JaColBERTv1 and trained using knowledge distillation with 31 negative examples per positive example. It was trained for 250k steps using a batch size of 32.
The information on this model card is minimal and intends to give a quick overview! It'll be updated once benchmarking is complete and a longer report is available.
# Why use a ColBERT-like approach for your RAG application?
Most retrieval methods have strong tradeoffs:
* __Traditional sparse approaches__, such as BM25, are strong baselines, __but__ do not leverage any semantic understanding, and thus hit a hard ceiling.
* __Cross-encoder__ retriever methods are powerful, __but__ prohibitively expensive over large datasets: they must process the query against every single known document to be able to output scores.
* __Dense retrieval__ methods, using dense embeddings in vector databases, are lightweight and perform well, __but__ are __not__ data-efficient (they often require hundreds of millions if not billions of training examples pairs to reach state-of-the-art performance) and generalise poorly in a lot of cases. This makes sense: representing every single aspect of a document, to be able to match it to any potential query, into a single vector is an extremely hard problem.
ColBERT and its variants, including JaColBERTv2, aim to combine the best of all worlds: by representing the documents as essentially *bags-of-embeddings*, we obtain superior performance and strong out-of-domain generalisation at much lower compute cost than cross-encoders.
# Training
### Training Data
The model is trained on the japanese split of MMARCO. It uses ColBERTv2 style training, meaning the model uses knowledge distillation from a cross-encoder model. We use the same cross-encoder scores as the original English ColBERTv2 training (as MMarco is a translated dataset, these are more or less well mapped). These scores are available [here](https://huggingface.co/colbert-ir/colbertv2.0_msmarco_64way).
Unlike English ColBERTv2, we use nway=32 rather than nway=64, meaning that we provide the model with 31 negative examples per positive examples. Furthermore, we downsample the original sets of triplets from over 19 million to 8 million examples.
### Training Method
JColBERT is trained for a single epoch (1-pass over every triplet, meaning 250000 trainings teps) on 8 NVidia A100 40GB GPUs. Total training time was around 30 hours.
JColBERT is initialised from [JaColBERT](https://huggingface.co/bclavie/JaColBERT), which itselfs builds upon Tohoku University's excellent [bert-base-japanese-v3](https://huggingface.co/cl-tohoku/bert-base-japanese-v3). Our experiments benefitted strongly from Nagoya University's work on building [strong Japanese SimCSE models](https://arxiv.org/abs/2310.19349), among other work.
JaColBERT is trained with an overall batch size of 32 and a learning rate of 1e-5, and a warmup of 20000 steps. Limited exploration was performed but those defaults outperformed other experiments.
JaColBERT, as mentioned above, uses knowledge distillation using cross-encoder scores generated by a MiniLM cross-encoder on the English version of MS Marco. Please refer to the original [ColBERTv2 paper](https://arxiv.org/abs/2112.01488) for more information on this approach.
# Results
We present the first results, on two datasets: JQaRa, a passage retrieval task composed of questions and wikipedia passages containing the answer, and JSQuAD, the Japanese translation of SQuAD. (Further evaluations on MIRACL and TyDi are running, but fairly slow due to how long it takes to run e5-large and bge-m3.)
JaColBERTv2 reaches state-of-the-art results on both datasets, outperforming models with 5x more parameters.
| | | | JQaRa | | | | JSQuAD | | |
| ------------------- | --- | --------- | --------- | --------- | --------- | --- | --------- | --------- | --------- |
| | | NDCG@10 | MRR@10 | NDCG@100 | MRR@100 | | R@1 | R@5 | R@10 |
| JaColBERTv2 | | **0.585** | **0.836** | **0.753** | **0.838** | | **0.921** | **0.977** | **0.982** |
| JaColBERT | | 0.549 | 0.811 | 0.730 | 0.814 | | 0.913 | 0.972 | 0.978 |
| bge-m3+all | | 0.576 | 0.818 | 0.745 | 0.820 | | N/A | N/A | N/A |
| bg3-m3+dense | | 0.539 | 0.785 | 0.721 | 0.788 | | 0.850 | 0.959 | 0.976 |
| m-e5-large | | 0.554 | 0.799 | 0.731 | 0.801 | | 0.865 | 0.966 | 0.977 |
| m-e5-base | | 0.471 | 0.727 | 0.673 | 0.731 | | *0.838* | *0.955* | 0.973 |
| m-e5-small | | 0.492 | 0.729 | 0.689 | 0.733 | | *0.840* | *0.954* | 0.973 |
| GLuCoSE | | 0.308 | 0.518 | 0.564 | 0.527 | | 0.645 | 0.846 | 0.897 |
| sup-simcse-ja-base | | 0.324 | 0.541 | 0.572 | 0.550 | | 0.632 | 0.849 | 0.897 |
| sup-simcse-ja-large | | 0.356 | 0.575 | 0.596 | 0.583 | | 0.603 | 0.833 | 0.889 |
| fio-base-v0.1 | | 0.372 | 0.616 | 0.608 | 0.622 | | 0.700 | 0.879 | 0.924 |
| | | | | | | | | | |
# Usage
## Installation
JaColBERT works using ColBERT+RAGatouille. You can install it and all its necessary dependencies by running:
```sh
pip install -U ragatouille
```
For further examples on how to use RAGatouille with ColBERT models, you can check out the [`examples` section it the github repository](https://github.com/bclavie/RAGatouille/tree/main/examples).
Specifically, example 01 shows how to build/query an index, 04 shows how you can use JaColBERTv2 as a re-ranker, and 06 shows how to use JaColBERTv2 for in-memory searching rather than using an index.
Notably, RAGatouille has metadata support, so check the examples out if it's something you need!
## Encoding and querying documents without an index
If you want to use JaColBERTv2 without building an index, it's very simple, you just need to load the model, `encode()` some documents, and then `search_encoded_docs()`:
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("bclavie/JaColBERTv2")
RAG.encode(['document_1', 'document_2', ...])
RAG.search_encoded_docs(query="your search query")
```
Subsequent calls to `encode()` will add to the existing in-memory collection. If you want to empty it, simply run `RAG.clear_encoded_docs()`.
## Indexing
In order for the late-interaction retrieval approach used by ColBERT to work, you must first build your index.
Think of it like using an embedding model, like e5, to embed all your documents and storing them in a vector database.
