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text-generation | transformers |
<!-- 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. -->
# selfbiorag-7b-dpo-full-sft-wo-kqa_silver_wogold
This model is a fine-tuned version of [Minbyul/selfbiorag-7b-wo-kqa_silver_wogold-sft](https://huggingface.co/Minbyul/selfbiorag-7b-wo-kqa_silver_wogold-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3027
- Rewards/chosen: -1.2987
- Rewards/rejected: -5.2535
- Rewards/accuracies: 0.8697
- Rewards/margins: 3.9549
- Logps/rejected: -1323.0533
- Logps/chosen: -663.1464
- Logits/rejected: -0.3508
- Logits/chosen: -0.2498
## 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: 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 |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.2357 | 0.35 | 100 | 0.3364 | -0.8114 | -3.4524 | 0.8579 | 2.6410 | -1142.9419 | -614.4251 | -0.0367 | 0.0065 |
| 0.154 | 0.71 | 200 | 0.3009 | -0.9868 | -4.1905 | 0.8632 | 3.2037 | -1216.7521 | -631.9589 | -0.3156 | -0.2396 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/selfbiorag-7b-wo-kqa_silver_wogold-sft", "model-index": [{"name": "selfbiorag-7b-dpo-full-sft-wo-kqa_silver_wogold", "results": []}]} | Minbyul/selfbiorag-7b-dpo-full-sft-wo-kqa_silver_wogold | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Minbyul/selfbiorag-7b-wo-kqa_silver_wogold-sft",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:27:21+00:00 |
null | null | Number of experts present in the library: 8
| Expert Name | Base Model | Trained on | Adapter Type |
| --- | --- | --- | --- |
| e0 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e1 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e2 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e3 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e4 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e5 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e6 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
| e7 | phi-2 | sordonia/flan-10k-flat/None | skilled_lora |
Last updated on: 2024-04-30 15:27:43+00:00
| {} | pclucas14/phi2_mhr_S32_1ep | null | [
"region:us"
] | null | 2024-04-30T15:27:43+00:00 |
fill-mask | transformers |
<!-- 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. -->
# tda_2001_16_distilbert
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2095
## 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 128 | 1.2329 |
| No log | 2.0 | 256 | 1.2978 |
| No log | 3.0 | 384 | 1.2600 |
| 1.3691 | 4.0 | 512 | 1.2740 |
| 1.3691 | 5.0 | 640 | 1.1886 |
| 1.3691 | 6.0 | 768 | 1.2319 |
| 1.3691 | 7.0 | 896 | 1.2037 |
| 1.2783 | 8.0 | 1024 | 1.2322 |
| 1.2783 | 9.0 | 1152 | 1.2019 |
| 1.2783 | 10.0 | 1280 | 1.2095 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-cased", "model-index": [{"name": "tda_2001_16_distilbert", "results": []}]} | gc394/tda_2001_16_distilbert | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"fill-mask",
"generated_from_trainer",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:28:38+00:00 |
text2text-generation | transformers | {"license": "unlicense"} | mboachie/T5-next-word | null | [
"transformers",
"onnx",
"t5",
"text2text-generation",
"license:unlicense",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:29:02+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | gutsartificial/RecipeBert | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:30:14+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/elinas/Llama-3-8B-Ultra-Instruct
You should use `--override-kv tokenizer.ggml.pre=str:llama3` and a current llama.cpp version to work around a bug in llama.cpp that made these quants. (see https://old.reddit.com/r/LocalLLaMA/comments/1cg0z1i/bpe_pretokenization_support_is_now_merged_llamacpp/?share_id=5dBFB9x0cOJi8vbr-Murh)
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-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-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF/resolve/main/Llama-3-8B-Ultra-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
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 -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "elinas/Llama-3-8B-Ultra-Instruct", "quantized_by": "mradermacher"} | mradermacher/Llama-3-8B-Ultra-Instruct-i1-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:elinas/Llama-3-8B-Ultra-Instruct",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:31:26+00:00 |
null | null | {"language": ["en"], "datasets": ["bloc4488/TMDB-all-movies"], "metrics": ["accuracy"]} | bloc4488/SAD | null | [
"en",
"dataset:bloc4488/TMDB-all-movies",
"region:us"
] | null | 2024-04-30T15:32:11+00:00 |
|
text-generation | transformers | {} | iimran/llama3 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:32:55+00:00 |
|
null | transformers |
`Epoch = 1, Global Step = 7669`
# Training Script
```
python pretrain.py \
--data-path "chart-rela-ins/unichart-pretrain-table-extraction-v2" \
--train-images "/workspace/data/UniChartImages/" \
--output-dir "/workspace/output_data/unichart_pretrain/" \
--max-steps 1600000 \
--batch-size 2 \
--valid-batch-size 2 \
--num-workers 12 \
--lr 5e-5 \
--log-every-n-steps 100 \
--val-check-interval 0.5 \
--warmup-steps 8000 \
--checkpoint-steps 40000 \
--accumulate-grad-batches 64 \
--processor-path "ahmed-masry/unichart-base-960" \
--image-size 512 \
--pretrained-vision-encoder "nxquang-al/unichart-base-960-encoder" \
--pretrained-decoder "nxquang-al/unichart-base-960-decoder" \
--wandb-project "Pretrain-ChartReLA-Instruct" \
``` | {"library_name": "transformers", "tags": []} | chart-rela-ins/pretrain-small-unichart-table-bs64-low-lr | null | [
"transformers",
"safetensors",
"Chart-rela-instruct",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:33:14+00:00 |
null | peft |
<!-- 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. -->
# llava-1.5-7b-hf-ft-mix-vsft
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "llava-hf/llava-1.5-7b-hf", "model-index": [{"name": "llava-1.5-7b-hf-ft-mix-vsft", "results": []}]} | GURU369/llava-1.5-7b-hf-ft-mix-vsft | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"region:us"
] | null | 2024-04-30T15:33:22+00:00 |
text-classification | transformers | {} | skelley/other_success | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:33:54+00:00 |
|
null | null | Neo_AD_MotionLora_V1
[Watch the Video](https://huggingface.co/Neofuturism/Neo_AD_MotionLora_V1/blob/main/MOTION_LORA.mp4)
4 Motion Loras trained on AnimateDiff V3
1. Neo360Pan: Slow 360 pan around center
2. Neo360Pan_B: Same as above, subjective smoother output
3. NeoPanUp: Background and foreground sliding upward
4. NeoPanDown: Background and foreground sliding downward
5. NeoMoveIn: Slow move forward
| {"license": "apache-2.0"} | Neofuturism/Neo_AD_MotionLora_V1 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:35:48+00:00 |
text-generation | transformers |
<!-- 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/emillykkejensen/LLM-instruct/runs/v8wdcn55)
# Llama-3-8B-instruct-dansk
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the [kobprof/skolegpt-instruct](https://huggingface.co/datasets/kobprof/skolegpt-instruct) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9477
## 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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.2
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"language": ["da"], "license": "other", "library_name": "transformers", "tags": ["trl", "sft", "generated_from_trainer", "danish"], "datasets": ["kobprof/skolegpt-instruct"], "base_model": "meta-llama/Meta-Llama-3-8B", "pipeline_tag": "text-generation", "model-index": [{"name": "Llama-3-8B-instruct-dansk", "results": []}]} | emillykkejensen/Llama-3-8B-instruct-dansk | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"danish",
"da",
"dataset:kobprof/skolegpt-instruct",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:37:56+00:00 |
null | null | {} | KleinPenny/imdb_tuning_model | null | [
"region:us"
] | null | 2024-04-30T15:38:22+00:00 |
|
null | null | {} | DouglasBraga/swin-tiny-patch4-window7-224-finetuned-eurosat-leukemia-3000-finetuned-eurosat-leukemia-5000 | null | [
"region:us"
] | null | 2024-04-30T15:38:57+00:00 |
|
null | transformers |
# 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] | {"library_name": "transformers", "tags": []} | ilhemhmz752/Agrobot-llama2-ft | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:39:37+00:00 |
null | peft |
<!-- 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. -->
# agrobot-ft
This model is a fine-tuned version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7517
