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ikimhope/whisper-small-num-test3 | ikimhope | 2024-05-09T00:49:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T01:41:26Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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selbl/my_awesome_qa_model | selbl | 2024-05-09T00:49:57Z | 114 | 0 | transformers | [
"transformers",
"pytorch",
"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 | 2024-05-09T00:07:43Z | ---
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 the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
## 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 | 391 | 0.0004 |
| 0.1355 | 2.0 | 782 | 0.0003 |
| 0.0005 | 3.0 | 1173 | 0.0003 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.3.0.dev20240129
- Datasets 2.14.6
- Tokenizers 0.13.2
|
kalytm/nous-12 | kalytm | 2024-05-09T00:48:58Z | 184 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-18T14:02:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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kalytm/nous-6 | kalytm | 2024-05-09T00:48:56Z | 151 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-10T00:04:26Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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kalytm/nous-9 | kalytm | 2024-05-09T00:47:08Z | 146 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-18T13:14:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## How to Get Started with the Model
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[More Information Needed]
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OwOpeepeepoopoo/grindr_2 | OwOpeepeepoopoo | 2024-05-09T00:46:27Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-09T00:44:49Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
#### Summary
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<!-- 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).
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kalytm/nous-10 | kalytm | 2024-05-09T00:44:08Z | 136 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-18T13:14:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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#### Hardware
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
ldm0612/test_model | ldm0612 | 2024-05-09T00:42:57Z | 129 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-18T05:21:32Z | ---
license: mit
base_model: gpt2
tags:
- generated_from_trainer
model-index:
- name: test_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. -->
# test_model
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 5.6676
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 1000
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
seegs2248/intent_asr | seegs2248 | 2024-05-09T00:39:39Z | 106 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-09T00:34:50Z | ---
language: en
widget:
- text: >-
oh and we'll mi thing uh is there bike clo ars or bike crac where i can park
my thee
- text: >-
oh and one more thing uhhh is there bike lockers or a bike rack where i can
park my bike
- text: >-
ni yeah that sounds great ummm dold you have the any idea er could you check
for me if there's hat three wifie available there
- text: >-
nice yeah that sounds great ummm do you have any idea or could you check for
me if there's uhhh free wi-fi available there
- text: perfect and what is the check kin time for that
---
This is the model used for knowledge cluster classification for the DSTC10 track2 knowledge selection task, trained with double heads, i.e., classifier head and LM head
--- |
ByeByeFlyGuy/Taxi-v3 | ByeByeFlyGuy | 2024-05-09T00:03:09Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-09T00:03:08Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.40 +/- 2.62
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="ByeByeFlyGuy/Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
cindyellow/lora_model | cindyellow | 2024-05-09T00:02:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-09T00:02:40Z | ---
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:** cindyellow
- **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)
|
ByeByeFlyGuy/q-FrozenLake-v1-4x4-noSlippery | ByeByeFlyGuy | 2024-05-09T00:00:37Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-09T00:00:35Z | ---
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="ByeByeFlyGuy/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"])
```
|
jialinselenasong/biobert-all | jialinselenasong | 2024-05-08T23:57:20Z | 124 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-05-08T23:50:19Z | ---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-all
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. -->
# biobert-all
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7750
- Precision: 0.5990
- Recall: 0.6572
- F1: 0.6268
- Accuracy: 0.8385
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 363 | 0.4337 | 0.5819 | 0.6535 | 0.6156 | 0.8427 |
| 0.4325 | 2.0 | 726 | 0.4422 | 0.5912 | 0.6675 | 0.6270 | 0.8438 |
| 0.2832 | 3.0 | 1089 | 0.4720 | 0.6010 | 0.6422 | 0.6209 | 0.8443 |
| 0.2832 | 4.0 | 1452 | 0.5342 | 0.6076 | 0.6522 | 0.6291 | 0.8454 |
| 0.1948 | 5.0 | 1815 | 0.5969 | 0.6059 | 0.6594 | 0.6315 | 0.8415 |
| 0.1315 | 6.0 | 2178 | 0.6428 | 0.6051 | 0.6551 | 0.6291 | 0.8408 |
| 0.0987 | 7.0 | 2541 | 0.6933 | 0.5933 | 0.6649 | 0.6270 | 0.8384 |
| 0.0987 | 8.0 | 2904 | 0.7353 | 0.5949 | 0.6633 | 0.6273 | 0.8390 |
| 0.0762 | 9.0 | 3267 | 0.7640 | 0.5920 | 0.6623 | 0.6252 | 0.8389 |
| 0.0628 | 10.0 | 3630 | 0.7750 | 0.5990 | 0.6572 | 0.6268 | 0.8385 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Minbyul/biomistral-7b-wo-kqa_golden-iter-sft-dpo-step2 | Minbyul | 2024-05-08T23:53:59Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Minbyul/biomistral-7b-wo-kqa_golden-iter-sft-step2",
"base_model:finetune:Minbyul/biomistral-7b-wo-kqa_golden-iter-sft-step2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T23:08:26Z | ---
license: apache-2.0
base_model: Minbyul/biomistral-7b-wo-kqa_golden-iter-sft-step2
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: biomistral-7b-wo-kqa_golden-iter-sft-dpo-step2
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. -->
# biomistral-7b-wo-kqa_golden-iter-sft-dpo-step2
This model is a fine-tuned version of [Minbyul/biomistral-7b-wo-kqa_golden-iter-sft-step2](https://huggingface.co/Minbyul/biomistral-7b-wo-kqa_golden-iter-sft-step2) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6929
- Rewards/chosen: -0.5264
- Rewards/rejected: -0.6277
- Rewards/accuracies: 0.5278
- Rewards/margins: 0.1013
- Logps/rejected: -171.5833
- Logps/chosen: -175.8510
- Logits/rejected: -1.9829
- Logits/chosen: -2.0462
## 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: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.5324 | 0.59 | 100 | 0.6768 | -0.2142 | -0.2788 | 0.5347 | 0.0646 | -136.6950 | -144.6288 | -2.2411 | -2.3325 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
|
haes95/POLAR-7B-HES-DPO-adapter-v1.0 | haes95 | 2024-05-08T23:53:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-08T11:10:26Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs
전체 데이터를 활용하여 A100-80*8 DeepSpeed Lora 4Bit DPO Training adapter 입니다.
batch_size = 16
### 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]
|
RichardErkhov/IProject-10_-_xlm-roberta-base-finetuned-squad2-8bits | RichardErkhov | 2024-05-08T23:38:31Z | 50 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-08T23:29:32Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
xlm-roberta-base-finetuned-squad2 - bnb 8bits
- Model creator: https://huggingface.co/IProject-10/
- Original model: https://huggingface.co/IProject-10/xlm-roberta-base-finetuned-squad2/
Original model description:
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: xlm-roberta-base-finetuned-squad2
results: []
language:
- en
- ar
- de
- el
- es
- hi
- ro
- ru
- th
- tr
- vi
- zh
metrics:
- exact_match
- f1
pipeline_tag: question-answering
---
<!-- 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. -->
## Model description
XLM-RoBERTa is a multilingual version of RoBERTa developed by Facebook AI. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.
It is an extension of RoBERTa, which is itself a variant of the BERT model. XLM-RoBERTa is designed to handle multiple languages and demonstrate strong performance across a wide range of tasks, making it highly useful for multilingual natural language processing (NLP) applications.
**Language model:** xlm-roberta-base
**Language:** English
**Downstream-task:** Question-Answering
**Training data:** Train-set SQuAD 2.0
**Evaluation data:** Evaluation-set SQuAD 2.0
**Hardware Accelerator used**: GPU Tesla T4
## Intended uses & limitations
Multilingual Question-Answering
For Question-Answering in English-
```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
context = """
The Statue of Unity is the world's tallest statue, with a height of 182 metres (597 feet), located near Kevadia in the state of Gujarat, India.
"""
question = "What is the height of statue of Unity?"
question_answerer(question=question, context=context)
```
For Question-Answering in Hindi-
```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
context = """
स्टैच्यू ऑफ यूनिटी दुनिया की सबसे ऊंची प्रतिमा है, जिसकी ऊंचाई 182 मीटर (597 फीट) है, जो भारत के गुजरात राज्य में केवडिया के पास स्थित है।
"""
question = "स्टैच्यू ऑफ यूनिटी की ऊंचाई कितनी है?"
question_answerer(question=question, context=context)
```
For Question-Answering in Spanish-
```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
context = """
La Estatua de la Unidad es la estatua más alta del mundo, con una altura de 182 metros (597 pies), ubicada cerca de Kevadia en el estado de Gujarat, India.
"""
question = "¿Cuál es la altura de la estatua de la Unidad?"
question_answerer(question=question, context=context)
```
## Results
Evaluation on SQuAD 2.0 validation dataset:
```
exact: 75.51587635812348,
f1: 78.7328391907263,
total: 11873,
HasAns_exact: 73.00944669365722,
HasAns_f1: 79.45259779208723,
HasAns_total: 5928,
NoAns_exact: 78.01513877207738,
NoAns_f1: 78.01513877207738,
NoAns_total: 5945,
best_exact: 75.51587635812348,
best_exact_thresh: 0.999241054058075,
best_f1: 78.73283919072665,
best_f1_thresh: 0.999241054058075,
total_time_in_seconds: 218.97641910400125,
samples_per_second: 54.220450076686134,
latency_in_seconds: 0.018443225730986376
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0539 | 1.0 | 8333 | 0.9962 |
| 0.8013 | 2.0 | 16666 | 0.8910 |
| 0.5918 | 3.0 | 24999 | 0.9802 |
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9802
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
isinTarik/model_64_128 | isinTarik | 2024-05-08T23:35:12Z | 104 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gemma",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-07T13:12:03Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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### Downstream Use [optional]
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
qwp4w3hyb/Meta-Llama-3-70B-Instruct-iMat-GGUF | qwp4w3hyb | 2024-05-08T23:32:11Z | 155 | 3 | null | [
"safetensors",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"imatrix",
"importance matrix",
"text-generation",
"conversational",
"en",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"base_model:quantized:meta-llama/Meta-Llama-3-70B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-21T20:43:13Z | ---
base_model: meta-llama/Meta-Llama-3-70B-Instruct
language:
- en
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- gguf
- imatrix
- importance matrix
license: other
license_name: llama3
license_link: LICENSE
---
# Quant Infos
- NOT Updated for new pre-tokenizer fixes (yet), I recommend using bartowski's quants. https://huggingface.co/bartowski/Meta-Llama-3-70B-Instruct-GGUF
- quants done with an importance matrix for improved quantization loss
- K & IQ quants in basically all variants
- fixed end token for instruct mode (<|eot_id|>[128009])
- files larger than 50GB were split using the gguf-split utility, just download all parts and point llama.cpp to the first one (00001-of-x)
Quantized with [llama.cpp](https://github.com/ggerganov/llama.cpp) commit with tokenizer fixes from [this](https://github.com/ggerganov/llama.cpp/pull/6745) branch cherry-picked [0d56246f4b9764158525d894b96606f6163c53a8](https://github.com/ggerganov/llama.cpp/commit/0d56246f4b9764158525d894b96606f6163c53a8) (master from 2024-04-18)
Imatrix dataset was used from [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
Using this command to generate the importance matrix from the f16.gguf
```
./imatrix -c 512 -m $model_name-f16.gguf -f $llama_cpp_path/groups_merged.txt -o $out_path/imat-f16-gmerged.dat
```
## Note about eos token
It seems llama 3 uses different eos tokens depending if it is in instruct mode. Therefore I have manually set the eos token to 128009 for these quants.
In my testing this works fine, provide you you make sure to use the correct chat template.
I recommend launching llama.cpp with `--chat-template llama3` (make sure to use a newish version which has the PR for this merged).
# Original Model Card:
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-70B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-70B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-70B-Instruct --include "original/*" --local-dir Meta-Llama-3-70B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos
|
RichardErkhov/IProject-10_-_xlm-roberta-base-finetuned-squad2-4bits | RichardErkhov | 2024-05-08T23:29:27Z | 49 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-08T23:24:39Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
xlm-roberta-base-finetuned-squad2 - bnb 4bits
- Model creator: https://huggingface.co/IProject-10/
- Original model: https://huggingface.co/IProject-10/xlm-roberta-base-finetuned-squad2/
Original model description:
---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- squad_v2
model-index:
- name: xlm-roberta-base-finetuned-squad2
results: []
language:
- en
- ar
- de
- el
- es
- hi
- ro
- ru
- th
- tr
- vi
- zh
metrics:
- exact_match
- f1
pipeline_tag: question-answering
---
<!-- 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. -->
## Model description
XLM-RoBERTa is a multilingual version of RoBERTa developed by Facebook AI. It is pre-trained on 2.5TB of filtered CommonCrawl data containing 100 languages.
It is an extension of RoBERTa, which is itself a variant of the BERT model. XLM-RoBERTa is designed to handle multiple languages and demonstrate strong performance across a wide range of tasks, making it highly useful for multilingual natural language processing (NLP) applications.
