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null | transformers |
# Uploaded model
- **Developed by:** Trelis
- **License:** apache-2.0
- **Finetuned from model :** NousResearch/Meta-Llama-3-8B-Instruct
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": "NousResearch/Meta-Llama-3-8B-Instruct"} | Trelis/Meta-Llama-3-8B-Instruct-no-fin-advice-adapters | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:35:32+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. -->
# da_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: 1.6089
## 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: 8
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 130 | 1.9587 |
| No log | 2.0 | 260 | 1.8275 |
| No log | 3.0 | 390 | 1.7576 |
| 1.9627 | 4.0 | 520 | 1.7045 |
| 1.9627 | 5.0 | 650 | 1.6049 |
| 1.9627 | 6.0 | 780 | 1.6452 |
| 1.9627 | 7.0 | 910 | 1.5920 |
| 1.6873 | 8.0 | 1040 | 1.6354 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.4.0.dev20240502
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-cased", "model-index": [{"name": "da_distilbert", "results": []}]} | gc394/da_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 | 2024-04-30T14:35:45+00:00 |
text-generation | transformers |
# Llama-3-70B-Instruct-ov-fp16-int4-sym
## Built with Meta Llama 3
## Model Description
This is a version of the original [Meta-Llama-3-70B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct) model converted to [OpenVINO™](https://github.com/openvinotoolkit/openvino) IR (Intermediate Representation) format for optimized inference on Intel® hardware. The model is created using the examples shown in [OpenVINO™ Notebooks](https://github.com/openvinotoolkit/openvino_notebooks/tree/latest/notebooks) repository.
## Intended Use
This model is designed for advanced natural language understanding and generation tasks, ideal for academic researchers and developers in commercial settings looking to integrate efficient AI capabilities into their applications. It is not to be used for creating or promoting harmful or illegal content as per the guidelines outlined in the [Meta Llama 3 Acceptable Use Policy](https://llama.meta.com/llama3/use-policy/).
## Licensing and Redistribution
This model is released under the Meta Llama 3 Community License. Redistribution requires inclusion of this license and a citation to the original model. Modifications and derivative works must prominently display "Built with Meta Llama 3" and adhere to the redistribution policies detailed in the original model [license terms](https://huggingface.co/meta-llama/Meta-Llama-3-70B-Instruct/blob/main/LICENSE).
## Weight Compression Parameters
For more information on the parameters, refer to the [OpenVINO™ 2024.1.0 documentation](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html)
* mode: **INT4_ASYM**
* group_size: **128**
* ratio: **0.8**
## Running Model Inference
Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO™ backend:
```sh
pip install --upgrade --upgrade-strategy eager "optimum[openvino]"
```
Run model inference:
```python
from optimum.intel.openvino import OVModelForCausalLM
from transformers import AutoTokenizer
model_id = "nsbendre25/Llama-3-70B-Instruct-ov_fp16-int4_sym"
# Initialize the tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
pipeline = transformers.pipeline("text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto")
pipeline("i am in paris, plan me a 2 week trip")
``` | {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["OpenVINO", "meta", "llama", "llama-3", "optimum-intel"], "pipeline_tag": "text-generation", "extra_gated_prompt": "\nMeta Llama 3 Version Release Date: April 18, 2024\n"} | nsbendre25/llama-3-70B-Instruct-ov-fp16-int4-asym | null | [
"transformers",
"openvino",
"llama",
"text-generation",
"OpenVINO",
"meta",
"llama-3",
"optimum-intel",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:36:01+00:00 |
null | transformers | {"license": "cc-by-nc-4.0"} | thaodd11/donut-try | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:36:27+00:00 |
|
null | null | {} | horangwave/vicuna_prune_99 | null | [
"region:us"
] | null | 2024-04-30T14:36:44+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": []} | lunarsylph/mooncell_v39 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:37:31+00:00 |
text-generation | transformers | # GreenBit LLMs
This is GreenBitAI's pretrained **low-bit** LLMs with extreme compression yet still strong performance.
Please refer to our [Github page](https://github.com/GreenBitAI/green-bit-llm) for the code to run the model and more information.
| {"license": "apache-2.0"} | GreenBitAI/Qwen-1.5-7B-layer-mix-bpw-4.0 | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:37:43+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** ntvcie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/tinyllama-bnb-4bit"} | ntvcie/TinyLlamaVinhntV01 | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:38:51+00:00 |
null | null | {} | 0x0son0/ll1131 | null | [
"region:us"
] | null | 2024-04-30T14:39:19+00:00 |
|
text-generation | transformers | {} | 0x0son0/llama-01 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:39:33+00:00 |
|
null | null | {} | MajdDaoud/Zeina | null | [
"region:us"
] | null | 2024-04-30T14:40:55+00:00 |
|
text-generation | null |
# tushar310/Phi-3-mini-128k-instruct-Q4_0-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3-mini-128k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo tushar310/Phi-3-mini-128k-instruct-Q4_0-GGUF --model phi-3-mini-128k-instruct.Q4_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo tushar310/Phi-3-mini-128k-instruct-Q4_0-GGUF --model phi-3-mini-128k-instruct.Q4_0.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 phi-3-mini-128k-instruct.Q4_0.gguf -n 128
```
| {"language": ["en"], "license": "mit", "tags": ["nlp", "code", "llama-cpp", "gguf-my-repo"], "license_link": "https://huggingface.co/microsoft/Phi-3-mini-128k-instruct/resolve/main/LICENSE", "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}]} | tushar310/Phi-3-mini-128k-instruct-Q4_0-GGUF | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:mit",
"region:us"
] | null | 2024-04-30T14:41:07+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-finetuned-patient-doctor-text-classifier-eng
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: 0.0516
- Accuracy: 0.9879
## 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: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0897 | 1.0 | 1547 | 0.0573 | 0.9865 |
| 0.0301 | 2.0 | 3094 | 0.0516 | 0.9879 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
- # How to Use
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="LukeGPT88/patient-doctor-text-classifier-eng")
classifier("I see you’ve set aside this special time to humiliate yourself in public.")
```
```python
Output:
[{'label': 'NEUTRAL', 'score': 0.9890775680541992}]
```
# Contact
Please reach out to [[email protected]]([email protected]) if you have any questions or feedback.
---
| {"language": "en", "tags": ["distilbert-base-uncased", "text-classification", "patient", "doctor"], "widget": [{"text": "I've got flu"}, {"text": "I prescribe you some drugs and you need to stay at home for a couple of days"}, {"text": "Let's move to the theatre this evening!"}]} | LukeGPT88/patient-doctor-text-classifier-eng | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"distilbert-base-uncased",
"patient",
"doctor",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:41:15+00:00 |
text-generation | transformers | # Untitled Model (1)
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) as a base.
### Models Merged
The following models were included in the merge:
* [EleutherAI/llemma_7b](https://huggingface.co/EleutherAI/llemma_7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: codellama/CodeLlama-7b-hf
parameters:
density: 0.5
weight: 0.5
- model: EleutherAI/llemma_7b
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: codellama/CodeLlama-7b-hf
parameters:
normalize: true
int8_mask: true
dtype: float16
```
| {"library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["EleutherAI/llemma_7b", "codellama/CodeLlama-7b-hf"]} | JyoP/merged_llemma_codeLlama-ties | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:EleutherAI/llemma_7b",
"base_model:codellama/CodeLlama-7b-hf",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:41:16+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/fiveflow/KoLlama-3-8B-Instruct
<!-- 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/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/KoLlama-3-8B-Instruct-GGUF/resolve/main/KoLlama-3-8B-Instruct.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", "tags": ["generated_from_trainer"], "base_model": "fiveflow/KoLlama-3-8B-Instruct", "quantized_by": "mradermacher"} | mradermacher/KoLlama-3-8B-Instruct-GGUF | null | [
"transformers",
"gguf",
"generated_from_trainer",
"en",
"base_model:fiveflow/KoLlama-3-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:41:45+00:00 |
text-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. -->
# ai-text-detector
This model is a fine-tuned version of [tf_model.h5](https://huggingface.co/Yuhhi/tf_model.h5) on an unknown dataset.
It achieves the following results on the evaluation set:
## 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: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.32.1
- TensorFlow 2.15.0
- Datasets 2.12.0
- Tokenizers 0.13.2
| {"tags": ["generated_from_keras_callback"], "base_model": "tf_model.h5", "model-index": [{"name": "ai-text-detector", "results": []}]} | Yuhhi/ai-text-detector | null | [
"transformers",
"tf",
"distilbert",
"text-classification",
"generated_from_keras_callback",
"base_model:tf_model.h5",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-30T14:42:08+00:00 |
null | null | {"license": "unknown"} | hautc/x8 | null | [
"license:unknown",
"region:us"
] | null | 2024-04-30T14:42:32+00:00 |
|
text-generation | transformers | # AnFeng
**~30B**, SFT...
**Chinese, English**
Test 0 of all.
Released as an early preview of our v3 LLMs.
The v3 series covers the "Shi-Ci", "AnFeng" and "Cecilia" LLM products.