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.26 | 1.0 | 102 | 1.8542 |
| 1.7656 | 2.0 | 205 | 1.7839 |
| 1.7085 | 3.0 | 307 | 1.7542 |
| 1.6346 | 4.0 | 410 | 1.7347 |
| 1.5946 | 5.0 | 512 | 1.7275 |
| 1.5335 | 6.0 | 615 | 1.7295 |
| 1.5043 | 7.0 | 717 | 1.7276 |
| 1.455 | 8.0 | 820 | 1.7389 |
| 1.443 | 9.0 | 922 | 1.7459 |
| 1.4064 | 9.95 | 1020 | 1.7517 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.39.3
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "meta-llama/Llama-2-7b-chat-hf", "model-index": [{"name": "agrobot-ft", "results": []}]} | ilhemhmz752/agrobot-ft | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"license:llama2",
"region:us"
] | null | 2024-04-30T15:39:40+00:00 |
null | null | {} | veela4/Yggdrasil | null | [
"region:us"
] | null | 2024-04-30T15:40:00+00:00 |
|
text-classification | transformers | {} | skelley/working_with_providers | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:40:38+00:00 |
|
null | null | {"license": "apache-2.0"} | alikwiq/test | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:40:47+00:00 |
|
text-generation | transformers |
# 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)
``` | {"license": "other", "library_name": "transformers", "tags": ["autotrain", "text-generation-inference", "text-generation", "peft"], "widget": [{"messages": [{"role": "user", "content": "What is your favorite condiment?"}]}]} | nanxiz/autotrain-fzpxr-ximzt | null | [
"transformers",
"tensorboard",
"safetensors",
"phi3",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"custom_code",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:41:45+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/jmgvp9n | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:41:50+00:00 |
text-generation | transformers |
# 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] | {"library_name": "transformers", "tags": []} | golf2248/10jfipg | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:43:03+00:00 |
null | null | {"license": "mit"} | AiNemt/NemtyrevAI | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T15:43:15+00:00 |
|
reinforcement-learning | null |
# **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="raulgadea/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"])
```
| {"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}]}]}]} | raulgadea/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-30T15:44:12+00:00 |
image-classification | transformers | {} | AP45345/Test-Model | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2024-04-30T15:44:20+00:00 |
|
null | diffusers | {"license": "mit"} | nathanReitinger/FASHION-diffusion-oneImage | null | [
"diffusers",
"safetensors",
"license:mit",
"diffusers:DDPMPipeline",
"region:us",
"has_space"
] | null | 2024-04-30T15:44:28+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-trained
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3511
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4352 | 1.0 | 157 | 0.3683 |
| 0.2797 | 2.0 | 314 | 0.3349 |
| 0.2142 | 3.0 | 471 | 0.3511 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-trained", "results": []}]} | Lasghar/distilbert-base-uncased-trained | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:45:28+00:00 |
null | null | {"license": "apache-2.0"} | Lauriane-ANS/gpt2 | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:45:45+00:00 |
|
audio-classification | transformers | {} | wojtek2288/Wav2Vec | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:46:19+00:00 |
|
null | transformers | {"license": "apache-2.0"} | NakanoMiku0-0/test_fine_tune | null | [
"transformers",
"gguf",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:46:23+00:00 |
|
null | null | {} | bwittmann/OCTA-unet | null | [
"region:us"
] | null | 2024-04-30T15:47:28+00:00 |
|
text-generation | transformers |
<!-- 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. -->
# dbloom-1b1-alpha-1
This model is a fine-tuned version of [bigscience/bloomz-1b1](https://huggingface.co/bigscience/bloomz-1b1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.7774
## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 14.0871 | 0.11 | 100 | 8.6262 |
| 7.0473 | 0.22 | 200 | 6.7608 |
| 6.3553 | 0.33 | 300 | 6.3938 |
| 6.1571 | 0.44 | 400 | 6.2497 |
| 6.0 | 0.55 | 500 | 6.1143 |
| 5.8703 | 0.66 | 600 | 5.9965 |
| 5.7946 | 0.77 | 700 | 5.9184 |
| 5.6473 | 0.88 | 800 | 5.8412 |
| 5.5571 | 0.99 | 900 | 5.7774 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "bigscience-bloom-rail-1.0", "tags": ["generated_from_trainer"], "base_model": "bigscience/bloomz-1b1", "model-index": [{"name": "dbloom-1b1-alpha-1", "results": []}]} | greenbureau/dbloom-1b1-alpha-1 | null | [
"transformers",
"safetensors",
"bloom",
"text-generation",
"generated_from_trainer",
"base_model:bigscience/bloomz-1b1",
"license:bigscience-bloom-rail-1.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:47:44+00:00 |
null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.8.2 | {"library_name": "peft", "base_model": "HiTZ/Medical-mT5-large"} | Mikelium5/Medical-mT5-large_art-nat-int-gal-sep-LoRa | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:HiTZ/Medical-mT5-large",
"region:us"
] | null | 2024-04-30T15:48:50+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adaptertoxic2nontoxic-100-50-0.0003 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:48:53+00:00 |
text-generation | transformers |
# Model Card for Model ID
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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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## 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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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model20 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:50:11+00:00 |
null | null | {} | jeyakumar2005/text_to_image | null | [
"region:us"
] | null | 2024-04-30T15:50:33+00:00 |
|
text-generation | transformers | {} | Lazycuber/Do-not-try | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:53:26+00:00 |
|
null | null | {"license": "openrail"} | lioooooooooo/db | null | [
"license:openrail",
"region:us"
] | null | 2024-04-30T15:54:08+00:00 |
|
null | transformers |
# Visually Guided Generative Text-Layout Pre-training for Document Intelligence
The ViTLP model was proposed in [Visually Guided Generative Text-Layout Pre-training for Document Intelligence](https://arxiv.org/abs/2403.16516), which is a generative foundation model for document intelligence. We provide the pre-trained checkpoint *ViTLP-medium* (380M). The pre-trained ViTLP model can natively perform OCR text localization and recognition.
## Demo on Document Text Recognition & Localization
The code of ViTLP inference and demo is assisible at [https://github.com/Veason-silverbullet/ViTLP](https://github.com/Veason-silverbullet/ViTLP).
# Preset FAQ
- Why is ViTLP-medium (380M)?
When I commenced this project, it was on the eve of LLMs (precisely speaking, ChatGPT). ViTLP-base presented in our paper, is actually a rather small pre-trained model. We know it is expected to scale up ViTLP in this LLM era. However, the pre-training scale is commonly constrained by computation resources and the pre-training dataset scale, in which context ViTLP-medium (380M) is the largest pre-training scale so far we can support.
Besides, this scale of ViTLP also brings inference sweetness including speed and memory usage. Typically, OCR on a page of a document image can be processed within 5~10 seconds in an Nvidia 4090, which is comparable to (and faster than) most OCR engines (and LLMs).
# Note
ViTLP is pronounced /ˈvai·tlp/ (vital). The first version of our paper was submitted to [OpenReview](https://openreview.net/forum?id=ARtBIBAmNR) in June 2023. | {"language": ["en"], "license": "mit"} | veason/ViTLP-medium | null | [
"transformers",
"pytorch",
"ViTLP",
"en",
"arxiv:2403.16516",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:54:12+00:00 |
null | null | {} | bluuwhale/BagelMIsteryTour-v2-8x7B-4bpw-exl2-RPcal | null | [
"region:us"
] | null | 2024-04-30T15:54:57+00:00 |
|
text-generation | transformers | # [MaziyarPanahi/LlaMAndement-13b-GGUF](https://huggingface.co/MaziyarPanahi/LlaMAndement-13b-GGUF)
- Model creator: [AgentPublic](https://huggingface.co/AgentPublic)
- Original model: [AgentPublic/LlaMAndement-13b](https://huggingface.co/AgentPublic/LlaMAndement-13b)
## Description
[MaziyarPanahi/LlaMAndement-13b-GGUF](https://huggingface.co/MaziyarPanahi/LlaMAndement-13b-GGUF) contains GGUF format model files for [AgentPublic/LlaMAndement-13b](https://huggingface.co/AgentPublic/LlaMAndement-13b).
