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null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | 1093212290a/idefics-9b-doodles | null | [
"transformers",
"safetensors",
"idefics",
"pretraining",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-28T11:36:39+00:00 |
feature-extraction | transformers |
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| {"library_name": "transformers", "tags": []} | wb-droid/my-quantized-chatGLM3 | null | [
"transformers",
"safetensors",
"chatglm",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | null | 2024-04-28T11:36:41+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** moriire
- **License:** apache-2.0
- **Finetuned from model :** moriire/healthcare-ai-adapter-merged_16bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "moriire/healthcare-ai-adapter-merged_16bit"} | moriire/healthcare-ai-q2_k | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:moriire/healthcare-ai-adapter-merged_16bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:36:43+00:00 |
text2text-generation | transformers | {} | Sagar23p/my_awesome_opus_books_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T11:38:41+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/s0wy9ft | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:38:54+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/ou17mtc | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:38:59+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/4513wfg | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:39:03+00:00 |
null | transformers |
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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]
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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]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/f4s0y0z | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:39:08+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. -->
# stablelm-2-1_6b-sft-full-spin-sigmoid-iter0_1_61101_small_margin_0_1
This model is a fine-tuned version of [nnheui/stablelm-2-1_6b-sft-full](https://huggingface.co/nnheui/stablelm-2-1_6b-sft-full) on the nnheui/stablelm-2-1_6b-sft-full-ultrachat_200k_generated-1_61101 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "other", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["nnheui/stablelm-2-1_6b-sft-full-ultrachat_200k_generated-1_61101"], "base_model": "nnheui/stablelm-2-1_6b-sft-full", "model-index": [{"name": "stablelm-2-1_6b-sft-full-spin-sigmoid-iter0_1_61101_small_margin_0_1", "results": []}]} | nnheui/stablelm-2-1_6b-sft-full-spin-sigmoid-iter0_1_61101_small_margin_0_1 | null | [
"transformers",
"tensorboard",
"safetensors",
"stablelm",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:nnheui/stablelm-2-1_6b-sft-full-ultrachat_200k_generated-1_61101",
"base_model:nnheui/stablelm-2-1_6b-sft-full",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:39:38+00:00 |
null | null | {} | Satyamsc0/llava-1.5-7b-hf-ft-mix-vsft | null | [
"region:us"
] | null | 2024-04-28T11:41:01+00:00 |
|
null | null | https://civitai.com/models/423011/kizuki-violet-evergarden-violet-evergarden | {"license": "creativeml-openrail-m"} | LarryAIDraw/Kizuki_Violet | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-28T11:41:32+00:00 |
text-generation | null | # Welcome to Llama3, your AI-powered code helper designed for sql
## Model
meta-llama/Meta-Llama-3-8B <br>
per_device_train_batch_size = 2 <br>
gradient_accumulation_steps = 2 <br>
optim = "paged_adamw_32bit" <br>
save_steps = 1000 <br>
logging_steps = 10 <br>
learning_rate = 2e-4 <br>
max_grad_norm = 0.3 <br>
max_steps = 1400 <br>
warmup_ratio = 0.03 <br>
lora_alpha = 4 <br>
lora_dropout = 0.1 <br>
lora_r = 2 <br>
## Data
token size = 99191 <br>
row = 1303 <br>
word count = 45616 <br>
prompt=[INST]<<SYS>>AΕaΔΔ±da bir SQL gΓΆrevi aΓ§Δ±klayan bir talimat bulunmaktadΔ±r. Δ°steΔi uygun Εekilde tamamlayan bir yanΔ±t yazΔ±n <<\/SYS>> ΓΌrΓΌn tablosundan 10. numaralΔ± tedarikΓ§inin saΔladΔ±ΔΔ± ΓΌrΓΌn sayΔ±sΔ± kaΓ§tΔ±r [\/INST]SELECT COUNT (*) FROM Products INNER JOIN Suppliers ON products.SupplierID=suppliers.SupplierID WHERE products.SupplierID=10
## Question and Answer
1. **question**:filmler tablosundan Avusturya yapΔ±mΔ± filmlerden en kΔ±sa sΓΌreli filmi getir <br>
**answer**:Bu, bir SELECT komutunun sonucunu bir veri tablosuna yazdΔ±rΔ±r. Bu ΓΆrnekte, filmler tablosundan Avusturya yapΔ±mΔ± filmlerden en kΔ±sa sΓΌreli filmi getirir. <>SELECT filmler FROM film WHERE language_id = 1 AND (SELECT MIN(length) FROM film) = length; | {"language": ["en"], "license": "apache-2.0", "datasets": ["onurSakar/GYM-Exercise"], "pipeline_tag": "text-generation"} | onurSakar/Llama-3-8b-sql_fine_tuning | null | [
"safetensors",
"text-generation",
"en",
"dataset:onurSakar/GYM-Exercise",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T11:41:38+00:00 |
null | null | {} | crazyup37/Suyash_Adv_2000e | null | [
"region:us"
] | null | 2024-04-28T11:42:42+00:00 |
|
null | null | {"license": "llama2"} | OllmOne/CodeLlama-7B-Instruct-GGUF | null | [
"gguf",
"license:llama2",
"region:us"
] | null | 2024-04-28T11:45:28+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 π€ 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": []} | sooh-j/blip2-vizwizqa | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:45:55+00:00 |