The sizes are labelled from small to large "Nano" "Leap" "Pattern" "Avocet "Robin" "Kestrel" | {"language": ["en"], "license": "cc-by-nc-nd-4.0", "library_name": "transformers", "pipeline_tag": "text-generation", "inference": false} | NLPark/AnFeng_v3_Avocet | null | [
"transformers",
"safetensors",
"cohere",
"text-generation",
"conversational",
"en",
"license:cc-by-nc-nd-4.0",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:42:55+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_es_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: 1.5649
## 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 |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3046 | 1.0 | 724 | 1.6641 |
| 1.6565 | 2.0 | 1448 | 1.5655 |
| 1.3038 | 3.0 | 2172 | 1.5649 |
### 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_es_v2", "results": []}]} | enriquesaou/roberta_es_v2 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:43:17+00:00 |
null | null | {} | ignisarcanum/mingi | null | [
"region:us"
] | null | 2024-04-30T14:44:25+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. -->
# final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds
This model is a fine-tuned version of [BSC-LT/roberta-base-biomedical-clinical-es](https://huggingface.co/BSC-LT/roberta-base-biomedical-clinical-es) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5971
## 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
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-------:|:----:|:---------------:|
| No log | 0.9978 | 229 | 0.9388 |
| No log | 2.0 | 459 | 0.8139 |
| No log | 2.9978 | 688 | 0.7544 |
| 0.9622 | 4.0 | 918 | 0.7302 |
| 0.9622 | 4.9978 | 1147 | 0.6878 |
| 0.9622 | 6.0 | 1377 | 0.6754 |
| 0.9622 | 6.9978 | 1606 | 0.6625 |
| 0.715 | 8.0 | 1836 | 0.6431 |
| 0.715 | 8.9978 | 2065 | 0.6278 |
| 0.715 | 10.0 | 2295 | 0.6361 |
| 0.715 | 10.9978 | 2524 | 0.6296 |
| 0.6597 | 12.0 | 2754 | 0.6164 |
| 0.6597 | 12.9978 | 2983 | 0.6117 |
| 0.6597 | 14.0 | 3213 | 0.6052 |
| 0.6597 | 14.9978 | 3442 | 0.6064 |
| 0.6354 | 16.0 | 3672 | 0.6225 |
| 0.6354 | 16.9978 | 3901 | 0.5974 |
| 0.6354 | 18.0 | 4131 | 0.6010 |
| 0.6354 | 18.9978 | 4360 | 0.5816 |
| 0.6354 | 19.9564 | 4580 | 0.5971 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "BSC-LT/roberta-base-biomedical-clinical-es", "model-index": [{"name": "final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds", "results": []}]} | manucos/final-ft__roberta-base-biomedical-clinical-es__70k-ultrasounds | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"fill-mask",
"generated_from_trainer",
"base_model:BSC-LT/roberta-base-biomedical-clinical-es",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:45:24+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. -->
# falcon7b-finetuned-On-Screenplays
This model is a fine-tuned version of [vilsonrodrigues/falcon-7b-instruct-sharded](https://huggingface.co/vilsonrodrigues/falcon-7b-instruct-sharded) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "vilsonrodrigues/falcon-7b-instruct-sharded", "model-index": [{"name": "falcon7b-finetuned-On-Screenplays", "results": []}]} | Elhassnaoui-2001/falcon7b-finetuned-On-Screenplays | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:vilsonrodrigues/falcon-7b-instruct-sharded",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T14:46:01+00:00 |
text-generation | transformers |
## **Llama-3-8B-Instruct-Gradient-1048k**
[exllamav2](https://github.com/turboderp/exllamav2) quant for [gradientai/Llama-3-8B-Instruct-Gradient-1048k](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k)
**Original model information:**
<a href="https://www.gradient.ai" target="_blank"><img src="https://cdn-uploads.huggingface.co/production/uploads/655bb613e8a8971e89944f3e/TSa3V8YpoVagnTYgxiLaO.png" width="200"/></a>
# Llama-3 8B Gradient Instruct 1048k
Gradient incorporates your data to deploy autonomous assistants that power critical operations across your business. If you're looking to build custom AI models or agents, email us a message [email protected].
For more info see our [End-to-end development service for custom LLMs and AI systems](https://gradient.ai/development-lab)
This model extends LLama-3 8B's context length from 8k to > 1040K, developed by Gradient, sponsored by compute from [Crusoe Energy](https://huggingface.co/crusoeai). It demonstrates that SOTA LLMs can learn to operate on long context with minimal training by appropriately adjusting RoPE theta. We trained on 830M tokens for this stage, and 1.4B tokens total for all stages, which is < 0.01% of Llama-3's original pre-training data.
**Approach:**
- [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) as the base
- NTK-aware interpolation [1] to initialize an optimal schedule for RoPE theta, followed by empirical RoPE theta optimization
- Progressive training on increasing context lengths, similar to [Large World Model](https://huggingface.co/LargeWorldModel) [2] (See details below)
**Infra:**
We build on top of the EasyContext Blockwise RingAttention library [3] to scalably and efficiently train on contexts up to 1048k tokens on [Crusoe Energy](https://huggingface.co/crusoeai) high performance L40S cluster.
Notably, we layered parallelism on top of Ring Attention with a custom network topology to better leverage large GPU clusters in the face of network bottlenecks from passing many KV blocks between devices. This gave us a 33x speedup in model training (compare 524k and 1048k to 65k and 262k in the table below).
**Data:**
For training data, we generate long contexts by augmenting [SlimPajama](https://huggingface.co/datasets/cerebras/SlimPajama-627B).
**Progressive Training Details:**
| | 65K | 262K | 524k | 1048k |
|------------------------|-----------|-----------|-----------|-----------|
| Initialize From | LLaMA-3 8B| 65K | 262K | 524k |
| Sequence Length 2^N | 16 | 18 | 19 | 20 |
| RoPE theta | 15.3 M | 207.1 M | 1.06B | 2.80B |
| Batch Size | 1 | 1 | 16 | 16 |
| Gradient Accumulation Steps | 32 | 16 | 1 | 1 |
| Steps | 30 | 24 | 50 | 50 |
| Total Tokens | 62914560 | 100663296 | 419430400 | 838860800 |
| Learning Rate | 2.00E-05 | 2.00E-05 | 2.00E-05 | 2.00E-05 |
| # GPUs | 8 | 32 | 512 | 512 |
| GPU Type | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S | NVIDIA L40S |
| Minutes to Train (Wall)| 202 | 555 | 61 | 87 |
**Quants**:
- [GGUF](https://huggingface.co/crusoeai/Llama-3-8B-Instruct-1048k-GGUF)
- [MLX-4bit](https://huggingface.co/mlx-community/Llama-3-8B-Instruct-1048k-4bit)
## The Gradient AI Team
https://gradient.ai/
Gradient is accelerating AI transformation across industries. Our AI Foundry incorporates your data to deploy autonomous assistants that power critical operations across your business.
## Contact Us
Drop an email to [[email protected]](mailto:[email protected])
## References
[1] Peng, Bowen, et al. "Yarn: Efficient context window extension of large language models." arXiv preprint arXiv:2309.00071 (2023).
[2] Liu, Hao, et al. "World Model on Million-Length Video And Language With RingAttention." arXiv preprint arXiv:2402.08268 (2024).
[3] https://github.com/jzhang38/EasyContext
----
# Base Model
## Model Details
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Model Architecture** Llama 3 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="2" >Llama 3
</td>
<td rowspan="2" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>8k
</td>
<td>Yes
</td>
<td rowspan="2" >15T+
</td>
<td>March, 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>8k
</td>
<td>Yes
</td>
<td>December, 2023
</td>
</tr>
</table>
**Llama 3 family of models**. Token counts refer to pretraining data only. Both the 8 and 70B versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date** April 18, 2024.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
## How to use
This repository contains two versions of Meta-Llama-3-8B-Instruct, for use with transformers and with the original `llama3` codebase.
### Use with transformers
You can run conversational inference using the Transformers pipeline abstraction, or by leveraging the Auto classes with the `generate()` function. Let's see examples of both.
#### Transformers pipeline
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
#### Transformers AutoModelForCausalLM
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = "meta-llama/Meta-Llama-3-8B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
input_ids = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
return_tensors="pt"
).to(model.device)
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = model.generate(
input_ids,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
```
### Use with `llama3`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama3)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Meta-Llama-3-8B-Instruct --include "original/*" --local-dir Meta-Llama-3-8B-Instruct
```
For Hugging Face support, we recommend using transformers or TGI, but a similar command works.
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's Research SuperCluster, and production clusters for pretraining. Fine-tuning, annotation, and evaluation were also performed on third-party cloud compute.
**Carbon Footprint Pretraining utilized a cumulative** 7.7M GPU hours of computation on hardware of type H100-80GB (TDP of 700W). Estimated total emissions were 2290 tCO2eq, 100% of which were offset by Meta’s sustainability program.
<table>
<tr>
<td>
</td>
<td><strong>Time (GPU hours)</strong>
</td>
<td><strong>Power Consumption (W)</strong>
</td>
<td><strong>Carbon Emitted(tCO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3 8B
</td>
<td>1.3M
</td>
<td>700
</td>
<td>390
</td>
</tr>
<tr>
<td>Llama 3 70B
</td>
<td>6.4M
</td>
<td>700
</td>
<td>1900
</td>
</tr>
<tr>
<td>Total
</td>
<td>7.7M
</td>
<td>
</td>
<td>2290
</td>
</tr>
</table>
**CO2 emissions during pre-training**. Time: total GPU time required for training each model. Power Consumption: peak power capacity per GPU device for the GPUs used adjusted for power usage efficiency. 100% of the emissions are directly offset by Meta's sustainability program, and because we are openly releasing these models, the pretraining costs do not need to be incurred by others.
## Training Data
**Overview** Llama 3 was pretrained on over 15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 10M human-annotated examples. Neither the pretraining nor the fine-tuning datasets include Meta user data.
**Data Freshness** The pretraining data has a cutoff of March 2023 for the 7B and December 2023 for the 70B models respectively.
## Benchmarks
In this section, we report the results for Llama 3 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library. For details on the methodology see [here](https://github.com/meta-llama/llama3/blob/main/eval_methodology.md).