Indexing is the slowest step retrieval is extremely quick. There are some tricks to speed it up, but the default settings work fairly well:
```python
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("bclavie/JaColBERT")
documents = [ "マクドナルドのフライドポテトの少量のカロリーはいくつですか？マクドナルドの小さなフライドポテトのカロリーマクドナルドのウェブサイトには、次のように記載されています。フライドポテトの小さな注文で230カロリーケチャップで25カロリー、ケチャップパケットで15カロリー。",]
RAG.index(name="My_first_index", collection=documents)
```
The index files are stored, by default, at `.ragatouille/colbert/indexes/{index_name}`.
And that's it! Let it run, and your index and all its representations (compressed to 2bits by default) will have been generated.
## Searching
Once you have created an index, searching through it is just as simple! If you're in the same session and `RAG` is still loaded, you can directly search the newly created index.
Otherwise, you'll want to load it from disk:
```python
RAG = RAGPretrainedModel.from_index(".ragatouille/colbert/indexes/My_first_index")
```
And then query it:
```python
RAG.search(query="QUERY")
> [{'content': 'TEXT OF DOCUMENT ONE',
'score': float,
'rank': 1,
'document_id': str,
'document_metadata': dict},
{'content': 'TEXT OF DOCUMENT TWO',
'score': float,
'rank': 2,
'document_id': str,
'document_metadata': dict},
[...]
]
```
# Citation
If you'd like to cite this work, please cite the JaColBERT technical report:
```
@misc{clavié2023jacolbert,
title={JaColBERT and Hard Negatives, Towards Better Japanese-First Embeddings for Retrieval: Early Technical Report},
author={Benjamin Clavié},
year={2023},
eprint={2312.16144},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
``` |
tingting/llama3_lora_model_Data_50 | tingting | 2024-04-29T21:54:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T21:53:46Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** tingting
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
GreenBitAI/Llama-2-13B-Chat-layer-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:52:48Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-15T06:55:37Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Llama-2-13B-channel-mix-bpw-2.2 | GreenBitAI | 2024-04-29T21:52:08Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-07T18:50:02Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Llama-2-13B-Chat-layer-mix-bpw-2.2 | GreenBitAI | 2024-04-29T21:51:50Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-15T06:55:10Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
YasaminAbb/Idefics2-8b-multimodal | YasaminAbb | 2024-04-29T21:51:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T21:08:08Z | ---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
GreenBitAI/Llama-2-13B-channel-mix-bpw-2.5 | GreenBitAI | 2024-04-29T21:51:37Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-07T19:29:37Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Llama-2-13B-channel-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:51:25Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-07T18:53:53Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Llama-2-13B-layer-mix-bpw-2.5 | GreenBitAI | 2024-04-29T21:50:52Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-09T19:43:33Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Llama-2-7B-channel-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:50:42Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-07T18:42:13Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Llama-2-7B-Chat-layer-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:50:03Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-14T14:40:57Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
hugozanini/fine-tunning-tutorial | hugozanini | 2024-04-29T21:49:50Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T21:45:04Z | ---
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]
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[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:**
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**APA:**
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## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Litzy619/O0428HMA1 | Litzy619 | 2024-04-29T21:48:39Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"base_model:finetune:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T06:24:48Z | ---
license: apache-2.0
base_model: allenai/OLMo-1B
tags:
- generated_from_trainer
model-index:
- name: O0428HMA1
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. -->
# O0428HMA1
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0540
## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.8099 | 0.09 | 10 | 0.1912 |
| 0.1798 | 0.18 | 20 | 0.1531 |
| 0.1494 | 0.27 | 30 | 0.1613 |
| 0.1557 | 0.36 | 40 | 0.1576 |
| 0.1505 | 0.45 | 50 | 0.1489 |
| 0.1502 | 0.54 | 60 | 0.1467 |
| 0.1486 | 0.63 | 70 | 0.1468 |
| 0.1478 | 0.73 | 80 | 0.1530 |
| 0.1418 | 0.82 | 90 | 0.1254 |
| 0.1393 | 0.91 | 100 | 0.1264 |
| 0.114 | 1.0 | 110 | 0.0868 |
| 0.0713 | 1.09 | 120 | 0.0721 |
| 0.0753 | 1.18 | 130 | 0.1096 |
| 0.0868 | 1.27 | 140 | 0.0649 |
| 0.124 | 1.36 | 150 | 0.0621 |
| 0.058 | 1.45 | 160 | 0.0572 |
| 0.0688 | 1.54 | 170 | 0.0600 |
| 0.0626 | 1.63 | 180 | 0.0618 |
| 0.0673 | 1.72 | 190 | 0.0575 |
| 0.0579 | 1.81 | 200 | 0.0574 |
| 0.0592 | 1.9 | 210 | 0.0554 |
| 0.0577 | 1.99 | 220 | 0.0546 |
| 0.0568 | 2.08 | 230 | 0.0548 |
| 0.0807 | 2.18 | 240 | 0.0912 |
| 0.0728 | 2.27 | 250 | 0.0610 |
| 0.0629 | 2.36 | 260 | 0.0589 |
| 0.0554 | 2.45 | 270 | 0.0552 |
| 0.0523 | 2.54 | 280 | 0.0547 |
| 0.0544 | 2.63 | 290 | 0.0560 |
| 0.0551 | 2.72 | 300 | 0.0541 |
| 0.056 | 2.81 | 310 | 0.0539 |
| 0.0574 | 2.9 | 320 | 0.0540 |
| 0.0586 | 2.99 | 330 | 0.0540 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
|
GreenBitAI/Mistral-7B-v0.1-channel-mix-bpw-2.5 | GreenBitAI | 2024-04-29T21:46:20Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-07T19:22:24Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Mistral-7B-v0.1-channel-mix-bpw-2.2 | GreenBitAI | 2024-04-29T21:46:11Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-04T20:16:08Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Mistral-7B-Instruct-v0.2-layer-mix-bpw-2.2 | GreenBitAI | 2024-04-29T21:45:39Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-02T19:37:39Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/Mistral-7B-Instruct-v0.2-layer-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:45:21Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-02T18:43:02Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
|
GreenBitAI/01-Yi-9B-channel-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:40:07Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-15T09:23:16Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
GreenBitAI/01-Yi-9B-layer-mix-bpw-2.5 | GreenBitAI | 2024-04-29T21:39:46Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-15T09:11:51Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
Odeusys/mistral_emails | Odeusys | 2024-04-29T21:39:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T18:48:52Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **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. -->
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## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
GreenBitAI/01-Yi-9B-layer-mix-bpw-2.2 | GreenBitAI | 2024-04-29T21:39:09Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-15T09:12:02Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
GreenBitAI/01-Yi-34B-layer-mix-bpw-2.5 | GreenBitAI | 2024-04-29T21:38:44Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-09T19:12:27Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
nem012/gemma2b-lrl | nem012 | 2024-04-29T21:37:14Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T19:01:04Z | ---
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
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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GreenBitAI/Qwen-1.5-14B-channel-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:36:31Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-14T12:58:28Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
GreenBitAI/Qwen-1.5-4B-channel-mix-bpw-3.0 | GreenBitAI | 2024-04-29T21:36:09Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-14T12:57:43Z | ---
license: apache-2.0
---
# GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
moetezsa/OpenHermes_finetued_on_scigen_v2 | moetezsa | 2024-04-29T21:35:54Z | 1 | 1 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:teknium/OpenHermes-2.5-Mistral-7B",
"base_model:adapter:teknium/OpenHermes-2.5-Mistral-7B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-29T20:25:16Z | ---
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: teknium/OpenHermes-2.5-Mistral-7B
model-index:
- name: OpenHermes_finetued_on_scigen_v2
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. -->
# OpenHermes_finetued_on_scigen_v2
This model is a fine-tuned version of [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 64
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 64
- total_train_batch_size: 4096
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 30
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.19.0
- Tokenizers 0.19.1 |
nem012/gemma2b-lrm | nem012 | 2024-04-29T21:34:21Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T19:06:18Z | ---
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.