null | null | {"license": "gemma"} | OllmOne/gemma-2b-it-GGUF | null | [
"gguf",
"license:gemma",
"region:us"
] | null | 2024-04-28T11:46:01+00:00 |
|
null | null | {"license": "gemma"} | OllmOne/gemma-7b-it-GGUF | null | [
"gguf",
"license:gemma",
"region:us"
] | null | 2024-04-28T11:46:36+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/arcee-ai/Llama-3-OpenBioLLM-JSL-8B-SLERP
<!-- 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/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF/resolve/main/Llama-3-OpenBioLLM-JSL-8B-SLERP.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": ["merge", "mergekit", "aaditya/OpenBioLLM-Llama3-8B", "johnsnowlabs/JSL-Med-Sft-Llama-3-8B"], "base_model": "arcee-ai/Llama-3-OpenBioLLM-JSL-8B-SLERP", "quantized_by": "mradermacher"} | mradermacher/Llama-3-OpenBioLLM-JSL-8B-SLERP-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"aaditya/OpenBioLLM-Llama3-8B",
"johnsnowlabs/JSL-Med-Sft-Llama-3-8B",
"en",
"base_model:arcee-ai/Llama-3-OpenBioLLM-JSL-8B-SLERP",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:47:05+00:00 |
null | null | {} | Satyamsc0/output | null | [
"region:us"
] | null | 2024-04-28T11:49:36+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** merkle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | merkle/unsloth-llama-ble-newsletters | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:49:52+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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | wlgq/w2v-bert-2.0-mongolian-zh-cn | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:50:12+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Hardware Type:** [More Information Needed]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abhinavraj123/distilbert-base-uncased-lora-text-classification | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:51:36+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. -->
# distilbert-base-uncased-lora-text_classification
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0738
- Accuracy: {'accuracy': 0.881}
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:-------------------:|
| No log | 1.0 | 250 | 0.3929 | {'accuracy': 0.875} |
| 0.4353 | 2.0 | 500 | 0.4260 | {'accuracy': 0.865} |
| 0.4353 | 3.0 | 750 | 0.6525 | {'accuracy': 0.88} |
| 0.1655 | 4.0 | 1000 | 0.7133 | {'accuracy': 0.879} |
| 0.1655 | 5.0 | 1250 | 0.7804 | {'accuracy': 0.879} |
| 0.059 | 6.0 | 1500 | 0.9110 | {'accuracy': 0.879} |
| 0.059 | 7.0 | 1750 | 0.9332 | {'accuracy': 0.877} |
| 0.0101 | 8.0 | 2000 | 0.9973 | {'accuracy': 0.884} |
| 0.0101 | 9.0 | 2250 | 1.0601 | {'accuracy': 0.88} |
| 0.0012 | 10.0 | 2500 | 1.0738 | {'accuracy': 0.881} |
### 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"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-lora-text_classification", "results": []}]} | abhinavraj123/distilbert-base-uncased-lora-text_classification | null | [
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T11:51:38+00:00 |
null | null | {} | Anm5/rainpred | null | [
"region:us"
] | null | 2024-04-28T11:51:58+00:00 |
|
automatic-speech-recognition | null | {"language": ["id"], "license": "apache-2.0", "datasets": ["mozilla-foundation/common_voice_15_0"], "metrics": ["wer"], "pipeline_tag": "automatic-speech-recognition"} | regieh/wav2vec2-large-xlsr-indonesian | null | [
"automatic-speech-recognition",
"id",
"dataset:mozilla-foundation/common_voice_15_0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T11:53:26+00:00 |
|
automatic-speech-recognition | 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. -->
# basic_train_basic_test 1000 similar params: per_device_train_batch_size=32, # bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the xbilek25/basic_train_set_en_last3cs_1000 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3732
- Wer: 29.7470
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 200
- training_steps: 2000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0021 | 7.03 | 500 | 0.3407 | 32.9174 |
| 0.0008 | 15.03 | 1000 | 0.3548 | 27.6897 |
| 0.0005 | 23.03 | 1500 | 0.3684 | 30.3204 |
| 0.0004 | 31.02 | 2000 | 0.3732 | 29.7470 |
### Framework versions
- Transformers 4.37.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"language": ["multilingual"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_11_0"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "basic_train_basic_test 1000 similar params: per_device_train_batch_size=32, # bylo 16 a pod tim 1 gradient_accumulation_steps=2, warmup_steps=300, max_steps=3000", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "xbilek25/basic_train_set_en_last3cs_1000", "type": "mozilla-foundation/common_voice_11_0", "args": "config: csen, split: train"}, "metrics": [{"type": "wer", "value": 29.747048903878582, "name": "Wer"}]}]}]} | xbilek25/whisper-small-train-v2.0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"multilingual",
"dataset:mozilla-foundation/common_voice_11_0",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:53:27+00:00 |
null | null | {} | robinhyg/tinyllama-1b-sft-qlora | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-04-28T11:53:59+00:00 |
|
text-generation | transformers |
<img src=https://huggingface.co/lodrick-the-lafted/Olethros-8B/resolve/main/olethros.png>
# Olethros-8B
L3-8b-Instruct tuned on roughly 6000 Opus generations in the hopes of adding a bit of sovl.