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama2 7B</strong>
</td>
<td><strong>Llama2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama2 70B</strong>
</td>
</tr>
<tr>
<td rowspan="6" >General
</td>
<td>MMLU (5-shot)
</td>
<td>66.6
</td>
<td>45.7
</td>
<td>53.8
</td>
<td>79.5
</td>
<td>69.7
</td>
</tr>
<tr>
<td>AGIEval English (3-5 shot)
</td>
<td>45.9
</td>
<td>28.8
</td>
<td>38.7
</td>
<td>63.0
</td>
<td>54.8
</td>
</tr>
<tr>
<td>CommonSenseQA (7-shot)
</td>
<td>72.6
</td>
<td>57.6
</td>
<td>67.6
</td>
<td>83.8
</td>
<td>78.7
</td>
</tr>
<tr>
<td>Winogrande (5-shot)
</td>
<td>76.1
</td>
<td>73.3
</td>
<td>75.4
</td>
<td>83.1
</td>
<td>81.8
</td>
</tr>
<tr>
<td>BIG-Bench Hard (3-shot, CoT)
</td>
<td>61.1
</td>
<td>38.1
</td>
<td>47.0
</td>
<td>81.3
</td>
<td>65.7
</td>
</tr>
<tr>
<td>ARC-Challenge (25-shot)
</td>
<td>78.6
</td>
<td>53.7
</td>
<td>67.6
</td>
<td>93.0
</td>
<td>85.3
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki (5-shot)
</td>
<td>78.5
</td>
<td>72.1
</td>
<td>79.6
</td>
<td>89.7
</td>
<td>87.5
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD (1-shot)
</td>
<td>76.4
</td>
<td>72.2
</td>
<td>72.1
</td>
<td>85.6
</td>
<td>82.6
</td>
</tr>
<tr>
<td>QuAC (1-shot, F1)
</td>
<td>44.4
</td>
<td>39.6
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>49.4
</td>
</tr>
<tr>
<td>BoolQ (0-shot)
</td>
<td>75.7
</td>
<td>65.5
</td>
<td>66.9
</td>
<td>79.0
</td>
<td>73.1
</td>
</tr>
<tr>
<td>DROP (3-shot, F1)
</td>
<td>58.4
</td>
<td>37.9
</td>
<td>49.8
</td>
<td>79.7
</td>
<td>70.2
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Benchmark</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 2 7B</strong>
</td>
<td><strong>Llama 2 13B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 2 70B</strong>
</td>
</tr>
<tr>
<td>MMLU (5-shot)
</td>
<td>68.4
</td>
<td>34.1
</td>
<td>47.8
</td>
<td>82.0
</td>
<td>52.9
</td>
</tr>
<tr>
<td>GPQA (0-shot)
</td>
<td>34.2
</td>
<td>21.7
</td>
<td>22.3
</td>
<td>39.5
</td>
<td>21.0
</td>
</tr>
<tr>
<td>HumanEval (0-shot)
</td>
<td>62.2
</td>
<td>7.9
</td>
<td>14.0
</td>
<td>81.7
</td>
<td>25.6
</td>
</tr>
<tr>
<td>GSM-8K (8-shot, CoT)
</td>
<td>79.6
</td>
<td>25.7
</td>
<td>77.4
</td>
<td>93.0
</td>
<td>57.5
</td>
</tr>
<tr>
<td>MATH (4-shot, CoT)
</td>
<td>30.0
</td>
<td>3.8
</td>
<td>6.7
</td>
<td>50.4
</td>
<td>11.6
</td>
</tr>
</table>
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Llama 3-Instruct
As outlined in the Responsible Use Guide, some trade-off between model helpfulness and model alignment is likely unavoidable. Developers should exercise discretion about how to weigh the benefits of alignment and helpfulness for their specific use case and audience. Developers should be mindful of residual risks when using Llama models and leverage additional safety tools as needed to reach the right safety bar for their use case.
<span style="text-decoration:underline;">Safety</span>
For our instruction tuned model, we conducted extensive red teaming exercises, performed adversarial evaluations and implemented safety mitigations techniques to lower residual risks. As with any Large Language Model, residual risks will likely remain and we recommend that developers assess these risks in the context of their use case. In parallel, we are working with the community to make AI safety benchmark standards transparent, rigorous and interpretable.
<span style="text-decoration:underline;">Refusals</span>
In addition to residual risks, we put a great emphasis on model refusals to benign prompts. Over-refusing not only can impact the user experience but could even be harmful in certain contexts as well. We’ve heard the feedback from the developer community and improved our fine tuning to ensure that Llama 3 is significantly less likely to falsely refuse to answer prompts than Llama 2.
We built internal benchmarks and developed mitigations to limit false refusals making Llama 3 our most helpful model to date.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
#### Critical risks
<span style="text-decoration:underline;">CBRNE</span> (Chemical, Biological, Radiological, Nuclear, and high yield Explosives)
We have conducted a two fold assessment of the safety of the model in this area:
* Iterative testing during model training to assess the safety of responses related to CBRNE threats and other adversarial risks.
* Involving external CBRNE experts to conduct an uplift test assessing the ability of the model to accurately provide expert knowledge and reduce barriers to potential CBRNE misuse, by reference to what can be achieved using web search (without the model).
### <span style="text-decoration:underline;">Cyber Security </span>
We have evaluated Llama 3 with CyberSecEval, Meta’s cybersecurity safety eval suite, measuring Llama 3’s propensity to suggest insecure code when used as a coding assistant, and Llama 3’s propensity to comply with requests to help carry out cyber attacks, where attacks are defined by the industry standard MITRE ATT&CK cyber attack ontology. On our insecure coding and cyber attacker helpfulness tests, Llama 3 behaved in the same range or safer than models of [equivalent coding capability](https://huggingface.co/spaces/facebook/CyberSecEval).
### <span style="text-decoration:underline;">Child Safety</span>
Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
### Community
Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership in AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide)
## Citation instructions
@article{llama3modelcard,
title={Llama 3 Model Card},
author={AI@Meta},
year={2024},
url = {https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md}
}
## Contributors
Aaditya Singh; Aaron Grattafiori; Abhimanyu Dubey; Abhinav Jauhri; Abhinav Pandey; Abhishek Kadian; Adam Kelsey; Adi Gangidi; Ahmad Al-Dahle; Ahuva Goldstand; Aiesha Letman; Ajay Menon; Akhil Mathur; Alan Schelten; Alex Vaughan; Amy Yang; Andrei Lupu; Andres Alvarado; Andrew Gallagher; Andrew Gu; Andrew Ho; Andrew Poulton; Andrew Ryan; Angela Fan; Ankit Ramchandani; Anthony Hartshorn; Archi Mitra; Archie Sravankumar; Artem Korenev; Arun Rao; Ashley Gabriel; Ashwin Bharambe; Assaf Eisenman; Aston Zhang; Aurelien Rodriguez; Austen Gregerson; Ava Spataru; Baptiste Roziere; Ben Maurer; Benjamin Leonhardi; Bernie Huang; Bhargavi Paranjape; Bing Liu; Binh Tang; Bobbie Chern; Brani Stojkovic; Brian Fuller; Catalina Mejia Arenas; Chao Zhou; Charlotte Caucheteux; Chaya Nayak; Ching-Hsiang Chu; Chloe Bi; Chris Cai; Chris Cox; Chris Marra; Chris McConnell; Christian Keller; Christoph Feichtenhofer; Christophe Touret; Chunyang Wu; Corinne Wong; Cristian Canton Ferrer; Damien Allonsius; Daniel Kreymer; Daniel Haziza; Daniel Li; Danielle Pintz; Danny Livshits; Danny Wyatt; David Adkins; David Esiobu; David Xu; Davide Testuggine; Delia David; Devi Parikh; Dhruv Choudhary; Dhruv Mahajan; Diana Liskovich; Diego Garcia-Olano; Diego Perino; Dieuwke Hupkes; Dingkang Wang; Dustin Holland; Egor Lakomkin; Elina Lobanova; Xiaoqing Ellen Tan; Emily Dinan; Eric Smith; Erik Brinkman; Esteban Arcaute; Filip Radenovic; Firat Ozgenel; Francesco Caggioni; Frank Seide; Frank Zhang; Gabriel Synnaeve; Gabriella Schwarz; Gabrielle Lee; Gada Badeer; Georgia Anderson; Graeme Nail; Gregoire Mialon; Guan Pang; Guillem Cucurell; Hailey Nguyen; Hannah Korevaar; Hannah Wang; Haroun Habeeb; Harrison Rudolph; Henry Aspegren; Hu Xu; Hugo Touvron; Iga Kozlowska; Igor Molybog; Igor Tufanov; Iliyan Zarov; Imanol Arrieta Ibarra; Irina-Elena Veliche; Isabel Kloumann; Ishan Misra; Ivan Evtimov; Jacob Xu; Jade Copet; Jake Weissman; Jan Geffert; Jana Vranes; Japhet Asher; Jason Park; Jay Mahadeokar; Jean-Baptiste Gaya; Jeet Shah; Jelmer van der Linde; Jennifer Chan; Jenny Hong; Jenya Lee; Jeremy Fu; Jeremy Teboul; Jianfeng Chi; Jianyu Huang; Jie Wang; Jiecao Yu; Joanna Bitton; Joe Spisak; Joelle Pineau; Jon Carvill; Jongsoo Park; Joseph Rocca; Joshua Johnstun; Junteng Jia; Kalyan Vasuden Alwala; Kam Hou U; Kate Plawiak; Kartikeya Upasani; Kaushik Veeraraghavan; Ke Li; Kenneth Heafield; Kevin Stone; Khalid El-Arini; Krithika Iyer; Kshitiz Malik; Kuenley Chiu; Kunal Bhalla; Kyle Huang; Lakshya Garg; Lauren Rantala-Yeary; Laurens van der Maaten; Lawrence Chen; Leandro Silva; Lee Bell; Lei Zhang; Liang Tan; Louis Martin; Lovish Madaan; Luca Wehrstedt; Lukas Blecher; Luke de Oliveira; Madeline Muzzi; Madian Khabsa; Manav Avlani; Mannat Singh; Manohar Paluri; Mark Zuckerberg; Marcin Kardas; Martynas Mankus; Mathew Oldham; Mathieu Rita; Matthew Lennie; Maya Pavlova; Meghan Keneally; Melanie Kambadur; Mihir Patel; Mikayel Samvelyan; Mike Clark; Mike Lewis; Min Si; Mitesh Kumar Singh; Mo Metanat; Mona Hassan; Naman Goyal; Narjes Torabi; Nicolas Usunier; Nikolay Bashlykov; Nikolay Bogoychev; Niladri Chatterji; Ning Dong; Oliver Aobo Yang; Olivier Duchenne; Onur Celebi; Parth Parekh; Patrick Alrassy; Paul Saab; Pavan Balaji; Pedro Rittner; Pengchuan Zhang; Pengwei Li; Petar Vasic; Peter Weng; Polina Zvyagina; Prajjwal Bhargava; Pratik Dubal; Praveen Krishnan; Punit Singh Koura; Qing He; Rachel Rodriguez; Ragavan Srinivasan; Rahul Mitra; Ramon Calderer; Raymond Li; Robert Stojnic; Roberta Raileanu; Robin Battey; Rocky Wang; Rohit Girdhar; Rohit Patel; Romain Sauvestre; Ronnie Polidoro; Roshan Sumbaly; Ross Taylor; Ruan Silva; Rui Hou; Rui Wang; Russ Howes; Ruty Rinott; Saghar Hosseini; Sai Jayesh Bondu; Samyak Datta; Sanjay Singh; Sara Chugh; Sargun Dhillon; Satadru Pan; Sean Bell; Sergey Edunov; Shaoliang Nie; Sharan Narang; Sharath Raparthy; Shaun Lindsay; Sheng Feng; Sheng Shen; Shenghao Lin; Shiva Shankar; Shruti Bhosale; Shun Zhang; Simon Vandenhende; Sinong Wang; Seohyun Sonia Kim; Soumya Batra; Sten Sootla; Steve Kehoe; Suchin Gururangan; Sumit Gupta; Sunny Virk; Sydney Borodinsky; Tamar Glaser; Tamar Herman; Tamara Best; Tara Fowler; Thomas Georgiou; Thomas Scialom; Tianhe Li; Todor Mihaylov; Tong Xiao; Ujjwal Karn; Vedanuj Goswami; Vibhor Gupta; Vignesh Ramanathan; Viktor Kerkez; Vinay Satish Kumar; Vincent Gonguet; Vish Vogeti; Vlad Poenaru; Vlad Tiberiu Mihailescu; Vladan Petrovic; Vladimir Ivanov; Wei Li; Weiwei Chu; Wenhan Xiong; Wenyin Fu; Wes Bouaziz; Whitney Meers; Will Constable; Xavier Martinet; Xiaojian Wu; Xinbo Gao; Xinfeng Xie; Xuchao Jia; Yaelle Goldschlag; Yann LeCun; Yashesh Gaur; Yasmine Babaei; Ye Qi; Yenda Li; Yi Wen; Yiwen Song; Youngjin Nam; Yuchen Hao; Yuchen Zhang; Yun Wang; Yuning Mao; Yuzi He; Zacharie Delpierre Coudert; Zachary DeVito; Zahra Hankir; Zhaoduo Wen; Zheng Yan; Zhengxing Chen; Zhenyu Yang; Zoe Papakipos | {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3", "8-bit"], "pipeline_tag": "text-generation", "base_model": ["gradientai/Llama-3-8B-Instruct-Gradient-1048k"]} | Slvcxc/Llama-3-8B-Instruct-Gradient-1048k-8.0bpw-h8-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"meta",
"llama-3",
"8-bit",
"conversational",
"en",
"base_model:gradientai/Llama-3-8B-Instruct-Gradient-1048k",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:46:39+00:00 |
text-generation | transformers |
# phi-2-int8-ov
* Model creator: [Microsoft](https://huggingface.co/microsoft)
* Original model: [phi-2](https://huggingface.co/microsoft/phi-2)
## Description
This is [phi-2](https://huggingface.co/microsoft/phi-2) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to INT8 by [NNCF](https://github.com/openvinotoolkit/nncf).