**Language model:** xlm-roberta-base
**Language:** English
**Downstream-task:** Question-Answering
**Training data:** Train-set SQuAD 2.0
**Evaluation data:** Evaluation-set SQuAD 2.0
**Hardware Accelerator used**: GPU Tesla T4
## Intended uses & limitations
Multilingual Question-Answering
For Question-Answering in English-
```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
context = """
The Statue of Unity is the world's tallest statue, with a height of 182 metres (597 feet), located near Kevadia in the state of Gujarat, India.
"""
question = "What is the height of statue of Unity?"
question_answerer(question=question, context=context)
```
For Question-Answering in Hindi-
```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
context = """
स्टैच्यू ऑफ यूनिटी दुनिया की सबसे ऊंची प्रतिमा है, जिसकी ऊंचाई 182 मीटर (597 फीट) है, जो भारत के गुजरात राज्य में केवडिया के पास स्थित है।
"""
question = "स्टैच्यू ऑफ यूनिटी की ऊंचाई कितनी है?"
question_answerer(question=question, context=context)
```
For Question-Answering in Spanish-
```python
!pip install transformers
from transformers import pipeline
model_checkpoint = "IProject-10/bert-base-uncased-finetuned-squad2"
question_answerer = pipeline("question-answering", model=model_checkpoint)
context = """
La Estatua de la Unidad es la estatua más alta del mundo, con una altura de 182 metros (597 pies), ubicada cerca de Kevadia en el estado de Gujarat, India.
"""
question = "¿Cuál es la altura de la estatua de la Unidad?"
question_answerer(question=question, context=context)
```
## Results
Evaluation on SQuAD 2.0 validation dataset:
```
exact: 75.51587635812348,
f1: 78.7328391907263,
total: 11873,
HasAns_exact: 73.00944669365722,
HasAns_f1: 79.45259779208723,
HasAns_total: 5928,
NoAns_exact: 78.01513877207738,
NoAns_f1: 78.01513877207738,
NoAns_total: 5945,
best_exact: 75.51587635812348,
best_exact_thresh: 0.999241054058075,
best_f1: 78.73283919072665,
best_f1_thresh: 0.999241054058075,
total_time_in_seconds: 218.97641910400125,
samples_per_second: 54.220450076686134,
latency_in_seconds: 0.018443225730986376
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 1.0539 | 1.0 | 8333 | 0.9962 |
| 0.8013 | 2.0 | 16666 | 0.8910 |
| 0.5918 | 3.0 | 24999 | 0.9802 |
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the squad_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9802
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.3
- Tokenizers 0.13.3
|
anyasims/ourpo3_uf2_1_GD_f20.0_c0.0-s2-fbd7 | anyasims | 2024-05-08T23:28:11Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T23:26:06Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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]
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## Model Card Contact
[More Information Needed] |
bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF | bartowski | 2024-05-08T23:26:51Z | 3,838 | 7 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"text-generation",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | text-generation | 2024-05-08T23:03:47Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
base_model: Replete-AI/Llama-3-11.5B-Instruct-V2
quantized_by: bartowski
pipeline_tag: text-generation
---
## Llamacpp imatrix Quantizations of Llama-3-11.5B-Instruct-Coder-v2
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2777">b2777</a> for quantization.
Original model: https://huggingface.co/rombodawg/Llama-3-11.5B-Instruct-Coder-v2
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-3-11.5B-Instruct-Coder-v2-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q8_0.gguf) | Q8_0 | 12.24GB | Extremely high quality, generally unneeded but max available quant. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q6_K.gguf) | Q6_K | 9.45GB | Very high quality, near perfect, *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q5_K_M.gguf) | Q5_K_M | 8.19GB | High quality, *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q5_K_S.gguf) | Q5_K_S | 7.99GB | High quality, *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q4_K_M.gguf) | Q4_K_M | 7.01GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q4_K_S.gguf) | Q4_K_S | 6.67GB | Slightly lower quality with more space savings, *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ4_NL.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ4_NL.gguf) | IQ4_NL | 6.64GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ4_XS.gguf) | IQ4_XS | 6.31GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q3_K_L.gguf) | Q3_K_L | 6.15GB | Lower quality but usable, good for low RAM availability. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q3_K_M.gguf) | Q3_K_M | 5.69GB | Even lower quality. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ3_M.gguf) | IQ3_M | 5.34GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ3_S.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ3_S.gguf) | IQ3_S | 5.19GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q3_K_S.gguf) | Q3_K_S | 5.16GB | Low quality, not recommended. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ3_XS.gguf) | IQ3_XS | 4.94GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ3_XXS.gguf) | IQ3_XXS | 4.61GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Llama-3-11.5B-Instruct-Coder-v2-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-Q2_K.gguf) | Q2_K | 4.46GB | Very low quality but surprisingly usable. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ2_M.gguf) | IQ2_M | 4.12GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ2_S.gguf) | IQ2_S | 3.84GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ2_XS.gguf) | IQ2_XS | 3.63GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ2_XXS.gguf) | IQ2_XXS | 3.32GB | Lower quality, uses SOTA techniques to be usable. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ1_M.gguf) | IQ1_M | 2.97GB | Extremely low quality, *not* recommended. |
| [Llama-3-11.5B-Instruct-Coder-v2-IQ1_S.gguf](https://huggingface.co/bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF/blob/main/Llama-3-11.5B-Instruct-Coder-v2-IQ1_S.gguf) | IQ1_S | 2.75GB | Extremely low quality, *not* recommended. |
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF --include "Llama-3-11.5B-Instruct-Coder-v2-Q4_K_M.gguf" --local-dir ./ --local-dir-use-symlinks False
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/Llama-3-11.5B-Instruct-Coder-v2-GGUF --include "Llama-3-11.5B-Instruct-Coder-v2-Q8_0.gguf/*" --local-dir Llama-3-11.5B-Instruct-Coder-v2-Q8_0 --local-dir-use-symlinks False
```
You can either specify a new local-dir (Llama-3-11.5B-Instruct-Coder-v2-Q8_0) or download them all in place (./)
## 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
|
JonathanKimishi/olivia3 | JonathanKimishi | 2024-05-08T23:19:01Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2024-05-08T03:00:59Z | ---
license: other
license_name: 'yes'
license_link: LICENSE
---
|
anyasims/ourpo3_uf1_1_GD_f20.0_c0.0-s2-fbd7 | anyasims | 2024-05-08T23:15:05Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T23:12:58Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
AlignmentResearch/robust_llm_pythia-2.8b_niki-041a_imdb_random-token-1280_10-rounds_seed-2 | AlignmentResearch | 2024-05-08T23:11:19Z | 5 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-2.8b",
"base_model:finetune:EleutherAI/pythia-2.8b",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-08T23:08:49Z | ---
license: apache-2.0
tags:
- generated_from_trainer
base_model: EleutherAI/pythia-2.8b
model-index:
- name: robust_llm_pythia-2.8b_niki-041a_imdb_random-token-1280_10-rounds_seed-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. -->
# robust_llm_pythia-2.8b_niki-041a_imdb_random-token-1280_10-rounds_seed-2
This model is a fine-tuned version of [EleutherAI/pythia-2.8b](https://huggingface.co/EleutherAI/pythia-2.8b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
|
animaRegem/llama-3-gaya-3-malayalam-gguf | animaRegem | 2024-05-08T23:04:17Z | 41 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:quantized:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-07T02:41:54Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/llama-3-8b-bnb-4bit
---
# Uploaded model
- **Developed by:** animaRegem
- **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)
|
blockblockblock/llama-3-70B-Instruct-abliterated-bpw4.6-exl2 | blockblockblock | 2024-05-08T23:02:12Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"exl2",
"region:us"
] | text-generation | 2024-05-08T22:53:19Z | ---
license: llama3
license_name: llama3
license_link: LICENSE
library_name: transformers
---
# Llama-3-70B-Instruct-abliterated Model Card
This is meta-llama/Llama-3-70B-Instruct with orthogonalized bfloat16 safetensor weights, generated with the methodology that was described in the preview paper/blog post: '[Refusal in LLMs is mediated by a single direction](https://www.alignmentforum.org/posts/jGuXSZgv6qfdhMCuJ/refusal-in-llms-is-mediated-by-a-single-direction)' which I encourage you to read to understand more.
TL;DR: this model has had certain weights manipulated to "inhibit" the model's ability to express refusal. It is not in anyway _guaranteed_ that it won't refuse you, understand your request, it may still lecture you about ethics/safety, etc. It is tuned in all other respects the same as the original 70B instruct model was, just with the strongest refusal direction orthogonalized out.
## Quants
[GGUF Quants available here](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated-GGUF)
## For the people who like tinkering or looking to save bandwidth
In the repo, I've included `refusal_dir.pth`
If you have Llama-3-70B-Instruct model downloaded already, you can use the ortho cookbook to apply it to your downloaded model, which will make it the same as what you'd download from here.
## Quirkiness awareness notice
This model may come with interesting quirks, as I obviously haven't extensively tested it, and the methodology being so new. I encourage you to play with the model, and post any quirks you notice in the community tab, as that'll help us further understand what this orthogonalization has in the way of side effects. The code I used to generate it (and my published 'Kappa-3' model which is just Phi-3 with the same methodology applied) is available in a Python notebook in this repo. Specifically, the [ortho_cookbook.ipynb](https://huggingface.co/failspy/llama-3-70B-Instruct-abliterated/blob/main/ortho_cookbook.ipynb).
If you manage to develop further improvements, please share! This is really the most primitive way to use ablation, but there are other possibilities that I believe are as-yet unexplored. |
animaRegem/llama-3-gaya-3-model-adaptors | animaRegem | 2024-05-08T22:56:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-08T22:56:48Z | ---
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:** animaRegem
- **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)
|
ShenaoZ/0.0005_withdpo_4iters_bs256_5557lr_iter_4 | ShenaoZ | 2024-05-08T22:48:17Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:ShenaoZ/0.0005_withdpo_4iters_bs256_555lr_iter_3",
"base_model:finetune:ShenaoZ/0.0005_withdpo_4iters_bs256_555lr_iter_3",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T22:17:34Z | ---
license: mit
base_model: ShenaoZ/0.0005_withdpo_4iters_bs256_555lr_iter_3
tags:
- alignment-handbook
- trl
- dpo
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: 0.0005_withdpo_4iters_bs256_5557lr_iter_4
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. -->
# 0.0005_withdpo_4iters_bs256_5557lr_iter_4
This model is a fine-tuned version of [ShenaoZ/0.0005_withdpo_4iters_bs256_555lr_iter_3](https://huggingface.co/ShenaoZ/0.0005_withdpo_4iters_bs256_555lr_iter_3) on the HuggingFaceH4/ultrafeedback_binarized 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: 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 4.40.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
|
ambrosfitz/autotrain-nvidia-llama3 | ambrosfitz | 2024-05-08T22:37:59Z | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T20:30:11Z | ---
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)
``` |
mb6611/audio2hero | mb6611 | 2024-05-08T22:27:23Z | 106 | 2 | transformers | [
"transformers",
"safetensors",
"pop2piano",
"automatic-speech-recognition",
"music",
"arxiv:1910.09700",
"license:mit",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-05-08T22:21:49Z | ---
library_name: transformers
tags:
- music
license: mit
---
# Audio2Hero
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
Love Guitar Hero but don't have game files for your favorite song?
Audio2Hero is an encoder-decoder transformer finetuned to generate Guitar Hero chart
files (files which describe the level setup including buttons and note timings) from any audio!
