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text-generation | transformers | {} | vapegod/stable2 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:42:29+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model47 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:43:33+00:00 |
text-generation | transformers |
# Model Card for free-evo-qwen72b-v0.8
## Developed by : [Freewheelin](https://freewheelin-recruit.oopy.io/) Pulley - AI Technical Team
## Method
- We were inspired by this [Sakana project](https://sakana.ai/evolutionary-model-merge/)
## Base Architecture
- QWEN | {"language": ["ko", "en"], "license": "mit"} | freewheelin/free-evo-qwen72b-v0.8 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"ko",
"en",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:44:15+00:00 |
null | null | {} | dhrumeen/pokemon-lora | null | [
"region:us"
] | null | 2024-05-02T23:45:52+00:00 |
|
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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## How to Get Started with the Model
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## Training Details
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.9.0 | {"library_name": "peft", "base_model": "NousResearch/Llama-2-7b-chat-hf"} | CNBOOMBOOM/r_8_llama2_chat_hivetalk | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | 2024-05-02T23:45:52+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** qbitmaze
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/tinyllama-bnb-4bit"} | qbitmaze/ibraind | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:45:59+00:00 |
text-generation | transformers |
# llama-3-bophades-v3-8B
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
[nbeerbower/llama-3-wissenschaft-8B](https://huggingface.co/nbeerbower/nbeerbower/llama-3-wissenschaft-8B) finetuned on [jondurbin/truthy-dpo-v0.1](https://huggingface.co/datasets/jondurbin/truthy-dpo-v0.1) and [kyujinpy/orca_math_dpo](https://huggingface.co/datasets/kyujinpy/orca_math_dpo).
### 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 preperation and message formatting:
```python
def chatml_format(example):
# Initialize formatted system message
system = ""
# Check if 'system' field exists and is not None
if example.get('system'):
system = "<|im_start|>system\n" + example['system'] + "<|im_end|>\n"
# Format instruction
instruction = ""
if example.get('prompt'):
instruction = example['prompt']
if example.get('question'):
instruction = example['question']
prompt = "<|im_start|>user\n" + instruction + "<|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",
"kyujinpy/orca_math_dpo"
]
# 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=2,
gradient_accumulation_steps=4,
gradient_checkpointing=True,
learning_rate=5e-5,
lr_scheduler_type="cosine",
max_steps=1000,
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,
max_prompt_length=2048,
max_length=4096,
force_use_ref_model=True
)
# Fine-tune model with DPO
dpo_trainer.train()
``` | {"license": "other", "library_name": "transformers", "datasets": ["jondurbin/truthy-dpo-v0.1", "kyujinpy/orca_math_dpo"], "base_model": ["nbeerbower/llama-3-wissenschaft-8B"], "license_name": "llama3"} | nbeerbower/llama-3-bophades-v3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"dataset:jondurbin/truthy-dpo-v0.1",
"dataset:kyujinpy/orca_math_dpo",
"base_model:nbeerbower/llama-3-wissenschaft-8B",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:47:28+00:00 |
text-generation | transformers |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | dwb2023/gen-z-translate-llama-3-instruct-v1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:47:31+00:00 |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed]
### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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| {"library_name": "transformers", "tags": []} | shallow6414/ddqndrq | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:48:03+00:00 |
null | null | {} | Mrteymoorian/path-to-save-model | null | [
"region:us"
] | null | 2024-05-02T23:48:05+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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#### Testing Data
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-v2-3e-05_AdamW_1876 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:49:39+00:00 |
token-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# output_deberta_orca_v3_on_new_dataset_v2_base_eval_each_step_lr_1e_5_10_epochs
This model is a fine-tuned version of [truongpdd/deberta_v3_base_orca_math_word_problems_6_epochs](https://huggingface.co/truongpdd/deberta_v3_base_orca_math_word_problems_6_epochs) on the truongpdd/new_dataset_v2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0129
- Precision: 0.8840
- Recall: 0.8840
- F1: 0.8840
- Accuracy: 0.9951
## 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: 10.0
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:------:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0176 | 1.0 | 19381 | 0.0187 | 0.7782 | 0.7782 | 0.7782 | 0.9906 |
| 0.0159 | 2.0 | 38762 | 0.0155 | 0.8310 | 0.8310 | 0.8310 | 0.9929 |
| 0.0148 | 3.0 | 58143 | 0.0152 | 0.8418 | 0.8419 | 0.8419 | 0.9933 |
| 0.0134 | 4.0 | 77524 | 0.0140 | 0.8514 | 0.8514 | 0.8514 | 0.9937 |
| 0.0123 | 5.0 | 96905 | 0.0137 | 0.8565 | 0.8565 | 0.8565 | 0.9939 |
| 0.0107 | 6.0 | 116286 | 0.0132 | 0.8700 | 0.8700 | 0.8700 | 0.9945 |
| 0.0106 | 7.0 | 135667 | 0.0130 | 0.8765 | 0.8765 | 0.8765 | 0.9948 |
| 0.0095 | 8.0 | 155048 | 0.0127 | 0.8817 | 0.8817 | 0.8817 | 0.9950 |
| 0.0092 | 9.0 | 174429 | 0.0126 | 0.8830 | 0.8831 | 0.8831 | 0.9951 |
| 0.0084 | 10.0 | 193810 | 0.0129 | 0.8840 | 0.8840 | 0.8840 | 0.9951 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "datasets": ["truongpdd/new_dataset_v2"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "truongpdd/deberta_v3_base_orca_math_word_problems_6_epochs", "model-index": [{"name": "output_deberta_orca_v3_on_new_dataset_v2_base_eval_each_step_lr_1e_5_10_epochs", "results": [{"task": {"type": "token-classification", "name": "Token Classification"}, "dataset": {"name": "truongpdd/new_dataset_v2", "type": "truongpdd/new_dataset_v2"}, "metrics": [{"type": "precision", "value": 0.8839960440229244, "name": "Precision"}, {"type": "recall", "value": 0.8839960440229244, "name": "Recall"}, {"type": "f1", "value": 0.8839960440229244, "name": "F1"}, {"type": "accuracy", "value": 0.9951002995302736, "name": "Accuracy"}]}]}]} | truongpdd/output_deberta_orca_v3_on_new_dataset_v2_base_eval_each_step_lr_1e_5_10_epochs | null | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"generated_from_trainer",
"dataset:truongpdd/new_dataset_v2",
"base_model:truongpdd/deberta_v3_base_orca_math_word_problems_6_epochs",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:49:53+00:00 |
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lebretou/code-human-ai | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:51:45+00:00 |