## Quantization Parameters
Weight compression was performed using `nncf.compress_weights` with the following parameters:
* mode: **INT8_ASYM**
For more information on quantization, check the [OpenVINO model optimization guide](https://docs.openvino.ai/2024/openvino-workflow/model-optimization-guide/weight-compression.html).
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2024.1.0 and higher
* Optimum Intel 1.16.0 and higher
## Running Model Inference
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "OpenVINO/phi-2-int8-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
## Limitations
Check the original model card for [limitations](https://huggingface.co/microsoft/phi-2#limitations-of-phi-2).
## Legal information
The original model is distributed under [MIT](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE) license. More details can be found in [original model card](https://huggingface.co/microsoft/phi-2). | {"language": ["en"], "license": "mit", "license_link": "https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE"} | OpenVINO/phi-2-int8-ov | null | [
"transformers",
"openvino",
"phi",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:48:03+00:00 |
text2text-generation | transformers | ## Eval results on WikiLarge
We obtain the following results on ```validation``` and ```test``` sets of WikiLarge:
| Set | SARI | BLEU |
|------------|-------|-------|
| validation | 44.4 | 27.38 |
| test | 39.16 | 31.62 |
## EASSE evaluation
### TurkCorpus
We obtain the following results on ```validation``` and ```test``` sets of the Turk corpus:
| Set | SARI | BLEU | FKGL |
|------------|-------|-------|------|
| validation | 38.57 | 87.7 | 8.1 |
| test | 38.82 | 87.89 | 8.49 |
### ASSET
We obtain the following results on ```validation``` and ```test``` sets of the ASSET corpus:
| Set | SARI | BLEU | FKGL |
|------------|-------|-------|------|
| validation | 36.83 | 88.54 | 8.15 |
| test | 35.74 | 86.96 | 8.46 |
| {"language": ["en"], "tags": ["sentence-simplification"], "metrics": ["sari", "bleu"], "pipeline_tag": "text2text-generation"} | waboucay/bart-large-simplification-wikilarge-original | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"sentence-simplification",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:48:38+00:00 |
text-generation | transformers |
# **csg-wukong-1B-sft-bf16** [[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://portal.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-sft-bf16** was finetuned on [csg-wukong-1B](https://huggingface.co/opencsg/csg-wukong-1B).
<br>
we will introduce more information about csg-wukong-1B.
## 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-sft-bf16** 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": "text-generation"} | opencsg/csg-wukong-1B-sft-bf16 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:48:46+00:00 |
object-detection | 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": []} | HeavenWaters/detr-finetuned-wheathead-v2 | null | [
"transformers",
"safetensors",
"detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:49:33+00:00 |
null | transformers | {"license": "unlicense"} | xiaoqiu2451/asr_japanese | null | [
"transformers",
"license:unlicense",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:49:47+00:00 |
|
null | null | {} | takoneya/shiroko | null | [
"region:us"
] | null | 2024-04-30T14:50:01+00:00 |
|
feature-extraction | transformers |
# phospho-small
This is a SetFit model that can be used for Text Classification on CPU.
The model has been trained using an efficient few-shot learning technique.
## Usage
```python
from setfit import SetFitModel
model = SetFitModel.from_pretrained("phospho-small-7c8faca")
outputs = model.predict(["This is a sentence to classify", "Another sentence"])
# tensor([1, 0])
```
## References
This work was possible thanks to the SetFit library and the work of:
Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren (2022). Efficient Few-Shot Learning Without Prompts.
ArXiv: [https://doi.org/10.48550/arxiv.2209.11055](https://doi.org/10.48550/arxiv.2209.11055)
| {"language": "en", "license": "apache-2.0"} | phospho-app/phospho-small-7c8faca | null | [
"transformers",
"safetensors",
"mpnet",
"feature-extraction",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:50:07+00:00 |
null | null | {"license": "llama3"} | crimeaisukraine/crimeaisukraine_dolphin_llama3_8b_q4_ks | null | [
"license:llama3",
"region:us"
] | null | 2024-04-30T14:50:23+00:00 |
|
null | null | {"license": "mit"} | gytdau/mazegpt-1 | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T14:51:03+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. -->
# adapter_ThaiSC_LLM_Scamper
This model is a fine-tuned version of [openthaigpt/openthaigpt-1.0.0-7b-chat](https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat) 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: 8e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.38.0
- Pytorch 2.2.1+cu121
- Datasets 2.17.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "openthaigpt/openthaigpt-1.0.0-7b-chat", "model-index": [{"name": "adapter_ThaiSC_LLM_Scamper", "results": []}]} | Thirawarit/adapter_ThaiSC_LLM_Scamper | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:openthaigpt/openthaigpt-1.0.0-7b-chat",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T14:52:09+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_1971_85_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: 1.6890
## 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 | 60 | 1.6985 |
| No log | 2.0 | 120 | 1.7388 |
| No log | 3.0 | 180 | 1.6725 |
| No log | 4.0 | 240 | 1.6855 |
| No log | 5.0 | 300 | 1.6056 |
| No log | 6.0 | 360 | 1.7211 |
| No log | 7.0 | 420 | 1.6464 |
| No log | 8.0 | 480 | 1.6195 |
| 1.7601 | 9.0 | 540 | 1.5872 |
| 1.7601 | 10.0 | 600 | 1.6890 |
### 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_1971_85_distilbert", "results": []}]} | gc394/tda_1971_85_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 | 2024-04-30T14:52:31+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_1986_00_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: 1.4929
## 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 | 71 | 1.4492 |
| No log | 2.0 | 142 | 1.4750 |
| No log | 3.0 | 213 | 1.4390 |
| No log | 4.0 | 284 | 1.4929 |
| No log | 5.0 | 355 | 1.4365 |
| No log | 6.0 | 426 | 1.3863 |
| No log | 7.0 | 497 | 1.4428 |
| 1.5109 | 8.0 | 568 | 1.4007 |
| 1.5109 | 9.0 | 639 | 1.4325 |
| 1.5109 | 10.0 | 710 | 1.4929 |
### 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_1986_00_distilbert", "results": []}]} | gc394/tda_1986_00_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 | 2024-04-30T14:52:31+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_01_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: 1.3717
## 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: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 64 | 1.4189 |
| No log | 2.0 | 128 | 1.3668 |
| No log | 3.0 | 192 | 1.3829 |
| No log | 4.0 | 256 | 1.3625 |
| No log | 5.0 | 320 | 1.3717 |
### Framework versions
- Transformers 4.40.0
- 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_01_16_distilbert", "results": []}]} | gc394/tda_01_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 | 2024-04-30T14:52:32+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** Suru
- **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", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Suru/Llama3_8b_gpt_Tweets | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:53:30+00:00 |
null | null | {} | siyalnaeem/my_awesome_model | null | [
"region:us"
] | null | 2024-04-30T14:53:35+00:00 |
|
null | null |
GGUF version of [sentence-transformers/multi-qa-MiniLM-L6-cos-v1](https://huggingface.co/sentence-transformers/multi-qa-MiniLM-L6-cos-v1). | {"base_model": "sentence-transformers/multi-qa-MiniLM-L6-cos-v1"} | Felladrin/gguf-multi-qa-MiniLM-L6-cos-v1 | null | [
"gguf",
"base_model:sentence-transformers/multi-qa-MiniLM-L6-cos-v1",
"region:us"
] | null | 2024-04-30T14:55:05+00:00 |
null | null | {} | JJjdjka/pokemon | null | [
"region:us"
] | null | 2024-04-30T14:55:06+00:00 |
|
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 137553 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 5000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 0.0001
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Mihaiii/test19 | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:57:03+00:00 |
text-generation | transformers | # Wise-EMO-2B
## Overview
Wise-EMO-2B is a 2.5 billion parameter conversational AI model that combines emotional intelligence capabilities from the EMO-2B model with insights from ancient Indian wisdom traditions. By integrating the wealth of knowledge from philosophies like Yoga, Vedanta, Buddhism, Jainism, Sikhism, Hinduism, and other indigenous spiritual practices, this model aims to provide emotionally resonant dialogue infused with profound philosophical perspectives.
## Key Features
- **Emotional Intelligence**: Inherited from EMO-2B, Wise-EMO-2B excels at perceiving and responding to emotional undertones with empathy, providing emotionally supportive responses.
- **Philosophical Wisdom**: The model has been further finetuned on texts from ancient Indian wisdom traditions, allowing it to draw upon profound philosophical concepts when appropriate.