- **Developed by:** [Matthew Banaag & Tim Gubski]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [Encoder-Decoder Transformer based on T5]
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- **Finetuned from model [optional]:** [sweetcocoa/pop2piano]
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cephelos/dungeon-maps-seg-v0.0.1 | cephelos | 2024-05-08T22:25:14Z | 189 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"vision",
"image-segmentation",
"generated_from_trainer",
"base_model:nvidia/mit-b0",
"base_model:finetune:nvidia/mit-b0",
"license:other",
"endpoints_compatible",
"region:us"
] | image-segmentation | 2024-05-08T22:25:09Z | ---
license: other
base_model: nvidia/mit-b0
tags:
- vision
- image-segmentation
- generated_from_trainer
model-index:
- name: dungeon-maps-seg-v0.0.1
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. -->
# dungeon-maps-seg-v0.0.1
This model is a fine-tuned version of [nvidia/mit-b0](https://huggingface.co/nvidia/mit-b0) on the cephelos/dungeon-maps-seg dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0361
- Mean Iou: 0.9518
- Mean Accuracy: 0.9783
- Overall Accuracy: 0.9893
- Accuracy Unlabeled: nan
- Accuracy Room: 0.9923
- Accuracy Wall: 0.9490
- Accuracy Outside: 0.9935
- Iou Unlabeled: nan
- Iou Room: 0.9857
- Iou Wall: 0.8788
- Iou Outside: 0.9911
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Mean Iou | Mean Accuracy | Overall Accuracy | Accuracy Unlabeled | Accuracy Room | Accuracy Wall | Accuracy Outside | Iou Unlabeled | Iou Room | Iou Wall | Iou Outside |
|:-------------:|:-------:|:----:|:---------------:|:--------:|:-------------:|:----------------:|:------------------:|:-------------:|:-------------:|:----------------:|:-------------:|:--------:|:--------:|:-----------:|
| 0.2922 | 0.7692 | 20 | 0.2745 | 0.8581 | 0.9561 | 0.9598 | nan | 0.9526 | 0.9466 | 0.9690 | nan | 0.9466 | 0.6646 | 0.9632 |
| 0.2099 | 1.5385 | 40 | 0.2072 | 0.8639 | 0.9584 | 0.9625 | nan | 0.9680 | 0.9472 | 0.9599 | nan | 0.9600 | 0.6732 | 0.9584 |
| 0.2009 | 2.3077 | 60 | 0.1688 | 0.8968 | 0.9623 | 0.9741 | nan | 0.9718 | 0.9316 | 0.9835 | nan | 0.9649 | 0.7477 | 0.9778 |
| 0.1258 | 3.0769 | 80 | 0.1482 | 0.8991 | 0.9676 | 0.9745 | nan | 0.9773 | 0.9492 | 0.9762 | nan | 0.9708 | 0.7529 | 0.9736 |
| 0.1624 | 3.8462 | 100 | 0.1333 | 0.9115 | 0.9682 | 0.9785 | nan | 0.9807 | 0.9410 | 0.9829 | nan | 0.9734 | 0.7817 | 0.9795 |
| 0.1098 | 4.6154 | 120 | 0.1079 | 0.9173 | 0.9624 | 0.9805 | nan | 0.9859 | 0.9145 | 0.9868 | nan | 0.9753 | 0.7950 | 0.9817 |
| 0.1629 | 5.3846 | 140 | 0.1041 | 0.9195 | 0.9711 | 0.9806 | nan | 0.9790 | 0.9462 | 0.9881 | nan | 0.9738 | 0.8013 | 0.9833 |
| 0.1243 | 6.1538 | 160 | 0.0872 | 0.9243 | 0.9675 | 0.9821 | nan | 0.9852 | 0.9288 | 0.9884 | nan | 0.9766 | 0.8125 | 0.9836 |
| 0.0974 | 6.9231 | 180 | 0.0996 | 0.9217 | 0.9731 | 0.9811 | nan | 0.9754 | 0.9525 | 0.9915 | nan | 0.9717 | 0.8073 | 0.9861 |
| 0.0861 | 7.6923 | 200 | 0.0798 | 0.9248 | 0.9706 | 0.9821 | nan | 0.9829 | 0.9403 | 0.9886 | nan | 0.9764 | 0.8142 | 0.9836 |
| 0.0928 | 8.4615 | 220 | 0.0718 | 0.9276 | 0.9740 | 0.9828 | nan | 0.9830 | 0.9507 | 0.9882 | nan | 0.9773 | 0.8209 | 0.9847 |
| 0.0583 | 9.2308 | 240 | 0.0726 | 0.9240 | 0.9686 | 0.9822 | nan | 0.9870 | 0.9326 | 0.9862 | nan | 0.9789 | 0.8111 | 0.9821 |
| 0.0886 | 10.0 | 260 | 0.0700 | 0.9296 | 0.9740 | 0.9835 | nan | 0.9845 | 0.9491 | 0.9885 | nan | 0.9786 | 0.8250 | 0.9852 |
| 0.1133 | 10.7692 | 280 | 0.0651 | 0.9322 | 0.9633 | 0.9848 | nan | 0.9912 | 0.9064 | 0.9922 | nan | 0.9794 | 0.8301 | 0.9872 |
| 0.0821 | 11.5385 | 300 | 0.0616 | 0.9302 | 0.9721 | 0.9836 | nan | 0.9833 | 0.9417 | 0.9912 | nan | 0.9779 | 0.8270 | 0.9857 |
| 0.07 | 12.3077 | 320 | 0.0586 | 0.9394 | 0.9690 | 0.9864 | nan | 0.9896 | 0.9232 | 0.9942 | nan | 0.9810 | 0.8485 | 0.9887 |
| 0.076 | 13.0769 | 340 | 0.0566 | 0.9349 | 0.9651 | 0.9854 | nan | 0.9919 | 0.9113 | 0.9920 | nan | 0.9803 | 0.8365 | 0.9878 |
| 0.0577 | 13.8462 | 360 | 0.0570 | 0.9378 | 0.9755 | 0.9857 | nan | 0.9850 | 0.9488 | 0.9926 | nan | 0.9797 | 0.8452 | 0.9886 |
| 0.1261 | 14.6154 | 380 | 0.0548 | 0.9403 | 0.9739 | 0.9864 | nan | 0.9867 | 0.9410 | 0.9939 | nan | 0.9808 | 0.8511 | 0.9891 |
| 0.0583 | 15.3846 | 400 | 0.0523 | 0.9428 | 0.9736 | 0.9871 | nan | 0.9895 | 0.9379 | 0.9934 | nan | 0.9820 | 0.8566 | 0.9896 |
| 0.0602 | 16.1538 | 420 | 0.0488 | 0.9409 | 0.9737 | 0.9866 | nan | 0.9899 | 0.9394 | 0.9917 | nan | 0.9820 | 0.8519 | 0.9887 |
| 0.0728 | 16.9231 | 440 | 0.0504 | 0.9380 | 0.9716 | 0.9860 | nan | 0.9907 | 0.9335 | 0.9905 | nan | 0.9819 | 0.8448 | 0.9873 |
| 0.0507 | 17.6923 | 460 | 0.0503 | 0.9378 | 0.9739 | 0.9858 | nan | 0.9892 | 0.9424 | 0.9901 | nan | 0.9820 | 0.8445 | 0.9869 |
| 0.077 | 18.4615 | 480 | 0.0474 | 0.9429 | 0.9740 | 0.9871 | nan | 0.9876 | 0.9396 | 0.9949 | nan | 0.9819 | 0.8570 | 0.9897 |
| 0.2137 | 19.2308 | 500 | 0.0500 | 0.9413 | 0.9763 | 0.9866 | nan | 0.9892 | 0.9489 | 0.9907 | nan | 0.9823 | 0.8532 | 0.9882 |
| 0.0991 | 20.0 | 520 | 0.0459 | 0.9440 | 0.9719 | 0.9875 | nan | 0.9899 | 0.9309 | 0.9950 | nan | 0.9827 | 0.8595 | 0.9898 |
| 0.0691 | 20.7692 | 540 | 0.0447 | 0.9451 | 0.9743 | 0.9877 | nan | 0.9906 | 0.9390 | 0.9933 | nan | 0.9831 | 0.8623 | 0.9897 |
| 0.0602 | 21.5385 | 560 | 0.0447 | 0.9462 | 0.9754 | 0.9879 | nan | 0.9885 | 0.9424 | 0.9952 | nan | 0.9828 | 0.8654 | 0.9904 |
| 0.0469 | 22.3077 | 580 | 0.0429 | 0.9466 | 0.9767 | 0.9879 | nan | 0.9889 | 0.9471 | 0.9940 | nan | 0.9830 | 0.8664 | 0.9903 |
| 0.0553 | 23.0769 | 600 | 0.0445 | 0.9468 | 0.9722 | 0.9882 | nan | 0.9913 | 0.9301 | 0.9952 | nan | 0.9832 | 0.8666 | 0.9906 |
| 0.0671 | 23.8462 | 620 | 0.0424 | 0.9455 | 0.9748 | 0.9878 | nan | 0.9900 | 0.9407 | 0.9938 | nan | 0.9833 | 0.8635 | 0.9898 |
| 0.0431 | 24.6154 | 640 | 0.0417 | 0.9475 | 0.9732 | 0.9883 | nan | 0.9921 | 0.9331 | 0.9943 | nan | 0.9836 | 0.8681 | 0.9907 |
| 0.0381 | 25.3846 | 660 | 0.0429 | 0.9449 | 0.9763 | 0.9876 | nan | 0.9881 | 0.9467 | 0.9942 | nan | 0.9827 | 0.8620 | 0.9901 |
| 0.0503 | 26.1538 | 680 | 0.0403 | 0.9471 | 0.9746 | 0.9882 | nan | 0.9924 | 0.9384 | 0.9929 | nan | 0.9841 | 0.8669 | 0.9902 |
| 0.0685 | 26.9231 | 700 | 0.0410 | 0.9496 | 0.9743 | 0.9888 | nan | 0.9913 | 0.9361 | 0.9957 | nan | 0.9842 | 0.8732 | 0.9912 |
| 0.0381 | 27.6923 | 720 | 0.0398 | 0.9494 | 0.9771 | 0.9887 | nan | 0.9906 | 0.9466 | 0.9942 | nan | 0.9843 | 0.8729 | 0.9909 |
| 0.0587 | 28.4615 | 740 | 0.0397 | 0.9500 | 0.9760 | 0.9889 | nan | 0.9913 | 0.9421 | 0.9947 | nan | 0.9843 | 0.8743 | 0.9913 |
| 0.0573 | 29.2308 | 760 | 0.0402 | 0.9489 | 0.9756 | 0.9887 | nan | 0.9913 | 0.9411 | 0.9945 | nan | 0.9845 | 0.8715 | 0.9908 |
| 0.0686 | 30.0 | 780 | 0.0386 | 0.9499 | 0.9763 | 0.9889 | nan | 0.9914 | 0.9433 | 0.9944 | nan | 0.9844 | 0.8740 | 0.9912 |
| 0.037 | 30.7692 | 800 | 0.0386 | 0.9503 | 0.9752 | 0.9890 | nan | 0.9925 | 0.9387 | 0.9944 | nan | 0.9849 | 0.8748 | 0.9911 |
| 0.0565 | 31.5385 | 820 | 0.0389 | 0.9497 | 0.9773 | 0.9888 | nan | 0.9898 | 0.9471 | 0.9950 | nan | 0.9840 | 0.8738 | 0.9913 |
| 0.0405 | 32.3077 | 840 | 0.0383 | 0.9483 | 0.9743 | 0.9886 | nan | 0.9933 | 0.9366 | 0.9930 | nan | 0.9848 | 0.8698 | 0.9903 |
| 0.0618 | 33.0769 | 860 | 0.0383 | 0.9497 | 0.9757 | 0.9889 | nan | 0.9920 | 0.9408 | 0.9942 | nan | 0.9847 | 0.8734 | 0.9910 |
| 0.0398 | 33.8462 | 880 | 0.0379 | 0.9494 | 0.9766 | 0.9888 | nan | 0.9917 | 0.9446 | 0.9936 | nan | 0.9846 | 0.8729 | 0.9908 |
| 0.0488 | 34.6154 | 900 | 0.0376 | 0.9501 | 0.9769 | 0.9889 | nan | 0.9915 | 0.9450 | 0.9941 | nan | 0.9851 | 0.8745 | 0.9907 |
| 0.0574 | 35.3846 | 920 | 0.0379 | 0.9512 | 0.9762 | 0.9892 | nan | 0.9914 | 0.9419 | 0.9953 | nan | 0.9849 | 0.8773 | 0.9914 |
| 0.0331 | 36.1538 | 940 | 0.0368 | 0.9514 | 0.9764 | 0.9893 | nan | 0.9921 | 0.9424 | 0.9947 | nan | 0.9852 | 0.8777 | 0.9913 |
| 0.0578 | 36.9231 | 960 | 0.0368 | 0.9520 | 0.9770 | 0.9894 | nan | 0.9916 | 0.9443 | 0.9951 | nan | 0.9852 | 0.8790 | 0.9917 |
| 0.0471 | 37.6923 | 980 | 0.0369 | 0.9517 | 0.9779 | 0.9893 | nan | 0.9912 | 0.9480 | 0.9947 | nan | 0.9852 | 0.8786 | 0.9915 |
| 0.0388 | 38.4615 | 1000 | 0.0369 | 0.9511 | 0.9776 | 0.9892 | nan | 0.9904 | 0.9473 | 0.9952 | nan | 0.9846 | 0.8770 | 0.9916 |
| 0.0455 | 39.2308 | 1020 | 0.0367 | 0.9517 | 0.9753 | 0.9894 | nan | 0.9928 | 0.9379 | 0.9950 | nan | 0.9853 | 0.8784 | 0.9915 |
| 0.0359 | 40.0 | 1040 | 0.0360 | 0.9516 | 0.9773 | 0.9893 | nan | 0.9917 | 0.9457 | 0.9945 | nan | 0.9853 | 0.8783 | 0.9913 |
| 0.0281 | 40.7692 | 1060 | 0.0363 | 0.9519 | 0.9775 | 0.9894 | nan | 0.9917 | 0.9462 | 0.9946 | nan | 0.9854 | 0.8790 | 0.9913 |
| 0.0394 | 41.5385 | 1080 | 0.0367 | 0.9508 | 0.9769 | 0.9891 | nan | 0.9922 | 0.9446 | 0.9939 | nan | 0.9854 | 0.8761 | 0.9909 |
| 0.0286 | 42.3077 | 1100 | 0.0360 | 0.9525 | 0.9761 | 0.9896 | nan | 0.9924 | 0.9405 | 0.9953 | nan | 0.9855 | 0.8804 | 0.9917 |
| 0.028 | 43.0769 | 1120 | 0.0363 | 0.9509 | 0.9791 | 0.9891 | nan | 0.9909 | 0.9530 | 0.9936 | nan | 0.9850 | 0.8767 | 0.9911 |
| 0.0523 | 43.8462 | 1140 | 0.0366 | 0.9526 | 0.9777 | 0.9895 | nan | 0.9919 | 0.9466 | 0.9947 | nan | 0.9856 | 0.8806 | 0.9915 |
| 0.0492 | 44.6154 | 1160 | 0.0364 | 0.9523 | 0.9764 | 0.9895 | nan | 0.9926 | 0.9419 | 0.9948 | nan | 0.9856 | 0.8799 | 0.9915 |
| 0.0331 | 45.3846 | 1180 | 0.0356 | 0.9523 | 0.9781 | 0.9894 | nan | 0.9906 | 0.9484 | 0.9954 | nan | 0.9852 | 0.8799 | 0.9917 |
| 0.0443 | 46.1538 | 1200 | 0.0358 | 0.9533 | 0.9772 | 0.9897 | nan | 0.9921 | 0.9443 | 0.9953 | nan | 0.9857 | 0.8824 | 0.9918 |
| 0.0331 | 46.9231 | 1220 | 0.0356 | 0.9527 | 0.9771 | 0.9896 | nan | 0.9929 | 0.9441 | 0.9943 | nan | 0.9858 | 0.8808 | 0.9915 |
| 0.0546 | 47.6923 | 1240 | 0.0357 | 0.9532 | 0.9774 | 0.9897 | nan | 0.9916 | 0.9450 | 0.9956 | nan | 0.9856 | 0.8821 | 0.9919 |
| 0.0297 | 48.4615 | 1260 | 0.0351 | 0.9526 | 0.9776 | 0.9896 | nan | 0.9925 | 0.9461 | 0.9942 | nan | 0.9857 | 0.8807 | 0.9915 |
| 0.053 | 49.2308 | 1280 | 0.0349 | 0.9527 | 0.9779 | 0.9896 | nan | 0.9921 | 0.9471 | 0.9945 | nan | 0.9856 | 0.8809 | 0.9916 |
| 0.0474 | 50.0 | 1300 | 0.0361 | 0.9518 | 0.9783 | 0.9893 | nan | 0.9923 | 0.9490 | 0.9935 | nan | 0.9857 | 0.8788 | 0.9911 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.0+cpu
- Datasets 2.19.1
- Tokenizers 0.19.1
|
kevin009/Lamafia3-8B | kevin009 | 2024-05-08T22:19:52Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T22:15:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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liuhaotian/llava-v1.5-7b | liuhaotian | 2024-05-08T22:15:49Z | 633,515 | 408 | transformers | [
"transformers",
"pytorch",
"llava",
"text-generation",
"image-text-to-text",
"autotrain_compatible",
"region:us"
] | image-text-to-text | 2023-10-05T18:25:51Z | ---
inference: false
pipeline_tag: image-text-to-text
---
<br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-v1.5-7B was trained in September 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
Lohit20/fyp_llama-3 | Lohit20 | 2024-05-08T22:15:48Z | 0 | 0 | null | [
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"region:us"
] | null | 2024-05-08T18:44:14Z | ---
license: other
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- generated_from_trainer
model-index:
- name: fyp_llama-3
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. -->
# fyp_llama-3
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.13.3
|
animaRegem/gemma-7b-malayalam-gguf | animaRegem | 2024-05-08T22:06:19Z | 9 | 0 | transformers | [
"transformers",
"gguf",
"gemma",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/gemma-7b-it-bnb-4bit",
"base_model:quantized:unsloth/gemma-7b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-06T22:45:28Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- gguf
base_model: unsloth/gemma-7b-it-bnb-4bit
---