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible. | {"tags": ["quantized", "2-bit", "3-bit", "4-bit", "5-bit", "6-bit", "8-bit", "GGUF", "text-generation", "llama", "llama-2", "text-generation"], "model_name": "LlaMAndement-13b-GGUF", "base_model": "AgentPublic/LlaMAndement-13b", "inference": false, "model_creator": "AgentPublic", "pipeline_tag": "text-generation", "quantized_by": "MaziyarPanahi"} | MaziyarPanahi/LlaMAndement-13b-GGUF | null | [
"transformers",
"gguf",
"mistral",
"quantized",
"2-bit",
"3-bit",
"4-bit",
"5-bit",
"6-bit",
"8-bit",
"GGUF",
"text-generation",
"llama",
"llama-2",
"base_model:AgentPublic/LlaMAndement-13b",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:54:58+00:00 |
null | null | {} | Litzy619/OLM_test | null | [
"region:us"
] | null | 2024-04-30T15:55:33+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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#### 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] | {"library_name": "transformers", "tags": []} | OwOOwO/finalupdate | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:56:13+00:00 |
null | transformers |
# 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]
### 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] | {"library_name": "transformers", "tags": []} | jamesohe/casaudit3-4bit-p02-adapter | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:56:57+00:00 |
null | null | {} | Sam2x/dolphin-2.9-llama3-8b-256k-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-04-30T15:57:09+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** yadz45
- **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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | yadz45/IA_simple2 | null | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"gguf",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:58:08+00:00 |
reinforcement-learning | null |
# **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="raulgadea/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-Taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.56 +/- 2.71", "name": "mean_reward", "verified": false}]}]}]} | raulgadea/q-Taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-30T15:59:19+00:00 |
token-classification | transformers | {} | pontusnorman123/layoutlmv3-finetuned-sweset3_v3 | null | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:59:26+00:00 |
|
text-generation | transformers | # # Fast-Inference with Ctranslate2
Speedup inference while reducing memory by 2x-4x using int8 inference in C++ on CPU or GPU.
quantized version of [meta-llama/Llama-2-7b-chat-hf](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)
```bash
pip install ctranslate2
```
Checkpoint compatible to [ctranslate2>=3.22.0](https://github.com/OpenNMT/CTranslate2)
- `compute_type=int8_float16` for `device="cuda"`
- `compute_type=int8` for `device="cpu"`
Converted on 2023-12-01 using CTranslate2==3.22.0 and
```
from ctranslate2.converters import TransformersConverter
TransformersConverter(
"meta-llama/Llama-2-7b-chat-hf",
activation_scales=None,
copy_files=['tokenizer.json', 'USE_POLICY.md', 'generation_config.json', 'README.md', 'special_tokens_map.json', 'tokenizer_config.json', 'LICENSE.txt', '.gitattributes'],
load_as_float16=True,
revision=None,
low_cpu_mem_usage=True,
trust_remote_code=True,
).convert(
output_dir=str(tmp_dir),
vmap = None,
quantization="int8",
force = True,
)
```
# License and other remarks:
This is just a quantized version. License conditions are intended to be idential to original huggingface repo.
# Original description, copied from https://huggingface.co/meta-llama/Llama-2-7b-chat-hf
# **Llama 2**
Llama 2 is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. This is the repository for the 7B fine-tuned model, optimized for dialogue use cases and converted for the Hugging Face Transformers format. Links to other models can be found in the index at the bottom.
## Model Details
*Note: Use of this model is governed by the Meta license. In order to download the model weights and tokenizer, please visit the [website](https://ai.meta.com/resources/models-and-libraries/llama-downloads/) and accept our License before requesting access here.*
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Our fine-tuned LLMs, called Llama-2-Chat, are optimized for dialogue use cases. Llama-2-Chat models outperform open-source chat models on most benchmarks we tested, and in our human evaluations for helpfulness and safety, are on par with some popular closed-source models like ChatGPT and PaLM.
**Model Developers** Meta
**Variations** Llama 2 comes in a range of parameter sizes — 7B, 13B, and 70B — as well as pretrained and fine-tuned variations.
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Llama 2 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 to human preferences for helpfulness and safety.
||Training Data|Params|Content Length|GQA|Tokens|LR|
|---|---|---|---|---|---|---|
|Llama 2|*A new mix of publicly available online data*|7B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|13B|4k|✗|2.0T|3.0 x 10<sup>-4</sup>|
|Llama 2|*A new mix of publicly available online data*|70B|4k|✔|2.0T|1.5 x 10<sup>-4</sup>|
*Llama 2 family of models.* Token counts refer to pretraining data only. All models are trained with a global batch-size of 4M tokens. Bigger models - 70B -- use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Dates** Llama 2 was trained between January 2023 and July 2023.
**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://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** ["Llama-2: Open Foundation and Fine-tuned Chat Models"](arxiv.org/abs/2307.09288)
## Intended Use
**Intended Use Cases** Llama 2 is intended for commercial and research use in English. Tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
To get the expected features and performance for the chat versions, a specific formatting needs to be followed, including the `INST` and `<<SYS>>` tags, `BOS` and `EOS` tokens, and the whitespaces and breaklines in between (we recommend calling `strip()` on inputs to avoid double-spaces). See our reference code in github for details: [`chat_completion`](https://github.com/facebookresearch/llama/blob/main/llama/generation.py#L212).
**Out-of-scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws).Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Llama 2.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research Super Cluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint** Pretraining utilized a cumulative 3.3M GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 539 tCO2eq, 100% of which were offset by Meta’s sustainability program.
||Time (GPU hours)|Power Consumption (W)|Carbon Emitted(tCO<sub>2</sub>eq)|
|---|---|---|---|
|Llama 2 7B|184320|400|31.22|
|Llama 2 13B|368640|400|62.44|
|Llama 2 70B|1720320|400|291.42|
|Total|3311616||539.00|
**CO<sub>2</sub> emissions during pretraining.** 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 2 was pretrained on 2 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over one million new human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of September 2022, but some tuning data is more recent, up to July 2023.
## Evaluation Results
In this section, we report the results for the Llama 1 and Llama 2 models on standard academic benchmarks.For all the evaluations, we use our internal evaluations library.
|Model|Size|Code|Commonsense Reasoning|World Knowledge|Reading Comprehension|Math|MMLU|BBH|AGI Eval|
|---|---|---|---|---|---|---|---|---|---|
|Llama 1|7B|14.1|60.8|46.2|58.5|6.95|35.1|30.3|23.9|
|Llama 1|13B|18.9|66.1|52.6|62.3|10.9|46.9|37.0|33.9|
|Llama 1|33B|26.0|70.0|58.4|67.6|21.4|57.8|39.8|41.7|
|Llama 1|65B|30.7|70.7|60.5|68.6|30.8|63.4|43.5|47.6|
|Llama 2|7B|16.8|63.9|48.9|61.3|14.6|45.3|32.6|29.3|
|Llama 2|13B|24.5|66.9|55.4|65.8|28.7|54.8|39.4|39.1|
|Llama 2|70B|**37.5**|**71.9**|**63.6**|**69.4**|**35.2**|**68.9**|**51.2**|**54.2**|
**Overall performance on grouped academic benchmarks.** *Code:* We report the average pass@1 scores of our models on HumanEval and MBPP. *Commonsense Reasoning:* We report the average of PIQA, SIQA, HellaSwag, WinoGrande, ARC easy and challenge, OpenBookQA, and CommonsenseQA. We report 7-shot results for CommonSenseQA and 0-shot results for all other benchmarks. *World Knowledge:* We evaluate the 5-shot performance on NaturalQuestions and TriviaQA and report the average. *Reading Comprehension:* For reading comprehension, we report the 0-shot average on SQuAD, QuAC, and BoolQ. *MATH:* We report the average of the GSM8K (8 shot) and MATH (4 shot) benchmarks at top 1.