null | null |
# HermesAqua-7B
HermesAqua-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
* [mlabonne/OrpoLlama-3-8B](https://huggingface.co/mlabonne/OrpoLlama-3-8B)
## 🧩 Configuration
```yaml
models:
- model: NousResearch/Meta-Llama-3-8B
# No parameters necessary for base model
- model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
density: 0.6
weight: 0.5
- model: mlabonne/OrpoLlama-3-8B
parameters:
density: 0.55
weight: 0.05
merge_method: dare_ties
base_model: NousResearch/Meta-Llama-3-8B
parameters:
int8_mask: true
dtype: float16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/HermesAqua-7B"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct", "mlabonne/OrpoLlama-3-8B"]} | automerger/HermesAqua-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"base_model:mlabonne/OrpoLlama-3-8B",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T23:53:05+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** qbitmaze
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "unsloth/tinyllama-bnb-4bit"} | qbitmaze/ibrain_model | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:53:33+00:00 |
null | null | {} | k4d3/cascade | null | [
"region:us"
] | null | 2024-05-02T23:55:19+00:00 |
|
null | null | {} | k4d3/cascade-prior | null | [
"region:us"
] | null | 2024-05-02T23:55:32+00:00 |
|
translation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# m2m_egyArabic_to_english
This model is a fine-tuned version of [facebook/m2m100_418M](https://huggingface.co/facebook/m2m100_418M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.8747
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.6965 | 0.16 | 100 | 2.8276 |
| 1.8822 | 0.32 | 200 | 2.8354 |
| 1.9255 | 0.48 | 300 | 2.8868 |
| 1.9824 | 0.64 | 400 | 2.5958 |
| 1.8749 | 0.8 | 500 | 2.5445 |
| 1.8816 | 0.96 | 600 | 2.5431 |
| 1.5409 | 1.12 | 700 | 2.3189 |
| 1.2783 | 1.28 | 800 | 2.2877 |
| 1.2672 | 1.43 | 900 | 2.2608 |
| 1.2834 | 1.59 | 1000 | 2.1690 |
| 1.2396 | 1.75 | 1100 | 2.1359 |
| 1.2143 | 1.91 | 1200 | 2.0771 |
| 1.0131 | 2.07 | 1300 | 2.0340 |
| 0.656 | 2.23 | 1400 | 2.0304 |
| 0.6463 | 2.39 | 1500 | 1.9823 |
| 0.6116 | 2.55 | 1600 | 1.9585 |
| 0.6336 | 2.71 | 1700 | 1.9306 |
| 0.6128 | 2.87 | 1800 | 1.8854 |
| 0.52 | 3.03 | 1900 | 1.8826 |
| 0.2979 | 3.19 | 2000 | 1.8785 |
| 0.271 | 3.35 | 2100 | 1.8895 |
| 0.2819 | 3.51 | 2200 | 1.8815 |
| 0.2694 | 3.67 | 2300 | 1.8782 |
| 0.2634 | 3.83 | 2400 | 1.8754 |
| 0.1998 | 3.99 | 2500 | 1.8747 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2 | {"language": ["ar", "en"], "license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/m2m100_418M", "pipeline_tag": "translation", "model-index": [{"name": "m2m_egyArabic_to_english", "results": []}]} | Amr-khaled/m2m_egyArabic_to_english | null | [
"transformers",
"safetensors",
"m2m_100",
"text2text-generation",
"generated_from_trainer",
"translation",
"ar",
"en",
"base_model:facebook/m2m100_418M",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:55:36+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_v23e-05_AdamW | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:56:23+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | golf2248/iqax1e0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:56:58+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | cilantro9246/mocyy2l | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:58:00+00:00 |
null | null | {} | ttc0000/mistral_HFTrainer_instruct02_Sample1_lora_r64_a128 | null | [
"safetensors",
"region:us"
] | null | 2024-05-02T23:59:27+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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| {"library_name": "transformers", "tags": []} | shallow6414/6yskuag | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:59:39+00:00 |
text-generation | transformers | {} | kiwikiw/llama1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T23:59:40+00:00 |
|
text-classification | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
You can use this classifier on Google Ads to determine which of the 5 types of Ad promotions a Google Ad pertains to.
## Label Mappings
- LABEL_0: product promotion
- LABEL_1: service promotion
- LABEL_2: sales promotion
- LABEL_3: lead generation
- LABEL_4: brand awareness
## Model Description
This classifier is a trained checkpoint of Bert, specifically bert-base-uncased, trained on 10,000 Google Ad inputs. These ads were scraped, cleaned, and labeled using OpenAi 3.5 turbo. | {"library_name": "transformers", "pipeline_tag": "text-classification", "widget": [{"text": "Shop Popular Prescription Food & Treats from Trusted Brands at Chewy.", "example_title": "Product Promotion Example"}, {"text": "Protect your pet with wellness & insurance plans at Chewy. Rx coverage, no payout limits!", "example_title": "Service Promotion Example"}, {"text": "Food your pet will love or we'll make it right with our 100% satisfaction guarantee Fast delivery of pet food so you can spend more time playing & less time shopping. Low Prices. Free 365-Day Returns. 24/7 Customer Service.", "example_title": "Sales Promotion Example"}, {"text": "Join Our Treats\u2122 Rewards Program to Start Earning Points & Receive Exclusive Discounts. Autoship ...", "example_title": "Lead Generation Example"}, {"text": "Be prepared for the unexpected with CarePlus.", "example_title": "Brand Awareness Example"}, {"text": "New Customers Are Welcome at PetSmart\u00ae for Exclusive Deals on the Most Trusted Brands. Get all Your Pet Essentials With Free Same-Day Delivery or Curbside Pickup at PetSmart! Weekend Adoption Events. Pet Training.", "example_title": "Variety Example"}]} | tejasc/AdTypeClassifier | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T23:59:51+00:00 |
null | null | {} | BeaverAI/Llama-3SOME-8B-v2-rc1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-03T00:00:24+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** xkiwilabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | xkiwilabs/lora_opLLama3_modelv8 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:01:35+00:00 |
text-generation | transformers |
# Model Card for Model ID
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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| {"library_name": "transformers", "tags": []} | golf2248/tynk33y | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:01:58+00:00 |
token-classification | transformers | {} | truongpdd/output_deberta_large_v3_on_new_dataset_v2_base_eval_each_step_lr_1e_5_10_epochs | null | [
"transformers",
"safetensors",
"deberta-v2",
"token-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:02:34+00:00 |
|
reinforcement-learning | transformers |
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a simulated human feedback. The model was fine-tuned for classification of cancer / diabetes based on clinical notes.