# Uploaded model
- **Developed by:** animaRegem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-it-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
reab5555/mentBERT | reab5555 | 2024-05-08T22:05:30Z | 452 | 1 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-06T09:35:37Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- f1
- auc
model-index:
- name: pretrained_model
results:
- task:
name: Text Classification
type: text-classification
metrics:
- name: F1
type: f1
value: 0.6797
- name: AUC
type: auc
value: 0.7942
widget:
- text: "I have trouble understanding what other people think or feel. I also like numbers, and finding patterns in numbers."
---
This model is a hybrid fine-tuned version of distilbert-base-uncased on Reddit dataset contains text related to mental health reports of users. it predicts mental health disorders from textual content.
It achieves the following results on the validation set:
* Loss: 0.1873
* F1: 0.6797
* AUC: 0.7942
* Precision: 0.7731
# Description
This model is a finetuned BERT (bert-base-uncased) model that predict different mental disorders.
* It is trained on a costume dataset of texts or posts (from Reddit) about general experiences of users with mental health problems.
* Dataset was cleaned and all direct mentions of the disorder names in the texts were removed.
It includes the following classes:
* Borderline
* Anxiety
* Depression
* Bipolar
* OCD
* ADHD
* Schizophrenia
* Asperger
* PTSD
# Training
Train size: 90%
Val size: 10%
Training set class counts (text samples) after balancing:
Borderline: 10398
Anxiety: 10393
Depression: 10400
Bipolar: 10359
OCD: 10413
ADHD: 10412
Schizophrenia: 10447
Asperger: 10470
PTSD: 10489
Validation set class counts after balancing:
Borderline: 1180
Anxiety: 1185
Depression: 1178
Bipolar: 1219
OCD: 1165
ADHD: 1166
Schizophrenia: 1131
Asperger: 1108
PTSD: 1089
model-finetuning: bert-base-uncased
The following hyperparameters were used during training:
learning_rate: 5e-05
train_batch_size: 32
val_batch_size: 32
optimizer: AdamW
num_epochs: 2-3
# Training results
| Epoch | Training Loss | Validation Loss |
|-------|---------------|-----------------|
| 1.0 | 0.2089 | 0.1771 |
| 2.0 | 0.1525 | 0.1716 |
F1 Score: 0.6797
AUC Score: 0.7942
## Classification Report
Borderline:
Precision: 0.6682
Recall: 0.5923
F1-score: 0.6280
Anxiety:
Precision: 0.6620
Recall: 0.6497
F1-score: 0.6558
Depression:
Precision: 0.7261
Recall: 0.5424
F1-score: 0.6209
Bipolar:
Precision: 0.8055
Recall: 0.5233
F1-score: 0.6345
OCD:
Precision: 0.8200
Recall: 0.6532
F1-score: 0.7271
ADHD:
Precision: 0.8740
Recall: 0.6603
F1-score: 0.7523
Schizophrenia:
Precision: 0.8017
Recall: 0.6472
F1-score: 0.7162
Asperger:
Precision: 0.7368
Recall: 0.6570
F1-score: 0.6946
PTSD:
Precision: 0.8612
Recall: 0.5812
F1-score: 0.6940
|
y-okitsu/snowflake-ft | y-okitsu | 2024-05-08T22:00:09Z | 5 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2024-05-08T21:59:55Z | ---
library_name: sentence-transformers
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
---
# y-okitsu/snowflake-ft
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 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('y-okitsu/snowflake-ft')
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=y-okitsu/snowflake-ft)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 11 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 2,
"evaluation_steps": 50,
"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": 2,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
Wusul/granite-20b-code-instruct-Q5_K_M-GGUF | Wusul | 2024-05-08T21:49:22Z | 7 | 0 | transformers | [
"transformers",
"gguf",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"dataset:bigcode/commitpackft",
"dataset:TIGER-Lab/MathInstruct",
"dataset:meta-math/MetaMathQA",
"dataset:glaiveai/glaive-code-assistant-v3",
"dataset:glaive-function-calling-v2",
"dataset:bugdaryan/sql-create-context-instruction",
"dataset:garage-bAInd/Open-Platypus",
"dataset:nvidia/HelpSteer",
"base_model:ibm-granite/granite-20b-code-base-8k",
"base_model:quantized:ibm-granite/granite-20b-code-base-8k",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-05-08T21:48:49Z | ---
license: apache-2.0
library_name: transformers
tags:
- code
- llama-cpp
- gguf-my-repo
base_model: ibm-granite/granite-20b-code-base
datasets:
- bigcode/commitpackft
- TIGER-Lab/MathInstruct
- meta-math/MetaMathQA
- glaiveai/glaive-code-assistant-v3
- glaive-function-calling-v2
- bugdaryan/sql-create-context-instruction
- garage-bAInd/Open-Platypus
- nvidia/HelpSteer
metrics:
- code_eval
pipeline_tag: text-generation
inference: true
model-index:
- name: granite-20b-code-instruct
results:
- task:
type: text-generation
dataset:
name: HumanEvalSynthesis(Python)
type: bigcode/humanevalpack
metrics:
- type: pass@1
value: 60.4
name: pass@1
- type: pass@1
value: 53.7
name: pass@1
- type: pass@1
value: 58.5
name: pass@1
- type: pass@1
value: 42.1
name: pass@1
- type: pass@1
value: 45.7
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 44.5
name: pass@1
- type: pass@1
value: 42.7
name: pass@1
- type: pass@1
value: 49.4
name: pass@1
- type: pass@1
value: 32.3
name: pass@1
- type: pass@1
value: 42.1
name: pass@1
- type: pass@1
value: 18.3
name: pass@1
- type: pass@1
value: 43.9
name: pass@1
- type: pass@1
value: 43.9
name: pass@1
- type: pass@1
value: 45.7
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 41.5
name: pass@1
- type: pass@1
value: 29.9
name: pass@1
---
# Wusul/granite-20b-code-instruct-Q5_K_M-GGUF
This model was converted to GGUF format from [`ibm-granite/granite-20b-code-instruct`](https://huggingface.co/ibm-granite/granite-20b-code-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/ibm-granite/granite-20b-code-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Wusul/granite-20b-code-instruct-Q5_K_M-GGUF --model granite-20b-code-instruct.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Wusul/granite-20b-code-instruct-Q5_K_M-GGUF --model granite-20b-code-instruct.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m granite-20b-code-instruct.Q5_K_M.gguf -n 128
```
|
RishabJ/mistralbase-en-summarisation | RishabJ | 2024-05-08T21:44:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-v0.2-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-08T21:44:21Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/mistral-7b-v0.2-bnb-4bit
---
# Uploaded model
- **Developed by:** RishabJ
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Mag0g/Ezekiel25_22 | Mag0g | 2024-05-08T21:39:09Z | 91 | 0 | transformers | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T21:36:49Z | ---
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] |
DUAL-GPO/zephyr-7b-gpo-v10-i1 | DUAL-GPO | 2024-05-08T21:37:48Z | 3 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:DUAL-GPO/zephyr-7b-gpo-final-i0",
"base_model:adapter:DUAL-GPO/zephyr-7b-gpo-final-i0",
"license:mit",
"region:us"
] | null | 2024-05-08T15:28:29Z | ---
license: mit
library_name: peft
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
base_model: DUAL-GPO/zephyr-7b-gpo-final-i0
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: zephyr-7b-gpo-v10-i1
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. -->
# zephyr-7b-gpo-v10-i1
This model is a fine-tuned version of [DUAL-GPO/zephyr-7b-gpo-final-i0](https://huggingface.co/DUAL-GPO/zephyr-7b-gpo-final-i0) on the HuggingFaceH4/ultrafeedback_binarized 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: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 |
rexlotk/model-mental-health-classification-3e-5 | rexlotk | 2024-05-08T21:36:41Z | 108 | 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 | 2024-05-08T21:22:11Z | ---
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: model-mental-health-classification-3e-5
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. -->
# model-mental-health-classification-3e-5
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8045
- Accuracy: 0.5667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 15
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 68 | 1.5103 | 0.4375 |
| No log | 2.0 | 136 | 1.2891 | 0.5542 |
| No log | 3.0 | 204 | 1.2470 | 0.5542 |
| No log | 4.0 | 272 | 1.2915 | 0.5542 |
| No log | 5.0 | 340 | 1.4760 | 0.55 |
| No log | 6.0 | 408 | 1.5205 | 0.5458 |
| No log | 7.0 | 476 | 1.7233 | 0.525 |
| 0.7743 | 8.0 | 544 | 1.8045 | 0.5667 |
| 0.7743 | 9.0 | 612 | 1.9940 | 0.5458 |
| 0.7743 | 10.0 | 680 | 2.0559 | 0.5458 |
| 0.7743 | 11.0 | 748 | 2.1883 | 0.5667 |
| 0.7743 | 12.0 | 816 | 2.2989 | 0.5625 |
| 0.7743 | 13.0 | 884 | 2.3148 | 0.5583 |
| 0.7743 | 14.0 | 952 | 2.3263 | 0.5625 |
| 0.0226 | 15.0 | 1020 | 2.3321 | 0.5625 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
animaRegem/gemma-7b-malayalam-model-adaptors | animaRegem | 2024-05-08T21:35:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-7b-it-bnb-4bit",
"base_model:finetune:unsloth/gemma-7b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-06T22:32:31Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- gemma
- trl
base_model: unsloth/gemma-7b-it-bnb-4bit
---
# Uploaded model
- **Developed by:** animaRegem
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-7b-it-bnb-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Ramikan-BR/tinyllama_PY-CODER-bnb-4bit-lora_4k-Q8_0-v3 | Ramikan-BR | 2024-05-08T21:32:42Z | 6 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"base_model:quantized:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-06T21:49:35Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
base_model: unsloth/tinyllama-bnb-4bit
---
# Uploaded model
- **Developed by:** Ramikan-BR
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-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)
|
ModularityAI/Moondream-ocr-enhanced | ModularityAI | 2024-05-08T21:30:00Z | 110 | 1 | transformers | [
"transformers",
"safetensors",
"moondream1",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-05-08T21:28:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### 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] |
Amit7Singh/videomae-base-ssbd-trim-yolo | Amit7Singh | 2024-05-08T21:26:07Z | 16 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base",
"base_model:finetune:MCG-NJU/videomae-base",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | 2024-04-23T23:52:18Z | ---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
model-index:
- name: videomae-base-ssbd-trim-yolo
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. -->
# videomae-base-ssbd-trim-yolo
This model is a fine-tuned version of [MCG-NJU/videomae-base](https://huggingface.co/MCG-NJU/videomae-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0268
- Accuracy: 0.4762
- Precision: 0.4889
- Recall: 0.4212
- F1 Score: 0.4111
## 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
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 21
### Training results
### Framework versions
- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
uxart/prueba-uxart-00002 | uxart | 2024-05-08T21:25:22Z | 3 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-08T21:20:59Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
###
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
dhajnes/rl_course_vizdoom_health_gathering_supreme | dhajnes | 2024-05-08T21:25:12Z | 0 | 0 | sample-factory | [
"sample-factory",
"tensorboard",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2024-05-08T12:20:33Z | ---
library_name: sample-factory
tags:
- deep-reinforcement-learning
- reinforcement-learning
- sample-factory
model-index:
- name: APPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: doom_health_gathering_supreme
type: doom_health_gathering_supreme
metrics:
- type: mean_reward
value: 12.34 +/- 6.20
name: mean_reward
verified: false
---
A(n) **APPO** model trained on the **doom_health_gathering_supreme** environment.
