modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-28 06:27:35
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 500
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-06-28 06:24:42
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
TroyDoesAI/BlackSheep-RP-3xMoE | TroyDoesAI | 2024-10-18T02:16:27Z | 10 | 0 | null | [
"pytorch",
"safetensors",
"mixtral",
"license:artistic-2.0",
"region:us"
] | null | 2024-08-28T22:52:39Z | ---
license: artistic-2.0
---
Base Model : TroyDoesAI/BlackSheep-4B
Overview
The difference between training an LLM on a single persona (e.g., the `<|assistant|>` role focused on positivity and confidence) versus using a dataset format that dynamically assigns personas (like in the earlier prompt format) would significantly impact the model’s behavior, flexibility, and adaptability. Let’s compare the two approaches and how they would affect the model’s ability to generate responses optimally.
### Single Persona (Traditional `<|assistant|>` Role):
In the traditional format, the model assumes a fixed persona (`<|assistant|>`) that typically focuses on being helpful, positive, confident, and neutral. Here's how this affects the LLM:
#### Characteristics of `<|assistant|>`-Only Training:
1. **Consistency**:
- The model will consistently exhibit positivity, confidence, and helpfulness in its responses. It’s predictable and uniform, which can be ideal for customer service, general inquiries, or providing factual information.
- There’s no need to switch between different personas or emotional states because the model is hard-anchored to a specific type of interaction.
2. **Limited Flexibility**:
- Since the model is only trained in one voice (positive, confident), it struggles to adapt to other contexts where different emotional tones, levels of depth, or character-specific behaviors are needed.
- For example, the model may find it difficult to take on complex personas that require vulnerability, shyness, or even negative emotional states like anger or confusion.
3. **Generic Dialogue**:
- The focus on confidence and positivity means the model tends to generate more generalized, surface-level responses. Even in creative contexts, it might be more inclined to "play it safe" by being overly helpful or encouraging without diving deep into unique personalities or scenarios.
- This approach is ideal for applications requiring straightforward, consistent responses (like a friendly virtual assistant or customer support chatbot), but it doesn’t perform well for character-driven storytelling, role-playing, or immersive scenarios.
4. **Predictable Emotional Arc**:
- Since the model is hardwired for confidence and positivity, it often fails to reflect complex emotions or a diverse emotional arc (e.g., shifting from shy to brave, or from fear to excitement).
### Dynamic Persona Switching (Dataset Dictating Characters):
In the dynamic persona-driven format (where the dataset assigns who’s speaking, such as `<|Ariana|>`, `<|Daiki|>`, etc.), the LLM learns to embody multiple, distinct personalities, adapting its responses based on the specific character assigned in each interaction.
#### Characteristics of Persona-Based Training:
1. **Persona Diversity**:
- The model is trained to take on different personas, each with its own traits, backstories, emotional states, and goals. It doesn’t always speak with the same voice; instead, it adapts its behavior to the character or context at hand.
- In the example of Ariana, the model learns to be confident, flirtatious, and emotionally complex. For Daiki, it learns to embody awkwardness, shyness, or nerdy charm.
2. **Emotional and Contextual Flexibility**:
- The LLM can handle a wide range of emotions, tones, and narrative progressions. It can switch from one emotional state to another depending on the character and scenario.
- For instance, Ariana can show vulnerability despite her confident exterior, while Daiki might exhibit a transformation from awkwardness to emotional openness over the course of the conversation.
3. **Rich, Character-Driven Responses**:
- By giving the model context-specific personas, the responses become more nuanced and immersive. Each reply isn’t just informative or positive; it aligns with the emotional and psychological depth of the character.
- For example, the model might generate dialogue that moves the story forward, revealing hidden emotions or intentions that align with the character's backstory (e.g., Ariana realizing deeper feelings for Daiki in an intimate moment).
4. **Scenario-Specific Adaptation**:
- The model’s responses are anchored not just by the persona, but by the situation. In a role-playing setting, for example, it could transition between different characters based on whose perspective it’s generating at the moment.
- It’s not bound to the same emotional trajectory for every response (like the `<|assistant|>` format); instead, it can reflect the emotional arc of the character or the shifting dynamics of the interaction.
### How Dynamic Persona Improves Performance:
1. **Improved Immersive Storytelling**:
- In applications like interactive fiction, role-playing games, or any context where characters need to exhibit distinct personalities, the persona-driven dataset approach would drastically improve immersion. The model doesn’t just provide answers—it embodies the character fully, responding in line with their motivations, emotional state, and persona arc.
- This is critical for games, simulations, or narrative-driven platforms, where characters must seem real and multi-dimensional.
2. **Enhanced Creative Flexibility**:
- Dynamic personas allow the model to express a broader range of creative, emotional, and scenario-driven responses. It’s not just about positivity and confidence—it could handle characters that are timid, angry, confused, or mischievous. This leads to much richer dialogue interactions.
- For instance, when characters interact, the model can generate more believable, layered conversations that reflect real emotional dynamics, rather than sticking to a “confident helper” role.
