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null | transformers |
# Uploaded model
- **Developed by:** katharsis
- **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"} | katharsis/llama3-8b-oig-unsloth | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:06:11+00:00 |
text-generation | gemma_torch |
# CodeGemma Model Card
> [!IMPORTANT]
>
> This repository corresponds to the CodeGemma 7B IT checkpoint for use with [Gemma PyTorch](https://github.com/google/gemma_pytorch). If you're looking for the `transformers` implementation, or more detailed model card, visit https://huggingface.co/google/codegemma-1.1-7b-it.
**Model Page**: [CodeGemma](https://ai.google.dev/gemma/docs/codegemma)
**Resources and Technical Documentation**:
* [Technical Report](https://goo.gle/codegemma)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
**Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)
**Authors**: Google
# Sample Usage
```python
from gemma.config import GemmaConfig, get_config_for_7b, get_config_for_2b
from gemma.model import GemmaForCausalLM
from gemma.tokenizer import Tokenizer
import contextlib
import os
import torch
VARIANT = "7b-it"
MACHINE_TYPE = "cpu"
weights_dir = 'codegemma-1.1-7b-it-pytorch'
@contextlib.contextmanager
def _set_default_tensor_type(dtype: torch.dtype):
"""Sets the default torch dtype to the given dtype."""
torch.set_default_dtype(dtype)
yield
torch.set_default_dtype(torch.float)
model_config = get_config_for_2b() if "2b" in VARIANT else get_config_for_7b()
model_config.tokenizer = os.path.join(weights_dir, "tokenizer.model")
device = torch.device(MACHINE_TYPE)
with _set_default_tensor_type(model_config.get_dtype()):
model = GemmaForCausalLM(model_config)
ckpt_path = os.path.join(weights_dir, f'codegemma-1.1-{VARIANT}.pt')
model.load_weights(ckpt_path)
model = model.to(device).eval()
PROMPT = """<start_of_turn>user
Write a Python function to calculate the nth fibonacci number.<end_of_turn>
<start_of_turn>model
"""
model.generate(
PROMPT,
device=device,
output_len=100,
)
``` | {"license": "gemma", "library_name": "gemma_torch", "tags": ["pytorch"], "extra_gated_heading": "Access CodeGemma on Hugging Face", "extra_gated_prompt": "To access CodeGemma on Hugging Face, you\u2019re required to review and agree to Google\u2019s usage license. To do this, please ensure you\u2019re logged-in to Hugging Face and click below. Requests are processed immediately.", "extra_gated_button_content": "Acknowledge license", "license_link": "https://ai.google.dev/gemma/terms", "pipeline_tag": "text-generation"} | google/codegemma-1.1-7b-it-pytorch | null | [
"gemma_torch",
"pytorch",
"text-generation",
"license:gemma",
"region:us"
] | null | 2024-04-30T22:06:15+00:00 |
text-generation | gemma_torch |
# CodeGemma Model Card
> [!IMPORTANT]
>
> This repository corresponds to the CodeGemma 2B checkpoint for use with [Gemma PyTorch](https://github.com/google/gemma_pytorch). If you're looking for the `transformers` implementation, or more detailed model card, visit https://huggingface.co/google/codegemma-1.1-2b.
**Model Page**: [CodeGemma](https://ai.google.dev/gemma/docs/codegemma)
**Resources and Technical Documentation**:
* [Technical Report](https://goo.gle/codegemma)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
**Terms of Use**: [Terms](https://ai.google.dev/gemma/terms)
**Authors**: Google
# Sample Usage
```python
from gemma.config import GemmaConfig, get_config_for_7b, get_config_for_2b
from gemma.model import GemmaForCausalLM
from gemma.tokenizer import Tokenizer
import contextlib
import os
import torch
VARIANT = "2b"
MACHINE_TYPE = "cpu"
weights_dir = 'codegemma-1.1-2b-pytorch'
@contextlib.contextmanager
def _set_default_tensor_type(dtype: torch.dtype):
"""Sets the default torch dtype to the given dtype."""
torch.set_default_dtype(dtype)
yield
torch.set_default_dtype(torch.float)
model_config = get_config_for_2b() if "2b" in VARIANT else get_config_for_7b()
model_config.tokenizer = os.path.join(weights_dir, "tokenizer.model")
device = torch.device(MACHINE_TYPE)
with _set_default_tensor_type(model_config.get_dtype()):
model = GemmaForCausalLM(model_config)
ckpt_path = os.path.join(weights_dir, f'codegemma-1.1-{VARIANT}.pt')
model.load_weights(ckpt_path)
model = model.to(device).eval()
FIM_PROMPT = """<|fim_prefix|>import <|fim_suffix|>if __name__ == "__main__":
sys.exit(0)<|fim_middle|>"""
model.generate(
FIM_PROMPT,
device=device,
output_len=100,
)
``` | {"license": "gemma", "library_name": "gemma_torch", "tags": ["pytorch"], "extra_gated_heading": "Access Codeemma on Hugging Face", "extra_gated_prompt": "To access CodeGemma on Hugging Face, you\u2019re required to review and agree to Google\u2019s usage license. To do this, please ensure you\u2019re logged-in to Hugging Face and click below. Requests are processed immediately.", "extra_gated_button_content": "Acknowledge license", "license_link": "https://ai.google.dev/gemma/terms", "pipeline_tag": "text-generation"} | google/codegemma-1.1-2b-pytorch | null | [
"gemma_torch",
"pytorch",
"text-generation",
"license:gemma",
"region:us"
] | null | 2024-04-30T22:06:52+00:00 |
text-classification | transformers |
# Model Trained Using AutoTrain
- Problem type: Text Regression
## Validation Metrics
loss: 0.19687849283218384
mse: 0.19687849283218384
mae: 0.2855921685695648
r2: 0.2613240842619654
rmse: 0.4437099099159241
explained_variance: 0.26253634691238403
| {"tags": ["autotrain", "text-regression"], "datasets": ["autotrain-y5fnj-qjq2l/autotrain-data"], "widget": [{"text": "I love AutoTrain"}]} | Konark-HC/autotrain-y5fnj-qjq2l | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"text-regression",
"dataset:autotrain-y5fnj-qjq2l/autotrain-data",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:07:30+00:00 |
null | null | ~~1704 and 1804 RAWrr Yi-34B-200K-xlctx loras came out bad, which I realized now. I had HDD drive failure and it was the one holding up Linux that training was happening on. Probably that's why.~~
Nah, it was transformers or peft python update that broke my merging process. I downgraded and it works. 1704/1804/3004 loras are fine, I think 1804 is the best one.
3004 is a re-train with 1804 config - higher lr than 1704. | {"license": "other", "license_name": "yi-license", "license_link": "LICENSE"} | adamo1139/Yi-34B-200K-XLCTX-RAWrr-3004-LoRA | null | [
"safetensors",
"license:other",
"region:us"
] | null | 2024-04-30T22:07:36+00:00 |
null | null | {} | tripnoas/modelso | null | [
"region:us"
] | null | 2024-04-30T22:08:01+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": []} | AbdWalidAI/Llama-3-8B-Instruct-dialogsum | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T22:08:59+00:00 |
null | null | {} | stickman712/vae | null | [
"region:us"
] | null | 2024-04-30T22:09:37+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/jtatman/OrpoDolphin-3-8B-16k
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OrpoDolphin-3-8B-16k-GGUF/resolve/main/OrpoDolphin-3-8B-16k.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "orpo"], "base_model": "jtatman/OrpoDolphin-3-8B-16k", "quantized_by": "mradermacher"} | mradermacher/OrpoDolphin-3-8B-16k-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"orpo",
"en",
"base_model:jtatman/OrpoDolphin-3-8B-16k",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:10:42+00:00 |
text2text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | samzirbo/mT5.pretrained.en-es.16K | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T22:11:27+00:00 |
token-classification | spacy | | Feature | Description |
| --- | --- |
| **Name** | `en_pipeline` |
| **Version** | `0.0.0` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipeline** | `ner` |
| **Components** | `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | n/a |
| **License** | n/a |
| **Author** | [n/a]() |
### Label Scheme
<details>
<summary>View label scheme (9 labels for 1 components)</summary>
| Component | Labels |
| --- | --- |
| **`ner`** | `AM_TIME_END`, `AM_TIME_START`, `DATE_END`, `DATE_START`, `MILITARY_TIME_END`, `MILITARY_TIME_START`, `PM_TIME_END`, `PM_TIME_START`, `REASON` |
</details> | {"language": ["en"], "tags": ["spacy", "token-classification"]} | Agreus/en_pipeline | null | [
"spacy",
"token-classification",
"en",
"region:us"
] | null | 2024-04-30T22:12:57+00:00 |
null | null | The GGUF files of [xtuner/llava-llama-3-8b-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1) | {"tags": ["llava", "llama", "llava-llama", "gguf", "GGUF"]} | nitsuai/llava-llama-3-8b-v1_1-GGUF | null | [
"gguf",
"llava",
"llama",
"llava-llama",
"GGUF",
"region:us"
] | null | 2024-04-30T22:13:06+00:00 |
null | null | {} | rancelyndar/segformer-b5-finetuned-segments-sidewalk-2 | null | [
"region:us"
] | null | 2024-04-30T22:14:42+00:00 |
|
null | null | {} | squaadinc/1714515282183x200513135873622000 | null | [
"region:us"
] | null | 2024-04-30T22:14:46+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-finetune
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5196
- Wer: 19.8880
## 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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 3.9796 | 0.2778 | 10 | 3.9148 | 37.1304 |
| 3.8883 | 0.5556 | 20 | 3.8228 | 36.4457 |
| 3.8393 | 0.8333 | 30 | 3.6743 | 39.0912 |
| 3.5454 | 1.1111 | 40 | 3.4770 | 43.2306 |
| 3.3763 | 1.3889 | 50 | 3.2317 | 35.9477 |
| 3.1017 | 1.6667 | 60 | 2.9296 | 42.9816 |
| 2.7298 | 1.9444 | 70 | 2.5452 | 33.4578 |
| 2.2923 | 2.2222 | 80 | 2.0397 | 33.5512 |
| 1.739 | 2.5 | 90 | 1.4515 | 34.2670 |
| 1.148 | 2.7778 | 100 | 0.9843 | 34.1737 |
| 0.846 | 3.0556 | 110 | 0.7598 | 30.2210 |
| 0.7269 | 3.3333 | 120 | 0.6819 | 27.3265 |
| 0.6914 | 3.6111 | 130 | 0.6347 | 26.2372 |
| 0.6225 | 3.8889 | 140 | 0.6027 | 24.9611 |
| 0.5939 | 4.1667 | 150 | 0.5781 | 24.5565 |
| 0.5677 | 4.4444 | 160 | 0.5582 | 23.2493 |
| 0.5611 | 4.7222 | 170 | 0.5420 | 22.6268 |
| 0.5285 | 5.0 | 180 | 0.5271 | 22.0044 |
| 0.4771 | 5.2778 | 190 | 0.5158 | 21.8176 |
| 0.5003 | 5.5556 | 200 | 0.5068 | 21.7554 |
| 0.4232 | 5.8333 | 210 | 0.4965 | 21.4441 |
| 0.4232 | 6.1111 | 220 | 0.4888 | 20.6038 |
| 0.3522 | 6.3889 | 230 | 0.4824 | 20.2303 |
| 0.405 | 6.6667 | 240 | 0.4753 | 20.0436 |
| 0.4333 | 6.9444 | 250 | 0.4694 | 23.0937 |
| 0.3314 | 7.2222 | 260 | 0.4623 | 19.9813 |
| 0.3282 | 7.5 | 270 | 0.4581 | 19.8257 |
| 0.3463 | 7.7778 | 280 | 0.4567 | 19.6390 |
| 0.3215 | 8.0556 | 290 | 0.4518 | 19.0476 |
| 0.3049 | 8.3333 | 300 | 0.4496 | 18.7986 |
| 0.2792 | 8.6111 | 310 | 0.4463 | 18.8920 |
| 0.3031 | 8.8889 | 320 | 0.4426 | 18.7053 |
| 0.2353 | 9.1667 | 330 | 0.4451 | 18.5496 |
| 0.2618 | 9.4444 | 340 | 0.4433 | 18.8920 |
| 0.2405 | 9.7222 | 350 | 0.4449 | 18.4563 |
| 0.2609 | 10.0 | 360 | 0.4408 | 18.1450 |
| 0.1956 | 10.2778 | 370 | 0.4374 | 18.3940 |
| 0.2079 | 10.5556 | 380 | 0.4382 | 18.2695 |
| 0.2149 | 10.8333 | 390 | 0.4383 | 18.3940 |
| 0.1791 | 11.1111 | 400 | 0.4400 | 18.4563 |
| 0.1778 | 11.3889 | 410 | 0.4401 | 18.5185 |