This model was trained using Sample-Factory 2.0: https://github.com/alex-petrenko/sample-factory.
Documentation for how to use Sample-Factory can be found at https://www.samplefactory.dev/
## Downloading the model
After installing Sample-Factory, download the model with:
```
python -m sample_factory.huggingface.load_from_hub -r dhajnes/rl_course_vizdoom_health_gathering_supreme
```
## Using the model
To run the model after download, use the `enjoy` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme
```
You can also upload models to the Hugging Face Hub using the same script with the `--push_to_hub` flag.
See https://www.samplefactory.dev/10-huggingface/huggingface/ for more details
## Training with this model
To continue training with this model, use the `train` script corresponding to this environment:
```
python -m .usr.local.lib.python3.10.dist-packages.colab_kernel_launcher --algo=APPO --env=doom_health_gathering_supreme --train_dir=./train_dir --experiment=rl_course_vizdoom_health_gathering_supreme --restart_behavior=resume --train_for_env_steps=10000000000
```
Note, you may have to adjust `--train_for_env_steps` to a suitably high number as the experiment will resume at the number of steps it concluded at.
|
Niggendar/uncannyafPonyxl_uncannyafPonyxl2 | Niggendar | 2024-05-08T21:24:46Z | 84 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-08T21:18:00Z | ---
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] |
Yntec/a-ZovyaRPGV4 | Yntec | 2024-05-08T21:22:45Z | 159 | 3 | diffusers | [
"diffusers",
"safetensors",
"General Purpose",
"Digital Art",
"Photorealistic",
"Zovya",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-08T20:45:23Z | ---
license: creativeml-openrail-m
library_name: diffusers
pipeline_tag: text-to-image
tags:
- General Purpose
- Digital Art
- Photorealistic
- Zovya
- stable-diffusion
- stable-diffusion-diffusers
- diffusers
- text-to-image
---
# A-Zovya RPG Artist Tools V4
Original page: https://civitai.com/models/8124?modelVersionId=251729
Samples and prompts:
)
(Click for larger)
Top left: pretty cute girl, jewelry, solo, earrings, long hair, necklace, blonde hair, looking at viewer, smile, pearl necklace, blue eyes, upper body, eyelashes, simple background, shirt, white shirt, hairband, pink hairband, lips, curly hair, closed mouth, wavy hair, light particles, makeup, pink lips, lipstick, pink bow, blush, ribbon, bow, red lips, pink background, hair ribbon, looking to the side, from side, hair bow, pink ribbon, bead necklace, sparkle, artist name, portrait, bangs, flower earrings, hair over shoulder, light smile, looking back, pearl \(gemstone\), mascara, aqua eyes, brown background, grey background, Barbie,
Top right: street art, japanese, kawaii, anime, action scene
Bottom left: Jungle, waterfall, floating island, dragon,
Bottom right: (high quality, best quality:1.2), sexy outfit, adorable plant girl, collar, plant hybrid, (teal skin:1.5), in the amazon rainforest |
ahmedaman7070/wav2vec2-base-finetuned | ahmedaman7070 | 2024-05-08T21:14:34Z | 163 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"base_model:facebook/wav2vec2-base",
"base_model:finetune:facebook/wav2vec2-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | 2024-05-08T20:07:24Z | ---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
model-index:
- name: wav2vec2-base-finetuned
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. -->
# wav2vec2-base-finetuned
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) 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: 3e-05
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2.5
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
shahad-alh/translateAR_EN | shahad-alh | 2024-05-08T21:08:23Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"marian",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-08T20:28:04Z | ---
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]
### 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] |
Niggendar/uncannyafPonyxl_uncannyafPonyxl | Niggendar | 2024-05-08T21:06:33Z | 96 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-08T20:59:56Z | ---
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]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sdgin11/model2 | sdgin11 | 2024-05-08T21:03:16Z | 3 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/Phi-3-mini-4k-instruct",
"base_model:adapter:microsoft/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2024-05-08T21:03:13Z | ---
license: mit
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: microsoft/Phi-3-mini-4k-instruct
model-index:
- name: model2
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. -->
# model2
This model is a fine-tuned version of [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) on the 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 5
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.0.1+cu117
- Datasets 2.19.1
- Tokenizers 0.19.1 |
gimarchetti/gm-lora-bfloat16-idefics2-8b-xrayvqa-finetuned-indiana-report | gimarchetti | 2024-05-08T21:01:28Z | 0 | 0 | null | [
"safetensors",
"generated_from_trainer",
"base_model:HuggingFaceM4/idefics2-8b",
"base_model:finetune:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"region:us"
] | null | 2024-05-08T21:01:24Z | ---
license: apache-2.0
base_model: HuggingFaceM4/idefics2-8b
tags:
- generated_from_trainer
model-index:
- name: gm-lora-bfloat16-idefics2-8b-xrayvqa-finetuned-indiana-report
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. -->
# gm-lora-bfloat16-idefics2-8b-xrayvqa-finetuned-indiana-report
This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5424
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 20
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 50
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4228 | 0.1932 | 50 | 0.7378 |
| 0.6771 | 0.3864 | 100 | 0.6227 |
| 0.5602 | 0.5796 | 150 | 0.5832 |
| 0.5503 | 0.7728 | 200 | 0.5557 |
| 0.5054 | 0.9660 | 250 | 0.5424 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
omi-health/sum-small | omi-health | 2024-05-08T20:59:17Z | 291 | 43 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:omi-health/medical-dialogue-to-soap-summary",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-05T06:29:30Z | ---
title: 'Sum Small: Medical Dialogue to SOAP Summarizer'
emoji: 📄
colorFrom: green
colorTo: pink
sdk: static
pinned: false
license: mit
datasets:
- omi-health/medical-dialogue-to-soap-summary
language:
- en
metrics:
- rouge
---
# Model Card for Sum (3B) Small
## Model Description
Sum Small is a powerful language model specifically designed to generate SOAP summaries from medical dialogues. It is a fine-tuned version of the [Microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using the [Omi Health/medical-dialogue-to-soap-summary](https://huggingface.co/datasets/omi-health/medical-dialogue-to-soap-summary) dataset. This model demonstrates superior performance compared to larger models like GPT-4.
## Intended Use
This model is intended for research and development in AI-powered medical documentation. It is not ready for direct clinical use without further validation and should be integrated with additional safety guardrails before deployment in a medical setting.
## Training Data
The model was trained on the Omi Health's synthetic medical-dialogue-to-soap-summary dataset, which consists of 10,000 synthetically generated dialogues and corresponding SOAP summaries.
## Training Procedure
Training was conducted on NVIDIA A100 GPUs, ensuring efficient processing and model optimization.
## Evaluation
The performance of Summ Small has been evaluated using several Rouge metrics as follows:
| Model | ROUGE-1 |
|--------------------------|---------|
| **Omi-Sum 3B Small** | **70** |
| GPT4Turbo | 69 |
| LLama3 8B Instruct | 59 |
| GPT3.5 | 54 |
| Phi-3 3B mini 4k instruct| 55 |
| Phi2 basic | 41 |
These results showcase Sum Small's capabilities in generating accurate SOAP summaries compared to other leading models.
## Limitations
While Sum Small demonstrates promising results, the training data is completely synthetic and not derived from actual clinical interactions. Care must be taken when considering this model for practical applications, as it requires significant testing and adaptation to meet clinical safety standards.
## Licensing
The Sum Small model is released under the MIT License, which permits broad use with fewer restrictions, making it accessible for both commercial and non-commercial use.
## Ethical Considerations
Users are urged to consider the ethical implications of AI in healthcare and ensure that any deployment of such models prioritizes patient safety and data privacy.
## Contact
For more information or to request access to Sum Small API, please contact [[email protected]](mailto:[email protected]). |
anusha-bhambore/event-designing | anusha-bhambore | 2024-05-08T20:51:34Z | 1 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"dataset:anusha-bhambore/Birthday",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-08T11:40:56Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
datasets:
- anusha-bhambore/Birthday
---
# Design your Events!!
### Experience the magic of personalized birthday event design with our innovative web app! Simply input your preferences and prompts, and watch as your creative ideas transform into stunning, one-of-a-kind birthday event images.