3. **More Natural and Believable Dialogue**:
- By embedding unique personas, the LLM avoids the generic quality that often comes from a one-size-fits-all approach. Instead, each character’s response feels tailored to the moment, driving the story forward with emotional depth and personality traits specific to the situation.
- For example, Ariana’s dialogue is flirtatious and reflective, while Daiki’s would be awkward and hesitant. The model learns to shift styles based on which persona it’s playing, making interactions feel more organic and authentic.
4. **Role Switching and Adaptation**:
- With this persona-driven format, the model could switch between characters seamlessly, assuming the voice of one character for a stretch and then switching to another as needed. This ability is crucial for multi-character dialogues in games, collaborative storytelling, or simulations.
### Comparison of Impact on LLM Behavior:
| **Feature** | **Single Persona (Assistant)** | **Dynamic Persona (Per Entry)** |
|----------------------------------|--------------------------------------------------------------------------|---------------------------------------------------------------|
| **Character Flexibility** | Limited to one persona (confidence, positivity) | Can assume a variety of distinct characters with unique traits |
| **Emotional Range** | Restricted (positive, helpful, confident) | Broad emotional range, reflecting the character’s personality |
| **Scenario-Specific Responses** | Generalized, consistent responses | Tailored responses based on persona and scenario |
| **Storytelling Capabilities** | Limited to simple, linear narrative generation | Complex, immersive storytelling with diverse characters |
| **Adaptability** | Less adaptable to nuanced contexts or situations | Adapts responses to fit the emotional tone and scene at hand |
| **Dialog Quality** | Predictable, positive, but can become formulaic | Nuanced, character-driven dialogue that feels more authentic |
| **Creativity** | Constrained by a consistent tone and emotional profile | High creativity, allowing for deeper engagement and emotional shifts |
### Conclusion:
Training the LLM with a persona-driven format (where each dataset entry specifies who’s talking and how they should react) would significantly increase its adaptability, emotional depth, and immersion. Instead of responding with a generic, consistent voice (as in the `<|assistant|>` format), the model can switch between personas, reflect complex emotional arcs, and deliver more nuanced, scenario-specific dialogue. This makes it far more suitable for applications requiring rich, character-driven interactions, such as role-playing games, simulations, or interactive storytelling platforms.
|
neulab/UIX-Qwen2-Mind2Web | neulab | 2024-10-18T02:15:07Z | 133 | 3 | null | [
"safetensors",
"qwen2",
"arxiv:2410.13824",
"license:odc-by",
"region:us"
] | null | 2024-09-29T09:13:03Z | ---
license: odc-by
---
#### Model for the paper: [Harnessing Webpage Uis For Text Rich Visual Understanding](https://arxiv.org/abs/2410.13824)
🌐 [Homepage](https://neulab.github.io/MultiUI/) | 🐍 [GitHub](https://github.com/neulab/multiui) | 📖 [arXiv](https://arxiv.org/abs/2410.13824)
## Introduction
We introduce **MultiUI**, a dataset containing 7.3 million samples from 1 million websites, covering diverse multi- modal tasks and UI layouts. Models trained on **MultiUI** not only excel in web UI tasks—achieving up to a 48% improvement on VisualWebBench and a 19.1% boost in action accuracy on a web agent dataset Mind2Web—but also generalize surprisingly well to non-web UI tasks and even to non-UI domains, such as document understanding, OCR, and chart interpretation.
<video controls autoplay src="https://cdn-uploads.huggingface.co/production/uploads/65403d8781a8731a1c09a584/vk7yT4Y7ydBOHM6BojmlI.mp4"></video>
## Model Performance
## Contact
* Junpeng Liu: [email protected]
* Xiang Yue: [email protected]
## Citation
If you find this work helpful, please cite out paper:
````
@misc{liu2024harnessingwebpageuistextrich,
title={Harnessing Webpage UIs for Text-Rich Visual Understanding},
author={Junpeng Liu and Tianyue Ou and Yifan Song and Yuxiao Qu and Wai Lam and Chenyan Xiong and Wenhu Chen and Graham Neubig and Xiang Yue},
year={2024},
eprint={2410.13824},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2410.13824},
}
```` |
BroAlanTaps/GPT2-large-4-50000steps | BroAlanTaps | 2024-10-18T02:12:43Z | 136 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-18T02:10:43Z | ---
library_name: transformers
tags: []
---
# 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] |
BroAlanTaps/Llama3-instruct-4-50000steps | BroAlanTaps | 2024-10-18T02:10:41Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-18T02:08:16Z | ---
library_name: transformers
tags: []
---
# 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] |
qly/path-to-save-model | qly | 2024-10-18T02:08:24Z | 5 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"base_model:finetune:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-17T12:00:44Z | ---
base_model: CompVis/stable-diffusion-v1-4