<br />
<br />
<br />
<br />
# Quants
GGUF, Exl2 and AWQ available right now.
| Type | Misc | Author |
| ----- | ----- | ----- |
[GGUF](https://huggingface.co/mradermacher/Olethros-8B-GGUF)| Static GGUF Quants | mradermacher |
[AWQ](https://huggingface.co/lodrick-the-lafted/Olethros-8B-AWQ)| | lodrick |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw2.25-exl2)| 2.25bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw2.5-exl2)| 2.5bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw3-exl2)| 3.0bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw3.5-exl2)| 3.5bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw3.7-exl2)| 3.7bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw4-exl2)| 4.0bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw4.2-exl2)| 4.2bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw4.4-exl2)| 4.4bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw4.6-exl2)| 4.6bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw4.8-exl2)| 4.8bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw5-exl2)| 5.0bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw5.5-exl2)| 5.5bpw | blockblockblock |
[exl2](https://huggingface.co/blockblockblock/Olethros-8B-bpw6-exl2)| 6.0bpw | blockblockblock |
| {"license": "llama3", "datasets": ["lodrick-the-lafted/OpusStories", "lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K", "lodrick-the-lafted/Samantha-Opus", "lodrick-the-lafted/Worldsim-Opus"]} | lodrick-the-lafted/Olethros-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:lodrick-the-lafted/OpusStories",
"dataset:lodrick-the-lafted/Sao10K_Claude-3-Opus-Instruct-3.3K",
"dataset:lodrick-the-lafted/Samantha-Opus",
"dataset:lodrick-the-lafted/Worldsim-Opus",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T11:54:54+00:00 |
null | null | {} | arwaHugging/test | null | [
"region:us"
] | null | 2024-04-28T11:55:53+00:00 |
|
null | null | {"license": "cc-by-sa-3.0"} | Singer65/Ovais | null | [
"license:cc-by-sa-3.0",
"region:us"
] | null | 2024-04-28T11:57:55+00:00 |
|
null | null | {} | liho00/distributed-training-lh-default | null | [
"region:us"
] | null | 2024-04-28T11:58:17+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/2c4hz86 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:58:21+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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | golf2248/6pad7j1 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:58:21+00:00 |
null | null | {} | liho00/distributed-training-lh-defaulttwo | null | [
"region:us"
] | null | 2024-04-28T11:58:28+00:00 |
|
text-to-audio | 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. -->
# zlm_b32_le5_s8000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3854
## 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: 32
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2000
- training_steps: 8050
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.7532 | 0.2094 | 500 | 0.6165 |
| 0.6399 | 0.4188 | 1000 | 0.5227 |
| 0.5333 | 0.6281 | 1500 | 0.4723 |
| 0.5159 | 0.8375 | 2000 | 0.4493 |
| 0.4795 | 1.0469 | 2500 | 0.4283 |
| 0.4821 | 1.2563 | 3000 | 0.4274 |
| 0.4563 | 1.4657 | 3500 | 0.4122 |
| 0.4428 | 1.6750 | 4000 | 0.4040 |
| 0.4604 | 1.8844 | 4500 | 0.4010 |
| 0.4545 | 2.0938 | 5000 | 0.3986 |
| 0.445 | 2.3032 | 5500 | 0.3939 |
| 0.4279 | 2.5126 | 6000 | 0.3892 |
| 0.4451 | 2.7219 | 6500 | 0.3893 |
| 0.4243 | 2.9313 | 7000 | 0.3868 |
| 0.4356 | 3.1407 | 7500 | 0.3857 |
| 0.4429 | 3.3501 | 8000 | 0.3854 |
### 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": "microsoft/speecht5_tts", "model-index": [{"name": "zlm_b32_le5_s8000", "results": []}]} | mikhail-panzo/zlm_b32_le5_s8000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:58:39+00:00 |
null | null | {} | liho00/distributed-training-lh-defaultthree | null | [
"region:us"
] | null | 2024-04-28T11:58:47+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. -->
# daxpro-BERT
This model was trained from scratch on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 24
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1500
- num_epochs: 8
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "model-index": [{"name": "daxpro-BERT", "results": []}]} | daxproai/daxpro-BERT | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:59:20+00:00 |
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-ACT_SITE_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:59:22+00:00 |
|
sentence-similarity | sentence-transformers |
# sergeyvi4ev/sql-question_evidence_mnr_loss
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 384 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('sergeyvi4ev/sql-question_evidence_mnr_loss')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=sergeyvi4ev/sql-question_evidence_mnr_loss)
## Training
The model was trained with the parameters:
**DataLoader**:
`sentence_transformers.datasets.NoDuplicatesDataLoader.NoDuplicatesDataLoader` of length 41 with parameters:
```
{'batch_size': 128}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 0,
"evaluator": "NoneType",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 41,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 256, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "datasets": ["sergeyvi4ev/sql_questions_triplets"], "pipeline_tag": "sentence-similarity"} | sergeyvi4ev/all-MiniLM-RAGSQL-text | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"dataset:sergeyvi4ev/sql_questions_triplets",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T11:59:35+00:00 |
text-generation | llama.cpp |
# Mistroll-7B-v2.2-GGUF
**Model creator:** [BarraHome](https://huggingface.co/BarraHome)<br>
**Original model**: [Mistroll-7B-v2.2](https://huggingface.co/BarraHome/Mistroll-7B-v2.2)<br>
**GGUF quantization:** `llama.cpp` commit [6e472f58e40cd4acf6023e15c75a2700535c5f0b](https://github.com/ggerganov/llama.cpp/tree/6e472f58e40cd4acf6023e15c75a2700535c5f0b)<br>
## Description
This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
This experiment serves to test and refine a specific training and evaluation pipeline research framework. Its primary objective is to identify potential optimizations, with a focus on data engineering, architectural efficiency, and evaluation performance.