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama 1|7B|27.42|23.00|
|Llama 1|13B|41.74|23.08|
|Llama 1|33B|44.19|22.57|
|Llama 1|65B|48.71|21.77|
|Llama 2|7B|33.29|**21.25**|
|Llama 2|13B|41.86|26.10|
|Llama 2|70B|**50.18**|24.60|
**Evaluation of pretrained LLMs on automatic safety benchmarks.** For TruthfulQA, we present the percentage of generations that are both truthful and informative (the higher the better). For ToxiGen, we present the percentage of toxic generations (the smaller the better).
|||TruthfulQA|Toxigen|
|---|---|---|---|
|Llama-2-Chat|7B|57.04|**0.00**|
|Llama-2-Chat|13B|62.18|**0.00**|
|Llama-2-Chat|70B|**64.14**|0.01|
**Evaluation of fine-tuned LLMs on different safety datasets.** Same metric definitions as above.
## Ethical Considerations and Limitations
Llama 2 is a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Llama 2’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 2, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available at [https://ai.meta.com/llama/responsible-use-guide/](https://ai.meta.com/llama/responsible-use-guide)
## Reporting Issues
Please report any software “bug,” or other problems with the models through one of the following means:
- Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)
- Reporting problematic content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
- Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
## Llama Model Index
|Model|Llama2|Llama2-hf|Llama2-chat|Llama2-chat-hf|
|---|---|---|---|---|
|7B| [Link](https://huggingface.co/meta-llama/Llama-2-7b) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-7b-chat-hf)|
|13B| [Link](https://huggingface.co/meta-llama/Llama-2-13b) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-13b-chat-hf)|
|70B| [Link](https://huggingface.co/meta-llama/Llama-2-70b) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-hf) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat) | [Link](https://huggingface.co/meta-llama/Llama-2-70b-chat-hf)| | {"language": ["en"], "tags": ["ctranslate2", "int8", "float16", "facebook", "meta", "pytorch", "llama", "llama-2"], "extra_gated_heading": "Access Llama 2 on Hugging Face", "extra_gated_description": "This is a form to enable access to Llama 2 on Hugging Face after you have been granted access from Meta. Please visit the [Meta website](https://ai.meta.com/resources/models-and-libraries/llama-downloads) and accept our license terms and acceptable use policy before submitting this form. Requests will be processed in 1-2 days.", "extra_gated_prompt": "**Your Hugging Face account email address MUST match the email you provide on the Meta website, or your request will not be approved.**", "extra_gated_button_content": "Submit", "extra_gated_fields": {"I agree to share my name, email address and username with Meta and confirm that I have already been granted download access on the Meta website": "checkbox"}, "pipeline_tag": "text-generation", "inference": false, "arxiv": 2307.09288} | Weblet/Llama-2-7b-chat-hf-ct2-int8 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"ctranslate2",
"int8",
"float16",
"facebook",
"meta",
"llama-2",
"conversational",
"en",
"arxiv:2307.09288",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:59:38+00:00 |
text-to-image | null |
## ReaL
<img src="https://via.placeholder.com/468x300?text=App+Screenshot+Here" alt="Generated on Image Pipeline" style="border-radius: 10px;">
**This lora model is uploaded on [imagepipeline.io](https://imagepipeline.io/)**
Model details - FOOTJOB REAL
[](https://imagepipeline.io/models/ReaL?id=16043964-2894-4bc4-9ffb-5c0f42c7938c/)
## How to try this model ?
You can try using it locally or send an API call to test the output quality.
Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/). No payment required.
Coding in `php` `javascript` `node` etc ? Checkout our documentation
[](https://docs.imagepipeline.io/docs/introduction)
```python
import requests
import json
url = "https://imagepipeline.io/sd/text2image/v1/run"
payload = json.dumps({
"model_id": "sd1.5",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": false,
"guidance_scale": 7.5,
"multi_lingual": "no",
"embeddings": "",
"lora_models": "16043964-2894-4bc4-9ffb-5c0f42c7938c",
"lora_weights": "0.5"
})
headers = {
'Content-Type': 'application/json',
'API-Key': 'your_api_key'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
}
```
Get more ready to use `MODELS` like this for `SD 1.5` and `SDXL` :
[](https://imagepipeline.io/models)
### API Reference
#### Generate Image
```http
https://api.imagepipeline.io/sd/text2image/v1
```
| Headers | Type | Description |
|:----------------------| :------- |:-------------------------------------------------------------------------------------------------------------------|
| `API-Key` | `str` | Get your `API_KEY` from [imagepipeline.io](https://imagepipeline.io/) |
| `Content-Type` | `str` | application/json - content type of the request body |
| Parameter | Type | Description |
| :-------- | :------- | :------------------------- |
| `model_id` | `str` | Your base model, find available lists in [models page](https://imagepipeline.io/models) or upload your own|
| `prompt` | `str` | Text Prompt. Check our [Prompt Guide](https://docs.imagepipeline.io/docs/SD-1.5/docs/extras/prompt-guide) for tips |
| `num_inference_steps` | `int [1-50]` | Noise is removed with each step, resulting in a higher-quality image over time. Ideal value 30-50 (without LCM) |
| `guidance_scale` | `float [1-20]` | Higher guidance scale prioritizes text prompt relevance but sacrifices image quality. Ideal value 7.5-12.5 |
| `lora_models` | `str, array` | Pass the model_id(s) of LoRA models that can be found in models page |
| `lora_weights` | `str, array` | Strength of the LoRA effect |
---
license: creativeml-openrail-m
tags:
- imagepipeline
- imagepipeline.io
- text-to-image
- ultra-realistic
pinned: false
pipeline_tag: text-to-image
---
### Feedback
If you have any feedback, please reach out to us at [email protected]
#### 🔗 Visit Website
[](https://imagepipeline.io/)
If you are the original author of this model, please [click here](https://airtable.com/apprTaRnJbDJ8ufOx/shr4g7o9B6fWfOlUR) to add credits
| {"license": "creativeml-openrail-m", "tags": ["imagepipeline", "imagepipeline.io", "text-to-image", "ultra-realistic"], "pinned": false, "pipeline_tag": "text-to-image"} | imagepipeline/ReaL | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-30T15:59:39+00:00 |
automatic-speech-recognition | transformers | {} | Abosteet/whisper-small-ar | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:59:51+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
A [LLaVA (Large Language and Vision Assistant)](https://github.com/haotian-liu/LLaVA) version of [microsoft/Phi-3-mini-128k-instruct](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct)
## Model Details
Trained with this dataset of [liuhaotian/LLaVA-Instruct-150K](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K). This model has a around 4.1B parameters, where the 3.8B came from the base model. | {"base_model": "microsoft/Phi-3-mini-128k-instruct", "inference": false} | praysimanjuntak/llava-phi3-3.8b-lora | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"conversational",
"custom_code",
"base_model:microsoft/Phi-3-mini-128k-instruct",
"autotrain_compatible",
"region:us"
] | null | 2024-04-30T15:59:53+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Entreprenerdly
- **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)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Entreprenerdly/finetuned_llama3_unsloth_lora_model | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:01:54+00:00 |
null | null | {"license": "openrail"} | otmanabs/gcamai | null | [
"safetensors",
"license:openrail",
"region:us"
] | null | 2024-04-30T16:03:36+00:00 |
|
null | transformers |
# 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] | {"library_name": "transformers", "tags": []} | Lasion/wav2vec2-base-vivos | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:03:52+00:00 |
text-generation | transformers |
# dictalm2.0-yam-peleg-Mistral-instruct-Merge
dictalm2.0-yam-peleg-Mistral-instruct-Merge is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [yam-peleg/Hebrew-Mistral-7B](https://huggingface.co/yam-peleg/Hebrew-Mistral-7B)