library_name: diffusers
license: creativeml-openrail-m
tags:
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- dreambooth
- diffusers-training
- stable-diffusion
- stable-diffusion-diffusers
inference: true
instance_prompt: a photo of sks dog
---
<!-- 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. -->
# DreamBooth - qly/path-to-save-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## 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] |
QuantFactory/starcoder2-3b-instruct-v0.1-GGUF | QuantFactory | 2024-10-18T01:58:37Z | 60 | 1 | transformers | [
"transformers",
"gguf",
"dataset:bigcode/self-oss-instruct-sc2-exec-filter-50k",
"base_model:bigcode/starcoder2-3b",
"base_model:quantized:bigcode/starcoder2-3b",
"license:bigcode-openrail-m",
"endpoints_compatible",
"region:us"
] | null | 2024-10-18T01:40:24Z |
---
license: bigcode-openrail-m
datasets:
- bigcode/self-oss-instruct-sc2-exec-filter-50k
base_model:
- bigcode/starcoder2-3b
library_name: transformers
---
[](https://hf.co/QuantFactory)
# QuantFactory/starcoder2-3b-instruct-v0.1-GGUF
This is quantized version of [onekq-ai/starcoder2-3b-instruct-v0.1](https://huggingface.co/onekq-ai/starcoder2-3b-instruct-v0.1) created using llama.cpp
# Original Model Card
Starcoder2-3b fined the same way as https://huggingface.co/bigcode/starcoder2-15b-instruct-v0.1 using https://huggingface.co/datasets/bigcode/self-oss-instruct-sc2-exec-filter-50k
Epochs: 1
Learning Rate: 0.0001
Lora Rank: 8
Batch Size: 16
Evaluation Split: 0
|
dzagardo/mia_ziggy_llama_750m_2000_steps_orca_mini_10k | dzagardo | 2024-10-18T01:57:49Z | 48 | 0 | transformers | [
"transformers",
"pytorch",
"my_transformer",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-18T01:56:22Z | ---
library_name: transformers
tags: []
---
# 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] |
maic1995/my_awesome_model_destibert | maic1995 | 2024-10-18T01:49:10Z | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-09-10T23:15:45Z | ---
library_name: transformers
tags: []
---
# 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] |
lemonTree5366/model_1017_llama3.2_gguf | lemonTree5366 | 2024-10-18T01:38:18Z | 12 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:Bllossom/llama-3.2-Korean-Bllossom-3B",
"base_model:quantized:Bllossom/llama-3.2-Korean-Bllossom-3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-18T01:34:08Z | ---
base_model: Bllossom/llama-3.2-Korean-Bllossom-3B
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** lemonTree5366
- **License:** apache-2.0
- **Finetuned from model :** Bllossom/llama-3.2-Korean-Bllossom-3B
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)
|
QuantFactory/starcoder2-3b-instruct-GGUF | QuantFactory | 2024-10-18T01:35:08Z | 66 | 1 | transformers | [
"transformers",
"gguf",
"code",
"starcoder2",
"text-generation",
"license:bigcode-openrail-m",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-18T01:19:14Z |
---
tags:
- code
- starcoder2
library_name: transformers
pipeline_tag: text-generation
license: bigcode-openrail-m
---
[](https://hf.co/QuantFactory)
# QuantFactory/starcoder2-3b-instruct-GGUF
This is quantized version of [TechxGenus/starcoder2-3b-instruct](https://huggingface.co/TechxGenus/starcoder2-3b-instruct) created using llama.cpp
# Original Model Card
<p align="center">
<img width="300px" alt="starcoder2-instruct" src="https://huggingface.co/TechxGenus/starcoder2-3b-instruct/resolve/main/starcoder2-instruct.jpg">
</p>
### starcoder2-instruct
We've fine-tuned starcoder2-3b with an additional 0.7 billion high-quality, code-related tokens for 3 epochs. We used DeepSpeed ZeRO 3 and Flash Attention 2 to accelerate the training process. It achieves **65.9 pass@1** on HumanEval-Python. This model operates using the Alpaca instruction format (excluding the system prompt).
### Usage
Here give some examples of how to use our model:
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
PROMPT = """### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
tokenizer = AutoTokenizer.from_pretrained("TechxGenus/starcoder2-3b-instruct")
model = AutoModelForCausalLM.from_pretrained(
"TechxGenus/starcoder2-3b-instruct",
torch_dtype=torch.bfloat16,
device_map="auto",
)
inputs = tokenizer.encode(prompt, return_tensors="pt")
outputs = model.generate(input_ids=inputs.to(model.device), max_new_tokens=2048)
print(tokenizer.decode(outputs[0]))
```
With text-generation pipeline:
```python
from transformers import pipeline
import torch
PROMPT = """### Instruction
{instruction}
### Response
"""
instruction = <Your code instruction here>
prompt = PROMPT.format(instruction=instruction)
generator = pipeline(
model="TechxGenus/starcoder2-3b-instruct",
task="text-generation",
torch_dtype=torch.bfloat16,
device_map="auto",
)
result = generator(prompt, max_length=2048)
print(result[0]["generated_text"])
```
### Note
Model may sometimes make errors, produce misleading contents, or struggle to manage tasks that are not related to coding. It has undergone very limited testing. Additional safety testing should be performed before any real-world deployments.