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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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
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## 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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andersonbcdefg/tiny-emb-2024-04-29_21-26-53 | andersonbcdefg | 2024-04-29T21:34:07Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-04-29T21:26:53Z | ---
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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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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whizzzzkid/nose_gemma_ft9 | whizzzzkid | 2024-04-29T21:32:40Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-24T21:21:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Software
[More Information Needed]
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adperem/entregable2 | adperem | 2024-04-29T21:31:34Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-04-29T21:31:31Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
Joanton/sd-class-butterflies-32 | Joanton | 2024-04-29T21:28:15Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | 2024-04-29T21:28:05Z | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Joanton/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview-qdora-v0.1 | sosoai | 2024-04-29T21:25:56Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T21:15:59Z | base model = beomi-Llama-3-Open-Ko-8B-Instruct-preview
base model = hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview (Trained via Axolotl)
dora_train config
(from fsdp_qlora repo)
```
export CUDA_VISIBLE_DEVICES=0,1
python train.py \
--train_type bnb_dora \
--model_name sosoai/hansoldeco-beomi-Llama-3-Open-Ko-8B-Instruct-preview \
--dataset orca_math \
--dataset_samples 193789 \
--batch_size 4 \
--context_length 8192 \
--gradient_accumulation_steps 2 \
--sharding_strategy full_shard \
--use_gradient_checkpointing true \
--reentrant_checkpointing true \
--use_cpu_offload false \
--use_activation_cpu_offload false \
--log_to wandb \
--project_name "sosoai-fsdp-quantized-ft-exps" \
--save_model true \
--output_dir models/llama-8b-orca-math-10k-bnb-QDoRA
```
Dataset = hansoldeco domain own dataset (Non open)
Dataset = kuotient/orca-math-word-problems-193k-korean
|
ShenaoZhang/0.01_4iters_bs256_nodpo_full6w_userresponse_iter_1 | ShenaoZhang | 2024-04-29T21:22:04Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"base_model:finetune:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T20:23:41Z | ---
license: mit
base_model: HuggingFaceH4/mistral-7b-sft-beta
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- updated
- original
model-index:
- name: 0.01_4iters_bs256_nodpo_full6w_userresponse_iter_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.01_4iters_bs256_nodpo_full6w_userresponse_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
rahaiduc/paisajes | rahaiduc | 2024-04-29T21:18:30Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-04-29T21:18:26Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
GreenBitAI/Llama-3-70B-layer-mix-bpw-4.0 | GreenBitAI | 2024-04-29T21:10:16Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T08:35:58Z | ---
license: apache-2.0
---
# GreenBit LLaMA
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information. |
sanjay920/mistral-9.5-fc-yaml-v1 | sanjay920 | 2024-04-29T21:01:51Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llama-factory",
"freeze",
"generated_from_trainer",
"conversational",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T20:56:01Z | ---
license: other
base_model: models/rubra-9.5b-base
tags:
- llama-factory
- freeze
- generated_from_trainer
model-index:
- name: rubra-9.5b-yaml_v1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# rubra-9.5b-yaml_v1
This model is a fine-tuned version of [models/rubra-9.5b-base](https://huggingface.co/models/rubra-9.5b-base) on the yaml-simple, the yaml-multiple, the yaml-parallel, the yaml-parallel_multiple, the yaml-relevance, the yaml-sql, the yaml-rest, the yaml-gptscript-x8 and the yaml-chain_of_function datasets.
## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 9.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
hugodk-sch/norllm-ai-normistral-7b-sft-qlora | hugodk-sch | 2024-04-29T21:01:43Z | 5 | 1 | peft | [
"peft",
"safetensors",
"mistral",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:hugodk-sch/aftonposten_title_sft",
"base_model:NorwAI/NorwAI-Mistral-7B",
"base_model:adapter:NorwAI/NorwAI-Mistral-7B",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2024-04-29T17:57:16Z | ---
library_name: peft
tags:
- alignment-handbook
- trl
- sft
- generated_from_trainer
base_model: NorLLM-AI/NorMistral-7B
datasets:
- hugodk-sch/aftonposten_title_sft
model-index:
- name: norllm-ai-normistral-7b-sft-qlora
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. -->
# norllm-ai-normistral-7b-sft-qlora
This model is a fine-tuned version of [NorLLM-AI/NorMistral-7B](https://huggingface.co/NorLLM-AI/NorMistral-7B) on the hugodk-sch/aftonposten_title_sft dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4403
## 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: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.7274 | 1.0 | 274 | 1.9432 |
| 1.1514 | 2.0 | 549 | 1.7111 |
| 0.645 | 3.0 | 823 | 1.5109 |
| 0.4291 | 4.0 | 1098 | 1.4415 |
| 0.3392 | 4.99 | 1370 | 1.4403 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.1 |
nicoprofeai/llama3-8b-oig-unsloth-merged | nicoprofeai | 2024-04-29T21:00:42Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T21:00:41Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** nicorprofe
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
sergeyvi4ev/all-MiniLM-RAGSQL-code | sergeyvi4ev | 2024-04-29T21:00:29Z | 132 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"dataset:sergeyvi4ev/sql_questions_triplets",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-04-29T21:00:17Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
datasets:
- sergeyvi4ev/sql_questions_triplets
---
# sergeyvi4ev/all-MiniLM-ragsql-code
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('sergeyvi4ev/all-MiniLM-ragsql-code')
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=sergeyvi4ev/all-MiniLM-ragsql-code)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 41 with parameters:
```
{'batch_size': 128}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 41,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, '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 --> |
hltcoe/psq_translation_tables | hltcoe | 2024-04-29T20:57:26Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T03:09:22Z | ---
license: mit
---
# Translation Tables for Probablistic Structured Queries
This repository contains the raw translation tables for tha package [`fast_psq`](https://github.com/hltcoe/PSQ).