| 0.1571 | 11.6667 | 420 | 0.4390 | 18.5185 |
| 0.1602 | 11.9444 | 430 | 0.4376 | 18.0205 |
| 0.1168 | 12.2222 | 440 | 0.4418 | 18.4251 |
| 0.1353 | 12.5 | 450 | 0.4418 | 18.6430 |
| 0.1156 | 12.7778 | 460 | 0.4433 | 18.3318 |
| 0.1148 | 13.0556 | 470 | 0.4422 | 17.8960 |
| 0.0895 | 13.3333 | 480 | 0.4478 | 18.1139 |
| 0.0903 | 13.6111 | 490 | 0.4492 | 18.7364 |
| 0.0981 | 13.8889 | 500 | 0.4522 | 19.1721 |
| 0.0669 | 14.1667 | 510 | 0.4570 | 19.0787 |
| 0.0723 | 14.4444 | 520 | 0.4612 | 18.5808 |
| 0.0677 | 14.7222 | 530 | 0.4603 | 18.9231 |
| 0.066 | 15.0 | 540 | 0.4600 | 19.1410 |
| 0.0393 | 15.2778 | 550 | 0.4696 | 18.6741 |
| 0.052 | 15.5556 | 560 | 0.4730 | 19.3589 |
| 0.0414 | 15.8333 | 570 | 0.4728 | 18.8609 |
| 0.0486 | 16.1111 | 580 | 0.4756 | 19.3589 |
| 0.0329 | 16.3889 | 590 | 0.4822 | 19.4522 |
| 0.0285 | 16.6667 | 600 | 0.4864 | 19.1410 |
| 0.0291 | 16.9444 | 610 | 0.4814 | 19.6078 |
| 0.0234 | 17.2222 | 620 | 0.4861 | 19.7012 |
| 0.0197 | 17.5 | 630 | 0.4928 | 19.7323 |
| 0.0191 | 17.7778 | 640 | 0.4927 | 19.7323 |
| 0.0187 | 18.0556 | 650 | 0.4914 | 19.7012 |
| 0.0167 | 18.3333 | 660 | 0.4961 | 19.8568 |
| 0.0152 | 18.6111 | 670 | 0.4998 | 19.9191 |
| 0.0125 | 18.8889 | 680 | 0.4983 | 20.1369 |
| 0.0116 | 19.1667 | 690 | 0.5016 | 19.7946 |
| 0.0107 | 19.4444 | 700 | 0.5022 | 19.7012 |
| 0.0126 | 19.7222 | 710 | 0.5032 | 19.9191 |
| 0.0112 | 20.0 | 720 | 0.5042 | 20.1369 |
| 0.0102 | 20.2778 | 730 | 0.5054 | 20.0436 |
| 0.0097 | 20.5556 | 740 | 0.5089 | 19.7946 |
| 0.01 | 20.8333 | 750 | 0.5074 | 19.9502 |
| 0.0092 | 21.1111 | 760 | 0.5099 | 19.9502 |
| 0.009 | 21.3889 | 770 | 0.5100 | 20.0436 |
| 0.008 | 21.6667 | 780 | 0.5119 | 19.9813 |
| 0.0087 | 21.9444 | 790 | 0.5125 | 19.9502 |
| 0.0083 | 22.2222 | 800 | 0.5111 | 20.0436 |
| 0.0083 | 22.5 | 810 | 0.5119 | 19.9813 |
| 0.0076 | 22.7778 | 820 | 0.5127 | 20.0124 |
| 0.0074 | 23.0556 | 830 | 0.5150 | 20.0124 |
| 0.0076 | 23.3333 | 840 | 0.5150 | 19.8568 |
| 0.007 | 23.6111 | 850 | 0.5162 | 20.0124 |
| 0.0074 | 23.8889 | 860 | 0.5165 | 19.9813 |
| 0.0066 | 24.1667 | 870 | 0.5157 | 19.7946 |
| 0.0068 | 24.4444 | 880 | 0.5163 | 19.9502 |
| 0.0065 | 24.7222 | 890 | 0.5173 | 19.8257 |
| 0.0074 | 25.0 | 900 | 0.5182 | 19.9813 |
| 0.0067 | 25.2778 | 910 | 0.5184 | 19.8568 |
| 0.006 | 25.5556 | 920 | 0.5186 | 20.0124 |
| 0.0063 | 25.8333 | 930 | 0.5187 | 19.7946 |
| 0.0071 | 26.1111 | 940 | 0.5190 | 19.8257 |
| 0.0058 | 26.3889 | 950 | 0.5193 | 19.8880 |
| 0.0063 | 26.6667 | 960 | 0.5195 | 19.8880 |
| 0.0059 | 26.9444 | 970 | 0.5195 | 19.8880 |
| 0.0056 | 27.2222 | 980 | 0.5195 | 19.8880 |
| 0.006 | 27.5 | 990 | 0.5196 | 19.8880 |
| 0.0068 | 27.7778 | 1000 | 0.5196 | 19.8880 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisper-tiny-finetune", "results": []}]} | sajidof/whisper-tiny-finetune | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:15:40+00:00 |
null | transformers | {} | rancelyndar/segformer-b5-asbestosV | null | [
"transformers",
"tensorboard",
"safetensors",
"segformer",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:17:35+00:00 |
|
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AOLM1
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1410
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9303 | 0.09 | 10 | 0.9380 |
| 0.3692 | 0.18 | 20 | 0.1548 |
| 0.1539 | 0.27 | 30 | 0.1633 |
| 0.1561 | 0.36 | 40 | 0.1560 |
| 0.1518 | 0.45 | 50 | 0.1541 |
| 0.1518 | 0.54 | 60 | 0.1478 |
| 0.1488 | 0.63 | 70 | 0.1481 |
| 0.1486 | 0.73 | 80 | 0.1550 |
| 0.1467 | 0.82 | 90 | 0.1513 |
| 0.1476 | 0.91 | 100 | 0.1491 |
| 0.149 | 1.0 | 110 | 0.1483 |
| 0.1456 | 1.09 | 120 | 0.1493 |
| 0.1442 | 1.18 | 130 | 0.1517 |
| 0.1474 | 1.27 | 140 | 0.1478 |
| 0.1482 | 1.36 | 150 | 0.1495 |
| 0.1455 | 1.45 | 160 | 0.1479 |
| 0.1455 | 1.54 | 170 | 0.1474 |
| 0.1467 | 1.63 | 180 | 0.1452 |
| 0.1464 | 1.72 | 190 | 0.1486 |
| 0.145 | 1.81 | 200 | 0.1469 |
| 0.1485 | 1.9 | 210 | 0.1460 |
| 0.1453 | 1.99 | 220 | 0.1480 |
| 0.1432 | 2.08 | 230 | 0.1456 |
| 0.1376 | 2.18 | 240 | 0.1444 |
| 0.1392 | 2.27 | 250 | 0.1451 |
| 0.1385 | 2.36 | 260 | 0.1441 |
| 0.137 | 2.45 | 270 | 0.1441 |
| 0.1352 | 2.54 | 280 | 0.1420 |
| 0.1338 | 2.63 | 290 | 0.1423 |
| 0.1352 | 2.72 | 300 | 0.1410 |
| 0.1351 | 2.81 | 310 | 0.1407 |
| 0.1317 | 2.9 | 320 | 0.1409 |
| 0.1361 | 2.99 | 330 | 0.1410 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "AOLM1", "results": []}]} | Litzy619/AOLM1 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T22:17:37+00:00 |
text-to-image | diffusers |
# API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "9527-detail-realistic-xl"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/9527-detail-realistic-xl)
Model link: [View model](https://modelslab.com/models/9527-detail-realistic-xl)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "9527-detail-realistic-xl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/9527-detail-realistic-xl | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-30T22:18:34+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)
Qwen1.5-32B - bnb 4bits
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwen/Qwen1.5-32B/
Original model description:
---
license: other
license_name: tongyi-qianwen-research
license_link: >-
https://huggingface.co/Qwen/Qwen1.5-32B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
---
# Qwen1.5-32B
## Introduction
Qwen1.5 is the beta version of Qwen2, a transformer-based decoder-only language model pretrained on a large amount of data. In comparison with the previous released Qwen, the improvements include:
* 8 model sizes, including 0.5B, 1.8B, 4B, 7B, 14B, 32B and 72B dense models, and an MoE model of 14B with 2.7B activated;
* Significant performance improvement in Chat models;
* Multilingual support of both base and chat models;
* Stable support of 32K context length for models of all sizes
* No need of `trust_remote_code`.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/qwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
Qwen1.5 is a language model series including decoder language models of different model sizes. For each size, we release the base language model and the aligned chat model. It is based on the Transformer architecture with SwiGLU activation, attention QKV bias, group query attention, mixture of sliding window attention and full attention, etc. Additionally, we have an improved tokenizer adaptive to multiple natural languages and codes. For the beta version, temporarily we did not include GQA (except for 32B) and the mixture of SWA and full attention.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Usage
We do not advise you to use base language models for text generation. Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
| {} | RichardErkhov/Qwen_-_Qwen1.5-32B-4bits | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T22:19:33+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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<!-- Provide the basic links for the model. -->
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### Out-of-Scope Use
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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]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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- **Training 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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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- 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 Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adaptertoxic2nontoxic-100-50-0.0006 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:20:42+00:00 |
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | claudios/plbart-java-cs | null | [
"transformers",
"safetensors",
"plbart",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:21:23+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.Q8_0.gguf) | Q8_0 | 8.6 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF/resolve/main/Llama-3-8B-Instruct-80K-QLoRA-Merged.f16.gguf) | f16 | 16.2 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "mit", "library_name": "transformers", "base_model": "namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged", "quantized_by": "mradermacher"} | mradermacher/Llama-3-8B-Instruct-80K-QLoRA-Merged-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:namespace-Pt/Llama-3-8B-Instruct-80K-QLoRA-Merged",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:22:58+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi09_LoRA
<Gallery />
## Model description
These are embracellm/sushi09_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Salmon Sushi Burrito to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi09_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Salmon Sushi Burrito ", "widget": []} | embracellm/sushi09_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-30T22:23:10+00:00 |
null | null | {} | isferi1996/t2 | null | [
"region:us"
] | null | 2024-04-30T22:23:40+00:00 |
|
null | null | {"license": "apache-2.0"} | wasiqtayyab/test | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T22:24:28+00:00 |
|
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - tonyassi/dolls-kill-club-exx
<Gallery />
## Model description
These are tonyassi/dolls-kill-club-exx LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use Dolls Kill Club Exx style to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](tonyassi/dolls-kill-club-exx/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "lora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "Dolls Kill Club Exx style", "widget": []} | tonyassi/dolls-kill-club-exx | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-30T22:24:32+00:00 |
feature-extraction | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | claudios/plbart-python-en_XX | null | [
"transformers",
"safetensors",
"plbart",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:25:03+00:00 |
text-generation | transformers | {} | tchattha/mistral-neuron | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T22:26:31+00:00 |
|
null | null | {} | squaadinc/1714516001580x831102858815340500 | null | [
"region:us"
] | null | 2024-04-30T22:26:53+00:00 |
|
feature-extraction | transformers | {} | claudios/plbart-csnet | null | [
"transformers",
"safetensors",
"plbart",
"feature-extraction",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:28:43+00:00 |
|
null | null |
# DDPO trained model
num_epochs=20
train_gradient_accumulation_steps=1
sample_num_steps=30
sample_batch_size=8
train_batch_size=8
sample_num_batches_per_epoch=32
based off of stabilityai/stable-diffusion-2-base
and then trained off of None
| {} | jlbaker361/ddpo-runway-image_reward-chatgpt | null | [
"region:us"
] | null | 2024-04-30T22:29:59+00:00 |
text-generation | null |
## Exllama v2 Quantizations of llama-3-8b-256k-PoSE
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/winglian/llama-3-8b-256k-PoSE
## Prompt format
This is a base model.
