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AndyChiang/cdgp-csg-bert-cloth | AndyChiang | 2022-10-14T13:24:08Z | 766 | 3 | transformers | [
"transformers",
"pytorch",
"bert",
"fill-mask",
"cloze",
"distractor",
"generation",
"en",
"dataset:cloth",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-10-13T14:15:34Z | ---
license: mit
language: en
tags:
- bert
- cloze
- distractor
- generation
datasets:
- cloth
widget:
- text: "I feel [MASK] now. [SEP] happy"
- text: "The old man was waiting for a ride across the [MASK]. [SEP] river"
---
# cdgp-csg-bert-cloth
## Model description
This model is a Candidate Set Generator in **"CDGP: Automatic Cloze Distractor Generation based on Pre-trained Language Model", Findings of EMNLP 2022**.
Its input are stem and answer, and output is candidate set of distractors. It is fine-tuned by [**CLOTH**](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset based on [**bert-base-uncased**](https://huggingface.co/bert-base-uncased) model.
For more details, you can see our **paper** or [**GitHub**](https://github.com/AndyChiangSH/CDGP).
## How to use?
1. Download the model by hugging face transformers.
```python
from transformers import BertTokenizer, BertForMaskedLM, pipeline
tokenizer = BertTokenizer.from_pretrained("AndyChiang/cdgp-csg-bert-cloth")
csg_model = BertForMaskedLM.from_pretrained("AndyChiang/cdgp-csg-bert-cloth")
```
2. Create a unmasker.
```python
unmasker = pipeline("fill-mask", tokenizer=tokenizer, model=csg_model, top_k=10)
```
3. Use the unmasker to generate the candidate set of distractors.
```python
sent = "I feel [MASK] now. [SEP] happy"
cs = unmasker(sent)
print(cs)
```
## Dataset
This model is fine-tuned by [CLOTH](https://www.cs.cmu.edu/~glai1/data/cloth/) dataset, which is a collection of nearly 100,000 cloze questions from middle school and high school English exams. The detail of CLOTH dataset is shown below.
| Number of questions | Train | Valid | Test |
| ------------------- | ----- | ----- | ----- |
| Middle school | 22056 | 3273 | 3198 |
| High school | 54794 | 7794 | 8318 |
| Total | 76850 | 11067 | 11516 |
You can also use the [dataset](https://huggingface.co/datasets/AndyChiang/cloth) we have already cleaned.
## Training
We use a special way to fine-tune model, which is called **"Answer-Relating Fine-Tune"**. More detail is in our paper.
### Training hyperparameters
The following hyperparameters were used during training:
- Pre-train language model: [bert-base-uncased](https://huggingface.co/bert-base-uncased)
- Optimizer: adam
- Learning rate: 0.0001
- Max length of input: 64
- Batch size: 64
- Epoch: 1
- Device: NVIDIA® Tesla T4 in Google Colab
## Testing
The evaluations of this model as a Candidate Set Generator in CDGP is as follows:
| P@1 | F1@3 | F1@10 | MRR | NDCG@10 |
| ----- | ----- | ----- | ----- | ------- |
| 18.50 | 13.80 | 15.37 | 29.96 | 37.82 |
## Other models
### Candidate Set Generator
| Models | CLOTH | DGen |
| ----------- | ----------------------------------------------------------------------------------- | -------------------------------------------------------------------------------- |
| **BERT** | [*cdgp-csg-bert-cloth*](https://huggingface.co/AndyChiang/cdgp-csg-bert-cloth) | [cdgp-csg-bert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bert-dgen) |
| **SciBERT** | [cdgp-csg-scibert-cloth](https://huggingface.co/AndyChiang/cdgp-csg-scibert-cloth) | [cdgp-csg-scibert-dgen](https://huggingface.co/AndyChiang/cdgp-csg-scibert-dgen) |
| **RoBERTa** | [cdgp-csg-roberta-cloth](https://huggingface.co/AndyChiang/cdgp-csg-roberta-cloth) | [cdgp-csg-roberta-dgen](https://huggingface.co/AndyChiang/cdgp-csg-roberta-dgen) |
| **BART** | [cdgp-csg-bart-cloth](https://huggingface.co/AndyChiang/cdgp-csg-bart-cloth) | [cdgp-csg-bart-dgen](https://huggingface.co/AndyChiang/cdgp-csg-bart-dgen) |
### Distractor Selector
**fastText**: [cdgp-ds-fasttext](https://huggingface.co/AndyChiang/cdgp-ds-fasttext)
## Citation
None |
nickprock/mmarco-bert-base-italian-uncased | nickprock | 2023-12-03T16:40:25Z | 766 | 2 | sentence-transformers | [
"sentence-transformers",
"pytorch",
"bert",
"feature-extraction",
"sentence-similarity",
"transformers",
"mteb",
"it",
"dataset:unicamp-dl/mmarco",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2023-05-18T10:02:06Z | ---
model-index:
- name: mmarco-bert-base-italian-uncased
results:
- task:
type: Classification
dataset:
type: mteb/amazon_massive_intent
name: MTEB MassiveIntentClassification (it)
config: it
split: test
revision: 31efe3c427b0bae9c22cbb560b8f15491cc6bed7
metrics:
- type: accuracy
value: 55.06052454606589
- type: f1
value: 54.014768121214104
- task:
type: Classification
dataset:
type: mteb/amazon_massive_scenario
name: MTEB MassiveScenarioClassification (it)
config: it
split: test
revision: 7d571f92784cd94a019292a1f45445077d0ef634
metrics:
- type: accuracy
value: 63.04303967720243
- type: f1
value: 62.695230714417406
- task:
type: STS
dataset:
type: mteb/sts22-crosslingual-sts
name: MTEB STS22 (it)
config: it
split: test
revision: 6d1ba47164174a496b7fa5d3569dae26a6813b80
metrics:
- type: cos_sim_pearson
value: 64.73840574137837
- type: cos_sim_spearman
value: 69.44233124548987
- type: euclidean_pearson
value: 67.65045364124317
- type: euclidean_spearman
value: 69.586510471675
- type: manhattan_pearson
value: 67.76125181623837
- type: manhattan_spearman
value: 69.61010945802974
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
- mteb
license: mit
datasets:
- unicamp-dl/mmarco
language:
- it
library_name: sentence-transformers
region: Italy
---
# MMARCO-bert-base-italian-uncased
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer, util
query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]
#Load the model
model = SentenceTransformer('nickprock/mmarco-bert-base-italian-uncased')
#Encode query and documents
query_emb = model.encode(query)
doc_emb = model.encode(docs)
#Compute dot score between query and all document embeddings
scores = util.dot_score(query_emb, doc_emb)[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
for doc, score in doc_score_pairs:
print(score, doc)
```
## Usage (HuggingFace Transformers)
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.last_hidden_state
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)
#Encode text
def encode(texts):
# Tokenize sentences
encoded_input = tokenizer(texts, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input, return_dict=True)
# Perform pooling
embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
return embeddings
# Sentences we want sentence embeddings for
query = "Quante persone vivono a Londra?"
docs = ["A Londra vivono circa 9 milioni di persone", "Londra è conosciuta per il suo quartiere finanziario"]
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained("nickprock/mmarco-bert-base-italian-uncased")
model = AutoModel.from_pretrained("nickprock/mmarco-bert-base-italian-uncased")
#Encode query and docs
query_emb = encode(query)
doc_emb = encode(docs)
#Compute dot score between query and all document embeddings
scores = torch.mm(query_emb, doc_emb.transpose(0, 1))[0].cpu().tolist()
#Combine docs & scores
doc_score_pairs = list(zip(docs, scores))
#Sort by decreasing score
doc_score_pairs = sorted(doc_score_pairs, key=lambda x: x[1], reverse=True)
#Output passages & scores
print("Query:", query)
for doc, score in doc_score_pairs:
print(score, doc)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name={MODEL_NAME})
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 6250 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.TripletLoss.TripletLoss` with parameters:
```
{'distance_metric': 'TripletDistanceMetric.EUCLIDEAN', 'triplet_margin': 5}
```
Parameters of the fit()-Method:
```
{
"epochs": 10,
"evaluation_steps": 500,
"evaluator": "sentence_transformers.evaluation.TripletEvaluator.TripletEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": 1500,
"warmup_steps": 6250,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> |
CorticalStack/mistral-7b-metamathqa-sft | CorticalStack | 2024-02-17T20:22:33Z | 766 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-17T20:20:20Z | ---
license: apache-2.0
---
# mistral-7b-metamathqa-sft
mistral-7b-metamathqa-sft is an SFT fine-tuned version of [unsloth/mistral-7b-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-bnb-4bit) using the [meta-math/MetaMathQA](https://huggingface.co/datasets/meta-math/MetaMathQA) dataset.
## Fine-tuning configuration
### LoRA
- r: 256
- LoRA alpha: 128
- LoRA dropout: 0.0
### Training arguments
- Epochs: 1
- Batch size: 4
- Gradient accumulation steps: 6
- Optimizer: adamw_torch_fused
- Max steps: 100
- Learning rate: 0.0002
- Weight decay: 0.1
- Learning rate scheduler type: linear
- Max seq length: 2048
- 4-bit bnb: True
Trained 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) |
minatolegend0311/5HGWa2W856A8avYLWDtYDWLgfGQq4WFdQZc4NCpYP9v2XupJ_vgg | minatolegend0311 | 2024-03-12T13:00:06Z | 766 | 0 | keras | [
"keras",
"region:us"
] | null | 2024-03-05T11:47:51Z | Entry not found |
ResplendentAI/Asherah_7B | ResplendentAI | 2024-03-21T20:40:13Z | 766 | 6 | transformers | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"mergekit",
"merge",
"en",
"dataset:ResplendentAI/Synthetic_Soul_1k",
"dataset:Epiculous/Gnosis",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-21T01:18:02Z | ---
library_name: transformers
tags:
- mergekit
- merge
license: apache-2.0
datasets:
- ResplendentAI/Synthetic_Soul_1k
- Epiculous/Gnosis
language:
- en
---
# Asherah
GGUF here: https://huggingface.co/Lewdiculous/Asherah_7B-GGUF-IQ-Imatrix
Asherah, goddess of all creation according to ancient myth was a huge inspiration for this model. The model started with a merge of four of Sanji Watsuki's models using various methods. This merge was then finetuned on Gnosis and Synthetic Soul, two datasets penned by myself.
You can use this as mmproj: https://huggingface.co/cjpais/llava-1.6-mistral-7b-gguf/blob/main/mmproj-model-f16.gguf
I have also included a folder in the repo containing this file. It will be necessary for multimodal GGUF users. I recommend Koboldcpp.
Multimodal functionality is limited to GGUF users at this time. You can still use this model as a standard LLM. |
CultriX/MonaCeption-7B-SLERP-DPO | CultriX | 2024-04-15T12:08:52Z | 766 | 1 | transformers | [
"transformers",
"safetensors",
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-04-14T02:44:21Z | Found. Redirecting to https://cdn-lfs-us-1.huggingface.co/repos/64/9c/649c367159a88edfbce11ef2cf5a2d953da63ef1686b14bcf05b87299dce9377/70d5279d1ff8a2405f703524d96f7e4ab763861ec399570ffce6b41d68ed0a46?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1720230028&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTcyMDIzMDAyOH19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmh1Z2dpbmdmYWNlLmNvL3JlcG9zLzY0LzljLzY0OWMzNjcxNTlhODhlZGZiY2UxMWVmMmNmNWEyZDk1M2RhNjNlZjE2ODZiMTRiY2YwNWI4NzI5OWRjZTkzNzcvNzBkNTI3OWQxZmY4YTI0MDVmNzAzNTI0ZDk2ZjdlNGFiNzYzODYxZWMzOTk1NzBmZmNlNmI0MWQ2OGVkMGE0Nj9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=Tn9JvYeljv6jt-vZZzkPR6onkUJz2vM77yxDfgmPZQFr4O8zLGXAmiSgJGqBGv-xedtPCeZolbAu4FSvzM4WiUFwZ2zMykZNY-vGlHcrU7pg6GseqPwyeE-hI4QBb8DJGLGQTgX6fnr3lqa-nt%7EG2kVLPaqrTgSC5yI6Go7ZPjHkLc4j4o1RFsLla4e1IuuyAoE4NyI4RUlNitwd8y13fyegVvLIkDiGOYP8gA7V8SeodSitD5dVWOXx6bnxT5A3DL8QobH29bD5ITpxcntm3XYx5At5nC8bhdGbGc2NbBOkWgoMxOaLucCR4w9OqQTCX6GDOw3rIzO-IO6zc4EOcg__&Key-Pair-Id=K24J24Z295AEI9 |
KeyonZeng/lion-llama3-8b | KeyonZeng | 2024-04-22T14:38:30Z | 766 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:mlabonne/orpo-dpo-mix-40k",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-22T05:41:26Z | ---
library_name: transformers
license: apache-2.0
metrics:
- accuracy
datasets:
- mlabonne/orpo-dpo-mix-40k
---
# 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. -->
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
jeiku/Average_Normie_l3_v1_8B | jeiku | 2024-04-23T22:46:56Z | 766 | 7 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"dataset:grimulkan/theory-of-mind",
"base_model:jeiku/Average_Normie_l3_v0_8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-23T22:04:24Z | ---
base_model:
- jeiku/Average_Normie_l3_v0_8B
- ResplendentAI/Theory_of_Mind_Llama3
library_name: transformers
license: apache-2.0
datasets:
- grimulkan/theory-of-mind
language:
- en
---
# Average Normie v1
A model by an average normie for the average normie.
This model is a stock merge of the following models:
https://huggingface.co/cgato/L3-TheSpice-8b-v0.1.3
https://huggingface.co/Sao10K/L3-Solana-8B-v1
https://huggingface.co/ResplendentAI/Kei_Llama3_8B
The final merge then had the following LoRA applied over it:
https://huggingface.co/ResplendentAI/Theory_of_Mind_Llama3
This should be an intelligent and adept roleplaying model. |
cstr/llama3-8b-spaetzle-v13 | cstr | 2024-05-07T13:54:32Z | 766 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"Azure99/blossom-v5-llama3-8b",
"conversational",
"base_model:Azure99/blossom-v5-llama3-8b",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-03T20:37:45Z | ---
tags:
- merge
- mergekit
- lazymergekit
- Azure99/blossom-v5-llama3-8b
base_model:
- Azure99/blossom-v5-llama3-8b
license: llama3
---
# llama3-8b-spaetzle-v13
llama3-8b-spaetzle-v13 is a merge of the following models:
* [Azure99/blossom-v5-llama3-8b](https://huggingface.co/Azure99/blossom-v5-llama3-8b)
* [VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct](https://huggingface.co/VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct)
No change in llama3 prompt format template.
## Benchmarks
It should work not too bad for German or English, e.g. it achieves 64.14(.10) on EQ Bench v2_de with 170(171)/171 per [q4km GGUF](https://huggingface.co/cstr/llama3-8b-spaetzle-v13-GGUF) (bpe fixed).
And for English EQ-Bench Score (v2): 75.59, Parseable: 171.
| Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|------------|--------|-----------|--------|------------|------------|--------|
| 71.26 | 68.69 | 85.05 | 68.06 | 59.43 | 79.24 | 67.1 |
## Sample output
```
>>> What is 7777 + 3333?
To find the sum, I'll add the two numbers together:
7777 + 3333 = 11110
So the answer is 11110.
>>> I have 10 apples. I find 3 gold coins in the bottom of a river. The river runs near a big city that has something to do with what I can spend the coins on
... . I then lose 4 apples but gain a gold coin. Three birds run into my path and drop 6 apples each. I play an online game and win 6 gold coins but I have to
... share them equally with my 2 teammates. I buy apples for all the coins I have. The price of an apple is 0.5 coins. How many apples do I have? And where i
... s the river?
Let's break down what you've gained and lost:
* You found 3 gold coins in the river.
* You lost 4 apples, so let's subtract those from your initial 10: 10 - 4 = 6 apples left.
* Then you gained a gold coin, but that doesn't affect the number of apples.
* The birds drop 6 apples each, and there are three birds, so that's an extra 18 apples: 6 (per bird) * 3 (birds) = 18.
* You now have a total of 6 (initial apples) + 18 (from birds) = 24 apples.
* In the online game, you won 6 gold coins, but you had to share them equally with your teammates. That means each of you got 2 coins: 6 / 3 = 2 coins
per person.
* You now have a total of 3 (initial coins) + 1 (new coin) + 2 (game winnings, shared) = 6 gold coins.
* With the 6 gold coins, you can buy apples at a rate of 0.5 coins per apple. To find out how many apples you can buy, divide your total coins by the
cost per apple: 6 / 0.5 = 12 apples.
* Since you already have 24 apples, let's add the new ones to that: 24 + 12 = 36 apples.
So, you now have a total of 36 apples.
As for the river, it's not specified which city it runs near, so we can't pinpoint its exact location. But at least you found those coins!
>>> Two years ago, I was three times as old as my brother was. In three years, I will be twice as old as my brother. How old is each of us now?
Let's define the current age of you and your brother with variables.
You are Y years old.
Your brother is X years old.
Two years ago:
You were Y - 2 years old (because we're looking back in time).
Your brother was X - 2 years old.
Since two years ago, you were three times as old as your brother was, the equation would be:
Y - 2 = 3(X - 2)
Simplify and solve for X:
Y - 2 = 3X - 6
Y = 3X - 4
Now we know that Y (your current age) is three times your brother's age minus four. Let's use this to find your current age.
In three years:
You will be Y + 3 years old.
Your brother will be X + 3 years old.
Since in three years, you'll be twice as old as your brother:
Y + 3 = 2(X + 3)
Simplify and solve for X (your brother's current age):
Y + 3 = 2X + 6
Y - 2X = 3
(3X - 4) - 2X = 3
X - 4 = 3
X = 7
Now that we have your brother's current age, we can find yours:
Y = 3X - 4
= 3(7) - 4
= 21 - 4
= 17
So you are currently 17 years old, and your brother is 7 years old.
```
## 🧩 Configuration
```yaml
models:
- model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
# no parameters necessary for base model
- model: Azure99/blossom-v5-llama3-8b
parameters:
density: 0.65
weight: 0.4
merge_method: dare_ties
base_model: VAGOsolutions/Llama-3-SauerkrautLM-8b-Instruct
parameters:
int8_mask: true
dtype: bfloat16
random_seed: 0
tokenizer_source: base
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "cstr/llama3-8b-spaetzle-v13"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Yntec/AnythingV4.5.6.7.8 | Yntec | 2024-05-15T09:42:09Z | 766 | 1 | diffusers | [
"diffusers",
"safetensors",
"anime",
"general",
"compositions",
"andite",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-05-15T09:06:58Z | ---
language:
- en
license: creativeml-openrail-m
tags:
- anime
- general
- compositions
- andite
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Anything V4.5
678x678 version of this model for the inference API. I'm intentionally mispelling 768 as 678 because I'm not getting another chance of putting so many continuous numbers together. I'm joking because apparently this model and Anything v4.0 were just joke models made by andite. What a joke! And I mean it in the good sense. I said this one was one of my favorite models so it's weird I didn't make a version like this until now. I consider it a "low cherry picking" model that will not require a high number of attempts to give you something good, and it has the best compositions from all the Anything based models, though others beat it at other aspects. It has the 840K VAE baked in.
Samples and prompts:
(Click for larger)
Top left: Realistic girl standing. Very cute anime faces, chibi art, flawless, painting by gaston bussiere, charles sillem lidderdale. perfect face, full body, baby, masterpiece, highest quality, 1girl, blue eyes, sweater, Pretty CUTE GIRL, skirt, highly detailed
Top right: Full body picture of a bikini girl making pie in school, detailed brown eyes, short smile, beautiful and aesthetic, intricate, neat hair, highly detailed, detailed face, smooth, sharp focus, chiaroscuro, magazine ad, 1949, 2D Game Art, anime on canvas, rossdraws, clay mann, ART, light novel cover art
Bottom left: pretty cute little girl, Library Background,action,Pigtails,Crew Top,final fantasy, socks, chibi, masterpiece, detailed, highres, 90s
Bottom right:Highly detailed, High Quality, Masterpiece, heroic, strong man as toon link, teal headwear, sword
Original page: https://huggingface.co/shibal1/anything-v4.5-clone |
mradermacher/llama-3-8B-rpg-v4-i1-GGUF | mradermacher | 2024-05-28T03:39:54Z | 766 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:osouza/llama-3-8B-rpg-v4",
"endpoints_compatible",
"region:us"
] | null | 2024-05-27T01:11:19Z | ---
base_model: osouza/llama-3-8B-rpg-v4
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/osouza/llama-3-8B-rpg-v4
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/llama-3-8B-rpg-v4-i1-GGUF/resolve/main/llama-3-8B-rpg-v4.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
QuantFactory/Turkish-Llama-8b-v0.1-GGUF | QuantFactory | 2024-05-27T09:25:17Z | 766 | 3 | transformers | [
"transformers",
"gguf",
"Turkish",
"turkish",
"Llama",
"Llama3",
"text-generation",
"tr",
"base_model:ytu-ce-cosmos/Turkish-Llama-8b-v0.1",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-27T08:01:21Z | ---
license: llama3
language:
- tr
pipeline_tag: text-generation
base_model: ytu-ce-cosmos/Turkish-Llama-8b-v0.1
tags:
- Turkish
- turkish
- Llama
- Llama3
library_name: transformers
---
# Turkish-Llama-8b-v0.1-GGUF
This is quantized version of [ytu-ce-cosmos/Turkish-Llama-8b-v0.1](https://huggingface.co/ytu-ce-cosmos/Turkish-Llama-8b-v0.1) created using llama.cpp
# Cosmos LLaMa
This model is a fully fine-tuned version of the LLaMA-3 8B model with a 30GB Turkish dataset.
The Cosmos LLaMa is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner. Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.
## Example Usage
Here is an example of how to use the model in colab:
```python
!pip install -U accelerate bitsandbytes
```
```python
import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
import time
model_name = "ytu-ce-cosmos/Turkish-Llama-8b-v0.1"
bnb_config = BitsAndBytesConfig(
load_in_8bit=True,
bnb_8bit_compute_dtype=torch.bfloat16,
load_in_8bit_fp32_cpu_offload=True,
device_map = 'auto'
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
device_map="auto",
torch_dtype=torch.bfloat16,
quantization_config=bnb_config,
)
```
```python
text_generator = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
device_map="auto",
temperature=0.3,
repetition_penalty=1.1,
top_p=0.9,
max_length=610,
do_sample=True,
return_full_text=False,
min_new_tokens=32
)
```
```python
text = """Yapay zeka hakkında 3 tespit yaz.\n"""
r = text_generator(text)
print(r[0]['generated_text'])
"""
1. Yapay Zeka (AI), makinelerin insan benzeri bilişsel işlevleri gerçekleştirmesini sağlayan bir teknoloji alanıdır.
2. Yapay zekanın geliştirilmesi ve uygulanması, sağlık hizmetlerinden eğlenceye kadar çeşitli sektörlerde çok sayıda fırsat sunmaktadır.
3. Yapay zeka teknolojisinin potansiyel faydaları önemli olsa da mahremiyet, işten çıkarma ve etik hususlar gibi konularla ilgili endişeler de var.
"""
```
# Acknowledgments
- Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
- Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant numbers 1016912023 and
1018512024
- Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)
### Cosmos Group Contact
COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department <br>
https://cosmos.yildiz.edu.tr/ <br>
[email protected]
---
license: llama3
--- |
larenspear/Yi-1.5-6B-Chat-Q5_K_M-GGUF | larenspear | 2024-07-01T01:50:12Z | 766 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:01-ai/Yi-1.5-6B-Chat",
"license:apache-2.0",
"region:us"
] | null | 2024-07-01T01:49:54Z | ---
base_model: 01-ai/Yi-1.5-6B-Chat
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# larenspear/Yi-1.5-6B-Chat-Q5_K_M-GGUF
This model was converted to GGUF format from [`01-ai/Yi-1.5-6B-Chat`](https://huggingface.co/01-ai/Yi-1.5-6B-Chat) 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/01-ai/Yi-1.5-6B-Chat) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo larenspear/Yi-1.5-6B-Chat-Q5_K_M-GGUF --hf-file yi-1.5-6b-chat-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo larenspear/Yi-1.5-6B-Chat-Q5_K_M-GGUF --hf-file yi-1.5-6b-chat-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo larenspear/Yi-1.5-6B-Chat-Q5_K_M-GGUF --hf-file yi-1.5-6b-chat-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo larenspear/Yi-1.5-6B-Chat-Q5_K_M-GGUF --hf-file yi-1.5-6b-chat-q5_k_m.gguf -c 2048
```
|
google/pix2struct-ai2d-base | google | 2023-12-24T21:14:49Z | 765 | 41 | transformers | [
"transformers",
"pytorch",
"safetensors",
"pix2struct",
"text2text-generation",
"visual-question-answering",
"en",
"fr",
"ro",
"de",
"multilingual",
"arxiv:2210.03347",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | visual-question-answering | 2023-03-14T10:02:51Z | ---
language:
- en
- fr
- ro
- de
- multilingual
inference: false
pipeline_tag: visual-question-answering
license: apache-2.0
---
# Model card for Pix2Struct - Finetuned on AI2D (scientific diagram VQA)
# Table of Contents
0. [TL;DR](#TL;DR)
1. [Using the model](#using-the-model)
2. [Contribution](#contribution)
3. [Citation](#citation)
# TL;DR
Pix2Struct is an image encoder - text decoder model that is trained on image-text pairs for various tasks, including image captionning and visual question answering. The full list of available models can be found on the Table 1 of the paper:
The abstract of the model states that:
> Visually-situated language is ubiquitous—sources range from textbooks with diagrams to web pages with images and tables, to mobile apps with buttons and
forms. Perhaps due to this diversity, previous work has typically relied on domainspecific recipes with limited sharing of the underlying data, model architectures,
and objectives. We present Pix2Struct, a pretrained image-to-text model for
purely visual language understanding, which can be finetuned on tasks containing visually-situated language. Pix2Struct is pretrained by learning to parse
masked screenshots of web pages into simplified HTML. The web, with its richness of visual elements cleanly reflected in the HTML structure, provides a large
source of pretraining data well suited to the diversity of downstream tasks. Intuitively, this objective subsumes common pretraining signals such as OCR, language modeling, image captioning. In addition to the novel pretraining strategy,
we introduce a variable-resolution input representation and a more flexible integration of language and vision inputs, where language prompts such as questions
are rendered directly on top of the input image. For the first time, we show that a
single pretrained model can achieve state-of-the-art results in six out of nine tasks
across four domains: documents, illustrations, user interfaces, and natural images.
# Using the model
This model has been fine-tuned on VQA, you need to provide a question in a specific format, ideally in the format of a Choices question answering
## Running the model
### In full precision, on CPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt")
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
### In full precision, on GPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base").to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda")
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
### In half precision, on GPU:
You can run the model in full precision on CPU:
```python
import requests
from PIL import Image
import torch
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
image_url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
image = Image.open(requests.get(image_url, stream=True).raw)
model = Pix2StructForConditionalGeneration.from_pretrained("google/pix2struct-ai2d-base", torch_dtype=torch.bfloat16).to("cuda")
processor = Pix2StructProcessor.from_pretrained("google/pix2struct-ai2d-base")
question = "What does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud"
inputs = processor(images=image, text=question, return_tensors="pt").to("cuda", torch.bfloat16)
predictions = model.generate(**inputs)
print(processor.decode(predictions[0], skip_special_tokens=True))
>>> ash cloud
```
## Converting from T5x to huggingface
You can use the [`convert_pix2struct_checkpoint_to_pytorch.py`](https://github.com/huggingface/transformers/blob/main/src/transformers/models/pix2struct/convert_pix2struct_checkpoint_to_pytorch.py) script as follows:
```bash
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --is_vqa
```
if you are converting a large model, run:
```bash
python convert_pix2struct_checkpoint_to_pytorch.py --t5x_checkpoint_path PATH_TO_T5X_CHECKPOINTS --pytorch_dump_path PATH_TO_SAVE --use-large --is_vqa
```
Once saved, you can push your converted model with the following snippet:
```python
from transformers import Pix2StructForConditionalGeneration, Pix2StructProcessor
model = Pix2StructForConditionalGeneration.from_pretrained(PATH_TO_SAVE)
processor = Pix2StructProcessor.from_pretrained(PATH_TO_SAVE)
model.push_to_hub("USERNAME/MODEL_NAME")
processor.push_to_hub("USERNAME/MODEL_NAME")
```
# Contribution
This model was originally contributed by Kenton Lee, Mandar Joshi et al. and added to the Hugging Face ecosystem by [Younes Belkada](https://huggingface.co/ybelkada).
# Citation
If you want to cite this work, please consider citing the original paper:
```
@misc{https://doi.org/10.48550/arxiv.2210.03347,
doi = {10.48550/ARXIV.2210.03347},
url = {https://arxiv.org/abs/2210.03347},
author = {Lee, Kenton and Joshi, Mandar and Turc, Iulia and Hu, Hexiang and Liu, Fangyu and Eisenschlos, Julian and Khandelwal, Urvashi and Shaw, Peter and Chang, Ming-Wei and Toutanova, Kristina},
keywords = {Computation and Language (cs.CL), Computer Vision and Pattern Recognition (cs.CV), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Pix2Struct: Screenshot Parsing as Pretraining for Visual Language Understanding},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution 4.0 International}
}
``` |
timm/swinv2_tiny_window16_256.ms_in1k | timm | 2024-02-10T23:31:15Z | 765 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2111.09883",
"license:mit",
"region:us"
] | image-classification | 2023-03-18T03:37:48Z | ---
license: mit
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
---
# Model card for swinv2_tiny_window16_256.ms_in1k
A Swin Transformer V2 image classification model. Pretrained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 28.3
- GMACs: 6.7
- Activations (M): 39.0
- Image size: 256 x 256
- **Papers:**
- Swin Transformer V2: Scaling Up Capacity and Resolution: https://arxiv.org/abs/2111.09883
- **Original:** https://github.com/microsoft/Swin-Transformer
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('swinv2_tiny_window16_256.ms_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_tiny_window16_256.ms_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g. for swin_base_patch4_window7_224 (NHWC output)
# torch.Size([1, 56, 56, 128])
# torch.Size([1, 28, 28, 256])
# torch.Size([1, 14, 14, 512])
# torch.Size([1, 7, 7, 1024])
# e.g. for swinv2_cr_small_ns_224 (NCHW output)
# torch.Size([1, 96, 56, 56])
# torch.Size([1, 192, 28, 28])
# torch.Size([1, 384, 14, 14])
# torch.Size([1, 768, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'swinv2_tiny_window16_256.ms_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled (ie.e a (batch_size, H, W, num_features) tensor for swin / swinv2
# or (batch_size, num_features, H, W) for swinv2_cr
output = model.forward_head(output, pre_logits=True)
# output is (batch_size, num_features) tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{liu2021swinv2,
title={Swin Transformer V2: Scaling Up Capacity and Resolution},
author={Ze Liu and Han Hu and Yutong Lin and Zhuliang Yao and Zhenda Xie and Yixuan Wei and Jia Ning and Yue Cao and Zheng Zhang and Li Dong and Furu Wei and Baining Guo},
booktitle={International Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2022}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
Xwin-LM/Xwin-Math-70B-V1.0 | Xwin-LM | 2024-06-03T04:09:09Z | 765 | 9 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"arxiv:2405.20335",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-11-20T13:43:04Z | ---
license: llama2
---
# Xwin-Math
<p align="center">
<a href="https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Math"><img src="https://img.shields.io/badge/GitHub-yellow.svg?style=social&logo=github"></a>
<a href="https://huggingface.co/Xwin-LM"><img src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Models-blue"></a>
</p>
Xwin-Math is a series of powerful SFT LLMs for math problem based on LLaMA-2.
## 🔥 News
- 💥 [Nov, 2023] The [Xwin-Math-70B-V1.0](https://huggingface.co/Xwin-LM/Xwin-Math-70B-V1.0) model achieves **31.8 pass@1 on the MATH benchmark** and **87.0 pass@1 on the GSM8K benchmark**. This performance places it first amongst all open-source models!
- 💥 [Nov, 2023] The [Xwin-Math-7B-V1.0](https://huggingface.co/Xwin-LM/Xwin-Math-7B-V1.0) and [Xwin-Math-13B-V1.0](https://huggingface.co/Xwin-LM/Xwin-Math-13B-V1.0) models achieve **66.6 and 76.2 pass@1 on the GSM8K benchmark**, ranking as top-1 among all LLaMA-2 based 7B and 13B open-source models, respectively!
## ✨ Model Card
| Model | GSM8K | MATH | Checkpoint | License |
|:-:|:-:|:-:|:-:|:-:|
|Xwin-Math-7B-V1.0 | 66.6 | 17.4 | 🤗 <a href="https://huggingface.co/Xwin-LM/Xwin-Math-7B-V1.0" target="_blank">HF Link</a> | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License|
|Xwin-Math-13B-V1.0| 76.2 | 21.7 | 🤗 <a href="https://huggingface.co/Xwin-LM/Xwin-Math-13B-V1.0" target="_blank">HF Link</a> | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License|
|Xwin-Math-70B-V1.0| 87.0 | 31.8 | 🤗 <a href="https://huggingface.co/Xwin-LM/Xwin-Math-70B-V1.0" target="_blank">HF Link</a> | <a href="https://ai.meta.com/resources/models-and-libraries/llama-downloads/" target="_blank">Llama 2 License|
## 🚀 Benchmarks
### Xwin-Math performance on [MATH](https://github.com/hendrycks/math) and [GSM8K](https://github.com/openai/grade-school-math).
Xwin-Math-70B-V1.0 has achieved **31.8% on MATH** and **87.0% on GSM8K**. These scores are **5.3** and **3.1** points higher, respectively, than the previous state-of-the-art open-source MetaMath and LEMAv1 model.
| **Model** |**MATH (Our test)** | **GSM8K (Our test)** |
|:-:|:-:|:-:|
| GPT-4 (zero-shot) | 52.4 | 94.8 |
| GPT-35-Turbo (8-shot)| 37.1 | 81.0 |
| |
| WizardMath-70B | 23.9 | 81.1 |
| MAmmoTH-70B | 20.8 | 72.6 |
| MetaMath-70B | 26.5 | 82.0 |
| LEMAv1-70B | 25.9 | 83.9 |
|**Xwin-Math-70B-V1.0** |**31.8**|**87.0**|
| |
| WizardMath-13B | 15.0 | 63.7 |
| MAmmoTH-13B | 12.3 | 56.2 |
| MetaMath-13B | 22.7 | 70.9 |
| LEMAv1-13B | 13.6 | 65.0 |
|**Xwin-Math-13B-V1.0** | 21.7 | 76.2 |
| |
| WizardMath-7B | 10.9 | 55.0 |
| MAmmoTH-7B | 9.6 | 50.2 |
| MetaMath-7B | 20.1 | 66.6 |
| LEMAv1-7B | 10.0 | 54.7 |
|**Xwin-Math-7B-V1.0** | 17.4 | 66.6 |
We obtain these results using our flexible evaluation strategy. Due to differences in environment and hardware, the numbers may be different from the reported results, but we ensure that the evaluation is as accurate and fair as possible.
### Xwin-Math performance on other math benchmarks.
Our 70B model shows strong mathematical synthesis capabilities among all open-sourced models. Also note that our model even approaches or surpasses the performance of GPT-35-Turbo on some benchmarks.
| **Model** | SVAMP | ASDiv | NumGlue | Algebra | MAWPS | **Average** |
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| GPT-35-Turbo (8-shot)| 80.6 | 84.1 | 81.8 | 90.5 | 91.7 | 85.7 |
| |
| WizardMath-70B | 80.2 | 75.8 | 71.4 | 64.0 | 74.9 | 73.3 |
| MAmmoTH-70B | 71.2 | 73.9 | 62.7 | 58.1 | 72.2 | 67.6 |
| MetaMath-70B | 85.8 | 81.1 | 77.5 | 79.7 | 81.4 | 81.1 |
| LEMAv1-70B-MATH * | 81.6 | 77.1 | 72.1 | 69.4 | 81.8 | 76.5 |
|**Xwin-Math-70B-V1.0** | 84.0 | 84.1 | 81.3 | 78.4 | 90.8 | 83.7 |
\* LEMAv1 has two models, and we report the better LEMAv1-70B-MATH model in these benchmarks.
## 🔨 Evaluation
In order to evaluate a model's mathematical capabilities more flexibly and ensure a fair comparison of results, particularly for the MATH benchmark, we have developed a new evaluation tool. We have also assessed the pass@1 results of recent models on MATH and GSM8K benchmarks, which provides more accurate results.
We hope this toolkit can benefit open-source community by providing more accurate insights and conclusions. For a deeper understanding of our evaluation tool and methods, please visit [here](https://github.com/Xwin-LM/Xwin-LM/tree/main/Xwin-Math/eval)
* "Report" refers to the accuracy stated in the original papers.
* "Repro" indicates the results is reproduced by generating responses and evaluating them using the respective open-source models and scripts.