|
DeepalteredL/Goodmorningpepe | DeepalteredL | 2024-10-18T01:27:46Z | 41 | 2 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-18T00:35:44Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: GMP
---
# Goodmorningpepe
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `GMP` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('DeepalteredL/Goodmorningpepe', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
lightsout19/gpt2-moe-top1-8-partitioned-sst2 | lightsout19 | 2024-10-18T01:07:57Z | 5 | 0 | null | [
"tensorboard",
"safetensors",
"gpt2",
"generated_from_trainer",
"region:us"
] | null | 2024-10-18T00:25:51Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gpt2-moe-top1-8-partitioned-sst2-new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-moe-top1-8-partitioned-sst2-new
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3785
- Accuracy: 0.8406
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.385 | 1.0 | 2105 | 0.3990 | 0.8280 |
| 0.3091 | 2.0 | 4210 | 0.3743 | 0.8314 |
| 0.2916 | 3.0 | 6315 | 0.3740 | 0.8509 |
| 0.2651 | 4.0 | 8420 | 0.3993 | 0.8360 |
| 0.2501 | 5.0 | 10525 | 0.4068 | 0.8429 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
QuantFactory/gemma-7b-aps-it-GGUF | QuantFactory | 2024-10-18T01:06:33Z | 54 | 3 | transformers | [
"transformers",
"gguf",
"text-generation",
"arxiv:2406.19803",
"base_model:google/gemma-7b",
"base_model:quantized:google/gemma-7b",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-18T00:09:43Z |
---
base_model: google/gemma-7b
library_name: transformers
license: gemma
pipeline_tag: text-generation
extra_gated_heading: Access Gemma on Hugging Face
extra_gated_prompt: To access Gemma on Hugging Face, you’re required to review and
agree to Google’s usage license. To do this, please ensure you’re logged in to Hugging
Face and click below. Requests are processed immediately.
extra_gated_button_content: Acknowledge license
---
[](https://hf.co/QuantFactory)
# QuantFactory/gemma-7b-aps-it-GGUF
This is quantized version of [google/gemma-7b-aps-it](https://huggingface.co/google/gemma-7b-aps-it) created using llama.cpp
# Original Model Card
# Gemma Model Card
**Model Page**: [Gemma](https://ai.google.dev/gemma/docs)
This model card corresponds to the 7B finetuned version of the Gemma-APS model.
You can also visit the model card of the [2B finetuned model](https://huggingface.co/google/gemma-2b-aps-it).
**Resources and Technical Documentation**:
* [Scalable and Domain-General Abstractive Proposition Segmentation](https://arxiv.org/abs/2406.19803)
* [Gemma Technical Report](https://storage.googleapis.com/deepmind-media/gemma/gemma-report.pdf)
* [Responsible Generative AI Toolkit](https://ai.google.dev/responsible)
* [Gemma on Kaggle](https://www.kaggle.com/models/google/gemma)
**Terms of Use**: [Terms](https://www.kaggle.com/models/google/gemma/license/consent/verify/huggingface?returnModelRepoId=google/gemma-7b-aps-it)
**Authors**: Mohammad Javad Hosseini, Yang Gao, Tim Baumgärtner, Alex Fabrikant, Reinald Kim Amplayo
## Model Information
Summary description and brief definition of inputs and outputs.
### Description
Gemma-APS is a generative model and a research tool for **abstractive proposition segmentation** (APS for short), a.k.a. claim extraction.
Given a text passage, the model segments the content into the individual facts, statements, and ideas expressed in the text, and restates
them in full sentences with small changes to the original text.
This model can be used for research where there is a need to break down text content into meaningful components. Applications include
grounding, retrieval, fact-checking, and evaluation of generation tasks (such as summarization) where it can be useful to divide up
individual propositions (claims) so that they can be processed independently. For more information, check out the [research paper](https://arxiv.org/abs/2406.19803).
### Context Length
Models are trained on a context length of 8192 tokens.
### Usage
Below we share some code snippets on how to get quickly started with running the model. First make sure to `pip install -U transformers nltk`,
then copy the snippet from the section that is relevant for your usecase.
For ease-of-use, we define two helper functions for pre-processing input and post-processing output of the model:
```py
import nltk
import re
nltk.download('punkt')
start_marker = '<s>'
end_marker = '</s>'
separator = '\n'
def create_propositions_input(text: str) -> str:
input_sents = nltk.tokenize.sent_tokenize(text)
propositions_input = ''
for sent in input_sents:
propositions_input += f'{start_marker} ' + sent + f' {end_marker}{separator}'
propositions_input = propositions_input.strip(f'{separator}')
return propositions_input
def process_propositions_output(text):
pattern = re.compile(f'{re.escape(start_marker)}(.*?){re.escape(end_marker)}', re.DOTALL)
output_grouped_strs = re.findall(pattern, text)
predicted_grouped_propositions = []
for grouped_str in output_grouped_strs:
grouped_str = grouped_str.strip(separator)
props = [x[2:] for x in grouped_str.split(separator)]
predicted_grouped_propositions.append(props)
return predicted_grouped_propositions
```
#### Usage with the `pipeline` API
```py
from transformers import pipeline
import torch
generator = pipeline('text-generation', 'google/gemma-7b-aps-it', device_map='auto', torch_dtype=torch.bfloat16)
passage = 'Sarah Stage, 30, welcomed James Hunter into the world on Tuesday.\nThe baby boy weighed eight pounds seven ounces and was 22 inches long.'
messages = [{'role': 'user', 'content': create_propositions_input(passage)}]
output = generator(messages, max_new_tokens=4096, return_full_text=False)
result = process_propositions_output(output[0]['generated_text'])
print(result)
```
<details>
<summary>Example output</summary>
```json
[
[
"Sarah Stage welcomed James Hunter into the world.",
"Sarah Stage is 30 years old.",
"James Hunter was welcomed on Tuesday."