Please refer to the GitHub for more information.
The following is a brief example for using the tables.
## Get started
`fast_psq` is available on PyPI.
```bash
pip install fast_psq ir_datasets ir_measures
```
The following is an example indexing command.
```bash
python -m fast_psq.index \
--doc_file irds:neuclir/1/zh/trec-2022 \
--lang zh \
--psq_file hltcoe/psq_translation_tables:zh.table.dict.gz \
--min_translation_prob 0.00010 \
--max_translation_alternatives 64 \
--max_translation_cdf 0.99 \
--docid doc_id \
--title title \
--body text \
--min_translation_prob 1e-4 \
--max_translation_alternatives 64 \
--output_dir ./indexes/neuclir-zh.f32/ \
--compression \
--nworkers 64
```
The following command is an example for searching.
```bash
python -m fast_psq.search \
--query_source irds:neuclir/1/zh/trec-2022 \
--query_field title \
--index_dir ./indexes/neuclir-zh.f32/ \
--qrels irds:neuclir/1/zh/trec-2022 \
--query_lang en \
--output_file ./neuclir-zh.en.title.f32.trec
```
## Citation
```bibtex
@article{psq-repro,
title = {Efficiency-Effectiveness Tradeoff of Probabilistic Structured Queries for Cross-Language Information Retrieval},
author = {Eugene Yang and Suraj Nair and Dawn Lawrie and James Mayfield and Douglas W. Oard and Kevin Duh},
journal = {arXiv preprint arXiv},
year = {2024}
}
``` |
TIGER-Lab/cosxl | TIGER-Lab | 2024-04-29T20:55:13Z | 0 | 1 | null | [
"text-to-image",
"license:other",
"region:us"
] | text-to-image | 2024-04-29T20:51:49Z | ---
pipeline_tag: text-to-image
license: other
license_name: cosxl-nc-community
license_link: LICENSE
extra_gated_prompt: "STABILITY AI NON-COMMERCIAL RESEARCH COMMUNITY LICENSE AGREEMENT\t Dated: April 7th, 2024\nBy clicking “I Accept” below or by using or distributing any portion or element of the Models, Software, Software Products or Derivative Works, you agree to the terms of this License. If you do not agree to this License, then you do not have any rights to use the Software Products or Derivative Works through this License, and you must immediately cease using the Software Products or Derivative Works. If you are agreeing to be bound by the terms of this License on behalf of your employer or other entity, you represent and warrant to Stability AI that you have full legal authority to bind your employer or such entity to this License. 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Non-Commercial Uses does not include any production use of the Software Products or any Derivative Works. \n\"Stability AI\" or \"we\" means Stability AI Ltd. and its affiliates.\n\n\"Software\" means Stability AI’s proprietary software made available under this Agreement. \n“Software Products” means the Models, Software and Documentation, individually or in any combination. \n\n\n1. License Rights and Redistribution. \n a. Subject to your compliance with this Agreement, the AUP (which is hereby incorporated herein by reference), and the Documentation, Stability AI grants you a non-exclusive, worldwide, non-transferable, non-sublicensable, revocable, royalty free and limited license under Stability AI’s intellectual property or other rights owned or controlled by Stability AI embodied in the Software Products to use, reproduce, distribute, and create Derivative Works of, the Software Products, in each case for Non-Commercial Uses only. \n b. You may not use the Software Products or Derivative Works to enable third parties to use the Software Products or Derivative Works as part of your hosted service or via your APIs, whether you are adding substantial additional functionality thereto or not. Merely distributing the Software Products or Derivative Works for download online without offering any related service (ex. by distributing the Models on HuggingFace) is not a violation of this subsection. If you wish to use the Software Products or any Derivative Works for commercial or production use or you wish to make the Software Products or any Derivative Works available to third parties via your hosted service or your APIs, contact Stability AI at https://stability.ai/contact. \n c. If you distribute or make the Software Products, or any Derivative Works thereof, available to a third party, the Software Products, Derivative Works, or any portion thereof, respectively, will remain subject to this Agreement and you must (i) provide a copy of this Agreement to such third party, and (ii) retain the following attribution notice within a \"Notice\" text file distributed as a part of such copies: \"This Stability AI Model is licensed under the Stability AI Non-Commercial Research Community License, Copyright (c) Stability AI Ltd. All Rights Reserved.” If you create a Derivative Work of a Software Product, you may add your own attribution notices to the Notice file included with the Software Product, provided that you clearly indicate which attributions apply to the Software Product and you must state in the NOTICE file that you changed the Software Product and how it was modified.\n2. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE SOFTWARE PRODUCTS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \"AS IS\" BASIS, WITHOUT WARRANTIES OF ANY KIND, EITHER EXPRESS OR IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE SOFTWARE PRODUCTS, DERIVATIVE WORKS OR ANY OUTPUT OR RESULTS AND ASSUME ANY RISKS ASSOCIATED WITH YOUR USE OF THE SOFTWARE PRODUCTS, DERIVATIVE WORKS AND ANY OUTPUT AND RESULTS. 3. Limitation of Liability. IN NO EVENT WILL STABILITY AI OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT, FOR ANY LOST PROFITS OR ANY DIRECT, INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN IF STABILITY AI OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING. 4. Intellectual Property.\n a. No trademark licenses are granted under this Agreement, and in connection with the Software Products or Derivative Works, neither Stability AI nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Software Products or Derivative Works. \n b. Subject to Stability AI’s ownership of the Software Products and Derivative Works made by or for Stability AI, with respect to any Derivative Works that are made by you, as between you and Stability AI, you are and will be the owner of such Derivative Works \n c. If you institute litigation or other proceedings against Stability AI (including a cross-claim or counterclaim in a lawsuit) alleging that the Software Products, Derivative Works or associated outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Stability AI from and against any claim by any third party arising out of or related to your use or distribution of the Software Products or Derivative Works in violation of this Agreement. \n5. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Software Products and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Stability AI may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of any Software Products or Derivative Works. Sections 2-4 shall survive the termination of this Agreement. \n6. Governing Law. This Agreement will be governed by and construed in accordance with the laws of the United States and the State of California without regard to choice of law \n principles. "
extra_gated_description: CosXL License Agreement
extra_gated_button_content: Submit
extra_gated_fields:
Name: text
Company Name (if applicable): text
Email: text
By clicking here, you accept the License agreement, and will use the Software Products and Derivative Works for non-commercial or research purposes only: checkbox
---
# Cos Stable Diffusion XL 1.0 and Cos Stable Diffusion XL 1.0 Edit
Cos Stable Diffusion XL 1.0 Base is tuned to use a Cosine-Continuous EDM VPred schedule. The most notable feature of this schedule change is its capacity to produce the full color range from pitch black to pure white, alongside more subtle improvements to the model's rate-of-change to images across each step.