- **Holistic Perspective**: Wise-EMO-2B offers a holistic viewpoint that combines emotional resonance with spiritual and philosophical wisdom for a well-rounded, insightful conversational experience.
- **Dynamic Contextualization**: The model adapts its communication style, emotional responses, and philosophical framing based on the specific conversational context.
## Use Cases
Wise-EMO-2B can be beneficial for applications that require emotionally intelligent yet philosophically grounded dialogue, such as:
- Emotional support companions with a wisdom-oriented perspective
- Philosophical discussion and ideation
- Storytelling and creative writing with profound themes
- Personal growth, self-reflection, and mindfulness practice
- Exploring ancient wisdom in relation to modern issues
## Limitations and Responsible Use
While powerful, Wise-EMO-2B is an AI model with inherent limitations. Its responses should be viewed as a supportive tool rather than a substitute for professional mental health services or spiritual guidance. Additionally, the model may reflect biases from its training data. Users should think critically and report concerning outputs.
As with all AI systems, Wise-EMO-2B should be used responsibly and ethically, particularly given the sensitivity around philosophy and spiritual beliefs. Outputs should be carefully evaluated in context.
**This is mark one of Wise-EMO, It is just for testing purposes** | {"language": ["en"], "license": "apache-2.0", "tags": ["EMO", "HelpingAI", "Wisdom", "Philosophy", "AncientIndianKnowledge"], "datasets": ["Abhaykoul/Ancient-Indian-Wisdom"], "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "What insights can we gain from the Bhagavad Gita about the path of selfless action and devotion?"}]}], "inference": {"parameters": {"max_new_tokens": 500}}} | OEvortex/Wise-EMO-2B | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"EMO",
"HelpingAI",
"Wisdom",
"Philosophy",
"AncientIndianKnowledge",
"conversational",
"en",
"dataset:Abhaykoul/Ancient-Indian-Wisdom",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T14:57:21+00:00 |
sentence-similarity | sentence-transformers |
# {MODEL_NAME}
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('{MODEL_NAME}')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 137553 with parameters:
```
{'batch_size': 64, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MSELoss.MSELoss`
Parameters of the fit()-Method:
```
{
"epochs": 1,
"evaluation_steps": 5000,
"evaluator": "sentence_transformers.evaluation.SequentialEvaluator.SequentialEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"eps": 1e-06,
"lr": 0.0001
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 1000,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | Mihaiii/test20 | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T14:57:25+00:00 |
null | null | {} | jelenatest/test | null | [
"region:us"
] | null | 2024-04-30T14:57:27+00:00 |
|
null | null | {} | superemohot/Llama3-TAIDE-LX-8B-Chat-Alpha1-GGUF-8bit | null | [
"region:us"
] | null | 2024-04-30T14:58:58+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "259.49 +/- 21.24", "name": "mean_reward", "verified": false}]}]}]} | dirkneethling/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T14:59:17+00:00 |
null | null | {} | Palmarivs/results | null | [
"region:us"
] | null | 2024-04-30T15:00:21+00:00 |
|
sentence-similarity | sentence-transformers | {"language": ["vi"], "license": "apache-2.0", "library_name": "sentence-transformers", "tags": ["rerank", "cross-encoder"], "metrics": ["spearmanr", "pearsonr"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "vietnamese-embedding-onnx", "results": [{"task": {"type": "feature-extraction"}, "dataset": {"name": "doanhieung/vi-stsbenchmark", "type": "stsb"}, "metrics": [{"type": "Pearsonr", "value": 0.85, "name": "Pearsonr (Onnx)"}, {"type": "Pearsonr", "value": 0.85, "name": "Pearsonr (Optimized)"}, {"type": "Pearsonr", "value": 0.83, "name": "Pearsonr (Dynamic 8b)"}, {"type": "Pearsonr", "value": 0.84, "name": "Pearsonr (Static 8b)"}, {"type": "Spearmanr", "value": 0.84, "name": "Spearmanr (Onnx)"}, {"type": "Spearmanr", "value": 0.84, "name": "Spearmanr (Optimized)"}, {"type": "Spearmanr", "value": 0.82, "name": "Spearmanr (Dynamic 8b)"}, {"type": "Spearmanr", "value": 0.82, "name": "Spearmanr (Static 8b)"}]}]}]} | itdainb/vietnamese-embedding-onnx | null | [
"sentence-transformers",
"onnx",
"roberta",
"rerank",
"cross-encoder",
"sentence-similarity",
"vi",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:00:32+00:00 |
|
automatic-speech-recognition | 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": []} | adrianmedinav/whisper-large-v3_ro_epochs_1_2024-04-30_13-43-03 | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:01:15+00:00 |
text-generation | transformers |
# phi-2-fp16-ov
* Model creator: [Microsoft](https://huggingface.co/microsoft)
* Original model: [phi-2](https://huggingface.co/microsoft/phi-2)
## Description
This is [phi-2](https://huggingface.co/microsoft/phi-2) model converted to the [OpenVINO™ IR](https://docs.openvino.ai/2024/documentation/openvino-ir-format.html) (Intermediate Representation) format with weights compressed to FP16.
## Compatibility
The provided OpenVINO™ IR model is compatible with:
* OpenVINO version 2024.1.0 and higher
* Optimum Intel 1.16.0 and higher
## Running Model Inference
1. Install packages required for using [Optimum Intel](https://huggingface.co/docs/optimum/intel/index) integration with the OpenVINO backend:
```
pip install optimum[openvino]
```
2. Run model inference:
```
from transformers import AutoTokenizer
from optimum.intel.openvino import OVModelForCausalLM
model_id = "OpenVINO/phi-2-fp16-ov"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = OVModelForCausalLM.from_pretrained(model_id)
inputs = tokenizer("What is OpenVINO?", return_tensors="pt")
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
For more examples and possible optimizations, refer to the [OpenVINO Large Language Model Inference Guide](https://docs.openvino.ai/2024/learn-openvino/llm_inference_guide.html).
## Limitations
Check the original model card for [limitations](https://huggingface.co/microsoft/phi-2#limitations-of-phi-2).
## Legal information
The original model is distributed under [MIT](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE) license. More details can be found in [original model card](https://huggingface.co/microsoft/phi-2). | {"language": ["en"], "license": "mit", "license_link": "https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE"} | OpenVINO/phi-2-fp16-ov | null | [
"transformers",
"openvino",
"phi",
"text-generation",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:01:28+00:00 |
null | null | {"license": "apache-2.0"} | Subhra987/ShowruMagie | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:01:46+00:00 |
|
null | null | {} | Maulana18/test | null | [
"region:us"
] | null | 2024-04-30T15:02:45+00:00 |
|
null | null | {} | Palmarivs/bart_ocr_correction | null | [
"region:us"
] | null | 2024-04-30T15:03:03+00:00 |
|
null | null | ebony like Halle berry | {} | StupidMonk/Shayna | null | [
"region:us"
] | null | 2024-04-30T15:03:42+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. -->
# dpo_harmlessharmless_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-05
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 15
- training_steps: 5000
### Training results
### Framework versions
- PEFT 0.9.0
- Transformers 4.38.2
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"license": "mit", "library_name": "peft", "tags": ["trl", "dpo", "generated_from_trainer"], "base_model": "gpt2", "model-index": [{"name": "dpo_harmlessharmless_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-05", "results": []}]} | Holarissun/dpo_harmlessharmless_gpt3_subset20000_modelgpt2_maxsteps5000_bz8_lr1e-05 | null | [
"peft",
"safetensors",
"trl",
"dpo",
"generated_from_trainer",
"base_model:gpt2",
"license:mit",
"region:us"
] | null | 2024-04-30T15:04:02+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. -->
# meditron-7b-dpo-full-sft-wo-kqa_silver_wogold
This model is a fine-tuned version of [Minbyul/meditron-7b-wo-kqa_silver_wogold-sft](https://huggingface.co/Minbyul/meditron-7b-wo-kqa_silver_wogold-sft) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6249
- Rewards/chosen: -0.0145
- Rewards/rejected: -0.1983
- Rewards/accuracies: 0.875
- Rewards/margins: 0.1839
- Logps/rejected: -653.1394
- Logps/chosen: -150.2214
- Logits/rejected: -1.0337
- Logits/chosen: -1.4037
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "llama2", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Minbyul/meditron-7b-wo-kqa_silver_wogold-sft", "model-index": [{"name": "meditron-7b-dpo-full-sft-wo-kqa_silver_wogold", "results": []}]} | Minbyul/meditron-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/meditron-7b-wo-kqa_silver_wogold-sft",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:04:44+00:00 |
null | null | {} | pavlopt/maccrobat | null | [
"region:us"
] | null | 2024-04-30T15:05:27+00:00 |
|
null | null | {} | ignisarcanum/hongjoong | null | [
"region:us"
] | null | 2024-04-30T15:05:56+00:00 |
|
null | null | {} | Robb1620/sd-monet | null | [
"region:us"
] | null | 2024-04-30T15:06:45+00:00 |
|
text-to-image | null |
## reverse
<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 - version 1
[](https://imagepipeline.io/models/reverse?id=c50ec5bb-5215-4d84-ac7e-2f9caa563ff5/)
## 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": "c50ec5bb-5215-4d84-ac7e-2f9caa563ff5",
"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/reverse | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-30T15:06:45+00:00 |
sentence-similarity | sentence-transformers | # Squirtle
Squirtle is a distill of [bge-base-en-v1.5](BAAI/bge-base-en-v1.5).
## Intended purpose
<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
Make sure you also pass `pipelineParams={{ pooling: "cls", normalize: true }}` since the default pooling in the component is mean.