```bash
pip install torch transformers trl peft
```
## Usage
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
from peft import LoraConfig
tokenizer_kwargs = {
"padding": "max_length",
"truncation": True,
"return_tensors": "pt",
"padding_side": "left"
}
tokenizer = AutoTokenizer.from_pretrained("hanyinwang/layer-project-diagnostic-mistral", **tokenizer_kwargs)
tokenizer.pad_token = tokenizer.eos_token
generation_kwargs = {
"min_length": -1,
"top_k": 40,
"top_p": 0.95,
"do_sample": True,
"pad_token_id": tokenizer.eos_token_id,
"max_new_tokens":11,
"temperature":0.1,
"repetition_penalty":1.2
}
model = AutoModelForCausalLMWithValueHead.from_pretrained("hanyinwang/layer-project-diagnostic-mistral").cuda()
def format_prompt_mistral(text, condition):
prompt = """<s>[INST]You are a medical doctor specialized in %s diagnosis.
From the provided document, assert if the patient historically and currently has %s.
For each condition, only pick from "YES", "NO", or "MAYBE". And you must follow format without anything further. The results have to be directly parseable with python json.loads().
Sample output: {"%s": "MAYBE"}
Never output anything beyond the format.[/INST]
Provided document: %s"""%(condition, condition, condition, text)
return prompt
query_tensors = tokenizer.encode(format_prompt_mistral(<note>, <condition>), return_tensors="pt")
# <note>: clinical note
# <condition>: "cancer" or "diabetes"
prompt_length = query_tensors.shape[1]
outputs = model.generate(query_tensors.cuda(), **generation_kwargs)
response = tokenizer.decode(outputs[0][prompt_length:])
``` | {"language": ["en"], "license": "apache-2.0", "tags": ["trl", "ppo", "transformers", "reinforcement-learning"]} | hanyinwang/layer-project-diagnostic-mistral | null | [
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:02:36+00:00 |
text-classification | null | # LUNA-ProACT - PROtective Analysis for Cyber Threats
LUNA-ProACT is a context-aware machine learning model designed to protect users from online threats, primarily focusing on cybergrooming. It is created to work in association with LUNA, an AI-powered cybergrooming prevention application.
LUNA-ProACT uses natural language processing (NLP) to analyze language use in real-time and detects unusual patterns that could indicate potential online threats. It operates locally on iOS and Android mobile devices using TensorFlow Lite, providing real-time protection without requiring network traffic.
This model results from a collective effort during the TRUE CRIME HACKATHON 2023. Special thanks go to the Polizei Niedersachsen for inspiring the LUNA Cybergrooming Prevention App idea.
## Key Features
- **Real-Time Analysis:** LUNA-ProACT scans text input on the fly, examining for patterns and language use typically associated with cyber threats.
- **Context Awareness:** Recognizes cultural, geographical, and age-specific contexts for accurate and relevant threat detection.
- **Local Processing:** Employs TensorFlow Lite to analyze data directly on the user's device, ensuring maximum privacy.
## Dependencies
- Python 3.7+
- TensorFlow 2.5.0
- HuggingFace Transformers
## Usage
1. **Installation:** Import the LUNA-ProACT model into your Python project.
```python
from transformers import AutoTokenizer, TFAutoModel
tokenizer = AutoTokenizer.from_pretrained("LUNA-ProACT")
model = TFAutoModel.from_pretrained("LUNA-ProACT")
```
2. **Inference:** Use the model for threat detection.
```python
input_text = "Your text here..."
inputs = tokenizer.encode(input_text, return_tensors='tf')
outputs = model(inputs)[0]
# Outputs return the likelihood of the text being a potential cyber threat.
threat_probability = tf.sigmoid(outputs)
if threat_probability > 0.5:
print("Potential cyber threat detected!")
```
## About Us
LUNA-ProACT has been released by **Phichayut 'Florentin' Sakwiset** from WE-MAKE.IO, with contributions from **Leon Lukaszewski**, **Lisa Adolf** & **Klemens Karboswki**.
## License
LUNA-ProACT is the property of the Polizei Niedersachsen. It is licensed under the GNU Affero General Public License (AGPL) Version 3 and is available for non-commercial use as an open-source project.
Please see the [LICENSE (TBA)](LICENSE) file for more details.
## Contribution
We welcome contributions to help improve LUNA-ProACT. If you have any issues or feature requests, please open a new issue or pull request.
Remember to follow our [contribution guidelines](https://huggingface.co/WE-MAKE-IO/LUNA-ProACT/blob/main/CONTRIBUTING.md) to ensure a smooth collaboration process. Thank you for your interest in contributing to LUNA-ProACT.
## Contact Us
For any further information or inquiries, please reach us at [[email protected]](mailto:[email protected]).
[](mailto:[email protected])
---
### 💙 Thanks to the Polizei Niedersachsen
*We appreciate the platform provided by Polizei Niedersachsen through the TRUE CRIME HACKATHON 2023, which played a crucial role in the inception and development of LUNA-ProACT.
Let's work together to make our digital world safer.* | {"language": ["de"], "pipeline_tag": "text-classification"} | WEMAKE-CX/LUNA-ProACT | null | [
"text-classification",
"de",
"region:us"
] | null | 2024-05-03T00:03:41+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | shallow6414/91iajdi | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:04:31+00:00 |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model49 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:05:15+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-v2-1e-05_Adam_938 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:06:43+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10_Hyperparameter_ES
This model is a fine-tuned version of [unsloth/mistral-7b-instruct-v0.2-bnb-4bit](https://huggingface.co/unsloth/mistral-7b-instruct-v0.2-bnb-4bit) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5674
- Accuracy: 0.438
- Chrf: 0.845
- Bleu: 0.73
- Sacrebleu: 0.7
- Rouge1: 0.769
- Rouge2: 0.65
- Rougel: 0.752
- Rougelsum: 0.769
- Meteor: 0.711
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.1795554702080496e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 3407
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 5
- num_epochs: 10
- early stopping 0.01 instead of the previous 0.1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Chrf | Bleu | Sacrebleu | Rouge1 | Rouge2 | Rougel | Rougelsum | Meteor |
|:-------------:|:------:|:----:|:---------------:|:--------:|:-----:|:-----:|:---------:|:------:|:------:|:------:|:---------:|:------:|
| 0.9838 | 0.9890 | 45 | 0.9139 | 0.439 | 0.798 | 0.676 | 0.7 | 0.666 | 0.499 | 0.622 | 0.664 | 0.577 |
| 0.8502 | 2.0 | 91 | 0.7414 | 0.439 | 0.814 | 0.688 | 0.7 | 0.708 | 0.569 | 0.679 | 0.707 | 0.638 |
| 0.8674 | 2.9890 | 136 | 0.6565 | 0.439 | 0.832 | 0.744 | 0.7 | 0.739 | 0.612 | 0.715 | 0.738 | 0.678 |
| 0.9702 | 4.0 | 182 | 0.6025 | 0.438 | 0.837 | 0.744 | 0.7 | 0.758 | 0.633 | 0.739 | 0.757 | 0.692 |
| 0.7919 | 4.9890 | 227 | 0.5674 | 0.438 | 0.845 | 0.73 | 0.7 | 0.769 | 0.65 | 0.752 | 0.769 | 0.711 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.16.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "unsloth", "generated_from_trainer"], "metrics": ["accuracy", "bleu", "sacrebleu", "rouge"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit", "model-index": [{"name": "mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10_Hyperparameter_ES", "results": []}]} | vdavidr/mistral-7b-instruct-v0.2-bnb-4bit_Finetuned_usloth_dataset_size_364_epochs_10_Hyperparameter_ES | null | [
"peft",
"safetensors",
"trl",
"sft",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"region:us"
] | null | 2024-05-03T00:07:14+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** KillerShoaib
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | KillerShoaib/llama3-8b-4bit-bangla | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:08:17+00:00 |
null | null | {"license": "creativeml-openrail-m"} | casque/0286_blue_bodysuit_v1 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-05-03T00:09:12+00:00 |
|
image-classification | transformers | {} | pablo-marinozi/vit-base-oxford-iiit-pets | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:10:07+00:00 |
|
text-generation | transformers |
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | shallow6414/o28myji | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:13:40+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0001_withdpo_3iters_bs256_531lr_iter_2
This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_3iters_bs256_531lr_iter_1](https://huggingface.co/ShenaoZ/0.0001_withdpo_3iters_bs256_531lr_iter_1) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_3iters_bs256_531lr_iter_1", "model-index": [{"name": "0.0001_withdpo_3iters_bs256_531lr_iter_2", "results": []}]} | ShenaoZ/0.0001_withdpo_3iters_bs256_531lr_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.0001_withdpo_3iters_bs256_531lr_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:16:20+00:00 |
text-generation | null |
# MoMonir/Llama-3-8B-Instruct-Gradient-1048k-GGUF
This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) 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/gradientai/Llama-3-8B-Instruct-Gradient-1048k) for more details on the model.