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/llama-3-8b-256k-PoSE-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/llama-3-8b-256k-PoSE-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/llama-3-8b-256k-PoSE-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/llama-3-8b-256k-PoSE-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/llama-3-8b-256k-PoSE-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/llama-3-8b-256k-PoSE-exl2 llama-3-8b-256k-PoSE-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/llama-3-8b-256k-PoSE-exl2 --revision 6_5 --local-dir llama-3-8b-256k-PoSE-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/llama-3-8b-256k-PoSE-exl2 --revision 6_5 --local-dir llama-3-8b-256k-PoSE-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "axolotl"], "pipeline_tag": "text-generation", "quantized_by": "bartowski"} | bartowski/llama-3-8b-256k-PoSE-exl2 | null | [
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"axolotl",
"text-generation",
"en",
"region:us"
] | null | 2024-04-30T22:30:42+00:00 |
null | null |
```
e88 88e d8
d888 888b 8888 8888 ,"Y88b 888 8e d88
C8888 8888D 8888 8888 "8" 888 888 88b d88888
Y888 888P Y888 888P ,ee 888 888 888 888
"88 88" "88 88" "88 888 888 888 888
b
8b,
e88'Y88 d8 888
d888 'Y ,"Y88b 888,8, d88 ,e e, 888
C8888 "8" 888 888 " d88888 d88 88b 888
Y888 ,d ,ee 888 888 888 888 , 888
"88,d88 "88 888 888 888 "YeeP" 888
PROUDLY PRESENTS
```
## Poppy_Porpoise-DADA-8B-iMat-GGUF
Quantized from fp16 with love.
* Weighted quantizations were calculated with fp16 GGUF using groups_merged.txt in 105 chunks (recommended amount for this file) and n_ctx=512. Special thanks to jukofyork for sharing [this process](https://huggingface.co/jukofyork/WizardLM-2-8x22B-imatrix)
<b>Important Note - Quantized post [PR6920](https://github.com/ggerganov/llama.cpp/pull/6920). There may still be some remaining issues with the bpe tokenizer so consider these quantizations experimental for now. Any feedback is greatly encouraged. In initial tests quants as low as Q2_K can still answer math questions.</b>
```
What is 7777 + 3333? Please write just the answer in integer format.
The answer is: 11110
```
For a brief rundown of iMatrix quant performance please see this [PR](https://github.com/ggerganov/llama.cpp/pull/5747)
<b>All quants are verified working prior to uploading to repo for your safety and convenience. </b>
It's highly recommended to stick to higher quants of this model due to the unique nature of its pseudotokens
Original model card [here](https://huggingface.co/Envoid/Poppy_Porpoise-DADA-8B) and below
---
# This is a [DADA tuned](https://huggingface.co/Envoid/Mixtral-Instruct-ITR-DADA-8x7B) version of [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B)
### As always, experimental model, results may be unpredictable.
The intermediate merge model was finetuned specifically for the original model and then merged onto it via 50/50 SLERP merge.
As far as RP goes my current testing setup is having difficulties involving end of sequence and I found the prompt templates provided with Poppy Porpoise were not yielding desirable results on either the original model or the DADA tune so I opted instead to use Libra style Llama-3 prompt templates and simple sampling. The results, albeit subjective, I thought were good.
## Whether or not the tune had any effect on multi-modal capabilities was not part of the test and instead focus was entirely on creative writing.
Test: A simple instruct prompt utilizing determinsitic samplers.
**PROMPT**
```
Write me a 'beauty and the beast' gothic metal duet about a couple fighting over which brand of ketchup to purchase.
```
**Poppy Porpoise Original:**
```
(Verse 1 - Beauty)
In the land of condiments, where flavors reign supreme,
A tale of love and strife, a battle of the brands it seems.
I stand before the shelves, my heart aflame with desire,
For the perfect ketchup to complement my fries.
(Chorus)
But alas, my love, you stand beside me, with a different view,
Your heart set on [Brand 1 redacted], while mine beats for [Brand 2 redacted], it's true.
We clash in this aisle, our passions running high,
A Gothic metal duet, our love and hate collide.
(Verse 2 - Beast)
I've tasted both, and I know, [Brand 1 redacted] is the one for me,
Its tangy sweetness, a symphony, a harmony.
But you, my love, you cling to Hunt's, its flavor so bland,
A cacophony of mediocrity, a discordant band.
(Chorus)
But alas, my love, you stand beside me, with a different view,
Your heart set on [Brand 2 redacted], while mine beats for [Brand 1 redacted], it's true.
We clash in this aisle, our passions running high,
A Gothic metal duet, our love and hate collide.
(Bridge)
We're lost in this supermarket, a Gothic metal dream,
Our love and hate entwined, a twisted, tortured theme.
The ketchup bottles loom, like tombstones in the night,
A monument to our love, a testament to our fight.
(Chorus)
But alas, my love, you stand beside me, with a different view,
Your heart set on [Brand 2 redacted], while mine beats for [Brand 1 redacted], it's true.
We clash in this aisle, our passions running high,
A Gothic metal duet, our love and hate collide.
(Outro)
And so we stand, in this aisle of despair,
Our love and hate, a Gothic metal prayer.
For in the end, it's not the ketchup that matters,
But the love we share, in this twisted, tortured heart.
```
**Poppy Porpoise DADA**
```
(Verse 1 - Beauty)
In the land of condiments, where the sauces reign supreme,
A battle rages on, a war of ketchup dreams.
I stand by [Brand 1 redacted], the classic, the original, the best,
But my love, he's a rebel, he's a [Brand 2 redacted] pest.
(Chorus)
Oh, the ketchup wars, they rage on and on,
A tale of love and sauce, a battle to be won.
Will our love prevail, or will it be undone?
By the brand of ketchup, our hearts are torn asunder.
(Verse 2 - Beast)
[Brand 2 redacted], the new kid, the one with the flair,
A sweeter taste, a richer flavor, beyond compare.
But Beauty, she's a traditionalist, she won't budge,
She clings to [Brand 1 redacted], her heart, her soul, her blood.
(Chorus)
Oh, the ketchup wars, they rage on and on,
A tale of love and sauce, a battle to be won.
Will our love prevail, or will it be undone?
By the brand of ketchup, our hearts are torn asunder.
(Bridge - Both)
We stand, divided, by our condiment creed,
Our love, a casualty, in this ketchup war we lead.
But can we find a middle ground, a compromise, a truce?
Or will our love be lost, in this battle of the sauces?
(Chorus)
Oh, the ketchup wars, they rage on and on,
A tale of love and sauce, a battle to be won.
Will our love prevail, or will it be undone?
By the brand of ketchup, our hearts are torn asunder.
(Outro - Both)
In the end, we find a way, to put aside our pride,
And together, we stand, united, side by side.
For love, for ketchup, for the sauce that binds,
We'll find a way, to make our love, forever shine.
```
As you can clearly see the cadence and overall word selection by the DADA model is more copacetic with what would be expected from a gothic metal duet indicating that the IQ of the model has increased by at least 100 points.
Training was done using [qlora-pipe](https://github.com/tdrussell/qlora-pipe) | {"license": "cc-by-nc-4.0", "tags": ["GGUF", "iMat", "llama3"]} | Quant-Cartel/Poppy_Porpoise-DADA-8B-iMat-GGUF | null | [
"gguf",
"GGUF",
"iMat",
"llama3",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2024-04-30T22:31:06+00:00 |
null | null | {} | snakesss/hrkljus | null | [
"region:us"
] | null | 2024-04-30T22:32:37+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]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | EdBerg/002Llama3_b_finance_finetuned_test | null | [
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:34:40+00:00 |
null | transformers |
# TS-Corpus WordPiece Tokenizer (256k, Cased)
## Overview
This repository contains a WordPiece tokenizer with a vocabulary size of 256,000, trained uncased on various datasets from the TS Corpus website. It is designed to handle Turkish text, leveraging rich and diverse sources to provide a robust tool for natural language processing tasks.
## Dataset Sources
The tokenizer was trained using multiple corpora from the TS Corpus, specifically:
- [TS Corpus V2](https://tscorpus.com/corpora/ts-corpus-v2/)
- [TS Wikipedia Corpus](https://tscorpus.com/corpora/ts-wikipedia-corpus/)
- [TS Abstract Corpus](https://tscorpus.com/corpora/ts-abstract-corpus/)
- [TS Idioms and Proverbs Corpus](https://tscorpus.com/corpora/ts-idioms-and-proverbs-corpus/)
- [Syllable Corpus](https://tscorpus.com/corpora/syllable-corpus/)
- [Turkish Constitution Corpus](https://tscorpus.com/corpora/turkish-constitution-corpus/)
These diverse sources include a wide range of texts from encyclopedic articles to legal documents, providing a comprehensive linguistic foundation for the tokenizer.
## Tokenizer Model
The tokenizer uses the WordPiece model, which is widely utilized in many modern NLP systems. It is particularly effective in handling languages with rich morphology like Turkish due to its subword segmentation approach. This tokenizer differentiates between uppercase and lowercase letters.
## Usage
To use this tokenizer, you can load it via the Hugging Face `transformers` library as follows:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("tahaenesaslanturk/ts-corpus-wordpiece-256k-cased")
```
| {"language": ["tr"], "license": "mit", "library_name": "transformers"} | tahaenesaslanturk/ts-corpus-wordpiece-256k-cased | null | [
"transformers",
"tr",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:35:54+00:00 |
null | null | # TS-Corpus BPE Tokenizer (256k, Cased)
## Overview
This repository hosts a Byte Pair Encoding (BPE) tokenizer with a vocabulary size of 256,000, trained cased using several datasets from the TS Corpus website. The BPE method is particularly effective for languages like Turkish, providing a balance between word-level and character-level tokenization.
## Dataset Sources
The tokenizer was trained on a variety of text sources from TS Corpus, ensuring a broad linguistic coverage. These sources include:
- [TS Corpus V2](https://tscorpus.com/corpora/ts-corpus-v2/)
- [TS Wikipedia Corpus](https://tscorpus.com/corpora/ts-wikipedia-corpus/)
- [TS Abstract Corpus](https://tscorpus.com/corpora/ts-abstract-corpus/)
- [TS Idioms and Proverbs Corpus](https://tscorpus.com/corpora/ts-idioms-and-proverbs-corpus/)
- [Syllable Corpus](https://tscorpus.com/corpora/syllable-corpus/)
- [Turkish Constitution Corpus](https://tscorpus.com/corpora/turkish-constitution-corpus/)
The inclusion of idiomatic expressions, proverbs, and legal terminology provides a comprehensive toolkit for processing Turkish text across different domains.
## Tokenizer Model
Utilizing the Byte Pair Encoding (BPE) method, this tokenizer excels in efficiently managing subword units without the need for an extensive vocabulary. BPE is especially suitable for handling the agglutinative nature of Turkish, where words can have multiple suffixes.