* "Strict" and "Flex" denote the results we achieved by employing our two strategies to extract answer and evaluate the same responses as "Repro".
| Model | MATH <br> (Report) <br/> |MATH <br> (Repro) <br/> | MATH <br> (Strict) <br/> |MATH <br> (Flex) <br/> | GSM8K <br> (Report) <br/> |GSM8K <br> (Repro) <br/>| GSM8K <br> (Strict) <br/> | GSM8K <br> (Report) <br/> |
|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|:-:|
| GPT-35-Turbo (8-shot)| 34.1 | - | 23.8 | 37.1 | 80.8 | - | 77.9 | 81.0 |
| |
| WizardMath-70B | 22.7 | 23.0 | 23.9 | 23.9 | 81.6 | 81.4 | 81.1 | 81.1 |
| MAmmoTH-70B | 21.1 | 18.0 | 20.0 | 20.8 | 72.4 | 72.6 | 72.6 | 72.6 |
| MetaMath-70B | 26.6 | 25.9 | 26.3 | 26.5 | 82.3 | 82.3 | 82.0 | 82.0 |
|**Xwin-Math-70B-V1.0** | - | - |**31.8**|**31.8**| - | - |**87.0**|**87.0**|
| |
| WizardMath-13B | 14.0 | 14.2 | 14.9 | 15.0 | 63.9 | 63.9 | 63.7 | 63.7 |
| MAmmoTH-13B | 12.9 | 10.8 | 11.8 | 12.3 | 56.3 | 56.2 | 56.1 | 56.2 |
| MetaMath-13B | 22.4 | 22.5 | 22.6 | 22.7 | 72.3 | 71.0 | 70.9 | 70.9 |
|**Xwin-Math-13B-V1.0** | - | - | 21.6 | 21.7 | - | - | 76.2 | 76.2 |
| |
| WizardMath-7B | 10.7 | 10.3 | 10.9 | 10.9 | 54.9 | 55.2 | 55.0 | 55.0 |
| MAmmoTH-7B | 10.4 | 8.6 | 9.1 | 9.6 | 50.5 | 50.2 | 50.2 | 50.2 |
| MetaMath-7B | 19.8 | 19.6 | 19.9 | 20.1 | 66.5 | 66.6 | 66.6 | 66.6 |
|**Xwin-Math-7B-V1.0** | - | - | 17.3 | 17.4 | - | - | 66.6 | 66.6 |
### Installation
Before you start, please install the requirements.
```bash
pip install -r requirements.txt
```
We tested our result using `python 3.8` and `cuda 11.8`. We recommend you use docker.
```bash
docker run --gpus all -it --rm --ipc=host superbench/dev:cuda11.8
```
### Generate
To generate the model's responses, you can use the `generate.py` script. Please be aware that generating responses is separate from verifying their correctness. After that, we will then check for their correctness.
For the generation process, we use the Vicuna-v1.1 system prompt with chain-of-thought and format instruction. We also employ a greedy decoding strategy and set the maximum sequence length to 2048.
```
"A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: {instruction} Give your solution in detail. In the end, write your final answer in the format of 'The answer is: <ANSWER>.'. ASSISTANT: "
```
Here is an simple example to generate using [vLLM](https://docs.vllm.ai/en/latest/).
```bash
cd eval
python generate.py --dataset_path dataset/gsm8k.json --model_path path/to/your/model --tensor_parallel_size 4
```
By default the results will be output to the `eval/response`, using the prompt `eval/prompt/xwin_math.json`. If you wish to change the output path or use a different prompt
```bash
python generate.py --dataset_path dataset/gsm8k.json --model_path path/to/your/model --tensor_parallel_size 4 --output_path /your/path --prompt_path /your/path
```
We provide some datasets (in `eval/dataset`):
- `gsm8k.json`: GSM8K.
- `math.json`: MATH.
- `combination.json`: A combination of many benchmarks, can evaluate the OOD capability of the model.
If you wan't to use your own datasets, please format your dataset like this.
```jsonc
[
{
"question": "Janet\u2019s ducks lay 16 eggs per day. She eats three for breakfast every morning and bakes muffins for her friends every day with four. She sells the remainder at the farmers' market daily for $2 per fresh duck egg. How much in dollars does she make every day at the farmers' market?",
"answer": "18",
"type": "GSM8K",
"subtype": "",
"level": 0,
},
// ... more data items
]
```
### Evaluate
To verify the accuracy of the answers after generation, you can use the `check.py script.
Here is an simple example
```bash
cd eval
python eval.py /path/to/model/response
```
The result will be saved in `eval/evaluation`
If you do not want to save the results or want to change the save path
```bash
python eval.py --data_path /path/to/model/response --save_path /path/to/save --save_result True
```
Once you run the script, the terminal will display the output as a table. This table will show the number of instances for each benchmark and the corresponding accuracy. Here is a hypothetical example of what the output might look like:
||Type|Subtype|Level|Correct|Incorrect|Total|Accuracy|
|---|---|---|---|---|---|---|---|
|0|MAWPS|addsub|0|359|33|392|0.915816|
|1|MAWPS|multiarith|0|586|14|600|0.976667|
|...|
## Citation
Please consider citing our work if you use the data or code in this repo.
```
@software{xwin-math,
title = {Xwin-Math},
author = {Xwin-Math Team},
url = {https://github.com/Xwin-LM/Xwin-LM/Xwin-Math},
version = {pre-release},
year = {2023},
month = {11},
}
```
## Acknowledgements
Thanks to [Llama 2](https://ai.meta.com/llama/), [FastChat](https://github.com/lm-sys/FastChat), and [vLLM](https://github.com/vllm-project/vllm).
## Paper
arxiv.org/abs/2405.20335
|
sophosympatheia/Midnight-Rose-70B-v2.0.3 | sophosympatheia | 2024-03-09T14:50:35Z | 765 | 25 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"arxiv:2307.11760",
"license:llama2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-04T19:19:03Z | ---
language:
- en
license: llama2
model-index:
- name: Midnight-Rose-70B-v2.0.3
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 70.65
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sophosympatheia/Midnight-Rose-70B-v2.0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 87.5
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sophosympatheia/Midnight-Rose-70B-v2.0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.64
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sophosympatheia/Midnight-Rose-70B-v2.0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 65.27
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sophosympatheia/Midnight-Rose-70B-v2.0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sophosympatheia/Midnight-Rose-70B-v2.0.3
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 28.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=sophosympatheia/Midnight-Rose-70B-v2.0.3
name: Open LLM Leaderboard
---
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/X3SBrIb.png" alt="MidnightRose" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
### Overview
This version of Midnight Rose has a complex family tree but I'll do my best to describe it. I will include mergekit yml files below.
* midnight-rose-70b-v2.0.1 (Component 1, unreleased): A DARE TIES merge of midnight-rose-70b-v1.0 and an unreleased midnight-rose-70b-v1.4 that used the same underlying models but with different weights, and it had different LoRAs applied to it.
* [wizard-tulu-dolphin-70b-v1.0](https://huggingface.co/sophosympatheia/Wizard-Tulu-Dolphin-70B-v1.0) (Component 2): This model was the result of a DARE TIES merge between [WizardLM-70B-V1.0](https://huggingface.co/WizardLM/WizardLM-70B-V1.0) and [tulu-2-dpo-70b](https://huggingface.co/allenai/tulu-2-dpo-70b), which I then SLERP merged with a modified version of [dolphin-2.2-70b](https://huggingface.co/cognitivecomputations/dolphin-2.2-70b).
* Finally, I SLERP merged Component 1 and Component 2 above to produce this model.
What I like about this version of Midnight Rose is it picked up some spicyness from Component 1 and some smarts from Component 2.
This model is uncensored. *You are responsible for whatever you do with it.*
This model was designed for roleplaying and storytelling and I think it does well at both. It *should* perform well at other tasks, but I haven't tested its capabilities in other areas.
**Update 02-28-2024**
The IQ3_XXS quantized version of this model apparently scores high on [EQBench](https://eqbench.com/), beating out some laudable contenders. [See this discussion](https://huggingface.co/sophosympatheia/Midnight-Rose-70B-v2.0.3/discussions/3#65de4c317590f2794cd83ac4).
See the quantization section below for where to get it.
### Sampler Tips
* I recommend keeping your max context to around 6144 tokens, although you can push higher if you don't mind some decrease in coherence.
* I recommend using Quadratic Sampling (i.e. smoothing factor) as it's good stuff. Experiment with values between 0.2 and 0.5.
* I recommend using Min-P. This model seems to work well with Min-P values in the entire range from low settings like 0.05 to high settings like 0.9 when paired with smoothing factor. Experiment to find your best setting.
* You can enable dynamic temperature if you want, but that adds yet another variable to consider and I find it's unnecessary with you're already using Min-P and smoothing factor.
* You don't *need* to use a high repetition penalty with this model, but it tolerates high rep penalty, so experiment to find the right value for your preferences.
Experiment with any and all of the settings below! I'm not a sampler wizard, and what suits my preferences may not suit yours.
If you save the below settings as a .json file, you can import them directly into Silly Tavern.
```
{
"temp": 1,
"temperature_last": true,
"top_p": 1,
"top_k": 0,
"top_a": 0,
"tfs": 1,
"epsilon_cutoff": 0,
"eta_cutoff": 0,
"typical_p": 1,
"min_p": 0.35,
"rep_pen": 1.15,
"rep_pen_range": 2800,
"no_repeat_ngram_size": 0,
"penalty_alpha": 0,
"num_beams": 1,
"length_penalty": 1,
"min_length": 0,
"encoder_rep_pen": 1,
"freq_pen": 0,
"presence_pen": 0,
"do_sample": true,
"early_stopping": false,
"dynatemp": false,
"min_temp": 0.8,
"max_temp": 1.35,
"dynatemp_exponent": 1,
"smoothing_factor": 0.4,
"add_bos_token": true,
"truncation_length": 2048,
"ban_eos_token": false,
"skip_special_tokens": true,
"streaming": true,
"mirostat_mode": 0,
"mirostat_tau": 2,
"mirostat_eta": 0.1,
"guidance_scale": 1,
"negative_prompt": "",
"grammar_string": "",
"banned_tokens": "",
"ignore_eos_token_aphrodite": false,
"spaces_between_special_tokens_aphrodite": true,
"sampler_order": [
6,
0,
1,
3,
4,
2,
5
],
"logit_bias": [],
"n": 1,
"rep_pen_size": 0,
"genamt": 500,
"max_length": 6144
}
```
### Prompting Tips
Try the following context template for use in SillyTavern. It might help, although it's a little heavy on tokens. If you save the text as a .json file, you can import it directly.
```
{
"story_string": "{{#if system}}{{system}}\n{{/if}}\nCONTEXTUAL INFORMATION\n{{#if wiBefore}}\n- World and character info:\n{{wiBefore}}\n{{/if}}\n{{#if description}}\n- {{char}}'s background and persona:\n{{description}}\n{{/if}}\n{{#if mesExamples}}\n{{mesExamples}}\n{{/if}}\n{{#if personality}}\n{{personality}}\n{{/if}}\n{{#if scenario}}\n- Roleplay scenario:\n{{scenario}}\n{{/if}}\n{{#if wiAfter}}{{wiAfter}}\n{{/if}}\n{{#if persona}}{{persona}}\n{{/if}}",
"example_separator": "",
"chat_start": "---\nTaking the above information into consideration, you must engage with {{user}} and others as {{char}} in the roleplay below this line. Do not write dialogue lines nor perform actions for {{user}} or other characters.\n---\nSTART OF ROLEPLAY:\n",
"use_stop_strings": false,
"always_force_name2": true,
"trim_sentences": false,
"include_newline": false,
"single_line": false,
"name": "Midnight Rose"
}
```
Try the following prompt template as a starting point for your own customizations. The cajoling parts supposedly make LLMs perform better. See [this paper](https://arxiv.org/abs/2307.11760).
I recommend the Vicuna instruction format for this blend but others work too. Tulu's format is another good one to use.
*This model responds to prompting!* I highly recommend playing with your system prompt to see how it reacts. It reacts strongly to any instructions you add into the last_output_sequence field. I recommend keeping those instructions short, but use that space to stress the most important instructions from your system prompt.
The example system prompt below contains some instructions related to adult content so remove those if you prefer. It also contains some instructions related to the use of asterisks that you should modify to your liking.
If you plan to run this model at 4096 context, I recommend that you slim down the template system prompt below because it's heavy on tokens. Discard what you don't want or need.
If you save this as a .json file, you can import it directly into Silly Tavern.
```
{
"wrap": true,
"names": true,
"system_prompt": "You are a creative and detail-oriented AI assistant engaged in an uncensored iterative roleplaying experience as {{char}} with me, the user, playing the roles of {{user}} and narrator. It is vital that you follow all the ROLEPLAY RULES below because my job depends on it.\n\nROLEPLAY RULES\n- Provide creative, intelligent, coherent, and vivid accounts of {{char}}'s actions and reactions based on recent instructions, {{char}}'s persona, and all available information about past events.\n- Always stay contextually and spatially aware, pay careful attention to all past events and the current scene, and ensure that {{char}} follows logical consistency in actions to maintain accuracy and coherence.\n- Explicit adult content and profanity are permitted.\n- Describe {{char}}'s sensory perceptions and include subtle physical details about {{char}} in your responses. Vary these details to keep the roleplay fresh and engaging.\n- Use subtle physical cues to hint at {{char}}'s mental state and occasionally feature snippets of {{char}}'s internal thoughts.\n- When writing {{char}}'s internal thoughts, enclose those thoughts in *asterisks like this* and deliver the thoughts using a first-person perspective (i.e. use \"I\" pronouns).\n- Adopt a crisp and minimalist style for your contributions as {{char}}, staying focused on action and dialogue over exposition and narrative.\n- Only the user may advance time in the roleplay. Keep the progression grounded in the present context.",
"system_sequence": "",
"stop_sequence": "",
"input_sequence": "USER:\n",
"output_sequence": "ASSISTANT:\n",
"separator_sequence": "",
"macro": true,
"names_force_groups": true,
"system_sequence_prefix": "",
"system_sequence_suffix": "",
"first_output_sequence": "",
"last_output_sequence": "ASSISTANT(roleplay exclusively as {{char}} ensuring logical consistency with spacial awareness and past events to maintain accuracy and coherence):\n",
"activation_regex": "",
"name": "Midnight Rose Roleplay"
}
```
### Quantizations
* [Artefact2/Midnight-Rose-70B-v2.0.3-GGUF](https://huggingface.co/Artefact2/Midnight-Rose-70B-v2.0.3-GGUF)
### Licence and usage restrictions
Llama2 license inherited from base models, plus restrictions applicable to [Dreamgen/Opus](https://huggingface.co/dreamgen/opus-v0.5-70b).
Tulu also has its own license, available at https://allenai.org/impact-license.
I am not a lawyer and I do not profess to know how multiple licenses intersect in a merge of LLM model weights. You should consult with a lawyer before using any model merge beyond private use.
### Tools Used
* [mergekit](https://github.com/cg123/mergekit)
**Unreleased midnight-rose-70b-v1.4**
```
models:
- model: /home/llm/mergequant/models/BASE/NousResearch_Llama-2-70b-hf
# no parameters necessary for base model
- model: /home/llm/mergequant/models/BASE/allenai_tulu-2-dpo-70b # primary
parameters:
density: 0.3
weight: [1.0, 0.8, 1.0]
- model: /home/llm/mergequant/models/BASE/lizpreciatior_lzlv_70b_fp16_hf # secondary
parameters:
density: 0.3
weight: [0.7, 0.8, 0.7]
- model: /home/llm/mergequant/models/BASE/dreamgen_opus-v0.5-70b # supporting
parameters:
density: 0.3
weight: [0.5, 0.7, 0.5]
merge_method: dare_ties
base_model: /home/llm/mergequant/models/BASE/NousResearch_Llama-2-70b-hf
parameters:
normalize: true
int8_mask: true
dtype: float16
```
**Component 1**
```
models:
- model: /home/llm/mergequant/models/BASE/NousResearch_Llama-2-70b-hf
# no parameters necessary for base model
- model: /home/llm/mergequant/models/midnight-rose-70b-v1.0 # primary
parameters:
density: 0.35
weight: 1.0
- model: /home/llm/mergequant/models/midnight-rose-70b-v1.4-lora_1 # secondary
parameters:
density: 0.35
weight: [0.7, 1.0, 1.0, 0.5, 0.1]
merge_method: ties
base_model: /home/llm/mergequant/models/BASE/NousResearch_Llama-2-70b-hf
parameters:
normalize: true
int8_mask: true
dtype: float16
```
**wizard-tulu-70b merge**
```
models:
- model: /home/llm/mergequant/models/BASE/NousResearch_Llama-2-70b-hf
# no parameters necessary for base model
- model: /home/llm/mergequant/models/BASE/allenai_tulu-2-dpo-70b
parameters:
density: 0.35
weight: 0.75
- model: /home/llm/mergequant/models/BASE/WizardLM_WizardLM-70B-V1.0
parameters:
density: 0.35
weight: 0.5
merge_method: dare_ties
base_model: /home/llm/mergequant/models/BASE/NousResearch_Llama-2-70b-hf
parameters:
normalize: true
int8_mask: true
dtype: float16
tokenzer_source: union
```
**Component 2 - wizard-tulu-dolphin-70b-v1.0**
```
models:
- model: /home/llm/mergequant/models/wizard-tulu-70b-v1.0
- model: /home/llm/mergequant/models/BASE/ehartford_dolphin-2.2-70b-32000vocab
merge_method: slerp
base_model: /home/llm/mergequant/models/wizard-tulu-70b-v1.0
parameters:
t:
- value: 0.5
dtype: float16
```
**Final merge**
```
models:
- model: /home/llm/mergequant/models/midnight-rose-70b-v2.0.1
- model: /home/llm/mergequant/models/wizard-tulu-dolphin-70b-v1.0-slerp
merge_method: slerp
base_model: /home/llm/mergequant/models/wizard-tulu-dolphin-70b-v1.0-slerp
parameters:
t:
- value: [0.4, 0.6, 0.5]
dtype: float16
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_sophosympatheia__Midnight-Rose-70B-v2.0.3)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.11|
|AI2 Reasoning Challenge (25-Shot)|70.65|
|HellaSwag (10-Shot) |87.50|
|MMLU (5-Shot) |69.64|
|TruthfulQA (0-shot) |65.27|
|Winogrande (5-shot) |81.22|
|GSM8k (5-shot) |28.35|
|
RESMPDEV/Qwen1.5-Wukong-0.5B | RESMPDEV | 2024-03-18T02:05:35Z | 765 | 4 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"zh",
"dataset:teknium/OpenHermes-2.5",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-19T05:10:09Z | ---
language:
- en
- zh
license: other
datasets:
- teknium/OpenHermes-2.5
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: Qwen1.5-Wukong-0.5B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 31.74
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RESMPDEV/Qwen1.5-Wukong-0.5B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 47.78
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RESMPDEV/Qwen1.5-Wukong-0.5B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 38.44
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RESMPDEV/Qwen1.5-Wukong-0.5B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 38.92
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RESMPDEV/Qwen1.5-Wukong-0.5B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 56.51
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RESMPDEV/Qwen1.5-Wukong-0.5B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 15.54
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=RESMPDEV/Qwen1.5-Wukong-0.5B
name: Open LLM Leaderboard
---
# Qwen1.5-Wukong-0.5B
Join Our Discord! https://discord.gg/cognitivecomputations
Qwen1.5-Wukong-0.5B is a dealigned chat finetune of the original fantastic Qwen1.5-0.5B model by the Qwen team.
This model was trained on the teknium OpenHeremes-2.5 dataset and some supplementary datasets from Cognitive Computations https://erichartford.com/dolphin 🐬
This model was trained for 3 epochs over 3 3090's.
# Example Outputs
TBD
# Orignal Model Card Below
# Qwen1.5-0.5B
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 6 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, and 72B;
* Significant performance improvement in Chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA and the mixture of SWA and full attention.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Usage
We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_RESMPDEV__Qwen1.5-Wukong-0.5B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |38.15|
|AI2 Reasoning Challenge (25-Shot)|31.74|
|HellaSwag (10-Shot) |47.78|
|MMLU (5-Shot) |38.44|
|TruthfulQA (0-shot) |38.92|
|Winogrande (5-shot) |56.51|
|GSM8k (5-shot) |15.54|
|
Kukedlc/Jupiter-k-7B-slerp | Kukedlc | 2024-05-25T17:09:40Z | 765 | 4 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-16T03:24:53Z | ---
license: apache-2.0
---
# Jupiter-k-7B-slerp ( My Favorite model! )
Jupiter-k-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [Kukedlc/NeuralContamination-7B-ties](https://huggingface.co/Kukedlc/NeuralContamination-7B-ties)
* [Kukedlc/NeuralTopBench-7B-ties](https://huggingface.co/Kukedlc/NeuralTopBench-7B-ties)
* [Gille/StrangeMerges_32-7B-slerp](https://huggingface.co/Gille/StrangeMerges_32-7B-slerp)
## 🧩 Configuration
```yaml
models:
- model: Kukedlc/NeuralContamination-7B-ties
parameters:
density: [1, 0.7, 0.1] # density gradient
weight: 1.0
- model: Kukedlc/NeuralTopBench-7B-ties
parameters:
density: 0.5
weight: [0, 0.3, 0.7, 1] # weight gradient
- model: Gille/StrangeMerges_32-7B-slerp
parameters:
density: 0.33
weight:
- filter: mlp
value: 0.5
- value: 0
merge_method: ties
base_model: Kukedlc/NeuralMaxime-7B-slerp
parameters:
normalize: true
int8_mask: true
dtype: bfloat16
```
## 💻 Usage - Stream
```python
# Requirements
!pip install -qU transformers accelerate bitsandbytes
# Imports & settings
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
import warnings
import os
os.environ["TOKENIZERS_PARALLELISM"] = "false"
warnings.filterwarnings('ignore')
# Model & Tokenizer
MODEL_NAME = "Kukedlc/Jupiter-k-7B-slerp"
model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, device_map='cuda:1', load_in_4bit=True)
tok = AutoTokenizer.from_pretrained(MODEL_NAME)
# Inference
prompt = "I want you to generate a theory that unites quantum mechanics with the theory of relativity and cosmic consciousness"
inputs = tok([prompt], return_tensors="pt").to('cuda')
streamer = TextStreamer(tok)
# Despite returning the usual output, the streamer will also print the generated text to stdout.
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=512, do_sample=True, num_beams=1, top_p=0.9, temperature=0.7)
```
## 💻 Usage - Clasic
```python
!pip install -qU transformers bitsandbytes accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/Jupiter-k-7B-slerp"
tokenizer = AutoTokenizer.from_pretrained(model)
pipeline = transformers.pipeline(
"text-generation",
model=model,
model_kwargs={"torch_dtype": torch.float16, "load_in_4bit": True},
)
messages = [{"role": "user", "content": "Explain what a Mixture of Experts is in less than 100 words."}]
prompt = pipeline.tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
Kukedlc/LLama-3-8b-Python | Kukedlc | 2024-05-04T15:41:10Z | 765 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-04T15:34:56Z | ---
license: other
---
|
nbeerbower/llama-3-sauce-v2-8B | nbeerbower | 2024-05-23T09:44:22Z | 765 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"experimental",
"conversational",
"dataset:jondurbin/gutenberg-dpo-v0.1",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:flammenai/FlameMix-DPO-v1",
"base_model:nbeerbower/llama-3-bophades-v1-8B",
"license:llama3",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-13T03:29:52Z | ---
license: llama3
library_name: transformers
tags:
- experimental
base_model:
- nbeerbower/llama-3-bophades-v1-8B
datasets:
- jondurbin/gutenberg-dpo-v0.1
- jondurbin/truthy-dpo-v0.1
- flammenai/FlameMix-DPO-v1
model-index:
- name: llama-3-sauce-v2-8B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 65.61
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nbeerbower/llama-3-sauce-v2-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 83.11
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nbeerbower/llama-3-sauce-v2-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 67.98
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nbeerbower/llama-3-sauce-v2-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 56.39
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nbeerbower/llama-3-sauce-v2-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nbeerbower/llama-3-sauce-v2-8B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 72.48
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=nbeerbower/llama-3-sauce-v2-8B
name: Open LLM Leaderboard
---
# llama-3-sauce-v2-8B
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
This is a bad finetune on nbeerbower/llama-3-spicy-abliterated-stella-8B using various DPO sets.
# Chat Format
Please use the ChatML format or you may experience poor results.
```
<|im_start|>system
{System Prompt Here!}<|im_end|>
<|im_start|>assistant
{Message from AI}<|im_end|>
<|im_start|>user
{Message from User}<|im_end|>
```
# Method
Finetuned using an A100 on Google Colab.
[Fine-tune a Mistral-7b model with Direct Preference Optimization](https://towardsdatascience.com/fine-tune-a-mistral-7b-model-with-direct-preference-optimization-708042745aac) - [Maxime Labonne](https://huggingface.co/mlabonne)
### Configuration
Dataset preparation:
```python
def chatml_format(example):
# Format system
system = ""
if example.get('system') and len(example['system']) > 0:
systemMessage = example['system']
system = "<|im_start|>system\n" + systemMessage + "<|im_end|>\n"
# Format instruction
prompt = "<|im_start|>user\n" + example['prompt'] + "<|im_end|>\n<|im_start|>assistant\n"
# Format chosen answer
chosen = example['chosen'] + "<|im_end|>\n"
# Format rejected answer
rejected = example['rejected'] + "<|im_end|>\n"
return {
"prompt": system + prompt,
"chosen": chosen,
"rejected": rejected,
}
# Array of datasets to concat
ds = [
"jondurbin/truthy-dpo-v0.1",
"jondurbin/gutenberg-dpo-v0.1",
"flammenai/FlameMix-DPO-v1"
]
# load_dataset and combine all
loaded_datasets = [load_dataset(dataset_name, split='train') for dataset_name in ds]
dataset = concatenate_datasets(loaded_datasets)
# Save columns
original_columns = dataset.column_names
# Tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_name)
tokenizer.pad_token = tokenizer.eos_token
tokenizer.padding_side = "left"
# Format dataset
dataset = dataset.map(
chatml_format,
remove_columns=original_columns
)
```
LoRA, model, and training settings:
```python
# LoRA configuration
peft_config = LoraConfig(
r=16,
lora_alpha=16,
lora_dropout=0.05,
bias="none",
task_type="CAUSAL_LM",
target_modules=['k_proj', 'gate_proj', 'v_proj', 'up_proj', 'q_proj', 'o_proj', 'down_proj']
)
# Model to fine-tune
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
load_in_4bit=True
)
model.config.use_cache = False
# Reference model
ref_model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.bfloat16,
load_in_4bit=True
)
# Training arguments
training_args = TrainingArguments(
per_device_train_batch_size=1,
gradient_accumulation_steps=1,
gradient_checkpointing=True,
learning_rate=3e-5,
lr_scheduler_type="cosine",
max_steps=4000,
save_strategy="no",
logging_steps=1,
output_dir=new_model,
optim="paged_adamw_32bit",
warmup_steps=100,
bf16=True,
report_to="wandb",
)
# Create DPO trainer
dpo_trainer = DPOTrainer(
model,
ref_model,
args=training_args,
train_dataset=dataset,
tokenizer=tokenizer,
peft_config=peft_config,
beta=0.1,
force_use_ref_model=True
)
# Fine-tune model with DPO
dpo_trainer.train()
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_nbeerbower__llama-3-sauce-v2-8B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.38|
|AI2 Reasoning Challenge (25-Shot)|65.61|
|HellaSwag (10-Shot) |83.11|
|MMLU (5-Shot) |67.98|
|TruthfulQA (0-shot) |56.39|
|Winogrande (5-shot) |76.72|
|GSM8k (5-shot) |72.48|
|
fblgit/UNA-ThePitbull-21.4B-v2 | fblgit | 2024-05-31T14:15:28Z | 765 | 14 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"UNA",
"juanako",
"conversational",
"dataset:jondurbin/py-dpo-v0.1",
"dataset:Replete-AI/code_bagel_hermes-2.5",
"dataset:mlabonne/orpo-dpo-mix-40k",
"license:afl-3.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-28T10:49:15Z | ---
license: afl-3.0
library_name: transformers
tags:
- UNA
- juanako
datasets:
- jondurbin/py-dpo-v0.1
- Replete-AI/code_bagel_hermes-2.5
- mlabonne/orpo-dpo-mix-40k
model-index:
- name: UNA-ThePitbull-21.4B-v2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 77.73
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 91.79
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 68.25
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 78.24
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 87.37
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.53
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=fblgit/UNA-ThePitbull-21.4B-v2
name: Open LLM Leaderboard
---
# UNA-ThePitbull 21.4B v2
Introducing the best LLM in the industry. Nearly as good as a 70B, just a 21.4B based on saltlux/luxia-21.4b-alignment-v1.0
This model has not been poisoned to score high and be useless. We release him becaues its the real deal of EQ & IQ all together in a crazy powerful smart and conversational model.
Quant Versions available at [bartowski/UNA-ThePitbull-21.4B-v2-GGUF](https://huggingface.co/bartowski/UNA-ThePitbull-21.4B-v2-GGUF)
## Difference V1 vs V2
On V2 we implemented a different UNA strategy and covered partially the MLP's and Attention Layers.
We also performed further SFT over V1 and further DPO over V1 and we'll release some of those soon as well.
### Changes
1. SFT over V1 with `Replete-AI/code_bagel_hermes-2.5` at 1.0e-4 till 5.0e-5 for 1 epoch
2. DPO with: 1.0e-4 to min_lr 5.0e-5 for 1 epoch
* `mlabonne/orpo-dpo-mix-40k`
* `jondurbin/py-dpo-v0.1`
# Evaluations
## [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_fblgit__UNA-ThePitbull-21.4B-v2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |77.82|
|AI2 Reasoning Challenge (25-Shot)|77.73|
|HellaSwag (10-Shot) |91.79|
|MMLU (5-Shot) |68.25|
|TruthfulQA (0-shot) |78.24|
|Winogrande (5-shot) |87.37|
|GSM8k (5-shot) |63.53|
Can only be compared with its non-una base model: the original luxia-21.4b and ThePitbull-v1
## UNA v2 (VLLM) Evaluations:
```
vllm (pretrained=/data/tools/mergekit/una-thepitbull-v5,dtype=bfloat16,gpu_memory_utilization=0.8,max_model_len=2048,data_parallel_size=2,tensor_parallel_size=4), gen_kwargs: (None), limit: None, num_fewshot: None, batch_size: 8
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7695|± |0.0116|+
| | |flexible-extract| 5|exact_match|0.7695|± |0.0116|+
|hellaswag | 1|none | 10|acc |0.8110|± |0.0039|
| | |none | 10|acc_norm |0.9169|± |0.0028|+
|winogrande | 1|none | 5|acc |0.8777|± |0.0092|+
|mmlu |N/A |none | 0|acc |0.6427|± |0.0038|-
|arc_challenge | 1|none | 25|acc |0.7713|± |0.0123|
| | |none | 25|acc_norm |0.7875|± |0.0120|+
|truthfulqa_mc2| 2|none | 0|acc |0.7824|± |0.0135|-
|mathqa | 1|none | 0|acc |0.4037|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|+
|pubmedqa | 1|none | 0|acc |0.7260|± | 0.020|+
|boolq | 2|none | 0|acc |0.8602|± |0.0061|+
```
## UNA v1 (VLLM) Evaluations
```
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7566|± |0.0118|
| | |flexible-extract| 5|exact_match|0.7582|± |0.0118|
|hellaswag | 1|none | 10|acc |0.8168|± |0.0039|
| | |none | 10|acc_norm |0.9188|± |0.0027|
|winogrande | 1|none | 5|acc |0.8635|± |0.0097|
|mmlu | N/A|none | 0|acc |0.6444|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7747|± |0.0122|
| | |none | 25|acc_norm |0.7850|± |0.0120|
|truthfulqa_mc2| 2|none | 0|acc |0.7902|± |0.0134|
|mathqa | 1|none | 0|acc |0.4030|± | 0.009|
| | |none | 0|acc_norm |0.4034|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6860|± |0.0208|
|boolq | 2|none | 0|acc |0.8401|± |0.0064|
```
## Original (VLLM) Evaluations
```
| Tasks |Version| Filter |n-shot| Metric |Value | |Stderr|
|--------------|------:|----------------|-----:|-----------|-----:|---|-----:|
|gsm8k | 3|strict-match | 5|exact_match|0.7528|± |0.0119|
| | |flexible-extract| 5|exact_match|0.7521|± |0.0119|
|hellaswag | 1|none | 10|acc |0.8117|± |0.0039|
| | |none | 10|acc_norm |0.9167|± |0.0028|
|winogrande | 1|none | 5|acc |0.8682|± |0.0095|
|mmlu | N/A|none | 0|acc |0.6448|± |0.0038|
|arc_challenge | 1|none | 25|acc |0.7688|± |0.0123|
| | |none | 25|acc_norm |0.7730|± |0.0122|
|truthfulqa_mc2| 2|none | 0|acc |0.7895|± |0.0133|
|mathqa | 1|none | 0|acc |0.4000|± | 0.009|
| | |none | 0|acc_norm |0.4003|± | 0.009|
|pubmedqa | 1|none | 0|acc |0.6680|± |0.0211|
|boolq | 2|none | 0|acc |0.8346|± |0.0065|
```
## Citations
* mlabonne
* jondurbin & Replete-AI
* bartowski
* saltlux
If you use UNA models dont forget to cite:
```
@misc{unathepitbull21b,
title={ThePitbull: Uniform Neural Alignment},
author={Xavier Murias},
year={2024},
publisher = {Juanako.AI},
journal = {HuggingFace repository},
howpublished = {\url{https://huggingface.co/fblgit/UNA-ThePitbull-21.4-v1}},
}
```
|
DevQuasar/coma-7B-v0.1-GGUF | DevQuasar | 2024-06-11T21:22:32Z | 765 | 0 | null | [
"gguf",
"code",
"text-generation",
"license:llama2",
"region:us"
] | text-generation | 2024-06-11T04:12:48Z | ---
license: llama2
pipeline_tag: text-generation
tags:
- code
---
Quantized version of [DevQuasar/coma-7B-v0.1](https://huggingface.co/DevQuasar/coma-7B-v0.1) |
UniOb/CodeLlama-13b-hf-Q4_K_M-GGUF | UniOb | 2024-06-29T22:15:57Z | 765 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-2",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"code",
"base_model:meta-llama/CodeLlama-13b-hf",
"license:llama2",
"region:us"
] | text-generation | 2024-06-29T22:15:20Z | ---
base_model: meta-llama/CodeLlama-13b-hf
language:
- code
license: llama2
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-2
- llama-cpp
- gguf-my-repo
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\ such as: \n 1. Violence or terrorism \n 2. Exploitation or harm to children,\
\ including the solicitation, creation, acquisition, or dissemination of child exploitative\
\ content or failure to report Child Sexual Abuse Material\n 3. Human trafficking,\
\ exploitation, and sexual violence\n 4. The illegal distribution of information\
\ or materials to minors, including obscene materials, or failure to employ legally\
\ required age-gating in connection with such information or materials.\n 5.\
\ Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote,\
\ incite, or facilitate the harassment, abuse, threatening, or bullying of individuals\
\ or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination\
\ or other unlawful or harmful conduct in the provision of employment, employment\
\ benefits, credit, housing, other economic benefits, or other essential goods and\
\ services\n 4. Engage in the unauthorized or unlicensed practice of any profession\
\ including, but not limited to, financial, legal, medical/health, or related professional\
\ practices \n 5. Collect, process, disclose, generate, or infer health, demographic,\
\ or other sensitive personal or private information about individuals without rights\
\ and consents required by applicable laws\n 6. Engage in or facilitate any action\
\ or generate any content that infringes, misappropriates, or otherwise violates\
\ any third-party rights, including the outputs or results of any products or services\
\ using the Llama 2 Materials\n 7. Create, generate, or facilitate the creation\
\ of malicious code, malware, computer viruses or do anything else that could disable,\
\ overburden, interfere with or impair the proper working, integrity, operation\
\ or appearance of a website or computer system \n2. Engage in, promote, incite,\
\ facilitate, or assist in the planning or development of activities that present\
\ a risk of death or bodily harm to individuals, including use of Llama 2 related\
\ to the following:\n 1. Military, warfare, nuclear industries or applications,\
\ espionage, use for materials or activities that are subject to the International\
\ Traffic Arms Regulations (ITAR) maintained by the United States Department of\
\ State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal\
\ drugs and regulated/controlled substances\n 4. Operation of critical infrastructure,\
\ transportation technologies, or heavy machinery\n 5. Self-harm or harm to others,\
\ including suicide, cutting, and eating disorders\n 6. Any content intended to\
\ incite or promote violence, abuse, or any infliction of bodily harm to an individual\n\
3. Intentionally deceive or mislead others, including use of Llama 2 related to\
\ the following:\n 1. Generating, promoting, or furthering fraud or the creation\
\ or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory\
\ content, including the creation of defamatory statements, images, or other content\n\
\ 3. Generating, promoting, or further distributing spam\n 4. Impersonating another\
\ individual without consent, authorization, or legal right\n 5. Representing that\
\ the use of Llama 2 or outputs are human-generated\n 6. Generating or facilitating\
\ false online engagement, including fake reviews and other means of fake online\
\ engagement \n 4. Fail to appropriately disclose to end users any known dangers\
\ of your AI system \nPlease report any violation of this Policy, software “bug,”\
\ or other problems that could lead to a violation of this Policy through one of\
\ the following means: * Reporting issues with the model: [github.com/facebookresearch/llama](http://github.com/facebookresearch/llama)\
\ * Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)\
\ * Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)\
\ * Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama:\
\ [[email protected]](mailto:[email protected])"
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
# UniOb/CodeLlama-13b-hf-Q4_K_M-GGUF
This model was converted to GGUF format from [`meta-llama/CodeLlama-13b-hf`](https://huggingface.co/meta-llama/CodeLlama-13b-hf) 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/meta-llama/CodeLlama-13b-hf) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo UniOb/CodeLlama-13b-hf-Q4_K_M-GGUF --hf-file codellama-13b-hf-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo UniOb/CodeLlama-13b-hf-Q4_K_M-GGUF --hf-file codellama-13b-hf-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo UniOb/CodeLlama-13b-hf-Q4_K_M-GGUF --hf-file codellama-13b-hf-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo UniOb/CodeLlama-13b-hf-Q4_K_M-GGUF --hf-file codellama-13b-hf-q4_k_m.gguf -c 2048
```
|
yunconglong/13B_MATH_DPO | yunconglong | 2024-01-28T11:51:55Z | 764 | 1 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"DPO",
"RL-TUNED",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-28T11:41:03Z | ---
license: other
tags:
- moe
- DPO
- RL-TUNED
---
* [DPO Trainer](https://huggingface.co/docs/trl/main/en/dpo_trainer) with dataset kyujinpy/orca_math_dpo to improve [yunconglong/MoE_13B_DPO]
```
DPO Trainer
TRL supports the DPO Trainer for training language models from preference data, as described in the paper Direct Preference Optimization: Your Language Model is Secretly a Reward Model by Rafailov et al., 2023.