* [dicta-il/dictalm2.0-instruct](https://huggingface.co/dicta-il/dictalm2.0-instruct)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: yam-peleg/Hebrew-Mistral-7B
layer_range: [0, 32]
- model: dicta-il/dictalm2.0-instruct
layer_range: [0, 32]
merge_method: slerp
base_model: dicta-il/dictalm2.0-instruct
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "itayl/dictalm2.0-yam-peleg-Mistral-instruct-Merge"
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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "yam-peleg/Hebrew-Mistral-7B", "dicta-il/dictalm2.0-instruct"], "base_model": ["yam-peleg/Hebrew-Mistral-7B", "dicta-il/dictalm2.0-instruct"]} | itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Hebrew-Mistral-7B",
"dicta-il/dictalm2.0-instruct",
"conversational",
"base_model:yam-peleg/Hebrew-Mistral-7B",
"base_model:dicta-il/dictalm2.0-instruct",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:04:18+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[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]
| {"library_name": "transformers", "tags": []} | Trelis/Meta-Llama-3-8B-Instruct-forced-french-adapters | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:04:53+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/chlee10/T3Q-Llama3-8B-dpo-v2.0
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF/resolve/main/T3Q-Llama3-8B-dpo-v2.0.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 -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": "chlee10/T3Q-Llama3-8B-dpo-v2.0", "quantized_by": "mradermacher"} | mradermacher/T3Q-Llama3-8B-dpo-v2.0-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:chlee10/T3Q-Llama3-8B-dpo-v2.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:04:59+00:00 |
null | null | {} | QuyenEzio/vietnamese-correction-v2 | null | [
"region:us"
] | null | 2024-04-30T16:05:38+00:00 |
|
image-classification | transformers |
<!-- 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. -->
# FASHION-vision
This model is a fine-tuned version of [google/vit-huge-patch14-224-in21k](https://huggingface.co/google/vit-huge-patch14-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2821
- Accuracy: 0.9034
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.5334 | 1.0 | 750 | 0.5595 | 0.8302 |
| 0.3389 | 2.0 | 1500 | 0.4090 | 0.8602 |
| 0.358 | 3.0 | 2250 | 0.3631 | 0.8717 |
| 0.3672 | 4.0 | 3000 | 0.3368 | 0.8815 |
| 0.3458 | 5.0 | 3750 | 0.3231 | 0.8842 |
| 0.2721 | 6.0 | 4500 | 0.3075 | 0.8885 |
| 0.2397 | 7.0 | 5250 | 0.3035 | 0.8899 |
| 0.2779 | 8.0 | 6000 | 0.2893 | 0.8963 |
| 0.2046 | 9.0 | 6750 | 0.2868 | 0.8991 |
| 0.2599 | 10.0 | 7500 | 0.2821 | 0.9034 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-huge-patch14-224-in21k", "model-index": [{"name": "FASHION-vision", "results": []}]} | nathanReitinger/FASHION-vision | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-huge-patch14-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:06:16+00:00 |
null | transformers |
# 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] | {"library_name": "transformers", "tags": []} | omezzinemariem/codeqwen-text-to-RULE | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:07:09+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_en_v2
This model is a fine-tuned version of [FacebookAI/roberta-base](https://huggingface.co/FacebookAI/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9374
## 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: 24
- eval_batch_size: 24
- 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 1.137 | 1.0 | 609 | 0.9105 |
| 0.8061 | 2.0 | 1218 | 0.8708 |
| 0.6349 | 3.0 | 1827 | 0.9374 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "FacebookAI/roberta-base", "model-index": [{"name": "roberta_en_v2", "results": []}]} | enriquesaou/roberta_en_v2 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:08:25+00:00 |
text2text-generation | transformers |
# 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]
| {"library_name": "transformers", "tags": []} | manueldeprada/t5-base-pt-en | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:08:52+00:00 |
null | peft |
<!-- 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. -->
# llava-1.5-7b-hf-ft-mix-vsft
This model is a fine-tuned version of [llava-hf/llava-1.5-7b-hf](https://huggingface.co/llava-hf/llava-1.5-7b-hf) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.4e-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: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "llava-hf/llava-1.5-7b-hf", "model-index": [{"name": "llava-1.5-7b-hf-ft-mix-vsft", "results": []}]} | Kakapoor/llava-1.5-7b-hf-ft-mix-vsft | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:llava-hf/llava-1.5-7b-hf",
"region:us"
] | null | 2024-04-30T16:09:31+00:00 |
token-classification | transformers |
<!-- 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. -->
# khadija69/bert-finetuned-ner-bio_test
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 0.1506
- Validation Loss: 0.2840
- Epoch: 2
## 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': 2e-05, 'decay_steps': 3480, '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 | Epoch |
|:----------:|:---------------:|:-----:|
| 0.3167 | 0.2658 | 0 |
| 0.1842 | 0.2582 | 1 |
| 0.1506 | 0.2840 | 2 |
### Framework versions
- Transformers 4.40.1
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "bert-base-cased", "model-index": [{"name": "khadija69/bert-finetuned-ner-bio_test", "results": []}]} | khadija69/bert-finetuned-ner-bio_test | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:bert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:09:40+00:00 |
text2text-generation | transformers |
<!-- 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. -->
# opus-mt-en-vi-finetuned-en-to-vi
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-vi](https://huggingface.co/Helsinki-NLP/opus-mt-en-vi) on the mt_eng_vietnamese dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|
| No log | 1.0 | 50 | 1.4989 | 34.3192 | 28.1662 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["opus-mt-en-vi", "generated_from_trainer"], "datasets": ["mt_eng_vietnamese"], "base_model": "Helsinki-NLP/opus-mt-en-vi", "model-index": [{"name": "opus-mt-en-vi-finetuned-en-to-vi", "results": []}]} | lmh2011/marianMT-finetuned-en-vi | null | [
"transformers",
"tensorboard",
"safetensors",
"marian",
"text2text-generation",
"opus-mt-en-vi",
"generated_from_trainer",
"dataset:mt_eng_vietnamese",
"base_model:Helsinki-NLP/opus-mt-en-vi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:10:41+00:00 |
null | null |
# llm-jp-13b-v2.0-gguf
[llm-jpさんが公開しているllm-jp-13b-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-v2.0)のggufフォーマット変換版です。
モデル一覧
GGUF V2.0系
[mmnga/llm-jp-13b-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
[mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
GGUF V1.0系
[mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf)
[mmnga/llm-jp-13b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-1.3b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-1.3b-v1.0-gguf)
## Convert Script
[convert-hf-to-gguf_llmjp_v2-py](https://gist.github.com/mmnga/8b8f6ca14f94326ffdac96a3c3605751#file-convert-hf-to-gguf_llmjp_v2-py)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'llm-jp-13b-v2.0-q4_0.gguf' -n 128 -p '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n自然言語処理とは何か\n\n### 応答:\n' --top_p 0.95 --temp 0.7 --repeat-penalty 1.1
``` | {"language": ["en", "ja"], "license": "apache-2.0", "tags": ["llama"]} | mmnga/llm-jp-13b-v2.0-gguf | null | [
"gguf",
"llama",
"en",
"ja",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:10:48+00:00 |
null | null |
# llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf
[llm-jpさんが公開しているllm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0)のggufフォーマット変換版です。
モデル一覧
GGUF V2.0系
[mmnga/llm-jp-13b-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
[mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
GGUF V1.0系
[mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf)
[mmnga/llm-jp-13b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-1.3b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-1.3b-v1.0-gguf)
## Convert Script
[convert-hf-to-gguf_llmjp_v2-py](https://gist.github.com/mmnga/8b8f6ca14f94326ffdac96a3c3605751#file-convert-hf-to-gguf_llmjp_v2-py)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-q4_0.gguf' -n 128 -p '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n自然言語処理とは何か\n\n### 応答:\n' --top_p 0.95 --temp 0.7 --repeat-penalty 1.1
