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null | null | {} | 5p33ch3xpr/t5-lora | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-04-27T09:46:38+00:00 |
|
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/qhjajc7 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:46:50+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/78gd6y1 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/wnb6yqs | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/anpj1l6 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/d4j2o6l | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/i2p445c | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/viwznvk | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | pruning/vzl6orn | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:47:08+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** saksornr
- **License:** apache-2.0
- **Finetuned from model :** saksornr/SeaLLM-7B-v2.5-4bit
This gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "gemma", "trl"], "base_model": "saksornr/SeaLLM-7B-v2.5-4bit"} | saksornr/SeaLLM-7B-v2.5-sample-finetune | null | [
"transformers",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:saksornr/SeaLLM-7B-v2.5-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:49:09+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | zandfj/LLaMA2-7B-Chat_-sft-sft-3epo-moren_042716 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:50:37+00:00 |
text-generation | transformers | Repeting [this experiemnt](https://huggingface.co/maxim-saplin/parrot-1_6B) of teaching LLM to follow chat structure and reply with all CAPS. This time with Gemma 2B as the base model. Compared to Stable LM 1.6B this model took 68 minutes (vs 11) and didn't learn the capability for RU language.
| {"language": ["en"], "pipeline_tag": "text-generation"} | maxim-saplin/parrot-gemma-2B | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:51:24+00:00 |
feature-extraction | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | aashish-249/Sarcasm_classification | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:53:14+00:00 |
sentence-similarity | sentence-transformers | # Bulbasaur
This is a distill of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny) trained using [qa-assistant](https://huggingface.co/datasets/Mihaiii/qa-assistant).
## Intended purpose
<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
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('Mihaiii/Bulbasaur')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Mihaiii/Bulbasaur')
model = AutoModel.from_pretrained('Mihaiii/Bulbasaur')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small))
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. | {"license": "mit", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "gte", "mteb"], "datasets": ["Mihaiii/qa-assistant"], "pipeline_tag": "sentence-similarity", "model-index": [{"name": "Bulbasaur", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 71.86567164179104}, {"type": "ap", "value": 34.08685244750869}, {"type": "f1", "value": 65.66014356237362}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": 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"value": 49.18667699457785}, {"type": "dot_recall", "value": 67.01846965699208}, {"type": "euclidean_accuracy", "value": 84.30589497526375}, {"type": "euclidean_ap", "value": 68.07824251821404}, {"type": "euclidean_f1", "value": 65.09073543457498}, {"type": "euclidean_precision", "value": 59.44177932839075}, {"type": "euclidean_recall", "value": 71.92612137203166}, {"type": "manhattan_accuracy", "value": 84.24032902187518}, {"type": "manhattan_ap", "value": 67.76838044141897}, {"type": "manhattan_f1", "value": 64.75698520779525}, {"type": "manhattan_precision", "value": 58.333333333333336}, {"type": "manhattan_recall", "value": 72.77044854881267}, {"type": "max_accuracy", "value": 84.30589497526375}, {"type": "max_ap", "value": 68.07824251821404}, {"type": "max_f1", "value": 65.09073543457498}]}, {"task": {"type": "PairClassification"}, "dataset": {"name": "MTEB TwitterURLCorpus", "type": "mteb/twitterurlcorpus-pairclassification", "config": "default", "split": "test", "revision": "8b6510b0b1fa4e4c4f879467980e9be563ec1cdf"}, "metrics": [{"type": "cos_sim_accuracy", "value": 88.2951061435169}, {"type": "cos_sim_ap", "value": 84.74905878045149}, {"type": "cos_sim_f1", "value": 77.01659871869538}, {"type": "cos_sim_precision", "value": 73.0392156862745}, {"type": "cos_sim_recall", "value": 81.45210963966738}, {"type": "dot_accuracy", "value": 86.37598478674273}, {"type": "dot_ap", "value": 79.17253140971533}, {"type": "dot_f1", "value": 73.19411657889958}, {"type": "dot_precision", "value": 69.27201484842236}, {"type": "dot_recall", "value": 77.58700338774254}, {"type": "euclidean_accuracy", "value": 88.29122521054062}, {"type": "euclidean_ap", "value": 84.64901724668165}, {"type": "euclidean_f1", "value": 76.99685189252507}, {"type": "euclidean_precision", "value": 73.39148639218422}, {"type": "euclidean_recall", "value": 80.97474591931014}, {"type": "manhattan_accuracy", "value": 88.29316567702877}, {"type": "manhattan_ap", "value": 84.5869003947086}, {"type": "manhattan_f1", "value": 76.9094138543517}, {"type": "manhattan_precision", "value": 74.03818751781134}, {"type": "manhattan_recall", "value": 80.01231906375116}, {"type": "max_accuracy", "value": 88.2951061435169}, {"type": "max_ap", "value": 84.74905878045149}, {"type": "max_f1", "value": 77.01659871869538}]}]}]} | Mihaiii/Bulbasaur | null | [
"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"gte",
"mteb",
"dataset:Mihaiii/qa-assistant",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:53:29+00:00 |
null | null | # umiyuki-Japanese-Chat-Umievo-itr001-7b-gguf
[umiyukiさんが公開しているJapanese-Chat-Umievo-itr001-7b](https://huggingface.co/umiyuki/Japanese-Chat-Umievo-itr001-7b)のggufフォーマット変換版です。
imatrixのデータは[TFMC/imatrix-dataset-for-japanese-llm](https://huggingface.co/datasets/TFMC/imatrix-dataset-for-japanese-llm)を使用して作成しました。
## Usage
```
git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
make -j
./main -m 'umiyuki-Japanese-Chat-Umievo-itr001-7b-Q4_0.gguf' -p "[INST] 今晩の夕食のレシピを教えて [/INST] " -n 128
``` | {"language": ["en", "ja"], "license": "apache-2.0", "datasets": ["TFMC/imatrix-dataset-for-japanese-llm"]} | mmnga/umiyuki-Japanese-Chat-Umievo-itr001-7b-gguf | null | [
"gguf",
"en",
"ja",
"dataset:TFMC/imatrix-dataset-for-japanese-llm",
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T09:55:38+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | siddharth-magesh/lora-flan-t5-large-chat | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T09:56:29+00:00 |
null | null | {} | ccie1111/trainingtest | null | [
"region:us"
]
| null | 2024-04-27T09:57:21+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep36 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:58:05+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold5
This model is a fine-tuned version of [microsoft/swinv2-tiny-patch4-window16-256](https://huggingface.co/microsoft/swinv2-tiny-patch4-window16-256) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0138
- Accuracy: 0.6603
## 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: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.5961 | 1.0 | 924 | 1.4478 | 0.5094 |
| 1.4174 | 2.0 | 1848 | 1.3398 | 0.5451 |
| 1.1688 | 3.0 | 2772 | 1.1674 | 0.5993 |
| 0.927 | 4.0 | 3696 | 1.1084 | 0.6281 |
| 1.0425 | 5.0 | 4620 | 1.0311 | 0.6484 |
| 1.1655 | 6.0 | 5544 | 1.0304 | 0.6519 |
| 0.9514 | 7.0 | 6468 | 1.0135 | 0.6528 |
| 0.8508 | 8.0 | 7392 | 1.0348 | 0.6511 |
| 0.9113 | 9.0 | 8316 | 1.0275 | 0.6549 |
| 0.8186 | 10.0 | 9240 | 1.0138 | 0.6603 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-tiny-patch4-window16-256", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold5", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6603415559772297, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-tiny-patch4-window16-256_fold5 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-tiny-patch4-window16-256",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T09:59:56+00:00 |
text-generation | transformers | # Llama-3-8B-UltraMedical
> Experience it in our 🤗 [Huggingface Space Demo](https://huggingface.co/spaces/TsinghuaC3I/UltraMedical-LM)!
<!-- Provide a quick summary of what the model is/does. -->
Llama-3-8B-UltraMedical is an open-access large language model (LLM) specialized in biomedicine. Developed by the [Tsinghua C3I Lab](https://github.com/TsinghuaC3I), this model aims to enhance medical examination access, literature comprehension, and clinical knowledge.
Building on the foundation of Meta's Llama-3-8B, Llama-3-8B-UltraMedical is trained on our [UltraMedical](https://github.com/TsinghuaC3I/UltraMedical) dataset, which includes 410,000 diverse entries comprising both synthetic and manually curated samples.
Llama-3-8B-UltraMedical has achieved top average scores across several popular medical benchmarks, including MedQA, MedMCQA, PubMedQA, and MMLU-Medical.
In these benchmarks, Llama-3-8B-UltraMedical significantly outperforms Flan-PaLM, OpenBioLM-8B, Gemini-1.0, GPT-3.5, and Meditron-70b.
We extend our gratitude to Meta for the Llama model, which provided an excellent foundation for our fine-tuning efforts.
## Usage
### Input Examples
This model utilizes the Llama-3 default chat template without a system prompt.
Below, we provide input examples for multi-choice QA, PubMedQA, and open-ended questions.
> Note: To reproduce our evaluation results for the medical QA benchmark, we recommend using the following format to organize questions and multiple-choice options.