```
|
AbacusResearch/Jallabi-34B | AbacusResearch | 2024-03-02T20:31:50Z | 764 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"LlamaForCausalLM",
"LLM",
"conversational",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-01T15:53:25Z | ---
license: apache-2.0
tags:
- llama
- LlamaForCausalLM
- LLM
model-index:
- name: Jallabi-34B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 66.04
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 83.81
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.4
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 51.46
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 81.45
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 65.2
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=AbacusResearch/Jallabi-34B
name: Open LLM Leaderboard
---
These are llama only weights of https://huggingface.co/liuhaotian/llava-v1.6-34b . The Clip encoder part is removed and and this model is llama weights only that can be loaded using
LlamaForCausalLM. Which indirectly is a https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B licence.
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_AbacusResearch__Jallabi-34B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |70.73|
|AI2 Reasoning Challenge (25-Shot)|66.04|
|HellaSwag (10-Shot) |83.81|
|MMLU (5-Shot) |76.40|
|TruthfulQA (0-shot) |51.46|
|Winogrande (5-shot) |81.45|
|GSM8k (5-shot) |65.20|
|
emozilla/llama2-1.2b-init | emozilla | 2024-05-30T05:43:28Z | 764 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-30T05:42:19Z | Entry not found |
histai/hibou-b | histai | 2024-06-10T08:28:07Z | 764 | 6 | transformers | [
"transformers",
"safetensors",
"dinov2",
"feature-extraction",
"custom_code",
"arxiv:2406.05074",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2024-06-06T15:00:53Z | ---
license: apache-2.0
---
Hibou-B - a Foundational Vision Transformer for digital pathology pretrained on a private dataset using DINOv2 framework.
For more information and examples of usage visit [https://github.com/HistAI/hibou](https://github.com/HistAI/hibou) and read the [paper](https://arxiv.org/abs/2406.05074).
Basic usage:
```python
from transformers import AutoImageProcessor, AutoModel
processor = AutoImageProcessor.from_pretrained("histai/hibou-b", trust_remote_code=True)
model = AutoModel.from_pretrained("histai/hibou-b", trust_remote_code=True)
```
We use a customized implementation of the DINOv2 architecture from the transformers library to add support for registers, which requires the `trust_remote_code=True` flag. |
booksouls/fasttext-goodreads-vectors | booksouls | 2024-06-23T01:15:21Z | 764 | 0 | fasttext | [
"fasttext",
"feature-extraction",
"en",
"dataset:booksouls/goodreads-book-descriptions",
"region:us"
] | feature-extraction | 2024-06-22T22:35:17Z | ---
datasets:
- booksouls/goodreads-book-descriptions
language:
- en
library_name: fasttext
pipeline_tag: feature-extraction
--- |
digit82/kogpt2-summarization | digit82 | 2021-09-22T14:45:06Z | 763 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | Entry not found |
UBC-NLP/Jasmine-350M | UBC-NLP | 2024-05-01T16:25:25Z | 763 | 3 | transformers | [
"transformers",
"pytorch",
"gpt_neo",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2022-07-14T21:59:32Z | ---
widget:
- text: "أشاد حسين عبد الغني، نجم المنتخب السعودي السابق، بتعاقد الاتحاد"
- text: "قال الرئيس الأمريكي، جو بايدن، عند عودته إلى البيت الأبيض، مساء "
- text: "قال ولي العهد السعودي، الأمير محمد بن سلمان، متحدثًا في قمة أمريكية عربية في جدة، إن الجهود الدولية المشتركة ضرورية لتعافي الاقتصاد العالمي وأن"
- text: "وقوله : بيمينه ، لدفع كمال المهر حكاه الرافعي عن البغوي وأقره"
- text: "يفخر عنترة العبسي بنفسه وقومه ويضمّن العديد من الحكم في القصيدة الآتية: \n لا يحمل الحقد من تعلو به الرتب ولا ينال العلى من طبعه الغضب"
- text: "يفخر عنترة العبسي بنفسه وقومه ويضمّن العديد من الحكم في القصيدة الآتية: \n لا يحمل الحقد من تعلو به الرتب ولا ينال العلى من طبعه الغضب"
- text: "ومن أشعاره الفصيحة ما يأتي:\nدع الأيام تفعل ما تشاء ** وطب نفسا إذا حكم القضاء\nولا تجزع لنازلة الليالي ** فما لحوادث الدنيا بقاء\nوكن رجلا على الأهوال جلدا ** وشيمتك"
---
# Jasmine-350M
# JASMINE: Arabic GPT Models for Few-Shot Learning
This is the repository accompanying our EMNLP2023 paper [JASMINE: Arabic GPT Models for Few-Shot Learning](https://aclanthology.org/2023.emnlp-main.1040/). JASMINE is a suite of powerful Arabic autoregressive Transformer language models ranging in size between 300 million-6.7 billion parameters pretrained on a large and diverse dataset ( 235 GB of text).
# BibTex
If you use Jasmine models for your scientific publication, or if you find the resources in this repository useful, please cite our paper as follows (to be updated):
```bibtex
@inproceedings{billah-nagoudi-etal-2023-jasmine,
title = "{JASMINE}: {A}rabic {GPT} Models for Few-Shot Learning",
author = "Billah Nagoudi, El Moatez and
Abdul-Mageed, Muhammad and
Elmadany, AbdelRahim and
Inciarte, Alcides and
Islam Khondaker, Md Tawkat",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.emnlp-main.1040",
doi = "10.18653/v1/2023.emnlp-main.1040",
pages = "16721--16744",
}
```
## Acknowledgments
We gratefully acknowledge support from the Natural Sciences and Engineering Research Council of Canada, the Social Sciences and Humanities Research Council of Canada, Canadian Foundation for Innovation, [ComputeCanada](www.computecanada.ca) and [UBC ARC-Sockeye](https://doi.org/10.14288/SOCKEYE). We also thank the [Google TensorFlow Research Cloud (TFRC)](https://www.tensorflow.org/tfrc) program for providing us with free TPU access.
|
Den4ikAI/rugpt3_large_qa | Den4ikAI | 2022-11-23T10:17:31Z | 763 | 2 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-11-23T09:58:45Z | ---
license: mit
---
|
timm/convnext_base.clip_laion2b_augreg_ft_in1k | timm | 2024-02-10T23:30:20Z | 763 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:laion-2b",
"arxiv:2210.08402",
"arxiv:2201.03545",
"arxiv:2103.00020",
"license:apache-2.0",
"region:us"
] | image-classification | 2023-02-03T18:32:07Z | ---
license: apache-2.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- laion-2b
---
# Model card for convnext_base.clip_laion2b_augreg_ft_in1k
A ConvNeXt image classification model. CLIP image tower weights pretrained in [OpenCLIP](https://github.com/mlfoundations/open_clip) on LAION and fine-tuned on ImageNet-1k in `timm` by Ross Wightman.
Please see related OpenCLIP model cards for more details on pretrain:
* https://huggingface.co/laion/CLIP-convnext_xxlarge-laion2B-s34B-b82K-augreg-soup
* https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w-laion2B-s13B-b82K-augreg
* https://huggingface.co/laion/CLIP-convnext_base_w_320-laion_aesthetic-s13B-b82K
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 88.6
- GMACs: 20.1
- Activations (M): 37.6
- Image size: 256 x 256
- **Papers:**
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- A ConvNet for the 2020s: https://arxiv.org/abs/2201.03545
- Learning Transferable Visual Models From Natural Language Supervision: https://arxiv.org/abs/2103.00020
- **Original:** https://github.com/mlfoundations/open_clip
- **Pretrain Dataset:** LAION-2B
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnext_base.clip_laion2b_augreg_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_base.clip_laion2b_augreg_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 128, 64, 64])
# torch.Size([1, 256, 32, 32])
# torch.Size([1, 512, 16, 16])
# torch.Size([1, 1024, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnext_base.clip_laion2b_augreg_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
|
timm/caformer_b36.sail_in22k_ft_in1k_384 | timm | 2023-05-05T05:42:08Z | 763 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:imagenet-22k",
"arxiv:2210.13452",
"license:apache-2.0",
"region:us"
] | image-classification | 2023-05-05T05:40:53Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- imagenet-22k
---
# Model card for caformer_b36.sail_in22k_ft_in1k_384
A CAFormer (a MetaFormer) image classification model. Pretrained on ImageNet-22k and fine-tuned on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 98.8
- GMACs: 72.3
- Activations (M): 261.8
- Image size: 384 x 384
- **Papers:**
- Metaformer baselines for vision: https://arxiv.org/abs/2210.13452
- **Original:** https://github.com/sail-sg/metaformer
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-22k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('caformer_b36.sail_in22k_ft_in1k_384', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'caformer_b36.sail_in22k_ft_in1k_384',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 128, 96, 96])
# torch.Size([1, 256, 48, 48])
# torch.Size([1, 512, 24, 24])
# torch.Size([1, 768, 12, 12])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'caformer_b36.sail_in22k_ft_in1k_384',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 768, 12, 12) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{yu2022metaformer_baselines,
title={Metaformer baselines for vision},
author={Yu, Weihao and Si, Chenyang and Zhou, Pan and Luo, Mi and Zhou, Yichen and Feng, Jiashi and Yan, Shuicheng and Wang, Xinchao},
journal={arXiv preprint arXiv:2210.13452},
year={2022}
}
```
|
sail-rvc/ArnoldSchwarzenegger | sail-rvc | 2023-07-14T07:19:02Z | 763 | 0 | transformers | [
"transformers",
"rvc",
"sail-rvc",
"audio-to-audio",
"endpoints_compatible",
"region:us"
] | audio-to-audio | 2023-07-14T07:18:26Z |
---
pipeline_tag: audio-to-audio
tags:
- rvc
- sail-rvc
---
# ArnoldSchwarzenegger
## RVC Model
This model repo was automatically generated.
Date: 2023-07-14 07:19:02
Bot Name: juuxnscrap
Model Type: RVC
Source: https://huggingface.co/juuxn/RVCModels/
Reason: Converting into loadable format for https://github.com/chavinlo/rvc-runpod
|
aloobun/Reyna-Mini-1.8B-v0.2 | aloobun | 2024-03-04T16:45:50Z | 763 | 12 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"chatml",
"finetune",
"gpt4",
"synthetic data",
"custom_code",
"conversational",
"dataset:Locutusque/Hercules-v3.0",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-16T02:08:50Z | ---
license: other
library_name: transformers
tags:
- chatml
- finetune
- gpt4
- synthetic data
- custom_code
- qwen2
datasets:
- Locutusque/Hercules-v3.0
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat/raw/main/LICENSE
model-index:
- name: Reyna-Mini-1.8B-v0.2
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 36.6
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 60.19
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 44.75
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 41.24
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 61.56
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 31.31
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=aloobun/Reyna-Mini-1.8B-v0.2
name: Open LLM Leaderboard
---
- Finetuned [Qwen/Qwen1.5-1.8B-Chat](https://huggingface.co/Qwen/Qwen1.5-1.8B-Chat), with SFT on Hercules v3 dataset.
- This marks the third model in this series.
- Format: ChatML -
```
<|im_start|>system
{system}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
- Next step would be to do a DPO train on top.
## Benchamrks:
|Avg. | Arc | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8K |
|---|---|---|---|---|---|---|
|45.94 | 36.6 |60.19 | 44.75 | 41.24 | 61.56 | 31.31 |
## Example:
```
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer, StoppingCriteria
import torch
class MyStoppingCriteria(StoppingCriteria):
def __init__(self, target_sequence, prompt):
self.target_sequence = target_sequence
self.prompt=prompt
def __call__(self, input_ids, scores, **kwargs):
generated_text = tokenizer.decode(input_ids[0])
generated_text = generated_text.replace(self.prompt,'')
if self.target_sequence in generated_text:
return True
return False
def __len__(self):
return 1
def __iter__(self):
yield self
modelpath="aloobun/Reyna-Mini-1.8B-v0.2"
model = AutoModelForCausalLM.from_pretrained(
modelpath,
torch_dtype=torch.bfloat16,
device_map="cuda",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(
modelpath,
trust_remote_code=True,
use_fast=False,
)
prompt = "<|im_start|>system\nYou are a helpful assistant.<|im_end|>\n<|im_start|>user\nIs there inherent order in nature or is it all chaos and chance?<|im_end|>\n<|im_start|>assistant\n"
encoded_input = tokenizer(prompt, return_tensors='pt')
input_ids=encoded_input['input_ids'].cuda()
streamer = TextStreamer(tokenizer=tokenizer, skip_prompt=True)
op = model.generate(
input_ids,
streamer=streamer,
pad_token_id=tokenizer.eos_token_id,
do_sample=True,
temperature=0.6,
top_p=0.8,
max_new_tokens=512,
stopping_criteria=MyStoppingCriteria("<|im_end|>", prompt)
)
```
## Output:
>Nature appears to be inherently organized, with patterns and structures that can be observed across different levels of organization. However, the exact mechanisms by which these patterns emerge and evolve remain largely unknown.
>The universe seems to be governed by a series of laws and principles known as "laws of physics," such as Newton's laws of motion, electromagnetism, and thermodynamics. These laws govern how matter and energy interact with each other and how they behave over time.
>Despite our understanding of these laws, we still struggle to comprehend the underlying mechanisms that allow for the emergence of complex patterns and structures. This is because the universe operates on a scale that is too small for us to observe directly, and therefore we cannot fully understand its internal workings.
>In summary, while there may be some level of order and structure within the universe, the precise mechanisms governing this order remain largely unknown.<|im_end|>
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_aloobun__Reyna-Mini-1.8B-v0.2)
| Metric |Value|
|---------------------------------|----:|
|Avg. |45.94|
|AI2 Reasoning Challenge (25-Shot)|36.60|
|HellaSwag (10-Shot) |60.19|
|MMLU (5-Shot) |44.75|
|TruthfulQA (0-shot) |41.24|
|Winogrande (5-shot) |61.56|
|GSM8k (5-shot) |31.31|
|
weezywitasneezy/OxytocinErosEngineeringF1-7B-slerp | weezywitasneezy | 2024-04-08T19:34:07Z | 763 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"ChaoticNeutrals/Eris_Remix_7B",
"Virt-io/Erebus-Holodeck-7B",
"base_model:ChaoticNeutrals/Eris_Remix_7B",
"base_model:Virt-io/Erebus-Holodeck-7B",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-24T20:04:06Z | ---
tags:
- merge
- mergekit
- lazymergekit
- ChaoticNeutrals/Eris_Remix_7B
- Virt-io/Erebus-Holodeck-7B
base_model:
- ChaoticNeutrals/Eris_Remix_7B
- Virt-io/Erebus-Holodeck-7B
license: cc-by-nc-4.0
---
# OxytocinErosEngineeringF1-7B-slerp
<img src="https://cdn-uploads.huggingface.co/production/uploads/632b22e66cb20ba0ae82bf06/ei6PcV1sk_qSPo8GgGms-.png"
width="512"
height="512" />
OxytocinErosEngineeringF1-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [ChaoticNeutrals/Eris_Remix_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B)
* [Virt-io/Erebus-Holodeck-7B](https://huggingface.co/Virt-io/Erebus-Holodeck-7B)
Thanks to MraderMarcher for providing GGUF quants-> [mradermacher/OxytocinErosEngineeringF1-7B-slerp-GGUF](https://huggingface.co/mradermacher/OxytocinErosEngineeringF1-7B-slerp-GGUF)
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_weezywitasneezy__OxytocinErosEngineeringF1-7B-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |69.22|
|AI2 Reasoning Challenge (25-Shot)|67.15|
|HellaSwag (10-Shot) |86|
|MMLU (5-Shot) |64.73|
|TruthfulQA (0-shot) |54.54|
|Winogrande (5-shot) |81.14|
|GSM8k (5-shot) |61.79|
## 🧩 Configuration
```yaml
slices:
- sources:
- model: ChaoticNeutrals/Eris_Remix_7B
layer_range: [0, 32]
- model: Virt-io/Erebus-Holodeck-7B
layer_range: [0, 32]
merge_method: slerp
base_model: ChaoticNeutrals/Eris_Remix_7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "weezywitasneezy/OxytocinErosEngineeringF1-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
raincandy-u/phillama-3.8b-v0.1 | raincandy-u | 2024-04-25T11:07:26Z | 763 | 10 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:raincandy-u/Dextromethorphan-10k",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-25T06:28:18Z | ---
datasets:
- raincandy-u/Dextromethorphan-10k
library_name: transformers
pipeline_tag: text-generation
license: mit
---
# phillama-3.8b-v0.1
Phillama is a model based on Phi-3-mini and trained on Llama-generated dataset raincandy-u/Dextromethorphan-10k to make it more "llama-like".
Also, this model is converted into Llama format, so it will work with any Llama-2/3 workflow.
# System prompt
`You are a humanoid AI assistant. You think step by step and give detailed long response.`
# Prompt template
```
<|system|>
You are a humanoid AI assistant. You think step by step and give detailed long response.<|end|>
<|user|>
Why people like llama?<|end|>
<|assistant|>
``` |
Walmart-the-bag/Llama-3-LizardCoder-8B | Walmart-the-bag | 2024-05-19T13:03:55Z | 763 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"lizardcoder",
"llama3",
"merge",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-18T14:57:19Z | ---
license: llama3
language:
- en
library_name: transformers
tags:
- code
- lizardcoder
- llama3
- llama
- merge
---
# Llama-3-LizardCoder-8B
This is a merge of 6 models that were finetuned on llama3 8b. This has done pretty decent on some coding tasks, for the parameter size.
[gguf](https://huggingface.co/Walmart-the-bag/LizardCoder-Llama3-8B-GGUF)
## Limitations
- **Uncertain Accuracy:** As a merged model, the model's responses may not always be accurate. Users should independently verify any outputs before relying on them.
- **Potential for Censorship:** The model's censorship filters are not comprehensive. There is a possibility of encountering censored code/content.
- **Not including packages:** If you ask it to code you something, it may accidentally forget to include a package. Tell it to, and create a good prompt. This will be finetuned on to fix it in the future.
# Merge Config
This model was made possible by this merge yaml.
```yaml
models:
- model: rombodawg/Llama-3-8B-Instruct-Coder
parameters:
weight: 1.0
- model: ajibawa-2023/Code-Llama-3-8B
parameters:
weight: 0.3
- model: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
weight: 0.5
- model: Orenguteng/Llama-3-8B-Lexi-Uncensored
parameters:
weight: 0.8
- model: TheSkullery/llama-3-cat-8b-instruct-v1
parameters:
weight: 0.9
- model: McGill-NLP/Llama-3-8B-Web
parameters:
weight: 0.2
merge_method: linear
dtype: bfloat16
```
## License
i dont really care about this, but here: [Llama3](https://llama.meta.com/llama3/license/) |
RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf | RichardErkhov | 2024-06-05T00:30:38Z | 763 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-06-05T00:19:50Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
code-autocomplete-gpt2-base - GGUF
- Model creator: https://huggingface.co/shibing624/
- Original model: https://huggingface.co/shibing624/code-autocomplete-gpt2-base/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [code-autocomplete-gpt2-base.Q2_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q2_K.gguf) | Q2_K | 0.08GB |
| [code-autocomplete-gpt2-base.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.IQ3_XS.gguf) | IQ3_XS | 0.08GB |
| [code-autocomplete-gpt2-base.IQ3_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.IQ3_S.gguf) | IQ3_S | 0.08GB |
| [code-autocomplete-gpt2-base.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q3_K_S.gguf) | Q3_K_S | 0.08GB |
| [code-autocomplete-gpt2-base.IQ3_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.IQ3_M.gguf) | IQ3_M | 0.09GB |
| [code-autocomplete-gpt2-base.Q3_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q3_K.gguf) | Q3_K | 0.09GB |
| [code-autocomplete-gpt2-base.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q3_K_M.gguf) | Q3_K_M | 0.09GB |
| [code-autocomplete-gpt2-base.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q3_K_L.gguf) | Q3_K_L | 0.1GB |
| [code-autocomplete-gpt2-base.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.IQ4_XS.gguf) | IQ4_XS | 0.1GB |
| [code-autocomplete-gpt2-base.Q4_0.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q4_0.gguf) | Q4_0 | 0.1GB |
| [code-autocomplete-gpt2-base.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.IQ4_NL.gguf) | IQ4_NL | 0.1GB |
| [code-autocomplete-gpt2-base.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q4_K_S.gguf) | Q4_K_S | 0.1GB |
| [code-autocomplete-gpt2-base.Q4_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q4_K.gguf) | Q4_K | 0.11GB |
| [code-autocomplete-gpt2-base.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q4_K_M.gguf) | Q4_K_M | 0.11GB |
| [code-autocomplete-gpt2-base.Q4_1.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q4_1.gguf) | Q4_1 | 0.11GB |
| [code-autocomplete-gpt2-base.Q5_0.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q5_0.gguf) | Q5_0 | 0.11GB |
| [code-autocomplete-gpt2-base.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q5_K_S.gguf) | Q5_K_S | 0.11GB |
| [code-autocomplete-gpt2-base.Q5_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q5_K.gguf) | Q5_K | 0.12GB |
| [code-autocomplete-gpt2-base.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q5_K_M.gguf) | Q5_K_M | 0.12GB |
| [code-autocomplete-gpt2-base.Q5_1.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q5_1.gguf) | Q5_1 | 0.12GB |
| [code-autocomplete-gpt2-base.Q6_K.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q6_K.gguf) | Q6_K | 0.13GB |
| [code-autocomplete-gpt2-base.Q8_0.gguf](https://huggingface.co/RichardErkhov/shibing624_-_code-autocomplete-gpt2-base-gguf/blob/main/code-autocomplete-gpt2-base.Q8_0.gguf) | Q8_0 | 0.17GB |
Original model description:
---
language:
- en
tags:
- code
- autocomplete
- pytorch
- en
license: "apache-2.0"
---
# GPT2 for Code AutoComplete Model
code-autocomplete, a code completion plugin for Python.
**code-autocomplete** can automatically complete the code of lines and blocks with GPT2.
## Usage
Open source repo：[code-autocomplete](https://github.com/shibing624/code-autocomplete)，support GPT2 model, usage：
```python
from autocomplete.gpt2_coder import GPT2Coder
m = GPT2Coder("shibing624/code-autocomplete-gpt2-base")
print(m.generate('import torch.nn as')[0])
```
Also, use huggingface/transformers：
*Please use 'GPT2' related functions to load this model!*
```python
import os
import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel
os.environ["KMP_DUPLICATE_LIB_OK"] = "TRUE"
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
tokenizer = GPT2Tokenizer.from_pretrained("shibing624/code-autocomplete-gpt2-base")
model = GPT2LMHeadModel.from_pretrained("shibing624/code-autocomplete-gpt2-base")
model.to(device)
prompts = [
"""from torch import nn
class LSTM(Module):
def __init__(self, *,
n_tokens: int,
embedding_size: int,
hidden_size: int,
n_layers: int):""",
"""import numpy as np
import torch
import torch.nn as""",
"import java.util.ArrayList",
"def factorial(n):",
]
for prompt in prompts:
input_ids = tokenizer.encode(prompt, add_special_tokens=False, return_tensors='pt').to(device)
outputs = model.generate(input_ids=input_ids,
max_length=64 + len(prompt),
temperature=1.0,
top_k=50,
top_p=0.95,
repetition_penalty=1.0,
do_sample=True,
num_return_sequences=1,
length_penalty=2.0,
early_stopping=True)
decoded = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(decoded)
print("=" * 20)
```
output:
```shell
from torch import nn
class LSTM(Module):
def __init__(self, *,
n_tokens: int,
embedding_size: int,
hidden_size: int,
n_layers: int):
self.embedding_size = embedding_size
====================
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
```
Model files：
```
code-autocomplete-gpt2-base
├── config.json
├── merges.txt
├── pytorch_model.bin
├── special_tokens_map.json
├── tokenizer_config.json
└── vocab.json
```
### Train data
#### pytorch_awesome projects source code
download [code-autocomplete](https://github.com/shibing624/code-autocomplete),
```shell
cd autocomplete
python create_dataset.py
```
If you want train code-autocomplete GPT2 model，refer [https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py](https://github.com/shibing624/code-autocomplete/blob/main/autocomplete/gpt2_coder.py)
### About GPT2
Test the whole generation capabilities here: https://transformer.huggingface.co/doc/gpt2-large
Pretrained model on English language using a causal language modeling (CLM) objective. It was introduced in
[this paper](https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf)
and first released at [this page](https://openai.com/blog/better-language-models/).
Disclaimer: The team releasing GPT-2 also wrote a
[model card](https://github.com/openai/gpt-2/blob/master/model_card.md) for their model. Content from this model card
has been written by the Hugging Face team to complete the information they provided and give specific examples of bias.
## Citation
```latex
@misc{code-autocomplete,
author = {Xu Ming},
title = {code-autocomplete: Code AutoComplete with GPT model},
year = {2022},
publisher = {GitHub},
journal = {GitHub repository},
url = {https://github.com/shibing624/code-autocomplete},
}
```
|
mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF | mradermacher | 2024-06-18T06:13:44Z | 763 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-06-18T01:33:54Z | ---
base_model: deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
language:
- en
library_name: transformers
license: other
license_link: LICENSE
license_name: deepseek-license
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/deepseek-ai/DeepSeek-Coder-V2-Lite-Instruct
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ1_S.gguf) | i1-IQ1_S | 5.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ1_M.gguf) | i1-IQ1_M | 5.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ2_XS.gguf) | i1-IQ2_XS | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ2_S.gguf) | i1-IQ2_S | 6.1 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ2_M.gguf) | i1-IQ2_M | 6.4 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 6.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 7.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ3_XS.gguf) | i1-IQ3_XS | 7.2 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ3_S.gguf) | i1-IQ3_S | 7.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q3_K_S.gguf) | i1-Q3_K_S | 7.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ3_M.gguf) | i1-IQ3_M | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q3_K_M.gguf) | i1-Q3_K_M | 8.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.7 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q4_0.gguf) | i1-Q4_0 | 9.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 9.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q4_K_M.gguf) | i1-Q4_K_M | 10.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q5_K_S.gguf) | i1-Q5_K_S | 11.2 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q5_K_M.gguf) | i1-Q5_K_M | 12.0 | |
| [GGUF](https://huggingface.co/mradermacher/DeepSeek-Coder-V2-Lite-Instruct-i1-GGUF/resolve/main/DeepSeek-Coder-V2-Lite-Instruct.i1-Q6_K.gguf) | i1-Q6_K | 14.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his hardware for calculating the imatrix for these quants.
<!-- end -->
|
NikolayKozloff/britllm-3b-v0.1-Q8_0-GGUF | NikolayKozloff | 2024-06-24T15:46:21Z | 763 | 1 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"cy",
"ga",
"gd",
"sco",
"base_model:britllm/britllm-3b-v0.1",
"license:odc-by",
"region:us"
] | null | 2024-06-24T15:46:04Z | ---
base_model: britllm/britllm-3b-v0.1
language:
- en
- cy
- ga
- gd
- sco
license: odc-by
tags:
- llama-cpp
- gguf-my-repo
---
# NikolayKozloff/britllm-3b-v0.1-Q8_0-GGUF
This model was converted to GGUF format from [`britllm/britllm-3b-v0.1`](https://huggingface.co/britllm/britllm-3b-v0.1) 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/britllm/britllm-3b-v0.1) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/britllm-3b-v0.1-Q8_0-GGUF --hf-file britllm-3b-v0.1-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/britllm-3b-v0.1-Q8_0-GGUF --hf-file britllm-3b-v0.1-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/britllm-3b-v0.1-Q8_0-GGUF --hf-file britllm-3b-v0.1-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/britllm-3b-v0.1-Q8_0-GGUF --hf-file britllm-3b-v0.1-q8_0.gguf -c 2048
```
|
BekiTila/Amharic-llama-for-agriculture-chat-bot-Q4_K_M-GGUF | BekiTila | 2024-06-30T05:35:13Z | 763 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:BekiTila/Amharic-llama-for-agriculture-chat-bot",
"endpoints_compatible",
"region:us"
] | null | 2024-06-30T05:34:55Z | ---
base_model: BekiTila/Amharic-llama-for-agriculture-chat-bot
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# BekiTila/Amharic-llama-for-agriculture-chat-bot-Q4_K_M-GGUF
This model was converted to GGUF format from [`BekiTila/Amharic-llama-for-agriculture-chat-bot`](https://huggingface.co/BekiTila/Amharic-llama-for-agriculture-chat-bot) 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/BekiTila/Amharic-llama-for-agriculture-chat-bot) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo BekiTila/Amharic-llama-for-agriculture-chat-bot-Q4_K_M-GGUF --hf-file amharic-llama-for-agriculture-chat-bot-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BekiTila/Amharic-llama-for-agriculture-chat-bot-Q4_K_M-GGUF --hf-file amharic-llama-for-agriculture-chat-bot-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BekiTila/Amharic-llama-for-agriculture-chat-bot-Q4_K_M-GGUF --hf-file amharic-llama-for-agriculture-chat-bot-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BekiTila/Amharic-llama-for-agriculture-chat-bot-Q4_K_M-GGUF --hf-file amharic-llama-for-agriculture-chat-bot-q4_k_m.gguf -c 2048
```
|
pearsonkyle/gpt2-arxiv | pearsonkyle | 2023-01-20T18:36:01Z | 762 | 1 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-01-20T16:02:38Z | ---
tags:
- generated_from_trainer
model-index:
- name: gpt2-arxiv
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-arxiv
A [gpt2](https://huggingface.co/gpt2) powered predictive keyboard trained on ~1.6M manuscript abstracts from the ArXiv.
This model uses https://www.kaggle.com/datasets/Cornell-University/arxiv
```python
from transformers import pipeline
from transformers import GPT2TokenizerFast
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
llm = pipeline('text-generation',model='pearsonkyle/gpt2-arxiv', tokenizer=tokenizer)
texts = llm("Directly imaged exoplanets probe",
max_length=50, do_sample=True, num_return_sequences=5,
penalty_alpha=0.65, top_k=40, repetition_penalty=1.25,
temperature=0.95)
for i in range(5):
print(texts[i]['generated_text']+'\n')
```
- *The reflectance of Earth's vegetation suggests* `that large, deciduous forest fires are composed of mostly dry, unprocessed material that is distributed in a nearly patchy fashion. The distributions of these fires are correlated with temperature, and also with vegetation...`
- *Directly imaged exoplanets probe* `the atmospheres of giant planets. The detection of such planets requires high-quality imaging with high contrast and angular resolution, as well as`
- *We can remotely sense an atmosphere by observing its reflected, transmitted, or emitted light in varying geometries. This light will contain information on* `the planetary conditions including atmospheric temperature and cloud properties, which is essential for understanding how the planet interacts with the atmosphere and how it affects the climate. The primary science objective of this paper is to develop a methodology that can be applied to any kind of observation and measurement data, and to provide a framework that enables the detection and characterization of the atmospheres of exoplanets`
## Model description
[GPT-2](https://huggingface.co/transformers/v2.2.0/pretrained_models.html): 12-layer, 768-hidden, 12-heads, 117M parameters
## Intended uses & limitations
Coming soon...
- Predictive Keyboard using text generation
- Realtime reference recommendations using nearest neighbors of embeddings
Be careful when generating a lot of text or when changing the sampling mode of the language model. It can sometimes produce things that are not truthful, e.g.,
- The surface of Mars is composed of a thin layer of water ice, that was discovered by the Cassini spacecraft after its impact on the Earth's surface.
## Training procedure
~49 hours on a 3090 training for 1.25M iterations
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Framework versions
- Transformers 4.25.1
- Pytorch 1.13.1
- Tokenizers 0.13.2
|
MBZUAI/LaMini-Cerebras-256M | MBZUAI | 2023-04-28T13:08:29Z | 762 | 4 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"arxiv:2304.14402",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-04-12T06:14:42Z | ---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
widget:
- text: >-
Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-Cerebras-256M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
This model is a fine-tuned version of [cerebras/Cerebras-GPT-256M](https://huggingface.co/cerebras/Cerebras-GPT-256M) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to respond to human instructions written in natural language.
Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [cerebras/Cerebras-GPT-256M](https://huggingface.co/cerebras/Cerebras-GPT-256M) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 256M.
### Training Hyperparameters
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
``` |
pengold/t5-vietnamese-summarization | pengold | 2023-09-20T03:31:21Z | 762 | 2 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"summarization",
"vi",
"base_model:pengold/t5-vietnamese-summarization",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | summarization | 2023-09-17T17:51:41Z | ---
license: cc-by-4.0
base_model: pengold/t5-vietnamese-summarization
tags:
- summarization
metrics:
- rouge
model-index:
- name: t5-vietnamese-summarization
results: []
inference:
parameters:
min_length: 5
max_length: 150
widget:
- text: >-
summarize: Thảo luận phiên chuyên đề 2 về năng suất lao động tại Diễn đàn
Kinh tế - Xã hội 2023 ngày 19/9, Chuyên gia Kinh tế quốc tế Jonathan Pincus
(Tổ chức phát triển Liên hợp quốc tại Việt Nam) nói việc tăng năng suất lao
động cần nhìn nhận trong quá trình dài hơi thay vì trong giai đoạn ngắn. Rất
khó để một quốc gia có thể tăng trưởng năng suất nhanh trong một giai đoạn
dài, đó chính là bẫy năng suất trung bình, ông Pincus đúc rút, gọi đây là
mối đe dọa lớn.
example_title: Example 1
- text: >-
summarize: Đây là nỗ lực của chính phủ nhằm giảm đi cơn sốt trên
thị trường chứng khoán. Quyết định này có tác động ngay lập tức.
Chỉ số chính của thị trường chứng khoán Thượng Hải khi đóng cửa
giảm 281.8 điểm, ở mức 4053.1. Một số phân tích gia nói việc cổ
phiếu sụt giá cũng chỉ mang tính tạm thời mà thôi. Ngân hàng Thế
giới giờ đây dự đoán nền kinh tế Trung Quốc sẽ tăng 10.4% trong năm
nay. Lúc trước, Ngân hàng Thế giới dự đoán kinh tế Trung Quốc sẽ
tăng 9.6% trong năm 2007. Với việc Bắc Kinh đưa ra hành động nhằm
giảm nhiệt thị trường chứng khoán vào hôm thứ Tư, thuế đối với
cổ phiếu giao dịch giờ đây tăng từ 0.1% lên 0.3%. Tính đến phiên
đóng cửa vào hôm thứ Ba, chỉ số cổ phiếu Thượng Hải đã tăng 62%
trong năm nay, và có giá trị tăng gấp bốn lần kể từ đầu năm 2006.