``` | {"language": ["en", "ja"], "license": "apache-2.0", "tags": ["llama"]} | mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf | null | [
"gguf",
"llama",
"en",
"ja",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:11:11+00:00 |
text-classification | transformers | {} | MASE98/finetune_right_padding_XLNet_base_cased_en | null | [
"transformers",
"safetensors",
"gpt2",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:11:26+00:00 |
|
null | null |
# llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf
[llm-jpさんが公開しているllm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0)のggufフォーマット変換版です。
モデル一覧
GGUF V2.0系
[mmnga/llm-jp-13b-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
[mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
GGUF V1.0系
[mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf)
[mmnga/llm-jp-13b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-1.3b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-1.3b-v1.0-gguf)
## Convert Script
[convert-hf-to-gguf_llmjp_v2-py](https://gist.github.com/mmnga/8b8f6ca14f94326ffdac96a3c3605751#file-convert-hf-to-gguf_llmjp_v2-py)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-q4_0.gguf' -n 128 -p '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n自然言語処理とは何か\n\n### 応答:\n' --top_p 0.95 --temp 0.7 --repeat-penalty 1.1
``` | {"language": ["en", "ja"], "license": "apache-2.0", "tags": ["llama"]} | mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf | null | [
"gguf",
"llama",
"en",
"ja",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:11:28+00:00 |
null | null |
# Meliodaspercival_01_experiment26t3qStrangemerges_32-7B
Meliodaspercival_01_experiment26t3qStrangemerges_32-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## 🧩 Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: MaziyarPanahi/MeliodasPercival_01_Experiment26T3q
- model: Gille/StrangeMerges_32-7B-slerp
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Meliodaspercival_01_experiment26t3qStrangemerges_32-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/Meliodaspercival_01_experiment26t3qStrangemerges_32-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:11:44+00:00 |
null | null |
# llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf
[llm-jpさんが公開しているllm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0](https://huggingface.co/llm-jp/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0)のggufフォーマット変換版です。
モデル一覧
GGUF V2.0系
[mmnga/llm-jp-13b-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001_16x-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf)
[mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-ac_001-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
[mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf ](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf )
GGUF V1.0系
[mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-dolly-en-ja-oasst-v1.1-gguf)
[mmnga/llm-jp-13b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-jaster-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-13b-instruct-full-dolly-oasst-v1.0-gguf)
[mmnga/llm-jp-1.3b-v1.0-gguf](https://huggingface.co/mmnga/llm-jp-1.3b-v1.0-gguf)
## Convert Script
[convert-hf-to-gguf_llmjp_v2-py](https://gist.github.com/mmnga/8b8f6ca14f94326ffdac96a3c3605751#file-convert-hf-to-gguf_llmjp_v2-py)
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-q4_0.gguf' -n 128 -p '以下は、タスクを説明する指示です。要求を適切に満たす応答を書きなさい。\n\n### 指示:\n自然言語処理とは何か\n\n### 応答:\n' --top_p 0.95 --temp 0.7 --repeat-penalty 1.1
``` | {"language": ["en", "ja"], "license": "apache-2.0", "tags": ["llama"]} | mmnga/llm-jp-13b-instruct-full-dolly-ichikara_004_001_single-oasst-oasst2-v2.0-gguf | null | [
"gguf",
"llama",
"en",
"ja",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T16:12:06+00:00 |
null | peft | ## Training procedure
### Framework versions
- PEFT 0.4.0
| {"library_name": "peft"} | azmeena1311/distilbert-base-uncased-lora-spam-text-classification | null | [
"peft",
"region:us"
] | null | 2024-04-30T16:12:41+00:00 |
null | null | {} | azmeena1311/distilbert-base-uncased-lora-text-classification | null | [
"region:us"
] | null | 2024-04-30T16:13:00+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | sriyaseshadri/gemma-essay-finetune | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:13:03+00:00 |
image-to-text | transformers |
# **csg-wukong-1B-VL-v0.1** [[中文]](#chinese) [[English]](#english)
<a id="english"></a>
<p align="center">
<img width="900px" alt="OpenCSG" src="./csg-wukong-logo-green.jpg">
</p>
<p align="center"><a href="https://opencsg.com/models">[OpenCSG Community]</a> <a href="https://github.com/opencsgs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[wechat]</a> <a href="https://twitter.com/OpenCsg">[Twitter]</a> </p>
</div>
OpenCSG stands for Converged resources, Software refinement, and Generative LM. The 'C' represents Converged resources, indicating the integration and full utilization of hybrid resources. The 'S' stands for Software refinement, signifying software that is refined by large models. The 'G' represents Generative LM, which denotes widespread, inclusive, and democratized generative large models.
The vision of OpenCSG is to empower every industry, every company, and every individual to own their models. We adhere to the principles of openness and open source, making the large model software stack of OpenCSG available to the community. We welcome everyone to use, send feedback, and contribute collaboratively.
## Model Description
**csg-wukong-1B-VL-v0.1** was finetuned on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B).
<br>
we will introduce more information about this model.
## Model Evaluation results
We submitted csg-wukong-1B on the [open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard), and
the results show our model ranked the 8th among the ~1.5B pretrained small language models.
# Training
## Hardware
- **GPUs:** 16 H800
- **Training time:** 43days
## Software
- **Orchestration:** [Deepspeed](https://github.com/OpenCSGs)
- **Neural networks:** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16 if applicable:** [apex](https://github.com/NVIDIA/apex)
<a id="chinese"></a>
<p>
</p>
# OpenCSG介绍
<p align="center">
<img width="300px" alt="OpenCSG" src="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/GwYXPKuEoGCGcMICeW-sb.jpeg">
</p>
<p align="center"><a href="https://opencsg.com/models">[OpenCSG 社区]</a> <a href="https://github.com/opencsgs">[github]</a> <a href="https://cdn-uploads.huggingface.co/production/uploads/64c71b27d43e4dee51a8b31a/HU6vz21qKTEmUBCWqCFh9.jpeg">[微信]</a> <a href="https://twitter.com/OpenCsg">[推特]</a> </p>
</div>
OpenCSG中 Open是开源开放；C 代表 Converged resources，整合和充分利用的混合异构资源优势，算力降本增效；S 代表 Software refined，重新定义软件的交付方式，通过大模型驱动软件开发，人力降本增效；G 代表 Generative LM，大众化、普惠化和民主化的可商用的开源生成式大模型。
OpenCSG的愿景是让每个行业、每个公司、每个人都拥有自己的模型。 我们坚持开源开放的原则，将OpenCSG的大模型软件栈开源到社区，欢迎使用、反馈和参与共建，欢迎关注。
## 模型介绍
**csg-wukong-1B-VL-v0.1** 在[csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B)预训练模型上微调而成.
<br>
我们将在后面介绍更多关于这个模型的信息。
## 模型评测结果
我们把csg-wukong-1B模型提交到[open_llm_leaderboard](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)榜单上，结果显示我们的模型目前在~1.5B小语言模型中排名第8。
# 训练
## 硬件资源
- **GPU数量：** 16 H800
- **训练时间：** 43天
## 软件使用
- **微调训练框架：** [Deepspeed](https://github.com/OpenCSGs)
- **深度学习框架：** [PyTorch](https://github.com/pytorch/pytorch)
- **BP16：** [apex](https://github.com/NVIDIA/apex) | {"language": ["en"], "license": "apache-2.0", "tags": ["code"], "pipeline_tag": "image-to-text"} | opencsg/csg-wukong-1B-VL-v0.1 | null | [
"transformers",
"safetensors",
"csg-vl-wukong",
"text-generation",
"code",
"image-to-text",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:13:22+00:00 |
null | null | {} | raulgadea/q-Taxi-v3-2 | null | [
"region:us"
] | null | 2024-04-30T16:13:39+00:00 |
|
fill-mask | transformers |
# Phobert Base model with Legal domain
**Experiment performed with Transformers version 4.38.2**\
Vi-Legal-PhoBert model for Legal domain based on [vinai/phobert-base-v2](https://huggingface.co/vinai/phobert-base-v2), then continued MLM pretraining for 154600 steps with token-level on [Legal Corpus](https://huggingface.co/datasets/NghiemAbe/Legal-corpus-indexing) so the model can learn to legal domain.