],
[
"James Hunter weighed eight pounds seven ounces.",
"James Hunter was 22 inches long."
]
]
```
</details>
#### Usage with `AutoModel` and `AutoTokenizer` APIs
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
model_id = 'google/gemma-7b-aps-it'
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map='auto',
torch_dtype=torch.bfloat16,
)
passage = "For more than 40 years, the lyrics of American Pie have been puzzled over. This week the handwritten lyrics sold for more than $1 million at auction. The verses contain hidden references to seminal events of the 50s and 60s. It includes nods to Buddy Holly, Charles Manson and Martin Luther King."
messages = [{'role': 'user', 'content': create_propositions_input(passage)}]
inputs = tokenizer.apply_chat_template(messages, return_tensors='pt', add_generation_prompt=True, return_dict=True).to(model.device)
output = model.generate(**inputs, max_new_tokens=4096, do_sample=False)
generated_text = tokenizer.batch_decode(output[:, inputs['input_ids'].shape[1]:], skip_special_tokens=True)[0]
result = process_propositions_output(generated_text)
print(result)
```
<details>
<summary>Example output</summary>
```json
[
[
"The lyrics of American Pie have been puzzled over for more than 40 years."
],
[
"The handwritten lyrics sold for more than $1 million.",
"The handwritten lyrics sold at auction.",
"The handwritten lyrics sold this week."
],
[
"The verses contain hidden references to seminal events of the 50s.",
"The verses contain hidden references to seminal events of the 60s."
],
[
"The lyrics include nods to Buddy Holly.",
"The lyrics include nods to Charles Manson.",
"The lyrics include nods to Martin Luther King."
]
]
```
</details>
### Inputs and outputs
* **Input:** A text passage.
* **Output:** List of propositions for all the sentences in the text passage. The propositions for each sentence are grouped separately.
## Model Data
Data used for model training and how the data was processed.
### Training Dataset
* The training data contains synthetically generated examples, where each example has (input passage, propositions list) pairs, with the
propositions list containing propositions for all the sentences in the input passage (one group of propositions for each sentence).
* The input passages are generated by few-shot prompting Gemini Ultra.
* The propositions list is generated by applying a teacher LLM on the input passage. The teacher LLM is a Gemini Pro model trained on
a filtered version of the ROSE dataset.
See the [research paper](https://arxiv.org/abs/2406.19803) for all the details.
### Data Preprocessing
* We filtered example passages that have >=4 tokens overlap with any of the few-shot examples used for prompting Gemini Ultra.
* We used the ROSE dataset for training the teacher LLM (Gemini Pro). We filtered ROSE examples using an entailment model to remove
cases that do not satisfy desired properties of propositions.
## Implementation Information
Details about the model internals.
### Hardware
Similar to Gemma, Gemma-APS was trained on [TPUv5e](https://cloud.google.com/tpu/docs/intro-to-tpu?_gl=1*18wi411*_ga*MzE3NDU5OTY1LjE2MzQwNDA4NDY.*_ga_WH2QY8WWF5*MTcxMTA0MjUxMy4xNy4wLjE3MTEwNDI1MTkuMC4wLjA.&_ga=2.239449409.-317459965.1634040846).
Training large language models requires significant computational power. TPUs, designed specifically for matrix operations common in machine learning, offer several advantages in this domain:
Performance: TPUs are specifically designed to handle the massive computations involved in training LLMs. They can speed up training considerably compared to CPUs.
Memory: TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training. This can lead to better model quality.
Scalability: TPU Pods (large clusters of TPUs) provide a scalable solution for handling the growing complexity of large foundation models. You can distribute training across multiple TPU devices for faster and more efficient processing.
Cost-effectiveness: In many scenarios, TPUs can provide a more cost-effective solution for training large models compared to CPU-based infrastructure, especially when considering the time and resources saved due to faster training.
These advantages are aligned with [Google's commitments to operate sustainably](https://sustainability.google/operating-sustainably/).
### Software
Training was done using [JAX](https://github.com/jax-ml/jax).
JAX allows researchers to leverage the latest generation of hardware, including TPUs, for faster and more efficient training of large models.
## Evaluation
Model evaluation metrics and results.
### Benchmark Results
Evaluation was done on one existing in-domain dataset (development set of the [ROSE](https://github.com/Yale-LILY/ROSE) dataset filtered by an entailment model) and two out-of-domain datasets introduced in the paper. Evaluation was performed based on our new metrics for the abstractive proposition segmentation task.