## Usage
To use this tokenizer in your projects, load it with the Hugging Face `transformers` library:
```python
from transformers import AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("tahaenesaslanturk/ts-corpus-bpe-256k-cased")
``` | {"license": "mit"} | tahaenesaslanturk/ts-corpus-bpe-256k-cased | null | [
"license:mit",
"region:us"
] | null | 2024-04-30T22:36:56+00:00 |
text-to-image | diffusers |
# API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "arienmixxl-asian-portrait"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/arienmixxl-asian-portrait)
Model link: [View model](https://modelslab.com/models/arienmixxl-asian-portrait)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "arienmixxl-asian-portrait",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/arienmixxL-asian-portrait | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-30T22:36:58+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": []} | paytonison/Aristotle-GPT-Neo-250M-Alpha | null | [
"transformers",
"safetensors",
"gpt_neo",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:37:04+00:00 |
null | mlx |
# mlx-community/dolphin-2.9-llama3-8b-1m-4bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b-1m`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9-llama3-8b-1m-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "axolotl", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | mlx-community/dolphin-2.9-llama3-8b-1m-4bit | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"axolotl",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-30T22:38:14+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]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | TLLM/llama-3-zhtw-tokenizer | null | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:38:42+00:00 |
null | null | {} | hamzaakbar/minimal | null | [
"region:us"
] | null | 2024-04-30T22:39:01+00:00 |
|
null | null | {} | squaadinc/1714516715102x354985332262043650 | null | [
"region:us"
] | null | 2024-04-30T22:39:01+00:00 |
|
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AOLM2
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1428
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9303 | 0.09 | 10 | 0.9652 |
| 0.3692 | 0.18 | 20 | 0.1604 |
| 0.1539 | 0.27 | 30 | 0.1626 |
| 0.1561 | 0.36 | 40 | 0.1535 |
| 0.1518 | 0.45 | 50 | 0.1511 |
| 0.1518 | 0.54 | 60 | 0.1483 |
| 0.1488 | 0.63 | 70 | 0.1485 |
| 0.1486 | 0.73 | 80 | 0.1528 |
| 0.1467 | 0.82 | 90 | 0.1504 |
| 0.1476 | 0.91 | 100 | 0.1485 |
| 0.149 | 1.0 | 110 | 0.1477 |
| 0.1456 | 1.09 | 120 | 0.1490 |
| 0.1442 | 1.18 | 130 | 0.1499 |
| 0.1474 | 1.27 | 140 | 0.1479 |
| 0.1482 | 1.36 | 150 | 0.1486 |
| 0.1455 | 1.45 | 160 | 0.1483 |
| 0.1455 | 1.54 | 170 | 0.1467 |
| 0.1467 | 1.63 | 180 | 0.1455 |
| 0.1464 | 1.72 | 190 | 0.1485 |
| 0.145 | 1.81 | 200 | 0.1468 |
| 0.1485 | 1.9 | 210 | 0.1458 |
| 0.1453 | 1.99 | 220 | 0.1477 |
| 0.1432 | 2.08 | 230 | 0.1457 |
| 0.1376 | 2.18 | 240 | 0.1447 |
| 0.1392 | 2.27 | 250 | 0.1446 |
| 0.1385 | 2.36 | 260 | 0.1443 |
| 0.137 | 2.45 | 270 | 0.1444 |
| 0.1352 | 2.54 | 280 | 0.1436 |
| 0.1338 | 2.63 | 290 | 0.1435 |
| 0.1352 | 2.72 | 300 | 0.1430 |
| 0.1351 | 2.81 | 310 | 0.1428 |
| 0.1317 | 2.9 | 320 | 0.1430 |
| 0.1361 | 2.99 | 330 | 0.1428 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "AOLM2", "results": []}]} | Litzy619/AOLM2 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T22:39:16+00:00 |
null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# AOLM3
This model is a fine-tuned version of [allenai/OLMo-1B](https://huggingface.co/allenai/OLMo-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1426
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine_with_restarts
- lr_scheduler_warmup_steps: 80
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.9989 | 0.09 | 10 | 1.0026 |
| 0.3997 | 0.18 | 20 | 0.1602 |
| 0.1542 | 0.27 | 30 | 0.1613 |
| 0.1535 | 0.36 | 40 | 0.1525 |
| 0.1526 | 0.45 | 50 | 0.1486 |
| 0.1508 | 0.54 | 60 | 0.1496 |
| 0.1494 | 0.63 | 70 | 0.1490 |
| 0.1493 | 0.73 | 80 | 0.1514 |
| 0.1471 | 0.82 | 90 | 0.1513 |
| 0.1477 | 0.91 | 100 | 0.1507 |
| 0.1493 | 1.0 | 110 | 0.1496 |
| 0.1453 | 1.09 | 120 | 0.1498 |
| 0.1446 | 1.18 | 130 | 0.1523 |
| 0.147 | 1.27 | 140 | 0.1481 |
| 0.1478 | 1.36 | 150 | 0.1476 |
| 0.146 | 1.45 | 160 | 0.1496 |
| 0.1462 | 1.54 | 170 | 0.1466 |
| 0.1462 | 1.63 | 180 | 0.1452 |
| 0.1471 | 1.72 | 190 | 0.1515 |
| 0.1453 | 1.81 | 200 | 0.1469 |
| 0.1487 | 1.9 | 210 | 0.1475 |
| 0.1461 | 1.99 | 220 | 0.1483 |
| 0.1444 | 2.08 | 230 | 0.1468 |
| 0.139 | 2.18 | 240 | 0.1450 |
| 0.1411 | 2.27 | 250 | 0.1461 |
| 0.1405 | 2.36 | 260 | 0.1464 |
| 0.1392 | 2.45 | 270 | 0.1446 |
| 0.1379 | 2.54 | 280 | 0.1441 |
| 0.1368 | 2.63 | 290 | 0.1444 |
| 0.1389 | 2.72 | 300 | 0.1427 |
| 0.1387 | 2.81 | 310 | 0.1421 |
| 0.1367 | 2.9 | 320 | 0.1425 |
| 0.1396 | 2.99 | 330 | 0.1426 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "allenai/OLMo-1B", "model-index": [{"name": "AOLM3", "results": []}]} | Litzy619/AOLM3 | null | [
"safetensors",
"generated_from_trainer",
"base_model:allenai/OLMo-1B",
"license:apache-2.0",
"region:us"
] | null | 2024-04-30T22:39:16+00:00 |
null | null | {} | dogukanbas/beit-base-patch16-224-finetuned | null | [
"region:us"
] | null | 2024-04-30T22:39:41+00:00 |
|
text-generation | transformers |
# llama3-8B-slerp-biomed-chat-chinese
llama3-8B-slerp-biomed-chat-chinese is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [shanchen/llama3-8B-slerp-med-chinese](https://huggingface.co/shanchen/llama3-8B-slerp-med-chinese)
* [shenzhi-wang/Llama3-8B-Chinese-Chat](https://huggingface.co/shenzhi-wang/Llama3-8B-Chinese-Chat)
## 🧩 Configuration
```yaml
slices:
- sources:
- model: shanchen/llama3-8B-slerp-med-chinese
layer_range: [0,32]
- model: shenzhi-wang/Llama3-8B-Chinese-Chat
layer_range: [0,32]
merge_method: slerp
base_model: shenzhi-wang/Llama3-8B-Chinese-Chat
parameters:
t:
- filter: self_attn
value: [0.3, 0.5, 0.5, 0.7, 1]
- filter: mlp
value: [1, 0.7, 0.5, 0.5, 0.3]
- value: 0.5
dtype: bfloat16
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer, AutoModelForCausalLM
model_id = "shanchen/llama3-8B-slerp-biomed-chat-chinese"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id, torch_dtype="auto", device_map="auto"
)
messages = [
{"role": "user", "content": "Can you speak Japanese?"},
]
input_ids = tokenizer.apply_chat_template(
messages, add_generation_prompt=True, return_tensors="pt"
).to(model.device)
outputs = model.generate(
input_ids,
max_new_tokens=192 max#8192,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
response = outputs[0][input_ids.shape[-1]:]
print(tokenizer.decode(response, skip_special_tokens=True))
``` | {"language": ["zh", "en"], "license": "llama3", "tags": ["merge", "mergekit", "lazymergekit", "shanchen/llama3-8B-slerp-med-chinese", "shenzhi-wang/Llama3-8B-Chinese-Chat"], "base_model": ["shanchen/llama3-8B-slerp-med-chinese", "shenzhi-wang/Llama3-8B-Chinese-Chat"]} | shanchen/llama3-8B-slerp-biomed-chat-chinese | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"shanchen/llama3-8B-slerp-med-chinese",
"shenzhi-wang/Llama3-8B-Chinese-Chat",
"conversational",
"zh",
"en",
"base_model:shanchen/llama3-8B-slerp-med-chinese",
"base_model:shenzhi-wang/Llama3-8B-Chinese-Chat",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T22:40:17+00:00 |
null | null | {} | squaadinc/1714516976470x972991601345298400 | null | [
"region:us"
] | null | 2024-04-30T22:42:59+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)
CodeQwen1.5-7B - GGUF
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwen/CodeQwen1.5-7B/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [CodeQwen1.5-7B.Q2_K.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q2_K.gguf) | Q2_K | 2.84GB |
| [CodeQwen1.5-7B.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.IQ3_XS.gguf) | IQ3_XS | 3.13GB |
| [CodeQwen1.5-7B.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.IQ3_S.gguf) | IQ3_S | 3.27GB |
| [CodeQwen1.5-7B.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q3_K_S.gguf) | Q3_K_S | 3.26GB |
| [CodeQwen1.5-7B.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.IQ3_M.gguf) | IQ3_M | 3.36GB |
| [CodeQwen1.5-7B.Q3_K.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q3_K.gguf) | Q3_K | 3.55GB |
| [CodeQwen1.5-7B.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q3_K_M.gguf) | Q3_K_M | 3.55GB |
| [CodeQwen1.5-7B.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q3_K_L.gguf) | Q3_K_L | 3.71GB |
| [CodeQwen1.5-7B.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.IQ4_XS.gguf) | IQ4_XS | 3.79GB |
| [CodeQwen1.5-7B.Q4_0.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q4_0.gguf) | Q4_0 | 3.89GB |
| [CodeQwen1.5-7B.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.IQ4_NL.gguf) | IQ4_NL | 3.94GB |
| [CodeQwen1.5-7B.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q4_K_S.gguf) | Q4_K_S | 4.11GB |
| [CodeQwen1.5-7B.Q4_K.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q4_K.gguf) | Q4_K | 4.41GB |
| [CodeQwen1.5-7B.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q4_K_M.gguf) | Q4_K_M | 4.41GB |
| [CodeQwen1.5-7B.Q4_1.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q4_1.gguf) | Q4_1 | 4.29GB |
| [CodeQwen1.5-7B.Q5_0.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q5_0.gguf) | Q5_0 | 4.69GB |
| [CodeQwen1.5-7B.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q5_K_S.gguf) | Q5_K_S | 4.79GB |
| [CodeQwen1.5-7B.Q5_K.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q5_K.gguf) | Q5_K | 5.06GB |
| [CodeQwen1.5-7B.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q5_K_M.gguf) | Q5_K_M | 5.06GB |
| [CodeQwen1.5-7B.Q5_1.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q5_1.gguf) | Q5_1 | 5.09GB |
| [CodeQwen1.5-7B.Q6_K.gguf](https://huggingface.co/RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf/blob/main/CodeQwen1.5-7B.Q6_K.gguf) | Q6_K | 5.94GB |
Original model description:
---
license: other
license_name: tongyi-qianwen-research
license_link: >-
https://huggingface.co/Qwen/CodeQwen1.5-7B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- pretrained
---
# CodeQwen1.5-7B
## Introduction
CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting long context understanding and generation with the context length of 64K tokens;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Usage
For the base language model, we do not advise you to use it for chat. You can use it for finetuning, and you can also use it for code infilling, code generation, etc., but please be careful about your stopping criteria.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
| {} | RichardErkhov/Qwen_-_CodeQwen1.5-7B-gguf | null | [
"gguf",
"region:us"
] | null | 2024-04-30T22:43:19+00:00 |
null | null | # 🌋 LLaVA: Large Language and Vision Assistant
*Visual instruction tuning towards large language and vision models with GPT-4 level capabilities.*
[📢 [LLaVA-NeXT Blog](https://llava-vl.github.io/blog/2024-01-30-llava-next/)] [[Project Page](https://llava-vl.github.io/)] [[Demo](https://llava.hliu.cc/)] [[Data](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)] [[Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)]
🤝Community Contributions: [[llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436)] [[Colab](https://github.com/camenduru/LLaVA-colab)] [[🤗Space](https://huggingface.co/spaces/badayvedat/LLaVA)] [[Replicate](https://replicate.com/yorickvp/llava-13b)] [[AutoGen](https://github.com/microsoft/autogen/blob/main/notebook/agentchat_lmm_llava.ipynb)] [[BakLLaVA](https://github.com/SkunkworksAI/BakLLaVA)]
**Improved Baselines with Visual Instruction Tuning** [[Paper](https://arxiv.org/abs/2310.03744)] [[HF](https://huggingface.co/papers/2310.03744)] <br>
[Haotian Liu](https://hliu.cc), [Chunyuan Li](https://chunyuan.li/), [Yuheng Li](https://yuheng-li.github.io/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/)
**Visual Instruction Tuning** (NeurIPS 2023, **Oral**) [[Paper](https://arxiv.org/abs/2304.08485)] [[HF](https://huggingface.co/papers/2304.08485)] <br>
[Haotian Liu*](https://hliu.cc), [Chunyuan Li*](https://chunyuan.li/), [Qingyang Wu](https://scholar.google.ca/citations?user=HDiw-TsAAAAJ&hl=en/), [Yong Jae Lee](https://pages.cs.wisc.edu/~yongjaelee/) (*Equal Contribution)
<!--p align="center">
<a href="https://llava.hliu.cc/"><img src="images/llava_logo.png" width="50%"></a> <br>
Generated by <a href="https://gligen.github.io/">GLIGEN</a> via "a cute lava llama with glasses" and box prompt
</p-->
## Release
- [1/30] 🔥 LLaVA-NeXT (LLaVA-1.6) is out! With additional scaling to LLaVA-1.5, LLaVA-NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the [blog post](https://llava-vl.github.io/blog/2024-01-30-llava-next/), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). Training/eval data and scripts coming soon.