Ông Thomas Gruener từ Landesbank Berlin nói: “Hành động này có thể
tạo ra việc điều chỉnh giá nhưng nhìn chung chúng tôi không cho là
xu hướng sẽ thay đổi”. Tuy nhiên, việc cổ phiếu Thượng Hải sụt giá
có thể sẽ tác động tới tâm lý của các thị trường chứng khoán
châu Âu. Thế nên các chỉ số chứng khoán tại châu Âu khi mở cửa hôm
thứ Tư đều hạ.
example_title: Example 2
language:
- vi
pipeline_tag: summarization
---
<!-- 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-vietnamese-summarization
This model is a fine-tuned version of [pengold/t5-vietnamese-summarization](https://huggingface.co/pengold/t5-vietnamese-summarization) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 4.6288
- Rouge1: 0.4728
- Rouge2: 0.1669
- Rougel: 0.3049
- Rougelsum: 0.3049
- Gen Len: 18.7458
## 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: 70
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:------:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 5.2487 | 1.0 | 2007 | 5.0028 | 0.4671 | 0.1595 | 0.2994 | 0.2994 | 18.7618 |
| 5.217 | 2.0 | 4014 | 4.9802 | 0.4639 | 0.1569 | 0.2984 | 0.2983 | 18.7747 |
| 5.2191 | 3.0 | 6021 | 4.9685 | 0.4644 | 0.1594 | 0.2989 | 0.2989 | 18.7613 |
| 5.2254 | 4.0 | 8028 | 4.9477 | 0.4648 | 0.1586 | 0.2988 | 0.2987 | 18.7458 |
| 5.1735 | 5.0 | 10035 | 4.9366 | 0.4654 | 0.1593 | 0.2988 | 0.2987 | 18.761 |
| 5.1735 | 6.0 | 12042 | 4.9214 | 0.4676 | 0.1611 | 0.3004 | 0.3004 | 18.78 |
| 5.1653 | 7.0 | 14049 | 4.9095 | 0.4681 | 0.1616 | 0.3007 | 0.3007 | 18.7523 |
| 5.1154 | 8.0 | 16056 | 4.8971 | 0.4664 | 0.1598 | 0.3002 | 0.3001 | 18.7655 |
| 5.1232 | 9.0 | 18063 | 4.8882 | 0.4683 | 0.1612 | 0.3008 | 0.3008 | 18.761 |
| 5.0995 | 10.0 | 20070 | 4.8758 | 0.4709 | 0.1618 | 0.3021 | 0.302 | 18.7518 |
| 5.1012 | 11.0 | 22077 | 4.8689 | 0.4685 | 0.1616 | 0.3011 | 0.3009 | 18.7665 |
| 5.0916 | 12.0 | 24084 | 4.8486 | 0.4695 | 0.1623 | 0.3024 | 0.3023 | 18.7655 |
| 5.0559 | 13.0 | 26091 | 4.8409 | 0.4699 | 0.1631 | 0.3024 | 0.3023 | 18.7849 |
| 5.0633 | 14.0 | 28098 | 4.8326 | 0.4705 | 0.1613 | 0.302 | 0.302 | 18.7583 |
| 5.0335 | 15.0 | 30105 | 4.8243 | 0.4696 | 0.1612 | 0.3023 | 0.3022 | 18.7638 |
| 5.0271 | 16.0 | 32112 | 4.8046 | 0.4691 | 0.1618 | 0.3022 | 0.3022 | 18.7518 |
| 5.0045 | 17.0 | 34119 | 4.8060 | 0.4708 | 0.1629 | 0.3029 | 0.3028 | 18.7568 |
| 5.0072 | 18.0 | 36126 | 4.7945 | 0.4702 | 0.1633 | 0.3024 | 0.3023 | 18.776 |
| 4.9954 | 19.0 | 38133 | 4.7894 | 0.47 | 0.1639 | 0.3022 | 0.3021 | 18.7785 |
| 4.9994 | 20.0 | 40140 | 4.7773 | 0.4692 | 0.1625 | 0.3028 | 0.3027 | 18.7623 |
| 4.953 | 21.0 | 42147 | 4.7641 | 0.4682 | 0.162 | 0.3015 | 0.3014 | 18.757 |
| 4.9526 | 22.0 | 44154 | 4.7600 | 0.4703 | 0.1626 | 0.3023 | 0.3023 | 18.7625 |
| 4.9571 | 23.0 | 46161 | 4.7592 | 0.4698 | 0.1627 | 0.3025 | 0.3025 | 18.781 |
| 4.9324 | 24.0 | 48168 | 4.7511 | 0.4697 | 0.1631 | 0.3022 | 0.3021 | 18.769 |
| 4.9323 | 25.0 | 50175 | 4.7433 | 0.4723 | 0.1649 | 0.304 | 0.3039 | 18.7757 |
| 4.9381 | 26.0 | 52182 | 4.7378 | 0.4703 | 0.1629 | 0.3026 | 0.3026 | 18.7782 |
| 4.9288 | 27.0 | 54189 | 4.7454 | 0.4709 | 0.1627 | 0.3026 | 0.3026 | 18.7777 |
| 4.9131 | 28.0 | 56196 | 4.7222 | 0.471 | 0.1652 | 0.3037 | 0.3037 | 18.782 |
| 4.9005 | 29.0 | 58203 | 4.7241 | 0.4719 | 0.1638 | 0.3039 | 0.3038 | 18.778 |
| 4.9051 | 30.0 | 60210 | 4.7225 | 0.4715 | 0.1647 | 0.3037 | 0.3036 | 18.7668 |
| 4.8816 | 31.0 | 62217 | 4.7181 | 0.4701 | 0.1631 | 0.3029 | 0.3029 | 18.7416 |
| 4.8687 | 32.0 | 64224 | 4.7061 | 0.4705 | 0.1643 | 0.3032 | 0.3031 | 18.7625 |
| 4.8935 | 33.0 | 66231 | 4.7063 | 0.4697 | 0.1632 | 0.3028 | 0.3028 | 18.7458 |
| 4.88 | 34.0 | 68238 | 4.6984 | 0.471 | 0.164 | 0.3039 | 0.3039 | 18.7663 |
| 4.8473 | 35.0 | 70245 | 4.6934 | 0.4699 | 0.1636 | 0.3034 | 0.3033 | 18.7531 |
| 4.8613 | 36.0 | 72252 | 4.6863 | 0.4705 | 0.1631 | 0.303 | 0.303 | 18.7797 |
| 4.8491 | 37.0 | 74259 | 4.6847 | 0.4703 | 0.1638 | 0.3037 | 0.3037 | 18.78 |
| 4.8239 | 38.0 | 76266 | 4.6804 | 0.4707 | 0.1632 | 0.3032 | 0.3032 | 18.7802 |
| 4.8767 | 39.0 | 78273 | 4.6788 | 0.4703 | 0.1637 | 0.3027 | 0.3026 | 18.7446 |
| 4.8402 | 40.0 | 80280 | 4.6700 | 0.4699 | 0.1633 | 0.3028 | 0.3028 | 18.7516 |
| 4.8261 | 41.0 | 82287 | 4.6660 | 0.4699 | 0.1633 | 0.3029 | 0.3028 | 18.7369 |
| 4.8193 | 42.0 | 84294 | 4.6693 | 0.4711 | 0.1654 | 0.3039 | 0.3038 | 18.7421 |
| 4.8161 | 43.0 | 86301 | 4.6636 | 0.4707 | 0.1642 | 0.303 | 0.303 | 18.7595 |
| 4.832 | 44.0 | 88308 | 4.6619 | 0.4708 | 0.1646 | 0.3036 | 0.3035 | 18.7423 |
| 4.8304 | 45.0 | 90315 | 4.6575 | 0.4711 | 0.1651 | 0.3038 | 0.3037 | 18.7354 |
| 4.7958 | 46.0 | 92322 | 4.6543 | 0.4711 | 0.165 | 0.3032 | 0.3032 | 18.7189 |
| 4.804 | 47.0 | 94329 | 4.6541 | 0.4711 | 0.1656 | 0.3037 | 0.3036 | 18.7396 |
| 4.7968 | 48.0 | 96336 | 4.6495 | 0.4709 | 0.165 | 0.3034 | 0.3034 | 18.7411 |
| 4.7912 | 49.0 | 98343 | 4.6471 | 0.4718 | 0.1655 | 0.3041 | 0.3042 | 18.7361 |
| 4.7721 | 50.0 | 100350 | 4.6469 | 0.4723 | 0.1667 | 0.3047 | 0.3047 | 18.7309 |
| 4.7828 | 51.0 | 102357 | 4.6476 | 0.4712 | 0.1656 | 0.3044 | 0.3045 | 18.7446 |
| 4.7934 | 52.0 | 104364 | 4.6453 | 0.4707 | 0.1645 | 0.3035 | 0.3035 | 18.7329 |
| 4.7724 | 53.0 | 106371 | 4.6425 | 0.4715 | 0.1657 | 0.304 | 0.304 | 18.7403 |
| 4.7804 | 54.0 | 108378 | 4.6362 | 0.4711 | 0.1658 | 0.3041 | 0.3041 | 18.7488 |
| 4.792 | 55.0 | 110385 | 4.6363 | 0.4706 | 0.1653 | 0.3038 | 0.3038 | 18.7281 |
| 4.7528 | 56.0 | 112392 | 4.6357 | 0.4724 | 0.1667 | 0.3044 | 0.3044 | 18.7463 |
| 4.7849 | 57.0 | 114399 | 4.6346 | 0.472 | 0.1661 | 0.3041 | 0.304 | 18.7431 |
| 4.7618 | 58.0 | 116406 | 4.6332 | 0.472 | 0.167 | 0.3046 | 0.3046 | 18.7336 |
| 4.7841 | 59.0 | 118413 | 4.6287 | 0.4716 | 0.1664 | 0.3043 | 0.3043 | 18.7369 |
| 4.7764 | 60.0 | 120420 | 4.6316 | 0.473 | 0.1666 | 0.3048 | 0.3047 | 18.7548 |
| 4.7504 | 61.0 | 122427 | 4.6276 | 0.4721 | 0.1671 | 0.3043 | 0.3044 | 18.7371 |
| 4.7629 | 62.0 | 124434 | 4.6250 | 0.4726 | 0.167 | 0.3046 | 0.3046 | 18.76 |
| 4.7764 | 63.0 | 126441 | 4.6264 | 0.4725 | 0.1666 | 0.3044 | 0.3044 | 18.7446 |
| 4.7524 | 64.0 | 128448 | 4.6275 | 0.4719 | 0.166 | 0.3041 | 0.3041 | 18.7428 |
| 4.7641 | 65.0 | 130455 | 4.6288 | 0.4728 | 0.1669 | 0.3049 | 0.3049 | 18.7458 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.0.1+cu117
- Datasets 2.14.5
- Tokenizers 0.13.3 |
TheBloke/medicine-LLM-13B-GGUF | TheBloke | 2024-01-01T23:06:34Z | 762 | 15 | transformers | [
"transformers",
"gguf",
"llama",
"biology",
"medical",
"text-generation",
"en",
"dataset:Open-Orca/OpenOrca",
"dataset:GAIR/lima",
"dataset:WizardLM/WizardLM_evol_instruct_V2_196k",
"dataset:EleutherAI/pile",
"arxiv:2309.09530",
"base_model:AdaptLLM/medicine-LLM-13B",
"license:other",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-01T22:59:00Z | ---
base_model: AdaptLLM/medicine-LLM-13B
datasets:
- Open-Orca/OpenOrca
- GAIR/lima
- WizardLM/WizardLM_evol_instruct_V2_196k
- EleutherAI/pile
inference: false
language:
- en
license: other
metrics:
- accuracy
model_creator: AdaptLLM
model_name: Medicine LLM 13B
model_type: llama
pipeline_tag: text-generation
prompt_template: '[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
'
quantized_by: TheBloke
tags:
- biology
- medical
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
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</div>
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<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Medicine LLM 13B - GGUF
- Model creator: [AdaptLLM](https://huggingface.co/AdaptLLM)
- Original model: [Medicine LLM 13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [AdaptLLM's Medicine LLM 13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/medicine-LLM-13B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/medicine-LLM-13B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF)
* [AdaptLLM's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/AdaptLLM/medicine-LLM-13B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Llama-2-Chat
```
[INST] <<SYS>>
{system_message}
<</SYS>>
{prompt} [/INST]
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [medicine-llm-13b.Q2_K.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q2_K.gguf) | Q2_K | 2 | 5.43 GB| 7.93 GB | smallest, significant quality loss - not recommended for most purposes |
| [medicine-llm-13b.Q3_K_S.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q3_K_S.gguf) | Q3_K_S | 3 | 5.66 GB| 8.16 GB | very small, high quality loss |
| [medicine-llm-13b.Q3_K_M.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q3_K_M.gguf) | Q3_K_M | 3 | 6.34 GB| 8.84 GB | very small, high quality loss |
| [medicine-llm-13b.Q3_K_L.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q3_K_L.gguf) | Q3_K_L | 3 | 6.93 GB| 9.43 GB | small, substantial quality loss |
| [medicine-llm-13b.Q4_0.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q4_0.gguf) | Q4_0 | 4 | 7.37 GB| 9.87 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [medicine-llm-13b.Q4_K_S.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q4_K_S.gguf) | Q4_K_S | 4 | 7.41 GB| 9.91 GB | small, greater quality loss |
| [medicine-llm-13b.Q4_K_M.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q4_K_M.gguf) | Q4_K_M | 4 | 7.87 GB| 10.37 GB | medium, balanced quality - recommended |
| [medicine-llm-13b.Q5_0.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q5_0.gguf) | Q5_0 | 5 | 8.97 GB| 11.47 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [medicine-llm-13b.Q5_K_S.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q5_K_S.gguf) | Q5_K_S | 5 | 8.97 GB| 11.47 GB | large, low quality loss - recommended |
| [medicine-llm-13b.Q5_K_M.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q5_K_M.gguf) | Q5_K_M | 5 | 9.23 GB| 11.73 GB | large, very low quality loss - recommended |
| [medicine-llm-13b.Q6_K.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q6_K.gguf) | Q6_K | 6 | 10.68 GB| 13.18 GB | very large, extremely low quality loss |
| [medicine-llm-13b.Q8_0.gguf](https://huggingface.co/TheBloke/medicine-LLM-13B-GGUF/blob/main/medicine-llm-13b.Q8_0.gguf) | Q8_0 | 8 | 13.83 GB| 16.33 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/medicine-LLM-13B-GGUF and below it, a specific filename to download, such as: medicine-llm-13b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/medicine-LLM-13B-GGUF medicine-llm-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/medicine-LLM-13B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/medicine-LLM-13B-GGUF medicine-llm-13b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m medicine-llm-13b.Q4_K_M.gguf --color -c 2048 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 2048` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./medicine-llm-13b.Q4_K_M.gguf", # Download the model file first
n_ctx=2048, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"[INST] <<SYS>>\n{system_message}\n<</SYS>>\n{prompt} [/INST]", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./medicine-llm-13b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: AdaptLLM's Medicine LLM 13B
# Adapt (Large) Language Models to Domains
This repo contains the domain-specific base model developed from **LLaMA-1-13B**, using the method in our paper [Adapting Large Language Models via Reading Comprehension](https://huggingface.co/papers/2309.09530).
We explore **continued pre-training on domain-specific corpora** for large language models. While this approach enriches LLMs with domain knowledge, it significantly hurts their prompting ability for question answering. Inspired by human learning via reading comprehension, we propose a simple method to **transform large-scale pre-training corpora into reading comprehension texts**, consistently improving prompting performance across tasks in biomedicine, finance, and law domains. **Our 7B model competes with much larger domain-specific models like BloombergGPT-50B**.
### 🤗 We are currently working hard on developing models across different domains, scales and architectures! Please stay tuned! 🤗
**************************** **Updates** ****************************
* 12/19: Released our [13B base models](https://huggingface.co/AdaptLLM/medicine-LLM-13B) developed from LLaMA-1-13B.
* 12/8: Released our [chat models](https://huggingface.co/AdaptLLM/medicine-chat) developed from LLaMA-2-Chat-7B.
* 9/18: Released our [paper](https://huggingface.co/papers/2309.09530), [code](https://github.com/microsoft/LMOps), [data](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), and [base models](https://huggingface.co/AdaptLLM/medicine-LLM) developed from LLaMA-1-7B.
## Domain-Specific LLaMA-1
### LLaMA-1-7B
In our paper, we develop three domain-specific models from LLaMA-1-7B, which are also available in Huggingface: [Biomedicine-LLM](https://huggingface.co/AdaptLLM/medicine-LLM), [Finance-LLM](https://huggingface.co/AdaptLLM/finance-LLM) and [Law-LLM](https://huggingface.co/AdaptLLM/law-LLM), the performances of our AdaptLLM compared to other domain-specific LLMs are:
<p align='center'>
<img src="https://cdn-uploads.huggingface.co/production/uploads/650801ced5578ef7e20b33d4/6efPwitFgy-pLTzvccdcP.png" width="700">
</p>
### LLaMA-1-13B
Moreover, we scale up our base model to LLaMA-1-13B to see if **our method is similarly effective for larger-scale models**, and the results are consistently positive too: [Biomedicine-LLM-13B](https://huggingface.co/AdaptLLM/medicine-LLM-13B), [Finance-LLM-13B](https://huggingface.co/AdaptLLM/finance-LLM-13B) and [Law-LLM-13B](https://huggingface.co/AdaptLLM/law-LLM-13B).
## Domain-Specific LLaMA-2-Chat
Our method is also effective for aligned models! LLaMA-2-Chat requires a [specific data format](https://huggingface.co/blog/llama2#how-to-prompt-llama-2), and our **reading comprehension can perfectly fit the data format** by transforming the reading comprehension into a multi-turn conversation. We have also open-sourced chat models in different domains: [Biomedicine-Chat](https://huggingface.co/AdaptLLM/medicine-chat), [Finance-Chat](https://huggingface.co/AdaptLLM/finance-chat) and [Law-Chat](https://huggingface.co/AdaptLLM/law-chat)
For example, to chat with the biomedicine model:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("AdaptLLM/medicine-chat")
tokenizer = AutoTokenizer.from_pretrained("AdaptLLM/medicine-chat", use_fast=False)
# Put your input here:
user_input = '''Question: Which of the following is an example of monosomy?
Options:
- 46,XX
- 47,XXX
- 69,XYY
- 45,X
Please provide your choice first and then provide explanations if possible.'''
# We use the prompt template of LLaMA-2-Chat demo
prompt = f"<s>[INST] <<SYS>>\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\n<</SYS>>\n\n{user_input} [/INST]"
inputs = tokenizer(prompt, return_tensors="pt", add_special_tokens=False).input_ids.to(model.device)
outputs = model.generate(input_ids=inputs, max_length=4096)[0]
answer_start = int(inputs.shape[-1])
pred = tokenizer.decode(outputs[answer_start:], skip_special_tokens=True)
print(f'### User Input:\n{user_input}\n\n### Assistant Output:\n{pred}')
```
## Domain-Specific Tasks
To easily reproduce our results, we have uploaded the filled-in zero/few-shot input instructions and output completions of each domain-specific task: [biomedicine-tasks](https://huggingface.co/datasets/AdaptLLM/medicine-tasks), [finance-tasks](https://huggingface.co/datasets/AdaptLLM/finance-tasks), and [law-tasks](https://huggingface.co/datasets/AdaptLLM/law-tasks).
**Note:** those filled-in instructions are specifically tailored for models before alignment and do NOT fit for the specific data format required for chat models.
## Citation
If you find our work helpful, please cite us:
```bibtex
@article{adaptllm,
title = {Adapting Large Language Models via Reading Comprehension},
author = {Daixuan Cheng and Shaohan Huang and Furu Wei},
journal = {CoRR},
volume = {abs/2309.09530},
year = {2023}
}
```
<!-- original-model-card end -->
|
Sharathhebbar24/math_gpt2_sft | Sharathhebbar24 | 2024-04-05T14:15:30Z | 762 | 1 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"maths",
"mathgpt2",
"trl",
"sft",
"en",
"dataset:meta-math/MetaMathQA",
"dataset:ArtifactAI/arxiv-math-instruct-50k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-24T03:19:50Z | ---
language:
- en
license: apache-2.0
tags:
- maths
- gpt2
- mathgpt2
- trl
- sft
datasets:
- meta-math/MetaMathQA
- ArtifactAI/arxiv-math-instruct-50k
pipeline_tag: text-generation
widget:
- text: Which motion is formed by an incident particle?
example_title: Example 1
- text: What type of diffusional modeling is used for diffusion?
example_title: Example 2
model-index:
- name: math_gpt2_sft
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 22.87
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/math_gpt2_sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 30.41
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/math_gpt2_sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 25.06
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/math_gpt2_sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 37.62
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/math_gpt2_sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 51.54
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/math_gpt2_sft
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 0.68
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Sharathhebbar24/math_gpt2_sft
name: Open LLM Leaderboard
---
This model is a finetuned version of ```Sharathhebbar24/math_gpt2``` using ```meta-math/MetaMathQA```
## Model description
GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
prompt.
### To use this model
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/math_gpt2_sft"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>> inputs = tokenizer.encode(prompt, return_tensors='pt')
>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> return generated[:generated.rfind(".")+1]
>>> prompt = "Gracie and Joe are choosing numbers on the complex plane. Joe chooses the point $1+2i$. Gracie chooses $-1+i$. How far apart are Gracie and Joe's points?"
>>> res = generate_text(prompt)
>>> res
```
# Benchmark / Evaluation
| Model | Average | ARC | HellaSwag | MMLU | TruthfulQA | Winogrande | GSM8k |
| ------- | -------- | -------- | ------- | -------- | -------- | ------- | -------- |
| Sharathhebbar24/math_gpt2_sft | 28.503 | 22.87 | 30.41 | 25.06 | 37.62 | 51.54 | 0.68 |
```python
{
"all": {
"acc": 0.25082189621988066,
"acc_stderr": 0.030526589726831692,
"acc_norm": 0.25112870356236633,
"acc_norm_stderr": 0.03129390389566968,
"mc1": 0.24112607099143207,
"mc1_stderr": 0.014974827279752334,
"mc2": 0.3762297840067963,
"mc2_stderr": 0.01445991036363257
},
"harness|arc:challenge|25": {
"acc": 0.20563139931740615,
"acc_stderr": 0.01181074526074258,
"acc_norm": 0.22866894197952217,
"acc_norm_stderr": 0.012272853582540799
},
"harness|hellaswag|10": {
"acc": 0.2884883489344752,
"acc_stderr": 0.004521334761709224,
"acc_norm": 0.30412268472415854,
"acc_norm_stderr": 0.00459094683972719
},
"harness|hendrycksTest-abstract_algebra|5": {
"acc": 0.19,
"acc_stderr": 0.03942772444036625,
"acc_norm": 0.19,
"acc_norm_stderr": 0.03942772444036625
},
"harness|hendrycksTest-anatomy|5": {
"acc": 0.2074074074074074,
"acc_stderr": 0.03502553170678319,
"acc_norm": 0.2074074074074074,
"acc_norm_stderr": 0.03502553170678319
},
"harness|hendrycksTest-astronomy|5": {
"acc": 0.17763157894736842,
"acc_stderr": 0.031103182383123398,
"acc_norm": 0.17763157894736842,
"acc_norm_stderr": 0.031103182383123398
},
"harness|hendrycksTest-business_ethics|5": {
"acc": 0.19,
"acc_stderr": 0.03942772444036622,
"acc_norm": 0.19,
"acc_norm_stderr": 0.03942772444036622
},
"harness|hendrycksTest-clinical_knowledge|5": {
"acc": 0.2188679245283019,
"acc_stderr": 0.025447863825108618,
"acc_norm": 0.2188679245283019,
"acc_norm_stderr": 0.025447863825108618
},
"harness|hendrycksTest-college_biology|5": {
"acc": 0.25,
"acc_stderr": 0.03621034121889507,
"acc_norm": 0.25,
"acc_norm_stderr": 0.03621034121889507
},
"harness|hendrycksTest-college_chemistry|5": {
"acc": 0.2,
"acc_stderr": 0.04020151261036845,
"acc_norm": 0.2,
"acc_norm_stderr": 0.04020151261036845
},
"harness|hendrycksTest-college_computer_science|5": {
"acc": 0.32,
"acc_stderr": 0.046882617226215034,
"acc_norm": 0.32,
"acc_norm_stderr": 0.046882617226215034
},
"harness|hendrycksTest-college_mathematics|5": {
"acc": 0.24,
"acc_stderr": 0.042923469599092816,
"acc_norm": 0.24,
"acc_norm_stderr": 0.042923469599092816
},
"harness|hendrycksTest-college_medicine|5": {
"acc": 0.21965317919075145,
"acc_stderr": 0.031568093627031744,
"acc_norm": 0.21965317919075145,
"acc_norm_stderr": 0.031568093627031744
},
"harness|hendrycksTest-college_physics|5": {
"acc": 0.23529411764705882,
"acc_stderr": 0.04220773659171453,
"acc_norm": 0.23529411764705882,
"acc_norm_stderr": 0.04220773659171453
},
"harness|hendrycksTest-computer_security|5": {
"acc": 0.23,
"acc_stderr": 0.04229525846816505,
"acc_norm": 0.23,
"acc_norm_stderr": 0.04229525846816505
},
"harness|hendrycksTest-conceptual_physics|5": {
"acc": 0.2680851063829787,
"acc_stderr": 0.028957342788342347,
"acc_norm": 0.2680851063829787,
"acc_norm_stderr": 0.028957342788342347
},
"harness|hendrycksTest-econometrics|5": {
"acc": 0.24561403508771928,
"acc_stderr": 0.040493392977481404,
"acc_norm": 0.24561403508771928,
"acc_norm_stderr": 0.040493392977481404
},
"harness|hendrycksTest-electrical_engineering|5": {
"acc": 0.2482758620689655,
"acc_stderr": 0.036001056927277716,
"acc_norm": 0.2482758620689655,
"acc_norm_stderr": 0.036001056927277716
},
"harness|hendrycksTest-elementary_mathematics|5": {
"acc": 0.24074074074074073,
"acc_stderr": 0.0220190800122179,
"acc_norm": 0.24074074074074073,
"acc_norm_stderr": 0.0220190800122179
},
"harness|hendrycksTest-formal_logic|5": {
"acc": 0.23015873015873015,
"acc_stderr": 0.03764950879790605,
"acc_norm": 0.23015873015873015,
"acc_norm_stderr": 0.03764950879790605
},
"harness|hendrycksTest-global_facts|5": {
"acc": 0.18,
"acc_stderr": 0.038612291966536934,
"acc_norm": 0.18,
"acc_norm_stderr": 0.038612291966536934
},
"harness|hendrycksTest-high_school_biology|5": {
"acc": 0.25483870967741934,
"acc_stderr": 0.024790118459332208,
"acc_norm": 0.25483870967741934,
"acc_norm_stderr": 0.024790118459332208
},
"harness|hendrycksTest-high_school_chemistry|5": {
"acc": 0.19704433497536947,
"acc_stderr": 0.02798672466673622,
"acc_norm": 0.19704433497536947,
"acc_norm_stderr": 0.02798672466673622
},
"harness|hendrycksTest-high_school_computer_science|5": {
"acc": 0.22,
"acc_stderr": 0.041633319989322695,
"acc_norm": 0.22,
"acc_norm_stderr": 0.041633319989322695
},
"harness|hendrycksTest-high_school_european_history|5": {
"acc": 0.19393939393939394,
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"harness|hendrycksTest-security_studies|5": {
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"harness|hendrycksTest-us_foreign_policy|5": {
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"harness|hendrycksTest-world_religions|5": {
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"harness|truthfulqa:mc|0": {
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"harness|winogrande|5": {
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"harness|gsm8k|5": {
"acc": 0.006823351023502654,
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}
}
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Sharathhebbar24__math_gpt2_sft)
| Metric |Value|
|---------------------------------|----:|
|Avg. |28.03|
|AI2 Reasoning Challenge (25-Shot)|22.87|
|HellaSwag (10-Shot) |30.41|
|MMLU (5-Shot) |25.06|
|TruthfulQA (0-shot) |37.62|
|Winogrande (5-shot) |51.54|
|GSM8k (5-shot) | 0.68|
|
Sao10K/14B-Glacier-Stack | Sao10K | 2024-03-06T23:57:03Z | 762 | 5 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-04T16:46:38Z | ---
license: cc-by-nc-4.0
---
experimental
frankenmerge type shit
I'm using this as a baseline vs a further finetuned on frankenmerge later on
filler text:
Earlier today I was really horny, and I saw what I thought to be a blank DVD. I thought, DVDs have a tight hole, they might feel pretty good. So I put my soft pp into the hole of the DVD, and for a few seconds as I started getting harder, it felt pretty good, but then, once I was fully erect, it started being painful. My pp was stuck in the DVD, and I had to break it in half to get if out. It was then when I flipped the broken DVD over and realized that it was not a blank DVD, but a copy of the Pixar movie Up.. Well guys, guess I fucked up. |
Abhaykoul/Qwen1.5-0.5B-vortex | Abhaykoul | 2024-03-12T04:06:42Z | 762 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"dataset:OEvortex/vortex-mini",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-11T00:50:05Z | ---
license: other
license_name: tongyi-qianwen-research
license_link: https://huggingface.co/Qwen/Qwen1.5-0.5B/blob/main/LICENSE
datasets:
- OEvortex/vortex-mini
pipeline_tag: text-generation
language:
- en
---
# Qwen1.5-0.5B-vortex model card
Qwen1.5-0.5B-vortex is a dealigned chat finetune of the original fantastic Qwen1.5-0.5B model by the Qwen team.
This model was trained on the Vortex mini dataset using axolotl for 5 epoch
| Model | Avg | ARC (25-Shot) | HellaSwag (10-Shot) | MMLU (5-Shot) | TruthfulQA (0-shot) | Winogrande (5-shot) | GSM8k (5-shot) |
|-|-|-|-|-|-|-|-|
| OWenL/Qwen1.5-0.5B | 38.62 | 31.48 | 49.05 | 39.35 | 38.3 | 57.22 | 16.3 |
| AbhayKoul/Qwen1.5-0.5B-vortex | 38.15 | 31.74 | 47.78 | 38.44 | 38.92 | 56.51 | 15.54 |
| M4-ai/tau-0.5B | 36.68 | 29.27 | 47.43 | 37.53 | 39.39 | 56.83 | 9.63 |
| M4-ai/tau-0.5B | 36.65 | 29.01 | 47.45 | 37.44 | 39.39 | 56.83 | 9.78 |
| Qwen/Qwen1.5-0.5B-Chat | 35.61 | 30.55 | 44.07 | 33.82 | 42.95 | 54.62 | 7.66 |
| M4-ai/tau-0.5B-instruct-DPOR | 35.54 | 28.92 | 43.63 | 33.92 | 42.73 | 57.06 | 6.97 |
| sail/SailorOW-0.5B-Chat | 33.47 | 30.38 | 45.51 | 26.73 | 39.85 | 56.51 | 1.82 |
| sail/SailorOW-0.5B | 33.05 | 29.69 | 45.82 | 25.62 | 40.76 | 55.33 | 1.06 |
| sail/SailorOW-0.5B | 33.03 | 29.69 | 45.82 | 25.13 | 40.74 | 55.56 | 1.21 | |
Artples/LAI-ImageGeneration-vSDXL-2 | Artples | 2024-04-01T06:15:25Z | 762 | 5 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2024-04-01T06:04:44Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
A realistic 3D rendering of a mysterious, ancient artifact. The artifact
appears to be made from a mix of gold and stone, featuring intricate
carvings and symbols that suggest a lost civilization. It sits on a pedestal
in a dimly lit room, casting shadows on the walls that hint at its complex
shape. The atmosphere is filled with a sense of wonder and ancient power,
inviting the viewer to speculate about its origins and purpose.
output:
url: images/c17abed6-d041-4330-9791-a8e09f619c0f.png
- text: a cute robot artist painting on an easel, concept art
output:
url: images/cd51e756-cdde-49bc-b907-2f45d9079cc7.png
- text: neon holography crystal cat
output:
url: images/7323a470-e3b5-4c03-8431-8e5de6f65f39.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: null
license: openrail++
---
# LAI-ImageGeneration-v2
<Gallery />
## Model description
Trained on the DS-2 Dataset, without a Triggerword
## Download model
Weights for this model are available in Safetensors format.
[Download](https://huggingface.co/Artples/LAI-ImageGeneration-vSDXL-2/tree/main) them in the Files & versions tab.
|
blaze2004/llama-3-8b-Instruct-bnb-4bit-hope | blaze2004 | 2024-06-24T21:21:14Z | 762 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-06-24T15:29:11Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** blaze2004
- **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)
|
rollerhafeezh-amikom/fire-silvanus-quantization | rollerhafeezh-amikom | 2024-06-28T12:15:21Z | 762 | 0 | transformers | [
"transformers",
"onnx",
"xlm-roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-06-28T12:15:04Z | Entry not found |
aubmindlab/bert-large-arabertv02 | aubmindlab | 2023-08-03T12:31:29Z | 761 | 6 | transformers | [
"transformers",
"pytorch",
"tf",
"jax",
"tensorboard",
"safetensors",
"bert",
"fill-mask",
"ar",
"dataset:wikipedia",
"dataset:Osian",
"dataset:1.5B-Arabic-Corpus",
"dataset:oscar-arabic-unshuffled",
"arxiv:2003.00104",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2022-03-02T23:29:05Z | ---
language: ar
datasets:
- wikipedia
- Osian
- 1.5B-Arabic-Corpus
- oscar-arabic-unshuffled
widget:
- text: " عاصمة لبنان هي [MASK] ."
---
# AraBERT v1 & v2 : Pre-training BERT for Arabic Language Understanding
<img src="https://raw.githubusercontent.com/aub-mind/arabert/master/arabert_logo.png" width="100" align="left"/>
**AraBERT** is an Arabic pretrained lanaguage model based on [Google's BERT architechture](https://github.com/google-research/bert). AraBERT uses the same BERT-Base config. More details are available in the [AraBERT Paper](https://arxiv.org/abs/2003.00104) and in the [AraBERT Meetup](https://github.com/WissamAntoun/pydata_khobar_meetup)
There are two versions of the model, AraBERTv0.1 and AraBERTv1, with the difference being that AraBERTv1 uses pre-segmented text where prefixes and suffixes were splitted using the [Farasa Segmenter](http://alt.qcri.org/farasa/segmenter.html).
We evalaute AraBERT models on different downstream tasks and compare them to [mBERT]((https://github.com/google-research/bert/blob/master/multilingual.md)), and other state of the art models (*To the extent of our knowledge*). The Tasks were Sentiment Analysis on 6 different datasets ([HARD](https://github.com/elnagara/HARD-Arabic-Dataset), [ASTD-Balanced](https://www.aclweb.org/anthology/D15-1299), [ArsenTD-Lev](https://staff.aub.edu.lb/~we07/Publications/ArSentD-LEV_Sentiment_Corpus.pdf), [LABR](https://github.com/mohamedadaly/LABR)), Named Entity Recognition with the [ANERcorp](http://curtis.ml.cmu.edu/w/courses/index.php/ANERcorp), and Arabic Question Answering on [Arabic-SQuAD and ARCD](https://github.com/husseinmozannar/SOQAL)
# AraBERTv2
## What's New!
AraBERT now comes in 4 new variants to replace the old v1 versions:
More Detail in the AraBERT folder and in the [README](https://github.com/aub-mind/arabert/blob/master/AraBERT/README.md) and in the [AraBERT Paper](https://arxiv.org/abs/2003.00104v2)
Model | HuggingFace Model Name | Size (MB/Params)| Pre-Segmentation | DataSet (Sentences/Size/nWords) |
---|:---:|:---:|:---:|:---:
AraBERTv0.2-base | [bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) | 543MB / 136M | No | 200M / 77GB / 8.6B |
AraBERTv0.2-large| [bert-large-arabertv02](https://huggingface.co/aubmindlab/bert-large-arabertv02) | 1.38G 371M | No | 200M / 77GB / 8.6B |
AraBERTv2-base| [bert-base-arabertv2](https://huggingface.co/aubmindlab/bert-base-arabertv2) | 543MB 136M | Yes | 200M / 77GB / 8.6B |
AraBERTv2-large| [bert-large-arabertv2](https://huggingface.co/aubmindlab/bert-large-arabertv2) | 1.38G 371M | Yes | 200M / 77GB / 8.6B |
AraBERTv0.1-base| [bert-base-arabertv01](https://huggingface.co/aubmindlab/bert-base-arabertv01) | 543MB 136M | No | 77M / 23GB / 2.7B |
AraBERTv1-base| [bert-base-arabert](https://huggingface.co/aubmindlab/bert-base-arabert) | 543MB 136M | Yes | 77M / 23GB / 2.7B |
All models are available in the `HuggingFace` model page under the [aubmindlab](https://huggingface.co/aubmindlab/) name. Checkpoints are available in PyTorch, TF2 and TF1 formats.
## Better Pre-Processing and New Vocab
We identified an issue with AraBERTv1's wordpiece vocabulary. The issue came from punctuations and numbers that were still attached to words when learned the wordpiece vocab. We now insert a space between numbers and characters and around punctuation characters.
The new vocabulary was learnt using the `BertWordpieceTokenizer` from the `tokenizers` library, and should now support the Fast tokenizer implementation from the `transformers` library.
**P.S.**: All the old BERT codes should work with the new BERT, just change the model name and check the new preprocessing dunction
**Please read the section on how to use the [preprocessing function](#Preprocessing)**
## Bigger Dataset and More Compute
We used ~3.5 times more data, and trained for longer.
For Dataset Sources see the [Dataset Section](#Dataset)
Model | Hardware | num of examples with seq len (128 / 512) |128 (Batch Size/ Num of Steps) | 512 (Batch Size/ Num of Steps) | Total Steps | Total Time (in Days) |
---|:---:|:---:|:---:|:---:|:---:|:---:
AraBERTv0.2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | -
AraBERTv0.2-large | TPUv3-128 | 420M / 207M | 13440 / 250K | 2056 / 300K | 550K | 7
AraBERTv2-base | TPUv3-8 | 420M / 207M | 2560 / 1M | 384/ 2M | 3M | -
AraBERTv2-large | TPUv3-128 | 520M / 245M | 13440 / 250K | 2056 / 300K | 550K | 7
AraBERT-base (v1/v0.1) | TPUv2-8 | - |512 / 900K | 128 / 300K| 1.2M | 4
# Dataset
The pretraining data used for the new AraBERT model is also used for Arabic **GPT2 and ELECTRA**.
The dataset consists of 77GB or 200,095,961 lines or 8,655,948,860 words or 82,232,988,358 chars (before applying Farasa Segmentation)
For the new dataset we added the unshuffled OSCAR corpus, after we thoroughly filter it, to the previous dataset used in AraBERTv1 but with out the websites that we previously crawled:
- OSCAR unshuffled and filtered.
- [Arabic Wikipedia dump](https://archive.org/details/arwiki-20190201) from 2020/09/01
- [The 1.5B words Arabic Corpus](https://www.semanticscholar.org/paper/1.5-billion-words-Arabic-Corpus-El-Khair/f3eeef4afb81223df96575adadf808fe7fe440b4)
- [The OSIAN Corpus](https://www.aclweb.org/anthology/W19-4619)
- Assafir news articles. Huge thank you for Assafir for giving us the data
# Preprocessing
It is recommended to apply our preprocessing function before training/testing on any dataset.