## Ethics and Safety
Ethics and safety evaluation approach and results.
### Evaluation Approach
These models are only suitable for abstractive proposition segmentation for English text, not any other task or language. While we have tested the models on three evaluation datasets and have obtained positive results compared to strong baselines, the model might still have errors on some examples.
## Usage and Limitations
These models have certain limitations that users should be aware of.
### Intended Usage
These models are only suitable for abstractive proposition segmentation for English text, not any other task or language.
While we have tested it on three evaluation datasets and have obtained positive results compared to strong baselines,
the models might still have errors on some examples.
### Limitations
These models have certain limitations that users should be aware of.
* Training Data
* The quality and diversity of the training data significantly influence the
model's capabilities. Biases or gaps in the training data can lead to
limitations in the model's responses.
* The scope of the training dataset determines the subject areas the model can
handle effectively.
* We have tested our models on passages from different domains, where passages
contain a few sentences.
* This model supports abstractive proposition segmentation in English, not any
other language.
* Language Ambiguity and Nuance
* Natural language is inherently complex. LLMs might struggle to grasp subtle
nuances, sarcasm, or figurative language.
* Factual Accuracy
* LLMs generate responses based on information they learned from their
training datasets, but they are not knowledge bases. They may generate
incorrect or outdated factual statements.
* Common Sense
* LLMs rely on statistical patterns in language. They might lack the ability
to apply common sense reasoning in certain situations.
### Ethical Considerations and Risks
The development of large language models (LLMs) raises several ethical concerns.
In creating an open model, we have carefully considered the following:
* Bias and Fairness
* LLMs trained on large-scale, real-world text data can reflect socio-cultural
biases embedded in the training material. These models underwent careful
scrutiny, input data pre-processing described and posterior evaluations
reported in this card.
* Misinformation and Misuse
* LLMs can be misused to generate text that is false, misleading, or harmful.
* Guidelines are provided for responsible use with the model, see the
[Responsible Generative AI Toolkit](http://ai.google.dev/gemma/responsible).
* Transparency and Accountability:
* This model card summarizes details on the models' architecture,
capabilities, limitations, and evaluation processes.
* A responsibly developed open model offers the opportunity to share
innovation by making LLM technology accessible to developers and researchers
across the AI ecosystem.
Risks identified and mitigations:
* Perpetuation of biases: It's encouraged to perform continuous monitoring
(using evaluation metrics, human review) and the exploration of de-biasing
techniques during model training, fine-tuning, and other use cases.
* Generation of harmful content: Mechanisms and guidelines for content safety
are essential. Developers are encouraged to exercise caution and implement
appropriate content safety safeguards based on their specific product policies
and application use cases.
* Misuse for malicious purposes: Technical limitations and developer and
end-user education can help mitigate against malicious applications of LLMs.
Educational resources and reporting mechanisms for users to flag misuse are
provided. Prohibited uses of Gemma models are outlined in the