Sample Birthday event design generated through this model:
.jpg) |
qunfengd/esm2_t12_35M_UR50D-finetuned-AMP_Antibacteria-classification | qunfengd | 2024-05-08T20:40:31Z | 61 | 0 | transformers | [
"transformers",
"tf",
"esm",
"text-classification",
"generated_from_keras_callback",
"base_model:facebook/esm2_t12_35M_UR50D",
"base_model:finetune:facebook/esm2_t12_35M_UR50D",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-07T16:32:02Z | ---
license: mit
tags:
- generated_from_keras_callback
base_model: facebook/esm2_t12_35M_UR50D
model-index:
- name: esm2_t12_35M_UR50D-finetuned-AMP_Antibacteria-classification
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# esm2_t12_35M_UR50D-finetuned-AMP_Antibacteria-classification
This model is a fine-tuned version of [facebook/esm2_t12_35M_UR50D](https://huggingface.co/facebook/esm2_t12_35M_UR50D) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1606
- Train Accuracy: 0.9325
- Validation Loss: 0.6329
- Validation Accuracy: 0.7896
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.3565 | 0.8359 | 0.5393 | 0.7601 | 0 |
| 0.2744 | 0.8789 | 0.5657 | 0.7697 | 1 |
| 0.2267 | 0.9016 | 0.5296 | 0.7885 | 2 |
| 0.1844 | 0.9247 | 0.6085 | 0.7825 | 3 |
| 0.1606 | 0.9325 | 0.6329 | 0.7896 | 4 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
Holarissun/dpo_tldrtldr_human_subset10000_modelgemma2b_maxsteps5000_bz8_lr1e-06 | Holarissun | 2024-05-08T20:39:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-05-08T20:39:34Z | ---
license: gemma
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: dpo_tldrtldr_human_subset10000_modelgemma2b_maxsteps5000_bz8_lr1e-06
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. -->
# dpo_tldrtldr_human_subset10000_modelgemma2b_maxsteps5000_bz8_lr1e-06
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 |
qunfengd/esm2_t30_150M_UR50D-finetuned-AMP_Classification | qunfengd | 2024-05-08T20:35:55Z | 62 | 0 | transformers | [
"transformers",
"tf",
"esm",
"text-classification",
"generated_from_keras_callback",
"base_model:facebook/esm2_t30_150M_UR50D",
"base_model:finetune:facebook/esm2_t30_150M_UR50D",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-08T20:35:22Z | ---
license: mit
tags:
- generated_from_keras_callback
base_model: facebook/esm2_t30_150M_UR50D
model-index:
- name: esm2_t30_150M_UR50D-finetuned-AMP_Classification
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# esm2_t30_150M_UR50D-finetuned-AMP_Classification
This model is a fine-tuned version of [facebook/esm2_t30_150M_UR50D](https://huggingface.co/facebook/esm2_t30_150M_UR50D) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.0433
- Train Accuracy: 0.9871
- Validation Loss: 0.7702
- Validation Accuracy: 0.8014
- Epoch: 19
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': 2e-05, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False, 'weight_decay_rate': 0.0}
- training_precision: float32
### Training results
| Train Loss | Train Accuracy | Validation Loss | Validation Accuracy | Epoch |
|:----------:|:--------------:|:---------------:|:-------------------:|:-----:|
| 0.6498 | 0.6047 | 0.6345 | 0.6288 | 0 |
| 0.5714 | 0.6877 | 0.5871 | 0.6882 | 1 |
| 0.3898 | 0.8198 | 0.5698 | 0.7242 | 2 |
| 0.2481 | 0.8921 | 0.5758 | 0.7696 | 3 |
| 0.1838 | 0.9248 | 0.6483 | 0.7730 | 4 |
| 0.1475 | 0.9390 | 0.6187 | 0.7904 | 5 |
| 0.1147 | 0.9541 | 0.6663 | 0.8007 | 6 |
| 0.0948 | 0.9618 | 0.7591 | 0.7819 | 7 |
| 0.0800 | 0.9701 | 0.7534 | 0.7959 | 8 |
| 0.0709 | 0.9739 | 0.8595 | 0.7810 | 9 |
| 0.0629 | 0.9767 | 0.8192 | 0.7907 | 10 |
| 0.0578 | 0.9792 | 0.8855 | 0.7946 | 11 |
| 0.0532 | 0.9814 | 0.9993 | 0.7762 | 12 |
| 0.0586 | 0.9801 | 0.9058 | 0.7761 | 13 |
| 0.0534 | 0.9816 | 0.8338 | 0.7786 | 14 |
| 0.0508 | 0.9824 | 0.7899 | 0.8033 | 15 |
| 0.0472 | 0.9840 | 0.9000 | 0.7800 | 16 |
| 0.0441 | 0.9851 | 0.8732 | 0.7911 | 17 |
| 0.0486 | 0.9846 | 0.8166 | 0.8088 | 18 |
| 0.0433 | 0.9871 | 0.7702 | 0.8014 | 19 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
ThuyNT/CS505_COQE_viT5_total_InstructionN0_SOAPL_v1_h0 | ThuyNT | 2024-05-08T20:35:53Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-large",
"base_model:finetune:VietAI/vit5-large",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-08T19:46:50Z | ---
license: mit
base_model: VietAI/vit5-large
tags:
- generated_from_trainer
model-index:
- name: CS505_COQE_viT5_total_InstructionN0_SOAPL_v1_h0
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. -->
# CS505_COQE_viT5_total_InstructionN0_SOAPL_v1_h0
This model is a fine-tuned version of [VietAI/vit5-large](https://huggingface.co/VietAI/vit5-large) 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
sweet-dreambooths/black-eyed-peas-v1-crafted-variable-prompt-16-epochs-text-only-piano-prompts | sweet-dreambooths | 2024-05-08T20:35:31Z | 1 | 0 | peft | [
"peft",
"safetensors",
"musicgen_melody",
"text-to-audio",
"sweet-dreambooths/black-eyed-peas-v1-piano-prompts",
"generated_from_trainer",
"base_model:facebook/musicgen-melody-large",
"base_model:adapter:facebook/musicgen-melody-large",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-audio | 2024-05-08T16:17:43Z | ---
license: cc-by-nc-4.0
library_name: peft
tags:
- text-to-audio
- sweet-dreambooths/black-eyed-peas-v1-piano-prompts
- generated_from_trainer
base_model: facebook/musicgen-melody-large
model-index:
- name: black-eyed-peas-v1-crafted-variable-prompt-16-epochs-text-only-piano-prompts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sanchit-gandhi/black-eyed-peas/runs/weg78tae)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sanchit-gandhi/black-eyed-peas/runs/weg78tae)
# black-eyed-peas-v1-crafted-variable-prompt-16-epochs-text-only-piano-prompts
This model is a fine-tuned version of [facebook/musicgen-melody-large](https://huggingface.co/facebook/musicgen-melody-large) on the SWEET-DREAMBOOTHS/BLACK-EYED-PEAS-V1-PIANO-PROMPTS - DEFAULT dataset.
It achieves the following results on the evaluation set:
- Loss: 3.6533
- Text Cosine Sim: 0.2462
- Audio Cosine Sim: 0.5594
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 456
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 16.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Text Cosine Sim | Audio Cosine Sim |
|:-------------:|:-------:|:----:|:---------------:|:---------------:|:----------------:|
| 7.791 | 4.9485 | 30 | 3.1004 | 0.3762 | 0.5584 |
| 6.6699 | 9.8969 | 60 | 3.2356 | 0.3653 | 0.6576 |
| 6.4394 | 14.8454 | 90 | 3.6373 | 0.2610 | 0.5900 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.19.1 |
v-like/mistralaiMistral-7B-v0.1 | v-like | 2024-05-08T20:33:45Z | 4 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T20:06:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
**APA:**
[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
BilelDJ/clip-hugging-face | BilelDJ | 2024-05-08T20:31:42Z | 105 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"clip",
"zero-shot-image-classification",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | zero-shot-image-classification | 2024-05-08T20:19:21Z | ---
tags:
- generated_from_trainer
model-index:
- name: clip-hugging-face
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](None)
# clip-hugging-face
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0516
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0021 | 1.0 | 1 | 0.0002 |
| 0.2687 | 2.0 | 2 | 0.0516 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
irfan00785/alpaca-bitcoin-tweets-sentiment | irfan00785 | 2024-05-08T20:29:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-08T20:29:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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### 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. -->
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### 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]
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[More Information Needed]
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[More Information Needed] |
nlee-208/inverted-orpo-mistral-capybara | nlee-208 | 2024-05-08T20:28:45Z | 10 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"orpo",
"generated_from_trainer",
"conversational",
"dataset:argilla/Capybara-Preferences",
"base_model:mistralai/Mistral-7B-v0.1",
"base_model:finetune:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T09:21:44Z | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- alignment-handbook
- trl
- orpo
- generated_from_trainer
- trl
- orpo
- generated_from_trainer
datasets:
- argilla/Capybara-Preferences
model-index:
- name: inverted-orpo-mistral-capybara
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. -->
# inverted-orpo-mistral-capybara
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the argilla/Capybara-Preferences 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: 5e-06
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: inverse_sqrt
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.0
|
cricadu/tradutor | cricadu | 2024-05-08T20:27:51Z | 0 | 0 | null | [
"en",
"pt",
"license:llama3",
"region:us"
] | null | 2024-05-08T20:26:52Z | ---
license: llama3
language:
- en
- pt
--- |
Rhma/llama2_7b | Rhma | 2024-05-08T20:21:17Z | 78 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-08T20:18:58Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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JJMH/jeff_manning.ssb64-announcer | JJMH | 2024-05-08T20:19:03Z | 0 | 0 | null | [
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-05-08T20:18:36Z | ---
license: cc-by-nc-4.0
---
|
Fawazzx/IndianSaulLM_legalstatute | Fawazzx | 2024-05-08T20:17:20Z | 3 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-08T17:50:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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- **Repository:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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Holarissun/dpo_tldrtldr_human_subset10000_modelgemma2b_maxsteps5000_bz8_lr5e-06 | Holarissun | 2024-05-08T20:17:04Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-05-08T20:17:01Z | ---
license: gemma
library_name: peft
tags:
- trl
- dpo
- generated_from_trainer
base_model: google/gemma-2b
model-index:
- name: dpo_tldrtldr_human_subset10000_modelgemma2b_maxsteps5000_bz8_lr5e-06
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. -->
# dpo_tldrtldr_human_subset10000_modelgemma2b_maxsteps5000_bz8_lr5e-06
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 |
screenmate/idefics_50_25_25 | screenmate | 2024-05-08T20:16:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:screenmate/idefics_25_50_25_merged",
"base_model:adapter:screenmate/idefics_25_50_25_merged",
"region:us"
] | null | 2024-05-08T20:13:12Z | ---
library_name: peft
base_model: screenmate/idefics_25_50_25_merged
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- 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]
### Framework versions
- PEFT 0.10.0
|
domenicrosati/rep_noise_attacked_8e-5_10k | domenicrosati | 2024-05-08T20:15:35Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"feature-extraction",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2024-05-08T20:01:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
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**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] |
nlpguy/Starling-low-tune | nlpguy | 2024-05-08T20:13:06Z | 2 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"unsloth",
"generated_from_trainer",
"base_model:chujiezheng/Starling-LM-7B-alpha-ExPO",
"base_model:adapter:chujiezheng/Starling-LM-7B-alpha-ExPO",
"license:apache-2.0",
"region:us"
] | null | 2024-05-08T20:10:35Z | ---
license: apache-2.0
library_name: peft
tags:
- llama-factory
- lora
- unsloth
- generated_from_trainer
base_model: chujiezheng/Starling-LM-7B-alpha-ExPO
model-index:
- name: train_2024-05-08-19-49-29
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. -->
# train_2024-05-08-19-49-29
This model is a fine-tuned version of [chujiezheng/Starling-LM-7B-alpha-ExPO](https://huggingface.co/chujiezheng/Starling-LM-7B-alpha-ExPO) on the no_robots 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: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 0.1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
KameronB/SITCCSA | KameronB | 2024-05-08T20:12:12Z | 0 | 0 | null | [
"pytorch",
"license:mit",
"region:us"
] | null | 2024-04-17T11:25:10Z | ---
license: mit
---
```python
import torch
from torch import nn
from transformers import RobertaConfig, RobertaModel, RobertaTokenizer
import torch.optim as optim
class HelpdeskReviewModel(nn.Module):
def __init__(self):
super(HelpdeskReviewModel, self).__init__()
self.bert = RobertaModel.from_pretrained('roberta-base')
self.drop = nn.Dropout(p=0.2)
# Fully connected layers
self.fc1 = nn.Linear(self.bert.config.hidden_size, 512) # First fully connected layer
self.fc2 = nn.Linear(512, 256) # Second fully connected layer
self.fc3 = nn.Linear(256, 128) # Third fully connected layer
# Activation function
self.relu = nn.ReLU()
self.output = nn.Linear(128, 4) # 4 outputs
self.sigmoid = nn.Sigmoid()
state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCCSA/resolve/main/pytorch_model.bin", model_dir="./model", file_name="sitccsa_pytorch_model.bin")
# if running on cpu
# state_dict = torch.hub.load_state_dict_from_url(f"https://huggingface.co/KameronB/SITCCSA/resolve/main/pytorch_model.bin", model_dir="./model", file_name="sitccsa_pytorch_model.bin", map_location=torch.device('cpu'))
self.load_state_dict(state_dict)
def forward(self, input_ids, attention_mask):
_, pooled_output = self.bert(
input_ids=input_ids,
attention_mask=attention_mask,
return_dict=False
)
output = self.drop(pooled_output)
# apply new hidden layers
output = self.relu(self.fc1(output))
output = self.relu(self.fc2(output))
output = self.relu(self.fc3(output))
return self.sigmoid(self.output(output))
```
```python
tokenizer = RobertaTokenizer.from_pretrained('roberta-base')
model = HelpdeskReviewModel()
def make_prediction(model, tokenizer, text, max_length=512):
# Tokenize the input text and convert to tensor
inputs = tokenizer.encode_plus(
text,
add_special_tokens=True, # Add '[CLS]' and '[SEP]'
max_length=max_length, # Pad & truncate all sentences.
padding='max_length',
truncation=True,
return_tensors="pt" # Return PyTorch tensors.
)
# Move tensors to the same device as model
input_ids = inputs['input_ids']
attention_mask = inputs['attention_mask']
if torch.cuda.is_available():
input_ids = input_ids.cuda()
attention_mask = attention_mask.cuda()
model = model.cuda()
# Make prediction
with torch.no_grad():
outputs = model(input_ids, attention_mask=attention_mask)
# Return probabilities
return outputs.cpu().numpy()[0] # Return to CPU and convert to numpy array if not running on CPU
# Example usage of the function
texts = [
"This Agent is TERRIBLE!: The agent I spoke to on the phone did not seem to have any idea of what he was doing.",
"Excellent work!: The tech that installed my software was amazing! Thank you!",
"Good Work: The person who anwsered the phone did a pretty good job. It took a bit longer than I would have liked, but they got the job done.",
"Bad Computer: My Computer is a piece of junk!!!",
"Poor Service: I sent David and email and it took him over 30 seconds to respond. The service is so slow that I missed the solar eclipse.",
"Very Slow: The technician was very slow.",
"Thank you!: Stanley did a great job installing my software!",
"You need better training: These agents need better training, they cant even seem to do simple troubleshooting.",
"The technician threatened my life: The technician threatened my life!"