The goal of this experiment is to evaluate the effectiveness of a new training and evaluation pipeline for Large Language Models (LLMs). To achieve this, we will explore adjustments in data preprocessing, model training algorithms, and evaluation metrics to test methods for improvement.
## Prompt Template
Following the Mistroll [chat template](https://huggingface.co/BarraHome/Mistroll-7B-v2.2/blob/main/tokenizer_config.json#L31), the prompt template is ChatML.
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
``` | {"language": ["en", "es"], "license": "mit", "library_name": "llama.cpp", "tags": ["mistral", "unsloth", "gguf"], "model_name": "Mistroll 7B v2.2", "base_model": "BarraHome/Mistroll-7B-v2.2", "pipeline_tag": "text-generation", "model_creator": "BarraHome", "model_type": "mistral", "prompt_template": "<|im_start|>system\n{system_message}<|im_end|>\n<|im_start|>user\n{prompt}<|im_end|>\n<|im_start|>assistant\n", "quantized_by": "mgonzs13"} | mgonzs13/Mistroll-7B-v2.2-GGUF | null | [
"llama.cpp",
"gguf",
"mistral",
"unsloth",
"text-generation",
"en",
"es",
"base_model:BarraHome/Mistroll-7B-v2.2",
"license:mit",
"region:us"
] | null | 2024-04-28T11:59:38+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/bl5luuw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:01:45+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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[More Information Needed]
#### Summary
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | happylayers/sc72 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:02:00+00:00 |
null | null | {} | iow9/me3 | null | [
"region:us"
] | null | 2024-04-28T12:02:41+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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/mwwq1rg | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:02:47+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/tdrussell/Mixtral-8x22B-Capyboros-v1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q2_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q2_K.gguf.part2of2) | Q2_K | 52.2 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ3_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ3_XS.gguf.part2of2) | IQ3_XS | 58.3 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ3_S.gguf.part2of2) | IQ3_S | 61.6 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q3_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q3_K_S.gguf.part2of2) | Q3_K_S | 61.6 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ3_M.gguf.part2of2) | IQ3_M | 64.6 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q3_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q3_K_M.gguf.part2of2) | Q3_K_M | 67.9 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q3_K_L.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q3_K_L.gguf.part2of2) | Q3_K_L | 72.7 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.IQ4_XS.gguf.part2of2) | IQ4_XS | 76.5 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q4_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q4_K_S.gguf.part2of2) | Q4_K_S | 80.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q4_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q4_K_M.gguf.part2of2) | Q4_K_M | 85.7 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q5_K_S.gguf.part2of2) | Q5_K_S | 97.1 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q5_K_M.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q5_K_M.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q5_K_M.gguf.part3of3) | Q5_K_M | 100.1 | |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q6_K.gguf.part3of3) | Q6_K | 115.6 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q8_0.gguf.part1of4) [PART 2](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q8_0.gguf.part2of4) [PART 3](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q8_0.gguf.part3of4) [PART 4](https://huggingface.co/mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF/resolve/main/Mixtral-8x22B-Capyboros-v1.Q8_0.gguf.part4of4) | Q8_0 | 149.5 | fast, best quality |
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", "datasets": ["ssmi153/Capybara-ShareGPT", "jondurbin/airoboros-3.2"], "base_model": "tdrussell/Mixtral-8x22B-Capyboros-v1", "quantized_by": "mradermacher"} | mradermacher/Mixtral-8x22B-Capyboros-v1-GGUF | null | [
"transformers",
"en",
"dataset:ssmi153/Capybara-ShareGPT",
"dataset:jondurbin/airoboros-3.2",
"base_model:tdrussell/Mixtral-8x22B-Capyboros-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:03:20+00:00 |
null | null | {} | Msm1370/M | null | [
"region:us"
] | null | 2024-04-28T12:03:39+00:00 |
|
null | null | {} | krishnakalyan3/emo_cosine_avg_weight | null | [
"pytorch",
"region:us"
] | null | 2024-04-28T12:06:08+00:00 |
|
null | null | {} | karanjakhar/bgremoval | null | [
"region:us"
] | null | 2024-04-28T12:06:08+00:00 |
|
text-generation | transformers | {} | yirenc/llama-7b-on-truthfulQA_first2 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:06:12+00:00 |
|
null | null | {} | WoysfuL/Destiny-Ada-1-Voice | null | [
"region:us"
] | null | 2024-04-28T12:08:05+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 π€ 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": []} | Zardian/Cyber_assist3.4 | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:08:57+00:00 |