<!-- README_GGUF.md-about-gguf start -->
### About GGUF ([TheBloke](https://huggingface.co/TheBloke) Description)
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 -->
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo MoMonir/Llama-3-8B-Instruct-Gradient-1048k-GGUF --model llama-3-8b-instruct-gradient-1048k.Q5_K_M.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo MoMonir/Llama-3-8B-Instruct-Gradient-1048k-GGUF --model llama-3-8b-instruct-gradient-1048k.Q5_K_M.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-gradient-1048k.Q5_K_M.gguf -n 128
```
| {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"} | MoMonir/Llama-3-8B-Instruct-Gradient-1048k-GGUF | null | [
"gguf",
"meta",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"region:us"
] | null | 2024-05-03T00:16:21+00:00 |
null | null | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B
| {} | mradermacher/Llama3-ChatQA-1.5-8B-i1-GGUF | null | [
"gguf",
"region:us"
] | null | 2024-05-03T00:18:08+00:00 |
null | null | {} | amiradmehr/path-to-save-model | null | [
"region:us"
] | null | 2024-05-03T00:19:29+00:00 |
|
null | transformers |
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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<!-- 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.
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#### Speeds, Sizes, Times [optional]
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<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-v2-1e-05_Adam_1876 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:19:43+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- 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
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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#### Preprocessing [optional]
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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| {"library_name": "transformers", "tags": []} | golf2248/h5p0025 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:20:58+00:00 |
text-generation | transformers | # Locutusque/llama-3-neural-chat-v2.2-8B AWQ
- Model creator: [Locutusque](https://huggingface.co/Locutusque)
- Original model: [llama-3-neural-chat-v2.2-8B](https://huggingface.co/Locutusque/llama-3-neural-chat-v2.2-8B)
## Model Details
I fine-tuned llama-3 8B on an approach similar to Intel's neural chat language model. I have slightly modified the data sources so it is stronger in coding, math, and writing. I use both SFT and DPO-Positive.
DPO-Positive dramatically improves performance over DPO.
- **Developed by:** Locutusque
- **Model type:** Built with Meta Llama 3
- **Language(s) (NLP):** Many?
- **License:** Llama 3 license https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
AWQ models are currently supported on Linux and Windows, with NVidia GPUs only. macOS users: please use GGUF models instead.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - version 0.2.2 or later for support for all model types.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible"], "inference": false, "pipeline_tag": "text-generation", "quantized_by": "Suparious"} | solidrust/llama-3-neural-chat-v2.2-8B-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"en",
"license:apache-2.0",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:21:21+00:00 |
text-generation | transformers | <img src="./veteus_logo.svg" width="100%" height="20%" alt="">
# Our Models
- [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1)
- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1)
- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)
- [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k)
- [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k)
## This is a prototype of Vecteus-v1
## Model Card for VecTeus-Poet
The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1
VecTeus has the following changes compared to Mistral-7B-v0.1.
- Achieving both high quality Japanese and English generation
- Can be generated NSFW
- Memory ability that does not forget even after long-context generation
This model was created with the help of GPUs from the first LocalAI hackathon.
We would like to take this opportunity to thank
## List of Creation Methods
- Chatvector for multiple models
- Simple linear merging of result models
- Domain and Sentence Enhancement with LORA
- Context expansion
## Instruction format
Freed from templates. Congratulations
## Example prompts to improve (Japanese)
- BAD: あなたは○○として振る舞います
- GOOD: あなたは○○です
- BAD: あなたは○○ができます
- GOOD: あなたは○○をします
## Performing inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Local-Novel-LLM-project/Vecteus-Poet"
new_tokens = 1024
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- "
prompt = input("Enter a prompt: ")
system_prompt += prompt + "\n-------- "
model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)
print(tokenizer.batch_decode(generated_ids)[0])
````
## Other points to keep in mind
- The training data may be biased. Be careful with the generated sentences.
- Memory usage may be large for long inferences.
- If possible, we recommend inferring with llamacpp rather than Transformers. | {"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"} | Local-Novel-LLM-project/Vecteus-Poet | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"custom_code",
"en",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:23:37+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hfhfix -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/nvidia/Llama3-ChatQA-1.5-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-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/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama3-ChatQA-1.5-70B-GGUF/resolve/main/Llama3-ChatQA-1.5-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "llama3", "library_name": "transformers", "tags": ["nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "base_model": "nvidia/Llama3-ChatQA-1.5-70B", "quantized_by": "mradermacher"} | mradermacher/Llama3-ChatQA-1.5-70B-GGUF | null | [
"transformers",
"gguf",
"nvidia",
"chatqa-1.5",
"chatqa",
"llama-3",
"pytorch",
"en",
"base_model:nvidia/Llama3-ChatQA-1.5-70B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:23:38+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** Chord-Llama
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Chord-Llama/Llama-3-chord-llama-chechpoint-6 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:23:59+00:00 |
text-generation | transformers | <img src="./veteus_logo.svg" width="100%" height="20%" alt="">
# Our Models
- [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1)
- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1)
- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)
- [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k)
- [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k)
## This is a prototype of Vecteus-v1
## Model Card for VecTeus-Constant
The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1
VecTeus has the following changes compared to Mistral-7B-v0.1.