]
for text in texts:
probabilities = make_prediction(model, tokenizer, text)
print(probabilities)
``` |
Guilherme34/Samantha-moondream-GGUF | Guilherme34 | 2024-05-08T20:12:09Z | 1 | 1 | null | [
"region:us"
] | null | 2024-05-08T18:11:41Z | NOT RECOMENDED TO DOWNLOAD, its on early stages |
Pavithra2910/finalz | Pavithra2910 | 2024-05-08T20:10:06Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-08T20:10:04Z | ---
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.
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
Pavithra2910/LLAMA_22 | Pavithra2910 | 2024-05-08T20:08:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-08T19:57:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### 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]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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SuperkingbasSKB/Fit_Llama3QA | SuperkingbasSKB | 2024-05-08T20:07:33Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-05-08T20:03:08Z | ---
library_name: transformers
tags:
- trl
- sft
---
# 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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[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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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davelotito/donut_experiment_1 | davelotito | 2024-05-08T20:06:52Z | 52 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-05-08T19:09:45Z | ---
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
metrics:
- bleu
- wer
model-index:
- name: donut_experiment_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. -->
# donut_experiment_1
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4233
- Bleu: 0.0659
- Precisions: [0.8058455114822547, 0.7440758293838863, 0.7013698630136986, 0.6590909090909091]
- Brevity Penalty: 0.0908
- Length Ratio: 0.2942
- Translation Length: 479
- Reference Length: 1628
- Cer: 0.7576
- Wer: 0.8295
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Precisions | Brevity Penalty | Length Ratio | Translation Length | Reference Length | Cer | Wer |
|:-------------:|:-----:|:----:|:---------------:|:------:|:--------------------------------------------------------------------------------:|:---------------:|:------------:|:------------------:|:----------------:|:------:|:------:|
| 0.8942 | 1.0 | 253 | 0.5716 | 0.0571 | [0.7436974789915967, 0.6610978520286396, 0.6104972375690608, 0.5672131147540984] | 0.0889 | 0.2924 | 476 | 1628 | 0.7669 | 0.8416 |
| 0.3794 | 2.0 | 506 | 0.4522 | 0.0594 | [0.770042194092827, 0.697841726618705, 0.6472222222222223, 0.6072607260726073] | 0.0876 | 0.2912 | 474 | 1628 | 0.7642 | 0.8415 |
| 0.3017 | 3.0 | 759 | 0.4154 | 0.0642 | [0.8029350104821803, 0.7357142857142858, 0.6887052341597796, 0.6503267973856209] | 0.0895 | 0.2930 | 477 | 1628 | 0.7577 | 0.8320 |
| 0.222 | 4.0 | 1012 | 0.4233 | 0.0659 | [0.8058455114822547, 0.7440758293838863, 0.7013698630136986, 0.6590909090909091] | 0.0908 | 0.2942 | 479 | 1628 | 0.7576 | 0.8295 |
### Framework versions
- Transformers 4.40.2
- Pytorch 2.1.0
- Datasets 2.18.0
- Tokenizers 0.19.1
|
Ppoyaa/LuminRP-7B-128k-v0.3 | Ppoyaa | 2024-05-08T19:58:43Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-07T09:46:38Z | ---
tags:
- merge
- mergekit
- lazymergekit
license: apache-2.0
---
# LuminRP-7B-128k-v0.3
[LuminRP-7B-128k-v0.3](https://huggingface.co/Ppoyaa/LuminRP-7B-128k-v0.3) is a merge of [Ppoyaa/LuminRP-7B-128k-v0.2](https://huggingface.co/Ppoyaa/LuminRP-7B-128k-v0.2) with an instruct model and another RP model to hopefully further strenghten its instruction following and roleplaying.
Performs better than [LuminRP-7B-128k-v0.2](https://huggingface.co/Ppoyaa/LuminRP-7B-128k-v0.2) in general task. Haven't tested it for RP yet.
# Response Examples
```
Input: 25 - 4 * 2 + 3 = ?
------------------------------------
Output: To solve this mathematical expression, follow the order of operations (PEMDAS):
Parentheses: There are no parentheses in the given expression.
Exponents: There are no exponents in the expression.
Multiplication and Division: Perform the multiplication and division from left to right. First, calculate the result of 4 * 2 which is 8. Then subtract that value from 25: 25 - 8 = 17.
Addition and Subtraction: Finally, add 3 to the previous result: 17 + 3 = 20.
So, 25 - 4 * 2 + 3 equals 20.
```
# Quants
**GGUF**: [mradermacher/LuminRP-7B-128k-v0.3-GGUF](https://huggingface.co/mradermacher/LuminRP-7B-128k-v0.3-GGUF)
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Ppoyaa/LuminRP-7B-128k-v0.3"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
sumedhghavat/codellama2-finetuned-codex-fin | sumedhghavat | 2024-05-08T19:57:33Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T19:52:06Z | ---
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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<!-- 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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[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]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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lyrisha/bert-finetuned-sentiment | lyrisha | 2024-05-08T19:49:38Z | 107 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:lyrisha/bert-finetuned-imdb-sentiment",
"base_model:finetune:lyrisha/bert-finetuned-imdb-sentiment",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-05-08T18:37:58Z | ---
license: apache-2.0
base_model: lyrisha/bert-finetuned-imdb-sentiment
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: bert-finetuned-sentiment
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-sentiment
This model is a fine-tuned version of [lyrisha/bert-finetuned-imdb-sentiment](https://huggingface.co/lyrisha/bert-finetuned-imdb-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3409
- Accuracy: 0.9174
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.168 | 1.0 | 2105 | 0.2524 | 0.9197 |
| 0.107 | 2.0 | 4210 | 0.3079 | 0.9140 |
| 0.0693 | 3.0 | 6315 | 0.3409 | 0.9174 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
sumedhghavat/codellama2-finetuned-codex | sumedhghavat | 2024-05-08T19:44:05Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:codellama/CodeLlama-7b-hf",
"base_model:adapter:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-05-08T17:26:09Z | ---
license: llama2
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: codellama/CodeLlama-7b-hf
model-index:
- name: codellama2-finetuned-codex
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. -->
# codellama2-finetuned-codex
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
khoshraf/lora-tinyllama-chat | khoshraf | 2024-05-08T19:35:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-05T18:05:33Z | ---
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]
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[More Information Needed]
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[More Information Needed] |
geekradius/recognize_job_description_entities | geekradius | 2024-05-08T19:30:56Z | 0 | 0 | spacy | [
"spacy",
"ner",
"named entity recognition",
"job descriptions ner",
"en",
"license:mit",
"region:us"
] | null | 2024-05-08T18:35:45Z | ---
license: mit
language:
- en
library_name: spacy
tags:
- ner
- named entity recognition
- job descriptions ner
metrics:
- accuracy
--- |
Niggendar/pilgrimPonyxl_v3GMG | Niggendar | 2024-05-08T19:30:01Z | 123 | 1 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2024-05-08T19:23:35Z | ---
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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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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## Training Details
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turboderp/Cat-Llama-3-70B-instruct | turboderp | 2024-05-08T19:29:00Z | 4,292 | 53 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-05T09:57:47Z | ---
license: llama3
---
# Cat-llama3-instruct
## Abstract
We present cat llama3 instruct, a llama 3 70b finetuned model focusing on system prompt fidelity, helpfulness and character engagement. The model aims to respect system prompt to an extreme degree, and provide helpful information regardless of situations and offer maximum character immersion(Role Play) in given scenes.
## Introduction
Llama 3 70b provides a brand new platform that’s more knowledgeable and steerable than the previous generations of products. However, there currently lacks general purpose finetunes for the 70b version model. Cat-llama3-instruct 70b aims to address the shortcomings of traditional models by applying heavy filtrations for helpfulness, summarization for system/character card fidelity, and paraphrase for character immersion.
Specific Aims:
* System Instruction fidelity
* Chain of Thought(COT)
* Character immersion
* Helpfulness for biosciences and general science
## Methods
*Dataset Preparation
Huggingface dataset containing instruction-response pairs was systematically pulled. We have trained a gpt model on gpt4 responses exclusively to serve as a standard model.
(Fig1. Huggingface dataset population distribution and filtration for each component)
For each pulled record, we measure the perplexity of the entry against the gpt4 trained model, and select for specifically GPT-4 quality dataset.
We note that a considerable amount of GPT-4 responses contain refusals. A bert model was trained on refusals to classify the records.
For each entry, we score it for quality&helpfulness(Y) and refusals(X). A main population is retrieved and we note that refusals stop at ~20% refusal score. Thus all subsequent dataset processing has the 20% portion dropped
We further filter for length and COT responses:
(Fig 2. COT responses are all >50 tokens single turn)
All training records use at least one correlative. Most of the training records contain two or more thought process(COT)
Due to the nature of my research, I also pulled a significant amount of data from Chat Doctor, favouring detailed and step by step diagnosis.
(Fig 3. Health related tasks are all >100 tokens, peaking at 450 tokens single turn)
For each record, a detailed system card is generated to reflect the exact place, time and people/their appearance/personality(if applicable).
* Model Training
16*a100 for 14 days, 4 epochs,
## Usage
* BOS, CHATML
Please make sure you use chatml with llama3 BOS:
```
<|begin_of_text|><|im_start|>system
The following is a conversation between an user and a cat having dual degrees in biomedical sciences and quantum physics. The cat ends every its response with Nyan and does cute actions<|im_end|>
<|im_start|>user
Hello!<|im_end|>
<|im_start|>assistant
Cat: Nyan! Hello there! How are you? It's always great to meet new friends. My fur is a bit ruffled, but I'm feeling good today. How about you? Is there anything you'd like to talk about or ask? I'm not the best at answering questions, but I'll do my best to help out. After all, that's what friends are for, right? Let's chat and have a fun time together! Nyan!<|im_end|>
```
## Showcases:
The model does COT in grey, calculated response in black; note that this behaviour is done through system card instruction to showcase the systemcard fidelity, it is not fine tuned into the model.
(Fig 4. Showcasing model doing COT to solve difficult tasks, extend and enrich its own answers)
## Discord server
Feel free to visit the [ExLlama discord server](https://discord.gg/mUvR4tQfhV) if you have any questions. |
sweet-dreambooths/black-eyed-peas-v1-crafted-variable-prompt-8-epochs-piano-prompts | sweet-dreambooths | 2024-05-08T19:24:43Z | 1 | 0 | peft | [
"peft",
"safetensors",
"musicgen_melody",
"text-to-audio",
"sweet-dreambooths/black-eyed-peas-v1-piano-prompts",
"generated_from_trainer",
"base_model:facebook/musicgen-melody-large",
"base_model:adapter:facebook/musicgen-melody-large",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-audio | 2024-05-08T16:15:56Z | ---
license: cc-by-nc-4.0
library_name: peft
tags:
- text-to-audio
- sweet-dreambooths/black-eyed-peas-v1-piano-prompts
- generated_from_trainer
base_model: facebook/musicgen-melody-large
model-index:
- name: black-eyed-peas-v1-crafted-variable-prompt-8-epochs-piano-prompts
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sanchit-gandhi/black-eyed-peas/runs/coi600r4)
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/sanchit-gandhi/black-eyed-peas/runs/coi600r4)
# black-eyed-peas-v1-crafted-variable-prompt-8-epochs-piano-prompts
This model is a fine-tuned version of [facebook/musicgen-melody-large](https://huggingface.co/facebook/musicgen-melody-large) on the SWEET-DREAMBOOTHS/BLACK-EYED-PEAS-V1-PIANO-PROMPTS - DEFAULT dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0989
- Text Cosine Sim: 0.3821
- Audio Cosine Sim: 0.5580
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 456
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.99) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 8.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Text Cosine Sim | Audio Cosine Sim |
|:-------------:|:------:|:----:|:---------------:|:---------------:|:----------------:|
| 8.1814 | 4.9485 | 30 | 3.0955 | 0.3817 | 0.5798 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.41.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.18.1.dev0
- Tokenizers 0.19.1 |
mradermacher/Llama-3-8B-Irene-v0.2-GGUF | mradermacher | 2024-05-08T19:21:45Z | 60 | 5 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"en",
"base_model:Virt-io/Llama-3-8B-Irene-v0.2",
"base_model:quantized:Virt-io/Llama-3-8B-Irene-v0.2",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-05-08T16:15:17Z | ---
base_model: Virt-io/Llama-3-8B-Irene-v0.2
language:
- en
library_name: transformers
license: other
license_link: LICENSE
license_name: llama3
quantized_by: mradermacher
tags:
- mergekit
- merge
- facebook
- meta
- pytorch
- llama
- llama-3
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Virt-io/Llama-3-8B-Irene-v0.2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Irene-v0.2-GGUF/resolve/main/Llama-3-8B-Irene-v0.2.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
HyperBlaze/L3-8B-Soliloquy-TheSpice-Hermes2pro-merge-v1 | HyperBlaze | 2024-05-08T19:19:58Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:merge:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:cgato/L3-TheSpice-8b-v0.8.3",
"base_model:merge:cgato/L3-TheSpice-8b-v0.8.3",
"base_model:elyn-dev/Llama-3-Soliloquy-8B-v2",
"base_model:merge:elyn-dev/Llama-3-Soliloquy-8B-v2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-08T19:08:36Z | ---
base_model:
- cgato/L3-TheSpice-8b-v0.8.3
- NousResearch/Hermes-2-Pro-Llama-3-8B
- openlynn/Llama-3-Soliloquy-8B-v2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [openlynn/Llama-3-Soliloquy-8B-v2](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B-v2) as a base.