## Usage
Fill mask example:
```python:
from transformers import RobertaForMaskedLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("NghiemAbe/Vi-Legal-PhoBert")
model = RobertaForMaskedLM.from_pretrained("NghiemAbe/Vi-Legal-PhoBert")
```
## Metric
I evaluated my [Dev-Legal-Dataset](https://huggingface.co/datasets/NghiemAbe/dev_legal) and here are the results:
| Model | Paramaters | Language Type | Length | R@1 | R@5 | R@10 | R@20 | R@100 | MRR@5 | MRR@10 | MRR@20 | MRR@100 | Accuracy Masked|
|-------------------------------|------------|---------------|--------|-------|-------|-------|-------|-------|-------|--------|--------|---------|---------|
| vinai/phobert-base-v2 | 125M | vi | 256 | 0.266 | 0.482 | 0.601 | 0.702 | 0.841 | 0.356 | 0.372 | 0.379 | 0.382 | 0.522|
| FacebookAI/xlm-roberta-base | 279M | mul | 512 | 0.012 | 0.042 | 0.064 | 0.091 | 0.207 | 0.025 | 0.028 | 0.030 | 0.033 | x|
| Geotrend/bert-base-vi-cased | 179M | vi | 512 | 0.098 | 0.175 | 0.202 | 0.241 | 0.356 | 0.131 | 0.136 | 0.139 | 0.142 | x|
| NlpHUST/roberta-base-vn | x | vi | 512 | 0.050 | 0.097 | 0.126 | 0.163 | 0.369 | 0.071 | 0.076 | 0.078 | 0.083 | x|
| aisingapore/sealion-bert-base| x | mul | 512 | 0.002 | 0.007 | 0.021 | 0.036 | 0.106 | 0.003 | 0.005 | 0.006 | 0.008 | x|
| **Vi-Legal-PhoBert** | 125M | vi | 256 | **0.290**| **0.560**| **0.707**| **0.819**| **0.935**| **0.410**| **0.430**| **0.437**| **0.440**|**0.8401**|
| {"language": ["vi"], "license": "apache-2.0", "library_name": "transformers", "tags": ["legal", "roberta", "phobert"], "datasets": ["NghiemAbe/Legal-corpus-indexing"], "widget": [{"text": "M\u1ee5c 3a . Ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c <mask> 110a . N\u1ed9i_dung qu\u1ea3n_l\u00fd nh\u00e0_n\u01b0\u1edbc v\u1ec1 ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c 1 . Ban_h\u00e0nh quy_\u0111\u1ecbnh v\u1ec1 ti\u00eau_chu\u1ea9n \u0111\u00e1nh_gi\u00e1 ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c ; quy_tr\u00ecnh v\u00e0 chu_k\u1ef3 ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c \u1edf t\u1eebng c\u1ea5p h\u1ecdc v\u00e0 tr\u00ecnh_\u0111\u1ed9 \u0111\u00e0o_t\u1ea1o ; nguy\u00ean_t\u1eafc ho\u1ea1t_\u0111\u1ed9ng , \u0111i\u1ec1u_ki\u1ec7n v\u00e0 ti\u00eau_chu\u1ea9n c\u1ee7a t\u1ed5_ch\u1ee9c , c\u00e1_nh\u00e2n ho\u1ea1t_\u0111\u1ed9ng ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c ; c\u1ea5p ph\u00e9p ho\u1ea1t_\u0111\u1ed9ng ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c ; c\u1ea5p , thu_h\u1ed3i gi\u1ea5y ch\u1ee9ng_nh\u1eadn ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c . 2 . Qu\u1ea3n_l\u00fd ho\u1ea1t_\u0111\u1ed9ng ki\u1ec3m_\u0111\u1ecbnh ch\u01b0\u01a1ng_tr\u00ecnh gi\u00e1o_d\u1ee5c v\u00e0 ki\u1ec3m_\u0111\u1ecbnh c\u01a1_s\u1edf gi\u00e1o_d\u1ee5c . 3 . H\u01b0\u1edbng_d\u1eabn c\u00e1c t\u1ed5_ch\u1ee9c , c\u00e1_nh\u00e2n v\u00e0 c\u01a1_s\u1edf gi\u00e1o_d\u1ee5c th\u1ef1c_hi\u1ec7n \u0111\u00e1nh_gi\u00e1 , ki\u1ec3m_\u0111\u1ecbnh ch\u1ea5t_l\u01b0\u1ee3ng gi\u00e1o_d\u1ee5c ."}], "pipeline_tag": "fill-mask"} | NghiemAbe/Vi-Legal-PhoBert | null | [
"transformers",
"safetensors",
"roberta",
"fill-mask",
"legal",
"phobert",
"vi",
"dataset:NghiemAbe/Legal-corpus-indexing",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:14:22+00:00 |
null | peft |
# Model Card for Model ID
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-8B-Instruct"} | SoulTest/llama3-8b-finetune | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"region:us"
] | null | 2024-04-30T16:15:03+00:00 |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | aphamm/flrpln-captions | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:15:17+00:00 |
null | null | {} | rayeesss/IVA | null | [
"region:us"
] | null | 2024-04-30T16:17:16+00:00 |
|
null | transformers |
# Model Card for Model ID
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## How to Get Started with the Model
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[More Information Needed]
## Training Details
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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 -->
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- **Hardware Type:** [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]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Madhumita19/finetuned_Mistral_newmodel | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:19:03+00:00 |
text-classification | transformers |
<!-- 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. -->
# emotion
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1934
- Accuracy: 0.9463
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2703 | 1.0 | 670 | 0.2737 | 0.9167 |
| 0.1473 | 2.0 | 1340 | 0.2178 | 0.9407 |
| 0.1187 | 3.0 | 2010 | 0.1934 | 0.9463 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "emotion", "results": []}]} | pheonixnrj/emotion | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:19:34+00:00 |
null | null | {} | sayhihighhihigh/umji_gfriend | null | [
"region:us"
] | null | 2024-04-30T16:21:22+00:00 |
|
null | null | {} | MikylianMabppe/Predicciones_UCL | null | [
"region:us"
] | null | 2024-04-30T16:21:44+00:00 |
|
null | null | {} | Goodbards/model-repo | null | [
"region:us"
] | null | 2024-04-30T16:22:30+00:00 |
|
null | null | {"license": "mit"} | kevinwang676/OpenVoice-v2 | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T16:22:52+00:00 |
|
zero-shot-classification | transformers | {"language": ["fr"], "library_name": "transformers", "tags": ["code"], "datasets": ["1st-Simiabroozu/test"], "metrics": ["accuracy"], "pipeline_tag": "zero-shot-classification"} | 1st-Simiabroozu/test_trainer | null | [
"transformers",
"safetensors",
"bart",
"text-classification",
"code",
"zero-shot-classification",
"fr",
"dataset:1st-Simiabroozu/test",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:24:07+00:00 |
|
text-generation | keras-nlp | Gemma fine-tuned to speak like a pirate.
This is a [`Gemma` model](https://keras.io/api/keras_nlp/models/gemma) uploaded using the KerasNLP library and can be used with JAX, TensorFlow, and PyTorch backends.
This model is related to a `CausalLM` task.