- Achieving both high quality Japanese and English generation
- Can be generated NSFW
- Memory ability that does not forget even after long-context generation
This model was created with the help of GPUs from the first LocalAI hackathon.
We would like to take this opportunity to thank
## List of Creation Methods
- Chatvector for multiple models
- Simple linear merging of result models
- Domain and Sentence Enhancement with LORA
- Context expansion
## Instruction format
Freed from templates. Congratulations
## Example prompts to improve (Japanese)
- BAD: あなたは○○として振る舞います
- GOOD: あなたは○○です
- BAD: あなたは○○ができます
- GOOD: あなたは○○をします
## Performing inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Local-Novel-LLM-project/Vecteus-Constant"
new_tokens = 1024
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- "
prompt = input("Enter a prompt: ")
system_prompt += prompt + "\n-------- "
model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)
print(tokenizer.batch_decode(generated_ids)[0])
````
## Other points to keep in mind
- The training data may be biased. Be careful with the generated sentences.
- Memory usage may be large for long inferences.
- If possible, we recommend inferring with llamacpp rather than Transformers. | {"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"} | Local-Novel-LLM-project/Vecteus-Constant | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"en",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:25:59+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-final_v21e-05_Adam | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:26:21+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | golf2248/cgllp75 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:26:35+00:00 |
text-generation | transformers | <img src="./veteus_logo.svg" width="100%" height="20%" alt="">
# Our Models
- [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1)
- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1)
- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW)
- [Ninja-v1-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k)
- [Ninja-v1-NSFW-128k](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k)
## This is a prototype of Vecteus-v1
## Model Card for VecTeus-Forte
The Mistral-7B--based Large Language Model (LLM) is an noveldataset fine-tuned version of the Mistral-7B-v0.1
VecTeus has the following changes compared to Mistral-7B-v0.1.
- Achieving both high quality Japanese and English generation
- Can be generated NSFW
- Memory ability that does not forget even after long-context generation
This model was created with the help of GPUs from the first LocalAI hackathon.
We would like to take this opportunity to thank
## List of Creation Methods
- Chatvector for multiple models
- Simple linear merging of result models
- Domain and Sentence Enhancement with LORA
- Context expansion
## Instruction format
Freed from templates. Congratulations
## Example prompts to improve (Japanese)
- BAD: あなたは○○として振る舞います
- GOOD: あなたは○○です
- BAD: あなたは○○ができます
- GOOD: あなたは○○をします
## Performing inference
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_id = "Local-Novel-LLM-project/Vecteus-Forte"
new_tokens = 1024
model = AutoModelForCausalLM.from_pretrained(model_id, trust_remote_code=True, torch_dtype=torch.float16, attn_implementation="flash_attention_2", device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_id)
system_prompt = "あなたはプロの小説家です。\n小説を書いてください\n-------- "
prompt = input("Enter a prompt: ")
system_prompt += prompt + "\n-------- "
model_inputs = tokenizer([prompt], return_tensors="pt").to("cuda")
generated_ids = model.generate(**model_inputs, max_new_tokens=new_tokens, do_sample=True)
print(tokenizer.batch_decode(generated_ids)[0])
````
## Other points to keep in mind
- The training data may be biased. Be careful with the generated sentences.
- Memory usage may be large for long inferences.
- If possible, we recommend inferring with llamacpp rather than Transformers. | {"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"} | Local-Novel-LLM-project/Vecteus-Forte | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetuned",
"en",
"ja",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:26:49+00:00 |
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-3b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-3b-AWQ | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:27:38+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | shallow6414/osgxyhw | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:27:43+00:00 |
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-3b-s2 was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-3b-s2-AWQ | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:27:54+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-002e7-1x008-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:29:16+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | cilantro9246/9edjm55 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:29:16+00:00 |
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
Llama-3-VILA1.5-8b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/Llama-3-VILA1.5-8b-AWQ | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:29:22+00:00 |
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-13b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-13b-AWQ | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:29:41+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLlama-1.1B-intermediate-step-1431k-3T - bnb 4bits
- Model creator: https://huggingface.co/TinyLlama/
- Original model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T/
Original model description:
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| TinyLlama-1.1B-intermediate-step-1195k-2.5T | 2.5T | 58.96 | 34.40 | 58.72 | 31.91 | 56.78 | 63.21 | 73.07 | 53.86|
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99|
| {} | RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T00:30:11+00:00 |
text-generation | transformers |
# VILA Model Card
## Model details
**Model type:**
VILA is a visual language model (VLM) pretrained with interleaved image-text data at scale, enabling multi-image VLM. VILA is deployable on the edge, including Jetson Orin and laptop by AWQ 4bit quantization through TinyChat framework. We find: (1) image-text pairs are not enough, interleaved image-text is essential; (2) unfreezing LLM during interleaved image-text pre-training enables in-context learning; (3)re-blending text-only instruction data is crucial to boost both VLM and text-only performance. VILA unveils appealing capabilities, including: multi-image reasoning, in-context learning, visual chain-of-thought, and better world knowledge.
**Model date:**
VILA1.5-40b was trained in May 2024.
**Paper or resources for more information:**
https://github.com/Efficient-Large-Model/VILA
```
@misc{lin2023vila,
title={VILA: On Pre-training for Visual Language Models},
author={Ji Lin and Hongxu Yin and Wei Ping and Yao Lu and Pavlo Molchanov and Andrew Tao and Huizi Mao and Jan Kautz and Mohammad Shoeybi and Song Han},
year={2023},
eprint={2312.07533},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
## License
- The code is released under the Apache 2.0 license as found in the [LICENSE](./LICENSE) file.
- The pretrained weights are released under the [CC-BY-NC-SA-4.0 license](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.en).
- The service is a research preview intended for non-commercial use only, and is subject to the following licenses and terms:
- [Model License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA
- [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI
- [Dataset Licenses](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/LICENSE) for each one used during training.
**Where to send questions or comments about the model:**
https://github.com/Efficient-Large-Model/VILA/issues
## Intended use
**Primary intended uses:**
The primary use of VILA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
See [Dataset Preparation](https://github.com/Efficient-Large-Model/VILA/blob/main/data_prepare/README.md) for more details.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. | {"license": "cc-by-nc-4.0", "library_name": "transformers", "tags": ["VILA", "VLM"], "pipeline_tag": "text-generation"} | Efficient-Large-Model/VILA1.5-40b-AWQ | null | [
"transformers",
"safetensors",
"llava_llama",
"VILA",
"VLM",
"text-generation",
"arxiv:2312.07533",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:30:40+00:00 |
text-generation | transformers | ## Hypernova-experimental
Quantized to 4bit 128g using AutoGPTQ and 🤗 Optimum
Tried some new stuff this time around. Very different outcome than I expected.