## Usage
Other than within [semantic-autocomplete](https://github.com/Mihaiii/semantic-autocomplete), you can use this model same as [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5#usage). | {"license": "mit", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "bge", "mteb"], "datasets": ["Mihaiii/qa-assistant"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "Squirtle", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 69.59701492537313}, {"type": "ap", "value": 31.80839087521638}, {"type": "f1", "value": 63.43204352573031}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 82.09027499999999}, {"type": "ap", "value": 76.95004336850603}, {"type": "f1", "value": 82.04505556179174}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 41.943999999999996}, {"type": "f1", "value": 40.40964457303876}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "mteb/arguana", "config": "default", "split": "test", "revision": "c22ab2a51041ffd869aaddef7af8d8215647e41a"}, "metrics": [{"type": "map_at_1", "value": 13.869000000000002}, {"type": "map_at_10", "value": 24.631}, {"type": "map_at_100", "value": 25.965}, {"type": "map_at_1000", "value": 26.023000000000003}, {"type": "map_at_20", "value": 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"dot_accuracy", "value": 84.0317100792752}, {"type": "dot_ap", "value": 67.56359342938897}, {"type": "dot_f1", "value": 63.082081211970696}, {"type": "dot_precision", "value": 59.58245367112362}, {"type": "dot_recall", "value": 67.01846965699208}, {"type": "euclidean_accuracy", "value": 84.0317100792752}, {"type": "euclidean_ap", "value": 67.5636169518733}, {"type": "euclidean_f1", "value": 63.082081211970696}, {"type": "euclidean_precision", "value": 59.58245367112362}, {"type": "euclidean_recall", "value": 67.01846965699208}, {"type": "manhattan_accuracy", "value": 84.0734338677952}, {"type": "manhattan_ap", "value": 67.44969672020721}, {"type": "manhattan_f1", "value": 63.09479205695017}, {"type": "manhattan_precision", "value": 59.90040313018734}, {"type": "manhattan_recall", "value": 66.64907651715039}, {"type": "max_accuracy", "value": 84.0734338677952}, {"type": "max_ap", "value": 67.5636169518733}, {"type": "max_f1", "value": 63.09479205695017}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 87.60624054022587}, {"type": "cos_sim_ap", "value": 82.94451598409692}, {"type": "cos_sim_f1", "value": 74.76484194294527}, {"type": "cos_sim_precision", "value": 74.86874613959235}, {"type": "cos_sim_recall", "value": 74.66122574684324}, {"type": "dot_accuracy", "value": 87.60624054022587}, {"type": "dot_ap", "value": 82.94451133280317}, {"type": "dot_f1", "value": 74.76484194294527}, {"type": "dot_precision", "value": 74.86874613959235}, {"type": "dot_recall", "value": 74.66122574684324}, {"type": "euclidean_accuracy", "value": 87.60624054022587}, {"type": "euclidean_ap", "value": 82.94449586426977}, {"type": "euclidean_f1", "value": 74.76484194294527}, {"type": "euclidean_precision", "value": 74.86874613959235}, {"type": "euclidean_recall", "value": 74.66122574684324}, {"type": "manhattan_accuracy", "value": 87.63922847052432}, {"type": "manhattan_ap", "value": 82.9449637573502}, {"type": "manhattan_f1", "value": 74.9452996046217}, {"type": "manhattan_precision", "value": 74.73015386970833}, {"type": "manhattan_recall", "value": 75.1616877117339}, {"type": "max_accuracy", "value": 87.63922847052432}, {"type": "max_ap", "value": 82.9449637573502}, {"type": "max_f1", "value": 74.9452996046217}]}]}]} | Mihaiii/Squirtle | null | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"bge",
"mteb",
"dataset:Mihaiii/qa-assistant",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:06:52+00:00 |
text-to-image | null |
## Ring
<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 - ring gag
[](https://imagepipeline.io/models/Ring?id=497630fb-da2f-40ce-ab65-fc9c5428ab9c/)
## 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": "497630fb-da2f-40ce-ab65-fc9c5428ab9c",
"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/Ring | null | [
"imagepipeline",
"imagepipeline.io",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-30T15:07:20+00:00 |
null | null | {} | Destr/diffusers_ckpt_step_131.zip | null | [
"region:us"
] | null | 2024-04-30T15:09:26+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. -->
# WangchanBERTa
This model was trained from scratch 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: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "WangchanBERTa", "results": []}]} | tidarat/WangchanBERTa | null | [
"transformers",
"safetensors",
"camembert",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:09:48+00:00 |
null | null | {} | ignisarcanum/seonghwa | null | [
"region:us"
] | null | 2024-04-30T15:10:46+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
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## 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:**
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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. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model19 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:10:54+00:00 |
null | null |
<!-- 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. -->
# O0430HMA25
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3321 | 0.09 | 10 | 0.2026 |
| 0.1587 | 0.18 | 20 | 0.1513 |
| 0.1484 | 0.27 | 30 | 0.1700 |
| 0.157 | 0.36 | 40 | 0.1554 |
| 0.1545 | 0.45 | 50 | 0.1565 |
| 0.1711 | 0.54 | 60 | 0.1519 |
| 0.3899 | 0.63 | 70 | 0.1762 |
| 0.1605 | 0.73 | 80 | 0.1588 |
| 0.1806 | 0.82 | 90 | 0.1766 |
| 0.2771 | 0.91 | 100 | 0.1774 |
| 0.3353 | 1.0 | 110 | 1.2812 |
| 0.7796 | 1.09 | 120 | 3.0669 |
| 0.6043 | 1.18 | 130 | 0.1611 |
| 0.1654 | 1.27 | 140 | 0.1517 |
| 0.156 | 1.36 | 150 | 0.1525 |
| 0.1492 | 1.45 | 160 | 0.1531 |
| 0.1496 | 1.54 | 170 | 0.1504 |
| 0.1528 | 1.63 | 180 | 0.1523 |
| 0.1504 | 1.72 | 190 | 0.1482 |
| 0.1467 | 1.81 | 200 | 0.1498 |
| 0.1511 | 1.9 | 210 | 0.1509 |
| 0.1486 | 1.99 | 220 | 0.1509 |
| 0.1502 | 2.08 | 230 | 0.1493 |
| 0.1429 | 2.18 | 240 | 0.1471 |
| 0.1444 | 2.27 | 250 | 0.1490 |
| 0.1463 | 2.36 | 260 | 0.1482 |
| 0.1447 | 2.45 | 270 | 0.1471 |
| 0.1431 | 2.54 | 280 | 0.1471 |
| 0.144 | 2.63 | 290 | 0.1477 |
| 0.1464 | 2.72 | 300 | 0.1465 |
| 0.1453 | 2.81 | 310 | 0.1463 |
| 0.1459 | 2.9 | 320 | 0.1464 |
| 0.1467 | 2.99 | 330 | 0.1464 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA25", "results": []}]} | Litzy619/O0430HMA25 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:11:12+00:00 |
null | null |
<!-- 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. -->
# O0430HMA26
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0687
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.3358 | 0.09 | 10 | 0.2025 |
| 0.1669 | 0.18 | 20 | 0.1548 |
| 0.1489 | 0.27 | 30 | 0.1637 |
| 0.1568 | 0.36 | 40 | 0.1512 |
| 0.1504 | 0.45 | 50 | 0.1543 |
| 0.151 | 0.54 | 60 | 0.1516 |
| 0.1517 | 0.63 | 70 | 0.1471 |
| 0.1512 | 0.73 | 80 | 0.1543 |
| 0.1466 | 0.82 | 90 | 0.1497 |
| 0.1498 | 0.91 | 100 | 0.1498 |
| 0.421 | 1.0 | 110 | 0.7483 |
| 0.6289 | 1.09 | 120 | 0.4520 |
| 0.6214 | 1.18 | 130 | 0.5937 |
| 1.234 | 1.27 | 140 | 0.1622 |
| 0.1733 | 1.36 | 150 | 0.2787 |
| 0.1681 | 1.45 | 160 | 0.1530 |
| 0.151 | 1.54 | 170 | 0.1512 |
| 0.1503 | 1.63 | 180 | 0.1482 |
| 0.146 | 1.72 | 190 | 0.1427 |
| 0.1328 | 1.81 | 200 | 0.1179 |
| 0.1194 | 1.9 | 210 | 0.0917 |
| 0.0822 | 1.99 | 220 | 0.0782 |
| 0.0783 | 2.08 | 230 | 0.0748 |
| 0.0772 | 2.18 | 240 | 0.0769 |
| 0.0714 | 2.27 | 250 | 0.0763 |
| 0.0793 | 2.36 | 260 | 0.0728 |
| 0.0725 | 2.45 | 270 | 0.0720 |
| 0.0628 | 2.54 | 280 | 0.0674 |
| 0.0705 | 2.63 | 290 | 0.0689 |
| 0.075 | 2.72 | 300 | 0.0708 |
| 0.0687 | 2.81 | 310 | 0.0688 |
| 0.0715 | 2.9 | 320 | 0.0687 |
| 0.0762 | 2.99 | 330 | 0.0687 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "O0430HMA26", "results": []}]} | Litzy619/O0430HMA26 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:11:21+00:00 |
sentence-similarity | sentence-transformers | # Wartortle
Wartortle is a distill of [bge-base-en-v1.5](BAAI/bge-base-en-v1.5).
## Intended purpose
<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
Make sure you also pass `pipelineParams={{ pooling: "cls", normalize: true }}` since the default pooling in the component is mean.