- Input example for MedQA and MedMCQA:
```
A 42-year-old homeless man is brought to the emergency room after he was found unconscious in a park. He has alcohol on his breath and is known to have a history of chronic alcoholism. A noncontrast CT scan of the head is normal. The patient is treated for acute alcohol intoxication and admitted to the hospital. The next day, the patient demands to be released. His vital signs are a pulse 120/min, a respiratory rate 22/min, and blood pressure 136/88 mm Hg. On physical examination, the patient is confused, agitated, and sweating profusely, particularly from his palms. Generalized pallor is present. What is the mechanism of action of the drug recommended to treat this patient_s most likely condition?
A. It increases the duration of GABA-gated chloride channel opening.
B. It increases the frequency of GABA-gated chloride channel opening.
C. It decreases the frequency of GABA-gated chloride channel opening.
D. It decreases the duration of GABA-gated chloride channel opening.
```
- Input example for PubMedQA: We organize the context and questions in a multi-choice format, similar to [MedPrompt](https://github.com/microsoft/promptbase).
```
Context: Pediatric glioblastoma is a malignant disease with an extremely poor clinical outcome. Patients usually suffer from resistance to radiation therapy, so targeted drug treatment may be a new possibility for glioblastoma therapy. Survivin is also overexpressed in glioblastoma. YM155, a novel small-molecule survivin inhibitor, has not been examined for its use in glioblastoma therapy.
Context: The human glioblastoma cell line M059K, which expresses normal DNA-dependent protein kinase (DNA-PK) activity and is radiation-resistant, and M059J, which is deficient in DNA-PK activity and radiation-sensitive, were used in the study. Cell viability, DNA fragmentation, and the expression of survivin and securin following YM155 treatment were examined using MTT (methylthiazolyldiphenyl-tetrazolium) assay, ELISA assay, and Western blot analysis, respectively.
Context: YM155 caused a concentration-dependent cytotoxic effect, inhibiting the cell viability of both M059K and M059J cells by 70% after 48 hours of treatment with 50 nM YM155. The half-maximal inhibitory concentration (IC50) was around 30-35 nM for both cell lines. Apoptosis was determined to have occurred in both cell lines because immunoreactive signals from the DNA fragments in the cytoplasm were increased 24 hours after treatment with 30 nM YM155. The expression of survivin and securin in the M059K cells was greater than that measured in the M059J cells. Treatment with 30 nM YM155, for both 24 and 48 hours, significantly suppressed the expression of survivin and securin in both cell lines.
Does novel survivin inhibitor YM155 elicit cytotoxicity in glioblastoma cell lines with normal or deficiency DNA-dependent protein kinase activity?
A. maybe
B. yes
C. no
```
- Input example for open-ended questions:
```
hi doctor,i am chaitanya.age 28,from hyderabad.my problem is ....i got thyroid in my frist preganacy .my delivary date was on july 24th 2009 but on july 6th early morning around 7 oclock suddenly heany bleeding started and i rushed to the hospital but they could not save the baby(boy)...i lost my frist baby.then after 6 month i concevied again but doctors said that baby is having some heart problem and the sevarity of the problem can be known after the baby birth and i should go for a planned delivery.doctors did a c section on cotober 21 2010.doctors said that babys problem is not that serious but it is a heart problem so we need wait and see for 7 days.on 5th day the baby is dead.i want to know is their any problem in me that it is happing like this...do i need o go for any test before planning for next baby.i had 2 c section till now.what are the chances for me for the next baby.how long do i need to wait and plan for next preganacy.
```
```
Investigate the mechanistic implications of statins, primarily used for lipid modulation, on the immunomodulatory pathways, with an emphasis on delineating their therapeutic impact in the context of managing clinical outcomes for individuals afflicted with cardiovascular diseases, including a requirement to discuss the implications for atherosclerotic disease progression.
```
### Inference with vLLM
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
llm = LLM(model="TsinghuaC3I/Llama-3-8B-UltraMedical", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("TsinghuaC3I/Llama-3-8B-UltraMedical")
sampling_params = SamplingParams(temperature=0.7, top_p=0.9, max_tokens=1024, stop=["<|eot_id|>"])
messages = [
{"role": "user", "content": """The question format used in the above input examples。"""},
]
prompts = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
print(prompts[0])
"""
<|begin_of_text|><|start_header_id|>user<|end_header_id|>
{question}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
outputs = llm.generate(prompts=prompts, sampling_params=sampling_params)
print(outputs[0].outputs[0].text)
```
Note: This version of the model supports only single-turn dialog and has limited capabilities in multi-turn dialogue. We plan to enhance this in the next update.
## Evaluation Results
Llama-3-8B-UltraMedical achieved the best average results among 7B-level models on popular medical benchmarks, including MedQA, MedMCQA, PubMedQA, and MMLU-Medical. We would like to acknowledge Meta's remarkable Llama model, which served as an excellent base for our fine-tuning process.
| Released Date | Model | Average | MedQA | MedMCQA | PubMedQA | MMLU.ck | MMLU.mg | MMLU.an | MMLU.pm | MMLU.cb | MMLU.cm |
|:-------------:|:--------------------------------------:|:-------:|:-----:|:-------:|:--------:|:-------:|:-------:|:-------:|:-------:|:-------:|:-------:|
| 2024.04 | **Llama-3-8B-UltraMedical (Ensemble)** | 77.77 | 77.5 | 63.8 | 78.2 | 77.4 | 88.0 | 74.8 | 84.6 | 79.9 | 75.7 |
| 2024.04 | **Llama-3-8B-UltraMedical (Greedy)** | 75.20 | 73.3 | 61.5 | 77.0 | 78.9 | 78.0 | 74.1 | 83.8 | 78.5 | 71.7 |
| 2024.04 | OpenBioLM-8B | 72.48 | 59.0 | 56.9 | 74.1 | 76.1 | 86.1 | 69.8 | 78.2 | 84.2 | 68.0 |
| 2024.04 | Llama-3-8B-Instruct (Ensemble) | 71.23 | 62.4 | 56.5 | 75.8 | 72.5 | 84.0 | 71.1 | 70.6 | 80.6 | 67.6 |
| 2024.04 | Llama-3-8B-Instruct (Greedy) | 68.56 | 60.9 | 50.7 | 73.0 | 72.1 | 76.0 | 63.0 | 77.2 | 79.9 | 64.2 |
| 2024.04 | Internist-7B | 67.79 | 60.5 | 55.8 | 79.4 | 70.6 | 71.0 | 65.9 | 76.1 | - | 63.0 |
| 2024.02 | Gemma-7B | 64.18 | 47.2 | 49.0 | 76.2 | 69.8 | 70.0 | 59.3 | 66.2 | 79.9 | 60.1 |
| 2024.03 | Meerkat-7B (Ensemble) | 63.94 | 74.3 | 60.7 | - | 61.9 | 70.4 | 61.5 | 69.5 | 55.4 | 57.8 |
| 2023.03 | MedAlpaca | 58.03 | 41.7 | 37.5 | 72.8 | 57.4 | 69.0 | 57.0 | 67.3 | 65.3 | 54.3 |
| 2024.02 | BioMistral-7B | 57.26 | 46.6 | 45.7 | 68.1 | 63.1 | 63.3 | 49.9 | 57.4 | 63.4 | 57.8 |
In the table above:
- For MedQA, we use the 4 options from the US set. For MedMCQA, we use the Dev split. For PubMedQA, we use the reasoning required set.
- For MMLU, we include Clinical Knowledge (CK), Medical Genetics (MG), Anatomy (An), Professional Medicine (PM), College Biology (CB), and College Medicine (CM) to maintain consistency with previous studies.
- Greedy search is employed as our default decoding strategy. We denote ensemble scores with self-consistency as `(Ensemble)`. In our experiments, we conduct 10 decoding trials, and final decisions are made via majority vote (temperature=0.7, top_p=0.9).
- Partial results for 7B pre-trained models are sourced from the [Open Medical-LLM Leaderboard](https://huggingface.co/spaces/openlifescienceai/open_medical_llm_leaderboard).
## Training Details
<!-- Provide a longer summary of what this model is. -->
This model is trained using the full parameters and the Fully Sharded Data Parallel (FSDP) framework.
The training process was performed on 8 x A6000 GPUs for about 50 hours.
Hyperparameters:
- torch type: bfloat16
- epochs: 3
- learning rate: 2e-5
- learning rate scheduler type: cosine
- warmup ratio: 0.04
- max length: 1024
- global batch size: 128
- **License:** [Meta Llama-3 License](https://llama.meta.com/llama3/license/).
- **Finetuned from model:** [Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
- **Finetuned on data:** [UltraMedical](https://github.com/TsinghuaC3I/UltraMedical)
## Limitations & Safe Use
While our model offers promising capabilities, it is crucial to exercise caution when using it in real-world clinical settings due to potential hallucination issues. Hallucinations, where the model generates incorrect or misleading information, can pose significant risks in clinical decision-making. Users are advised to validate the model's outputs with trusted medical sources and expert consultation to ensure safety and accuracy.