[Gemma Prohibited Use Policy](https://ai.google.dev/gemma/prohibited_use_policy).
* Privacy violations: Models were trained on data filtered for removal of PII
(Personally Identifiable Information). Developers are encouraged to adhere to
privacy regulations with privacy-preserving techniques.
### Benefits
These models are useful for academics working on abstractive proposition segmentation (claim extraction) research or other problems (e.g., grounding, retrieval, fact-checking) that could benefit from this task.
|
BroAlanTaps/Llama3-instruct-4-48000steps | BroAlanTaps | 2024-10-18T00:32:49Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-18T00:30:16Z | ---
library_name: transformers
tags: []
---
# 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] |
jonathansuru/detr-resnet-50-dc5-ano | jonathansuru | 2024-10-18T00:32:43Z | 28 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"detr",
"object-detection",
"generated_from_trainer",
"base_model:facebook/detr-resnet-50-dc5",
"base_model:finetune:facebook/detr-resnet-50-dc5",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-10-17T14:27:31Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/detr-resnet-50-dc5
tags:
- generated_from_trainer
model-index:
- name: detr-resnet-50-dc5-ano
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# detr-resnet-50-dc5-ano
This model is a fine-tuned version of [facebook/detr-resnet-50-dc5](https://huggingface.co/facebook/detr-resnet-50-dc5) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0928
- Map: 0.2104
- Map 50: 0.462
- Map 75: 0.1556
- Map Small: 0.2131
- Map Medium: 0.2995
- Map Large: -1.0
- Mar 1: 0.065
- Mar 10: 0.2862
- Mar 100: 0.4225
- Mar Small: 0.424
- Mar Medium: 0.3929
- Mar Large: -1.0
- Map Trophozoite: 0.032
- Mar 100 Trophozoite: 0.3135
- Map Wbc: 0.3889
- Mar 100 Wbc: 0.5315
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Map | Map 50 | Map 75 | Map Small | Map Medium | Map Large | Mar 1 | Mar 10 | Mar 100 | Mar Small | Mar Medium | Mar Large | Map Trophozoite | Mar 100 Trophozoite | Map Wbc | Mar 100 Wbc |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:----------:|:---------:|:------:|:------:|:-------:|:---------:|:----------:|:---------:|:---------------:|:-------------------:|:-------:|:-----------:|
| 2.62 | 0.1078 | 50 | 2.1271 | 0.0042 | 0.0131 | 0.0019 | 0.0042 | 0.0155 | -1.0 | 0.0062 | 0.0447 | 0.1115 | 0.1109 | 0.1214 | -1.0 | 0.005 | 0.1545 | 0.0034 | 0.0685 |
| 2.4526 | 0.2155 | 100 | 2.0394 | 0.0049 | 0.0165 | 0.0014 | 0.0049 | 0.0372 | -1.0 | 0.0061 | 0.0369 | 0.1148 | 0.1138 | 0.1357 | -1.0 | 0.0073 | 0.1877 | 0.0026 | 0.0419 |
| 1.9677 | 0.3233 | 150 | 1.9835 | 0.0059 | 0.0193 | 0.0022 | 0.0058 | 0.0453 | -1.0 | 0.0071 | 0.0353 | 0.1178 | 0.1167 | 0.1357 | -1.0 | 0.0086 | 0.2018 | 0.0032 | 0.0337 |
| 2.5926 | 0.4310 | 200 | 1.9593 | 0.0072 | 0.0236 | 0.0027 | 0.0068 | 0.0614 | -1.0 | 0.0068 | 0.0262 | 0.1156 | 0.1148 | 0.0929 | -1.0 | 0.0104 | 0.2154 | 0.0039 | 0.0158 |
| 1.8831 | 0.5388 | 250 | 1.9354 | 0.0086 | 0.0252 | 0.005 | 0.0081 | 0.0626 | -1.0 | 0.0093 | 0.0294 | 0.1237 | 0.1226 | 0.1286 | -1.0 | 0.0107 | 0.2271 | 0.0065 | 0.0203 |
| 1.7079 | 0.6466 | 300 | 1.9019 | 0.0087 | 0.0277 | 0.0031 | 0.0086 | 0.0713 | -1.0 | 0.0105 | 0.0294 | 0.1281 | 0.1268 | 0.15 | -1.0 | 0.0105 | 0.2292 | 0.0069 | 0.0269 |
| 2.1389 | 0.7543 | 350 | 1.8507 | 0.0135 | 0.0378 | 0.0057 | 0.0134 | 0.048 | -1.0 | 0.017 | 0.0465 | 0.147 | 0.1463 | 0.1286 | -1.0 | 0.011 | 0.235 | 0.016 | 0.059 |
| 2.3653 | 0.8621 | 400 | 1.8474 | 0.0173 | 0.0456 | 0.0103 | 0.0169 | 0.1147 | -1.0 | 0.0222 | 0.0557 | 0.1534 | 0.1529 | 0.1214 | -1.0 | 0.0136 | 0.2336 | 0.021 | 0.0731 |