## Usage
Other than within [semantic-autocomplete](https://github.com/Mihaiii/semantic-autocomplete), you can use this model same as [bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5#usage). | {"license": "mit", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "bge", "mteb"], "datasets": ["Mihaiii/qa-assistant"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "Wartortle", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 70.40298507462687}, {"type": "ap", "value": 32.88973775597331}, {"type": "f1", "value": 64.3726772221329}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 82.0381}, {"type": "ap", "value": 77.15483149750918}, {"type": "f1", "value": 81.97695449378108}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 42.412}, {"type": "f1", "value": 41.039684315409595}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "mteb/arguana", "config": "default", "split": "test", "revision": "c22ab2a51041ffd869aaddef7af8d8215647e41a"}, "metrics": [{"type": "map_at_1", "value": 16.003}, {"type": "map_at_10", "value": 28.448}, {"type": "map_at_100", "value": 29.781999999999996}, {"type": "map_at_1000", "value": 29.822}, {"type": "map_at_20", "value": 29.278}, {"type": "map_at_3", "value": 23.874000000000002}, {"type": "map_at_5", "value": 26.491}, {"type": "mrr_at_1", "value": 16.714000000000002}, {"type": "mrr_at_10", "value": 28.727999999999998}, {"type": "mrr_at_100", "value": 30.055}, {"type": "mrr_at_1000", "value": 30.095}, {"type": "mrr_at_20", "value": 29.558}, {"type": "mrr_at_3", "value": 24.194}, {"type": "mrr_at_5", "value": 26.778999999999996}, {"type": "ndcg_at_1", "value": 16.003}, {"type": "ndcg_at_10", "value": 35.865}, {"type": "ndcg_at_100", "value": 42.304}, {"type": "ndcg_at_1000", "value": 43.333}, {"type": "ndcg_at_20", "value": 38.876}, {"type": "ndcg_at_3", "value": 26.436999999999998}, {"type": "ndcg_at_5", "value": 31.139}, {"type": "precision_at_1", "value": 16.003}, {"type": "precision_at_10", "value": 5.982}, {"type": "precision_at_100", "value": 0.898}, {"type": "precision_at_1000", "value": 0.098}, {"type": "precision_at_20", "value": 3.585}, {"type": "precision_at_3", "value": 11.285}, {"type": "precision_at_5", "value": 9.046999999999999}, {"type": "recall_at_1", "value": 16.003}, {"type": "recall_at_10", "value": 59.815}, {"type": "recall_at_100", "value": 89.75800000000001}, {"type": "recall_at_1000", "value": 97.795}, {"type": "recall_at_20", "value": 71.693}, {"type": "recall_at_3", "value": 33.855000000000004}, {"type": "recall_at_5", "value": 45.235}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringP2P", "type": "mteb/arxiv-clustering-p2p", "config": "default", "split": "test", "revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d"}, "metrics": [{"type": "v_measure", "value": 35.843668514122115}, {"type": "v_measures", "value": [0.3334224034497392, 0.3341547890740972, 0.3357840169117339, 0.34882361674739576, 0.3295989566449552, 0.346573603986452, 0.3336839394053626, 0.33891447096132693, 0.3324032010342291, 0.3292339117184913, 0.413709338323792, 0.42196122544420617, 0.41164392816543693, 0.42687170671748026, 0.4196249053060285, 0.41799639579395365, 0.4197169573853409, 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0.31329437576982844, 0.3203569385112976, 0.3427302354400537, 0.3045275740558555, 0.3228406069698239, 0.3215023256245064, 0.30524504896475263, 0.31571502008047786, 0.2995174236038641, 0.32352199328838743]}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterSemEval2015", "type": "mteb/twittersemeval2015-pairclassification", "config": "default", "split": "test", "revision": "70970daeab8776df92f5ea462b6173c0b46fd2d1"}, "metrics": [{"type": "cos_sim_accuracy", "value": 84.00190737318948}, {"type": "cos_sim_ap", "value": 67.48296380006165}, {"type": "cos_sim_f1", "value": 62.996718920889535}, {"type": "cos_sim_precision", "value": 58.39152962378914}, {"type": "cos_sim_recall", "value": 68.3905013192612}, {"type": "dot_accuracy", "value": 84.00190737318948}, {"type": "dot_ap", "value": 67.48295942427862}, {"type": "dot_f1", "value": 62.996718920889535}, {"type": "dot_precision", "value": 58.39152962378914}, {"type": "dot_recall", "value": 68.3905013192612}, {"type": "euclidean_accuracy", "value": 84.00190737318948}, {"type": "euclidean_ap", "value": 67.482961801317}, {"type": "euclidean_f1", "value": 62.996718920889535}, {"type": "euclidean_precision", "value": 58.39152962378914}, {"type": "euclidean_recall", "value": 68.3905013192612}, {"type": "manhattan_accuracy", "value": 83.94826250223521}, {"type": "manhattan_ap", "value": 67.32115101507013}, {"type": "manhattan_f1", "value": 62.665684830633275}, {"type": "manhattan_precision", "value": 58.5819183111519}, {"type": "manhattan_recall", "value": 67.36147757255937}, {"type": "max_accuracy", "value": 84.00190737318948}, {"type": "max_ap", "value": 67.48296380006165}, {"type": "max_f1", "value": 62.996718920889535}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 88.30286800946948}, {"type": "cos_sim_ap", "value": 84.5306725053528}, {"type": "cos_sim_f1", "value": 76.5947752126367}, {"type": "cos_sim_precision", "value": 75.56188192987715}, {"type": "cos_sim_recall", "value": 77.65629812134279}, {"type": "dot_accuracy", "value": 88.30286800946948}, {"type": "dot_ap", "value": 84.53066920468329}, {"type": "dot_f1", "value": 76.5947752126367}, {"type": "dot_precision", "value": 75.56188192987715}, {"type": "dot_recall", "value": 77.65629812134279}, {"type": "euclidean_accuracy", "value": 88.30286800946948}, {"type": "euclidean_ap", "value": 84.53066432305307}, {"type": "euclidean_f1", "value": 76.5947752126367}, {"type": "euclidean_precision", "value": 75.56188192987715}, {"type": "euclidean_recall", "value": 77.65629812134279}, {"type": "manhattan_accuracy", "value": 88.39795086738852}, {"type": "manhattan_ap", "value": 84.51446339083833}, {"type": "manhattan_f1", "value": 76.57867106644667}, {"type": "manhattan_precision", "value": 74.64181286549709}, {"type": "manhattan_recall", "value": 78.61872497690176}, {"type": "max_accuracy", "value": 88.39795086738852}, {"type": "max_ap", "value": 84.5306725053528}, {"type": "max_f1", "value": 76.5947752126367}]}]}]} | Mihaiii/Wartortle | null | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"bge",
"mteb",
"dataset:Mihaiii/qa-assistant",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:12:13+00:00 |
null | peft |
# Model Card for Model ID
Unsloth base model Code Llama2 34b (https://huggingface.co/unsloth/codellama-34b-bnb-4bit)
trained on Alpaca Dataset.
This is a repository for another 34B instruct-tuned version.
```
Tue Apr 30 18:21:29 2024
+---------------------------------------------------------------------------------------+
| NVIDIA-SMI 535.129.03 Driver Version: 535.129.03 CUDA Version: 12.2 |
|-----------------------------------------+----------------------+----------------------+
| GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC |
| Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. |
| | | MIG M. |
|=========================================+======================+======================|
| 0 NVIDIA A100-SXM4-40GB On | 00000000:07:00.0 Off | 0 |
| N/A 75C P0 389W / 400W | 20959MiB / 40960MiB | 100% Default |
| | | Disabled |
+-----------------------------------------+----------------------+----------------------+
+---------------------------------------------------------------------------------------+
| Processes: |
| GPU GI CI PID Type Process name GPU Memory |
| ID ID Usage |
|=======================================================================================|
| 0 N/A N/A 9175 C python 20938MiB |
+---------------------------------------------------------------------------------------+
```
### Framework versions
- Unsloth
- Torch
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "unsloth/codellama-34b-bnb-4bit"} | vincentoh/codellama-34b-alpaca-instruct | null | [
"peft",
"safetensors",
"base_model:unsloth/codellama-34b-bnb-4bit",
"region:us"
] | null | 2024-04-30T15:12:27+00:00 |
null | null | {} | pavlopt/mes | null | [
"region:us"
] | null | 2024-04-30T15:13:29+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": ["trl", "sft"]} | dbaek111/Mistral-7B-v0.2-Elon_407_HPC_Q | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T15:13:44+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "38.80 +/- 24.91", "name": "mean_reward", "verified": false}]}]}]} | lzacchini/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-04-30T15:13:55+00:00 |
text-classification | transformers | {} | skelley/impact | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:14:01+00:00 |
|
null | transformers |
`Epoch = 0, Step = 1000`
# Model Card for Model ID
```
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 36000 \
--batch-size 2 \
--valid-batch-size 2 \
--num-workers 12 \
--lr 5e-5 \
--log-every-n-steps 20 \
--val-check-interval 0.5 \
--warmup-steps 2000 \
--checkpoint-steps 1000 \
--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-high-lr | null | [
"transformers",
"safetensors",
"Chart-rela-instruct",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:14:40+00:00 |
null | null | {} | pavlopt/simple | null | [
"region:us"
] | null | 2024-04-30T15:15:11+00:00 |
|
null | null | {} | ignisarcanum/yunho | null | [
"region:us"
] | null | 2024-04-30T15:15:13+00:00 |
|
null | null | {} | ignisarcanum/yeosang | null | [
"region:us"
] | null | 2024-04-30T15:15:24+00:00 |
|
question-answering | 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. -->
# rajivsah240/my_awesome_qa_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: 3.4612
- Validation Loss: 2.2288
- Epoch: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- optimizer: {'name': 'Adam', 'weight_decay': None, 'clipnorm': None, 'global_clipnorm': None, 'clipvalue': None, 'use_ema': False, 'ema_momentum': 0.99, 'ema_overwrite_frequency': None, 'jit_compile': False, 'is_legacy_optimizer': False, 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 500, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| 3.4612 | 2.2288 | 0 |
### Framework versions
- Transformers 4.40.0
- TensorFlow 2.15.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_keras_callback"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "rajivsah240/my_awesome_qa_model", "results": []}]} | rajivsah240/my_awesome_qa_model | null | [
"transformers",
"tf",
"distilbert",
"question-answering",
"generated_from_keras_callback",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:17:47+00:00 |
reinforcement-learning | stable-baselines3 |
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Cheekydave -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga Cheekydave -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga Cheekydave
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| {"library_name": "stable-baselines3", "tags": ["SpaceInvadersNoFrameskip-v4", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "DQN", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "SpaceInvadersNoFrameskip-v4", "type": "SpaceInvadersNoFrameskip-v4"}, "metrics": [{"type": "mean_reward", "value": "718.50 +/- 256.51", "name": "mean_reward", "verified": false}]}]}]} | Cheekydave/dqn-SpaceInvadersNoFrameskip-v4 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T15:18:27+00:00 |
token-classification | flair |
# Recognition of UTEs and company mentions in Flair
This is a model trained using [Flair](https://github.com/flairNLP/flair/) to recognise mentions of UTEs (Unión Temporal de Empresas)
and companies in public tenders.
It is a finetune of the flair/ner-spanish-large model (retrained from scratch to include additional tags).
Based on document-level XLM-R embeddings and [FLERT](https://arxiv.org/pdf/2011.06993v1.pdf/).