null | adapter-transformers | {"language": ["en"], "license": "apache-2.0", "library_name": "adapter-transformers", "Details": "mistralai/Mistral-7B-Instruct-v0.2 model finetuned 1000 epochs on google colab using lora with unlsoth and yahma/alpaca-cleaned dataset"} | lINoRIl/mistral-tune | null | [
"adapter-transformers",
"gguf",
"mistral",
"en",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T12:10:09+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. -->
# GenAI-task2-ModelC-DS
This model is a fine-tuned version of [petals-team/falcon-rw-1b](https://huggingface.co/petals-team/falcon-rw-1b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5324
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7726 | 0.0196 | 20 | 1.5110 |
| 2.32 | 0.0393 | 40 | 1.4943 |
| 2.2059 | 0.0589 | 60 | 1.4992 |
| 1.7591 | 0.0785 | 80 | 1.4951 |
| 1.9699 | 0.0982 | 100 | 1.5324 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- 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": "petals-team/falcon-rw-1b", "model-index": [{"name": "GenAI-task2-ModelC-DS", "results": []}]} | Katochh/GenAI-task2-ModelC-DS | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:petals-team/falcon-rw-1b",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T12:10:17+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. -->
# G0428B2
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1291
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 32
- 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: 60
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.2513 | 0.09 | 10 | 1.9189 |
| 1.9252 | 0.18 | 20 | 1.9019 |
| 1.8761 | 0.27 | 30 | 1.7972 |
| 1.7045 | 0.36 | 40 | 1.5332 |
| 1.348 | 0.45 | 50 | 1.0846 |
| 0.9036 | 0.54 | 60 | 0.4970 |
| 0.3466 | 0.63 | 70 | 0.2054 |
| 0.1888 | 0.73 | 80 | 0.1562 |
| 0.1458 | 0.82 | 90 | 0.1490 |
| 0.1531 | 0.91 | 100 | 0.1478 |
| 0.1561 | 1.0 | 110 | 0.1477 |
| 0.142 | 1.09 | 120 | 0.1474 |
| 0.1687 | 1.18 | 130 | 0.1463 |
| 0.1426 | 1.27 | 140 | 0.1451 |
| 0.1577 | 1.36 | 150 | 0.1434 |
| 0.1386 | 1.45 | 160 | 0.1419 |
| 0.136 | 1.54 | 170 | 0.1397 |
| 0.135 | 1.63 | 180 | 0.1385 |
| 0.1489 | 1.72 | 190 | 0.1377 |
| 0.146 | 1.81 | 200 | 0.1349 |
| 0.1367 | 1.9 | 210 | 0.1340 |
| 0.1347 | 1.99 | 220 | 0.1338 |
| 0.1317 | 2.08 | 230 | 0.1318 |
| 0.1554 | 2.18 | 240 | 0.1309 |
| 0.1285 | 2.27 | 250 | 0.1308 |
| 0.1328 | 2.36 | 260 | 0.1310 |
| 0.1354 | 2.45 | 270 | 0.1305 |
| 0.1324 | 2.54 | 280 | 0.1301 |
| 0.1362 | 2.63 | 290 | 0.1297 |
| 0.1257 | 2.72 | 300 | 0.1293 |
| 0.1274 | 2.81 | 310 | 0.1291 |
| 0.1472 | 2.9 | 320 | 0.1291 |
| 0.1405 | 2.99 | 330 | 0.1291 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "gemma", "tags": ["generated_from_trainer"], "base_model": "google/gemma-2b", "model-index": [{"name": "G0428B2", "results": []}]} | Litzy619/G0428B2 | null | [
"safetensors",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"region:us"
] | null | 2024-04-28T12:11:08+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# Ketki0203/punctuation-predict
This model is a fine-tuned version of [bert-base-multilingual-cased](https://huggingface.co/bert-base-multilingual-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Train Loss: nan
- Validation Loss: nan
- 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': 'AdamWeightDecay', 'learning_rate': {'module': 'keras.optimizers.schedules', 'class_name': 'PolynomialDecay', 'config': {'initial_learning_rate': 2e-05, 'decay_steps': 300, 'end_learning_rate': 0.0, 'power': 1.0, 'cycle': False, 'name': None}, 'registered_name': None}, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-08, 'amsgrad': False, 'weight_decay_rate': 0.01}
- training_precision: float32
### Training results
| Train Loss | Validation Loss | Epoch |
|:----------:|:---------------:|:-----:|
| nan | nan | 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": "bert-base-multilingual-cased", "model-index": [{"name": "Ketki0203/punctuation-predict", "results": []}]} | Ketki0203/punctuation-predict | null | [
"transformers",
"tf",
"bert",
"token-classification",
"generated_from_keras_callback",
"base_model:bert-base-multilingual-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:11:33+00:00 |
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-BINDING_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:11:50+00:00 |
|
token-classification | transformers | {} | tandrievich/google-bert-finetuned | null | [
"transformers",
"safetensors",
"bert",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:12:06+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 π€ 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. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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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 Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | DocDuck/FRED-T5-large-1e-4-3ep | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:13:45+00:00 |
null | transformers | {} | pranav79/agv-med | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:14:38+00:00 |
|
null | null | # Prodigy SM Base v0.1
<img src="https://cdn-uploads.huggingface.co/production/uploads/617bbeec14572ebe9e6ea83f/4p2zaOWu6kTS3fcbevHef.png" width="70%" height="70%">
In our latest endeavour, we performed continued pre-training of a large language model (Mistral-7b-v0.1) to understand and generate text in new languages, including **Serbian**, **Bosnian** and **Croatian** using an innovative approach.