Edit Stable Diffusion XL 1.0 Base is tuned to use a Cosine-Continuous EDM VPred schedule, and then upgraded to perform instructed image editing. This model takes a source image as input alongside a prompt, and interprets the prompt as an instruction for how to alter the image.
## Usage
It is recommended to use [Stable Swarm UI](https://github.com/Stability-AI/StableSwarmUI) to inference the CosXL model and the edit model.
Cos Stable Diffusion XL 1.0 can also be used as a regular checkpoint in [ComfyUI](https://github.com/comfyanonymous/ComfyUI)
For an example on how to use Edit Stable Diffusion XL 1.0 see [ComfyUI Example](https://comfyanonymous.github.io/ComfyUI_examples/edit_models/)
## Uses
### Direct Use
The model is for research purposes only. This model is not intended to be state of the art or for consumer use. |
Georgeb254/whisper-small-hi | Georgeb254 | 2024-04-29T20:49:32Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"hi",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-04-28T16:26:59Z | ---
language:
- hi
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Hi - Sanchit Gandhi George
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
config: hi
split: None
args: 'config: hi, split: test'
metrics:
- name: Wer
type: wer
value: 45.00362932494556
---
<!-- 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. -->
# Whisper Small Hi - Sanchit Gandhi George
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4181
- Wer: 45.0036
## 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
- lr_scheduler_warmup_steps: 50
- training_steps: 400
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3791 | 0.8 | 100 | 0.4591 | 50.6170 |
| 0.2183 | 1.6 | 200 | 0.4247 | 47.1570 |
| 0.0889 | 2.4 | 300 | 0.4151 | 45.3666 |
| 0.0628 | 3.2 | 400 | 0.4181 | 45.0036 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
moczard/ppo-Pyramids | moczard | 2024-04-29T20:49:12Z | 8 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2024-04-29T20:49:08Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
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: moczard/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
Revrse/icon-labelling | Revrse | 2024-04-29T20:49:12Z | 0 | 0 | null | [
"region:us"
] | null | 2024-04-29T12:05:54Z | ```python
from ultralyticsplus import YOLO, postprocess_classify_output
# load model
model = YOLO('Revrse/icon-labelling')
# set image
image = 'test/155.png'
# perform inference
prediction = model(image)
# observe results
print(prediction[0].names[prediction[0].probs.top1])
``` |
umairaziz719/summarization_model | umairaziz719 | 2024-04-29T20:49:05Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-04-24T13:29:36Z | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarization_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# summarization_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4079
- Rouge1: 0.1935
- Rouge2: 0.0918
- Rougel: 0.1631
- Rougelsum: 0.1629
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.4772 | 0.1595 | 0.0642 | 0.1328 | 0.1326 | 19.0 |
| No log | 2.0 | 124 | 2.4328 | 0.1864 | 0.087 | 0.1582 | 0.1578 | 19.0 |
| No log | 3.0 | 186 | 2.4154 | 0.1933 | 0.0916 | 0.163 | 0.1627 | 19.0 |
| No log | 4.0 | 248 | 2.4079 | 0.1935 | 0.0918 | 0.1631 | 0.1629 | 19.0 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
epsilon3/cbt-llama3-8b-finetuned | epsilon3 | 2024-04-29T20:48:48Z | 86 | 3 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-25T16:16:40Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for CBTLlama: Fine Tuning LLaMA for CBT Thought Distortions
## Model Details
### Model Description
Developed by David Schiff, this Hugging Face transformers model, dubbed CBTLlama, is fine-tuned on the LLaMA-3 8B architecture.
It is specifically tailored to enhance Cognitive Behavioral Therapy (CBT) by detecting thought distortions and raising possible challenges for them.
The model is trained on synthetic data generated by claude that includes a variety of different demographic and emotional states to produce CBT scenarios,
aiming to make CBT more accessible and effective.
This model is not inteded to use without any professional assistance!
## Disclaimer
### Limitation of Liability
The developer of CBTLlama ("the model") provides this model on an "AS IS" basis and makes no warranties regarding its performance, accuracy, reliability, or suitability for any particular task or to achieve any specific results. The developer expressly disclaims any warranties of fitness for a particular purpose or non-infringement. In no event shall the developer be liable for any direct, indirect, incidental, special, exemplary, or consequential damages (including, but not limited to, procurement of substitute goods or services; loss of use, data, or profits; or business interruption) however caused and on any theory of liability, whether in contract, strict liability, or tort (including negligence or otherwise) arising in any way out of the use of this model, even if advised of the possibility of such damage.
This model is not intended to be a substitute for professional advice, diagnosis, or treatment. Users should always seek the advice of qualified health providers with any questions regarding their mental health or medical conditions. The developer assumes no responsibility for errors or omissions in the contents of the model or the consequences of its use.