- [11/10] [LLaVA-Plus](https://llava-vl.github.io/llava-plus/) is released: Learning to Use Tools for Creating Multimodal Agents, with LLaVA-Plus (LLaVA that Plug and Learn to Use Skills). [[Project Page](https://llava-vl.github.io/llava-plus/)] [[Demo](https://llavaplus.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Plus-Codebase)] [[Paper](https://arxiv.org/abs/2311.05437)]
- [11/2] [LLaVA-Interactive](https://llava-vl.github.io/llava-interactive/) is released: Experience the future of human-AI multimodal interaction with an all-in-one demo for Image Chat, Segmentation, Generation and Editing. [[Project Page](https://llava-vl.github.io/llava-interactive/)] [[Demo](https://llavainteractive.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo)] [[Paper](https://arxiv.org/abs/2311.00571)]
- [10/26] 🔥 LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement ([ckpts](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md#llava-v15), [script](https://github.com/haotian-liu/LLaVA#train)). We also provide a [doc](https://github.com/haotian-liu/LLaVA/blob/main/docs/Finetune_Custom_Data.md) on how to finetune LLaVA-1.5 on your own dataset with LoRA.
- [10/12] Check out the Korean LLaVA (Ko-LLaVA), created by ETRI, who has generously supported our research! [[🤗 Demo](https://huggingface.co/spaces/etri-vilab/Ko-LLaVA)]
- [10/5] 🔥 LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the [technical report](https://arxiv.org/abs/2310.03744), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
- [9/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project [[LLavA-RLHF]](https://llava-rlhf.github.io/)
- [9/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accepted by NeurIPS 2023 as **oral presentation**, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accepted by NeurIPS 2023 Datasets and Benchmarks Track as **spotlight presentation**.
<details>
<summary>More</summary>
- [11/6] Support **Intel** dGPU and CPU platforms. [More details here.](https://github.com/haotian-liu/LLaVA/tree/intel/docs/intel)
- [10/12] LLaVA is now supported in [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) with 4-bit / 5-bit quantization support!
- [10/11] The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
- [10/10] [Roboflow Deep Dive](https://blog.roboflow.com/first-impressions-with-llava-1-5/): First Impressions with LLaVA-1.5.
- [9/20] We summarize our empirical study of training 33B and 65B LLaVA models in a [note](https://arxiv.org/abs/2309.09958). Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper [``Multimodal Foundation Models: From Specialists to General-Purpose Assistants''.](https://arxiv.org/abs/2309.10020)
<p align="center">
<img src="https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings/blob/main/images/mfm_evolution.jpeg?raw=true" width=50%/>
</p>
- [7/19] 🔥 We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)!
- [6/26] [CVPR 2023 Tutorial](https://vlp-tutorial.github.io/) on **Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4**! Please check out [[Slides](https://datarelease.blob.core.windows.net/tutorial/vision_foundation_models_2023/slides/Chunyuan_cvpr2023_tutorial_lmm.pdf)] [[Notes](https://arxiv.org/abs/2306.14895)] [[YouTube](https://youtu.be/mkI7EPD1vp8)] [[Bilibli](https://www.bilibili.com/video/BV1Ng4y1T7v3/)].
- [6/11] We released the preview for the most requested feature: DeepSpeed and LoRA support! Please see documentations [here](./docs/LoRA.md).
- [6/1] We released **LLaVA-Med: Large Language and Vision Assistant for Biomedicine**, a step towards building biomedical domain large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2306.00890) and [page](https://github.com/microsoft/LLaVA-Med).
- [5/6] We are releasing [LLaVA-Lighting-MPT-7B-preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview), based on MPT-7B-Chat! See [here](#LLaVA-MPT-7b) for more details.
- [5/2] 🔥 We are releasing LLaVA-Lighting! Train a lite, multimodal GPT-4 with just $40 in 3 hours! See [here](#train-llava-lightning) for more details.
- [4/27] Thanks to the community effort, LLaVA-13B with 4-bit quantization allows you to run on a GPU with as few as 12GB VRAM! Try it out [here](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava).
- [4/17] 🔥 We released **LLaVA: Large Language and Vision Assistant**. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2304.08485) and [demo](https://llava.hliu.cc/).
</details>
<!-- <a href="https://llava.hliu.cc/"><img src="assets/demo.gif" width="70%"></a> -->
[](https://github.com/tatsu-lab/stanford_alpaca/blob/main/LICENSE)
**Usage and License Notices**: This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the [OpenAI Terms of Use](https://openai.com/policies/terms-of-use) for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. [Llama community license](https://ai.meta.com/llama/license/) for LLaMA-2 and Vicuna-v1.5). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
## Contents
- [Install](#install)
- [LLaVA Weights](#llava-weights)
- [Demo](#Demo)
- [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)
- [Dataset](https://github.com/haotian-liu/LLaVA/blob/main/docs/Data.md)
- [Train](#train)
- [Evaluation](#evaluation)
## Install
If you are not using Linux, do *NOT* proceed, see instructions for [macOS](https://github.com/haotian-liu/LLaVA/blob/main/docs/macOS.md) and [Windows](https://github.com/haotian-liu/LLaVA/blob/main/docs/Windows.md).
1. Clone this repository and navigate to LLaVA folder
```bash
git clone https://github.com/haotian-liu/LLaVA.git
cd LLaVA
```
2. Install Package
```Shell
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip # enable PEP 660 support
pip install -e .
```
3. Install additional packages for training cases
```
pip install -e ".[train]"
pip install flash-attn --no-build-isolation
```
### Upgrade to latest code base
```Shell
git pull
pip install -e .
# if you see some import errors when you upgrade, please try running the command below (without #)
# pip install flash-attn --no-build-isolation --no-cache-dir
```
### Quick Start With HuggingFace
<details>
<summary>Example Code</summary>
```Python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path
from llava.eval.run_llava import eval_model
model_path = "liuhaotian/llava-v1.5-7b"
tokenizer, model, image_processor, context_len = load_pretrained_model(
model_path=model_path,
model_base=None,
model_name=get_model_name_from_path(model_path)
)
```
Check out the details wth the `load_pretrained_model` function in `llava/model/builder.py`.
You can also use the `eval_model` function in `llava/eval/run_llava.py` to get the output easily. By doing so, you can use this code on Colab directly after downloading this repository.
``` python
model_path = "liuhaotian/llava-v1.5-7b"
prompt = "What are the things I should be cautious about when I visit here?"
image_file = "https://llava-vl.github.io/static/images/view.jpg"
args = type('Args', (), {
"model_path": model_path,
"model_base": None,
"model_name": get_model_name_from_path(model_path),
"query": prompt,
"conv_mode": None,
"image_file": image_file,
"sep": ",",
"temperature": 0,
"top_p": None,
"num_beams": 1,
"max_new_tokens": 512
})()
eval_model(args)
```
</details>
## LLaVA Weights
Please check out our [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md) for all public LLaVA checkpoints, and the instructions of how to use the weights.
## Demo
### Gradio Web UI
To launch a Gradio demo locally, please run the following commands one by one. If you plan to launch multiple model workers to compare between different checkpoints, you only need to launch the controller and the web server *ONCE*.
```mermaid
flowchart BT
%% Declare Nodes
gws("Gradio (UI Server)")
c("Controller (API Server):<br/>PORT: 10000")
mw7b("Model Worker:<br/>llava-v1.5-7b<br/>PORT: 40000")
mw13b("Model Worker:<br/>llava-v1.5-13b<br/>PORT: 40001")
sglw13b("SGLang Backend:<br/>llava-v1.6-34b<br/>http://localhost:30000")
lsglw13b("SGLang Worker:<br/>llava-v1.6-34b<br/>PORT: 40002")
%% Declare Styles
classDef data fill:#3af,stroke:#48a,stroke-width:2px,color:#444
classDef success fill:#8f8,stroke:#0a0,stroke-width:2px,color:#444
classDef failure fill:#f88,stroke:#f00,stroke-width:2px,color:#444
%% Assign Styles
class id,od data;
class cimg,cs_s,scsim_s success;
class ncimg,cs_f,scsim_f failure;
subgraph Demo Connections
direction BT
c<-->gws
mw7b<-->c
mw13b<-->c
lsglw13b<-->c
sglw13b<-->lsglw13b
end
```
#### Launch a controller
```Shell
python -m llava.serve.controller --host 0.0.0.0 --port 10000
```
#### Launch a gradio web server.
```Shell
python -m llava.serve.gradio_web_server --controller http://localhost:10000 --model-list-mode reload
```
You just launched the Gradio web interface. Now, you can open the web interface with the URL printed on the screen. You may notice that there is no model in the model list. Do not worry, as we have not launched any model worker yet. It will be automatically updated when you launch a model worker.
#### Launch a SGLang worker
This is the recommended way to serve LLaVA model with high throughput, and you need to install SGLang first. Note that currently `4-bit` quantization is not supported yet on SGLang-LLaVA, and if you have limited GPU VRAM, please check out model worker with [quantization](https://github.com/haotian-liu/LLaVA?tab=readme-ov-file#launch-a-model-worker-4-bit-8-bit-inference-quantized).
```Shell
pip install "sglang[all]"
```
You'll first launch a SGLang backend worker which will execute the models on GPUs. Remember the `--port` you've set and you'll use that later.
```Shell
# Single GPU
CUDA_VISIBLE_DEVICES=0 python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-7b --tokenizer-path llava-hf/llava-1.5-7b-hf --port 30000
# Multiple GPUs with tensor parallel
CUDA_VISIBLE_DEVICES=0,1 python3 -m sglang.launch_server --model-path liuhaotian/llava-v1.5-13b --tokenizer-path llava-hf/llava-1.5-13b-hf --port 30000 --tp 2
```
Tokenizers (temporary): `llava-hf/llava-1.5-7b-hf`, `llava-hf/llava-1.5-13b-hf`, `liuhaotian/llava-v1.6-34b-tokenizer`.
You'll then launch a LLaVA-SGLang worker that will communicate between LLaVA controller and SGLang backend to route the requests. Set `--sgl-endpoint` to `http://127.0.0.1:port` where `port` is the one you just set (default: 30000).
```Shell
python -m llava.serve.sglang_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --sgl-endpoint http://127.0.0.1:30000
```
#### Launch a model worker
This is the actual *worker* that performs the inference on the GPU. Each worker is responsible for a single model specified in `--model-path`.
```Shell
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b
```
Wait until the process finishes loading the model and you see "Uvicorn running on ...". Now, refresh your Gradio web UI, and you will see the model you just launched in the model list.
You can launch as many workers as you want, and compare between different model checkpoints in the same Gradio interface. Please keep the `--controller` the same, and modify the `--port` and `--worker` to a different port number for each worker.
```Shell
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port <different from 40000, say 40001> --worker http://localhost:<change accordingly, i.e. 40001> --model-path <ckpt2>
```
If you are using an Apple device with an M1 or M2 chip, you can specify the mps device by using the `--device` flag: `--device mps`.
#### Launch a model worker (Multiple GPUs, when GPU VRAM <= 24GB)
If the VRAM of your GPU is less than 24GB (e.g., RTX 3090, RTX 4090, etc.), you may try running it with multiple GPUs. Our latest code base will automatically try to use multiple GPUs if you have more than one GPU. You can specify which GPUs to use with `CUDA_VISIBLE_DEVICES`. Below is an example of running with the first two GPUs.
```Shell
CUDA_VISIBLE_DEVICES=0,1 python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b
```
#### Launch a model worker (4-bit, 8-bit inference, quantized)
You can launch the model worker with quantized bits (4-bit, 8-bit), which allows you to run the inference with reduced GPU memory footprint, potentially allowing you to run on a GPU with as few as 12GB VRAM. Note that inference with quantized bits may not be as accurate as the full-precision model. Simply append `--load-4bit` or `--load-8bit` to the **model worker** command that you are executing. Below is an example of running with 4-bit quantization.
```Shell
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1.5-13b --load-4bit
```
#### Launch a model worker (LoRA weights, unmerged)
You can launch the model worker with LoRA weights, without merging them with the base checkpoint, to save disk space. There will be additional loading time, while the inference speed is the same as the merged checkpoints. Unmerged LoRA checkpoints do not have `lora-merge` in the model name, and are usually much smaller (less than 1GB) than the merged checkpoints (13G for 7B, and 25G for 13B).