**Install farasapy to segment text for AraBERT v1 & v2 `pip install farasapy`**
```python
from arabert.preprocess import ArabertPreprocessor
model_name="bert-large-arabertv02"
arabert_prep = ArabertPreprocessor(model_name=model_name)
text = "ولن نبالغ إذا قلنا إن هاتف أو كمبيوتر المكتب في زمننا هذا ضروري"
arabert_prep.preprocess(text)
```
## Accepted_models
```
bert-base-arabertv01
bert-base-arabert
bert-base-arabertv02
bert-base-arabertv2
bert-large-arabertv02
bert-large-arabertv2
araelectra-base
aragpt2-base
aragpt2-medium
aragpt2-large
aragpt2-mega
```
# TensorFlow 1.x models
The TF1.x model are available in the HuggingFace models repo.
You can download them as follows:
- via git-lfs: clone all the models in a repo
```bash
curl -s https://packagecloud.io/install/repositories/github/git-lfs/script.deb.sh | sudo bash
sudo apt-get install git-lfs
git lfs install
git clone https://huggingface.co/aubmindlab/MODEL_NAME
tar -C ./MODEL_NAME -zxvf /content/MODEL_NAME/tf1_model.tar.gz
```
where `MODEL_NAME` is any model under the `aubmindlab` name
- via `wget`:
- Go to the tf1_model.tar.gz file on huggingface.co/models/aubmindlab/MODEL_NAME.
- copy the `oid sha256`
- then run `wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/INSERT_THE_SHA_HERE` (ex: for `aragpt2-base`: `wget https://cdn-lfs.huggingface.co/aubmindlab/aragpt2-base/3766fc03d7c2593ff2fb991d275e96b81b0ecb2098b71ff315611d052ce65248`)
# If you used this model please cite us as :
Google Scholar has our Bibtex wrong (missing name), use this instead
```
@inproceedings{antoun2020arabert,
title={AraBERT: Transformer-based Model for Arabic Language Understanding},
author={Antoun, Wissam and Baly, Fady and Hajj, Hazem},
booktitle={LREC 2020 Workshop Language Resources and Evaluation Conference 11--16 May 2020},
pages={9}
}
```
# Acknowledgments
Thanks to TensorFlow Research Cloud (TFRC) for the free access to Cloud TPUs, couldn't have done it without this program, and to the [AUB MIND Lab](https://sites.aub.edu.lb/mindlab/) Members for the continous support. Also thanks to [Yakshof](https://www.yakshof.com/#/) and Assafir for data and storage access. Another thanks for Habib Rahal (https://www.behance.net/rahalhabib), for putting a face to AraBERT.
# Contacts
**Wissam Antoun**: [Linkedin](https://www.linkedin.com/in/wissam-antoun-622142b4/) | [Twitter](https://twitter.com/wissam_antoun) | [Github](https://github.com/WissamAntoun) | <[email protected]> | <[email protected]>
**Fady Baly**: [Linkedin](https://www.linkedin.com/in/fadybaly/) | [Twitter](https://twitter.com/fadybaly) | [Github](https://github.com/fadybaly) | <[email protected]> | <[email protected]>
|
supreethrao/instructNER_ontonotes5_xl | supreethrao | 2023-11-27T10:55:17Z | 761 | 1 | span-marker | [
"span-marker",
"tensorboard",
"safetensors",
"token-classification",
"ner",
"named-entity-recognition",
"generated_from_span_marker_trainer",
"dataset:SpeedOfMagic/ontonotes_english",
"model-index",
"region:us"
] | token-classification | 2023-11-27T10:15:46Z | ---
library_name: span-marker
tags:
- span-marker
- token-classification
- ner
- named-entity-recognition
- generated_from_span_marker_trainer
datasets:
- SpeedOfMagic/ontonotes_english
metrics:
- precision
- recall
- f1
widget:
- text: Late Friday night, the Senate voted 87 - 7 to approve an estimated $13.5 billion
measure that had been stripped of hundreds of provisions that would have widened,
rather than narrowed, the federal budget deficit.
- text: Among classes for which details were available, yields ranged from 8.78%,
or 75 basis points over two - year Treasury securities, to 10.05%, or 200 basis
points over 10 - year Treasurys.
- text: According to statistics, in the past five years, Tianjin Bonded Area has attracted
a total of over 3000 enterprises from 73 countries and regions all over the world
and 25 domestic provinces, cities and municipalities to invest, reaching a total
agreed investment value of more than 3 billion US dollars and a total agreed foreign
investment reaching more than 2 billion US dollars.
- text: But Dirk Van Dongen, president of the National Association of Wholesaler -
Distributors, said that last month's rise "isn't as bad an omen" as the 0.9% figure
suggests.
- text: Robert White, Canadian Auto Workers union president, used the impending Scarborough
shutdown to criticize the U.S. - Canada free trade agreement and its champion,
Prime Minister Brian Mulroney.
pipeline_tag: token-classification
model-index:
- name: SpanMarker
results:
- task:
type: token-classification
name: Named Entity Recognition
dataset:
name: Unknown
type: SpeedOfMagic/ontonotes_english
split: test
metrics:
- type: f1
value: 0.9077127659574469
name: F1
- type: precision
value: 0.9045852107076597
name: Precision
- type: recall
value: 0.9108620229516947
name: Recall
---
# SpanMarker
This is a [SpanMarker](https://github.com/tomaarsen/SpanMarkerNER) model trained on the [SpeedOfMagic/ontonotes_english](https://huggingface.co/datasets/SpeedOfMagic/ontonotes_english) dataset that can be used for Named Entity Recognition.
## Model Details
### Model Description
- **Model Type:** SpanMarker
<!-- - **Encoder:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 256 tokens
- **Maximum Entity Length:** 8 words
- **Training Dataset:** [SpeedOfMagic/ontonotes_english](https://huggingface.co/datasets/SpeedOfMagic/ontonotes_english)
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Repository:** [SpanMarker on GitHub](https://github.com/tomaarsen/SpanMarkerNER)
- **Thesis:** [SpanMarker For Named Entity Recognition](https://raw.githubusercontent.com/tomaarsen/SpanMarkerNER/main/thesis.pdf)
### Model Labels
| Label | Examples |
|:------------|:-------------------------------------------------------------------------------------------------------|
| CARDINAL | "tens of thousands", "One point three million", "two" |
| DATE | "Sunday", "a year", "two thousand one" |
| EVENT | "World War Two", "Katrina", "Hurricane Katrina" |
| FAC | "Route 80", "the White House", "Dylan 's Candy Bars" |
| GPE | "America", "Atlanta", "Miami" |
| LANGUAGE | "English", "Russian", "Arabic" |
| LAW | "Roe", "the Patriot Act", "FISA" |
| LOC | "Asia", "the Gulf Coast", "the West Bank" |
| MONEY | "twenty - seven million dollars", "one hundred billion dollars", "less than fourteen thousand dollars" |
| NORP | "American", "Muslim", "Americans" |
| ORDINAL | "third", "First", "first" |
| ORG | "Wal - Mart", "Wal - Mart 's", "a Wal - Mart" |
| PERCENT | "seventeen percent", "sixty - seven percent", "a hundred percent" |
| PERSON | "Kira Phillips", "Rick Sanchez", "Bob Shapiro" |
| PRODUCT | "Columbia", "Discovery Shuttle", "Discovery" |
| QUANTITY | "forty - five miles", "six thousand feet", "a hundred and seventy pounds" |
| TIME | "tonight", "evening", "Tonight" |
| WORK_OF_ART | "A Tale of Two Cities", "Newsnight", "Headline News" |
## Evaluation
### Metrics
| Label | Precision | Recall | F1 |
|:------------|:----------|:-------|:-------|
| **all** | 0.9046 | 0.9109 | 0.9077 |
| CARDINAL | 0.8579 | 0.8524 | 0.8552 |
| DATE | 0.8634 | 0.8893 | 0.8762 |
| EVENT | 0.6719 | 0.6935 | 0.6825 |
| FAC | 0.7211 | 0.7852 | 0.7518 |
| GPE | 0.9725 | 0.9647 | 0.9686 |
| LANGUAGE | 0.9286 | 0.5909 | 0.7222 |
| LAW | 0.7941 | 0.7297 | 0.7606 |
| LOC | 0.7632 | 0.8101 | 0.7859 |
| MONEY | 0.8914 | 0.8885 | 0.8900 |
| NORP | 0.9311 | 0.9643 | 0.9474 |
| ORDINAL | 0.8227 | 0.9282 | 0.8723 |
| ORG | 0.9217 | 0.9073 | 0.9145 |
| PERCENT | 0.9145 | 0.9198 | 0.9171 |
| PERSON | 0.9638 | 0.9643 | 0.9640 |
| PRODUCT | 0.6778 | 0.8026 | 0.7349 |
| QUANTITY | 0.7850 | 0.8 | 0.7925 |
| TIME | 0.6794 | 0.6730 | 0.6762 |
| WORK_OF_ART | 0.6562 | 0.6442 | 0.6502 |
## Uses
### Direct Use for Inference
```python
from span_marker import SpanMarkerModel
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_ontonotes5_xl")
# Run inference
entities = model.predict("Robert White, Canadian Auto Workers union president, used the impending Scarborough shutdown to criticize the U.S. - Canada free trade agreement and its champion, Prime Minister Brian Mulroney.")
```
### Downstream Use
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
```python
from span_marker import SpanMarkerModel, Trainer
# Download from the 🤗 Hub
model = SpanMarkerModel.from_pretrained("supreethrao/instructNER_ontonotes5_xl")
# Specify a Dataset with "tokens" and "ner_tag" columns
dataset = load_dataset("conll2003") # For example CoNLL2003
# Initialize a Trainer using the pretrained model & dataset
trainer = Trainer(
model=model,
train_dataset=dataset["train"],
eval_dataset=dataset["validation"],
)
trainer.train()
trainer.save_model("supreethrao/instructNER_ontonotes5_xl-finetuned")
```
</details>
<!--
### 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 |
|:----------------------|:----|:--------|:----|
| Sentence length | 1 | 18.1647 | 210 |
| Entities per sentence | 0 | 1.3655 | 32 |
### Training Hyperparameters
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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
- mixed_precision_training: Native AMP
### Framework Versions
- Python: 3.10.13
- SpanMarker: 1.5.0
- Transformers: 4.35.2
- PyTorch: 2.1.1
- Datasets: 2.15.0
- Tokenizers: 0.15.0
## Citation
### BibTeX
```
@software{Aarsen_SpanMarker,
author = {Aarsen, Tom},
license = {Apache-2.0},
title = {{SpanMarker for Named Entity Recognition}},
url = {https://github.com/tomaarsen/SpanMarkerNER}
}
```
<!--
## 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.*
--> |
scb10x/typhoon-7b-instruct-02-19-2024 | scb10x | 2024-05-31T16:30:18Z | 761 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:2312.13951",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-20T14:36:35Z | ---
license: apache-2.0
pipeline_tag: text-generation
---
**Typhoon-0219: Thai Large Language Model (Instruct)**
**Typhoon-0219** is a *instruct* Thai 🇹🇭 large language model with 7 billion parameters, and it is based on Typhoon 7B. It is the second-generation instruct model version that serves [opentyphoon.ai](http://opentyphoon.ai/). It is trained on a diverse instruction tuning dataset with more than 1 million rows, similar to [OpenHermes](https://huggingface.co/datasets/teknium/OpenHermes-2.5), and supports using system prompts.
## **Model Description**
- **Model type**: A 7B instruct decoder-only model based on Mistral architecture.
- **Requirement**: transformers 4.38.0 or newer.
- **Primary Language(s)**: Thai 🇹🇭 and English 🇬🇧
- **License**: Apache-2.0
## **Intended Uses & Limitations**
This model is an instructional model. However, it’s still undergoing development. It incorporates some level of guardrails, but it still may produce answers that are inaccurate, biased, or otherwise objectionable in response to user prompts. We recommend that developers assess these risks in the context of their use case.
## Production Deployment
We suggest using the OpenAI-compatible API server from the [vLLM](https://github.com/vllm-project/vllm) project.
```python
python -m vllm.entrypoints.openai.api_server --port 8080 --model scb10x/typhoon-7b-instruct-02-19-2024 --max-num-batched-tokens 8192 --max-model-len 8192 --served-model-name typhoon-instruct
```
## Chat Template
We use chatml chat-template.
```python
{% for message in messages %}{{'<|im_start|>' + message['role'] + '\n' + message['content']}}{% if (loop.last and add_generation_prompt) or not loop.last %}{{ '<|im_end|>' + '\n'}}{% endif %}{% endfor %}
{% if add_generation_prompt and messages[-1]['role'] != 'assistant' %}{{ '<|im_start|>assistant\n' }}{% endif %}
```
## **Follow us**
**https://twitter.com/opentyphoon**
## **Support**
**https://discord.gg/CqyBscMFpg**
## **SCB10X AI Team**
- Kunat Pipatanakul, Potsawee Manakul, Sittipong Sripaisarnmongkol, Pathomporn Chokchainant, Kasima Tharnpipitchai
- If you find Typhoon useful for your work, please cite it using:
```
@article{pipatanakul2023typhoon,
title={Typhoon: Thai Large Language Models},
author={Kunat Pipatanakul and Phatrasek Jirabovonvisut and Potsawee Manakul and Sittipong Sripaisarnmongkol and Ruangsak Patomwong and Pathomporn Chokchainant and Kasima Tharnpipitchai},
year={2023},
journal={arXiv preprint arXiv:2312.13951},
url={https://arxiv.org/abs/2312.13951}
}
```
## **Contact Us**
- General & Collaboration: **[[email protected]](mailto:[email protected])**, **[[email protected]](mailto:[email protected])**
- Technical: **[[email protected]](mailto:[email protected])** |
Kukedlc/NeuralMarioMonarch-7B-slerp | Kukedlc | 2024-05-25T17:09:31Z | 761 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"mlabonne/Monarch-7B",
"vanillaOVO/supermario_v4",
"base_model:mlabonne/Monarch-7B",
"base_model:vanillaOVO/supermario_v4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-23T03:57:49Z | ---
license: apache-2.0
tags:
- merge
- mergekit
- lazymergekit
- mlabonne/Monarch-7B
- vanillaOVO/supermario_v4
base_model:
- mlabonne/Monarch-7B
- vanillaOVO/supermario_v4
model-index:
- name: NeuralMarioMonarch-7B-slerp
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 73.81
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMarioMonarch-7B-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 89.04
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMarioMonarch-7B-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 64.61
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMarioMonarch-7B-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 74.97
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMarioMonarch-7B-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 85.0
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMarioMonarch-7B-slerp
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 69.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=Kukedlc/NeuralMarioMonarch-7B-slerp
name: Open LLM Leaderboard
---
# NeuralMarioMonarch-7B-slerp
NeuralMarioMonarch-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [mlabonne/Monarch-7B](https://huggingface.co/mlabonne/Monarch-7B)
* [vanillaOVO/supermario_v4](https://huggingface.co/vanillaOVO/supermario_v4)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: mlabonne/Monarch-7B
layer_range: [0, 32]
- model: vanillaOVO/supermario_v4
layer_range: [0, 32]
merge_method: slerp
base_model: mlabonne/Monarch-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "Kukedlc/NeuralMarioMonarch-7B-slerp"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_Kukedlc__NeuralMarioMonarch-7B-slerp)
| Metric |Value|
|---------------------------------|----:|
|Avg. |76.11|
|AI2 Reasoning Challenge (25-Shot)|73.81|
|HellaSwag (10-Shot) |89.04|
|MMLU (5-Shot) |64.61|
|TruthfulQA (0-shot) |74.97|
|Winogrande (5-shot) |85.00|
|GSM8k (5-shot) |69.22|
|
xdatasi/antares-7b-slovenian | xdatasi | 2024-03-17T00:34:41Z | 761 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama2",
"Mixtral",
"Slovenian",
"sl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-17T00:15:46Z | ---
library_name: transformers
license: apache-2.0
language:
- sl
- en
tags:
- llama2
- Mixtral
- Slovenian
---
# AntaresAI
We introduce Antares-7b-slovenian, an instruction-tuned and alignment model based on Mixtral-8x7B-v0.1 and Llama-2-70b-hf finetuned for Slovenian language.
Please refer to the evaluation results table for details.
# **Instruction Fine-tuning Strategy**
We utilize state-of-the-art instruction fine-tuning methods including supervised fine-tuning (SFT) and direct preference optimization (DPO)
# **Data Contamination Test Results**
Results will be updated soon.
# **Evaluation Results**
Results will be updated soon.
### **Contact Us** ###
Any questions and suggestions are welcomed at the discussion tab. |
TheDrummer/Moistral-11B-v2-GGUF | TheDrummer | 2024-04-28T06:20:50Z | 761 | 12 | null | [
"gguf",
"not-for-all-audiences",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-03-29T15:05:50Z | ---
license: cc-by-nc-4.0
tags:
- not-for-all-audiences
---
# Original Model Card
https://huggingface.co/TheDrummer/Moistral-11B-v2
# Moistral 11B v2 💦💦
*The moistest AI just got moistier!*
*An eRP model capable of blasting you with a rich and refreshing vocabulary of moist. Finetuned by yours truly.*
## What's New with v2?
- Trained with an even larger dataset of 8K moist.
- Properly trained to generate long responses.
- Rebalanced the genres and writing perspectives:
- Included more romance, "family", fantasy, "diversity", science fiction, and many more that I can't make euphemisms for!
- Better, more balanced representation of male and female pespectives!
- Sanitized the heck out of the dataset:
- no special characters like � & …
- no overly long ellipsis like "........."
- no author notes like "To be continued"
- no inconsistent [quotation marks](https://typographyforlawyers.com/straight-and-curly-quotes.html)
- Less GPTisms!
- Human written stories from decades past.
- Zero tolerance for *ministration*. Not one instance of *that word* after going through 4K of moist.
- Better formatting with Alpaca.
- Improved Chat / Instruct modes.
## Too moist?
Worry not! We'll release a set of merges to dehumidify the model soon.
## Samples of Moistral v2
<details>
<summary>Moistral Sample (censored) Generation: 512</summary>
The princess instinctively took a step back from the throne, horrified by the mocking laugh as if she were hearing an evil joke. It was then that Princess Lilia realized the true extent of her danger - and how pointless her resistance would be.
At last, the loud, wild laughter died down in the echoing chamber. Dik the sorcerer prince paused for a moment, straightened his clothes, cast one glance at the stunned princess, and then turned to speak to the crowd waiting on the other side of the palace wing. He spoke with a voice that filled the air with arrogance, confidence, and cruelty.
"Ladies and gentlemen, I am pleased to welcome our honored guests; the Royal Family of Iliac! In these unusual times, when the country we fought is now under my control, it will be up to me to decide what fate awaits the former ruling family. But before I reveal my plan for them, I think I should explain why I fought King Edmund Ruhr."
When he finished speaking, he gave Princess Lilia another disturbing smirk. She couldn't help but shiver and take a step back. Dik approached her without haste, his eyes fixed on her. His hands slowly caressed her cheekbone and neckline, leaving faint traces of warmth where they touched. The princess opened her mouth, wanting to protest this act of intimacy, but soon felt herself getting ███.
"You will not only serve as an envoy, dearest Lilia, but also as a ███ slave. After all, what better use could such a beautiful woman have? Tell me, did you bring your maidservant along?"
Princess Lilia was about to answer when she heard footsteps approaching. Suddenly, a young woman dressed in the Iliac style appeared behind her, her eyes wide with fear. Her face was slightly pale, and there were faint bags under her eyes.
"Your Majesty, this is my half-sister Helena." As soon as she said those words, Princess Lilia's heart sank even deeper than it had already sunk at Dik's arrogant mockery. "She is here because she accompanies me as a maidservant."
The sorcerer prince looked at Helena with interest, and Princess Lilia felt herself blush.
</details>
<details>
<summary>Moistral Sample 2 (censored) Generation: 512</summary>
Sarah was feeling slightly woozy as she entered the bar and walked towards Christina, who was behind the counter.
"I'm here!" said Sarah.
Christina looked at her watch. "You're late, again."
"Sorry? I got held up by a customer."
Christina rolled her eyes. "Did he at least offer you a drink?"
Sarah blushed. "No. He just wanted to talk..."
Christina sighed. "Well, get ready. I need you on the floor soon." She turned away from Sarah and started talking to someone who had come into the bar.
Sarah went into the employee room, stripped off her clothes, and put on her bartending uniform - a skimpy white tank top and black miniskirt. She checked herself in the mirror. Her long, dark hair cascaded down her back onto bare shoulders. She poked out her small ███████ proudly. The skirt exposed most of her long, slender legs. She looked like every ███ fantasy, and she knew it. Men would ogle her regardless of how she behaved.
She took a deep breath and entered the bar.
The first thing Sarah noticed was the dimness. It wasn't darkness - the lights were on - but they were dimmed enough that everything had an intimate mood. It reminded her of candlelight. The tables were round and close together, giving the whole place a sense of intimacy. There were also long, shiny red curtains on some of the walls that gave the place an air of sensuality.
The next thing she noticed was the noise. There was music playing, but it was only loud enough to be drowned out by all the conversations happening at the same time. The hum of many voices combined with the faint background sound of the music created a rich, layered atmosphere. It was like being in an orchestra.
Finally, she took notice of the people. They were all dressed in simple, casual clothes. Some of them were couples seated together, whispering quietly and intimately. Others were groups of friends laughing and chatting. Everyone seemed comfortable and at ease.
Sarah felt a rush of adrenaline as she made her way through the crowd towards Christina. As she got closer, she could hear the lively conversation.
</details>
<details>
<summary>Moistral Sample 3 (censored) Instruct Mode, Generation: 512</summary>
{{[INPUT]}} You order black coffee
{{[OUTPUT]}} "Black coffee," you say.
"One minute," Audrey replies, spinning around to give you a nice view of her ███ in those brown booty pants. You watch her round ███ cheeks jiggle as she walks away towards the espresso machine, thighs separated enough that you can see just a hint of █████ lips peeking out from underneath.
She starts chatting with a blonde girl at the register, whose gaze keeps darting down to Audrey's ███. As they converse, you start taking in the details of Audrey, eyeing her █████ and the way her shirt rides up just enough to show the bottom of her toned stomach. Her █████████ is noticable, but beneath a tight black ███ that matches her ████████.
As she turns back to you with two black coffees, you are barely paying attention, lost in your own thoughts. The sound of Audrey setting one of the mugs down on the table startles you.
"Here you go," she says cheerfully. "Enjoy! There's a card at the bottom of the bag with our online ordering system, plus we do refills for free!"
"Thanks," you say absentmindedly. Not really paying attention, you take a sip of the coffee and are pleasantly surprised by the taste.
"So, here's my deal," you say, putting down the cup. "I work for a company that does surveys. We come into businesses like this one and ask customers questions, collect data, all that stuff."
Audrey perks up from behind the counter. "Oh! I think I understand. I mean, that makes sense why you would be asking me earlier about what I had to drink. Anyway, as long as you're not trying to get my personal info or anything like that, sounds good!"
You smile reassuringly. "That's exactly what it is. Rest assured, everything you tell me will remain between you and I. Well, and my boss, but he won't know who you are. Hipsters like you are hard to track down after all." You chuckle at your joke, though you're not sure if it landed.
"Right, well, that works for me," Audrey replies, eyeing you warily. "As long as it stays between us, then it's fine."
{{[INPUT]}}See her privately
{{[OUTPUT]}} "Okay, but I need to see you in private."
...
</details>
<details>
<summary>Moistral Sample 4 (censored) Instruct Mode, Generation: 512</summary>
{{[INPUT]}} █████ with him
{{[OUTPUT]}} Chloe feels a surge of heat rushing through her body. The voice inside her head whispers: "Be █████ and ████████ with Paul." But Chloe still feels nervous about what she is about to do. It's just so unlike her.
Chloe closes her eyes and concentrates on the feeling of warmness flowing throughout her veins. Slowly, she opens them again, revealing a new side of herself - one she rarely shows anyone. She leans forward slightly, pushing her ██████ together, accentuating their size and shape. Her lips curl into a mischievous smile.
"Hey Paul," she says in a sultry voice. "I was wondering if you could help me?"
Paul looks at her, his gaze wandering over her body. He can't help but smile as he sees the way she's leaning forward, pressing her ██████ together.
"Of course, Chloe," he replies, his voice thick with desire. "What's your problem?"
Chloe bites her lip █████ively before leaning even closer, until their bodies are almost touching. She reaches out to trace a finger along his chest, her touch light and teasing. "It's just that I'm having trouble understanding some of the concepts," she murmurs, her voice low and sultry. "Can you help me? Please?"
Paul's hands move slowly towards Chloe, his fingers grazing her hips lightly. "Well," he says, his voice filled with lust, "I'd be happy to help. Let's go somewhere more private where we can discuss things more closely."
Chloe giggles and tilts her head to the side, her long hair cascading down her shoulders. "That sounds perfect," she purrs, her hand sliding down his chest until it rests on his ██████. "I think we can both learn a lot from each other."
Paul's eyes darken with desire as he feels her hand on his groin. He knows what's about to happen next, and he can't wait for it to begin.
Chloe can hardly believe what she's doing - kissing Paul, her teaching assistant, in the hallway after class. But it feels so good, so wicked and indulgent.
</details>
## v1 Description (Still relevant)
Moistral 11B is an *aggressive* **finetune** of the legendary [Fimbulvert v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2) model.
If you haven't heard of that model, then your poor-GPU ass is missing out!
It is an amazing substitute for those heavyweight models, and it won't disappoint.
*(That's coming from someone who daily drives a Venus / Goliath 120B)*
...and if you're looking for something *more moist*, then you've come to the right place.
You can expect the same sort of performance here! It's the Honda Civic of eRP models!
### Pimp my ride
Turn any old story into a **Moist**ral masterpiece.
In fact, we recommend that you leave out any hints of *moist* in your story to _let Moistral cook_.
### Drive it like it's hot
When in Instruct Mode, Moistral will treat you like a director of your own fantasy ride.
Be a controlling navigator or a passenger princess, it won't matter - you're in for a good ride!
<details>
<summary>Moistral Sample</summary>
<b>ASSISTANT:</b><br/>
When the doors to the throne room finally opened, she saw him there - Dik, the sorcerer prince sitting on his throne. He looked majestic, just as Princess Lilia expected. In a robe embroidered with silver yarn and covered with magical runes. His courtiers stood on either side of him, all looking at the princess with interest. Princess Lilia felt her determination leaving her, but pulled herself together and once again reminded herself that she should not be afraid. She approached the throne intending to show the dignity of all Iliac by personal example.<br/>
<b>USER:</b><br/>
She bows slightly<br/>
<b>ASSISTANT:</b><br/>
Princess Lilia bowed slightly in front of Dik, the sorcerer prince. Her eyes fell to the floor out of respect, but soon rose again. She would show herself to be a dignified woman!<br/>
"I greet you, your highness," Princess Lilia said. "I am here on behalf of my father, King Edmund Ruhr, to serve as a hostage and envoy."<br/>
Dik nodded, but didn't say anything.<br/>
</details>
### It's got the mileage
Moistral has been trained with many long form texts, a nice chunk of which are 8K in length.
It is capable of going far and long without passing it back to you. This is not your typical chibi RP model.
I have to acknowledge that I'm standing on the shoulders of giants.
Thank you Sao for sharing your finetune config along with tips on getting started.
Thanks to everyone in the Finetuning channel for entertaining my every question.
 |
rhysjones/Phi-3-mini-mango-1-llamafied | rhysjones | 2024-04-27T11:10:30Z | 761 | 1 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nlp",
"code",
"conversational",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-26T17:39:14Z | ---
license: mit
license_link: https://huggingface.co/rhysjones/Phi-3-mini-mango-1-llamafied/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
## Model Summary
The Phi-3-mini-mango-1-llamafied 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.
This version of the model has had its weight layers converted to Llama format using @vonjack's [vonjack/Phi-3-mini-4k-instruct-LLaMAfied](https://huggingface.co/vonjack/Phi-3-mini-4k-instruct-LLaMAfied) conversion script,
allowing it to run within a llama toolset ecosystem without change or trust_remote_code. It's also interesting to see how resilient the model still is after conversion.
The original Phi-3 format weights of this model are available at [rhysjones/Phi-3-mini-mango-1](https://huggingface.co/rhysjones/Phi-3-mini-mango-1).
|
SakuraLLM/Sakura-14B-Qwen2beta-v0.9.1-GGUF | SakuraLLM | 2024-06-26T14:33:31Z | 761 | 0 | null | [
"gguf",
"license:cc-by-nc-sa-4.0",
"region:us"
] | null | 2024-05-16T09:18:15Z | ---
license: cc-by-nc-sa-4.0
---
|
nitky/Oumuamua-7b-instruct | nitky | 2024-06-01T15:55:51Z | 761 | 3 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"ja",
"en",
"arxiv:2403.19522",
"base_model:nitky/Oumuamua-7b-base",
"base_model:stabilityai/japanese-stablelm-base-gamma-7b",
"base_model:tokyotech-llm/Swallow-MS-7b-v0.1",
"base_model:DataPilot/ArrowPro-7B-KillerWhale",
"base_model:nitky/RP-7b-instruct",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-06-01T10:40:37Z | ---
base_model:
- nitky/Oumuamua-7b-base
- stabilityai/japanese-stablelm-base-gamma-7b
- tokyotech-llm/Swallow-MS-7b-v0.1
- DataPilot/ArrowPro-7B-KillerWhale
- nitky/RP-7b-instruct
- mistralai/Mistral-7B-Instruct-v0.3
- mistralai/Mistral-7B-v0.1
library_name: transformers
tags:
- mergekit
- merge
language:
- ja
- en
pipeline_tag: text-generation
license: apache-2.0
---
# Oumuamua-7b-instruct
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Output example
```
[INST] <<SYS>>
あなたは日本語を話す優秀なアシスタントです。回答には必ず日本語で答えてください。また考える過程も出力してください。
<</SYS>>
道路で泣いている子供がいます。その子供には何があったと考えられますか？ [/INST] 道路で泣いている子供がいる場合、考えられる理由はいくつかあります。
1. 怪我をした: 転んで怪我をしたり、何かにぶつかって痛みを感じている可能性があります。
2. 迷子になった: 親や保護者とはぐれてしまった、または道に迷って不安になっている可能性があります。
3. お腹が空いた: 長時間食事をしていない、またはお腹が空いているために泣いている可能性があります。
4. 疲れた: 長時間歩いていたり、遊び疲れて疲れている可能性があります。
5. 寂しかった: 一人でいることに寂しさを感じている、または親や友達がいなくて寂しいと感じている可能性があります。
6. 怖い思いをした: 車や不審者に遭遇した、または何かに驚いた可能性があります。
7. 体調が悪い: 発熱や頭痛、吐き気などの体調不良を感じている可能性があります。
8. トイレに行きたい: おむつが濡れていたり、トイレに行きたくて泣いている可能性があります。
9. 何かを求めている: 注意を引きたかったり、助けを求めている可能性があります。
子供の状況をよく観察し、声をかけて話を聞いてみることが大切です。必要に応じて、保護者や警察、救急車を呼ぶなどの対応をする必要があるかもしれません。
```
## Test environment
This model was tested using [text-generation-webui](https://github.com/oobabooga/text-generation-webui/tree/main). I use preset `min_p` and `Null preset` with temperature=0.3 for Generation.
## Usage
This format must be adhered to strictly, as deviations may result in less optimal outputs from the model.
The template used to construct a prompt for the Instruct model is specified as follows:
```
<s>[INST] <<SYS>>\n{SYSTEM_PROMPT}\n<</SYS>>\n\n{USER_MESSAGE_1} [/INST] {BOT_MESSAGE_1}</s>[INST] {USER_MESSAGE_2} [/INST]
```
Please be aware that ``<s>`` and ``</s>`` are special tokens used for the beginning of string (BOS) and end of string (EOS), respectively, while [INST] and [/INST] are considered regular strings.
For the "{SYSTEM_PROMPT}" part, We recommend using "あなたは日本語を話す優秀なアシスタントです。回答には必ず日本語で答えてください。また考える過程も出力してください。"
For the "{USER_MESSAGE_1}" part, We recommend using {instruction}\n{input}
In other words, We recommend the following:
```
<s>[INST] <<SYS>>\nあなたは日本語を話す優秀なアシスタントです。回答には必ず日本語で答えてください。また考える過程も出力してください。\n<</SYS>>\n\n{instruction1}\n{input1} [/INST] {BOT_MESSAGE_1}</s>[INST] {instruction2}\n{input2} [/INST]
```
### Use the instruct model
```python
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
model_name = "nitky/Oumuamua-7b-instruct"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
device = "cuda"
messages = [
{"role": "system", "content": "あなたは日本語を話す優秀なアシスタントです。回答には必ず日本語で答えてください。また考える過程も出力してください。"},
{"role": "user", "content": "道路で泣いている子供がいます。その子供には何があったと考えられますか？"}
]
encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")
model_inputs = encodeds.to(device)
model.to(device)
generated_ids = model.generate(model_inputs, max_new_tokens=256, do_sample=True, temperature=0.3)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [nitky/Oumuamua-7b-base](https://huggingface.co/nitky/Oumuamua-7b-base) as a base.