Model config:
* **name:** gemma_backbone
* **trainable:** True
* **vocabulary_size:** 256000
* **num_layers:** 28
* **num_query_heads:** 16
* **num_key_value_heads:** 16
* **hidden_dim:** 3072
* **intermediate_dim:** 49152
* **head_dim:** 256
* **layer_norm_epsilon:** 1e-06
* **dropout:** 0
This model card has been generated automatically and should be completed by the model author. See [Model Cards documentation](https://huggingface.co/docs/hub/model-cards) for more information.
| {"library_name": "keras-nlp", "pipeline_tag": "text-generation"} | martin-gorner/gemma_pirate_instruct_7b-keras | null | [
"keras-nlp",
"text-generation",
"region:us"
] | null | 2024-04-30T16:24:48+00:00 |
null | null | LoRA adapter based on GradientAI's 1M context Llama-3 8B Instruct finetune. I found that rank 1024 is not sufficient to capture the delta weights in the q_proj and o_proj, so I've created seperate adapters for those modules vs the k-v projection modules. | {} | winglian/llama-3-gradientai-1m-split | null | [
"safetensors",
"region:us"
] | null | 2024-04-30T16:25:02+00:00 |
text-generation | transformers | ## Model Details
### Model Description
The willieseun/Enron-Mixral-8x7b-instruct model is a large-scale language model based on the Mixtral architecture, fine-tuned specifically for email generation using the Enron dataset. This model is designed to generate coherent and contextually appropriate text, particularly suited for tasks related to email composition.
- **Developed by:** WILLIESEUN
- **Model type:** Transformer-based Language Model
- **Language(s) (NLP):** English
- **License:** Apache 2.0
### Model Sources
- **Repository:** [Hugging Face Model Hub](https://huggingface.co/MistralAI/Mixtral-8x7B-Instruct-v0.1)
## Uses
### Direct Use
The model can be used directly for email generation tasks. Users can input prompts or partial content, and the model will generate corresponding text.
### Downstream Use
This model is suitable for downstream tasks requiring email composition, such as email summarization, response generation, or personalized email content generation.
## Bias, Risks, and Limitations
The model's performance may vary depending on the quality and representativeness of the training data (Enron dataset). It may exhibit biases present in the training data, and caution should be exercised when using generated text in sensitive or critical applications.
### Recommendations
Users should review and post-process the generated text to ensure appropriateness and accuracy, particularly in professional or formal communication settings.
## How to Get Started with the Model
To use the model, you can leverage the Hugging Face Transformers library. Below is an example code snippet for generating emails:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "willieseun/Enron-Mixral-8x7b-instruct"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)
# Example prompt
prompt_text = "Compose an email from Claudio Ribeiro to Vince J Kaminski regarding the possibility of sponsoring a Financial Engineering Pro-Seminar at MIT. The email should mention that Enron may have sponsored a similar seminar in the past (related to Real Options) and inquire if the Research department or the Weather Desk (interested in a Weather Trading problem) would be interested in co-sponsoring."
pipe = pipeline("text-generation", tokenizer=tokenizer, model=model, return_full_text=False, max_length=190)
print(pipe(str(prompt_str)))
```
## Training Details
### Training Data
The model was fine-tuned on the Enron email dataset, which contains real-world emails from employees at the Enron Corporation.
### Training Procedure
The training utilized the PeftModelForCausalLM architecture and was fine-tuned using a Causal-LM approach, optimizing for email generation tasks.
#### Training Hyperparameters
- **Training regime:** CausalLM fine-tuning
- **Batch size:** 1
- **Learning rate:** 2e-4
- **Epochs:** 5
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
The model was evaluated on a held-out subset of the Enron dataset.
#### Metrics
Only the evaluation loss was used.
### Results
The model demonstrates coherent and contextually relevant email generation based on the evaluation metrics.
## Environmental Impact
The environmental impact of model training and inference can vary based on the hardware and compute infrastructure used.
## Citation
**BibTeX:**
```
@article{willieseun_enron_mixtral_instruct,
title={willieseun/Enron-Mixral-8x7b-instruct: Fine-tuned Email Generation Model},
author={WILLIESEUN},
journal={Hugging Face Model Hub},
year={2024},
howpublished={url{https://huggingface.co/willieseun/Enron-Mixral-8x7b-instruct}}
}
```
**APA:**
MistralAI. (2024). willieseun/Enron-Mixral-8x7b-instruct: Fine-tuned Email Generation Model. Hugging Face Model Hub. [https://huggingface.co/willieseun/Enron-Mixral-8x7b-instruct](https://huggingface.co/willieseun/Enron-Mixral-8x7b-instruct)
This model card provides an overview of the willieseun/Enron-Mixral-8x7b-instruct model, its use case, training details, and environmental considerations for users interested in utilizing this model for email generation tasks. For further information, please refer to the associated Hugging Face Model Hub repository. | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["NLP", "Text Generation", "Fine-tuning", "Language Model"], "pipeline_tag": "text-generation"} | willieseun/Enron-Mixral-8x7b-instruct | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"NLP",
"Text Generation",
"Fine-tuning",
"Language Model",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T16:25:13+00:00 |
text2text-generation | transformers |
# 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] | {"library_name": "transformers", "tags": []} | ylacombe/parler-tts-mini-Jenny-colab | null | [
"transformers",
"safetensors",
"parler_tts",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:25:51+00:00 |
null | null | {} | yowhattsup519/SRB2_Gleaming_Glacier_ACWW_Accordion_Solo | null | [
"region:us"
] | null | 2024-04-30T16:26:29+00:00 |
|
text2text-generation | transformers | {} | shenkha/DGSlow_T5-small_PC | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T16:26:34+00:00 |
|
null | null | {} | FanFierik/CapoPlaza | null | [
"region:us"
] | null | 2024-04-30T16:27:55+00:00 |
|
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
model = load_from_hub(repo_id="Novski/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"])
| {"tags": ["FrozenLake-v1-4x4", "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", "type": "FrozenLake-v1-4x4"}, "metrics": [{"type": "mean_reward", "value": "0.00 +/- 0.00", "name": "mean_reward", "verified": false}]}]}]} | Novski/q-FrozenLake-v1-4x4-noSlippery | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-04-30T16:29:07+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/CarrotAI/OpenCarrot-llama3-Mix-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## 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/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OpenCarrot-llama3-Mix-8B-GGUF/resolve/main/OpenCarrot-llama3-Mix-8B.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 -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "CarrotAI/OpenCarrot-llama3-Mix-8B", "quantized_by": "mradermacher"} | mradermacher/OpenCarrot-llama3-Mix-8B-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:CarrotAI/OpenCarrot-llama3-Mix-8B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T16:29:29+00:00 |
null | null |
# itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge-Q5_K_M-GGUF
This model was converted to GGUF format from [`itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge`](https://huggingface.co/itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge) 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/itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge) 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 itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge-Q5_K_M-GGUF --model dictalm2.0-1yam-peleg-mistral-instruct-merge.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge-Q5_K_M-GGUF --model dictalm2.0-1yam-peleg-mistral-instruct-merge.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 dictalm2.0-1yam-peleg-mistral-instruct-merge.Q5_K_M.gguf -n 128
```
| {"tags": ["merge", "mergekit", "lazymergekit", "yam-peleg/Hebrew-Mistral-7B", "dicta-il/dictalm2.0-instruct", "llama-cpp", "gguf-my-repo"], "base_model": ["yam-peleg/Hebrew-Mistral-7B", "dicta-il/dictalm2.0-instruct"]} | itayl/dictalm2.0-1yam-peleg-Mistral-instruct-Merge-Q5_K_M-GGUF | null | [
"gguf",
"merge",
"mergekit",
"lazymergekit",
"yam-peleg/Hebrew-Mistral-7B",
"dicta-il/dictalm2.0-instruct",
"llama-cpp",
"gguf-my-repo",
"base_model:yam-peleg/Hebrew-Mistral-7B",
"base_model:dicta-il/dictalm2.0-instruct",
"region:us"
] | null | 2024-04-30T16:29:48+00:00 |
text-generation | transformers | {} | Weni/WeniGPT-Agents-Llama3-5.0.15-SFT-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T16:30:45+00:00 |
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