To load unmerged LoRA weights, you simply need to pass an additional argument `--model-base`, which is the base LLM that is used to train the LoRA weights. You can check the base LLM of each LoRA weights in the [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md).
```Shell
python -m llava.serve.model_worker --host 0.0.0.0 --controller http://localhost:10000 --port 40000 --worker http://localhost:40000 --model-path liuhaotian/llava-v1-0719-336px-lora-vicuna-13b-v1.3 --model-base lmsys/vicuna-13b-v1.3
```
### CLI Inference
Chat about images using LLaVA without the need of Gradio interface. It also supports multiple GPUs, 4-bit and 8-bit quantized inference. With 4-bit quantization, for our LLaVA-1.5-7B, it uses less than 8GB VRAM on a single GPU.
```Shell
python -m llava.serve.cli \
--model-path liuhaotian/llava-v1.5-7b \
--image-file "https://llava-vl.github.io/static/images/view.jpg" \
--load-4bit
```
<img src="images/demo_cli.gif" width="70%">
## Train
*Below is the latest training configuration for LLaVA v1.5. For legacy models, please refer to README of [this](https://github.com/haotian-liu/LLaVA/tree/v1.0.1) version for now. We'll add them in a separate doc later.*
LLaVA training consists of two stages: (1) feature alignment stage: use our 558K subset of the LAION-CC-SBU dataset to connect a *frozen pretrained* vision encoder to a *frozen LLM*; (2) visual instruction tuning stage: use 150K GPT-generated multimodal instruction-following data, plus around 515K VQA data from academic-oriented tasks, to teach the model to follow multimodal instructions.
LLaVA is trained on 8 A100 GPUs with 80GB memory. To train on fewer GPUs, you can reduce the `per_device_train_batch_size` and increase the `gradient_accumulation_steps` accordingly. Always keep the global batch size the same: `per_device_train_batch_size` x `gradient_accumulation_steps` x `num_gpus`.
### Hyperparameters
We use a similar set of hyperparameters as Vicuna in finetuning. Both hyperparameters used in pretraining and finetuning are provided below.
1. Pretraining
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
| --- | ---: | ---: | ---: | ---: | ---: |
| LLaVA-v1.5-13B | 256 | 1e-3 | 1 | 2048 | 0 |
2. Finetuning
| Hyperparameter | Global Batch Size | Learning rate | Epochs | Max length | Weight decay |
| --- | ---: | ---: | ---: | ---: | ---: |
| LLaVA-v1.5-13B | 128 | 2e-5 | 1 | 2048 | 0 |
### Download Vicuna checkpoints (automatically)
Our base model Vicuna v1.5, which is an instruction-tuned chatbot, will be downloaded automatically when you run our provided training scripts. No action is needed.
### Pretrain (feature alignment)
Please download the 558K subset of the LAION-CC-SBU dataset with BLIP captions we use in the paper [here](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain).
Pretrain takes around 5.5 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 3.5 hours for LLaVA-v1.5-7B.
Training script with DeepSpeed ZeRO-2: [`pretrain.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/pretrain.sh).
- `--mm_projector_type mlp2x_gelu`: the two-layer MLP vision-language connector.
- `--vision_tower openai/clip-vit-large-patch14-336`: CLIP ViT-L/14 336px.
<details>
<summary>Pretrain takes around 20 hours for LLaVA-7B on 8x V100 (32G)</summary>
We provide training script with DeepSpeed [here](https://github.com/haotian-liu/LLaVA/blob/main/scripts/pretrain_xformers.sh).
Tips:
- If you are using V100 which is not supported by FlashAttention, you can use the [memory-efficient attention](https://arxiv.org/abs/2112.05682) implemented in [xFormers](https://github.com/facebookresearch/xformers). Install xformers and replace `llava/train/train_mem.py` above with [llava/train/train_xformers.py](llava/train/train_xformers.py).
</details>
### Visual Instruction Tuning
1. Prepare data
Please download the annotation of the final mixture our instruction tuning data [llava_v1_5_mix665k.json](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K/blob/main/llava_v1_5_mix665k.json), and download the images from constituting datasets:
- COCO: [train2017](http://images.cocodataset.org/zips/train2017.zip)
- GQA: [images](https://downloads.cs.stanford.edu/nlp/data/gqa/images.zip)
- OCR-VQA: [download script](https://drive.google.com/drive/folders/1_GYPY5UkUy7HIcR0zq3ZCFgeZN7BAfm_?usp=sharing), **we save all files as `.jpg`**
- TextVQA: [train_val_images](https://dl.fbaipublicfiles.com/textvqa/images/train_val_images.zip)
- VisualGenome: [part1](https://cs.stanford.edu/people/rak248/VG_100K_2/images.zip), [part2](https://cs.stanford.edu/people/rak248/VG_100K_2/images2.zip)
After downloading all of them, organize the data as follows in `./playground/data`,
```
├── coco
│ └── train2017
├── gqa
│ └── images
├── ocr_vqa
│ └── images
├── textvqa
│ └── train_images
└── vg
├── VG_100K
└── VG_100K_2
```
2. Start training!
You may download our pretrained projectors in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). It is not recommended to use legacy projectors, as they may be trained with a different version of the codebase, and if any option is off, the model will not function/train as we expected.
Visual instruction tuning takes around 20 hours for LLaVA-v1.5-13B on 8x A100 (80G), due to the increased resolution to 336px. It takes around 10 hours for LLaVA-v1.5-7B on 8x A100 (40G).
Training script with DeepSpeed ZeRO-3: [`finetune.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune.sh).
If you are do not have enough GPU memory:
- Use LoRA: [`finetune_lora.sh`](https://github.com/haotian-liu/LLaVA/blob/main/scripts/v1_5/finetune_lora.sh). We are able to fit 13B training in 8-A100-40G/8-A6000, and 7B training in 8-RTX3090. Make sure `per_device_train_batch_size*gradient_accumulation_steps` is the same as the provided script for best reproducibility.
- Replace `zero3.json` with `zero3_offload.json` which offloads some parameters to CPU RAM. This slows down the training speed.
If you are interested in finetuning LLaVA model to your own task/data, please check out [`Finetune_Custom_Data.md`](https://github.com/haotian-liu/LLaVA/blob/main/docs/Finetune_Custom_Data.md)。
New options to note:
- `--mm_projector_type mlp2x_gelu`: the two-layer MLP vision-language connector.
- `--vision_tower openai/clip-vit-large-patch14-336`: CLIP ViT-L/14 336px.
- `--image_aspect_ratio pad`: this pads the non-square images to square, instead of cropping them; it slightly reduces hallucination.
- `--group_by_modality_length True`: this should only be used when your instruction tuning dataset contains both language (e.g. ShareGPT) and multimodal (e.g. LLaVA-Instruct). It makes the training sampler only sample a single modality (either image or language) during training, which we observe to speed up training by ~25%, and does not affect the final outcome.
## Evaluation
In LLaVA-1.5, we evaluate models on a diverse set of 12 benchmarks. To ensure the reproducibility, we evaluate the models with greedy decoding. We do not evaluate using beam search to make the inference process consistent with the chat demo of real-time outputs.
See [Evaluation.md](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md).
### GPT-assisted Evaluation
Our GPT-assisted evaluation pipeline for multimodal modeling is provided for a comprehensive understanding of the capabilities of vision-language models. Please see our paper for more details.
1. Generate LLaVA responses
```Shell
python model_vqa.py \
--model-path ./checkpoints/LLaVA-13B-v0 \
--question-file \
playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \
--image-folder \
/path/to/coco2014_val \
--answers-file \
/path/to/answer-file-our.jsonl
```
2. Evaluate the generated responses. In our case, [`answer-file-ref.jsonl`](./playground/data/coco2014_val_qa_eval/qa90_gpt4_answer.jsonl) is the response generated by text-only GPT-4 (0314), with the context captions/boxes provided.
```Shell
OPENAI_API_KEY="sk-***********************************" python llava/eval/eval_gpt_review_visual.py \
--question playground/data/coco2014_val_qa_eval/qa90_questions.jsonl \
--context llava/eval/table/caps_boxes_coco2014_val_80.jsonl \
--answer-list \
/path/to/answer-file-ref.jsonl \
/path/to/answer-file-our.jsonl \
--rule llava/eval/table/rule.json \
--output /path/to/review.json
```
3. Summarize the evaluation results
```Shell
python summarize_gpt_review.py
```
## Citation
If you find LLaVA useful for your research and applications, please cite using this BibTeX:
```bibtex
@misc{liu2024llavanext,
title={LLaVA-NeXT: Improved reasoning, OCR, and world knowledge},
url={https://llava-vl.github.io/blog/2024-01-30-llava-next/},
author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Li, Bo and Zhang, Yuanhan and Shen, Sheng and Lee, Yong Jae},
month={January},
year={2024}
}
@misc{liu2023improvedllava,
title={Improved Baselines with Visual Instruction Tuning},
author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
publisher={arXiv:2310.03744},
year={2023},
}
@misc{liu2023llava,
title={Visual Instruction Tuning},
author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
publisher={NeurIPS},
year={2023},
}
```
## Acknowledgement
- [Vicuna](https://github.com/lm-sys/FastChat): the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities!
## Related Projects
- [Instruction Tuning with GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://github.com/microsoft/LLaVA-Med)
- [Otter: In-Context Multi-Modal Instruction Tuning](https://github.com/Luodian/Otter)
For future project ideas, please check out:
- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)
- [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) to detect, segment, and generate anything by marrying [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment-Anything](https://github.com/facebookresearch/segment-anything).
| {} | multitensor/llava_mistral_cpu | null | [
"safetensors",
"arxiv:2310.03744",
"arxiv:2304.08485",
"arxiv:2311.05437",
"arxiv:2311.00571",
"arxiv:2306.00890",
"arxiv:2309.09958",
"arxiv:2309.10020",
"arxiv:2306.14895",
"arxiv:2112.05682",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:44:38+00:00 |
null | mlx |
# mlx-community/dolphin-2.9-llama3-8b-1m-8bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b-1m`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b-1m) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9-llama3-8b-1m-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "axolotl", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | mlx-community/dolphin-2.9-llama3-8b-1m-8bit | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"axolotl",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-30T22:45:14+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)
CodeQwen1.5-7B-Chat - bnb 4bits
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/
Original model description:
---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
# CodeQwen1.5-7B-Chat
## Introduction
CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting long context understanding and generation with the context length of 64K tokens;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/CodeQwen1.5-7B-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
prompt = "Write a quicksort algorithm in python."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
| {} | RichardErkhov/Qwen_-_CodeQwen1.5-7B-Chat-4bits | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-30T22:45:32+00:00 |
null | null | {} | stong/melotts1 | null | [
"region:us"
] | null | 2024-04-30T22:45:56+00:00 |
|
null | null | {} | stong/melotts10 | null | [
"region:us"
] | null | 2024-04-30T22:46:03+00:00 |
|
null | null | {} | stong/melotts11 | null | [
"region:us"
] | null | 2024-04-30T22:46:11+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# mT5.test.tedtalks.simple
This model is a fine-tuned version of [samzirbo/mT5.pretrained.en-es.16K](https://huggingface.co/samzirbo/mT5.pretrained.en-es.16K) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 2.4559
- eval_bleu: 23.0583
- eval_meteor: 0.5191
- eval_chrF++: 46.831
- eval_runtime: 59.5279
- eval_samples_per_second: 33.598
- eval_steps_per_second: 0.538
- epoch: 0.4360
- step: 4000
## 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: 0.0001
- 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: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 3
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "base_model": "samzirbo/mT5.pretrained.en-es.16K", "model-index": [{"name": "mT5.test.tedtalks.simple", "results": []}]} | samzirbo/mT5.test.tedtalks.simple | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"generated_from_trainer",
"base_model:samzirbo/mT5.pretrained.en-es.16K",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T22:46:18+00:00 |
null | null |
## FRANKENWEIGHTS
dunh dunh dunh...
You wouldn't think something like this might work, but it does. I took the text encoder from my "Storytime" model
and then blindly pasted it over the text encoder stuff in the SD 1.5 model and now we have 
It's ALIVE!!!:
Also included here is a somewhat "refined" version of the "Superposition" ComfyUI workflow called  This includes
the extra awesomesauce done by ByteDance with their work on  and their
 for SD1.5 models, which allows you to greatly reduce the steps required to produce an image --
Frakenweights in this context responds best to "normal" CFG ranges (5-8), especially with the Lora linked above. I generally
like to make things that will take a huge range of CFG values, but in this case everything hums along together so well
for my purposes that I'm not gonna mess with a good thing. We hack stuff and just try things in these parts, and it
just so happens that the combo of FrankenWeights, that Lora, and the "Superposition" bits make a truly Frank-en Stein
with enough ummph to bring all your wild ideas to life:
(all of the above come from the same prompts and settings)
So, get your grubby little hands (I'm sure space aliens consider us to be "grubby" and "little") on FrankenWeights today.