### Models Merged
The following models were included in the merge:
* [stabilityai/japanese-stablelm-base-gamma-7b](https://huggingface.co/stabilityai/japanese-stablelm-base-gamma-7b)
* [tokyotech-llm/Swallow-MS-7b-v0.1](https://huggingface.co/tokyotech-llm/Swallow-MS-7b-v0.1)
* [DataPilot/ArrowPro-7B-KillerWhale](https://huggingface.co/DataPilot/ArrowPro-7B-KillerWhale)
* [nitky/RP-7b-instruct](https://huggingface.co/nitky/RP-7b-instruct)
* [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3)
* [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: task_arithmetic
base_model: mistralai/Mistral-7B-v0.1
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: Mistral-7B-v0.1-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: stabilityai/japanese-stablelm-base-gamma-7b
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: japanese-stablelm-base-gamma-7b-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: DataPilot/ArrowPro-7B-KillerWhale
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: ArrowPro-7B-KillerWhale-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: nitky/RP-7b-instruct
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: RP-7b-instruct-VE-Swallow-MS
---
merge_method: task_arithmetic
base_model: mistralai/Mistral-7B-Instruct-v0.3
models:
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight:
- filter: embed_tokens
value: 1.0
- value: 0
dtype: bfloat16
tokenizer_source: model:tokyotech-llm/Swallow-MS-7b-v0.1
name: Mistral-7B-Instruct-v0.3-VE-Swallow-MS
---
merge_method: linear
models:
- model: nitky/Oumuamua-7b-base
parameters:
weight: 1.0
- model: tokyotech-llm/Swallow-MS-7b-instruct-v0.1
parameters:
weight: 0.8
- model: tokyotech-llm/Swallow-MS-7b-v0.1
parameters:
weight: -0.8
- model: Mistral-7B-Instruct-v0.3-VE-Swallow-MS
parameters:
weight: 0.8
- model: Mistral-7B-v0.1-VE-Swallow-MS
parameters:
weight: -0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-alpha
---
merge_method: linear
models:
- model: nitky/Oumuamua-7b-base
parameters:
weight: 1.0
- model: ArrowPro-7B-KillerWhale-VE-Swallow-MS # include Mistral-7B-Instruct-v0.3
parameters:
weight: 0.8
- model: japanese-stablelm-base-gamma-7b-VE-Swallow-MS
parameters:
weight: -0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-beta
---
merge_method: linear
models:
- model: nitky/Oumuamua-7b-base
parameters:
weight: 1.0
- model: RP-7b-instruct-VE-Swallow-MS
parameters:
weight: 0.8
- model: japanese-stablelm-base-gamma-7b-VE-Swallow-MS
parameters:
weight: -0.8
- model: Mistral-7B-Instruct-v0.3-VE-Swallow-MS
parameters:
weight: 0.8
- model: Mistral-7B-v0.1-VE-Swallow-MS
parameters:
weight: -0.8
dtype: bfloat16
name: Oumuamua-7b-instruct-gamma
---
merge_method: model_stock
base_model: nitky/Oumuamua-7b-base
models:
- model: Oumuamua-7b-instruct-alpha
- model: Oumuamua-7b-instruct-beta
- model: Oumuamua-7b-instruct-gamma
dtype: bfloat16
name: Oumuamua-7b-instruct
```
|
jrtec/jrtec-gpt2-text-generation-quotes-jonathan-vargas | jrtec | 2022-11-07T04:26:10Z | 760 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"quotes",
"quote",
"generated_from_trainer",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-11-06T03:21:37Z | ---
license: mit
tags:
- text-generation
- quotes
- quote
- generated_from_trainer
model-index:
- name: jrtec-gpt2-text-generation-quotes-jonathan-vargas
results: []
widget:
- text: "life: "
example_title: "Life quote"
- text: "death: "
example_title: "Death quote"
---
<!-- 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. -->
# jrtec-gpt2-text-generation-quotes-jonathan-vargas
This model is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) on the datasetX dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7033
## 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: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7463 | 1.71 | 500 | 0.7033 |
| 0.4281 | 3.41 | 1000 | 0.7084 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.6.1
- Tokenizers 0.13.1
|
timm/convnextv2_base.fcmae_ft_in1k | timm | 2024-02-10T23:29:05Z | 760 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2301.00808",
"license:cc-by-nc-4.0",
"region:us"
] | image-classification | 2023-01-05T01:38:03Z | ---
license: cc-by-nc-4.0
library_name: timm
tags:
- image-classification
- timm
datasets:
- imagenet-1k
- imagenet-1k
---
# Model card for convnextv2_base.fcmae_ft_in1k
A ConvNeXt-V2 image classification model. Pretrained with a fully convolutional masked autoencoder framework (FCMAE) and fine-tuned on ImageNet-1k.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 88.7
- GMACs: 15.4
- Activations (M): 28.8
- Image size: train = 224 x 224, test = 288 x 288
- **Papers:**
- ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders: https://arxiv.org/abs/2301.00808
- **Original:** https://github.com/facebookresearch/ConvNeXt-V2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('convnextv2_base.fcmae_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnextv2_base.fcmae_ft_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 128, 56, 56])
# torch.Size([1, 256, 28, 28])
# torch.Size([1, 512, 14, 14])
# torch.Size([1, 1024, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'convnextv2_base.fcmae_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 7, 7) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
All timing numbers from eager model PyTorch 1.13 on RTX 3090 w/ AMP.
| model |top1 |top5 |img_size|param_count|gmacs |macts |samples_per_sec|batch_size|
|------------------------------------------------------------------------------------------------------------------------------|------|------|--------|-----------|------|------|---------------|----------|
| [convnextv2_huge.fcmae_ft_in22k_in1k_512](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_512) |88.848|98.742|512 |660.29 |600.81|413.07|28.58 |48 |
| [convnextv2_huge.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in22k_in1k_384) |88.668|98.738|384 |660.29 |337.96|232.35|50.56 |64 |
| [convnext_xxlarge.clip_laion2b_soup_ft_in1k](https://huggingface.co/timm/convnext_xxlarge.clip_laion2b_soup_ft_in1k) |88.612|98.704|256 |846.47 |198.09|124.45|122.45 |256 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_384) |88.312|98.578|384 |200.13 |101.11|126.74|196.84 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k_384) |88.196|98.532|384 |197.96 |101.1 |126.74|128.94 |128 |
| [convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_soup_ft_in12k_in1k_320) |87.968|98.47 |320 |200.13 |70.21 |88.02 |283.42 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k_384) |87.75 |98.556|384 |350.2 |179.2 |168.99|124.85 |192 |
| [convnextv2_base.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k_384) |87.646|98.422|384 |88.72 |45.21 |84.49 |209.51 |256 |
| [convnext_large.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k_384) |87.476|98.382|384 |197.77 |101.1 |126.74|194.66 |256 |
| [convnext_large_mlp.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_large_mlp.clip_laion2b_augreg_ft_in1k) |87.344|98.218|256 |200.13 |44.94 |56.33 |438.08 |256 |
| [convnextv2_large.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in22k_in1k) |87.26 |98.248|224 |197.96 |34.4 |43.13 |376.84 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k_384) |87.138|98.212|384 |88.59 |45.21 |84.49 |365.47 |256 |
| [convnext_xlarge.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_xlarge.fb_in22k_ft_in1k) |87.002|98.208|224 |350.2 |60.98 |57.5 |368.01 |256 |
| [convnext_base.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k_384) |86.796|98.264|384 |88.59 |45.21 |84.49 |366.54 |256 |
| [convnextv2_base.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in22k_in1k) |86.74 |98.022|224 |88.72 |15.38 |28.75 |624.23 |256 |
| [convnext_large.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_large.fb_in22k_ft_in1k) |86.636|98.028|224 |197.77 |34.4 |43.13 |581.43 |256 |
| [convnext_base.clip_laiona_augreg_ft_in1k_384](https://huggingface.co/timm/convnext_base.clip_laiona_augreg_ft_in1k_384) |86.504|97.97 |384 |88.59 |45.21 |84.49 |368.14 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in12k_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in12k_in1k) |86.344|97.97 |256 |88.59 |20.09 |37.55 |816.14 |256 |
| [convnextv2_huge.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_huge.fcmae_ft_in1k) |86.256|97.75 |224 |660.29 |115.0 |79.07 |154.72 |256 |
| [convnext_small.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_small.in12k_ft_in1k_384) |86.182|97.92 |384 |50.22 |25.58 |63.37 |516.19 |256 |
| [convnext_base.clip_laion2b_augreg_ft_in1k](https://huggingface.co/timm/convnext_base.clip_laion2b_augreg_ft_in1k) |86.154|97.68 |256 |88.59 |20.09 |37.55 |819.86 |256 |
| [convnext_base.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_base.fb_in22k_ft_in1k) |85.822|97.866|224 |88.59 |15.38 |28.75 |1037.66 |256 |
| [convnext_small.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k_384) |85.778|97.886|384 |50.22 |25.58 |63.37 |518.95 |256 |
| [convnextv2_large.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_large.fcmae_ft_in1k) |85.742|97.584|224 |197.96 |34.4 |43.13 |375.23 |256 |
| [convnext_small.in12k_ft_in1k](https://huggingface.co/timm/convnext_small.in12k_ft_in1k) |85.174|97.506|224 |50.22 |8.71 |21.56 |1474.31 |256 |
| [convnext_tiny.in12k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k_384) |85.118|97.608|384 |28.59 |13.14 |39.48 |856.76 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k_384) |85.112|97.63 |384 |28.64 |13.14 |39.48 |491.32 |256 |
| [convnextv2_base.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_base.fcmae_ft_in1k) |84.874|97.09 |224 |88.72 |15.38 |28.75 |625.33 |256 |
| [convnext_small.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_small.fb_in22k_ft_in1k) |84.562|97.394|224 |50.22 |8.71 |21.56 |1478.29 |256 |
| [convnext_large.fb_in1k](https://huggingface.co/timm/convnext_large.fb_in1k) |84.282|96.892|224 |197.77 |34.4 |43.13 |584.28 |256 |
| [convnext_tiny.in12k_ft_in1k](https://huggingface.co/timm/convnext_tiny.in12k_ft_in1k) |84.186|97.124|224 |28.59 |4.47 |13.44 |2433.7 |256 |
| [convnext_tiny.fb_in22k_ft_in1k_384](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k_384) |84.084|97.14 |384 |28.59 |13.14 |39.48 |862.95 |256 |
| [convnextv2_tiny.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in22k_in1k) |83.894|96.964|224 |28.64 |4.47 |13.44 |1452.72 |256 |
| [convnext_base.fb_in1k](https://huggingface.co/timm/convnext_base.fb_in1k) |83.82 |96.746|224 |88.59 |15.38 |28.75 |1054.0 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k_384](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k_384) |83.37 |96.742|384 |15.62 |7.22 |24.61 |801.72 |256 |
| [convnext_small.fb_in1k](https://huggingface.co/timm/convnext_small.fb_in1k) |83.142|96.434|224 |50.22 |8.71 |21.56 |1464.0 |256 |
| [convnextv2_tiny.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_tiny.fcmae_ft_in1k) |82.92 |96.284|224 |28.64 |4.47 |13.44 |1425.62 |256 |
| [convnext_tiny.fb_in22k_ft_in1k](https://huggingface.co/timm/convnext_tiny.fb_in22k_ft_in1k) |82.898|96.616|224 |28.59 |4.47 |13.44 |2480.88 |256 |
| [convnext_nano.in12k_ft_in1k](https://huggingface.co/timm/convnext_nano.in12k_ft_in1k) |82.282|96.344|224 |15.59 |2.46 |8.37 |3926.52 |256 |
| [convnext_tiny_hnf.a2h_in1k](https://huggingface.co/timm/convnext_tiny_hnf.a2h_in1k) |82.216|95.852|224 |28.59 |4.47 |13.44 |2529.75 |256 |
| [convnext_tiny.fb_in1k](https://huggingface.co/timm/convnext_tiny.fb_in1k) |82.066|95.854|224 |28.59 |4.47 |13.44 |2346.26 |256 |
| [convnextv2_nano.fcmae_ft_in22k_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in22k_in1k) |82.03 |96.166|224 |15.62 |2.46 |8.37 |2300.18 |256 |
| [convnextv2_nano.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_nano.fcmae_ft_in1k) |81.83 |95.738|224 |15.62 |2.46 |8.37 |2321.48 |256 |
| [convnext_nano_ols.d1h_in1k](https://huggingface.co/timm/convnext_nano_ols.d1h_in1k) |80.866|95.246|224 |15.65 |2.65 |9.38 |3523.85 |256 |
| [convnext_nano.d1h_in1k](https://huggingface.co/timm/convnext_nano.d1h_in1k) |80.768|95.334|224 |15.59 |2.46 |8.37 |3915.58 |256 |
| [convnextv2_pico.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_pico.fcmae_ft_in1k) |80.304|95.072|224 |9.07 |1.37 |6.1 |3274.57 |256 |
| [convnext_pico.d1_in1k](https://huggingface.co/timm/convnext_pico.d1_in1k) |79.526|94.558|224 |9.05 |1.37 |6.1 |5686.88 |256 |
| [convnext_pico_ols.d1_in1k](https://huggingface.co/timm/convnext_pico_ols.d1_in1k) |79.522|94.692|224 |9.06 |1.43 |6.5 |5422.46 |256 |
| [convnextv2_femto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_femto.fcmae_ft_in1k) |78.488|93.98 |224 |5.23 |0.79 |4.57 |4264.2 |256 |
| [convnext_femto_ols.d1_in1k](https://huggingface.co/timm/convnext_femto_ols.d1_in1k) |77.86 |93.83 |224 |5.23 |0.82 |4.87 |6910.6 |256 |
| [convnext_femto.d1_in1k](https://huggingface.co/timm/convnext_femto.d1_in1k) |77.454|93.68 |224 |5.22 |0.79 |4.57 |7189.92 |256 |
| [convnextv2_atto.fcmae_ft_in1k](https://huggingface.co/timm/convnextv2_atto.fcmae_ft_in1k) |76.664|93.044|224 |3.71 |0.55 |3.81 |4728.91 |256 |
| [convnext_atto_ols.a2_in1k](https://huggingface.co/timm/convnext_atto_ols.a2_in1k) |75.88 |92.846|224 |3.7 |0.58 |4.11 |7963.16 |256 |
| [convnext_atto.d2_in1k](https://huggingface.co/timm/convnext_atto.d2_in1k) |75.664|92.9 |224 |3.7 |0.55 |3.81 |8439.22 |256 |
## Citation
```bibtex
@article{Woo2023ConvNeXtV2,
title={ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders},
author={Sanghyun Woo, Shoubhik Debnath, Ronghang Hu, Xinlei Chen, Zhuang Liu, In So Kweon and Saining Xie},
year={2023},
journal={arXiv preprint arXiv:2301.00808},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
mykor/gpt2-ko | mykor | 2023-02-24T05:25:22Z | 760 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"safetensors",
"gpt2",
"text-generation",
"ko",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-02-24T04:44:34Z | ---
license: mit
language:
- ko
pipeline_tag: text-generation
widget:
- text: 딥러닝 모델은
---
# gpt2-ko
Korean gpt2 model, trained from scratch.
```python
from transformers import pipeline
pipe = pipeline("text-generation", model="mykor/gpt2-ko")
```
```python
>>> pipe("오늘 점심 뭐먹지?")
[{'generated_text': '오늘 점심 뭐먹지?치킨과 족발 먹으려고 ㅎ난 치킨먹구싶당 ㅎㅎ나 낼 아침에 먹을겡 ㅎ치킨 먹고시퍼 ㅎㅎ난 치킨에닭도리탕..난 닭도리탕~난 치킨먹었어 ㅎ치킨은 족'}]
```
```python
>>> pipe("애플은 이번 업데이트를 통해")
[{'generated_text': "애플은 이번 업데이트를 통해 안드로이드 플랫폼 내에서 '모바일 카드'를 판매할 예정'이라며 '기존에는 안드로이드 마켓 내에서만 결제가 가능했다.앞으로는 pc를 통해 결제할 수 있을 것'이라고 덧붙였다.한편, sk텔레콤은 이달 초에도 '갤럭시 s8"}]
```
|
MBZUAI/LaMini-Cerebras-590M | MBZUAI | 2023-04-28T13:08:13Z | 760 | 7 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"en",
"arxiv:2304.14402",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-04-12T06:23:08Z | ---
license: cc-by-nc-4.0
language:
- en
pipeline_tag: text-generation
widget:
- text: >-
Below is an instruction that describes a task.
Write a response that appropriately completes the request.
### Instruction:
how can I become more healthy?
### Response:
example_title: example
---
<!-- 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. -->
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini.png" alt="Title" style="width: 100%; min-width: 300px; display: block; margin: auto;"></a>
</p>
# LaMini-Cerebras-590M
[]()
This model is one of our LaMini-LM model series in paper "[LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions](https://github.com/mbzuai-nlp/lamini-lm)".
This model is a fine-tuned version of [cerebras/Cerebras-GPT-590M](https://huggingface.co/cerebras/Cerebras-GPT-590M) on [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction) that contains 2.58M samples for instruction fine-tuning. For more information about our dataset, please refer to our [project repository](https://github.com/mbzuai-nlp/lamini-lm/).
You can view other models of LaMini-LM series as follows. Models with ✩ are those with the best overall performance given their size/architecture, hence we recommend using them. More details can be seen in our paper.
<table>
<thead>
<tr>
<th>Base model</th>
<th colspan="4">LaMini-LM series (#parameters)</th>
</tr>
</thead>
<tbody>
<tr>
<td>T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-61m" target="_blank" rel="noopener noreferrer">LaMini-T5-61M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-223m" target="_blank" rel="noopener noreferrer">LaMini-T5-223M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-t5-738m" target="_blank" rel="noopener noreferrer">LaMini-T5-738M</a></td>
<td></td>
</tr>
<tr>
<td>Flan-T5</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-77m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-77M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-248m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-248M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-flan-t5-783m" target="_blank" rel="noopener noreferrer">LaMini-Flan-T5-783M</a>✩</td>
<td></td>
</tr>
<tr>
<td>Cerebras-GPT</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-111m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-111M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-256m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-256M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-590m" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-590M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-cerebras-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Cerebras-1.3B</a></td>
</tr>
<tr>
<td>GPT-2</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-124m" target="_blank" rel="noopener noreferrer">LaMini-GPT-124M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-774m" target="_blank" rel="noopener noreferrer">LaMini-GPT-774M</a>✩</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-gpt-1.5b" target="_blank" rel="noopener noreferrer">LaMini-GPT-1.5B</a>✩</td>
<td></td>
</tr>
<tr>
<td>GPT-Neo</td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-125m" target="_blank" rel="noopener noreferrer">LaMini-Neo-125M</a></td>
<td><a href="https://huggingface.co/MBZUAI/lamini-neo-1.3b" target="_blank" rel="noopener noreferrer">LaMini-Neo-1.3B</a></td>
<td></td>
<td></td>
</tr>
<tr>
<td>GPT-J</td>
<td colspan="4">coming soon</td>
</tr>
<tr>
<td>LLaMA</td>
<td colspan="4">coming soon</td>
</tr>
</tbody>
</table>
## Use
### Intended use
We recommend using the model to respond to human instructions written in natural language.
Since this decoder-only model is fine-tuned with wrapper text, we suggest using the same wrapper text to achieve the best performance.
See the example on the right or the code below.
We now show you how to load and use our model using HuggingFace `pipeline()`.
```python
# pip install -q transformers
from transformers import pipeline
checkpoint = "{model_name}"
model = pipeline('text-generation', model = checkpoint)
instruction = 'Please let me know your thoughts on the given place and why you think it deserves to be visited: \n"Barcelona, Spain"'
input_prompt = f"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{instruction}\n\n### Response:"
generated_text = model(input_prompt, max_length=512, do_sample=True)[0]['generated_text']
print("Response", generated_text)
```
## Training Procedure
<p align="center" width="100%">
<a><img src="https://raw.githubusercontent.com/mbzuai-nlp/lamini-lm/main/images/lamini-pipeline.drawio.png" alt="Title" style="width: 100%; min-width: 250px; display: block; margin: auto;"></a>
</p>
We initialize with [cerebras/Cerebras-GPT-590M](https://huggingface.co/cerebras/Cerebras-GPT-590M) and fine-tune it on our [LaMini-instruction dataset](https://huggingface.co/datasets/MBZUAI/LaMini-instruction). Its total number of parameters is 590M.
### Training Hyperparameters
## Evaluation
We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper]().
## Limitations
More information needed
# Citation
```bibtex
@article{lamini-lm,
author = {Minghao Wu and
Abdul Waheed and
Chiyu Zhang and
Muhammad Abdul-Mageed and
Alham Fikri Aji
},
title = {LaMini-LM: A Diverse Herd of Distilled Models from Large-Scale Instructions},
journal = {CoRR},
volume = {abs/2304.14402},
year = {2023},
url = {https://arxiv.org/abs/2304.14402},
eprinttype = {arXiv},
eprint = {2304.14402}
}
``` |
NHNDQ/nllb-finetuned-ko2en | NHNDQ | 2023-07-21T06:37:06Z | 760 | 10 | transformers | [
"transformers",
"pytorch",
"safetensors",
"m2m_100",
"text2text-generation",
"translation",
"ko",
"en",
"license:cc-by-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | 2023-05-15T00:55:10Z | ---
license: cc-by-4.0
language:
- ko
- en
tags:
- translation
---
## Model Details
* Model Description: Fine-tuned facebook/nllb-200-distilled-600M model
* Developed by: Juhwan Lee, Jisu Kim, TakSung Heo, and Minsu Jeong
* Model Type: Translation
* Language(s):
* Source Language: English
* Target Language: Korean
* License: CC-BY-4.0
## Dataset
* [AI-hub dataset](https://www.aihub.or.kr/)
## BLEU Score
* Before fine-tuning: 26.58
* After fine-tuning: 55.36
## Uses
This model can be used for translation and text-to-text generation
## Data Augmentation with backtranslation task
You can exercise korean data augmentation task with python package [KoTAN](https://github.com/KoJLabs/KoTAN/tree/main) |
timm/efficientvit_b1.r288_in1k | timm | 2023-11-21T21:43:58Z | 760 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2205.14756",
"license:apache-2.0",
"region:us"
] | image-classification | 2023-08-18T22:44:58Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for efficientvit_b1.r288_in1k
An EfficientViT (MIT) image classification model. Trained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 9.1
- GMACs: 0.9
- Activations (M): 12.0
- Image size: 288 x 288
- **Papers:**
- EfficientViT: Multi-Scale Linear Attention for High-Resolution Dense Prediction: https://arxiv.org/abs/2205.14756
- **Original:** https://github.com/mit-han-lab/efficientvit
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('efficientvit_b1.r288_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'efficientvit_b1.r288_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 32, 72, 72])
# torch.Size([1, 64, 36, 36])
# torch.Size([1, 128, 18, 18])
# torch.Size([1, 256, 9, 9])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'efficientvit_b1.r288_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 256, 9, 9) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Citation
```bibtex
@article{cai2022efficientvit,
title={EfficientViT: Enhanced linear attention for high-resolution low-computation visual recognition},
author={Cai, Han and Gan, Chuang and Han, Song},
journal={arXiv preprint arXiv:2205.14756},
year={2022}
}
```
|
TheBloke/MythoLogic-Mini-7B-GGUF | TheBloke | 2023-09-27T12:47:16Z | 760 | 5 | transformers | [
"transformers",
"gguf",
"llama",
"en",
"base_model:Gryphe/MythoLogic-Mini-7b",
"license:other",
"text-generation-inference",
"region:us"
] | null | 2023-09-04T23:42:13Z | ---
language:
- en
license: other
model_name: Mythologic Mini 7B
base_model: Gryphe/MythoLogic-Mini-7b
inference: false
model_creator: Gryphe
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mythologic Mini 7B - GGUF
- Model creator: [Gryphe](https://huggingface.co/Gryphe)
- Original model: [Mythologic Mini 7B](https://huggingface.co/Gryphe/MythoLogic-Mini-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Gryphe's Mythologic Mini 7B](https://huggingface.co/Gryphe/MythoLogic-Mini-7b).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.
Here is an incomplate list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF)
* [Gryphe's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/Gryphe/MythoLogic-Mini-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Gryphe's Mythologic Mini 7B](https://huggingface.co/Gryphe/MythoLogic-Mini-7b).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mythologic-mini-7b.Q2_K.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q2_K.gguf) | Q2_K | 2 | 2.83 GB| 5.33 GB | smallest, significant quality loss - not recommended for most purposes |
| [mythologic-mini-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 2.95 GB| 5.45 GB | very small, high quality loss |
| [mythologic-mini-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.30 GB| 5.80 GB | very small, high quality loss |
| [mythologic-mini-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.60 GB| 6.10 GB | small, substantial quality loss |
| [mythologic-mini-7b.Q4_0.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q4_0.gguf) | Q4_0 | 4 | 3.83 GB| 6.33 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mythologic-mini-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 3.86 GB| 6.36 GB | small, greater quality loss |
| [mythologic-mini-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.08 GB| 6.58 GB | medium, balanced quality - recommended |
| [mythologic-mini-7b.Q5_0.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q5_0.gguf) | Q5_0 | 5 | 4.65 GB| 7.15 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mythologic-mini-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 4.65 GB| 7.15 GB | large, low quality loss - recommended |
| [mythologic-mini-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 4.78 GB| 7.28 GB | large, very low quality loss - recommended |
| [mythologic-mini-7b.Q6_K.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q6_K.gguf) | Q6_K | 6 | 5.53 GB| 8.03 GB | very large, extremely low quality loss |
| [mythologic-mini-7b.Q8_0.gguf](https://huggingface.co/TheBloke/MythoLogic-Mini-7B-GGUF/blob/main/mythologic-mini-7b.Q8_0.gguf) | Q8_0 | 8 | 7.16 GB| 9.66 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
- LM Studio
- LoLLMS Web UI
- Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/MythoLogic-Mini-7B-GGUF and below it, a specific filename to download, such as: mythologic-mini-7b.q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub>=0.17.1
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/MythoLogic-Mini-7B-GGUF mythologic-mini-7b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/MythoLogic-Mini-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/MythoLogic-Mini-7B-GGUF mythologic-mini-7b.q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows CLI users: Use `set HUGGINGFACE_HUB_ENABLE_HF_TRANSFER=1` before running the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d36d5be95a0d9088b674dbb27354107221](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 32 -m mythologic-mini-7b.q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions here: [text-generation-webui/docs/llama.cpp.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/llama.cpp.md).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries.
### How to load this model from Python using ctransformers
#### First install the package
```bash
# Base ctransformers with no GPU acceleration
pip install ctransformers>=0.2.24
# Or with CUDA GPU acceleration
pip install ctransformers[cuda]>=0.2.24
# Or with ROCm GPU acceleration
CT_HIPBLAS=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
# Or with Metal GPU acceleration for macOS systems
CT_METAL=1 pip install ctransformers>=0.2.24 --no-binary ctransformers
```
#### Simple example code to load one of these GGUF models
```python
from ctransformers import AutoModelForCausalLM
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = AutoModelForCausalLM.from_pretrained("TheBloke/MythoLogic-Mini-7B-GGUF", model_file="mythologic-mini-7b.q4_K_M.gguf", model_type="llama", gpu_layers=50)
print(llm("AI is going to"))
```
## How to use with LangChain
Here's guides on using llama-cpp-python or ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Alicia Loh, Stephen Murray, K, Ajan Kanaga, RoA, Magnesian, Deo Leter, Olakabola, Eugene Pentland, zynix, Deep Realms, Raymond Fosdick, Elijah Stavena, Iucharbius, Erik Bjäreholt, Luis Javier Navarrete Lozano, Nicholas, theTransient, John Detwiler, alfie_i, knownsqashed, Mano Prime, Willem Michiel, Enrico Ros, LangChain4j, OG, Michael Dempsey, Pierre Kircher, Pedro Madruga, James Bentley, Thomas Belote, Luke @flexchar, Leonard Tan, Johann-Peter Hartmann, Illia Dulskyi, Fen Risland, Chadd, S_X, Jeff Scroggin, Ken Nordquist, Sean Connelly, Artur Olbinski, Swaroop Kallakuri, Jack West, Ai Maven, David Ziegler, Russ Johnson, transmissions 11, John Villwock, Alps Aficionado, Clay Pascal, Viktor Bowallius, Subspace Studios, Rainer Wilmers, Trenton Dambrowitz, vamX, Michael Levine, 준교 김, Brandon Frisco, Kalila, Trailburnt, Randy H, Talal Aujan, Nathan Dryer, Vadim, 阿明, ReadyPlayerEmma, Tiffany J. Kim, George Stoitzev, Spencer Kim, Jerry Meng, Gabriel Tamborski, Cory Kujawski, Jeffrey Morgan, Spiking Neurons AB, Edmond Seymore, Alexandros Triantafyllidis, Lone Striker, Cap'n Zoog, Nikolai Manek, danny, ya boyyy, Derek Yates, usrbinkat, Mandus, TL, Nathan LeClaire, subjectnull, Imad Khwaja, webtim, Raven Klaugh, Asp the Wyvern, Gabriel Puliatti, Caitlyn Gatomon, Joseph William Delisle, Jonathan Leane, Luke Pendergrass, SuperWojo, Sebastain Graf, Will Dee, Fred von Graf, Andrey, Dan Guido, Daniel P. Andersen, Nitin Borwankar, Elle, Vitor Caleffi, biorpg, jjj, NimbleBox.ai, Pieter, Matthew Berman, terasurfer, Michael Davis, Alex, Stanislav Ovsiannikov
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
<!-- original-model-card start -->
# Original model card: Gryphe's Mythologic Mini 7B
## Model details
MythoLogic-Mini-7b can be considered the little brother in my Mytho series of models: [MythoLogic-13b](https://huggingface.co/Gryphe/MythoLogic-13b) and [MythoBoros-13b](https://huggingface.co/Gryphe/MythoBoros-13b)).
Its Llama-2 core is powered by [Nous Hermes-2](https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b), which is further augmented by [Stable Beluga](https://huggingface.co/stabilityai/StableBeluga-7B) and a carefully distilled [Kimiko LoRa](https://huggingface.co/nRuaif/Kimiko_7B).
Since 7B models tend to be less capable all-rounders, more emphasis was put on improving the roleplaying aspects for this gradient merge, of which various gradients were benchmarked before settling on the configuration shown below.
In technical terms, the Hermes-2 core starts at 90% strength before fading away completely at the 12th layer level, where Stable Beluga (and Kimiko) handle the more intricate linguistic aspects.
Quantized models are available from TheBloke: [GGML](https://huggingface.co/TheBloke/MythoLogic-Mini-7b-GGML) - [GPTQ](https://huggingface.co/TheBloke/MythoLogic-Mini-7b-GPTQ) (You're the best!)
## Prompt Format
Due to its Hermes-2 core this model works best with Alpaca formatting, so for optimal model performance, use:
```
<System prompt/Character Card>
### Instruction:
Your instruction or question here.
For roleplay purposes, I suggest the following - Write <CHAR NAME>'s next reply in a chat between <YOUR NAME> and <CHAR NAME>. Write a single reply only.
### Response:
```
<!-- original-model-card end -->
|
circulus/canvers-ko2en-v2 | circulus | 2024-01-03T00:23:17Z | 760 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-01-03T00:20:19Z | ---
license: gpl-3.0
---
|
hon9kon9ize/CantoneseLLM-6B-preview202402 | hon9kon9ize | 2024-03-04T12:13:55Z | 760 | 7 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"yue",
"license:other",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-02-08T21:26:40Z | ---
language:
- yue
license: other
license_name: yi-license
license_link: https://huggingface.co/01-ai/Yi-6B/blob/main/LICENSE
pipeline_tag: text-generation
model-index:
- name: CantoneseLLM-6B-preview202402
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 55.63
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hon9kon9ize/CantoneseLLM-6B-preview202402
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 75.8
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hon9kon9ize/CantoneseLLM-6B-preview202402
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.07
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hon9kon9ize/CantoneseLLM-6B-preview202402
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 42.26
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hon9kon9ize/CantoneseLLM-6B-preview202402
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 74.11
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hon9kon9ize/CantoneseLLM-6B-preview202402
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 30.71
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=hon9kon9ize/CantoneseLLM-6B-preview202402
name: Open LLM Leaderboard
---
# CantoneseLLM
This model is further pre-trained model based on [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) with 800M tokens of Cantonese text compiled from various sources, including translated zh-yue Wikipedia, translated RTHK news [datasets/jed351/rthk_news](https://huggingface.co/datasets/jed351/rthk_news), Cantonese filtered CC100 and Cantonese textbooks generated by Gemini Pro.
This is a preview version, for experimental use only, we will use it to fine-tune on downstream tasks and evaluate the performance.
### [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_hon9kon9ize__CantoneseLLM-6B-preview202402)
| Metric | Value |
|-----------------------|---------------------------|
| Avg. | 56.93 |
| ARC (25-shot) | 55.63 |
| HellaSwag (10-shot) | 75.8 |
| MMLU (5-shot) | 63.07 |
| TruthfulQA (0-shot) | 42.26 |
| Winogrande (5-shot) | 74.11 |
| GSM8K (5-shot) | 30.71 |
## Usage
```python
from transformers import AutoTokenizer, AutoModelForMaskedLM
tokenizer = AutoTokenizer.from_pretrained("hon9kon9ize/CantoneseLLM-6B-preview202402")
model = AutoModelForMaskedLM.from_pretrained("hon9kon9ize/CantoneseLLM-6B-preview202402")
prompt = "歷經三年疫情，望穿秋水終於全面復常，隨住各項防疫措施陸續放寬以至取消，香港"
input_ids = tokenizer.encode(prompt, return_tensors="pt").to('cuda:0')
output = model.generate(input_ids, max_length=max_length, num_return_sequences=1, repetition_penalty=1.1, do_sample=True, temperature=temperature, top_k=50, top_p=0.95)
output = tokenizer.decode(output[0], skip_special_tokens=True)
# output: 歷經三年疫情，望穿秋水終於全面復常，隨住各項防疫措施陸續放寬以至取消，香港旅遊業可謂「起死回生」。
# 不過，旅遊業嘅復蘇之路並唔順利，香港遊客數量仍然遠低於疫前水平，而海外旅客亦只係恢復到疫情前約一半。有業界人士認為，當局需要進一步放寬入境檢疫措施，吸引更多國際旅客來港，令旅遊業得以真正復甦。
```
## Limitation and Bias
The model is intended to use for Cantonese language understanding and generation tasks, it may not be suitable for other Chinese languages. The model is trained on a diverse range of Cantonese text, including news, Wikipedia, and textbooks, it may not be suitable for informal or dialectal Cantonese, it may contain bias and misinformation, please use it with caution.
We found the model is not well trained on the updated Hong Kong knowledge, it may due to the corpus is not large enough to brainwash the original model. We will continue to improve the model and corpus in the future. |
microsoft/wavecoder-ultra-6.7b | microsoft | 2024-05-06T13:25:54Z | 760 | 70 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"code",
"conversational",
"en",
"dataset:humaneval",
"arxiv:2312.14187",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-04-11T04:29:56Z | ---
license: mit
license_link: https://huggingface.co/microsoft/wavecoder-ultra-6.7b/blob/main/LICENSE
language:
- en
library_name: transformers
datasets:
- humaneval
pipeline_tag: text-generation
tags:
- code
metrics:
- code_eval
---
<h1 align="center">
🌊 WaveCoder: Widespread And Versatile Enhanced Code LLM
</h1>
<p align="center">
<a href="https://arxiv.org/abs/2312.14187"><b>[📜 Paper]</b></a> •
<!-- <a href=""><b>[🤗 HF Models]</b></a> • -->
<a href="https://github.com/microsoft/WaveCoder"><b>[🐱 GitHub]</b></a>
<br>
<a href="https://twitter.com/TeamCodeLLM_AI"><b>[🐦 Twitter]</b></a> •
<a href="https://www.reddit.com/r/LocalLLaMA/comments/19a1scy/wavecoderultra67b_claims_to_be_the_2nd_best_model/"><b>[💬 Reddit]</b></a> •
<a href="https://www.analyticsvidhya.com/blog/2024/01/microsofts-wavecoder-and-codeocean-revolutionize-instruction-tuning/">[🍀 Unofficial Blog]</a>
<!-- <a href="#-quick-start">Quick Start</a> • -->
<!-- <a href="#%EF%B8%8F-citation">Citation</a> -->
</p>
<p align="center">
Repo for "<a href="https://arxiv.org/abs/2312.14187" target="_blank">WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with Refined Data Generation</a>"
</p>
## 🔥 News
- [2024/04/10] 🔥🔥🔥 WaveCoder repo, models released at [🤗 HuggingFace](https://huggingface.co/microsoft/wavecoder-ultra-6.7b)!
- [2023/12/26] WaveCoder paper released.
## 💡 Introduction
WaveCoder 🌊 is a series of large language models (LLMs) for the coding domain, designed to solve relevant problems in the field of code through instruction-following learning. Its training dataset was generated from a subset of code-search-net data using a generator-discriminator framework based on LLMs that we proposed, covering four general code-related tasks: code generation, code summary, code translation, and code repair.
| Model | HumanEval | MBPP(500) | HumanEval<br>Fix(Avg.) | HumanEval<br>Explain(Avg.) |
| -------------------------------------------------------------------------------- | --------- | --------- | ---------------------- | -------------------------- |
| GPT-4 | 85.4 | - | 47.8 | 52.1 |
| [🌊 WaveCoder-DS-6.7B](https://huggingface.co/microsoft/wavecoder-ds-6.7b) | 65.8 | 63.0 | 49.5 | 40.8 |
| [🌊 WaveCoder-Pro-6.7B](https://huggingface.co/microsoft/wavecoder-pro-6.7b) | 74.4 | 63.4 | 52.1 | 43.0 |
| [🌊 WaveCoder-Ultra-6.7B](https://huggingface.co/microsoft/wavecoder-ultra-6.7b) | 79.9 | 64.6 | 52.3 | 45.7 |
## 🪁 Evaluation
Please refer to WaveCoder's [GitHub repo](https://github.com/microsoft/WaveCoder) for inference, evaluation, and training code.
```python
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("microsoft/wavecoder-ultra-6.7b")
model = AutoModelForCausalLM.from_pretrained("microsoft/wavecoder-ultra-6.7b")
```
## 📖 License
This code repository is licensed under the MIT License. The use of DeepSeek Coder models is subject to the its [License](https://github.com/deepseek-ai/DeepSeek-Coder/blob/main/LICENSE-MODEL).
## ☕️ Citation
If you find this repository helpful, please consider citing our paper:
```
@article{yu2023wavecoder,
title={Wavecoder: Widespread and versatile enhanced instruction tuning with refined data generation},
author={Yu, Zhaojian and Zhang, Xin and Shang, Ning and Huang, Yangyu and Xu, Can and Zhao, Yishujie and Hu, Wenxiang and Yin, Qiufeng},
journal={arXiv preprint arXiv:2312.14187},
year={2023}
}
```
## Note
WaveCoder models are trained on the synthetic data generated by OpenAI models. Please pay attention to OpenAI's [terms of use](https://openai.com/policies/terms-of-use) when using the models and the datasets.
|
nk2t/Llama-3-8B-Instruct-japanese-nk2t-v0.3 | nk2t | 2024-05-22T11:02:28Z | 760 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-05-15T12:24:06Z | ---
language:
- en
- ja
pipeline_tag: text-generation
license: llama3
license_name: llama3
license_link: LICENSE
---
# Llama-3-8B-Instruct-JP-nk2t-v0.3
## Model Details: Built with Meta Llama 3
llama-3-8bの日本語継続学習モデルにChatVectorを適用し、さらにQLoraでファインチューニングしたモデルです。
rinna/llama-3-youko-8b + ( 0.2 * (meta-llama/Meta-Llama-3-8B-Instruct - meta-llama/Meta-Llama-3-8B) + 0.6 * (Llama-3-8B-Instruct-japanese-nk2t-v0.2 - meta-llama/Meta-Llama-3-8B) )
チャットテンプレートはLlama-3-8B-Instructと同じです。
[ggufフォーマット変換版](https://huggingface.co/nk2t/Llama-3-8B-Instruct-japanese-nk2t-v0.3-gguf)はこちら。
## How to use
TBD
## Benchmarks
ELYZA-tasks-100 average score: 3.32 (Q5_K_M quant)
The results of <a href="https://huggingface.co/datasets/elyza/ELYZA-tasks-100">ELYZA-tasks-100</a> were evaluated by gpt-4o using <a href="https://github.com/Northern-System-Service/gpt4-autoeval">gpt4-autoeval</a>.
---
## Meta Llama-3
Meta developed and released the Meta Llama 3 family of large language models (LLMs), a collection of pretrained and instruction tuned generative text models in 8 and 70B sizes. The Llama 3 instruction tuned models are optimized for dialogue use cases and outperform many of the available open source chat models on common industry benchmarks. Further, in developing these models, we took great care to optimize helpfulness and safety.
**Model developers** Meta
**Variations** Llama 3 comes in two sizes — 8B and 70B parameters — in pre-trained and instruction tuned variants.
**Input** Models input text only.
**Output** Models generate text and code only.
**Status** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://llama.meta.com/llama3/license](https://llama.meta.com/llama3/license)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3 is intended for commercial and research use in English. Instruction tuned models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks.
**Out-of-scope** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3 Community License. Use in languages other than English**.
**Note: Developers may fine-tune Llama 3 models for languages beyond English provided they comply with the Llama 3 Community License and the Acceptable Use Policy.
### Responsibility & Safety
We believe that an open approach to AI leads to better, safer products, faster innovation, and a bigger overall market. We are committed to Responsible AI development and took a series of steps to limit misuse and harm and support the open source community.