This is an experimental model that was created for the development of NovaAI.
Good at chatting and some RP. Sometimes gets characters mixed up. Can occasionally struggle with context.
## Prompt Template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
### Models Merged
The following models were included in the merge:
* [Undi95/Emerald-13B](https://huggingface.co/Undi95/Emerald-13B)
* [Gryphe/MythoMax-L2-13b](https://huggingface.co/Gryphe/MythoMax-L2-13b)
Some finetuning done as well | {"language": ["en"], "license": "cc-by-sa-4.0", "library_name": "transformers", "base_model": ["Undi95/Emerald-13B", "Gryphe/MythoMax-L2-13b"], "inference": false} | theNovaAI/Hypernova-experimental-GPTQ | null | [
"transformers",
"llama",
"text-generation",
"en",
"base_model:Undi95/Emerald-13B",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T00:30:59+00:00 |
text-generation | transformers | {} | TwinDoc/H100_stage1_checkpoint-41760 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:31:10+00:00 |
|
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-004e7-1x008-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:31:14+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Environmental Impact
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| {"library_name": "transformers", "tags": []} | shallow6414/n7own33 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:31:20+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLlama-1.1B-intermediate-step-1431k-3T - bnb 8bits
- Model creator: https://huggingface.co/TinyLlama/
- Original model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T/
Original model description:
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| TinyLlama-1.1B-intermediate-step-1195k-2.5T | 2.5T | 58.96 | 34.40 | 58.72 | 31.91 | 56.78 | 63.21 | 73.07 | 53.86|
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99|
| {} | RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T00:31:46+00:00 |
null | null | {"license": "mit"} | hautc/z6 | null | [
"license:mit",
"region:us"
] | null | 2024-05-03T00:32:49+00:00 |
|
null | null | {"license": "mit"} | hautc/w2 | null | [
"license:mit",
"region:us"
] | null | 2024-05-03T00:33:10+00:00 |
|
null | null | {"license": "mit"} | hautc/y1 | null | [
"license:mit",
"region:us"
] | null | 2024-05-03T00:33:21+00:00 |
|
null | null | {"license": "mit"} | hautc/w3 | null | [
"license:mit",
"region:us"
] | null | 2024-05-03T00:33:35+00:00 |
|
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLlama-1.1B-intermediate-step-1431k-3T - GGUF
- Model creator: https://huggingface.co/TinyLlama/
- Original model: https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q2_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q2_K.gguf) | Q2_K | 0.4GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.IQ3_XS.gguf) | IQ3_XS | 0.44GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.IQ3_S.gguf) | IQ3_S | 0.47GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K_S.gguf) | Q3_K_S | 0.47GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.IQ3_M.gguf) | IQ3_M | 0.48GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K.gguf) | Q3_K | 0.51GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K_M.gguf) | Q3_K_M | 0.51GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q3_K_L.gguf) | Q3_K_L | 0.55GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.IQ4_XS.gguf) | IQ4_XS | 0.57GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_0.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_0.gguf) | Q4_0 | 0.59GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.IQ4_NL.gguf) | IQ4_NL | 0.6GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_K_S.gguf) | Q4_K_S | 0.6GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_K.gguf) | Q4_K | 0.62GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_K_M.gguf) | Q4_K_M | 0.62GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_1.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q4_1.gguf) | Q4_1 | 0.65GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_0.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_0.gguf) | Q5_0 | 0.71GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_K_S.gguf) | Q5_K_S | 0.71GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_K.gguf) | Q5_K | 0.73GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_K_M.gguf) | Q5_K_M | 0.73GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_1.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q5_1.gguf) | Q5_1 | 0.77GB |
| [TinyLlama-1.1B-intermediate-step-1431k-3T.Q6_K.gguf](https://huggingface.co/RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf/blob/main/TinyLlama-1.1B-intermediate-step-1431k-3T.Q6_K.gguf) | Q6_K | 0.84GB |
Original model description:
---
license: apache-2.0
datasets:
- cerebras/SlimPajama-627B
- bigcode/starcoderdata
language:
- en
---
<div align="center">
# TinyLlama-1.1B
</div>
https://github.com/jzhang38/TinyLlama
The TinyLlama project aims to **pretrain** a **1.1B Llama model on 3 trillion tokens**. With some proper optimization, we can achieve this within a span of "just" 90 days using 16 A100-40G GPUs 🚀🚀. The training has started on 2023-09-01.
<div align="center">
<img src="./TinyLlama_logo.png" width="300"/>
</div>
We adopted exactly the same architecture and tokenizer as Llama 2. This means TinyLlama can be plugged and played in many open-source projects built upon Llama. Besides, TinyLlama is compact with only 1.1B parameters. This compactness allows it to cater to a multitude of applications demanding a restricted computation and memory footprint.
#### This Collection
This collection contains all checkpoints after the 1T fix. Branch name indicates the step and number of tokens seen.