- **Developed by:** David Schiff
- **Model type:** Fine-tuned LLaMA-3 8B
- **Language(s) (NLP):** English
- **License:** MIT
- **Finetuned from model:** LLaMA-3 8B
### Model Sources
- **Repository:** (URL to GitHub or similar)
- **Paper [optional]:** (Link to any published research or documentation)
- **Demo [optional]:** (Link to a model demonstration or interactive API)
## Uses
### Direct Use
CBTLlama is intended to be used directly by mental health practitioners to train their patients in identifying cognitive distortions
and challenging them.
### Downstream Use
While primarily designed for CBT, this model could be extended to other forms of therapy that require scenario generation or tailored mental health interventions.
### Out-of-Scope Use
This model is not intended to replace therapists or make clinical decisions. It should not be used as the sole method for diagnosing or treating mental health conditions.
## Bias, Risks, and Limitations
The model might exhibit biases based on the demographic data it was trained on. Users should critically assess the scenarios it generates,
especially when using the model with diverse populations.
### Recommendations
It is recommended that all outputs be reviewed by qualified professionals to ensure they are appropriate and sensitive to individual circumstances.
## How to Get Started with the Model
To start using CBTLlama, you can access the model via the Hugging Face API or download it directly from the repository.
## Training Details
### Training Data
The training data comprised simulated CBT scenarios generated by Claude, based on diverse demographic profiles and emotional states, ensuring broad coverage of potential therapy situations.
dataset:
https://huggingface.co/datasets/epsilon3/cbt-triggering-events
https://huggingface.co/datasets/epsilon3/cbt-cognitive-distortions-analysis
### Training Procedure
#### Training Hyperparameters
- **Training regime:** Mixed precision training for efficiency
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Metrics
### Results
Results indicated that CBTLlama produces highly accurate detections and challenges of thought distortions.
#### Example
It's completely natural and understandable to feel terrible, remorseful, and regretful in this situation. Letting your team down and causing them unnecessary stress due to procrastination is a difficult thing to cope with, and your emotions are valid. It's important to acknowledge and process these feelings as part of learning and growth.
Here are some potential cognitive distortions and challenges based on the situation you described:
* Overgeneralization: "I always let my team down and cause them stress."
Challenge: Recognize that this specific incident does not define a permanent pattern. Consider times when you have been responsible and met deadlines on time, which discredits the overgeneralization.
* Labeling: "I'm a terrible team member for letting this happen."
Challenge: Avoid defining yourself based on a single event. You are a complex individual with many qualities, and one mistake does not negate your overall value as a team member.
* Magnification (catastrophizing): "This one mistake ruins everything and makes me a failure."
Challenge: Put the situation into perspective. While it was an important deadline, it does not negate all your other contributions and successes. Consider how much this specific incident will matter in the long run.
* Should statements: "I should have managed my time better" or "I shouldn't have let this happen."
Challenge: Replace these "should" statements with more realistic and compassionate language, such as "I wish I had managed my time better" or "I'm sorry this situation occurred." Recognize that everyone makes mistakes and that being hard on yourself is not productive.
Remember, everyone faces challenges and makes mistakes from time to time. The most important thing is to learn from this experience, take responsibility for your actions, and find ways to prevent similar situations in the future. Be kind to yourself and focus on moving forward productively.
## Technical Specifications
### Model Architecture and Objective
The model utilizes the LLaMA-3 architecture with modifications to specifically suit CBT cognitive distortions analysis
## Citation
CBTLlama: Fine Tuning Large Language Models
For Identifying Thought Distortions
David Schiff
[email protected]
|
SallySun/llama2_chatdoctor_finetuned | SallySun | 2024-04-29T20:44:13Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2024-04-29T19:35:33Z | ---
license: mit
---
This model is fine tuned by the dataset which is produced by the chatgpt and is about the 'medical area' |
ymoslem/whisper-small-ga2en-v3.1 | ymoslem | 2024-04-29T20:41:02Z | 9 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"ga",
"en",
"dataset:ymoslem/IWSLT2023-GA-EN",
"dataset:ymoslem/FLEURS-GA-EN",
"dataset:ymoslem/BitesizeIrish-GA-EN",
"dataset:ymoslem/SpokenWords-GA-EN-MTed",
"dataset:ymoslem/Tatoeba-Speech-Irish",
"dataset:ymoslem/Wikimedia-Speech-Irish",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-04-16T17:58:43Z | ---
language:
- ga
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- ymoslem/IWSLT2023-GA-EN
- ymoslem/FLEURS-GA-EN
- ymoslem/BitesizeIrish-GA-EN
- ymoslem/SpokenWords-GA-EN-MTed
- ymoslem/Tatoeba-Speech-Irish
- ymoslem/Wikimedia-Speech-Irish
metrics:
- bleu
- wer
model-index:
- name: Whisper Small GA-EN Speech Translation
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia
type: ymoslem/IWSLT2023-GA-EN
metrics:
- name: Bleu
type: bleu
value: 27.57
- name: Wer
type: wer
value: 70.64385411976588
---
<!-- 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. -->
# Whisper Small GA-EN Speech Translation
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the IWSLT-2023, FLEURS, BiteSize, SpokenWords, Tatoeba, and Wikimedia dataset.