## Citation
```latex
@misc{UltraMedical,
author = {Zhang, Kaiyan and Ding, Ning and Qi, Biqing and Zeng, Sihang and Li, Haoxin and Zhu, Xuekai and Chen, Zhang-Ren and Zhou, Bowen},
title = {UltraMedical: Building Specialized Generalists in Biomedicine.},
year = {2024},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/TsinghuaC3I/UltraMedical}},
}
``` | {"license": "llama3", "datasets": ["TsinghuaC3I/UltraMedical"]} | TsinghuaC3I/Llama-3-8B-UltraMedical | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:TsinghuaC3I/UltraMedical",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:00:27+00:00 |
text-generation | transformers | {} | sakethp2003/mathllm | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:01:27+00:00 |
|
null | null | {"license": "mit"} | xzzlink/comgpt | null | [
"license:mit",
"region:us"
]
| null | 2024-04-27T10:01:59+00:00 |
|
null | null | {} | JupiterJones04/mistral-finetuned-alpaca | null | [
"region:us"
]
| null | 2024-04-27T10:06:57+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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": []} | minhquy1624/fintune-bi-encoder-sts-regressor | null | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:07:03+00:00 |
null | null | {} | ThuyNT/CS505_COQE_viT5_total_Instruction0_SAPOL_v1 | null | [
"region:us"
]
| null | 2024-04-27T10:07:14+00:00 |
|
null | null | {} | pk3388/vit-base-watches-demo-v5 | null | [
"region:us"
]
| null | 2024-04-27T10:08:27+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 PPO
from huggingface_sb3 import load_from_hub
repo_id = "theegnas/ppo-LunarLander-v2" # The repo_id
filename = "ppo-LunarLander-v2.zip" # The model filename.zip
custom_objects = {
"learning_rate": 0.0,
"lr_schedule": lambda _: 0.0,
"clip_range": lambda _: 0.0,
}
checkpoint = load_from_hub(repo_id, filename)
model = PPO.load(checkpoint, custom_objects=custom_objects, print_system_info=True)
```
| {"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": "251.97 +/- 23.47", "name": "mean_reward", "verified": false}]}]}]} | theegnas/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| null | 2024-04-27T10:09:37+00:00 |
text-generation | transformers | # Uploaded model
- **Developed by:** suriya7
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b
This Gemma model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
### Requirements
```bash
pip install torch
pip install transformers
```
### Inference In Notebook
```python
import torch
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
from transformers import TextStreamer
tokenizer = AutoTokenizer.from_pretrained("suriya7/Gemma-2b-SFT")
model = AutoModelForCausalLM.from_pretrained("suriya7/Gemma-2b-SFT")
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
inputs = tokenizer(
[
alpaca_prompt.format(
"You are an AI assistant. Please ensure that the answers conclude with an end-of-sequence (EOS) token.", # instruction
"how to cook pizza?", # input goes here
"", # output - leave this blank for generation!
)
], return_tensors = "pt").to(device)
text_streamer = TextStreamer(tokenizer)
_ = model.generate(**inputs, streamer = text_streamer, max_new_tokens = 250,do_sample=True,temperature=0.7,top_k=2,repetition_penalty=1.5, # Penalize repeated responses
eos_token_id=model.config.eos_token_id)
```
### Recommended Prompt Template
```bash
alpaca_prompt = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
### Instruction:
{}
### Input:
{}
### Response:
{}"""
``` | {"license": "mit", "datasets": ["databricks/databricks-dolly-15"]} | suriya7/Gemma-2b-SFT | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"dataset:databricks/databricks-dolly-15",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us",
"has_space"
]
| null | 2024-04-27T10:09:48+00:00 |
sentence-similarity | sentence-transformers | # Ivysaur
This is a fine-tune of [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny) using [qa-assistant](https://huggingface.co/datasets/Mihaiii/qa-assistant).
## Intended purpose
<span style="color:blue">This model is designed for use in semantic-autocomplete ([click here for demo](https://mihaiii.github.io/semantic-autocomplete/)).</span>
## Usage (Sentence-Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
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('Mihaiii/Ivysaur')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers) (same as [gte-tiny](https://huggingface.co/TaylorAI/gte-tiny))
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('Mihaiii/Ivysaur')
model = AutoModel.from_pretrained('Mihaiii/Ivysaur')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
### Limitation (same as [gte-small](https://huggingface.co/thenlper/gte-small))
This model exclusively caters to English texts, and any lengthy texts will be truncated to a maximum of 512 tokens. | {"license": "mit", "library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "gte", "mteb"], "datasets": ["Mihaiii/qa-assistant"], "base_model": "TaylorAI/gte-tiny", "pipeline_tag": "sentence-similarity", "model-index": [{"name": "Ivysaur", "results": [{"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonCounterfactualClassification (en)", "type": "mteb/amazon_counterfactual", "config": "en", "split": "test", "revision": "e8379541af4e31359cca9fbcf4b00f2671dba205"}, "metrics": [{"type": "accuracy", "value": 72.1044776119403}, {"type": "ap", "value": 35.09105788324913}, {"type": "f1", "value": 66.26967715703572}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonPolarityClassification", "type": "mteb/amazon_polarity", "config": "default", "split": "test", "revision": "e2d317d38cd51312af73b3d32a06d1a08b442046"}, "metrics": [{"type": "accuracy", "value": 86.686075}, {"type": "ap", "value": 81.92716581685914}, {"type": "f1", "value": 86.65902299160209}]}, {"task": {"type": "Classification"}, "dataset": {"name": "MTEB AmazonReviewsClassification (en)", "type": "mteb/amazon_reviews_multi", "config": "en", "split": "test", "revision": "1399c76144fd37290681b995c656ef9b2e06e26d"}, "metrics": [{"type": "accuracy", "value": 42.698}, {"type": "f1", "value": 42.287785312461885}]}, {"task": {"type": "Retrieval"}, "dataset": {"name": "MTEB ArguAna", "type": "mteb/arguana", "config": "default", "split": "test", "revision": "c22ab2a51041ffd869aaddef7af8d8215647e41a"}, "metrics": [{"type": "map_at_1", "value": 30.441000000000003}, {"type": "map_at_10", "value": 46.951}, {"type": "map_at_100", "value": 47.788000000000004}, {"type": "map_at_1000", "value": 47.794}, {"type": "map_at_20", "value": 47.621}, {"type": "map_at_3", "value": 42.295}, {"type": "map_at_5", "value": 45.126}, {"type": "mrr_at_1", "value": 31.65}, {"type": "mrr_at_10", "value": 47.394999999999996}, {"type": "mrr_at_100", "value": 48.238}, {"type": "mrr_at_1000", "value": 48.245}, {"type": "mrr_at_20", "value": 48.069}, {"type": "mrr_at_3", "value": 42.852000000000004}, {"type": "mrr_at_5", "value": 45.58}, {"type": "ndcg_at_1", "value": 30.441000000000003}, {"type": "ndcg_at_10", "value": 55.783}, {"type": "ndcg_at_100", "value": 59.227}, {"type": "ndcg_at_1000", "value": 59.376}, {"type": "ndcg_at_20", "value": 58.18}, {"type": "ndcg_at_3", "value": 46.291}, {"type": "ndcg_at_5", "value": 51.405}, {"type": "precision_at_1", "value": 30.441000000000003}, {"type": "precision_at_10", "value": 8.378}, {"type": "precision_at_100", "value": 0.985}, {"type": "precision_at_1000", "value": 0.1}, {"type": "precision_at_20", "value": 4.659}, {"type": "precision_at_3", "value": 19.298000000000002}, {"type": "precision_at_5", "value": 14.068}, {"type": "recall_at_1", "value": 30.441000000000003}, {"type": "recall_at_10", "value": 83.784}, {"type": "recall_at_100", "value": 98.506}, {"type": "recall_at_1000", "value": 99.644}, {"type": "recall_at_20", "value": 93.172}, {"type": "recall_at_3", "value": 57.894999999999996}, {"type": "recall_at_5", "value": 70.341}]}, {"task": {"type": "Clustering"}, "dataset": {"name": "MTEB ArxivClusteringP2P", "type": "mteb/arxiv-clustering-p2p", "config": "default", "split": "test", "revision": "a122ad7f3f0291bf49cc6f4d32aa80929df69d5d"}, "metrics": [{"type": "v_measure", "value": 46.39249132731755}, {"type": "v_measures", "value": [0.462627943488718, 0.4670198046702645, 0.4799590043041496, 0.4769331119808875, 0.4676232129237324, 0.4776548131275231, 0.4670074065859379, 0.4796639656537766, 0.4618481699630812, 0.4663292111226376, 0.5293353429909269, 0.5398570175481274, 0.5399074383870329, 0.5363158656403061, 0.5377616813701683, 0.5375897664056992, 0.5391811647339062, 0.5408906197352437, 0.5330346186210795, 0.5333610235325786, 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"sentence-transformers",
"onnx",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"gte",
"mteb",
"dataset:Mihaiii/qa-assistant",
"base_model:TaylorAI/gte-tiny",
"license:mit",
"model-index",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:10:39+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. -->
# 0.001_5iters_bs256_nodpo_only4w_iter_2
This model is a fine-tuned version of [ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_1](https://huggingface.co/ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_1) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_1", "model-index": [{"name": "0.001_5iters_bs256_nodpo_only4w_iter_2", "results": []}]} | ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.001_5iters_bs256_nodpo_only4w_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:15:34+00:00 |
null | null | {} | saksornr/SeaLLM-7B-v2.5-gguf | null | [
"gguf",
"region:us"
]
| null | 2024-04-27T10:18:01+00:00 |
|
text-generation | transformers | # Description
4-bit AWQ-quantized version of [stylellm/SanGuoYanYi-6b](https://huggingface.co/stylellm/SanGuoYanYi-6b)
| {"license": "other", "license_name": "yi-license", "license_link": "https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE"} | stylellm/SanGuoYanYi-6b-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
]
| null | 2024-04-27T10:18:37+00:00 |
null | null | {} | Stereobill/jk | null | [
"region:us"
]
| null | 2024-04-27T10:20:20+00:00 |
|
null | null | {} | PaulM2000/merged_peft_model_18-22_Llama-2-7b-hf | null | [
"region:us"
]
| null | 2024-04-27T10:20:39+00:00 |
|
null | null | {} | ashishabraham22/t5-lora | null | [
"tensorboard",
"safetensors",
"region:us"
]
| null | 2024-04-27T10:23:15+00:00 |
|
null | null | {"license": "apache-2.0"} | Akosaynh/Demoninrise | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T10:23:24+00:00 |
|
text-generation | transformers | {} | fenguhao/hh-rlhf-ppo | null | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:24:13+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **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": []} | aashish-249/Sentiment_classification | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:24:36+00:00 |