#### Eval
| Model | Pretrain Tokens | HellaSwag | Obqa | WinoGrande | ARC_c | ARC_e | boolq | piqa | avg |
|-------------------------------------------|-----------------|-----------|------|------------|-------|-------|-------|------|-----|
| Pythia-1.0B | 300B | 47.16 | 31.40| 53.43 | 27.05 | 48.99 | 60.83 | 69.21 | 48.30 |
| TinyLlama-1.1B-intermediate-step-50K-104b | 103B | 43.50 | 29.80| 53.28 | 24.32 | 44.91 | 59.66 | 67.30 | 46.11|
| TinyLlama-1.1B-intermediate-step-240k-503b| 503B | 49.56 |31.40 |55.80 |26.54 |48.32 |56.91 |69.42 | 48.28 |
| TinyLlama-1.1B-intermediate-step-480k-1007B | 1007B | 52.54 | 33.40 | 55.96 | 27.82 | 52.36 | 59.54 | 69.91 | 50.22 |
| TinyLlama-1.1B-intermediate-step-715k-1.5T | 1.5T | 53.68 | 35.20 | 58.33 | 29.18 | 51.89 | 59.08 | 71.65 | 51.29 |
| TinyLlama-1.1B-intermediate-step-955k-2T | 2T | 54.63 | 33.40 | 56.83 | 28.07 | 54.67 | 63.21 | 70.67 | 51.64 |
| TinyLlama-1.1B-intermediate-step-1195k-2.5T | 2.5T | 58.96 | 34.40 | 58.72 | 31.91 | 56.78 | 63.21 | 73.07 | 53.86|
| TinyLlama-1.1B-intermediate-step-1431k-3T | 3T | 59.20 | 36.00 | 59.12 | 30.12 | 55.25 | 57.83 | 73.29 | 52.99|
| {} | RichardErkhov/TinyLlama_-_TinyLlama-1.1B-intermediate-step-1431k-3T-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-03T00:33:57+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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## Uses
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### Downstream Use [optional]
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- 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. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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## Citation [optional]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | rickfalcao/RF-Mistral-historias-V0.8 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:34:49+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0001_withdpo_3iters_bs256_511lr_iter_2
This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_3iters_bs256_511lr_iter_1](https://huggingface.co/ShenaoZ/0.0001_withdpo_3iters_bs256_511lr_iter_1) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_3iters_bs256_511lr_iter_1", "model-index": [{"name": "0.0001_withdpo_3iters_bs256_511lr_iter_2", "results": []}]} | ShenaoZ/0.0001_withdpo_3iters_bs256_511lr_iter_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.0001_withdpo_3iters_bs256_511lr_iter_1",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:34:54+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-008e7-1x008-1-1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:34:57+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-14m_niki-041a_imdb_random-token-1280_10-rounds_seed-1
This model is a fine-tuned version of [EleutherAI/pythia-14m](https://huggingface.co/EleutherAI/pythia-14m) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-14m", "model-index": [{"name": "robust_llm_pythia-14m_niki-041a_imdb_random-token-1280_10-rounds_seed-1", "results": []}]} | AlignmentResearch/robust_llm_pythia-14m_niki-041a_imdb_random-token-1280_10-rounds_seed-1 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-14m",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:36:08+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-016e7-1x008-1-1 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:36:19+00:00 |
|
null | null | {} | jbnextnext/finetuning-sentiment-model-3000-samples | null | [
"region:us"
] | null | 2024-05-03T00:36:28+00:00 |
|
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-032e7-1x008-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:39:50+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | shallow6414/idrkb1f | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:40:16+00:00 |
feature-extraction | transformers | {} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-064e7-1x008-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:41:58+00:00 |
|
text-generation | transformers |
# NeuralPipe-7B-slerp
NeuralPipe-7B-slerp is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [OpenPipe/mistral-ft-optimized-1218](https://huggingface.co/OpenPipe/mistral-ft-optimized-1218)
* [mlabonne/NeuralHermes-2.5-Mistral-7B](https://huggingface.co/mlabonne/NeuralHermes-2.5-Mistral-7B)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: OpenPipe/mistral-ft-optimized-1218
layer_range: [0, 32]
- model: mlabonne/NeuralHermes-2.5-Mistral-7B
layer_range: [0, 32]
merge_method: slerp
base_model: OpenPipe/mistral-ft-optimized-1218
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 = "roboraction/NeuralPipe-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"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B"], "base_model": ["OpenPipe/mistral-ft-optimized-1218", "mlabonne/NeuralHermes-2.5-Mistral-7B"]} | roboraction/NeuralPipe-7B-slerp | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"OpenPipe/mistral-ft-optimized-1218",
"mlabonne/NeuralHermes-2.5-Mistral-7B",
"base_model:OpenPipe/mistral-ft-optimized-1218",
"base_model:mlabonne/NeuralHermes-2.5-Mistral-7B",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:41:59+00:00 |
text-generation | null |
# Wizz13150/Llama-3-8B-Instruct-Gradient-1048k-Q4_0-GGUF
This model was converted to GGUF format from [`gradientai/Llama-3-8B-Instruct-Gradient-1048k`](https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k) 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/gradientai/Llama-3-8B-Instruct-Gradient-1048k) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo Wizz13150/Llama-3-8B-Instruct-Gradient-1048k-Q4_0-GGUF --model llama-3-8b-instruct-gradient-1048k.Q4_0.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo Wizz13150/Llama-3-8B-Instruct-Gradient-1048k-Q4_0-GGUF --model llama-3-8b-instruct-gradient-1048k.Q4_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m llama-3-8b-instruct-gradient-1048k.Q4_0.gguf -n 128
```
| {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3", "llama-cpp", "gguf-my-repo"], "pipeline_tag": "text-generation"} | Wizz13150/Llama-3-8B-Instruct-Gradient-1048k-Q4_0-GGUF | null | [
"gguf",
"meta",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:llama3",
"region:us"
] | null | 2024-05-03T00:42:40+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
| {"library_name": "transformers", "tags": []} | Fighoture/Llama-2-7b-chat-shortgpt-25-percent-sharegpt-further-lora | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:42:55+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the emotion dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2120
- Accuracy: 0.926
- F1: 0.9262
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.7923 | 1.0 | 250 | 0.3047 | 0.9085 | 0.9080 |
| 0.2399 | 2.0 | 500 | 0.2120 | 0.926 | 0.9262 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["emotion"], "metrics": ["accuracy", "f1"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-base-uncased-finetuned-emotion", "results": [{"task": {"type": "text-classification", "name": "Text Classification"}, "dataset": {"name": "emotion", "type": "emotion", "config": "split", "split": "validation", "args": "split"}, "metrics": [{"type": "accuracy", "value": 0.926, "name": "Accuracy"}, {"type": "f1", "value": 0.9261912892383632, "name": "F1"}]}]}]} | skaurl/distilbert-base-uncased-finetuned-emotion | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"dataset:emotion",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:45:38+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
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| {"library_name": "transformers", "tags": []} | golf2248/y2jc6oq | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:45:47+00:00 |
null | null | {} | ivykopal/german_prompt_mlqa_adapter_100k | null | [
"region:us"
] | null | 2024-05-03T00:45:56+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | OwOpeepeepoopoo/herewegoagain7 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:48:21+00:00 |
feature-extraction | transformers |
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| {"library_name": "transformers", "tags": []} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-128e7-1x128-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:49:56+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0001_withdpo_3iters_bs256_551lr_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the HuggingFaceH4/ultrafeedback_binarized dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "HuggingFaceH4/mistral-7b-sft-beta", "model-index": [{"name": "0.0001_withdpo_3iters_bs256_551lr_iter_1", "results": []}]} | ShenaoZ/0.0001_withdpo_3iters_bs256_551lr_iter_1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:50:31+00:00 |
text-generation | transformers | {} | TwinDoc/H100_stage1_checkpoint-47520 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:50:57+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
CodeLlama-7b-Instruct-hf - bnb 4bits
- Model creator: https://huggingface.co/codellama/
- Original model: https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf/
Original model description:
---
language:
- code
pipeline_tag: text-generation
tags:
- llama-2
license: llama2
---
# **Code Llama**
Code Llama is a collection of pretrained and fine-tuned generative text models ranging in scale from 7 billion to 34 billion parameters. This is the repository for the 7B instruct-tuned version in the Hugging Face Transformers format. This model is designed for general code synthesis and understanding. Links to other models can be found in the index at the bottom.