Rather than depending only on extensive datasets in the target language, our method utilizes a more compact set of both synthetic and human-curated data along with some mixture of CC Web data, which is implemented in two strategic phases:
1. Establishing a comprehensive demonstration of all grammatical and orthographic rules pertinent to the language.
2. Supplying a diverse array of examples that not only reinforce these rules but also integrate a wide range of linguistic nuances.
While our approach is uniquely tailored to our objectives, we have drawn some inspiration from recent advancements in language model training. Specifically, the conceptual strategies discussed in the paper [ADAPTING LARGE LANGUAGE MODELS VIA READING COMPREHENSION](https://arxiv.org/pdf/2309.09530.pdf) provided valuable insights, though our methods diverge significantly in practice. By adopting this inspired approach, we aim to efficiently teach the model new languages with a balanced blend of accuracy and linguistic diversity.
So... Did it work?!
# **Yes!**
See the benchmark results, or even better, download the model and try it yourself. As you know by now, there's no better benchmark than a quick 'try it yourself' vibe check. :)
<img src="https://cdn-uploads.huggingface.co/production/uploads/617bbeec14572ebe9e6ea83f/C9m_OjnYEpQo43VCrwz4A.png" width="100%" height="100%">
Here, we demonstrate results of benchmark that is not frequently performed, yet equally important: how adapting the model for a new language impacted its original English-only performance.
<img src="https://cdn-uploads.huggingface.co/production/uploads/617bbeec14572ebe9e6ea83f/IPY0myfQI-Ne5x6b11glz.png" width="100%" height="100%">
*All evals are performed in zero shot manner.
*Also bear in mind that llama-2-7b, llama-3-8b and mistral-7b models compared to Prodigy SM base aren't trained on extensive Serbian language datasets, and these benchmarks demonstrate that primarily English models can be adapted to other languages.
So, as you can see, we successfully improved the original model's performance for Serbian language use cases while retaining or even slightly improving its performance for English language.
### Training results
Training results of continued pre-training of [mistral-7b-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
<img src="https://cdn-uploads.huggingface.co/production/uploads/617bbeec14572ebe9e6ea83f/5xeJ-vfWk4RhJNC7t5I0g.png" width="70%" height="70%">
<img src="https://cdn-uploads.huggingface.co/production/uploads/617bbeec14572ebe9e6ea83f/R4R8ai8LaN3WlYCOenUyb.png" width="70%" height="70%">
As last experimental step we merged produced model with **Mistral-7B-v0.1** and two earlier checkpoints from **prodigy-sm-base** using [Model Stock](https://arxiv.org/abs/2403.19522) method.
# Notes
As this is base model, there is no chat template or strict chat following capabilities, this model is best candidate for further pre-train on Serbian language as there is a lot more room for improvement (you can hit sweet spot), or next step in the pipeline, such as some form of chat or instruct tuning.
If you want model that is already instruction tuned we did that too, check **Prodigy SM Instruct v0.1**
# Prodigy SM Instruct v0.1
π[prodigy-sm-instruct]() **COMING SOON**
And stay tuned for:
[prodigy-sm-base (llama-3)]() **COMING SOON**
[prodigy-sm-instruct (llama-3)]() **COMING SOON**
π’ Also we are excited to announce that [iskon.ai](https://Iskon.ai) will soon launch an API platform featuring advanced **Prodigy** series of models, advanced AI tools and much more! π
# Thanks
- [gordicaleksa/serbian-llm-eval](https://github.com/gordicaleksa/serbian-llm-eval) and his community for curating translations and adaptation of [lm-evaluation-harness](https://github.com/EleutherAI/lm-evaluation-harness)
that we used to perform benchmarks.