null | null | {} | 3DBreeze/lora1 | null | [
"region:us"
]
| null | 2024-04-27T10:27:46+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. -->
# sft_small_model
This model is a fine-tuned version of [deepseek-ai/deepseek-coder-1.3b-base](https://huggingface.co/deepseek-ai/deepseek-coder-1.3b-base) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.41e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "deepseek-ai/deepseek-coder-1.3b-base", "model-index": [{"name": "sft_small_model", "results": []}]} | stojchet/sft_small_model | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:deepseek-ai/deepseek-coder-1.3b-base",
"license:other",
"region:us"
]
| null | 2024-04-27T10:29:14+00:00 |
text-generation | transformers | {} | OsakanaTeishoku/dummy-3.6b | null | [
"transformers",
"safetensors",
"deepseek",
"text-generation",
"custom_code",
"autotrain_compatible",
"region:us"
]
| null | 2024-04-27T10:30:03+00:00 |
|
object-detection | 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. -->
# detr
This model is a fine-tuned version of [facebook/detr-resnet-50](https://huggingface.co/facebook/detr-resnet-50) 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.39.3
- Pytorch 2.3.0+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "facebook/detr-resnet-50", "model-index": [{"name": "detr", "results": []}]} | tooltest/detr | null | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:31:35+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2090
- Accuracy: 0.926
- F1: 0.9259
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8274 | 1.0 | 250 | 0.3057 | 0.904 | 0.9032 |
| 0.2419 | 2.0 | 500 | 0.2090 | 0.926 | 0.9259 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.2+cu118
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.926, "name": "Accuracy"}, {"type": "f1", "value": 0.925854377188877, "name": "F1"}]}]}]} | orenot/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:32:29+00:00 |
question-answering | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bertQA
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0003
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0011 | 1.0 | 250 | 0.0004 |
| 0.0009 | 2.0 | 500 | 0.0003 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "bert-base-uncased", "model-index": [{"name": "bertQA", "results": []}]} | krushnakant27/bertQA | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:bert-base-uncased",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:32:45+00:00 |
reinforcement-learning | ml-agents |
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: tarpalsus/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SoccerTwos", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SoccerTwos"]} | tarpalsus/poca-SoccerTwos | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
]
| null | 2024-04-27T10:34:50+00:00 |
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3752
- Accuracy: 0.5238
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| No log | 0.8889 | 4 | 1.7408 | 0.4444 |
| No log | 2.0 | 9 | 1.4351 | 0.4921 |
| 1.7494 | 2.6667 | 12 | 1.3752 | 0.5238 |
### 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"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swin-tiny-patch4-window7-224", "model-index": [{"name": "swin-tiny-patch4-window7-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.5238095238095238, "name": "Accuracy"}]}]}]} | sharmajai901/swin-tiny-patch4-window7-224-finetuned-eurosat | null | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:35:06+00:00 |
text-generation | transformers |
# BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
Reference: R. Luu and M.J. Buehler, "BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials," Adv. Science, 2023, DOI: https://doi.org/10.1002/advs.202306724
Abstract: The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.
# Model Card for Model ID
Fine-tuned LLM with domain knowledge in biological materials, mechanics of materials, modeling and simulation, and related fields.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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| {"language": ["en"], "library_name": "transformers", "tags": ["biology", "materials science", "code", "scientific AI", "biological materials", "bioinspiration", "machine learning", "generative"]} | lamm-mit/Bioinspired-Phi-3-mini-128k | null | [
"transformers",
"safetensors",
"gguf",
"phi3",
"text-generation",
"biology",
"materials science",
"code",
"scientific AI",
"biological materials",
"bioinspiration",
"machine learning",
"generative",
"conversational",
"custom_code",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:35:30+00:00 |
text-generation | transformers |
# BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials
Reference: R. Luu and M.J. Buehler, "BioinspiredLLM: Conversational Large Language Model for the Mechanics of Biological and Bio-Inspired Materials," Adv. Science, 2023, DOI: https://doi.org/10.1002/advs.202306724
Abstract: The study of biological materials and bio-inspired materials science is well established; however, surprisingly little knowledge is systematically translated to engineering solutions. To accelerate discovery and guide insights, an open-source autoregressive transformer large language model (LLM), BioinspiredLLM, is reported. The model is finetuned with a corpus of over a thousand peer-reviewed articles in the field of structural biological and bio-inspired materials and can be prompted to recall information, assist with research tasks, and function as an engine for creativity. The model has proven that it is able to accurately recall information about biological materials and is further strengthened with enhanced reasoning ability, as well as with Retrieval-Augmented Generation (RAG) to incorporate new data during generation that can also help to traceback sources, update the knowledge base, and connect knowledge domains. BioinspiredLLM also has shown to develop sound hypotheses regarding biological materials design and remarkably so for materials that have never been explicitly studied before. Lastly, the model shows impressive promise in collaborating with other generative artificial intelligence models in a workflow that can reshape the traditional materials design process. This collaborative generative artificial intelligence method can stimulate and enhance bio-inspired materials design workflows. Biological materials are at a critical intersection of multiple scientific fields and models like BioinspiredLLM help to connect knowledge domains.
# Model Card for Model ID
Fine-tuned LLM with domain knowledge in biological materials, mechanics of materials, modeling and simulation, and related fields.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### 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. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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| {"language": ["en"], "library_name": "transformers", "tags": ["biology", "materials science", "code", "scientific AI", "biological materials", "bioinspiration", "machine learning", "generative"]} | lamm-mit/Bioinspired-Phi-3-mini-4k | null | [
"transformers",
"safetensors",
"gguf",
"phi3",
"text-generation",
"biology",
"materials science",
"code",
"scientific AI",
"biological materials",
"bioinspiration",
"machine learning",
"generative",
"conversational",
"custom_code",
"en",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:37:24+00:00 |
question-answering | transformers | {} | tringuyen-uit/Evidence_Retrieval_model_vi_mrc_large | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"question-answering",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:41:16+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. -->
# 0.001_4iters_bs256_nodpo_only4w_zephyr_iter_4
This model is a fine-tuned version of [ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_zephyr_iter_3](https://huggingface.co/ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_zephyr_iter_3) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.40.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["alignment-handbook", "trl", "dpo", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_zephyr_iter_3", "model-index": [{"name": "0.001_4iters_bs256_nodpo_only4w_zephyr_iter_4", "results": []}]} | ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_zephyr_iter_4 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"trl",
"dpo",
"generated_from_trainer",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZhang/0.001_4iters_bs256_nodpo_only4w_zephyr_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:44:12+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. -->
# sft-model
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1932
## 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: 3407
- 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: 10
- training_steps: 596
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.1549 | 0.0757 | 120 | 3.1938 |
| 2.8129 | 0.1514 | 240 | 2.8597 |
| 2.5655 | 0.2271 | 360 | 2.5002 |
| 2.2148 | 0.3028 | 480 | 2.1932 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["trl", "sft", "generated_from_trainer"], "base_model": "meta-llama/Meta-Llama-3-8B-Instruct", "model-index": [{"name": "sft-model", "results": []}]} | Imran1/sft-model | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"generated_from_trainer",
"conversational",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:45:26+00:00 |
text-generation | transformers | {} | VQA-CityU/smooth-compare-mix | null | [
"transformers",
"pytorch",
"mplug_owl2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:45:48+00:00 |
|
text-generation | transformers | {} | Mahim47/llama_2_7B_code | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:46:41+00:00 |
|
null | span-marker | [Biopeak Male Enhancement](https://thecontingent.microsoftcrmportals.com/forums/general-discussion/80c05195-9d03-ef11-a73d-6045bd01c1cc) They can impede different prescriptions or intensify specific ailments. Aftereffects could incorporate skin break out, balding, state of mind swings, and changes in cholesterol levels.Guideline and Quality: The enhancement business isn't generally very much controlled, and item quality can differ. Guarantee you purchase supplements from legitimate brands that go through outsider testing for quality and safety.Medical Exhortation: Prior to beginning any enhancement routine, particularly testosterone promoters, examine it with a medical services proficient. They can assess your wellbeing status, give customized exhortation, and screen any likely incidental effects or associations.