The best model checkpoint (this version) based on ChrF is at step 2000, epoch 1.31, and it achieves the following results on the evaluation set:
- Loss: 1.1571
- Bleu: 30.25
- Chrf: 48.12
- Wer: 64.9707
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.03
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Bleu | Chrf | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:-----:|:-----:|:---------------:|:--------:|
| 2.6685 | 0.07 | 100 | 5.05 | 20.18 | 2.0544 | 139.8919 |
| 2.4028 | 0.13 | 200 | 12.29 | 29.72 | 1.7367 | 95.5425 |
| 2.1231 | 0.2 | 300 | 14.33 | 30.77 | 1.6141 | 101.3958 |
| 1.9192 | 0.26 | 400 | 16.86 | 35.65 | 1.4778 | 91.0851 |
| 1.7129 | 0.33 | 500 | 16.77 | 37.53 | 1.3811 | 93.8766 |
| 1.5398 | 0.39 | 600 | 18.85 | 39.0 | 1.3427 | 90.2296 |
| 1.4257 | 0.46 | 700 | 25.73 | 43.3 | 1.2784 | 70.3287 |
| 1.3044 | 0.53 | 800 | 25.43 | 44.33 | 1.2274 | 72.3548 |
| 1.2626 | 0.59 | 900 | 25.09 | 44.62 | 1.1875 | 72.6249 |
| 1.2801 | 0.66 | 1000 | 25.68 | 45.53 | 1.1571 | 71.0491 |
| 1.2876 | 0.72 | 1100 | 20.62 | 41.49 | 1.2193 | 85.8622 |
| 1.2609 | 0.79 | 1200 | 29.47 | 45.04 | 1.2079 | 65.2859 |
| 1.187 | 0.85 | 1300 | 24.65 | 43.73 | 1.2086 | 72.9851 |
| 1.0342 | 0.92 | 1400 | 30.34 | 47.62 | 1.1766 | 64.3854 |
| 1.0519 | 0.98 | 1500 | 29.39 | 47.69 | 1.1425 | 64.9707 |
| 0.5473 | 1.05 | 1600 | 28.02 | 46.27 | 1.1842 | 67.6722 |
| 0.4886 | 1.12 | 1700 | 26.62 | 46.37 | 1.1845 | 76.4971 |
| 0.4354 | 1.18 | 1800 | 23.63 | 45.16 | 1.1621 | 86.1324 |
| 0.4709 | 1.25 | 1900 | 27.86 | 47.3 | 1.1544 | 73.7506 |
| 0.4802 | 1.31 | 2000 | 30.25 | 48.12 | 1.1571 | 64.9707 |
| 0.4565 | 1.38 | 2100 | 24.75 | 44.7 | 1.2095 | 77.4426 |
| 0.4797 | 1.44 | 2200 | 28.46 | 46.03 | 1.2051 | 67.1769 |
| 0.423 | 1.51 | 2300 | 28.34 | 47.65 | 1.2079 | 68.6177 |
| 0.4254 | 1.58 | 2400 | 27.78 | 46.01 | 1.2251 | 67.8523 |
| 0.4493 | 1.64 | 2500 | 26.61 | 47.8 | 1.1898 | 71.1391 |
| 0.3614 | 1.71 | 2600 | 30.08 | 47.25 | 1.2079 | 64.2954 |
| 0.4052 | 1.77 | 2700 | 30.88 | 47.44 | 1.1975 | 64.2053 |
| 0.3541 | 1.84 | 2800 | 28.4 | 46.02 | 1.2006 | 70.2837 |
| 0.3736 | 1.9 | 2900 | 30.82 | 47.52 | 1.1906 | 64.1153 |
| 0.3326 | 1.97 | 3000 | 27.57 | 46.72 | 1.1870 | 70.6439 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
|
Niggendar/modelEX_v45 | Niggendar | 2024-04-29T20:31:01Z | 65 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-04-29T20:27:19Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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] |
omar2535/ppo-LunarLander-v2 | omar2535 | 2024-04-29T20:27:35Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-04-29T01:44:46Z | ---
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: 258.25 +/- 19.52
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
...
```
|
sszymczyk/snowflake-arctic-instruct-GGUF | sszymczyk | 2024-04-29T20:24:04Z | 38 | 3 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-04-25T13:47:22Z | ---
license: apache-2.0
---
Quantized version of https://huggingface.co/Snowflake/snowflake-arctic-instruct
If you downloaded older quants (with no folders) you have to redownload.
There is no support for this in mainline llama.cpp yet. You have to use snowflake-arctic branch: https://github.com/fairydreaming/llama.cpp/tree/snowflake-arctic
|
mlx-community/Llama-3-8B-Instruct-1048k-8bit | mlx-community | 2024-04-29T20:22:22Z | 24 | 17 | mlx | [
"mlx",
"safetensors",
"llama",
"meta",
"llama-3",
"text-generation",
"conversational",
"en",
"license:llama3",
"region:us"
] | text-generation | 2024-04-29T20:20:38Z | ---
language:
- en
license: llama3
tags:
- meta
- llama-3
- mlx
pipeline_tag: text-generation
---
# mlx-community/Llama-3-8B-Instruct-1048k-8bit
This model was converted to MLX format from [`gradientai/Llama-3-8B-Instruct-1048k`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-1048k) 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/Llama-3-8B-Instruct-1048k-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
C-Stuti/llama3-8b-oig-unsloth-copy | C-Stuti | 2024-04-29T20:19:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T20:19:18Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mlx-community/Llama-3-8B-Instruct-1048k-4bit | mlx-community | 2024-04-29T20:15:02Z | 10 | 25 | mlx | [
"mlx",
"safetensors",
"llama",
"meta",
"llama-3",
"text-generation",
"conversational",
"en",
"license:llama3",
"region:us"
] | text-generation | 2024-04-29T20:13:32Z | ---
language:
- en
license: llama3
tags:
- meta
- llama-3
- mlx
pipeline_tag: text-generation
---
# mlx-community/Llama-3-8B-Instruct-1048k-4bit
This model was converted to MLX format from [`gradientai/Llama-3-8B-Instruct-262k`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/gradientai/Llama-3-8B-Instruct-262k) 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/Llama-3-8B-Instruct-1048k-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
|
PapiMarkis/simson | PapiMarkis | 2024-04-29T20:14:40Z | 0 | 0 | fastai | [
"fastai",
"region:us"
] | null | 2024-04-29T20:14:38Z | ---
tags:
- fastai
---
# Amazing!
🥳 Congratulations on hosting your fastai model on the Hugging Face Hub!
# Some next steps
1. Fill out this model card with more information (see the template below and the [documentation here](https://huggingface.co/docs/hub/model-repos))!
2. Create a demo in Gradio or Streamlit using 🤗 Spaces ([documentation here](https://huggingface.co/docs/hub/spaces)).
3. Join the fastai community on the [Fastai Discord](https://discord.com/invite/YKrxeNn)!
Greetings fellow fastlearner 🤝! Don't forget to delete this content from your model card.
---
# Model card
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
|
amitprgx/setfit-ethos-multilabel-example | amitprgx | 2024-04-29T20:14:12Z | 4 | 0 | setfit | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"dataset:ethos",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
] | text-classification | 2024-04-28T17:09:21Z | ---
library_name: setfit
tags:
- setfit
- sentence-transformers
- text-classification
- generated_from_setfit_trainer
base_model: sentence-transformers/paraphrase-mpnet-base-v2
datasets:
- ethos
metrics:
- accuracy
widget:
- text: Roses are red. Your sweatshirt is blue. When a white girl gets raped, the
suspect for sure is you!