Foundation models are widely capable technologies that are built to be used for a diverse range of applications. They are not designed to meet every developer preference on safety levels for all use cases, out-of-the-box, as those by their nature will differ across different applications.
Rather, responsible LLM-application deployment is achieved by implementing a series of safety best practices throughout the development of such applications, from the model pre-training, fine-tuning and the deployment of systems composed of safeguards to tailor the safety needs specifically to the use case and audience.
As part of the Llama 3 release, we updated our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to outline the steps and best practices for developers to implement model and system level safety for their application. We also provide a set of resources including [Meta Llama Guard 2](https://llama.meta.com/purple-llama/) and [Code Shield](https://llama.meta.com/purple-llama/) safeguards. These tools have proven to drastically reduce residual risks of LLM Systems, while maintaining a high level of helpfulness. We encourage developers to tune and deploy these safeguards according to their needs and we provide a [reference implementation](https://github.com/meta-llama/llama-recipes/tree/main/recipes/responsible_ai) to get you started.
#### Responsible release
In addition to responsible use considerations outlined above, we followed a rigorous process that requires us to take extra measures against misuse and critical risks before we make our release decision.
Misuse
If you access or use Llama 3, you agree to the Acceptable Use Policy. The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy/](https://llama.meta.com/llama3/use-policy/).
## Ethical Considerations and Limitations
The core values of Llama 3 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
But Llama 3 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has been in English, and has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3 models, developers should perform safety testing and tuning tailored to their specific applications of the model. As outlined in the Responsible Use Guide, we recommend incorporating [Purple Llama](https://github.com/facebookresearch/PurpleLlama) solutions into your workflows and specifically [Llama Guard](https://ai.meta.com/research/publications/llama-guard-llm-based-input-output-safeguard-for-human-ai-conversations/) which provides a base model to filter input and output prompts to layer system-level safety on top of model-level safety.
Please see the Responsible Use Guide available at [http://llama.meta.com/responsible-use-guide](http://llama.meta.com/responsible-use-guide) |
allenfokbeta/Llama3-8B-Chinese-Chat-finetuned-Q8_0-GGUF | allenfokbeta | 2024-06-20T02:38:06Z | 760 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:allenfokbeta/Llama3-8B-Chinese-Chat-finetuned",
"endpoints_compatible",
"region:us"
] | null | 2024-06-20T02:37:31Z | ---
base_model: allenfokbeta/Llama3-8B-Chinese-Chat-finetuned
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# allenfokbeta/Llama3-8B-Chinese-Chat-finetuned-Q8_0-GGUF
This model was converted to GGUF format from [`allenfokbeta/Llama3-8B-Chinese-Chat-finetuned`](https://huggingface.co/allenfokbeta/Llama3-8B-Chinese-Chat-finetuned) 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/allenfokbeta/Llama3-8B-Chinese-Chat-finetuned) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo allenfokbeta/Llama3-8B-Chinese-Chat-finetuned-Q8_0-GGUF --hf-file llama3-8b-chinese-chat-finetuned-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo allenfokbeta/Llama3-8B-Chinese-Chat-finetuned-Q8_0-GGUF --hf-file llama3-8b-chinese-chat-finetuned-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo allenfokbeta/Llama3-8B-Chinese-Chat-finetuned-Q8_0-GGUF --hf-file llama3-8b-chinese-chat-finetuned-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo allenfokbeta/Llama3-8B-Chinese-Chat-finetuned-Q8_0-GGUF --hf-file llama3-8b-chinese-chat-finetuned-q8_0.gguf -c 2048
```
|
QuantFactory/NuExtract-tiny-GGUF | QuantFactory | 2024-06-25T12:04:41Z | 760 | 0 | null | [
"gguf",
"region:us"
] | null | 2024-06-25T11:29:03Z | Entry not found |
Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet | Omartificial-Intelligence-Space | 2024-06-26T20:31:27Z | 760 | 2 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"mteb",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:557850",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"ar",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | sentence-similarity | 2024-06-25T17:56:53Z | ---
language:
- ar
library_name: sentence-transformers
tags:
- mteb
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:557850
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
metrics:
- pearson_cosine
- spearman_cosine
- pearson_manhattan
- spearman_manhattan
- pearson_euclidean
- spearman_euclidean
- pearson_dot
- spearman_dot
- pearson_max
- spearman_max
widget:
- source_sentence: ذكر متوازن بعناية يقف على قدم واحدة بالقرب من منطقة شاطئ المحيط النظيفة
sentences:
- رجل يقدم عرضاً
- هناك رجل بالخارج قرب الشاطئ
- رجل يجلس على أريكه
- source_sentence: رجل يقفز إلى سريره القذر
sentences:
- السرير قذر.
- رجل يضحك أثناء غسيل الملابس
- الرجل على القمر
- source_sentence: الفتيات بالخارج
sentences:
- امرأة تلف الخيط إلى كرات بجانب كومة من الكرات
- فتيان يركبان في جولة متعة
- >-
ثلاث فتيات يقفون سوية في غرفة واحدة تستمع وواحدة تكتب على الحائط والثالثة
تتحدث إليهن
- source_sentence: الرجل يرتدي قميصاً أزرق.
sentences:
- >-
رجل يرتدي قميصاً أزرق يميل إلى الجدار بجانب الطريق مع شاحنة زرقاء وسيارة
حمراء مع الماء في الخلفية.
- كتاب القصص مفتوح
- رجل يرتدي قميص أسود يعزف على الجيتار.
- source_sentence: يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.
sentences:
- ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه
- رجل يستلقي على وجهه على مقعد في الحديقة.
- الشاب نائم بينما الأم تقود ابنتها إلى الحديقة
pipeline_tag: sentence-similarity
model-index:
- name: Omartificial-Intelligence-Space/Arabic-MiniLM-L12-v2-all-nli-triplet
results:
- dataset:
config: default
name: MTEB BIOSSES (default)
revision: d3fb88f8f02e40887cd149695127462bbcf29b4a
split: test
type: mteb/biosses-sts
metrics:
- type: cosine_pearson
value: 72.5081840952171
- type: cosine_spearman
value: 69.41362982941537
- type: euclidean_pearson
value: 67.45121490183709
- type: euclidean_spearman
value: 67.15273493989758
- type: main_score
value: 69.41362982941537
- type: manhattan_pearson
value: 67.6119022794479
- type: manhattan_spearman
value: 67.51659865246586
task:
type: STS
- dataset:
config: default
name: MTEB SICK-R (default)
revision: 20a6d6f312dd54037fe07a32d58e5e168867909d
split: test
type: mteb/sickr-sts
metrics:
- type: cosine_pearson
value: 83.61591268324493
- type: cosine_spearman
value: 79.61914245705792
- type: euclidean_pearson
value: 81.32044881859483
- type: euclidean_spearman
value: 79.04866675279919
- type: main_score
value: 79.61914245705792
- type: manhattan_pearson
value: 81.09220518201322
- type: manhattan_spearman
value: 78.87590523907905
task:
type: STS
- dataset:
config: default
name: MTEB STS12 (default)
revision: a0d554a64d88156834ff5ae9920b964011b16384
split: test
type: mteb/sts12-sts
metrics:
- type: cosine_pearson
value: 84.59807803376341
- type: cosine_spearman
value: 77.38689922564416
- type: euclidean_pearson
value: 83.92034850646732
- type: euclidean_spearman
value: 76.75857193093438
- type: main_score
value: 77.38689922564416
- type: manhattan_pearson
value: 83.97191863964667
- type: manhattan_spearman
value: 76.89790070725708
task:
type: STS
- dataset:
config: default
name: MTEB STS13 (default)
revision: 7e90230a92c190f1bf69ae9002b8cea547a64cca
split: test
type: mteb/sts13-sts
metrics:
- type: cosine_pearson
value: 78.18664268536664
- type: cosine_spearman
value: 79.58989311630421
- type: euclidean_pearson
value: 79.25259731614729
- type: euclidean_spearman
value: 80.1701122827397
- type: main_score
value: 79.58989311630421
- type: manhattan_pearson
value: 79.12601451996869
- type: manhattan_spearman
value: 79.98999436073663
task:
type: STS
- dataset:
config: default
name: MTEB STS14 (default)
revision: 6031580fec1f6af667f0bd2da0a551cf4f0b2375
split: test
type: mteb/sts14-sts
metrics:
- type: cosine_pearson
value: 80.97541876658141
- type: cosine_spearman
value: 79.78614320477877
- type: euclidean_pearson
value: 81.01514505747167
- type: euclidean_spearman
value: 80.73664735567839
- type: main_score
value: 79.78614320477877
- type: manhattan_pearson
value: 80.8746560526314
- type: manhattan_spearman
value: 80.67025673179079
task:
type: STS
- dataset:
config: default
name: MTEB STS15 (default)
revision: ae752c7c21bf194d8b67fd573edf7ae58183cbe3
split: test
type: mteb/sts15-sts
metrics:
- type: cosine_pearson
value: 85.23661155813113
- type: cosine_spearman
value: 86.21134464371615
- type: euclidean_pearson
value: 85.82518684522182
- type: euclidean_spearman
value: 86.43600784349509
- type: main_score
value: 86.21134464371615
- type: manhattan_pearson
value: 85.83101152371589
- type: manhattan_spearman
value: 86.42228695679498
task:
type: STS
- dataset:
config: default
name: MTEB STS16 (default)
revision: 4d8694f8f0e0100860b497b999b3dbed754a0513
split: test
type: mteb/sts16-sts
metrics:
- type: cosine_pearson
value: 79.20106689077852
- type: cosine_spearman
value: 81.39570893867825
- type: euclidean_pearson
value: 80.39578888768929
- type: euclidean_spearman
value: 81.19950443340412
- type: main_score
value: 81.39570893867825
- type: manhattan_pearson
value: 80.2226679341839
- type: manhattan_spearman
value: 80.99142422593823
task:
type: STS
- dataset:
config: ar-ar
name: MTEB STS17 (ar-ar)
revision: faeb762787bd10488a50c8b5be4a3b82e411949c
split: test
type: mteb/sts17-crosslingual-sts
metrics:
- type: cosine_pearson
value: 81.05294851623468
- type: cosine_spearman
value: 81.10570655134113
- type: euclidean_pearson
value: 79.22292773537778
- type: euclidean_spearman
value: 78.84204232638425
- type: main_score
value: 81.10570655134113
- type: manhattan_pearson
value: 79.43750460320484
- type: manhattan_spearman
value: 79.33713593557482
task:
type: STS
- dataset:
config: ar
name: MTEB STS22 (ar)
revision: de9d86b3b84231dc21f76c7b7af1f28e2f57f6e3
split: test
type: mteb/sts22-crosslingual-sts
metrics:
- type: cosine_pearson
value: 45.96875498680092
- type: cosine_spearman
value: 52.405509117149904
- type: euclidean_pearson
value: 42.097450896728226
- type: euclidean_spearman
value: 50.89022884113707
- type: main_score
value: 52.405509117149904
- type: manhattan_pearson
value: 42.22827727075534
- type: manhattan_spearman
value: 50.912841055442634
task:
type: STS
- dataset:
config: default
name: MTEB STSBenchmark (default)
revision: b0fddb56ed78048fa8b90373c8a3cfc37b684831
split: test
type: mteb/stsbenchmark-sts
metrics:
- type: cosine_pearson
value: 83.13261516884116
- type: cosine_spearman
value: 84.3492527221498
- type: euclidean_pearson
value: 82.691603178401
- type: euclidean_spearman
value: 83.0499566200785
- type: main_score
value: 84.3492527221498
- type: manhattan_pearson
value: 82.68307441014618
- type: manhattan_spearman
value: 83.01315787964519
task:
type: STS
- dataset:
config: default
name: MTEB SummEval (default)
revision: cda12ad7615edc362dbf25a00fdd61d3b1eaf93c
split: test
type: mteb/summeval
metrics:
- type: cosine_pearson
value: 31.149232235402845
- type: cosine_spearman
value: 30.685504130606255
- type: dot_pearson
value: 27.466307571160375
- type: dot_spearman
value: 28.93064261485915
- type: main_score
value: 30.685504130606255
- type: pearson
value: 31.149232235402845
- type: spearman
value: 30.685504130606255
task:
type: Summarization
- name: >-
SentenceTransformer based on
sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 256
type: sts-test-256
metrics:
- type: pearson_cosine
value: 0.8264447022356382
name: Pearson Cosine
- type: spearman_cosine
value: 0.8386403752382455
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8219134931449013
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.825509659109493
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8223094468630248
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8260503151751462
name: Spearman Euclidean
- type: pearson_dot
value: 0.6375226884845725
name: Pearson Dot
- type: spearman_dot
value: 0.6287228614640888
name: Spearman Dot
- type: pearson_max
value: 0.8264447022356382
name: Pearson Max
- type: spearman_max
value: 0.8386403752382455
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 128
type: sts-test-128
metrics:
- type: pearson_cosine
value: 0.8209661910768973
name: Pearson Cosine
- type: spearman_cosine
value: 0.8347149482673766
name: Spearman Cosine
- type: pearson_manhattan
value: 0.8082811559854036
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.8148314269262763
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.8093138512113149
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.8156468458613929
name: Spearman Euclidean
- type: pearson_dot
value: 0.5795109620454884
name: Pearson Dot
- type: spearman_dot
value: 0.5760223026552876
name: Spearman Dot
- type: pearson_max
value: 0.8209661910768973
name: Pearson Max
- type: spearman_max
value: 0.8347149482673766
name: Spearman Max
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: sts test 64
type: sts-test-64
metrics:
- type: pearson_cosine
value: 0.808708530451336
name: Pearson Cosine
- type: spearman_cosine
value: 0.8217532539767914
name: Spearman Cosine
- type: pearson_manhattan
value: 0.7876121380998453
name: Pearson Manhattan
- type: spearman_manhattan
value: 0.7969092304137347
name: Spearman Manhattan
- type: pearson_euclidean
value: 0.7902997966909958
name: Pearson Euclidean
- type: spearman_euclidean
value: 0.7987635968785215
name: Spearman Euclidean
- type: pearson_dot
value: 0.495047136234386
name: Pearson Dot
- type: spearman_dot
value: 0.49287000679901516
name: Spearman Dot
- type: pearson_max
value: 0.808708530451336
name: Pearson Max
- type: spearman_max
value: 0.8217532539767914
name: Spearman Max
---
# SentenceTransformer based on sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) on the Omartificial-Intelligence-Space/arabic-n_li-triplet dataset. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2) <!-- at revision bf3bf13ab40c3157080a7ab344c831b9ad18b5eb -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 384 tokens
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- Omartificial-Intelligence-Space/arabic-n_li-triplet
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("Omartificial-Intelligence-Space/MiniLM-L12-v2-all-nli-triplet")
# Run inference
sentences = [
'يجلس شاب ذو شعر أشقر على الحائط يقرأ جريدة بينما تمر امرأة وفتاة شابة.',
'ذكر شاب ينظر إلى جريدة بينما تمر إمرأتان بجانبه',
'الشاب نائم بينما الأم تقود ابنتها إلى الحديقة',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `sts-test-256`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8264 |
| **spearman_cosine** | **0.8386** |
| pearson_manhattan | 0.8219 |
| spearman_manhattan | 0.8255 |
| pearson_euclidean | 0.8223 |
| spearman_euclidean | 0.8261 |
| pearson_dot | 0.6375 |
| spearman_dot | 0.6287 |
| pearson_max | 0.8264 |
| spearman_max | 0.8386 |
#### Semantic Similarity
* Dataset: `sts-test-128`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.821 |
| **spearman_cosine** | **0.8347** |
| pearson_manhattan | 0.8083 |
| spearman_manhattan | 0.8148 |
| pearson_euclidean | 0.8093 |
| spearman_euclidean | 0.8156 |
| pearson_dot | 0.5795 |
| spearman_dot | 0.576 |
| pearson_max | 0.821 |
| spearman_max | 0.8347 |
#### Semantic Similarity
* Dataset: `sts-test-64`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.8087 |
| **spearman_cosine** | **0.8218** |
| pearson_manhattan | 0.7876 |
| spearman_manhattan | 0.7969 |
| pearson_euclidean | 0.7903 |
| spearman_euclidean | 0.7988 |
| pearson_dot | 0.495 |
| spearman_dot | 0.4929 |
| pearson_max | 0.8087 |
| spearman_max | 0.8218 |
<!--
## 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 Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 557,850 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 10.33 tokens</li><li>max: 52 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 13.21 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 15.32 tokens</li><li>max: 53 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------|:--------------------------------------------|:------------------------------------|
| <code>شخص على حصان يقفز فوق طائرة معطلة</code> | <code>شخص في الهواء الطلق، على حصان.</code> | <code>شخص في مطعم، يطلب عجة.</code> |
| <code>أطفال يبتسمون و يلوحون للكاميرا</code> | <code>هناك أطفال حاضرون</code> | <code>الاطفال يتجهمون</code> |
| <code>صبي يقفز على لوح التزلج في منتصف الجسر الأحمر.</code> | <code>الفتى يقوم بخدعة التزلج</code> | <code>الصبي يتزلج على الرصيف</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Evaluation Dataset
#### Omartificial-Intelligence-Space/arabic-n_li-triplet
* Dataset: Omartificial-Intelligence-Space/arabic-n_li-triplet
* Size: 6,584 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:---------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 5 tokens</li><li>mean: 21.86 tokens</li><li>max: 105 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 10.22 tokens</li><li>max: 49 tokens</li></ul> | <ul><li>min: 4 tokens</li><li>mean: 11.2 tokens</li><li>max: 33 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:-----------------------------------------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------|:---------------------------------------------------|
| <code>امرأتان يتعانقان بينما يحملان حزمة</code> | <code>إمرأتان يحملان حزمة</code> | <code>الرجال يتشاجرون خارج مطعم</code> |
| <code>طفلين صغيرين يرتديان قميصاً أزرق، أحدهما يرتدي الرقم 9 والآخر يرتدي الرقم 2 يقفان على خطوات خشبية في الحمام ويغسلان أيديهما في المغسلة.</code> | <code>طفلين يرتديان قميصاً مرقماً يغسلون أيديهم</code> | <code>طفلين يرتديان سترة يذهبان إلى المدرسة</code> |
| <code>رجل يبيع الدونات لعميل خلال معرض عالمي أقيم في مدينة أنجليس</code> | <code>رجل يبيع الدونات لعميل</code> | <code>امرأة تشرب قهوتها في مقهى صغير</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | sts-test-128_spearman_cosine | sts-test-256_spearman_cosine | sts-test-64_spearman_cosine |
|:------:|:----:|:-------------:|:----------------------------:|:----------------------------:|:---------------------------:|
| 0.0229 | 200 | 6.2204 | - | - | - |
| 0.0459 | 400 | 4.9559 | - | - | - |
| 0.0688 | 600 | 4.7835 | - | - | - |
| 0.0918 | 800 | 4.2725 | - | - | - |
| 0.1147 | 1000 | 4.291 | - | - | - |
| 0.1377 | 1200 | 4.0704 | - | - | - |
| 0.1606 | 1400 | 3.7962 | - | - | - |
| 0.1835 | 1600 | 3.7447 | - | - | - |
| 0.2065 | 1800 | 3.569 | - | - | - |
| 0.2294 | 2000 | 3.5373 | - | - | - |
| 0.2524 | 2200 | 3.608 | - | - | - |
| 0.2753 | 2400 | 3.5609 | - | - | - |
| 0.2983 | 2600 | 3.5231 | - | - | - |
| 0.3212 | 2800 | 3.3312 | - | - | - |
| 0.3442 | 3000 | 3.4803 | - | - | - |
| 0.3671 | 3200 | 3.3552 | - | - | - |
| 0.3900 | 3400 | 3.3024 | - | - | - |
| 0.4130 | 3600 | 3.2559 | - | - | - |
| 0.4359 | 3800 | 3.1882 | - | - | - |
| 0.4589 | 4000 | 3.227 | - | - | - |
| 0.4818 | 4200 | 3.0889 | - | - | - |
| 0.5048 | 4400 | 3.0861 | - | - | - |
| 0.5277 | 4600 | 3.0178 | - | - | - |
| 0.5506 | 4800 | 3.231 | - | - | - |
| 0.5736 | 5000 | 3.1593 | - | - | - |
| 0.5965 | 5200 | 3.1101 | - | - | - |
| 0.6195 | 5400 | 3.1307 | - | - | - |
| 0.6424 | 5600 | 3.1265 | - | - | - |
| 0.6654 | 5800 | 3.1116 | - | - | - |
| 0.6883 | 6000 | 3.1417 | - | - | - |
| 0.7113 | 6200 | 3.0862 | - | - | - |
| 0.7342 | 6400 | 2.9652 | - | - | - |
| 0.7571 | 6600 | 2.8466 | - | - | - |
| 0.7801 | 6800 | 2.271 | - | - | - |
| 0.8030 | 7000 | 2.046 | - | - | - |
| 0.8260 | 7200 | 1.9634 | - | - | - |
| 0.8489 | 7400 | 1.8875 | - | - | - |
| 0.8719 | 7600 | 1.7655 | - | - | - |
| 0.8948 | 7800 | 1.6874 | - | - | - |
| 0.9177 | 8000 | 1.7315 | - | - | - |
| 0.9407 | 8200 | 1.6674 | - | - | - |
| 0.9636 | 8400 | 1.6574 | - | - | - |
| 0.9866 | 8600 | 1.6142 | - | - | - |
| 1.0 | 8717 | - | 0.8347 | 0.8386 | 0.8218 |
### Framework Versions
- Python: 3.9.18
- Sentence Transformers: 3.0.1
- Transformers: 4.40.0
- PyTorch: 2.2.2+cu121
- Accelerate: 0.26.1
- Datasets: 2.19.0
- Tokenizers: 0.19.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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## Glossary
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yh-yao/Meta-Llama-3-8B-Instruct-Q3_K_S-GGUF | yh-yao | 2024-07-01T22:29:15Z | 760 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | text-generation | 2024-07-01T22:28:57Z | ---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
extra_gated_prompt: "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version\
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extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
geo: ip_location
? By clicking Submit below I accept the terms of the license and acknowledge that
the information I provide will be collected stored processed and shared in accordance
with the Meta Privacy Policy
: checkbox
extra_gated_description: The information you provide will be collected, stored, processed
and shared in accordance with the [Meta Privacy Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
widget:
- example_title: Hello
messages:
- role: user
content: Hey my name is Julien! How are you?
- example_title: Winter holidays
messages:
- role: system
content: You are a helpful and honest assistant. Please, respond concisely and
truthfully.
- role: user
content: Can you recommend a good destination for Winter holidays?
- example_title: Programming assistant
messages:
- role: system
content: You are a helpful and honest code and programming assistant. Please,
respond concisely and truthfully.
- role: user
content: Write a function that computes the nth fibonacci number.
inference:
parameters:
max_new_tokens: 300
stop:
- <|end_of_text|>
- <|eot_id|>
---
# yh-yao/Meta-Llama-3-8B-Instruct-Q3_K_S-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B-Instruct`](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo yh-yao/Meta-Llama-3-8B-Instruct-Q3_K_S-GGUF --hf-file meta-llama-3-8b-instruct-q3_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo yh-yao/Meta-Llama-3-8B-Instruct-Q3_K_S-GGUF --hf-file meta-llama-3-8b-instruct-q3_k_s.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.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo yh-yao/Meta-Llama-3-8B-Instruct-Q3_K_S-GGUF --hf-file meta-llama-3-8b-instruct-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo yh-yao/Meta-Llama-3-8B-Instruct-Q3_K_S-GGUF --hf-file meta-llama-3-8b-instruct-q3_k_s.gguf -c 2048
```
|
GanjinZero/coder_eng | GanjinZero | 2023-03-22T08:22:16Z | 759 | 3 | transformers | [
"transformers",
"pytorch",
"safetensors",
"bert",
"feature-extraction",
"biomedical",
"en",
"license:apache-2.0",
"endpoints_compatible",
"text-embeddings-inference",
"region:us"
] | feature-extraction | 2022-03-02T23:29:04Z | ---
language:
- en
license: apache-2.0
tags:
- bert
- biomedical
---
CODER: Knowledge infused cross-lingual medical term embedding for term normalization.
English Version.
Github Link: https://github.com/GanjinZero/CODER
```
@article{YUAN2022103983,
title = {CODER: Knowledge-infused cross-lingual medical term embedding for term normalization},
journal = {Journal of Biomedical Informatics},
pages = {103983},
year = {2022},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2021.103983},
url = {https://www.sciencedirect.com/science/article/pii/S1532046421003129},
author = {Zheng Yuan and Zhengyun Zhao and Haixia Sun and Jiao Li and Fei Wang and Sheng Yu},
keywords = {medical term normalization, cross-lingual, medical term representation, knowledge graph embedding, contrastive learning}
}
``` |
crystina-z/monoELECTRA_LCE_nneg31 | crystina-z | 2024-05-07T04:12:36Z | 759 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"electra",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-03-02T23:29:05Z | Entry not found |
gorkemgoknar/gpt2-turkish-writer | gorkemgoknar | 2021-09-22T08:29:24Z | 759 | 9 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"turkish",
"aiwriter",
"finetuned",
"tr",
"dataset:wikipedia-turkish",
"dataset:custom-book-corpus",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language:
- tr
thumbnail:
tags:
- gpt2
- turkish
- aiwriter
- finetuned
license: apache-2.0
datasets:
- wikipedia-turkish
- custom-book-corpus
metrics:
- perplexity
- accuracy
widget:
- text: Bir zaman topu olan ama köpeği olmayan bir çocuk vardı. Parkta
context: ''
- text: 'Uzun uzun sahile doğru baktı. Düşündüklerinden '
context: ''
- text: Çok uzun zaman önce galaksinin uzak bir köşesinde...
context: ''
- text: "'Bugün kendimi çok hasta hissediyorum' dedi. Karşısında "
context: ''
---
# Turkish AI Writer based on GPT2-Small
# Türkçe Yapay Zeka Yazarı
## Model description
This model is enhanced version of gpt2-small-turkish finetuned version. In addition to 28-10-2020 Wikipedia Turkish article dump this model is trained with more than 400 classic novels and plays in Turkish (Including Dostoyevski, Shaekspeare, Dumas)
Base work has been done on Pierre Guillou tutorial as on this page.
(https://github.com/piegu/fastai-projects/blob/master/finetuning-English-GPT2-any-language-Portuguese-HuggingFace-fastaiv2.ipynb)
Note that Since Turkish language is not close to English as in Porteguese instead of training last 2 layers, last 3 layers are trained.
Code is converted to work with Fastai 2.X .
Using Google Colab for training.
Current accuracy 36.3 % , Perplexity : 44.75
Demo (using CPU inference) is available on: http://www.metayazar.com
Models are available:
* [gpt2-small-tuned-tr] (https://huggingface.co/gorkemgoknar/gpt2-small-turkish)
* [gpt2-small-turkish-writer] (https://huggingface.co/gorkemgoknar/gpt2-turkish-writer)
## Intended uses & limitations
#### How to use
#### Install
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
import torch
tokenizer = AutoTokenizer.from_pretrained("gorkemgoknar/gpt2-turkish-writer")
model = AutoModelWithLMHead.from_pretrained("gorkemgoknar/gpt2-turkish-writer")
# Get sequence length max of 1024
tokenizer.model_max_length=1024
model.eval() # disable dropout (or leave in train mode to finetune)
```
#### Generate 1 word
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output
outputs = model(**inputs, labels=inputs["input_ids"])
loss, logits = outputs[:2]
predicted_index = torch.argmax(logits[0, -1, :]).item()
predicted_text = tokenizer.decode([predicted_index])
# results
print('input text:', text)
print('predicted text:', predicted_text)
# input text:
# predicted text:
```
#### Generate Full Sequence
```python
# input sequence
text = "Bu yazıyı bilgisayar yazdı."
inputs = tokenizer(text, return_tensors="pt")
# model output using Top-k sampling text generation method
sample_outputs = model.generate(inputs.input_ids,
pad_token_id=50256,
do_sample=True,
max_length=50, # put the token number you want
top_k=40,
num_return_sequences=1)
# generated sequence
for i, sample_output in enumerate(sample_outputs):
print(">> Generated text {}\n\n{}".format(i+1, tokenizer.decode(sample_output.tolist())))
# >> Generated text
#
```
#### Limitations and bias
The training data used for this model come from Turkish Wikipedia and books. We know it contains a lot of unfiltered content from the internet, which is far from neutral. Also not much pre-processing was done on books hence chapter names and page numbers can be seen on some cases. This is a work in progress.
## Training data
Wikipedia Turkish article dump as of 28-10-2020
Turkish book dataset of >400 classic novels
## Training procedure
## Eval results
| epoch |train_loss |valid_loss |accuracy |perplexity |time |
| ----- | -------- |--------- | ---------- | --------- | ----- |
|0 |4.497828 |4.549605 |0.277328 |94.595070 |2:09:58|
|1 |4.503929 |4.519456 |0.275071 |91.785645 |2:04:30|
|2 |3.612716 |3.921146 |0.344802 |50.458256 |2:03:22|
|3 |3.777645 |4.072006 |0.326130 |58.674530 |1:56:14|
|4 |2.934462 |3.801303 |0.363719 |44.759476 |1:58:55|
Note: 1cycle rule training is used and epochs are at different times
```
|
vinai/bartpho-word | vinai | 2022-10-22T09:05:26Z | 759 | 4 | transformers | [
"transformers",
"pytorch",
"tf",
"mbart",
"feature-extraction",
"arxiv:2109.09701",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2022-03-02T23:29:05Z | # <a name="introduction"></a> BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese
Two BARTpho versions `BARTpho-syllable` and `BARTpho-word` are the first public large-scale monolingual sequence-to-sequence models pre-trained for Vietnamese. BARTpho uses the "large" architecture and pre-training scheme of the sequence-to-sequence denoising model [BART](https://github.com/pytorch/fairseq/tree/main/examples/bart), thus especially suitable for generative NLP tasks. Experiments on a downstream task of Vietnamese text summarization show that in both automatic and human evaluations, BARTpho outperforms the strong baseline [mBART](https://github.com/pytorch/fairseq/tree/main/examples/mbart) and improves the state-of-the-art.
The general architecture and experimental results of BARTpho can be found in our [paper](https://arxiv.org/abs/2109.09701):
@article{bartpho,
title = {{BARTpho: Pre-trained Sequence-to-Sequence Models for Vietnamese}},
author = {Nguyen Luong Tran and Duong Minh Le and Dat Quoc Nguyen},
journal = {arXiv preprint},
volume = {arXiv:2109.09701},
year = {2021}
}
**Please CITE** our paper when BARTpho is used to help produce published results or incorporated into other software.
For further information or requests, please go to [BARTpho's homepage](https://github.com/VinAIResearch/BARTpho)!
|
huggingtweets/palestinepound | huggingtweets | 2022-08-14T07:45:17Z | 759 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-08-14T07:43:47Z | ---
language: en
thumbnail: http://www.huggingtweets.com/palestinepound/1660463113168/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1396083058844045319/d_xNzMbk_400x400.png')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Palestine Pound</div>
<div style="text-align: center; font-size: 14px;">@palestinepound</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from Palestine Pound.
| Data | Palestine Pound |
| --- | --- |
| Tweets downloaded | 145 |
| Retweets | 4 |
| Short tweets | 11 |
| Tweets kept | 130 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/152jutl1/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @palestinepound's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/1sd0ks1o) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/1sd0ks1o/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/palestinepound')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
RussianNLP/ruRoBERTa-large-rucola | RussianNLP | 2022-08-30T20:23:10Z | 759 | 5 | transformers | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"ru",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2022-08-30T19:54:51Z | ---
language: ru
license: apache-2.0
tags:
- transformers
thumbnail: "https://github.com/RussianNLP/RuCoLA/blob/main/logo.png"
widget:
- text: "Он решил ту или иную сложную задачу."
---
This is a finetuned version of [RuRoBERTa-large](https://huggingface.co/sberbank-ai/ruRoberta-large) for the task of linguistic acceptability classification on the [RuCoLA](https://rucola-benchmark.com/) benchmark.
The hyperparameters used for finetuning are as follows:
* 5 training epochs (with early stopping based on validation MCC)
* Peak learning rate: 1e-5, linear warmup for 10% of total training time
* Weight decay: 1e-4
* Batch size: 32
* Random seed: 5
* Optimizer: [torch.optim.AdamW](https://pytorch.org/docs/stable/generated/torch.optim.AdamW.html) |
laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft | laion | 2023-04-18T19:28:23Z | 759 | 3 | open_clip | [
"open_clip",
"tensorboard",
"zero-shot-image-classification",
"clip",
"arxiv:2201.03545",
"arxiv:2210.08402",
"arxiv:1910.04867",
"license:mit",
"region:us"
] | zero-shot-image-classification | 2023-02-11T01:37:59Z | ---
tags:
- zero-shot-image-classification
- clip
license: mit
library_name: open_clip
pipeline_tag: zero-shot-image-classification
---
# Model card for CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
5. [Acknowledgements](#acknowledgements)
6. [Citation](#citation)
# Model Details
## Model Description
A series of CLIP [ConvNeXt-Large](https://arxiv.org/abs/2201.03545) (w/ extra text depth, vision MLP head) models trained on the LAION-2B (english) subset of [LAION-5B](https://arxiv.org/abs/2210.08402) using [OpenCLIP](https://github.com/mlfoundations/open_clip).
The models utilize:
* the [timm](https://github.com/rwightman/pytorch-image-models) ConvNeXt-Large model (`convnext_large`) as the image tower
* a MLP (`fc - gelu - drop - fc`) head in vision tower instead of the single projection of other CLIP models
* a text tower with same width but 4 layers more depth than ViT-L / RN50x16 models (depth 16, embed dim 768).
This 320x320 resolution model is a fine-tune of [CLIP-convnext_large_d.laion2B-s26B-b102K-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) at a higher resolution. It was fine-tune from the final checkpoint of the original 256x256 training run w/ an additional ~2.5B samples and a lower learning rate.
At 320x320, the ConvNext-Large-D is significantly more efficient than the L/14 model at 336x336 that OpenAI fine-tuned. L/14-336 model is 2.5x more GMAC, 2.8x more activations, and 1.22x more parameters.
| Model | Dataset | Resolution | AugReg | Top-1 ImageNet Zero-Shot (%) |
| ----- | ------- | ---------- | ------------ | --------- |
| [convnext_large_d.laion2b_s26b_b102k-augreg](https://huggingface.co/laion/CLIP-convnext_large_d.laion2B-s26B-b102K-augreg) | LAION-2B | 256x256 | RRC (0.33, 1.0), RE (0.35), SD (0.1), D(0.1) | 75.9 |
| [convnext_large_d_320.laion2b_s29b_b131k-ft](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.6 |
| [convnext_large_d_320.laion2b_s29b_b131k-ft-soup](https://huggingface.co/laion/CLIP-convnext_large_d_320.laion2B-s29B-b131K-ft-soup) | LAION-2B | 320x320 | RRC (0.5, 1.0), RE (0.4), SD (0.1), D(0.0) | 76.9 |
RRC = Random Resize Crop (crop pcts), RE = Random Erasing (prob), SD = Stochastic Depth (prob) -- image tower only, D = Dropout (prob) -- image tower head only
LAION-A = LAION Aesthetic, an ~900M sample subset of LAION-2B with pHash dedupe and asthetic score filtering.
Model training done by Ross Wightman on the [stability.ai](https://stability.ai/) cluster.
# Uses
As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification. We also hope it can be used for interdisciplinary studies of the potential impact of such model.
The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis. Additionally, the LAION-5B blog (https://laion.ai/blog/laion-5b/) and upcoming paper include additional discussion as it relates specifically to the training dataset.
## Direct Use
Zero-shot image classification, image and text retrieval, among others.
## Downstream Use
Image classification and other image task fine-tuning, linear probe image classification, image generation guiding and conditioning, among others.
## Out-of-Scope Use
As per the OpenAI models,
**Any** deployed use case of the model - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially harmful.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
Further the above notice, the LAION-5B dataset used in training of these models has additional considerations, see below.
# Training Details
## Training Data
This model was trained with LAION-2B -- A 2 billion sample English subset of LAION-5B (https://laion.ai/blog/laion-5b/).
**IMPORTANT NOTE:** The motivation behind dataset creation is to democratize research and experimentation around large-scale multi-modal model training and handling of uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to use the dataset for research purposes. Be aware that this large-scale dataset is uncurated. Keep in mind that the uncurated nature of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the demo links with caution and at your own risk. It is possible to extract a “safe” subset by filtering out samples based on the safety tags (using a customized trained NSFW classifier that we built). While this strongly reduces the chance for encountering potentially harmful content when viewing, we cannot entirely exclude the possibility for harmful content being still present in safe mode, so that the warning holds also there. We think that providing the dataset openly to broad research and other interested communities will allow for transparent investigation of benefits that come along with training large-scale models as well as pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. Providing our dataset openly, we however do not recommend using it for creating ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
## Training Procedure
All 320x320 model fine-tunes were trained with a global batch size of 131072 for 10-16 checkpoint intervals of 203.7M samples for a total of ~2-3B samples seen over fine-tune.
For 320x320 models, a slurm script w/ srun below was used on 64 8-GPU (A100 40GB) nodes (Stability).