VISIT HERE FOR OFFICIAL WEBSITE:-https://thecontingent.microsoftcrmportals.com/forums/general-discussion/80c05195-9d03-ef11-a73d-6045bd01c1cc
| {"language": ["en"], "license": "bigscience-openrail-m", "library_name": "span-marker", "tags": ["Biopeak Male Enhancement"]} | maleenhancement/biopeakmaleenhancement | null | [
"span-marker",
"Biopeak Male Enhancement",
"en",
"license:bigscience-openrail-m",
"region:us"
]
| null | 2024-04-27T10:47:06+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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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]
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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]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | shallow6414/6ih4k2q | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:47:50+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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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. -->
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | Imran1/fi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:49:03+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** sourcetableteam
- **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"} | sourcetableteam/lora_model | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:49:08+00:00 |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) as a base.
### Models Merged
The following models were included in the merge:
* [Weyaxi/Einstein-v6.1-Llama3-8B](https://huggingface.co/Weyaxi/Einstein-v6.1-Llama3-8B)
* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: meta-llama/Meta-Llama-3-8B
#no parameters necessary for base model
- model: Weyaxi/Einstein-v6.1-Llama3-8B
parameters:
density: 0.5
weight: 0.5
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
density: 0.5
weight: 0.5
merge_method: ties
base_model: meta-llama/Meta-Llama-3-8B
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
``` | {"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["Weyaxi/Einstein-v6.1-Llama3-8B", "meta-llama/Meta-Llama-3-8B-Instruct", "meta-llama/Meta-Llama-3-8B"]} | Weyaxi/a | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"arxiv:2306.01708",
"base_model:Weyaxi/Einstein-v6.1-Llama3-8B",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:49:10+00:00 |
null | null | {"license": "apache-2.0"} | sandralopez/graudio | null | [
"license:apache-2.0",
"region:us"
]
| null | 2024-04-27T10:49:20+00:00 |
|
null | null | {} | ivykopal/slovak_prompt_100k | null | [
"region:us"
]
| null | 2024-04-27T10:49:23+00:00 |
|
null | null | {} | avikki/beit-base-finetuned-ade-640-640-finetuned-diode-240427-104927 | null | [
"region:us"
]
| null | 2024-04-27T10:49:31+00:00 |
|
null | transformers |
# s0br/Llama-3-8B-Web-Q6_K-GGUF
This model was converted to GGUF format from [`McGill-NLP/Llama-3-8B-Web`](https://huggingface.co/McGill-NLP/Llama-3-8B-Web) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/McGill-NLP/Llama-3-8B-Web) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo s0br/Llama-3-8B-Web-Q6_K-GGUF --model llama-3-8b-web.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo s0br/Llama-3-8B-Web-Q6_K-GGUF --model llama-3-8b-web.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-web.Q6_K.gguf -n 128
```
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["agents", "agent", "llm", "llama", "llama-cpp", "gguf-my-repo"], "datasets": ["McGill-NLP/WebLINX"]} | s0br/Llama-3-8B-Web-Q6_K-GGUF | null | [
"transformers",
"gguf",
"agents",
"agent",
"llm",
"llama",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:McGill-NLP/WebLINX",
"license:llama3",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:50:31+00:00 |
text-generation | transformers |
```
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("heegyu/TinyMistral-248M-v2.5-Instruct-orpo")
model = AutoModelForCausalLM.from_pretrained("heegyu/TinyMistral-248M-v2.5-Instruct-orpo")
conv = [
{
'role': 'user',
'content': 'What can I do with Large Language Model?'
}
]
prompt = tokenizer.apply_chat_template(conv, add_generation_prompt=True, return_tensors="pt")
output = model.generate(prompt, max_new_token=128)
print(tokenizer.decode(output[0]))
``` | {"license": "apache-2.0", "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "Locutusque/TinyMistral-248M-v2.5-Instruct"} | heegyu/TinyMistral-248M-v2.5-Instruct-orpo | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:Locutusque/TinyMistral-248M-v2.5-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:50:44+00:00 |
null | null | {} | Kkilz/florhome | null | [
"region:us"
]
| null | 2024-04-27T10:51:59+00:00 |
|
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Boya1_RMSProp_1-e5_10Epoch_swinv2-large-patch4_fold1
This model is a fine-tuned version of [microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft](https://huggingface.co/microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9171
- Accuracy: 0.6782
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1746 | 1.0 | 1848 | 1.0899 | 0.6182 |
| 0.8689 | 2.0 | 3696 | 1.0184 | 0.6602 |
| 0.8727 | 3.0 | 5544 | 0.9164 | 0.6871 |
| 0.4107 | 4.0 | 7392 | 1.0648 | 0.6811 |
| 0.3287 | 5.0 | 9240 | 1.1981 | 0.6847 |
| 0.2801 | 6.0 | 11088 | 1.3374 | 0.6803 |
| 0.706 | 7.0 | 12936 | 1.5676 | 0.6733 |
| 0.4501 | 8.0 | 14784 | 1.7520 | 0.6787 |
| 0.1707 | 9.0 | 16632 | 1.8480 | 0.6841 |
| 0.097 | 10.0 | 18480 | 1.9171 | 0.6782 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft", "model-index": [{"name": "Boya1_RMSProp_1-e5_10Epoch_swinv2-large-patch4_fold1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "test", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.6781546811397557, "name": "Accuracy"}]}]}]} | onizukal/Boya1_RMSProp_1-e5_10Epoch_swinv2-large-patch4_fold1 | null | [
"transformers",
"safetensors",
"swinv2",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swinv2-large-patch4-window12to16-192to256-22kto1k-ft",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:52:19+00:00 |
null | null | {} | cambridge-climb/suchir-demo-roberta-model | null | [
"region:us"
]
| null | 2024-04-27T10:52:53+00:00 |
|
text-generation | transformers | {} | arctic126/hospital_open_llama_3b | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T10:53:13+00:00 |
|
question-answering | 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 🤗 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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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
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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": []} | NeginShams/parsbert-extratranslation | null | [
"transformers",
"safetensors",
"bert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:53:52+00:00 |
text-classification | 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 🤗 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]
#### 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] | {"library_name": "transformers", "tags": []} | Yoav-Yosef/toxic-comment-detection-model | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:55:01+00:00 |
null | null | {} | AIcreatorGG/jag | null | [
"region:us"
]
| null | 2024-04-27T10:55:01+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 🤗 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.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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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]
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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).
- **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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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | BaoLoi2003/vinallama-peft-7b-math-solver | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:55:36+00:00 |
null | null | {} | NerdyCivilian/BitTensorSubnet25HK9 | null | [
"region:us"
]
| null | 2024-04-27T10:57:19+00:00 |
|
null | null | {} | NerdyCivilian/BitTensorSubnet25HK12 | null | [
"region:us"
]
| null | 2024-04-27T10:57:45+00:00 |
|
feature-extraction | transformers |
# Model Card for Model ID
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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
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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| {"library_name": "transformers", "tags": []} | aravindhank/valuenet-bart-base | null | [
"transformers",
"safetensors",
"bart",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T10:59:39+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-imdb-mlflow
This model is a fine-tuned version of [distilbert-base-cased](https://huggingface.co/distilbert-base-cased) 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cpu
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "distilbert-base-cased", "model-index": [{"name": "distilbert-imdb-mlflow", "results": []}]} | nirmeshdell/distilbert-imdb-mlflow | null | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-cased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:00:21+00:00 |
text-generation | transformers |
For some reason this model is SLOW!
but it is highly powerful: Create a Prompt and let it perform the TASK!
Im not sure about its conversive powers! (but task based !!!!!@)
## MODELS !! :: : - Why?
New base Mode Generation from the final Cybertron series model and the Final CyberSeries Models :|
It would seem that some models are not registering on the board ?? perhaps there is a limmit per person ! :
followers should know that the cyberboss was my highest model (renamed)
And my Cybertron models were heavily merged and trained on many datasets : Even containing thinking pardigms :
merging the collection back to base model give the model a great position to begin from !
hence a new base model marker (Untrained/Sharded)(totally unlocked)
I had noticed the reality of TopK=1000,TopP=0.78, Temp=0.86
as so,
Important with merged models allowing for the model to produce a bit more random results but also giving the model a larger pool to select from:
obviously for Role play the model requires Temp to be 1+
:::
## FineTuning ::
Fine tuning models close to 0.9 means that some information is totally Fixed and maynot return without focusing the model ! sometimes to train the model to 1.5+
allowing for loosly trained datas to surface :
when higher tempretures are applied ! hence role play datasets being trained at higher loss rates that codeing datasets and math datasets (close to overfitting)
Hence Merging playing animportant role in centering the model again !
## Merging is not just for fun and game!
it is a vital part of the training process and locking data into the model as well as sharing data!
remember data is not stored in the model:: only the probablity of the information being returned !