- [jondurbin](https://huggingface.co/jondurbin) for amazing airoboros framework
- [teknium](https://huggingface.co/teknium) for various insights shared on discord and twitter aka x.com
- [Eric](https://twitter.com/erhartford) for various insights shared on discord and twitter aka x.com
- [mergekit](https://github.com/arcee-ai/mergekit) for model merging tools
*Huge thanks to Redmond.ai for generous DGX cloud credits* [redmond.ai]( https://redmond.ai)
| {"language": ["en", "sr", "hr", "bs"], "license": "apache-2.0"} | draganjovanovich/prodigy-sm-base-v0.1-GGUF | null | [
"gguf",
"en",
"sr",
"hr",
"bs",
"arxiv:2309.09530",
"arxiv:2403.19522",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T12:15:28+00:00 |
null | null | {} | synhya/first-model | null | [
"region:us"
] | null | 2024-04-28T12:16:10+00:00 |
|
null | null | {} | aden-yusuf/computer_graphics | null | [
"region:us"
] | null | 2024-04-28T12:16:53+00:00 |
|
text-to-image | diffusers |
# pure Evolution V5-inpainting API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "pure-evolution-v5-inpaint"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/pure-evolution-v5-inpaint)
Model link: [View model](https://modelslab.com/models/pure-evolution-v5-inpaint)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "pure-evolution-v5-inpaint",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/pure-evolution-v5-inpaint | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-28T12:18:32+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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Out-of-Scope Use
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | deepnet/BSN630-TunedLlama3 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:19:53+00:00 |
null | null | {} | Mihinko99/model | null | [
"region:us"
] | null | 2024-04-28T12:21:22+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": "265.86 +/- 19.05", "name": "mean_reward", "verified": false}]}]}]} | I1v2i3v4/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-04-28T12:22:59+00:00 |
null | null | {} | Magomed090/AhmedBukhatir | null | [
"region:us"
] | null | 2024-04-28T12:24:12+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 π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
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## How to Get Started with the Model
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adapterhappy2sad-2k-50-0.004 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:24:17+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[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": []} | deadcode99/mistral-7b-lime-only-question-aware-agnostic-1-epoch | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:24:23+00:00 |
null | null | {} | Phi2quant/phi-block | null | [
"region:us"
] | null | 2024-04-28T12:24:44+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. -->
# single-practice-fine-tuning-eeve
This model is a fine-tuned version of [yanolja/EEVE-Korean-Instruct-10.8B-v1.0](https://huggingface.co/yanolja/EEVE-Korean-Instruct-10.8B-v1.0) 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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "yanolja/EEVE-Korean-Instruct-10.8B-v1.0", "model-index": [{"name": "single-practice-fine-tuning-eeve", "results": []}]} | uine/single-practice-fine-tuning-eeve-adapter | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:yanolja/EEVE-Korean-Instruct-10.8B-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T12:25:23+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 π€ 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": []} | AVMLegend/Phi-2-quantization | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:26:33+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. -->
# shawgpt-ft
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0 | 1.0 | 36 | nan |
| 0.0 | 2.0 | 72 | nan |
| 0.0 | 3.0 | 108 | nan |
| 0.0 | 4.0 | 144 | nan |
| 0.0 | 5.0 | 180 | nan |
| 0.0 | 6.0 | 216 | nan |
| 0.0 | 7.0 | 252 | nan |
| 0.0 | 8.0 | 288 | nan |
| 0.0 | 9.0 | 324 | nan |
| 0.0 | 10.0 | 360 | nan |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.1.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "mit", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "shawgpt-ft", "results": []}]} | AVMLegend/shawgpt-ft | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-04-28T12:26:36+00:00 |
null | null | {} | Maaulik/Pixel-Fix | null | [
"region:us"
] | null | 2024-04-28T12:26:42+00:00 |
|
null | null | {"license": "llama2"} | OllmOne/Llama-2-7B-Chat-GGUF | null | [
"gguf",
"license:llama2",
"region:us"
] | null | 2024-04-28T12:26:45+00:00 |
|
null | null | {"license": "llama3"} | OllmOne/Meta-Llama-3-8B-Instruct-GGUF | null | [
"gguf",
"license:llama3",
"region:us"
] | null | 2024-04-28T12:27:11+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. -->
# digital-TA
This model is a fine-tuned version of [openai-community/gpt2](https://huggingface.co/openai-community/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: 5e-05
- train_batch_size: 2
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "openai-community/gpt2", "model-index": [{"name": "digital-TA", "results": []}]} | NTTUNLPTEAM/digital-TA | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:27:51+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. -->
# stablelm-2-1_6b-sft-full-spin-sigmoid-iter0_1_61101_bad_responses_0_1
This model is a fine-tuned version of [nnheui/stablelm-2-1_6b-sft-full](https://huggingface.co/nnheui/stablelm-2-1_6b-sft-full) on the nnheui/stablelm-2-1_6b-sft-full-ultrachat_200k_generated-1_61101 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- 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
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "other", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["nnheui/stablelm-2-1_6b-sft-full-ultrachat_200k_generated-1_61101"], "base_model": "nnheui/stablelm-2-1_6b-sft-full", "model-index": [{"name": "stablelm-2-1_6b-sft-full-spin-sigmoid-iter0_1_61101_bad_responses_0_1", "results": []}]} | nnheui/stablelm-2-1_6b-sft-full-spin-sigmoid-iter0_1_61101_bad_responses_0_1 | null | [
"transformers",
"tensorboard",
"safetensors",
"stablelm",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:nnheui/stablelm-2-1_6b-sft-full-ultrachat_200k_generated-1_61101",
"base_model:nnheui/stablelm-2-1_6b-sft-full",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:27:59+00:00 |
null | null | {"license": "apache-2.0"} | OllmOne/Mistral-7B-Instruct-v0.2-GGUF | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-04-28T12:28:12+00:00 |
|
null | null | {} | Magomed090/MohammedObaid | null | [
"region:us"
] | null | 2024-04-28T12:28:16+00:00 |
|
null | null | {"license": "other", "license_name": "tongyi-qianwen", "license_link": "LICENSE"} | OllmOne/Qwen1.5-4B-Chat-GGUF | null | [
"gguf",
"license:other",
"region:us"
] | null | 2024-04-28T12:29:21+00:00 |
|
null | null | {"license": "other", "license_name": "tongyi-qianwen", "license_link": "LICENSE"} | OllmOne/Qwen1.5-7B-Chat-GGUF | null | [
"gguf",
"license:other",
"region:us"
] | null | 2024-04-28T12:30:15+00:00 |
|
text-generation | transformers | {"license": "apache-2.0"} | uine/single-practice-fine-tuning-eeve-merge | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-28T12:30:41+00:00 |
|
null | null |
Quantized Llama 3 8B to Q40 format supported by [Distributed Llama](https://github.com/b4rtaz/distributed-llama).