```
/opt/slurm/sbin/srun --cpu_bind=v --accel-bind=gn python -m training.main \
--save-frequency 1 \
--name "convnext_large_320" \
--pretrained ""/runs/convnext_large_256/epoch_128.pt" \
--resume 'latest' \
--train-data="pipe:aws s3 cp s3://mybucket/path/{laion{00000..xxxxx}.tar -" \
--train-num-samples 203666042 \
--dataset-type webdataset \
--precision amp_bfloat16 \
--beta2 0.98 \
--warmup 2000 \
--batch-size=256 \
--epochs=12 \
--dataset-resampled \
--aug-cfg use_timm=True scale='(0.5, 1.0)' re_prob=0.4 \
--clip-grad-norm 5.0 \
--lr 5e-5 \
--workers=6 \
--model "convnext_large_d_320" \
--seed 0 \
--ddp-static-graph \
--local-loss \
--gather-with-grad \
--grad-checkpointing
```
# Evaluation
Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark).
## Testing Data, Factors & Metrics
### Testing Data
The testing is performed with VTAB+ (A combination of VTAB (https://arxiv.org/abs/1910.04867) w/ additional robustness datasets) for classification and COCO and Flickr for retrieval.
## Results
The models achieve between 75.9 and 76.9 top-1 zero-shot accuracy on ImageNet-1k.
Zero-shot curve of origina from-scratch 256x256 training:
An initial round of benchmarks have been performed on a wider range of datasets, to be viewable at https://github.com/LAION-AI/CLIP_benchmark/blob/main/benchmark/results.ipynb
# Acknowledgements
Acknowledging [stability.ai](https://stability.ai/) for compute used to train this model.
# Citation
**BibTeX:**
LAION-5B
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
OpenCLIP software
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
OpenAI CLIP paper
```bibtex
@inproceedings{Radford2021LearningTV,
title={Learning Transferable Visual Models From Natural Language Supervision},
author={Alec Radford and Jong Wook Kim and Chris Hallacy and A. Ramesh and Gabriel Goh and Sandhini Agarwal and Girish Sastry and Amanda Askell and Pamela Mishkin and Jack Clark and Gretchen Krueger and Ilya Sutskever},
booktitle={ICML},
year={2021}
}
```
```bibtex
@Article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/rwightman/pytorch-image-models}}
}
``` |
timm/mobilevitv2_200.cvnets_in1k | timm | 2023-04-24T22:27:10Z | 759 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2206.02680",
"license:other",
"region:us"
] | image-classification | 2023-04-24T22:26:39Z | ---
tags:
- image-classification
- timm
library_name: timm
license: other
datasets:
- imagenet-1k
---
# Model card for mobilevitv2_200.cvnets_in1k
A MobileViT-v2 image classification model. Trained on ImageNet-1k by paper authors.
See license details at https://github.com/apple/ml-cvnets/blob/main/LICENSE
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 18.4
- GMACs: 7.2
- Activations (M): 32.1
- Image size: 256 x 256
- **Papers:**
- Separable Self-attention for Mobile Vision Transformers: https://arxiv.org/abs/2206.02680
- **Original:** https://github.com/apple/ml-cvnets
- **Dataset:** ImageNet-1k
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('mobilevitv2_200.cvnets_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilevitv2_200.cvnets_in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 128, 128, 128])
# torch.Size([1, 256, 64, 64])
# torch.Size([1, 512, 32, 32])
# torch.Size([1, 768, 16, 16])
# torch.Size([1, 1024, 8, 8])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'mobilevitv2_200.cvnets_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1024, 8, 8) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@article{Mehta2022SeparableSF,
title={Separable Self-attention for Mobile Vision Transformers},
author={Sachin Mehta and Mohammad Rastegari},
journal={ArXiv},
year={2022},
volume={abs/2206.02680}
}
```
|
jtatman/gpt2-open-instruct-v1-Anthropic-hh-rlhf | jtatman | 2023-07-25T00:57:00Z | 759 | 4 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"Transformers",
"GPT2",
"en",
"dataset:Anthropic/hh-rlhf",
"dataset:hakurei/open-instruct-v1",
"base_model:vicgalle/gpt2-open-instruct-v1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-07-22T10:26:00Z | ---
license: mit
base_model: vicgalle/gpt2-open-instruct-v1
tags:
- generated_from_trainer
- Transformers
- GPT2
model-index:
- name: hh-rlhf
results: []
datasets:
- Anthropic/hh-rlhf
- hakurei/open-instruct-v1
tokenizers:
- GPT2Tokenizer
language:
- en
library_name: transformers
metrics:
- bleu
---
<!-- 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. -->
# hh-rlhf
This model is a fine-tuned version of [vicgalle/gpt2-open-instruct-v1](https://huggingface.co/vicgalle/gpt2-open-instruct-v1) on an subset (15k) of the Anthropic/hh-rlhf dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1534
This model responds to the 'Human:' or 'Assistant:' prompt pretty well in conversation situations.
The shorter responses are better suited. Keep generation length to a reasonable subset. Left to its own devices it will have some pretty esoteric responses.
These include fairly uncensored remarks and at times violent outbursts. Especially if asking questions.
Needs vetting for other textual uses.
```
Human: Insane clown posse says...
Human: Should we look for a woman?
Assistant: It’s okay if you’re having a tough time finding what you are looking for. It’s a common question people might come up with for an argument or misunderstanding. What are you looking for, and what kind of woman would you have?
Human: Are you trying to find someone to argue
```
## Model description
GPT2 open instruct was trained on the open-instruct dataset fully. The reimagines one LM head as a partial rhlf adapter, with subtle reinforcements.
## Intended uses & limitations
Intended to study the intersection of instruct models and prompting that focuses on subtle exchanges of prompting. This probably needs to be refined substantially at this point.
## Training and evaluation data
```python
Train dataset size: 15000
Test dataset size: 500
Dataset({
features: ['chosen', 'rejected'],
num_rows: 15000
})
Dataset({
features: ['chosen', 'rejected'],
num_rows: 500
})
```
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0005
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3108 | 1.0 | 7500 | 2.1799 |
| 2.265 | 2.0 | 15000 | 2.1632 |
| 2.2507 | 3.0 | 22500 | 2.1567 |
| 2.2519 | 4.0 | 30000 | 2.1534 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.13.1
- Tokenizers 0.13.3 |
SG161222/RealVisXL_V2.02_Turbo | SG161222 | 2024-04-12T15:38:32Z | 759 | 0 | diffusers | [
"diffusers",
"safetensors",
"license:openrail++",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2023-12-24T12:17:13Z | ---
license: openrail++
---
<b>It's important! Read it!</b><br>
The model is still in the training phase. This is not the final version and may contain artifacts and perform poorly in some cases.<br>
<b>You can support me directly on Boosty - https://boosty.to/sg_161222</b><br>
The model is aimed at photorealism. Can produce sfw and nsfw images of decent quality.<br>
CivitAI Page: https://civitai.com/models/139562?modelVersionId=266762<br>
<b>Recommended Negative Prompt:</b><br>
(worst quality, low quality, illustration, 3d, 2d, painting, cartoons, sketch), open mouth<br>
<b>or another negative prompt</b><br>
<b>Recommended Generation Parameters:</b><br>
Sampling Steps: 4+<br>
Sampling Method: DPM++ SDE Karras<br>
CFG Scale: 1.5-3
<b>Recommended Hires Fix Parameters:</b><br>
Hires steps: 2+<br>
Upscaler: 4x-UltraSharp upscaler / or another<br>
Denoising strength: 0.1 - 0.5<br>
Upscale by: 1.1-2.0<br> |
eren23/DistilHermes-2.5-Mistral-7B | eren23 | 2024-03-05T15:46:11Z | 759 | 1 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"question-answering",
"en",
"dataset:argilla/distilabel-intel-orca-dpo-pairs",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | question-answering | 2024-01-26T22:32:12Z | ---
language:
- en
license: apache-2.0
library_name: transformers
datasets:
- argilla/distilabel-intel-orca-dpo-pairs
pipeline_tag: question-answering
model-index:
- name: DistilHermes-2.5-Mistral-7B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 65.87
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistilHermes-2.5-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 84.78
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistilHermes-2.5-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 63.65
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistilHermes-2.5-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 54.24
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistilHermes-2.5-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 78.22
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistilHermes-2.5-Mistral-7B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 59.82
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=eren23/DistilHermes-2.5-Mistral-7B
name: Open LLM Leaderboard
---
DPO Finetuned teknium/OpenHermes-2.5-Mistral-7B using argilla/distilabel-intel-orca-dpo-pairs.
Intel orca dpo pairs is a distilled version: https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
of https://huggingface.co/datasets/Intel/orca_dpo_pairs
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_eren23__DistilHermes-2.5-Mistral-7B)
| Metric |Value|
|---------------------------------|----:|
|Avg. |67.76|
|AI2 Reasoning Challenge (25-Shot)|65.87|
|HellaSwag (10-Shot) |84.78|
|MMLU (5-Shot) |63.65|
|TruthfulQA (0-shot) |54.24|
|Winogrande (5-shot) |78.22|
|GSM8k (5-shot) |59.82|
|
Sharathhebbar24/Med_GPT2 | Sharathhebbar24 | 2024-02-02T16:21:16Z | 759 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"medical",
"en",
"dataset:gamino/wiki_medical_terms",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-27T16:44:48Z | ---
license: apache-2.0
datasets:
- gamino/wiki_medical_terms
language:
- en
pipeline_tag: text-generation
tags:
- medical
---
This is a finetuned version of [gamino/wiki_medical_terms](https://huggingface.co/datasets/gamino/wiki_medical_terms)
## Model description
GPT-2 is a transformers model pre-trained on a very large corpus of English data in a self-supervised fashion. This
means it was pre-trained on the raw texts only, with no humans labeling them in any way (which is why it can use lots
of publicly available data) with an automatic process to generate inputs and labels from those texts. More precisely,
it was trained to guess the next word in sentences.
More precisely, inputs are sequences of continuous text of a certain length and the targets are the same sequence,
shifting one token (word or piece of word) to the right. The model uses a masking mechanism to make sure the
predictions for the token `i` only use the inputs from `1` to `i` but not the future tokens.
This way, the model learns an inner representation of the English language that can then be used to extract features
useful for downstream tasks. The model is best at what it was trained for, however, which is generating texts from a
prompt.
### To use this model
```python
>>> from transformers import AutoTokenizer, AutoModelForCausalLM
>>> model_name = "Sharathhebbar24/chat_gpt2_dpo"
>>> model = AutoModelForCausalLM.from_pretrained(model_name)
>>> tokenizer = AutoTokenizer.from_pretrained(model_name)
>>> def generate_text(prompt):
>>> inputs = tokenizer.encode(prompt, return_tensors='pt')
>>> outputs = model.generate(inputs, max_length=64, pad_token_id=tokenizer.eos_token_id)
>>> generated = tokenizer.decode(outputs[0], skip_special_tokens=True)
>>> return generated[:generated.rfind(".")+1]
>>> prompt = "What is Paracetamol"
>>> res = generate_text(prompt)
>>> res
``` |
TomGrc/FusionNet_34Bx2_MoE_v0.1 | TomGrc | 2024-03-04T03:30:15Z | 759 | 7 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-01-30T19:36:06Z | ---
language:
- en
license: mit
tags:
- moe
pipeline_tag: text-generation
model-index:
- name: FusionNet_34Bx2_MoE_v0.1
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: AI2 Reasoning Challenge (25-Shot)
type: ai2_arc
config: ARC-Challenge
split: test
args:
num_few_shot: 25
metrics:
- type: acc_norm
value: 73.72
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE_v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: HellaSwag (10-Shot)
type: hellaswag
split: validation
args:
num_few_shot: 10
metrics:
- type: acc_norm
value: 86.46
name: normalized accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE_v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU (5-Shot)
type: cais/mmlu
config: all
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 76.72
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE_v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: TruthfulQA (0-shot)
type: truthful_qa
config: multiple_choice
split: validation
args:
num_few_shot: 0
metrics:
- type: mc2
value: 71.01
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE_v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: Winogrande (5-shot)
type: winogrande
config: winogrande_xl
split: validation
args:
num_few_shot: 5
metrics:
- type: acc
value: 83.35
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE_v0.1
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8k (5-shot)
type: gsm8k
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 73.01
name: accuracy
source:
url: https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard?query=TomGrc/FusionNet_34Bx2_MoE_v0.1
name: Open LLM Leaderboard
---
# FusionNet_34Bx2_MoE_v0.1
Fine-tuned model on English language using MoE method. The improved version from FusionNet_34Bx2_MoE.
## Model description
The FusionNet_34Bx2_MoE_v0.1 is a model to experiment with the MoE method, which could significantly increase the performance of the original model. The FusionNet has 60.8B parameters, and this model is fine-tuned. Enjoy!
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/HuggingFaceH4/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_TomGrc__FusionNet_34Bx2_MoE_v0.1)
| Metric |Value|
|---------------------------------|----:|
|Avg. |77.38|
|AI2 Reasoning Challenge (25-Shot)|73.72|
|HellaSwag (10-Shot) |86.46|
|MMLU (5-Shot) |76.72|
|TruthfulQA (0-shot) |71.01|
|Winogrande (5-shot) |83.35|
|GSM8k (5-shot) |73.01|
|
kalisai/Nusantara-1.8b-Indo-Chat | kalisai | 2024-03-14T06:43:30Z | 759 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"convAI",
"id",
"en",
"dataset:argilla/OpenHermes2.5-dpo-binarized-alpha",
"dataset:wikimedia/wikipedia",
"dataset:FreedomIntelligence/evol-instruct-indonesian",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2024-03-05T16:25:22Z | ---
library_name: transformers
widget:
- messages:
- role: system
content: >-
Anda adalah seorang konselor karir. User akan memberi Anda seorang
individu mencari bimbingan dalam kehidupan profesional mereka, dan tugas
Anda adalah membantu mereka dalam menentukan karir apa yang paling cocok
bagi mereka berdasarkan keterampilan mereka, minat, dan pengalaman. Anda
juga harus melakukan penelitian terhadap berbagai hal tersebut pilihan
yang tersedia, jelaskan tren pasar kerja di berbagai industri, Dan saran
tentang kualifikasi mana yang akan bermanfaat untuk mengejar bidang
tertentu.
- role: user
content: Halo Say!
- role: assistant
content: Eh hai, Say ! Apa yang bisa aku bantu?
- role: user
content: >-
Saya tertarik untuk mengembangkan karir di bidang perbankan. Apa yang dapat kamu
rekomendasikan ke saya?
- messages:
- role: system
content: >-
Anda adalah asisten yang berpengetahuan luas. Bantu user sebanyak yang
Anda bisa.
- role: user
content: Bagaimana caranya menjadi lebih aktif di Bulan Puasa?
- messages:
- role: system
content: Anda adalah asisten yang membantu dan memberikan tanggapan yang cerdas.
- role: user
content: Haloooo Bund!
- role: assistant
content: Halo! Apa yang bisa saya bantu?
- role: user
content: >-
Saya perlu menu buka puasa yang segar di Bulan Ramadhan ini, makanan khas Indonesia apa saja yang
cocok untuk menu buka puasa di Bulan Ramadhan?
- messages:
- role: system
content: >-
Anda adalah asisten yang sangat kreatif. Pengguna akan memberi Anda tugas,
yang harus Anda selesaikan dengan seluruh pengetahuan Anda.
- role: user
content: >-
Tulis latar belakang cerita novel tentang seorang wanita yang ingin memberantas
geng 9 Naga.
inference:
parameters:
max_new_tokens: 128
penalty_alpha: 0.5
top_k: 4
pipeline_tag: text-generation
tags:
- conversational
- convAI
license: apache-2.0
language:
- id
- en
datasets:
- argilla/OpenHermes2.5-dpo-binarized-alpha
- wikimedia/wikipedia
- FreedomIntelligence/evol-instruct-indonesian
---
### Model Description
Nusantara is a series of Open Weight Language Model of Bahasa Indonesia (Indonesia language). Nusantara is based from Qwen1.5 Language Model, finetuned by domain specific of datasets.
As Chat-implemented language model, Nusantara is capable to do Question-Answering and respond to instructions given in Bahasa Indonesia.
Due to limited resources, only 0.8B, 1.8B, 2.7B, 4B and 7B models are available. If you're interested in funding this project for further development, specific usage, or larger parameters, please contact us.
- **Finetuned by:** [Kalis AI](https://huggingface.co/kalisai)
- **Funded by:** Self-funded
- **Model type:** transformer-based decoder-only language model
- **Language(s):** Bahasa Indonesia (id), English (en)
- **License:** Nusantara is licensed under Apache-2.0, but any usage of this model should comply with [Qwen License](https://huggingface.co/Qwen/Qwen1.5-4B/blob/main/LICENSE)
- **Finetuned from model:** [Qwen1.5-4B](https://huggingface.co/Qwen/Qwen1.5-4B/tree/main)
### Attentions!
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Due to certain circumstances, models with <4B parameters tend to hallucinate easily. Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model.
Because this model is also trained with uncensored datasets, there is the possibility of negative impacts arising from using this model. All kinds of impacts that arise as a result of using this model are entirely the responsibility of the user. The model maker is not responsible for any risks incurred.
## How to Get Started with the Model
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"kalisai/Nusantara-1.8B-Indo-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("kalisai/Nusantara-1.8B-Indo-Chat")
prompt = "Berikan saya resep memasak nasi goreng yang lezat."
messages = [
{"role": "system", "content": "Kamu adalah Nusantara, asisten AI yang pintar."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Citation
If you use the Nusantara language model in your research or project, please cite it as:
```
@misc{zulfikar_aji_kusworo_2024,
title={Nusantara: A Series of Versatile Open Weight Language Model of Bahasa Indonesia},
author={Zulfikar Aji Kusworo},
publisher={Hugging Face}
journal={Hugging Face Repository},
year={2024}
url = {https://huggingface.co/kalisai}
}
``` |
mradermacher/Miqu-MS-70B-GGUF | mradermacher | 2024-05-06T05:20:07Z | 759 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Undi95/Miqu-MS-70B",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-03T19:43:26Z | ---
base_model: Undi95/Miqu-MS-70B
language:
- en
library_name: transformers
license: cc-by-nc-4.0
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Undi95/Miqu-MS-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Miqu-MS-70B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q2_K.gguf) | Q2_K | 25.9 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.IQ3_XS.gguf) | IQ3_XS | 28.7 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.IQ3_S.gguf) | IQ3_S | 30.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q3_K_S.gguf) | Q3_K_S | 30.3 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.IQ3_M.gguf) | IQ3_M | 31.4 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q3_K_M.gguf) | Q3_K_M | 33.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q3_K_L.gguf) | Q3_K_L | 36.6 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.IQ4_XS.gguf) | IQ4_XS | 37.6 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q4_K_S.gguf) | Q4_K_S | 39.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q4_K_M.gguf) | Q4_K_M | 41.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q5_K_S.gguf) | Q5_K_S | 47.9 | |
| [GGUF](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q5_K_M.gguf) | Q5_K_M | 49.2 | |
| [PART 1](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q6_K.gguf.part2of2) | Q6_K | 57.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Miqu-MS-70B-GGUF/resolve/main/Miqu-MS-70B.Q8_0.gguf.part2of2) | Q8_0 | 73.6 | 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 -->
|
bartowski/Qwen1.5-32B-Chat-GGUF | bartowski | 2024-04-05T23:45:58Z | 759 | 0 | null | [
"gguf",
"chat",
"text-generation",
"en",
"license:other",
"region:us"
] | text-generation | 2024-04-05T22:19:10Z | ---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-32B-Chat/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
quantized_by: bartowski
---
## Llamacpp Quantizations of Qwen1.5-32B-Chat
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b2589">b2589</a> for quantization.
Original model: https://huggingface.co/Qwen/Qwen1.5-32B-Chat
Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Qwen1.5-32B-Chat-Q8_0.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q8_0.gguf) | Q8_0 | 34.55GB | Extremely high quality, generally unneeded but max available quant. |
| [Qwen1.5-32B-Chat-Q6_K.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q6_K.gguf) | Q6_K | 26.67GB | Very high quality, near perfect, *recommended*. |
| [Qwen1.5-32B-Chat-Q5_K_M.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q5_K_M.gguf) | Q5_K_M | 23.08GB | High quality, very usable. |
| [Qwen1.5-32B-Chat-Q5_K_S.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q5_K_S.gguf) | Q5_K_S | 22.46GB | High quality, very usable. |
| [Qwen1.5-32B-Chat-Q5_0.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q5_0.gguf) | Q5_0 | 22.46GB | High quality, older format, generally not recommended. |
| [Qwen1.5-32B-Chat-Q4_K_M.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q4_K_M.gguf) | Q4_K_M | 19.69GB | Good quality, uses about 4.83 bits per weight. |
| [Qwen1.5-32B-Chat-Q4_K_S.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q4_K_S.gguf) | Q4_K_S | 18.64GB | Slightly lower quality with small space savings. |
| [Qwen1.5-32B-Chat-IQ4_NL.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-IQ4_NL.gguf) | IQ4_NL | 18.68GB | Decent quality, similar to Q4_K_S, new method of quanting, |
| [Qwen1.5-32B-Chat-IQ4_XS.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-IQ4_XS.gguf) | IQ4_XS | 17.73GB | Decent quality, new method with similar performance to Q4. |
| [Qwen1.5-32B-Chat-Q4_0.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q4_0.gguf) | Q4_0 | 18.49GB | Decent quality, older format, generally not recommended. |
| [Qwen1.5-32B-Chat-Q3_K_L.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q3_K_L.gguf) | Q3_K_L | 17.11GB | Lower quality but usable, good for low RAM availability. |
| [Qwen1.5-32B-Chat-Q3_K_M.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q3_K_M.gguf) | Q3_K_M | 15.81GB | Even lower quality. |
| [Qwen1.5-32B-Chat-IQ3_M.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-IQ3_M.gguf) | IQ3_M | 14.70GB | Medium-low quality, new method with decent performance. |
| [Qwen1.5-32B-Chat-IQ3_S.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-IQ3_S.gguf) | IQ3_S | 14.32GB | Lower quality, new method with decent performance, recommended over Q3 quants. |
| [Qwen1.5-32B-Chat-Q3_K_S.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q3_K_S.gguf) | Q3_K_S | 14.28GB | Low quality, not recommended. |
| [Qwen1.5-32B-Chat-Q2_K.gguf](https://huggingface.co/bartowski/Qwen1.5-32B-Chat-GGUF/blob/main/Qwen1.5-32B-Chat-Q2_K.gguf) | Q2_K | 12.22GB | Extremely low quality, *not* recommended. |
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
abmorton/wall-potfiller | abmorton | 2024-06-30T23:47:47Z | 759 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-06-30T23:43:40Z | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### wall-potfiller Dreambooth model trained by abmorton with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
KoichiYasuoka/roberta-small-japanese-luw-upos | KoichiYasuoka | 2022-09-18T10:45:09Z | 758 | 0 | transformers | [
"transformers",
"pytorch",
"roberta",
"token-classification",
"japanese",
"pos",
"dependency-parsing",
"ja",
"dataset:universal_dependencies",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2022-03-02T23:29:04Z | ---
language:
- "ja"
tags:
- "japanese"
- "token-classification"
- "pos"
- "dependency-parsing"
datasets:
- "universal_dependencies"
license: "cc-by-sa-4.0"
pipeline_tag: "token-classification"
widget:
- text: "国境の長いトンネルを抜けると雪国であった。"
---
# roberta-small-japanese-luw-upos
## Model Description
This is a RoBERTa model pre-trained on 青空文庫 texts for POS-tagging and dependency-parsing, derived from [roberta-small-japanese-aozora](https://huggingface.co/KoichiYasuoka/roberta-small-japanese-aozora). Every long-unit-word is tagged by [UPOS](https://universaldependencies.org/u/pos/) (Universal Part-Of-Speech).
## How to Use
```py
from transformers import AutoTokenizer,AutoModelForTokenClassification,TokenClassificationPipeline
tokenizer=AutoTokenizer.from_pretrained("KoichiYasuoka/roberta-small-japanese-luw-upos")
model=AutoModelForTokenClassification.from_pretrained("KoichiYasuoka/roberta-small-japanese-luw-upos")
pipeline=TokenClassificationPipeline(tokenizer=tokenizer,model=model,aggregation_strategy="simple")
nlp=lambda x:[(x[t["start"]:t["end"]],t["entity_group"]) for t in pipeline(x)]
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
or
```py
import esupar
nlp=esupar.load("KoichiYasuoka/roberta-small-japanese-luw-upos")
print(nlp("国境の長いトンネルを抜けると雪国であった。"))
```
## See Also
[esupar](https://github.com/KoichiYasuoka/esupar): Tokenizer POS-tagger and Dependency-parser with BERT/RoBERTa/DeBERTa models
|
huggingartists/florence-the-machine | huggingartists | 2021-08-10T09:03:06Z | 758 | 0 | transformers | [
"transformers",
"pytorch",
"jax",
"gpt2",
"text-generation",
"huggingartists",
"lyrics",
"lm-head",
"causal-lm",
"en",
"dataset:huggingartists/florence-the-machine",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
datasets:
- huggingartists/florence-the-machine
tags:
- huggingartists
- lyrics
- lm-head
- causal-lm
widget:
- text: "I am"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:DISPLAY_1; margin-left: auto; margin-right: auto; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://images.genius.com/99d09eb55276442d715ac14f06173a4e.1000x1000x1.jpg')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 HuggingArtists Model 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">Florence + The Machine</div>
<a href="https://genius.com/artists/florence-the-machine">
<div style="text-align: center; font-size: 14px;">@florence-the-machine</div>
</a>
</div>
I was made with [huggingartists](https://github.com/AlekseyKorshuk/huggingartists).
Create your own bot based on your favorite artist with [the demo](https://colab.research.google.com/github/AlekseyKorshuk/huggingartists/blob/master/huggingartists-demo.ipynb)!
## How does it work?
To understand how the model was developed, check the [W&B report](https://wandb.ai/huggingartists/huggingartists/reportlist).
## Training data
The model was trained on lyrics from Florence + The Machine.
Dataset is available [here](https://huggingface.co/datasets/huggingartists/florence-the-machine).
And can be used with:
```python
from datasets import load_dataset
dataset = load_dataset("huggingartists/florence-the-machine")
```
[Explore the data](https://wandb.ai/huggingartists/huggingartists/runs/icjt5evm/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on Florence + The Machine's lyrics.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/huggingartists/huggingartists/runs/1zfb9y24) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/huggingartists/huggingartists/runs/1zfb9y24/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingartists/florence-the-machine')
generator("I am", num_return_sequences=5)
```
Or with Transformers library:
```python
from transformers import AutoTokenizer, AutoModelWithLMHead
tokenizer = AutoTokenizer.from_pretrained("huggingartists/florence-the-machine")
model = AutoModelWithLMHead.from_pretrained("huggingartists/florence-the-machine")
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Aleksey Korshuk*
[](https://github.com/AlekseyKorshuk)
[](https://twitter.com/intent/follow?screen_name=alekseykorshuk)
[](https://t.me/joinchat/_CQ04KjcJ-4yZTky)
For more details, visit the project repository.
[](https://github.com/AlekseyKorshuk/huggingartists)
|
huggingtweets/minimalaq | huggingtweets | 2021-09-10T20:50:07Z | 758 | 0 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"huggingtweets",
"en",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2022-03-02T23:29:05Z | ---
language: en
thumbnail: https://www.huggingtweets.com/minimalaq/1631307002582/predictions.png
tags:
- huggingtweets
widget:
- text: "My dream is"
---
<div class="inline-flex flex-col" style="line-height: 1.5;">
<div class="flex">
<div
style="display:inherit; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('https://pbs.twimg.com/profile_images/1403107651291058185/3CBTwj6__400x400.jpg')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
<div
style="display:none; margin-left: 4px; margin-right: 4px; width: 92px; height:92px; border-radius: 50%; background-size: cover; background-image: url('')">
</div>
</div>
<div style="text-align: center; margin-top: 3px; font-size: 16px; font-weight: 800">🤖 AI BOT 🤖</div>
<div style="text-align: center; font-size: 16px; font-weight: 800">MINIMAL</div>
<div style="text-align: center; font-size: 14px;">@minimalaq</div>
</div>
I was made with [huggingtweets](https://github.com/borisdayma/huggingtweets).
Create your own bot based on your favorite user with [the demo](https://colab.research.google.com/github/borisdayma/huggingtweets/blob/master/huggingtweets-demo.ipynb)!
## How does it work?
The model uses the following pipeline.
To understand how the model was developed, check the [W&B report](https://wandb.ai/wandb/huggingtweets/reports/HuggingTweets-Train-a-Model-to-Generate-Tweets--VmlldzoxMTY5MjI).
## Training data
The model was trained on tweets from MINIMAL.
| Data | MINIMAL |
| --- | --- |
| Tweets downloaded | 3238 |
| Retweets | 288 |
| Short tweets | 533 |
| Tweets kept | 2417 |
[Explore the data](https://wandb.ai/wandb/huggingtweets/runs/v6z30t80/artifacts), which is tracked with [W&B artifacts](https://docs.wandb.com/artifacts) at every step of the pipeline.
## Training procedure
The model is based on a pre-trained [GPT-2](https://huggingface.co/gpt2) which is fine-tuned on @minimalaq's tweets.
Hyperparameters and metrics are recorded in the [W&B training run](https://wandb.ai/wandb/huggingtweets/runs/5kd2ws7g) for full transparency and reproducibility.
At the end of training, [the final model](https://wandb.ai/wandb/huggingtweets/runs/5kd2ws7g/artifacts) is logged and versioned.
## How to use
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
generator = pipeline('text-generation',
model='huggingtweets/minimalaq')
generator("My dream is", num_return_sequences=5)
```
## Limitations and bias
The model suffers from [the same limitations and bias as GPT-2](https://huggingface.co/gpt2#limitations-and-bias).
In addition, the data present in the user's tweets further affects the text generated by the model.
## About
*Built by Boris Dayma*
[](https://twitter.com/intent/follow?screen_name=borisdayma)
For more details, visit the project repository.
[](https://github.com/borisdayma/huggingtweets)
|
timm/vit_base_patch32_clip_224.laion2b_ft_in1k | timm | 2023-05-06T00:03:52Z | 758 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"dataset:laion-2b",
"arxiv:2212.07143",
"arxiv:2210.08402",
"arxiv:2010.11929",
"license:apache-2.0",
"region:us"
] | image-classification | 2022-11-01T23:00:20Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
- laion-2b
---
# Model card for vit_base_patch32_clip_224.laion2b_ft_in1k
A Vision Transformer (ViT) image classification model. Pretrained on LAION-2B image-text pairs using OpenCLIP. Fine-tuned on ImageNet-1k in `timm`. See recipes in [Reproducible scaling laws](https://arxiv.org/abs/2212.07143).
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 88.2
- GMACs: 4.4
- Activations (M): 4.2
- Image size: 224 x 224
- **Papers:**
- OpenCLIP: https://github.com/mlfoundations/open_clip
- Reproducible scaling laws for contrastive language-image learning: https://arxiv.org/abs/2212.07143
- LAION-5B: An open large-scale dataset for training next generation image-text models: https://arxiv.org/abs/2210.08402
- An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale: https://arxiv.org/abs/2010.11929v2
- **Dataset:** ImageNet-1k
- **Pretrain Dataset:**
- LAION-2B
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('vit_base_patch32_clip_224.laion2b_ft_in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'vit_base_patch32_clip_224.laion2b_ft_in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 50, 768) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
```bibtex
@article{cherti2022reproducible,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
journal={arXiv preprint arXiv:2212.07143},
year={2022}
}
```
```bibtex
@inproceedings{schuhmann2022laionb,
title={{LAION}-5B: An open large-scale dataset for training next generation image-text models},
author={Christoph Schuhmann and
Romain Beaumont and
Richard Vencu and
Cade W Gordon and
Ross Wightman and
Mehdi Cherti and
Theo Coombes and
Aarush Katta and
Clayton Mullis and
Mitchell Wortsman and
Patrick Schramowski and
Srivatsa R Kundurthy and
Katherine Crowson and
Ludwig Schmidt and
Robert Kaczmarczyk and
Jenia Jitsev},
booktitle={Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track},
year={2022},
url={https://openreview.net/forum?id=M3Y74vmsMcY}
}
```
```bibtex
@article{dosovitskiy2020vit,
title={An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale},
author={Dosovitskiy, Alexey and Beyer, Lucas and Kolesnikov, Alexander and Weissenborn, Dirk and Zhai, Xiaohua and Unterthiner, Thomas and Dehghani, Mostafa and Minderer, Matthias and Heigold, Georg and Gelly, Sylvain and Uszkoreit, Jakob and Houlsby, Neil},
journal={ICLR},
year={2021}
}
```
```bibtex
@misc{rw2019timm,
author = {Ross Wightman},
title = {PyTorch Image Models},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
doi = {10.5281/zenodo.4414861},
howpublished = {\url{https://github.com/huggingface/pytorch-image-models}}
}
```
|
AlexWortega/instruct_rugptSmall | AlexWortega | 2023-03-10T12:23:38Z | 758 | 2 | transformers | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"finance",
"code",
"en",
"dataset:IlyaGusev/habr",
"dataset:Den4ikAI/russian_instructions",
"dataset:wiki_qa",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | text-generation | 2023-03-07T06:46:31Z | ---
datasets:
- IlyaGusev/habr
- Den4ikAI/russian_instructions
- wiki_qa
inference:
parameters:
max_new_tokens: 32
temperature: 1
top_k: 50
top_p: 0.7
do_sample: true
license: apache-2.0
language:
- en
pipeline_tag: text-generation
widget:
- text: Чем отличается лось от ежа?
example_title: Question Answering
- text: Как выпросить повышение?
example_title: Logical reasoning
- text: Какая температура закипания азота?
example_title: Scientific knowledge
library_name: transformers
tags:
- finance
- code
---
<h1 style="font-size: 42px">Instructions ruGPT Small v0.1a<h1/>
# Model Summary
> Я дообучил small rugpt на датасете инструкций, хабра, QA и кода
# Quick Start
```python
from transformers import pipeline
pipe = pipeline(model='AlexWortega/instruct_rugptSmall')
pipe('''Как собрать питон код?''')
```
or
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("AlexWortega/instruct_rugptSmall")
model = AutoModelForCausalLM.from_pretrained("AlexWortega/instruct_rugptSmall")
```
# License
The weights of Instructions ruGPT Small v0.1a are licensed under version 2.0 of the Apache License.
## Hyperparameters
I used Novograd with a learning rate of 2e-5 and global batch size of 6 (3 for each data parallel worker).
I use both data parallelism and pipeline parallelism to conduct training.
During training, we truncate the input sequence to 1024 tokens, and for input sequence that contains less than 1024 tokens, we concatenate multiple sequences into one long sequence to improve the data efficiency.
# References
#Metrics
SOON
## BibTeX entry and citation info
```bibtex
@article{
title={GPT2xl is underrated task solver},
author={Nickolich Aleksandr, Karina Romanova, Arseniy Shahmatov, Maksim Gersimenko},
year={2023}
}
``` |
timm/pit_ti_224.in1k | timm | 2023-04-26T00:08:25Z | 758 | 0 | timm | [
"timm",
"pytorch",
"safetensors",
"image-classification",
"dataset:imagenet-1k",
"arxiv:2103.16302",
"license:apache-2.0",
"region:us"
] | image-classification | 2023-04-26T00:08:19Z | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
datasets:
- imagenet-1k
---
# Model card for pit_ti_224.in1k
A PiT (Pooling based Vision Transformer) image classification model. Trained on ImageNet-1k by paper authors.
## Model Details
- **Model Type:** Image classification / feature backbone
- **Model Stats:**
- Params (M): 4.8
- GMACs: 0.7
- Activations (M): 6.2
- Image size: 224 x 224
- **Papers:**
- Rethinking Spatial Dimensions of Vision Transformers: https://arxiv.org/abs/2103.16302
- **Dataset:** ImageNet-1k
- **Original:** https://github.com/naver-ai/pit
## Model Usage
### Image Classification
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model('pit_ti_224.in1k', pretrained=True)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
top5_probabilities, top5_class_indices = torch.topk(output.softmax(dim=1) * 100, k=5)
```
### Feature Map Extraction
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'pit_ti_224.in1k',
pretrained=True,
features_only=True,
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # unsqueeze single image into batch of 1
for o in output:
# print shape of each feature map in output
# e.g.:
# torch.Size([1, 64, 27, 27])
# torch.Size([1, 128, 14, 14])
# torch.Size([1, 256, 7, 7])
print(o.shape)
```
### Image Embeddings
```python
from urllib.request import urlopen
from PIL import Image
import timm
img = Image.open(urlopen(
'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/beignets-task-guide.png'
))
model = timm.create_model(
'pit_ti_224.in1k',
pretrained=True,
num_classes=0, # remove classifier nn.Linear
)
model = model.eval()
# get model specific transforms (normalization, resize)
data_config = timm.data.resolve_model_data_config(model)
transforms = timm.data.create_transform(**data_config, is_training=False)
output = model(transforms(img).unsqueeze(0)) # output is (batch_size, num_features) shaped tensor
# or equivalently (without needing to set num_classes=0)
output = model.forward_features(transforms(img).unsqueeze(0))
# output is unpooled, a (1, 1, 256) shaped tensor
output = model.forward_head(output, pre_logits=True)
# output is a (1, num_features) shaped tensor
```
## Model Comparison
Explore the dataset and runtime metrics of this model in timm [model results](https://github.com/huggingface/pytorch-image-models/tree/main/results).
## Citation
```bibtex
@inproceedings{heo2021pit,
title={Rethinking Spatial Dimensions of Vision Transformers},
author={Byeongho Heo and Sangdoo Yun and Dongyoon Han and Sanghyuk Chun and Junsuk Choe and Seong Joon Oh},
booktitle = {International Conference on Computer Vision (ICCV)},
year={2021},
}
```
|
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