## From here to where ?
Currently there is a trend for evaluation !
evaluating the model to discover its weaknesses and threats , removing the specific layers identifed in the model with the ofensive content :
enabling for these layers to be trained and replaced ! replace with ??
Replacing layers in the model ; also requires a realignment of information throughout the network !
despite being a copied layer (Still preserving some content) once ofensive content is discovered the network can be trained with its counter argument; hence the evaluation process enabes for the creationn of a custom dataset: targetting these internalized datas!
Despite a neural network NOT being a storage system as the retrival process is based oñ probablliities :hence at points in the networ certain emebedding values are present and once translated or decodedd into standard tokens can actually be identidfed!
## WOW!!
So !
this also means at each layer the network is actually storing a probablity table , word to word matrix of probab.itys for the next token generation !
IT may even be possible to train a network for image recognition , as long as the images are tokenized into an embedding value associated with the image, Hence image tokenizers :
The embedding value produced should enable the output to contain the same images that were present in the training set , ie they have been tokenized and embedded into the model so it should be able to produce an embedding associated with this output !
Hence is should also be possible to retrive the image from the image tokenizer ? so tokens not decoded by the text tokenizer should be handed off to the image tokenizer! to dcode the embedding and return its original (cascade) / digital numercical value (each pixel is a number and with line encoding of images essentially each line can be reconstructed to produce an image, hence ALL images would nbeed to be BitMap/JPEG/PNG acording to the encoder!)
MISSION!
But still we will need to uinstall all the competition datasets into the mode , so that the original baselines can be established enabling for , after layer removal full realignment to the same dataset collection ! hence retaining all funcitonality, its worth noting that domain specific datasets should also be handled in the same way!
MORE TO COME!(look out for the SFT's and Merges)
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["baseModel"], "base_model": ["LeroyDyer/Mixtral_AI_Cyber_Boss", "LeroyDyer/Mixtral_AI_CyberUltron", "mistralai/Mistral-7B-Instruct-v0.2"]} | LeroyDyer/Mixtral_AI_2.0_7b | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"baseModel",
"en",
"base_model:LeroyDyer/Mixtral_AI_Cyber_Boss",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T11:00:31+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/saucam/Skyro-4X8B
<!-- 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/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q2_K.gguf) | Q2_K | 9.4 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.IQ3_XS.gguf) | IQ3_XS | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q3_K_S.gguf) | Q3_K_S | 11.0 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.IQ3_S.gguf) | IQ3_S | 11.1 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.IQ3_M.gguf) | IQ3_M | 11.2 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q3_K_M.gguf) | Q3_K_M | 12.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q3_K_L.gguf) | Q3_K_L | 13.1 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.IQ4_XS.gguf) | IQ4_XS | 13.7 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q4_K_S.gguf) | Q4_K_S | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q4_K_M.gguf) | Q4_K_M | 15.3 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q5_K_S.gguf) | Q5_K_S | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q5_K_M.gguf) | Q5_K_M | 17.8 | |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q6_K.gguf) | Q6_K | 20.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Skyro-4X8B-GGUF/resolve/main/Skyro-4X8B.Q8_0.gguf) | Q8_0 | 26.6 | 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", "tags": ["merge", "mergekit", "moe", "frankenmoe", "abacusai/Llama-3-Smaug-8B", "cognitivecomputations/dolphin-2.9-llama3-8b", "Weyaxi/Einstein-v6.1-Llama3-8B", "dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2"], "base_model": "saucam/Skyro-4X8B", "quantized_by": "mradermacher"} | mradermacher/Skyro-4X8B-GGUF | null | [
"transformers",
"gguf",
"merge",
"mergekit",
"moe",
"frankenmoe",
"abacusai/Llama-3-Smaug-8B",
"cognitivecomputations/dolphin-2.9-llama3-8b",
"Weyaxi/Einstein-v6.1-Llama3-8B",
"dreamgen-preview/opus-v1.2-llama-3-8b-base-run3.4-epoch2",
"en",
"base_model:saucam/Skyro-4X8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:00:59+00:00 |
question-answering | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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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]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | NeginShams/albert-extratranslation | null | [
"transformers",
"safetensors",
"bert",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:06:51+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** hunterlee27
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-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", "zh"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit"} | hunterlee27/chinese-llama3-full-model | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"zh",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:06:56+00:00 |
text-to-audio | transformers | {} | zizzimars/speecht5_finetuned_kaztts2_4attempt | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:07:02+00:00 |
|
text-generation | transformers |
## Model Summary
The Phi-3-mini-mango-1 is an instruct finetune of [Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) with 4K context and 3.8B parameters.
It is a first cut of finetuning Phi-3 (which is a great model!) to explore its properties and behaviour. More to follow.
You will need to update your local transformers to the development version to run this model (4.40.0-dev0 or above):
```
pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers
```
If this isn't possible, you can run the llamafied version of the model available at [rhysjones/Phi-3-mini-mango-1-llamafied](https://huggingface.co/rhysjones/Phi-3-mini-mango-1-llamafied) (incurrs a small degredation in the abilities of the model).
| {"language": ["en"], "license": "mit", "tags": ["nlp", "code"], "license_link": "https://huggingface.co/rhysjones/Phi-3-mini-mango-1/resolve/main/LICENSE", "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}]} | rhysjones/Phi-3-mini-mango-1 | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"nlp",
"code",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:07:49+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** kevinasyraf
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | kevinasyraf/llama-3-8b-instruct-id-v1 | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:08:36+00:00 |
null | peft |
# 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. -->
- **Developed by:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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- **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]
### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "ai-forever/FRED-T5-1.7B"} | chibeenot/aspect_tonality | null | [
"peft",
"arxiv:1910.09700",
"base_model:ai-forever/FRED-T5-1.7B",
"region:us"
]
| null | 2024-04-27T11:09:49+00:00 |
null | 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. -->
# speaker-segmentation-fine-tuned-simsamu
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/simsamu default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2302
- Der: 0.0911
- False Alarm: 0.0236
- Missed Detection: 0.0413
- Confusion: 0.0262
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.2179 | 1.0 | 111 | 0.2240 | 0.0964 | 0.0254 | 0.0470 | 0.0240 |
| 0.1678 | 2.0 | 222 | 0.2279 | 0.0943 | 0.0236 | 0.0447 | 0.0260 |
| 0.156 | 3.0 | 333 | 0.2327 | 0.0947 | 0.0222 | 0.0450 | 0.0274 |
| 0.1507 | 4.0 | 444 | 0.2301 | 0.0919 | 0.0237 | 0.0420 | 0.0262 |
| 0.1471 | 5.0 | 555 | 0.2302 | 0.0911 | 0.0236 | 0.0413 | 0.0262 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["speaker-diarization", "speaker-segmentation", "generated_from_trainer"], "datasets": ["diarizers-community/simsamu"], "base_model": "pyannote/segmentation-3.0", "model-index": [{"name": "speaker-segmentation-fine-tuned-simsamu", "results": []}]} | tgrhn/speaker-segmentation-fine-tuned-simsamu | null | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:diarizers-community/simsamu",
"base_model:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:10:03+00:00 |
text-classification | setfit |
# SetFit with sentence-transformers/paraphrase-mpnet-base-v2
This is a [SetFit](https://github.com/huggingface/setfit) model that can be used for Text Classification. This SetFit model uses [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2) as the Sentence Transformer embedding model. A [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance is used for classification.
The model has been trained using an efficient few-shot learning technique that involves:
1. Fine-tuning a [Sentence Transformer](https://www.sbert.net) with contrastive learning.
2. Training a classification head with features from the fine-tuned Sentence Transformer.
## Model Details
### Model Description
- **Model Type:** SetFit
- **Sentence Transformer body:** [sentence-transformers/paraphrase-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-mpnet-base-v2)
- **Classification head:** a [LogisticRegression](https://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html) instance
- **Maximum Sequence Length:** 512 tokens
- **Number of Classes:** 5 classes
<!-- - **Training Dataset:** [Unknown](https://huggingface.co/datasets/unknown) -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SetFit on GitHub](https://github.com/huggingface/setfit)
- **Paper:** [Efficient Few-Shot Learning Without Prompts](https://arxiv.org/abs/2209.11055)
- **Blogpost:** [SetFit: Efficient Few-Shot Learning Without Prompts](https://huggingface.co/blog/setfit)
### Model Labels
| Label | Examples |
|:------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| 2 | <ul><li>'I require assistance in altering certain elements of my policy.'</li><li>"Hey there, I've spotted a gap in my policy information."</li><li>'I need to rectify something within my policy documentation.'</li></ul> |
| 4 | <ul><li>"I am covered by health insurance through my employer's sponsorship."</li><li>'Is it permissible to transfer my health plan to ACKO?'</li><li>'My old health policy from another insurance provider is no longer in effect.'</li></ul> |
| 3 | <ul><li>'Can you reveal all policies under my profile?'</li><li>'I want to be informed about the status of all my insurance arrangements.'</li><li>"Is it possible for you to display my family's health insurance policies?"</li></ul> |
| 1 | <ul><li>'How is my vehicle claim proceeding?'</li><li>"I'm curious about the status of my car insurance claim."</li><li>'Am I required to provide additional evidence for my claims?'</li></ul> |
| 0 | <ul><li>'I need help selecting an appropriate health insurance plan for my family.'</li><li>"I'm looking for a health policy that will cover me along with my two kids."</li><li>"I'm in urgent need of a health insurance plan for my family's wellbeing."</li></ul> |
## Evaluation
### Metrics
| Label | Accuracy |
|:--------|:---------|
| **all** | 0.9160 |
## Uses
### Direct Use for Inference
First install the SetFit library:
```bash
pip install setfit
```
Then you can load this model and run inference.