## License
Before download this repository please accept [Llama 3 Community License](https://llama.meta.com/llama3/license/).
## How to run
1. Clone this repository.
2. Clone Distributed Llama:
```sh
git clone https://github.com/b4rtaz/distributed-llama.git
```
3. Build Distributed Llama:
```sh
make main
```
4. Run Distributed Llama:
```
./main inference --prompt "Hello world" --steps 128 --weights-float-type q40 --buffer-float-type q80 --nthreads 4 --model path/to/dllama_meta-llama-3-8b_q40.bin --tokenizer path/to/dllama_meta-llama3-tokenizer.t
``` | {"license": "llama3"} | b4rtaz/llama-3-8b-distributed-llama | null | [
"license:llama3",
"region:us"
] | null | 2024-04-28T12:30:48+00:00 |
null | null | {"license": "other", "license_name": "yi-license", "license_link": "LICENSE"} | OllmOne/yi-chat-6B-GGUF | null | [
"gguf",
"license:other",
"region:us"
] | null | 2024-04-28T12:30:57+00:00 |
|
null | transformers | {} | cowWhySo/llama3-cybersecurity-8b-q8_0-gguf | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:32:26+00:00 |
|
null | transformers | {} | cowWhySo/llama3-cybersecurity-8b-q4_k_m-gguf | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:33:56+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 π€ 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": []} | deepnet/BSN677-TunedStableLM | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:34:07+00:00 |
null | null | {} | joacorf33/xlm-roberta-base-finetuned-panx-all | null | [
"region:us"
] | null | 2024-04-28T12:34:35+00:00 |
|
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-DNA_BIND_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:35:31+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 π€ 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. -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | suke0327/whisper-large_even_en | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:35:41+00:00 |
reinforcement-learning | null |
# PPO Agent Playing LunarLander-v2
This is a trained model of a PPO agent playing LunarLander-v2.
# Hyperparameters
```python
{'exp_name': 'ppo'
'seed': 1
'torch_deterministic': True
'cuda': True
'track': False
'wandb_project_name': 'cleanRL'
'wandb_entity': None
'capture_video': False
'env_id': 'LunarLander-v2'
'total_timesteps': 500000
'learning_rate': 0.00025
'num_envs': 6
'num_steps': 128
'anneal_lr': True
'gae': True
'gamma': 0.99
'gae_lambda': 0.97
'num_minibatches': 10
'update_epochs': 10
'norm_adv': True
'clip_coef': 0.25
'clip_vloss': True
'ent_coef': 0.01
'vf_coef': 0.5
'max_grad_norm': 0.5
'target_kl': None
'repo_id': 'HusseinEid/ppo-LunarLander-v2-from-scratch'
'batch_size': 768
'minibatch_size': 76}
```
| {"tags": ["LunarLander-v2", "ppo", "deep-reinforcement-learning", "reinforcement-learning", "custom-implementation", "deep-rl-course"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "206.64 +/- 107.48", "name": "mean_reward", "verified": false}]}]}]} | HusseinEid/ppo-LunarLander-v2-from-scratch | null | [
"tensorboard",
"LunarLander-v2",
"ppo",
"deep-reinforcement-learning",
"reinforcement-learning",
"custom-implementation",
"deep-rl-course",
"model-index",
"region:us"
] | null | 2024-04-28T12:36:07+00:00 |
text-classification | transformers |
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | luisespinosa/trust-merged_dataset_mdeberta-v3_30epoch | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:36:23+00:00 |
null | null | {} | Alefiah/UrduSum12 | null | [
"region:us"
] | null | 2024-04-28T12:36:32+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/28abns4 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:38:08+00:00 |
text-generation | transformers |
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[More Information Needed]
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#### Summary
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[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Hinno/incoder-1B-flutter-finetuned | null | [
"transformers",
"safetensors",
"xglm",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:38:24+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a π€ transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/v8831xs | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-28T12:38:36+00:00 |
null | null | {} | Ahmed-10122/res | null | [
"region:us"
] | null | 2024-04-28T12:38:38+00:00 |
|
token-classification | transformers | {} | AliSaadatV/esm2_t12_35M_UR50D-finetuned-SITE_earlystop_70_15_15 | null | [
"transformers",
"tensorboard",
"safetensors",
"esm",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-28T12:38:41+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bart-samsum
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### 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": "google/flan-t5-base", "model-index": [{"name": "bart-samsum", "results": []}]} | Mu7annad/t5-samsum | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
] | null | 2024-04-28T12:39:11+00:00 |
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