- text: The Jew Faggot Behind The Financial Collapse.
- text: Magnificient. Contrary to what the media reports, I have hope for the people
of Europe to expel these leeches.
- text: I'd get my money back if I were you. Your sex change was a complete failure.
- text: If only there was a way to get children without having to get women pregnant
we had them all ready killed
pipeline_tag: text-classification
inference: false
model-index:
- name: SetFit with sentence-transformers/paraphrase-mpnet-base-v2
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ethos
type: ethos
split: test
metrics:
- type: accuracy
value: 0.2857142857142857
name: Accuracy
---
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model trained on the [ethos](https://huggingface.co/datasets/ethos) dataset that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A OneVsRestClassifier instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a OneVsRestClassifier instance
- **Maximum Sequence Length:** 512 tokens
<!-- - **Number of Classes:** Unknown -->
- **Training Dataset:** [ethos](https://huggingface.co/datasets/ethos)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.2857 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("amitprgx/setfit-ethos-multilabel-example")
# Run inference
preds = model("The Jew Faggot Behind The Financial Collapse.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:--------|:----|
| Word count | 3 | 17.4062 | 87 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:------:|:----:|:-------------:|:---------------:|
| 0.0063 | 1 | 0.3199 | - |
| 0.3125 | 50 | 0.0952 | - |
| 0.625 | 100 | 0.1276 | - |
| 0.9375 | 150 | 0.0956 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
embracellm/sushi03_LoRA | embracellm | 2024-04-29T20:12:17Z | 2 | 0 | diffusers | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-04-29T20:12:14Z | ---
license: openrail++
library_name: diffusers
tags:
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- dora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: a photo of sushi03
widget: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi03_LoRA
<Gallery />
## Model description
These are embracellm/sushi03_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of sushi03 to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi03_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
imrajeshkr/distilhubert-finetuned-gtzan | imrajeshkr | 2024-04-29T20:10:11Z | 3 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"hubert",
"audio-classification",
"generated_from_trainer",
"dataset:marsyas/gtzan",
"base_model:ntu-spml/distilhubert",
"base_model:finetune:ntu-spml/distilhubert",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-04-29T20:10:05Z | ---
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: distilhubert-finetuned-gtzan
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Speech_command_RK
type: marsyas/gtzan
metrics:
- name: Accuracy
type: accuracy
value: 0.9975728155339806
---
<!-- 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. -->
# distilhubert-finetuned-gtzan
This model is a fine-tuned version of [ntu-spml/distilhubert](https://huggingface.co/ntu-spml/distilhubert) on the Speech_command_RK dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2480
- Accuracy: 0.9976
## 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: 264
- eval_batch_size: 264
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.4512 | 1.0 | 25 | 2.2018 | 0.6638 |
| 1.2836 | 2.0 | 50 | 1.0664 | 0.9636 |
| 0.6447 | 3.0 | 75 | 0.5056 | 0.9891 |
| 0.3833 | 4.0 | 100 | 0.2985 | 0.9964 |
| 0.3167 | 5.0 | 125 | 0.2480 | 0.9976 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
|
tomaszki/stablelm-48 | tomaszki | 2024-04-29T20:02:04Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T20:00:33Z | ---
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] |
C-Stuti/llama3-8b-oig-unsloth | C-Stuti | 2024-04-29T19:59:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-29T19:59:25Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
C-Stuti/llama3-8b-oig-unsloth-merged | C-Stuti | 2024-04-29T19:59:14Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T19:54:48Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** Cognitus-Stuti
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ShenaoZhang/0.0_3iters_bs256_nodpo_full6w_iter_1 | ShenaoZhang | 2024-04-29T19:59:12Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"base_model:finetune:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-29T18:37:56Z | ---
license: mit
base_model: HuggingFaceH4/mistral-7b-sft-beta
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- updated
- original
model-index:
- name: 0.0_3iters_bs256_nodpo_full6w_iter_1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0_3iters_bs256_nodpo_full6w_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
xbilek25/whisper-small-train-v2.2 | xbilek25 | 2024-04-29T19:58:57Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"multilingual",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-04-29T13:28:54Z | ---
language:
- multilingual
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: 'basic_train_basic_test 1000 similar params: per_device_train_batch_size=32,
# bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000'
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: xbilek25/xbilek25/train_set_1000_en_de_en
type: mozilla-foundation/common_voice_11_0
args: 'config: csen, split: train'
metrics:
- name: Wer
type: wer
value: 10.81081081081081
---
<!-- 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. -->
# basic_train_basic_test 1000 similar params: per_device_train_batch_size=32, # bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the xbilek25/xbilek25/train_set_1000_en_de_en dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2957
- Wer: 10.8108
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0087 | 7.02 | 500 | 0.2377 | 11.3924 |
| 0.0024 | 15.02 | 1000 | 0.2643 | 11.8029 |
| 0.0006 | 23.02 | 1500 | 0.2832 | 10.8792 |
| 0.0004 | 31.02 | 2000 | 0.2901 | 10.6055 |
| 0.0003 | 39.01 | 2500 | 0.2941 | 10.7766 |
| 0.0003 | 47.01 | 3000 | 0.2957 | 10.8108 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
|
AnirudhVV/ACTSA-CARDIFFNLP | AnirudhVV | 2024-04-29T19:56:47Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"autotrain",
"dataset:ACTSA-CARDIFFNLP/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-04-29T17:22:37Z |
---
tags:
- autotrain
- text-classification
widget:
- text: "I love AutoTrain"
datasets:
- ACTSA-CARDIFFNLP/autotrain-data
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 1.0654646158218384
f1_macro: 0.2095479509928179
f1_micro: 0.4584103512014787
f1_weighted: 0.2881768494245037
precision_macro: 0.1528034504004929
precision_micro: 0.4584103512014787
precision_weighted: 0.21014005008866307
recall_macro: 0.3333333333333333
recall_micro: 0.4584103512014787
recall_weighted: 0.4584103512014787
accuracy: 0.4584103512014787
|
Niggendar/js2prony_v10 | Niggendar | 2024-04-29T19:53:20Z | 84 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-04-29T19:49:45Z | ---
library_name: diffusers
---
# Model Card for Model ID
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## Model Details
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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