```python
from setfit import SetFitModel
# Download from the 🤗 Hub
model = SetFitModel.from_pretrained("harshita23sh/setfit-model-intent-classification-insurance")
# Run inference
preds = model("I have my own health insurance policy.")
```
<!--
### Downstream Use
*List how someone could finetune this model on their own dataset.*
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Set Metrics
| Training set | Min | Median | Max |
|:-------------|:----|:-------|:----|
| Word count | 5 | 10.6 | 15 |
| Label | Training Sample Count |
|:------|:----------------------|
| 0 | 5 |
| 1 | 8 |
| 2 | 12 |
| 3 | 4 |
| 4 | 11 |
### Training Hyperparameters
- batch_size: (16, 16)
- num_epochs: (1, 1)
- max_steps: -1
- sampling_strategy: oversampling
- num_iterations: 20
- body_learning_rate: (2e-05, 2e-05)
- head_learning_rate: 2e-05
- loss: CosineSimilarityLoss
- distance_metric: cosine_distance
- margin: 0.25
- end_to_end: False
- use_amp: False
- warmup_proportion: 0.1
- seed: 42
- eval_max_steps: -1
- load_best_model_at_end: False
### Training Results
| Epoch | Step | Training Loss | Validation Loss |
|:-----:|:----:|:-------------:|:---------------:|
| 0.01 | 1 | 0.1388 | - |
| 0.5 | 50 | 0.0087 | - |
| 1.0 | 100 | 0.0029 | - |
### Framework Versions
- Python: 3.10.12
- SetFit: 1.0.3
- Sentence Transformers: 2.7.0
- Transformers: 4.40.0
- PyTorch: 2.2.1+cu121
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
```bibtex
@article{https://doi.org/10.48550/arxiv.2209.11055,
doi = {10.48550/ARXIV.2209.11055},
url = {https://arxiv.org/abs/2209.11055},
author = {Tunstall, Lewis and Reimers, Nils and Jo, Unso Eun Seo and Bates, Luke and Korat, Daniel and Wasserblat, Moshe and Pereg, Oren},
keywords = {Computation and Language (cs.CL), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Efficient Few-Shot Learning Without Prompts},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> | {"library_name": "setfit", "tags": ["setfit", "sentence-transformers", "text-classification", "generated_from_setfit_trainer"], "metrics": ["accuracy"], "base_model": "sentence-transformers/paraphrase-mpnet-base-v2", "widget": [{"text": "I need to repair an issue I found in my policy."}, {"text": "I'm wondering about the developments in my policy alteration."}, {"text": "Hi, I've noticed my policy is incomplete and needs additional details."}, {"text": "What's the latest on the status of my claim?"}, {"text": "I have my own health insurance policy."}], "pipeline_tag": "text-classification", "inference": true, "model-index": [{"name": "SetFit with sentence-transformers/paraphrase-mpnet-base-v2", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "Unknown", "type": "unknown", "split": "test"}, "metrics": [{"type": "accuracy", "value": 0.9159663865546218, "name": "Accuracy"}]}]}]} | harshita23sh/setfit-model-intent-classification-insurance | null | [
"setfit",
"safetensors",
"mpnet",
"sentence-transformers",
"text-classification",
"generated_from_setfit_trainer",
"arxiv:2209.11055",
"base_model:sentence-transformers/paraphrase-mpnet-base-v2",
"model-index",
"region:us"
]
| null | 2024-04-27T11:11:04+00:00 |
null | null | {} | adhityamw11/valencearousal | null | [
"region:us"
]
| null | 2024-04-27T11:11:18+00:00 |
|
null | 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. -->
# speaker-segmentation-fine-tuned-simsamu-2
This model is a fine-tuned version of [pyannote/segmentation-3.0](https://huggingface.co/pyannote/segmentation-3.0) on the diarizers-community/simsamu default dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2428
- Der: 0.0861
- False Alarm: 0.0245
- Missed Detection: 0.0384
- Confusion: 0.0232
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Der | False Alarm | Missed Detection | Confusion |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-----------:|:----------------:|:---------:|
| 0.2179 | 1.0 | 111 | 0.2259 | 0.0951 | 0.0239 | 0.0486 | 0.0227 |
| 0.1694 | 2.0 | 222 | 0.2379 | 0.0930 | 0.0230 | 0.0466 | 0.0234 |
| 0.1559 | 3.0 | 333 | 0.2305 | 0.0898 | 0.0223 | 0.0431 | 0.0244 |
| 0.149 | 4.0 | 444 | 0.2323 | 0.0893 | 0.0246 | 0.0398 | 0.0249 |
| 0.1416 | 5.0 | 555 | 0.2351 | 0.0884 | 0.0243 | 0.0399 | 0.0243 |
| 0.1369 | 6.0 | 666 | 0.2458 | 0.0904 | 0.0266 | 0.0370 | 0.0268 |
| 0.1367 | 7.0 | 777 | 0.2410 | 0.0882 | 0.0204 | 0.0434 | 0.0244 |
| 0.1306 | 8.0 | 888 | 0.2400 | 0.0866 | 0.0240 | 0.0393 | 0.0234 |
| 0.1301 | 9.0 | 999 | 0.2422 | 0.0860 | 0.0243 | 0.0387 | 0.0230 |
| 0.1276 | 10.0 | 1110 | 0.2428 | 0.0861 | 0.0245 | 0.0384 | 0.0232 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.17.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["speaker-diarization", "speaker-segmentation", "generated_from_trainer"], "datasets": ["diarizers-community/simsamu"], "base_model": "pyannote/segmentation-3.0", "model-index": [{"name": "speaker-segmentation-fine-tuned-simsamu-2", "results": []}]} | tgrhn/speaker-segmentation-fine-tuned-simsamu-2 | null | [
"transformers",
"tensorboard",
"safetensors",
"pyannet",
"speaker-diarization",
"speaker-segmentation",
"generated_from_trainer",
"dataset:diarizers-community/simsamu",
"base_model:pyannote/segmentation-3.0",
"license:mit",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:11:41+00:00 |
null | null | {} | NiCoSav/llama-3-8b-bnb-8bit | null | [
"region:us"
]
| null | 2024-04-27T11:12:08+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** Dogge
- **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"} | Dogge/llama-3-translate-enko | 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 | 2024-04-27T11:12:32+00:00 |
text-generation | transformers |
# mlx-community/Swallow-70b-instruct-v0.1-8bit
This model was converted to MLX format from [`tokyotech-llm/Swallow-70b-instruct-v0.1`]() using mlx-lm version **0.6.0**.