> [!NOTE]
> This is a non-official Code Llama repo. You can find the official Meta repository in the [Meta Llama organization](https://huggingface.co/meta-llama/CodeLlama-7b-Instruct-hf).
| | Base Model | Python | Instruct |
| --- | ----------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------------------- |
| 7B | [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) | [codellama/CodeLlama-7b-Python-hf](https://huggingface.co/codellama/CodeLlama-7b-Python-hf) | [codellama/CodeLlama-7b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf) |
| 13B | [codellama/CodeLlama-13b-hf](https://huggingface.co/codellama/CodeLlama-13b-hf) | [codellama/CodeLlama-13b-Python-hf](https://huggingface.co/codellama/CodeLlama-13b-Python-hf) | [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) |
| 34B | [codellama/CodeLlama-34b-hf](https://huggingface.co/codellama/CodeLlama-34b-hf) | [codellama/CodeLlama-34b-Python-hf](https://huggingface.co/codellama/CodeLlama-34b-Python-hf) | [codellama/CodeLlama-34b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf) |
| 70B | [codellama/CodeLlama-70b-hf](https://huggingface.co/codellama/CodeLlama-70b-hf) | [codellama/CodeLlama-70b-Python-hf](https://huggingface.co/codellama/CodeLlama-70b-Python-hf) | [codellama/CodeLlama-70b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-70b-Instruct-hf) |
## Model Use
To use this model, please make sure to install transformers:
```bash
pip install transformers accelerate
```
Model capabilities:
- [x] Code completion.
- [x] Infilling.
- [x] Instructions / chat.
- [ ] Python specialist.
## Model Details
*Note: Use of this model is governed by the Meta license. Meta developed and publicly released the Code Llama family of large language models (LLMs).
**Model Developers** Meta
**Variations** Code Llama comes in three model sizes, and three variants:
* Code Llama: base models designed for general code synthesis and understanding
* Code Llama - Python: designed specifically for Python
* Code Llama - Instruct: for instruction following and safer deployment
All variants are available in sizes of 7B, 13B and 34B parameters.
**This repository contains the Instruct version of the 7B parameters model.**
**Input** Models input text only.
**Output** Models generate text only.
**Model Architecture** Code Llama is an auto-regressive language model that uses an optimized transformer architecture.
**Model Dates** Code Llama and its variants have been trained between January 2023 and July 2023.
**Status** This is a static model trained on an offline dataset. Future versions of Code Llama - Instruct will be released as we improve model safety with community feedback.
**License** A custom commercial license is available at: [https://ai.meta.com/resources/models-and-libraries/llama-downloads/](https://ai.meta.com/resources/models-and-libraries/llama-downloads/)
**Research Paper** More information can be found in the paper "[Code Llama: Open Foundation Models for Code](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/)" or its [arXiv page](https://arxiv.org/abs/2308.12950).
## Intended Use
**Intended Use Cases** Code Llama and its variants is intended for commercial and research use in English and relevant programming languages. The base model Code Llama can be adapted for a variety of code synthesis and understanding tasks, Code Llama - Python is designed specifically to handle the Python programming language, and Code Llama - Instruct is intended to be safer to use for code assistant and generation applications.
**Out-of-Scope Uses** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in languages other than English. Use in any other way that is prohibited by the Acceptable Use Policy and Licensing Agreement for Code Llama and its variants.
## Hardware and Software
**Training Factors** We used custom training libraries. The training and fine-tuning of the released models have been performed Meta’s Research Super Cluster.
**Carbon Footprint** In aggregate, training all 9 Code Llama models required 400K GPU hours of computation on hardware of type A100-80GB (TDP of 350-400W). Estimated total emissions were 65.3 tCO2eq, 100% of which were offset by Meta’s sustainability program.
## Training Data
All experiments reported here and the released models have been trained and fine-tuned using the same data as Llama 2 with different weights (see Section 2 and Table 1 in the [research paper](https://ai.meta.com/research/publications/code-llama-open-foundation-models-for-code/) for details).
## Evaluation Results
See evaluations for the main models and detailed ablations in Section 3 and safety evaluations in Section 4 of the research paper.
## Ethical Considerations and Limitations
Code Llama and its variants are a new technology that carries risks with use. Testing conducted to date has been in English, and has not covered, nor could it cover all scenarios. For these reasons, as with all LLMs, Code Llama’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate or objectionable responses to user prompts. Therefore, before deploying any applications of Code Llama, developers should perform safety testing and tuning tailored to their specific applications of the model.
Please see the Responsible Use Guide available available at [https://ai.meta.com/llama/responsible-use-guide](https://ai.meta.com/llama/responsible-use-guide).
| {} | RichardErkhov/codellama_-_CodeLlama-7b-Instruct-hf-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:2308.12950",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T00:51:10+00:00 |
text-generation | transformers |
# Model Card for Model ID
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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| {"library_name": "transformers", "tags": []} | golf2248/27gfmdk | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:51:26+00:00 |
null | null | {} | PassionFriend/sn25 | null | [
"region:us"
] | null | 2024-05-03T00:52:32+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-email-model-full - bnb 4bits
- Model creator: https://huggingface.co/amichalski2/
- Original model: https://huggingface.co/amichalski2/tinyllama-email-model-full/
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- **Shared by [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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| {} | RichardErkhov/amichalski2_-_tinyllama-email-model-full-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-03T00:54:11+00:00 |
feature-extraction | transformers |
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| {"library_name": "transformers", "tags": []} | lyghter/2ch-wt-24-01-01-27480-3849-mel-512-pool-256e7-1x128-1-1 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:54:19+00:00 |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | TrevorAsbery/Mistral-7b-wdc-products | null | [
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-03T00:54:25+00:00 |
text-generation | transformers |
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| {"library_name": "transformers", "tags": []} | shallow6414/eafebwo | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-03T00:55:40+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
tinyllama-email-model-full - bnb 8bits
- Model creator: https://huggingface.co/amichalski2/
- Original model: https://huggingface.co/amichalski2/tinyllama-email-model-full/
Original model description:
---
library_name: transformers
tags: []
---
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| {} | RichardErkhov/amichalski2_-_tinyllama-email-model-full-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-03T00:56:40+00:00 |
null | null | <!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: spm -->
static quants of https://huggingface.co/cognitivecomputations/dolphin-2.9-mixtral-8x22b
| {} | mradermacher/dolphin-2.9-mixtral-8x22b-GGUF | null | [
"region:us"
] | null | 2024-05-03T00:59:10+00:00 |
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