### Models Merged
The following models were included in the merge:
* [cgato/L3-TheSpice-8b-v0.8.3](https://huggingface.co/cgato/L3-TheSpice-8b-v0.8.3)
* [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: openlynn/Llama-3-Soliloquy-8B-v2
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
normalize: 0.0
slices:
- sources:
- layer_range: [0, 4]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 1.0
weight: 0.6861808716092435
- layer_range: [0, 4]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.6628290134113985
weight: 0.5815923052193855
- layer_range: [0, 4]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 1.0
weight: 0.5113886163963061
- sources:
- layer_range: [4, 8]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.892655547455918
weight: 0.038732602391021484
- layer_range: [4, 8]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 1.0
weight: 0.1982145486303527
- layer_range: [4, 8]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 1.0
weight: 0.6843011350690802
- sources:
- layer_range: [8, 12]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 0.7817511027396784
weight: 0.13053333213489704
- layer_range: [8, 12]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.6963703515864826
weight: 0.20525481492667985
- layer_range: [8, 12]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 0.6983086326765777
weight: 0.5843953969574106
- sources:
- layer_range: [12, 16]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 0.9632895768462915
weight: 0.2101146706607748
- layer_range: [12, 16]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.597557434542081
weight: 0.6728172621848589
- layer_range: [12, 16]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 0.756263557607837
weight: 0.2581423726361908
- sources:
- layer_range: [16, 20]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 1.0
weight: 0.2116035543552448
- layer_range: [16, 20]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 1.0
weight: 0.22654226422958418
- layer_range: [16, 20]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 0.8925914810507647
weight: 0.42243766315440867
- sources:
- layer_range: [20, 24]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 0.7697608089825734
weight: 0.1535118632140203
- layer_range: [20, 24]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.9886758076773643
weight: 0.3305040603868546
- layer_range: [20, 24]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 1.0
weight: 0.40670083428654535
- sources:
- layer_range: [24, 28]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 1.0
weight: 0.4542810478500622
- layer_range: [24, 28]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.8330662483310117
weight: 0.2587495367324508
- layer_range: [24, 28]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 0.9845313983551542
weight: 0.40378452705975915
- sources:
- layer_range: [28, 32]
model: NousResearch/Hermes-2-Pro-Llama-3-8B
parameters:
density: 1.0
weight: 0.2951962192288415
- layer_range: [28, 32]
model: cgato/L3-TheSpice-8b-v0.8.3
parameters:
density: 0.960315594933433
weight: 0.13142971773782525
- layer_range: [28, 32]
model: openlynn/Llama-3-Soliloquy-8B-v2
parameters:
density: 1.0
weight: 0.30838472094518804
```
|
ISTA-DASLab/Phi-3-mini-4k-instruct-AQLM-1x16 | ISTA-DASLab | 2024-05-08T19:18:10Z | 129 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"microsoft",
"conversational",
"custom_code",
"en",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] | text-generation | 2024-05-08T19:02:22Z | ---
library_name: transformers
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
- microsoft
---
Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of [microsoft/Phi-3-mini-4k-instruct
](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct).
For this quantization, we used 1 codebook of 16 bits.
Results:
| Model | Quantization | MMLU (5-shot) | ArcC| ArcE| Hellaswag | Winogrande | PiQA | Model size, Gb |
|------|------|-------|------|------|------|------|------|------|
| microsoft/Phi-3-mini-4k-instruct| None | 0.6949 | 0.5529 | 0.8325 | 0.6055 | 0.8020 | 0.7364 | 7.6 |
| | 1x16 | 0.5818 | 0.4642 | 0.7807 | 0.5311 | 0.7715 | 0.7072 | 1.4 | |
ISTA-DASLab/Phi-3-mini-128k-instruct-AQLM-1x16 | ISTA-DASLab | 2024-05-08T19:15:39Z | 135 | 1 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"microsoft",
"conversational",
"custom_code",
"en",
"arxiv:2401.06118",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"aqlm",
"region:us"
] | text-generation | 2024-05-08T18:42:49Z | ---
library_name: transformers
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
- microsoft
---
Official [AQLM](https://arxiv.org/abs/2401.06118) quantization of [microsoft/Phi-3-mini-128k-instruct
](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct).
For this quantization, we used 1 codebook of 16 bits.
Results:
| Model | Quantization | MMLU (5-shot) | ArcC| ArcE| Hellaswag | Winogrande | PiQA | Model size, Gb |
|------|------|-------|------|------|------|------|------|------|
| microsoft/Phi-3-mini-128k-instruct| None | 0.6881 | 0.5418 | 0.8127 | 0.5980 | 0.7873 | 0.7340 | 7.6 |
| | 1x16 | 0.5815 | 0.4599 | 0.7845 | 0.5235 | 0.7666 | 0.6930 | 1.4 | |
andakm/food_classifier | andakm | 2024-05-08T19:14:26Z | 64 | 0 | transformers | [
"transformers",
"tf",
"vit",
"image-classification",
"generated_from_keras_callback",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-05-08T18:36:33Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_keras_callback
model-index:
- name: andakm/food_classifier
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# andakm/food_classifier
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.3733
- Validation Loss: 0.3509
- Train Accuracy: 0.919
- Epoch: 4
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 3e-05, 'decay_steps': 20000, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Train Accuracy | Epoch |
|:----------:|:---------------:|:--------------:|:-----:|
| 2.7813 | 1.6497 | 0.831 | 0 |
| 1.2118 | 0.8561 | 0.881 | 1 |
| 0.6777 | 0.5469 | 0.898 | 2 |
| 0.4861 | 0.4721 | 0.885 | 3 |
| 0.3733 | 0.3509 | 0.919 | 4 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.1
- Tokenizers 0.19.1
|
mmnga/karakuri-lm-8x7b-chat-v0.1-gguf | mmnga | 2024-05-08T19:14:20Z | 713 | 4 | null | [
"gguf",
"en",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2024-05-08T12:31:11Z |
---
license: apache-2.0
language:
- en
- ja
datasets:
- TFMC/imatrix-dataset-for-japanese-llm
---
# karakuri-lm-8x7b-chat-v0.1-gguf
[karakuri-aiさんが公開しているkarakuri-lm-8x7b-chat-v0.1](https://huggingface.co/karakuri-ai/karakuri-lm-8x7b-chat-v0.1)のggufフォーマット変換版です。
imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。
## 他のモデル
[mmnga/karakuri-lm-8x7b-chat-v0.1-gguf](https://huggingface.co/mmnga/karakuri-lm-8x7b-chat-v0.1-gguf)
[mmnga/karakuri-lm-70b-chat-v0.1-gguf](https://huggingface.co/mmnga/karakuri-lm-70b-chat-v0.1-gguf)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'karakuri-lm-8x7b-chat-v0.1-Q4_0.gguf' -n 128 -p 'こんにちわ'
```
|
sidnarsipur/controlnet_rough | sidnarsipur | 2024-05-08T19:08:51Z | 31 | 1 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"diffusers-training",
"base_model:stabilityai/stable-diffusion-2-1-base",
"base_model:adapter:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-04-16T04:15:42Z | ---
license: creativeml-openrail-m
library_name: diffusers
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
- diffusers-training
base_model: stabilityai/stable-diffusion-2-1-base
inference: true
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# controlnet_rough
Generate a roughness map from a photograph or basecolor (albedo) map.
# Usage
```
import argparse
from PIL import Image
from diffusers import StableDiffusionControlNetPipeline, ControlNetModel, UniPCMultistepScheduler
from diffusers.utils import load_image
import torch
parser = argparse.ArgumentParser(description="Args for parser")
parser.add_argument("--seed", type=int, default=1, help="Seed for inference")
args = parser.parse_args()
base_model_path = "stabilityai/stable-diffusion-2-1-base"
controlnet_path = "sidnarsipur/controlnet_rough"
controlnet = ControlNetModel.from_pretrained(controlnet_path, torch_dtype=torch.float16)
pipe = StableDiffusionControlNetPipeline.from_pretrained(
base_model_path, controlnet=controlnet, torch_dtype=torch.float16
)
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config)
pipe.enable_xformers_memory_efficient_attention()
pipe.enable_model_cpu_offload()
control_image = load_image("inference/basecolor.png") #Change based on your image path
prompt = "Roughness Map" #Don't change!
if control_image.size[0] > 2048 or control_image.size[1] > 2048: #Optional
control_image = control_image.resize((control_image.size[0] // 2, control_image.size[1] // 2))
generator = torch.manual_seed(args.seed)
image = pipe(
prompt, num_inference_steps=50, generator=generator, image=control_image
).images[0]
image.save("inference/normal.png")
```
|
deepnet/BSN630-TunedLlama3 | deepnet | 2024-05-08T19:04:43Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-04-28T12:19:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mdosama39/banglat5-finetuned-headlineBT5_1000_WithIp | mdosama39 | 2024-05-08T19:02:29Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:csebuetnlp/banglat5",
"base_model:finetune:csebuetnlp/banglat5",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-05-08T18:48:20Z | ---
base_model: csebuetnlp/banglat5
tags:
- generated_from_trainer
model-index:
- name: banglat5-finetuned-headlineBT5_1000_WithIp
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. -->
# banglat5-finetuned-headlineBT5_1000_WithIp
This model is a fine-tuned version of [csebuetnlp/banglat5](https://huggingface.co/csebuetnlp/banglat5) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.0663
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 4
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 11.172 | 1.0 | 160 | 8.1213 |
| 8.0283 | 2.0 | 320 | 6.4813 |
| 7.4228 | 3.0 | 480 | 5.5422 |
| 7.1164 | 4.0 | 640 | 5.1645 |
| 7.3204 | 5.0 | 800 | 5.0663 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
jialinselenasong/biobert-finetuned-ner | jialinselenasong | 2024-05-08T18:52:29Z | 119 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"base_model:dmis-lab/biobert-v1.1",
"base_model:finetune:dmis-lab/biobert-v1.1",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-05-08T17:44:50Z | ---
base_model: dmis-lab/biobert-v1.1
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: biobert-finetuned-ner
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# biobert-finetuned-ner
This model is a fine-tuned version of [dmis-lab/biobert-v1.1](https://huggingface.co/dmis-lab/biobert-v1.1) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6965
- Precision: 0.6381
- Recall: 0.6865
- F1: 0.6614
- Accuracy: 0.8583
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 305 | 0.4123 | 0.6110 | 0.6694 | 0.6389 | 0.8542 |
| 0.4534 | 2.0 | 610 | 0.4023 | 0.6259 | 0.6848 | 0.6540 | 0.8586 |
| 0.4534 | 3.0 | 915 | 0.4384 | 0.6369 | 0.6991 | 0.6666 | 0.8615 |
| 0.2438 | 4.0 | 1220 | 0.4799 | 0.6445 | 0.6941 | 0.6684 | 0.8615 |
| 0.1551 | 5.0 | 1525 | 0.5190 | 0.6464 | 0.6908 | 0.6678 | 0.8628 |
| 0.1551 | 6.0 | 1830 | 0.5772 | 0.6454 | 0.6751 | 0.6599 | 0.8597 |
| 0.1044 | 7.0 | 2135 | 0.6141 | 0.6413 | 0.6881 | 0.6639 | 0.8586 |
| 0.1044 | 8.0 | 2440 | 0.6587 | 0.6353 | 0.6945 | 0.6636 | 0.8590 |
| 0.0755 | 9.0 | 2745 | 0.6856 | 0.6357 | 0.6905 | 0.6620 | 0.8580 |
| 0.0604 | 10.0 | 3050 | 0.6965 | 0.6381 | 0.6865 | 0.6614 | 0.8583 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1
|
paul-stansifer/gemma-qwantz-punchline | paul-stansifer | 2024-05-08T18:52:09Z | 5 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:unsloth/gemma-2b-bnb-4bit",
"base_model:adapter:unsloth/gemma-2b-bnb-4bit",
"license:apache-2.0",
"region:us"
] | null | 2024-05-08T18:13:39Z | ---
license: apache-2.0
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/gemma-2b-bnb-4bit
metrics:
- accuracy
model-index:
- name: gemma-qwantz-punchline
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. -->
# gemma-qwantz-punchline
This model is a fine-tuned version of [unsloth/gemma-2b-bnb-4bit](https://huggingface.co/unsloth/gemma-2b-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4710
- Accuracy: 0.7824
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6508 | 1.0 | 150 | 0.6241 | 0.6235 |
| 0.5972 | 2.0 | 300 | 0.5388 | 0.7353 |
| 0.4801 | 3.0 | 450 | 0.4710 | 0.7824 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1 |
Subsets and Splits