Refer to the [original model card](https://huggingface.co/tokyotech-llm/Swallow-70b-instruct-v0.1) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Swallow-70b-instruct-v0.1-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"language": ["en", "ja"], "license": "llama2", "library_name": "transformers", "tags": ["mlx"], "pipeline_tag": "text-generation", "model_type": "llama"} | mlx-community/Swallow-70b-instruct-v0.1-8bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mlx",
"conversational",
"en",
"ja",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T11:12:42+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** Dogge
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Dogge/llama-3-translate-enko-bf16 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:13:53+00:00 |
question-answering | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | NeginShams/roberta-extratranslation | null | [
"transformers",
"safetensors",
"xlm-roberta",
"question-answering",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2024-04-27T11:15:11+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. -->
# t5-small-finetuned-xsum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5091
- Rouge1: 15.8853
- Rouge2: 5.7363
- Rougel: 13.5746
- Rougelsum: 13.6787
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:-------:|
| 1.5826 | 1.0 | 9258 | 1.5091 | 15.8853 | 5.7363 | 13.5746 | 13.6787 | 19.0 |
### Framework versions
- Transformers 4.30.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-small-finetuned-xsum", "results": []}]} | honganhle1903/t5-small-finetuned-xsum | null | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T11:15:34+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - egioia/corgy_coins_LoRA
<Gallery />
## Model description
These are egioia/corgy_coins_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of TOK coin to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](egioia/corgy_coins_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of TOK coin", "widget": []} | egioia/corgy_coins_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
]
| null | 2024-04-27T11:16:08+00:00 |
null | null | ---
license: cc-by-sa-4.0
---
# v4からの修正点
数字を全て一桁区切りに。
# 説明
wikipedia, mbpp, grade-school-mathで学習したトークナイザー。
## 学習に使ったデータ
- 英語：1.33GB (wiki40b)<br>
- 日本語：1.78GB (wiki40b) ※形態素単位で"||||"で事前分割してsentencepieceの学習時にpretokenization_delimiterを設定。<br>
- コード：172KB (mbpp) <br>
- 数学：2.1MB (grade-school-math)
-
## 語彙の追加
以下を参考に日本語の語彙を追加。
- wikitinary 目次一覧（名詞・形容詞・形容動詞・副詞・接尾辞・助詞・動詞などから一般的に使われると思われるものを定性的に選別。）
- wikitionary 日本語の基本語彙1000
- 文化庁「常用漢字一覧表」の例から一部をサンプリング。
- 時間・季節・方角に関する語
- 都道府県・観光地・東京23区
- 頻出する日本の苗字
- 定型表現（「こんにちは」「よろしく」「ございます」など）
その他、以下の語彙を追加。
- 記号
- 数字（漢数字・半角数字0~9・全角数字０〜９・上付き数字0〜9）
- 数学に出るギリシャ文字
## 語彙の割合
概算ですが、アルファベットが約6割、日本語（ひらがな・カタカナ・漢字）が約4割となっています。（その他記号や数字は1~2%程度）
## 参照
- https://aclanthology.org/2020.lrec-1.297.pdf
- https://www.tensorflow.org/datasets/catalog/wiki40b
- https://github.com/openai/grade-school-math
- https://github.com/google-research/google-research/tree/master/mbpp
- https://www.bunka.go.jp/kokugo_nihongo/sisaku/joho/joho/kakuki/14/pdf/jyouyou_kanjihyou.pdf
- https://ja.m.wiktionary.org/wiki/%E3%82%AB%E3%83%86%E3%82%B4%E3%83%AA:%E6%97%A5%E6%9C%AC%E8%AA%9E
-
## 設定
vocab_size=56,320（語彙サイズ）<br>
character_coverage=0.9995（文字のカバー率99.95%）<br>
model_type="unigram"（アルゴリズム）<br>
normalization="identity"（正規化なし）<br>
byte_fallback=True（バイト変換あり）<br>
split_digits=True（数字分割あり）<br>
allow_whitespace_only_pieces=True（空白のトークンを許可する）<br>
remove_extra_whitespaces=True（余分な空白の削除あり）<br>
## 形式
LlamaTokenizer<br>
※encode時に文頭にbos_tokenである"\<s\>"トークンが付きます。
# 使い方
```python
!pip install transformers>=4.34.0
from transformers import AutoTokenizer
test_tokenizer = AutoTokenizer.from_pretrained("geniacllm/ja-en-tokenizer-unigram-v5", use_fast=False)
```
```python
# text
text = "This is tokenizer test."
# tokenize
tokenized = test_tokenizer.tokenize(text)
print(tokenized)
# encode
encoded = test_tokenizer.encode(text)
print(encoded)
# decode
decoded = test_tokenizer.decode(encoded)
print(decoded)
# special_token
print(test_tokenizer.special_tokens_map)
# vocab size
print(len(test_tokenizer))
# all subwords in vocab
print(test_tokenizer.get_vocab())
```
| {"license": "cc-by-sa-4.0"} | geniacllm/ja-en-tokenizer-unigram-v5 | null | [
"license:cc-by-sa-4.0",
"region:us"
]
| null | 2024-04-27T11:16:10+00:00 |
null | null | {} | misterytoon/Grett_disventurecamp | null | [
"region:us"
]
| null | 2024-04-27T11:17:28+00:00 |
|
null | null | {"license": "unknown"} | hugRam/Firstai | null | [
"license:unknown",
"region:us"
]
| null | 2024-04-27T11:17:59+00:00 |
|
text-generation | transformers |
# miqu-evil-dpo
# **Model Details**
## Description
miqu-evil-dpo is fine-tuned model based on miqu, serving as a direct successor to PiVoT-0.1-Evil-a.
It is trained with evil-tune method applied.
<!-- prompt-template start -->
## Prompt template: Mistral Inst
```
<s> [INST] {inst} [/INST]
```
<!-- prompt-template end -->
## Disclaimer
The AI model provided herein is intended for experimental purposes only. The creator of this model makes no representations or warranties of any kind, either express or implied, as to the model's accuracy, reliability, or suitability for any particular purpose. The creator shall not be held liable for any outcomes, decisions, or actions taken on the basis of the information generated by this model. Users of this model assume full responsibility for any consequences resulting from its use.
| {"language": ["en"], "license": "other", "tags": ["not-for-all-audiences"], "license_name": "miqu-license", "license_link": "LICENSE", "pipeline_tag": "text-generation"} | blockblockblock/miqu-evil-dpo-bpw6-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"not-for-all-audiences",
"conversational",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"6-bit",
"region:us"
]
| null | 2024-04-27T11:18:03+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** Dogge
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Dogge/llama-3-translate-enko-4bit | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
]
| null | 2024-04-27T11:18:17+00:00 |
text-generation | transformers |
<p align="center" style="margin:0;padding:0">
<img src="https://huggingface.co/BramVanroy/fietje-2b-instruct/resolve/main/img/fietje-2b-banner-rounded.png" alt="Fietje banner" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
</p>
<div style="margin:auto; text-align:center">
<h1 style="margin-bottom: 0">Fietje 2B Instruct</h1>
<em>An open and efficient LLM for Dutch</em>
</div>
<blockquote class="tip" style="padding: 1.5em; border: 0">
<p align="center" style="text-align: center; margin: 0">
<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b">👱♀️ Base version</a> -
<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-instruct">🤖 Instruct version</a> (this one) -
<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-chat">💬 Chat version</a> -
<a rel="nofollow" href="https://huggingface.co/BramVanroy/fietje-2b-chat-GGUF">🚀 GGUF of Instruct</a>
</p>
<p align="center" style="text-align: center; margin: 0">
<a href="https://huggingface.co/spaces/BramVanroy/fietje-2b"><strong>Chat with Fietje here!</strong></a>
</p>
</blockquote>
This is the instruct version of Fietje, an SFT-tuned (instruction-tuned) variant of [the base model](https://huggingface.co/BramVanroy/fietje-2b). Fietje is an adapated version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2), tailored to Dutch text generation by training on 28B tokens. It is small and efficient with a size of 2.7 billion parameters while performing almost on par with more powerful Dutch LLMs of twice its size like [GEITje 7B Ultra](https://huggingface.co/BramVanroy/GEITje-7B-ultra).
A thorough description of the creation and evaluation of Fietje as well as usage examples are available in [this Github repository](https://github.com/BramVanroy/fietje).
## Intended uses & limitations
The same limitations as [phi-2](https://huggingface.co/microsoft/phi-2#limitations-of-phi-2), and LLMs in general, apply here. LLMs hallucinate, make mistakes, and should not be trusted. Use at your own risk!
## Training and evaluation data
Fietje 2B instruct was finetuned from [the base model](https://huggingface.co/BramVanroy/fietje-2b) on the following datasets. Number of training samples per dataset given in brackets, totalling 201,579 samples.
- [BramVanroy/ultrachat_200k_dutch](https://huggingface.co/datasets/BramVanroy/ultrachat_200k_dutch): gpt-4-1106-preview; multi-turn; fully generated (192,598)
- [BramVanroy/no_robots_dutch](https://huggingface.co/datasets/BramVanroy/no_robots_dutch): gpt-4-1106-preview; prompt translate, answer generated; some items have system messages (8181)
- [BramVanroy/belebele_dutch](https://huggingface.co/datasets/BramVanroy/belebele_dutch): Dutch portion of [belebele](https://huggingface.co/datasets/facebook/belebele), formatted into SFT format (800)
## Training procedure
I am thankful to the [Flemish Supercomputer Center](https://www.vscentrum.be/) (VSC) for providing the computational power to accomplish this project. Accounting for waiting for jobs, training took around a day on four nodes of 4x A100 80GB each (16 total). I cannot find the exact time anymore and I do not think that the runtime in `all_results.json` accounts for interrupted-and-continued jobs.
Training was done with the wonderful [alignment-handbook](https://github.com/huggingface/alignment-handbook), using DeepSpeed as a back-end. Exact training recipes and SLURM script are given in the [Github repository](https://github.com/BramVanroy/fietje).
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 6e-05
- train_batch_size: 42
- eval_batch_size: 42
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 672
- total_eval_batch_size: 672
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-07
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9325 | 1.0 | 178 | 0.9060 |
| 0.8687 | 2.0 | 356 | 0.8850 |
| 0.8385 | 3.0 | 534 | 0.8818 |
### Framework versions
- Transformers 4.39.1
- Pytorch 2.1.2+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"language": ["nl"], "license": "mit", "tags": ["trl", "fietje", "alignment-handbook", "sft"], "datasets": ["BramVanroy/ultrachat_200k_dutch", "BramVanroy/no_robots_dutch", "BramVanroy/belebele_dutch"], "base_model": "BramVanroy/fietje-2b", "pipeline_tag": "text-generation", "inference": false, "model-index": [{"name": "fietje-2b-instruct", "results": []}]} | BramVanroy/fietje-2b-instruct | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"trl",
"fietje",
"alignment-handbook",
"sft",
"conversational",
"nl",
"dataset:BramVanroy/ultrachat_200k_dutch",
"dataset:BramVanroy/no_robots_dutch",
"dataset:BramVanroy/belebele_dutch",
"base_model:BramVanroy/fietje-2b",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"region:us"
]
| null | 2024-04-27T11:18:30+00:00 |
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