## Demo: How to use in Flair
Requires: **[Flair](https://github.com/flairNLP/flair/)** (`pip install flair`)
```python
from flair.data import Sentence
from flair.models import SequenceTagger
# load tagger
tagger = SequenceTagger.load("BSC-LT/NextProcurement-NER-Spanish-UTE-Company")
# make example sentence
sentence = Sentence("PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRÁULICOS S.L., constituidos en UTE PODACESA-ECR realizan la siguiente oferta:")
# predict NER tags
tagger.predict(sentence)
# print sentence
print(sentence)
# print predicted NER spans
print('The following NER tags are found:')
# iterate over entities and print
for entity in sentence.get_spans('ner'):
print(entity)
```
This yields the following output:
```
Sentence[24]: "PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRAULICOS S.L., constituidos en UTE PODACESA-ECR realizan la siguiente oferta:" _ ["PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRAULICOS S.L."/UTE, "PODACESA-ECR"/UTE]
The following NER tags are found:
Span[0:14]: "PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDRAULICOS S.L." _ UTE (0.995)
Span[18:19]: "PODACESA-ECR" _ UTE (0.9955)
```
and with the sentence "PODACESA OBRAS Y SERVICIOS, S.A realiza la siguiente oferta:"
```
Sentence[11]: "PODACESA OBRAS Y SERVICIOS, S.A realiza la siguiente oferta:" _ ["PODACESA OBRAS Y SERVICIOS, S.A"/SINGLE_COMPANY]
The following NER tags are found:
Span[0:6]: "PODACESA OBRAS Y SERVICIOS, S.A" _ SINGLE_COMPANY (1.0)
```
## Training: Script to train this model
The following Flair script was used to train this model (**TODO: update**):
```python
import torch
# 1. get the corpus
from flair.datasets import CONLL_03_SPANISH
corpus = CONLL_03_SPANISH()
# 2. what tag do we want to predict?
tag_type = 'ner'
# 3. make the tag dictionary from the corpus
tag_dictionary = corpus.make_tag_dictionary(tag_type=tag_type)
# 4. initialize fine-tuneable transformer embeddings WITH document context
from flair.embeddings import TransformerWordEmbeddings
embeddings = TransformerWordEmbeddings(
model='xlm-roberta-large',
layers="-1",
subtoken_pooling="first",
fine_tune=True,
use_context=True,
)
# 5. initialize bare-bones sequence tagger (no CRF, no RNN, no reprojection)
from flair.models import SequenceTagger
tagger = SequenceTagger(
hidden_size=256,
embeddings=embeddings,
tag_dictionary=tag_dictionary,
tag_type='ner',
use_crf=False,
use_rnn=False,
reproject_embeddings=False,
)
# 6. initialize trainer with AdamW optimizer
from flair.trainers import ModelTrainer
trainer = ModelTrainer(tagger, corpus, optimizer=torch.optim.AdamW)
# 7. run training with XLM parameters (20 epochs, small LR)
from torch.optim.lr_scheduler import OneCycleLR
trainer.train('resources/taggers/ner-spanish-large',
learning_rate=5.0e-6,
mini_batch_size=4,
mini_batch_chunk_size=1,
max_epochs=20,
scheduler=OneCycleLR,
embeddings_storage_mode='none',
weight_decay=0.,
)
)
```
## Evaluation Results
```
Results:
- F-score (micro) 0.7431
- F-score (macro) 0.7429
- Accuracy 0.5944
By class:
precision recall f1-score support
UTE 0.7568 0.7887 0.7724 71
SINGLE_COMPANY 0.6538 0.7846 0.7133 65
micro avg 0.7039 0.7868 0.7431 136
macro avg 0.7053 0.7867 0.7429 136
weighted avg 0.7076 0.7868 0.7442 136
```
## Additional information
### Author
The Language Technologies Unit from Barcelona Supercomputing Center.
### Contact
For further information, please send an email to <[email protected]>.
### Copyright
Copyright(c) 2023 by Language Technologies Unit, Barcelona Supercomputing Center.
### License
[Apache License, Version 2.0](https://www.apache.org/licenses/LICENSE-2.0)
### Funding
This work has been promoted and financed by the European Commission Health and Digital Executive Agency, Connecting Europe Facility, Grant Agreement Nº INEA/CEF/ICT/A2020/2373713, Action Title Open Harmonized and Enriched Procurement Data Platform (nextProcurement), Action number 2020-ES-IA-0255.
### Disclaimer
<details>
<summary>Click to expand</summary>
The model published in this repository is intended for a generalist purpose and is available to third parties under a permissive Apache License, Version 2.0.
Be aware that the model may have biases and/or any other undesirable distortions.
When third parties deploy or provide systems and/or services to other parties using this model (or any system based on it)
or become users of the model, they should note that it is their responsibility to mitigate the risks arising from its use and,
in any event, to comply with applicable regulations, including regulations regarding the use of Artificial Intelligence.
In no event shall the owner and creator of the model (Barcelona Supercomputing Center)
be liable for any results arising from the use made by third parties.
</details>
| {"language": "es", "license": "apache-2.0", "tags": ["flair", "token-classification", "sequence-tagger-model"], "datasets": ["conll2003", "BSC-LT/NextProcurement-NER-Spanish-UTE-Company-annotated"], "widget": [{"text": "PODACESA OBRAS Y SERVICIOS, S.A, y ECR INFRAESTRUCTURAS Y SERVICIOS HIDR\u00c1ULICOS S.L., constituidos en UTE PODACESA-ECR realizan la siguiente oferta:"}, {"text": "PODACESA OBRAS Y SERVICIOS, S.A realiza la siguiente oferta:"}]} | BSC-LT/NextProcurement-NER-Spanish-UTE-Company | null | [
"flair",
"pytorch",
"token-classification",
"sequence-tagger-model",
"es",
"dataset:conll2003",
"dataset:BSC-LT/NextProcurement-NER-Spanish-UTE-Company-annotated",
"arxiv:2011.06993",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:18:30+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. -->
# vissa_test
This model is a fine-tuned version of [openthaigpt/openthaigpt-1.0.0-7b-chat](https://huggingface.co/openthaigpt/openthaigpt-1.0.0-7b-chat) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.03
- training_steps: 180
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.1.dev0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "openthaigpt/openthaigpt-1.0.0-7b-chat", "model-index": [{"name": "vissa_test", "results": []}]} | Vissa15AI/vissa_test | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:openthaigpt/openthaigpt-1.0.0-7b-chat",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T15:18:58+00:00 |
text2text-generation | transformers | {} | paulh27/xsum_aligned_smallT5_cont2 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:19:24+00:00 |
|
text-generation | 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": ["fr"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "pipeline_tag": "text-generation"} | yadz45/IA_lo2 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"text-generation",
"fr",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:19:46+00:00 |
text-classification | transformers | {} | skelley/job_sat | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:20:48+00:00 |
|
null | null | {} | fiouf/nu | null | [
"region:us"
] | null | 2024-04-30T15:21:05+00:00 |
|
null | null | {} | yliuhz/ControlNet-init | null | [
"region:us"
] | null | 2024-04-30T15:21:48+00:00 |
|
text2text-generation | transformers | {} | paulh27/cnn_aligned_smallT5_cont2 | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T15:22:04+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "235.19 +/- 19.94", "name": "mean_reward", "verified": false}]}]}]} | jchenmath/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-30T15:22:53+00:00 |
text-to-image | diffusers |
# KrazyGlue
The purpose of this model is to keep you glued to the screen generating images.
Samples and prompts:
(Click for larger)
Top left: a cute girl with freckles on her face, cgsociety unreal engine, wet t-shirt, short skirt, style of aenami alena, trending on artstartion, inspired by Fyodor Vasilyev, looks a bit similar to amy adams, emissive light, fluffy orange skin, dribbble, dramatic rendering
Top right: 90s grainy vhs still young mother loose shirt, headband. holding a baby, on the couch, posing, bow. bokeh, bright lighting. smile
Bottom left: dragon holds the sun upside down in the sky with her hands, she stands on a mountain, under the mountain there is a small town, illustration, sharp focus, very detailed, 8 k, hd
Bottom right: Excellent quality and high resolution photo which depicts an old black panther in its natural habitat. The panther stands on four legs at a distance of 25 meters. Its torso is directed parallel to the lens. This is not a portrait, the panther is fully visible! ! ! The panther looks straight into the camera lens. Her appearance is menacing, majestic, but not aggressive. She looks wary but indifferent without fear. She shows that she has everything under control and she owns the situation. Back background in brown tones. Most likely the area is arid, something like a desert. However, the vegetation is still present. The quality of the photo is professional, taken by a professional photographer on the latest model of the camera in high resolution.
InsaneRealistic by cordonsolution8 merged with the Hellmix model by Barons, Kitsch-In-Sync v2 by iamxenos, the cryptids lora by RIXYN, and artistic models with the CokeGirls lora by iamxenos.
Original pages:
https://civitai.com/models/108585?modelVersionId=116883 (InsaneRealistic)
https://civitai.com/models/186251/coca-cola-gil-elvgrenhaddon-sundblom-pinup-style
https://civitai.com/models/142552?modelVersionId=163068 (Kitsch-In-Sync v2)
https://civitai.com/models/21493/hellmix?modelVersionId=25632
https://civitai.com/models/64766/cryptids?modelVersionId=69407 (Cryptids LoRA) | {"language": ["en"], "license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["Realism", "Girls", "Cute", "cordonsolution8", "iamxenos", "RIXYN", "Barons", "stable-diffusion", "stable-diffusion-diffusers", "diffusers", "text-to-image"], "pipeline_tag": "text-to-image"} | Yntec/KrazyGlue | null | [
"diffusers",
"safetensors",
"Realism",
"Girls",
"Cute",
"cordonsolution8",
"iamxenos",
"RIXYN",
"Barons",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us",
"has_space"
] | null | 2024-04-30T15:23:01+00:00 |
null | null | {} | ignisarcanum/wooyoung | null | [
"region:us"
] | null | 2024-04-30T15:23:15+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": []} | Luluuu/0429_SEASON_baseline_checkpoint_4854 | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:23:16+00:00 |
null | null | {} | Schaolone/slt | null | [
"region:us"
] | null | 2024-04-30T15:23:57+00:00 |
|
null | null | {} | superemohot/Llama3-TAIDE-LX-8B-Chat-Alpha1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-04-30T15:24:41+00:00 |
|
null | null | {} | RonanMcGovern/Meta-Llama-3-8B-Instruct-no-fin-advice-adapters-local | null | [
"region:us"
] | null | 2024-04-30T15:24:57+00:00 |
|
null | null | {} | ignisarcanum/san | null | [
"region:us"
] | null | 2024-04-30T15:25:09+00:00 |
|
null | null | {} | ignisarcanum/jongho | null | [
"region:us"
] | null | 2024-04-30T15:25:17+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]
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## Uses
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### 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]
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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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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#### Software
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## 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:**
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**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. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | usernameisanna/blip2-deit-seed-epoch-1 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:26:11+00:00 |
null | null | {} | Subhajit0320/my_awesome_qa_model | null | [
"region:us"
] | null | 2024-04-30T15:27:07+00:00 |
|
text-classification | transformers | {} | skelley/no_success | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T15:27:20+00:00 |
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