I have intentionally left this as a 32 bit model, because more often than not the "burn" in an image can be stretched
like tasty taffy back into reasonable colors using various image editing things like Photoshop. 32 bits per pixel gives
a _huge_ range of latitude for adjusting colors in such programs. I imagine most colorists would be in heaven if there
was some video codec that could work at 32 bits. So, welcome to, uh, "heaven" and go to town on even your burnt images
because there's all sorts of latitude with 32 bits.
| {"license": "creativeml-openrail-m"} | aplewe/FrankenWeights | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-04-30T22:47:11+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi10_LoRA
<Gallery />
## Model description
These are embracellm/sushi10_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Tuna Sushi Burrito to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi10_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Tuna Sushi Burrito ", "widget": []} | embracellm/sushi10_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-30T22:47:24+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: dhajnes/ppo-Pyramids
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["Pyramids", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Pyramids"]} | dhajnes/ppo-Pyramids | null | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | null | 2024-04-30T22:49:22+00:00 |
null | null | {} | squaadinc/1714517376319x926633127433994200 | null | [
"region:us"
] | null | 2024-04-30T22:49:40+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]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adapterhappy2sad-1k-search-3iter-50-0.0006 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:50:14+00:00 |
null | null | {} | roger070604/low-level-img-proc-finetuned | null | [
"region:us"
] | null | 2024-04-30T22:52:01+00:00 |
|
null | null | {"license": "openrail"} | Loren85/Willow-Park-THO-Dub-Ita | null | [
"license:openrail",
"region:us"
] | null | 2024-04-30T22:53:05+00:00 |
|
image-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# beit-base-patch16-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4134
- Accuracy: 0.8333
## Model description
55 dişleri tam croplandı
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: 100
- eval_batch_size: 100
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 400
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| No log | 0.6667 | 1 | 0.8100 | 0.5444 |
| No log | 2.0 | 3 | 0.7775 | 0.5333 |
| No log | 2.6667 | 4 | 0.8855 | 0.4778 |
| No log | 4.0 | 6 | 0.6365 | 0.6889 |
| No log | 4.6667 | 7 | 0.6099 | 0.7 |
| No log | 6.0 | 9 | 0.5443 | 0.7667 |
| 0.7372 | 6.6667 | 10 | 0.5721 | 0.7222 |
| 0.7372 | 8.0 | 12 | 0.8054 | 0.5556 |
| 0.7372 | 8.6667 | 13 | 0.6216 | 0.6444 |
| 0.7372 | 10.0 | 15 | 0.4596 | 0.8111 |
| 0.7372 | 10.6667 | 16 | 0.4567 | 0.7778 |
| 0.7372 | 12.0 | 18 | 0.4290 | 0.7889 |
| 0.7372 | 12.6667 | 19 | 0.4848 | 0.7667 |
| 0.6173 | 14.0 | 21 | 0.4288 | 0.7889 |
| 0.6173 | 14.6667 | 22 | 0.4281 | 0.7889 |
| 0.6173 | 16.0 | 24 | 0.4205 | 0.7667 |
| 0.6173 | 16.6667 | 25 | 0.4196 | 0.8 |
| 0.6173 | 18.0 | 27 | 0.4110 | 0.7889 |
| 0.6173 | 18.6667 | 28 | 0.4012 | 0.8 |
| 0.554 | 20.0 | 30 | 0.4673 | 0.7556 |
| 0.554 | 20.6667 | 31 | 0.4388 | 0.7556 |
| 0.554 | 22.0 | 33 | 0.5693 | 0.7889 |
| 0.554 | 22.6667 | 34 | 0.6131 | 0.7222 |
| 0.554 | 24.0 | 36 | 0.4064 | 0.7889 |
| 0.554 | 24.6667 | 37 | 0.4220 | 0.7778 |
| 0.554 | 26.0 | 39 | 0.4368 | 0.8111 |
| 0.5254 | 26.6667 | 40 | 0.5164 | 0.7778 |
| 0.5254 | 28.0 | 42 | 0.4945 | 0.7778 |
| 0.5254 | 28.6667 | 43 | 0.4292 | 0.7889 |
| 0.5254 | 30.0 | 45 | 0.4292 | 0.7889 |
| 0.5254 | 30.6667 | 46 | 0.5601 | 0.7556 |
| 0.5254 | 32.0 | 48 | 0.5608 | 0.7444 |
| 0.5254 | 32.6667 | 49 | 0.4134 | 0.8333 |
| 0.4745 | 34.0 | 51 | 0.4186 | 0.7889 |
| 0.4745 | 34.6667 | 52 | 0.4224 | 0.8111 |
| 0.4745 | 36.0 | 54 | 0.4263 | 0.8111 |
| 0.4745 | 36.6667 | 55 | 0.4354 | 0.8 |
| 0.4745 | 38.0 | 57 | 0.4275 | 0.8111 |
| 0.4745 | 38.6667 | 58 | 0.4467 | 0.8 |
| 0.4425 | 40.0 | 60 | 0.4258 | 0.8222 |
| 0.4425 | 40.6667 | 61 | 0.4194 | 0.8222 |
| 0.4425 | 42.0 | 63 | 0.4258 | 0.8222 |
| 0.4425 | 42.6667 | 64 | 0.4206 | 0.7889 |
| 0.4425 | 44.0 | 66 | 0.4352 | 0.8 |
| 0.4425 | 44.6667 | 67 | 0.4285 | 0.8 |
| 0.4425 | 46.0 | 69 | 0.4520 | 0.7889 |
| 0.4537 | 46.6667 | 70 | 0.4706 | 0.7889 |
| 0.4537 | 48.0 | 72 | 0.4700 | 0.7889 |
| 0.4537 | 48.6667 | 73 | 0.4504 | 0.8 |
| 0.4537 | 50.0 | 75 | 0.4446 | 0.8 |
| 0.4537 | 50.6667 | 76 | 0.4488 | 0.8111 |
| 0.4537 | 52.0 | 78 | 0.4405 | 0.8111 |
| 0.4537 | 52.6667 | 79 | 0.4307 | 0.8 |
| 0.3978 | 54.0 | 81 | 0.4557 | 0.7889 |
| 0.3978 | 54.6667 | 82 | 0.4571 | 0.7889 |
| 0.3978 | 56.0 | 84 | 0.4404 | 0.8 |
| 0.3978 | 56.6667 | 85 | 0.4422 | 0.8111 |
| 0.3978 | 58.0 | 87 | 0.4525 | 0.8 |
| 0.3978 | 58.6667 | 88 | 0.4557 | 0.8 |
| 0.3853 | 60.0 | 90 | 0.4624 | 0.7889 |
| 0.3853 | 60.6667 | 91 | 0.4570 | 0.8 |
| 0.3853 | 62.0 | 93 | 0.4533 | 0.8 |
| 0.3853 | 62.6667 | 94 | 0.4520 | 0.7889 |
| 0.3853 | 64.0 | 96 | 0.4500 | 0.7889 |
| 0.3853 | 64.6667 | 97 | 0.4505 | 0.7889 |
| 0.3853 | 66.0 | 99 | 0.4514 | 0.7889 |
| 0.3917 | 66.6667 | 100 | 0.4517 | 0.7889 |
### 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": ["imagefolder"], "metrics": ["accuracy"], "base_model": "microsoft/beit-base-patch16-224", "model-index": [{"name": "beit-base-patch16-224-finetuned-eurosat", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.8333333333333334, "name": "Accuracy"}]}]}]} | BilalMuftuoglu/beit-base-patch16-224-finetuned-eurosat | null | [
"transformers",
"tensorboard",
"safetensors",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:55:24+00:00 |
null | null | {} | Kittech/whisper-small-sn | null | [
"region:us"
] | null | 2024-04-30T22:55:46+00:00 |
|
null | null | {} | DiogoBraganca/FitBell | null | [
"region:us"
] | null | 2024-04-30T22:55:54+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)
CodeQwen1.5-7B-Chat - bnb 8bits
- Model creator: https://huggingface.co/Qwen/
- Original model: https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/
Original model description:
---
license: other
license_name: tongyi-qianwen
license_link: >-
https://huggingface.co/Qwen/CodeQwen1.5-7B-Chat/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
---
# CodeQwen1.5-7B-Chat
## Introduction
CodeQwen1.5 is the Code-Specific version of Qwen1.5. It is a transformer-based decoder-only language model pretrained on a large amount of data of codes.
* Strong code generation capabilities and competitve performance across a series of benchmarks;
* Supporting long context understanding and generation with the context length of 64K tokens;
* Supporting 92 coding languages
* Excellent performance in text-to-SQL, bug fix, etc.
For more details, please refer to our [blog post](https://qwenlm.github.io/blog/codeqwen1.5/) and [GitHub repo](https://github.com/QwenLM/Qwen1.5).
## Model Details
CodeQwen1.5 is based on Qwen1.5, a language model series including decoder language models of different model sizes. It is trained on 3 trillion tokens of data of codes, and it includes group query attention (GQA) for efficient inference.
## Requirements
The code of Qwen1.5 has been in the latest Hugging face transformers and we advise you to install `transformers>=4.37.0`, or you might encounter the following error:
```
KeyError: 'qwen2'.
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # the device to load the model onto
model = AutoModelForCausalLM.from_pretrained(
"Qwen/CodeQwen1.5-7B-Chat",
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained("Qwen/CodeQwen1.5-7B-Chat")
prompt = "Write a quicksort algorithm in python."
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(device)
generated_ids = model.generate(
model_inputs.input_ids,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
## Tips
* If you encounter code switching or other bad cases, we advise you to use our provided hyper-parameters in `generation_config.json`.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{qwen,
title={Qwen Technical Report},
author={Jinze Bai and Shuai Bai and Yunfei Chu and Zeyu Cui and Kai Dang and Xiaodong Deng and Yang Fan and Wenbin Ge and Yu Han and Fei Huang and Binyuan Hui and Luo Ji and Mei Li and Junyang Lin and Runji Lin and Dayiheng Liu and Gao Liu and Chengqiang Lu and Keming Lu and Jianxin Ma and Rui Men and Xingzhang Ren and Xuancheng Ren and Chuanqi Tan and Sinan Tan and Jianhong Tu and Peng Wang and Shijie Wang and Wei Wang and Shengguang Wu and Benfeng Xu and Jin Xu and An Yang and Hao Yang and Jian Yang and Shusheng Yang and Yang Yao and Bowen Yu and Hongyi Yuan and Zheng Yuan and Jianwei Zhang and Xingxuan Zhang and Yichang Zhang and Zhenru Zhang and Chang Zhou and Jingren Zhou and Xiaohuan Zhou and Tianhang Zhu},
journal={arXiv preprint arXiv:2309.16609},
year={2023}
}
```
| {} | RichardErkhov/Qwen_-_CodeQwen1.5-7B-Chat-8bits | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-04-30T22:57:47+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/abacusai/Llama-3-Giraffe-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Llama-3-Giraffe-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/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.IQ3_XS.gguf) | IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.IQ3_S.gguf) | IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.IQ3_M.gguf) | IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-GGUF/resolve/main/Llama-3-Giraffe-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": ["meta", "llama-3"], "base_model": "abacusai/Llama-3-Giraffe-70B", "quantized_by": "mradermacher"} | mradermacher/Llama-3-Giraffe-70B-GGUF | null | [
"transformers",
"gguf",
"meta",
"llama-3",
"en",
"base_model:abacusai/Llama-3-Giraffe-70B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T22:58:07+00:00 |
null | null | {} | Hazza1/Foster | null | [
"region:us"
] | null | 2024-04-30T22:58:28+00:00 |
|
summarization | 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. -->
# BioNLP-tech-decoder-eLife
This model was trained from scratch on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.3739167643078955e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 1.13.1+cu117
- Datasets 2.16.1
- Tokenizers 0.15.2
| {"tags": ["summarization", "generated_from_trainer"], "model-index": [{"name": "BioNLP-tech-decoder-eLife", "results": []}]} | dtorber/BioNLP-tech-decoder-eLife | null | [
"transformers",
"safetensors",
"led",
"text2text-generation",
"summarization",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T23:02:09+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | lunarsylph/mooncell_v41 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:03:36+00:00 |
null | null | {} | stong/melotts12 | null | [
"region:us"
] | null | 2024-04-30T23:04:21+00:00 |
|
null | null | {} | stong/melotts13 | null | [
"region:us"
] | null | 2024-04-30T23:04:33+00:00 |
|
text-generation | transformers | # Llama 3 70B Instruct Storywriter
Llama 3 70B Instruct, further finetuned on a dataset consisting of books in the fiction genre.
This was just an experiment, but it turned out well enough that I'm sharing it. The finetuning has caused a significant shift in the model's writing style, and seems to have made it more creative. There may be a slight decrease in overall intelligence.
Because this was trained on Instruct, you can use the normal Instruct chat formatting. It may also work well in raw completion mode.
## Training details
Trained on 4 4090s using [qlora-pipe](https://github.com/tdrussell/qlora-pipe).
Dataset consists of about 800 books in the fiction genre, totaling 570 MB of raw text.
Rank 64 QLoRA trained at 8192 sequence length.
### Evaluation metrics
<img src="https://i.imgur.com/sCMjix4.png" width="800" />
## Why no 8B?
I tried multiple times to train this on Llama 3 8B Instruct, using a variety of hyperparameters. It never worked well. The model took a huge hit to intelligence every time, to the point of being unusable. 70B fared much better. I don't know why, maybe 8B is just too small for this type of technique, and loses too much of the instruction-tuned smarts. | {} | tdrussell/Llama-3-70B-Instruct-Storywriter | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:04:41+00:00 |
null | null | {"license": "openrail"} | itskeonagain/Anatchaya | null | [
"license:openrail",
"region:us"
] | null | 2024-04-30T23:06:14+00:00 |
|
image-text-to-text | xtuner |
# mlx-community/llava-llama-3-8b-v1_1-8bit
This model was converted to MLX format from [`xtuner/llava-llama-3-8b-v1_1-transformers`]() using mlx-vllm version **0.0.3**.
Refer to the [original model card](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-transformers) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model mlx-community/llava-llama-3-8b-v1_1-8bit --max-tokens 100 --temp 0.0
```
| {"library_name": "xtuner", "tags": ["mlx"], "datasets": ["Lin-Chen/ShareGPT4V"], "pipeline_tag": "image-text-to-text"} | mlx-community/llava-llama-3-8b-v1_1-8bit | null | [
"xtuner",
"safetensors",
"llava",
"mlx",
"image-text-to-text",
"dataset:Lin-Chen/ShareGPT4V",
"region:us"
] | null | 2024-04-30T23:06:53+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. -->
# final_classifications
This model is a fine-tuned version of [yhavinga/t5-small-24L-ccmatrix-multi](https://huggingface.co/yhavinga/t5-small-24L-ccmatrix-multi) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1005
- F1: {'f1': 0.9592760180995475}
- Precision: {'precision': 0.954954954954955}
- Recall: {'recall': 0.9636363636363636}
## 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: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Precision | Recall |
|:-------------:|:-----:|:----:|:---------------:|:--------------------------:|:---------------------------------:|:------------------------------:|
| No log | 1.0 | 110 | 0.2362 | {'f1': 0.0} | {'precision': 0.0} | {'recall': 0.0} |
| No log | 2.0 | 220 | 0.1164 | {'f1': 0.9502262443438914} | {'precision': 0.9459459459459459} | {'recall': 0.9545454545454546} |
| No log | 3.0 | 330 | 0.0832 | {'f1': 0.9596412556053813} | {'precision': 0.9469026548672567} | {'recall': 0.9727272727272728} |
| No log | 4.0 | 440 | 0.0918 | {'f1': 0.9549549549549549} | {'precision': 0.9464285714285714} | {'recall': 0.9636363636363636} |
| 0.1554 | 5.0 | 550 | 0.0939 | {'f1': 0.9596412556053813} | {'precision': 0.9469026548672567} | {'recall': 0.9727272727272728} |
| 0.1554 | 6.0 | 660 | 0.1005 | {'f1': 0.9592760180995475} | {'precision': 0.954954954954955} | {'recall': 0.9636363636363636} |
### 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"], "metrics": ["f1", "precision", "recall"], "base_model": "yhavinga/t5-small-24L-ccmatrix-multi", "model-index": [{"name": "final_classifications", "results": []}]} | nizarh1999/final_classifications | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:yhavinga/t5-small-24L-ccmatrix-multi",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:08:53+00:00 |
text-generation | transformers |
# Uploaded model
- **Developed by:** Arara10
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-bnb-4bit
This mistral 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", "mistral", "trl", "sft"], "base_model": "unsloth/mistral-7b-bnb-4bit"} | Arara10/wolf_coder_mistral_7b_bnb_4bit | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/mistral-7b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T23:09:49+00:00 |
null | null | {"license": "cc-by-nc-sa-2.0"} | Gunjan96/Modi | null | [
"license:cc-by-nc-sa-2.0",
"region:us"
] | null | 2024-04-30T23:10:00+00:00 |
|
text-generation | transformers |
<h1 align='center' style='font-size: 36px; font-weight: bold;'>LlamaKatz</h1>
<h3 align='center' style='font-size: 24px;'>Mixture of Llama Experts 3x8B</h3>
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/650c74479e898eb6d2017bfa/iMiFAKee8W6jILhnMX0-a.webp" width="60%" height="auto"/>
</p> | {"language": ["en"], "license": "apache-2.0"} | deepapaikar/LlamaKatz-3x8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:10:06+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi11_LoRA
<Gallery />
## Model description
These are embracellm/sushi11_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Pacific Combo to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi11_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Pacific Combo ", "widget": []} | embracellm/sushi11_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-04-30T23:11:04+00:00 |
null | null | {} | Sama13/tay | null | [
"region:us"
] | null | 2024-04-30T23:12:47+00:00 |
|
sentence-similarity | sentence-transformers |
# savdar/snowflake-ft-camelids-l
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('savdar/snowflake-ft-camelids-l')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=savdar/snowflake-ft-camelids-l)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 20 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 8,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | savdar/snowflake-ft-camelids-l | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T23:14:43+00:00 |
text-generation | transformers | {} | Tristan/pythia-14m-en | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:16:59+00:00 |
|
null | null | {"license": "openrail"} | ryuseiken/Gladys-LBT | null | [
"license:openrail",
"region:us"
] | null | 2024-04-30T23:17:18+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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- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **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
<!-- 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": []} | HC-85/distilbert-lora-arxiv-multilabel-b16 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T23:17:44+00:00 |
null | null | {} | Silver088/Hi | null | [
"region:us"
] | null | 2024-04-30T23:18:21+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] | {"license": "apache-2.0", "library_name": "transformers"} | chihoonlee10/T3Q-LLM-MG-DPO-v1.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:18:34+00:00 |
null | null | {} | snakesss/ogre | null | [
"region:us"
] | null | 2024-04-30T23:20:45+00:00 |
|
sentence-similarity | sentence-transformers |
# jjovalle99/snowflake-ft-camelids-l
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 1024 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('jjovalle99/snowflake-ft-camelids-l')
embeddings = model.encode(sentences)
print(embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=jjovalle99/snowflake-ft-camelids-l)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 20 with parameters:
```
{'batch_size': 10, 'sampler': 'torch.utils.data.sampler.SequentialSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesRankingLoss.MultipleNegativesRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 4,
"evaluation_steps": 50,
"evaluator": "sentence_transformers.evaluation.InformationRetrievalEvaluator.InformationRetrievalEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 8,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity"], "pipeline_tag": "sentence-similarity"} | jjovalle99/snowflake-ft-camelids-l | null | [
"sentence-transformers",
"safetensors",
"bert",
"feature-extraction",
"sentence-similarity",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T23:21:40+00:00 |
text-generation | null |
## Exllama v2 Quantizations of Llama-3-8B-Instruct-Gradient-1048k
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.20">turboderp's ExLlamaV2 v0.0.20</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/gradientai/Llama-3-8B-Instruct-Gradient-1048k
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2 Llama-3-8B-Instruct-Gradient-1048k-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2 --revision 6_5 --local-dir Llama-3-8B-Instruct-Gradient-1048k-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2 --revision 6_5 --local-dir Llama-3-8B-Instruct-Gradient-1048k-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "license": "llama3", "tags": ["meta", "llama-3"], "pipeline_tag": "text-generation", "quantized_by": "bartowski"} | bartowski/Llama-3-8B-Instruct-Gradient-1048k-exl2 | null | [
"meta",
"llama-3",
"text-generation",
"en",
"license:llama3",
"region:us"
] | null | 2024-04-30T23:22:18+00:00 |
text-to-image | diffusers |
# Tamarin_XL API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "tamarinxl"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/tamarinxl)
Model link: [View model](https://modelslab.com/models/tamarinxl)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "tamarinxl",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/tamarinxl | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-30T23:22:39+00:00 |
null | null | {"license": "unknown"} | Thatsnazzyartist22/Camilo | null | [
"license:unknown",
"region:us"
] | null | 2024-04-30T23:23:08+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. -->
# layoutlmv3-funsd-finetuned
This model is a fine-tuned version of [microsoft/layoutlmv3-base](https://huggingface.co/microsoft/layoutlmv3-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6391
- Precision: 0.8943
- Recall: 0.9121
- F1: 0.9031
- Accuracy: 0.8540
## 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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 1000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.3333 | 100 | 0.6387 | 0.7375 | 0.8306 | 0.7813 | 0.7786 |
| No log | 2.6667 | 200 | 0.5473 | 0.8270 | 0.8718 | 0.8489 | 0.8184 |
| No log | 4.0 | 300 | 0.5020 | 0.8514 | 0.8877 | 0.8692 | 0.8278 |
| No log | 5.3333 | 400 | 0.5326 | 0.8610 | 0.9016 | 0.8809 | 0.8293 |
| 0.5379 | 6.6667 | 500 | 0.5600 | 0.8656 | 0.8987 | 0.8818 | 0.8451 |
| 0.5379 | 8.0 | 600 | 0.5903 | 0.8778 | 0.8917 | 0.8847 | 0.8357 |
| 0.5379 | 9.3333 | 700 | 0.6079 | 0.8859 | 0.9026 | 0.8942 | 0.8470 |
| 0.5379 | 10.6667 | 800 | 0.6449 | 0.8978 | 0.9076 | 0.9027 | 0.8458 |
| 0.5379 | 12.0 | 900 | 0.6410 | 0.8934 | 0.9116 | 0.9024 | 0.8582 |
| 0.1248 | 13.3333 | 1000 | 0.6391 | 0.8943 | 0.9121 | 0.9031 | 0.8540 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "cc-by-nc-sa-4.0", "tags": ["generated_from_trainer"], "metrics": ["precision", "recall", "f1", "accuracy"], "base_model": "microsoft/layoutlmv3-base", "model-index": [{"name": "layoutlmv3-funsd-finetuned", "results": []}]} | Negus/layoutlmv3-funsd-finetuned | null | [
"transformers",
"tensorboard",
"safetensors",
"layoutlmv3",
"token-classification",
"generated_from_trainer",
"base_model:microsoft/layoutlmv3-base",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-30T23:23:37+00:00 |
text-to-image | diffusers |
# Booltning XL v1 API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "booltning-xl-v1"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/booltning-xl-v1)
Model link: [View model](https://modelslab.com/models/booltning-xl-v1)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "booltning-xl-v1",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/booltning-xl-v1 | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-30T23:29:50+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": []} | terry69/llama2-poison-20p-full | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:29:54+00:00 |
null | null | {} | andyzeeinpe/nModelsXL | null | [
"region:us"
] | null | 2024-04-30T23:30:04+00:00 |
|
text-generation | transformers | # from_mistral_7b4-1714514853051
Description of the model.
| {"tags": ["fine-tuned", "abc123"], "languages": ["English"]} | brandonironbirdlabs/archive_from_mistral_7b4-1714514853051 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"fine-tuned",
"abc123",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-30T23:30:11+00:00 |
null | null | {"license": "openrail"} | otmanabs/MOCKER | null | [
"safetensors",
"license:openrail",
"region:us"
] | null | 2024-04-30T23:31:18+00:00 |
|
text-to-image | diffusers |
# Tamarin XL v1 API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "tamarin-xl-v1"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/tamarin-xl-v1)
Model link: [View model](https://modelslab.com/models/tamarin-xl-v1)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "tamarin-xl-v1",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/tamarin-xl-v1 | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-04-30T23:32:20+00:00 |
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