| 1.866 | 0.9698 | 450 | 1.8293 | 0.0282 | 0.0677 | 0.0197 | 0.0278 | 0.1387 | -1.0 | 0.0331 | 0.0893 | 0.1938 | 0.1936 | 0.1714 | -1.0 | 0.0126 | 0.242 | 0.0439 | 0.1456 |
| 2.0982 | 1.0776 | 500 | 1.8596 | 0.0337 | 0.0776 | 0.0256 | 0.0336 | 0.1432 | -1.0 | 0.0355 | 0.0986 | 0.2035 | 0.2034 | 0.1929 | -1.0 | 0.0088 | 0.2267 | 0.0587 | 0.1804 |
| 1.5012 | 1.1853 | 550 | 1.7823 | 0.0355 | 0.0848 | 0.0242 | 0.0354 | 0.1608 | -1.0 | 0.0365 | 0.1135 | 0.2235 | 0.2228 | 0.2714 | -1.0 | 0.0104 | 0.2433 | 0.0606 | 0.2036 |
| 2.2179 | 1.2931 | 600 | 1.7600 | 0.0456 | 0.1073 | 0.0309 | 0.046 | 0.1035 | -1.0 | 0.0416 | 0.1439 | 0.2612 | 0.2604 | 0.35 | -1.0 | 0.0106 | 0.2499 | 0.0806 | 0.2725 |
| 1.9926 | 1.4009 | 650 | 1.6850 | 0.0535 | 0.1172 | 0.0402 | 0.0535 | 0.1301 | -1.0 | 0.0449 | 0.154 | 0.2803 | 0.2791 | 0.4071 | -1.0 | 0.012 | 0.2692 | 0.0949 | 0.2914 |
| 1.5461 | 1.5086 | 700 | 1.6928 | 0.061 | 0.1339 | 0.0481 | 0.0619 | 0.1446 | -1.0 | 0.0441 | 0.1662 | 0.2923 | 0.2917 | 0.3857 | -1.0 | 0.0104 | 0.2542 | 0.1116 | 0.3304 |
| 1.6376 | 1.6164 | 750 | 1.7107 | 0.0754 | 0.1674 | 0.0565 | 0.0763 | 0.1474 | -1.0 | 0.047 | 0.1771 | 0.3048 | 0.3044 | 0.4071 | -1.0 | 0.0096 | 0.2472 | 0.1412 | 0.3625 |
| 1.7446 | 1.7241 | 800 | 1.6416 | 0.0883 | 0.1997 | 0.0607 | 0.0899 | 0.1329 | -1.0 | 0.0494 | 0.2006 | 0.364 | 0.3643 | 0.4429 | -1.0 | 0.0108 | 0.261 | 0.1658 | 0.467 |
| 1.6513 | 1.8319 | 850 | 1.5899 | 0.0988 | 0.2155 | 0.0732 | 0.1003 | 0.1206 | -1.0 | 0.0523 | 0.2181 | 0.3856 | 0.387 | 0.3643 | -1.0 | 0.0118 | 0.2704 | 0.1857 | 0.5009 |
| 1.4462 | 1.9397 | 900 | 1.5681 | 0.1085 | 0.2465 | 0.0751 | 0.1098 | 0.1331 | -1.0 | 0.0523 | 0.2198 | 0.3817 | 0.384 | 0.2714 | -1.0 | 0.0116 | 0.2694 | 0.2054 | 0.4939 |
| 2.1774 | 2.0474 | 950 | 1.5550 | 0.1235 | 0.2747 | 0.0904 | 0.1251 | 0.1514 | -1.0 | 0.0535 | 0.2272 | 0.3895 | 0.3918 | 0.2857 | -1.0 | 0.0116 | 0.2721 | 0.2354 | 0.507 |
| 1.4214 | 2.1552 | 1000 | 1.5505 | 0.134 | 0.3 | 0.0941 | 0.1376 | 0.1319 | -1.0 | 0.0549 | 0.2282 | 0.3844 | 0.3868 | 0.2643 | -1.0 | 0.0117 | 0.2677 | 0.2563 | 0.501 |
| 2.1869 | 2.2629 | 1050 | 1.5522 | 0.1381 | 0.3067 | 0.0917 | 0.1397 | 0.1458 | -1.0 | 0.0569 | 0.2313 | 0.3848 | 0.3875 | 0.2286 | -1.0 | 0.0145 | 0.2754 | 0.2617 | 0.4942 |
| 1.7756 | 2.3707 | 1100 | 1.5011 | 0.1473 | 0.326 | 0.1012 | 0.1489 | 0.215 | -1.0 | 0.0555 | 0.2375 | 0.4078 | 0.4105 | 0.2786 | -1.0 | 0.0156 | 0.2795 | 0.279 | 0.536 |
| 1.4246 | 2.4784 | 1150 | 1.4837 | 0.1435 | 0.3249 | 0.0988 | 0.1457 | 0.2315 | -1.0 | 0.0552 | 0.2368 | 0.4049 | 0.4071 | 0.3286 | -1.0 | 0.0164 | 0.2756 | 0.2706 | 0.5343 |
| 1.3888 | 2.5862 | 1200 | 1.4599 | 0.1495 | 0.3364 | 0.1006 | 0.1516 | 0.2003 | -1.0 | 0.057 | 0.2316 | 0.4016 | 0.4042 | 0.2786 | -1.0 | 0.0127 | 0.2828 | 0.2864 | 0.5205 |
| 1.4362 | 2.6940 | 1250 | 1.4548 | 0.1528 | 0.3524 | 0.1043 | 0.1543 | 0.2426 | -1.0 | 0.0557 | 0.2301 | 0.3902 | 0.3922 | 0.3143 | -1.0 | 0.0117 | 0.2728 | 0.2939 | 0.5076 |
| 1.3783 | 2.8017 | 1300 | 1.4243 | 0.1614 | 0.366 | 0.1027 | 0.1645 | 0.1674 | -1.0 | 0.0549 | 0.2372 | 0.3914 | 0.3941 | 0.25 | -1.0 | 0.0118 | 0.275 | 0.3109 | 0.5078 |
| 1.91 | 2.9095 | 1350 | 1.4263 | 0.1669 | 0.3804 | 0.1082 | 0.1698 | 0.2609 | -1.0 | 0.0574 | 0.2453 | 0.3907 | 0.3926 | 0.3357 | -1.0 | 0.0126 | 0.2656 | 0.3212 | 0.5158 |
| 1.3072 | 3.0172 | 1400 | 1.4192 | 0.1604 | 0.3835 | 0.0959 | 0.1616 | 0.2433 | -1.0 | 0.0539 | 0.2325 | 0.3726 | 0.3749 | 0.2643 | -1.0 | 0.0123 | 0.2587 | 0.3085 | 0.4865 |
| 1.469 | 3.125 | 1450 | 1.4252 | 0.1615 | 0.3796 | 0.1009 | 0.1638 | 0.2479 | -1.0 | 0.0546 | 0.2418 | 0.392 | 0.3941 | 0.3071 | -1.0 | 0.0134 | 0.282 | 0.3097 | 0.502 |
| 1.6321 | 3.2328 | 1500 | 1.4402 | 0.1609 | 0.3781 | 0.1006 | 0.1639 | 0.1707 | -1.0 | 0.0544 | 0.237 | 0.3739 | 0.3756 | 0.3214 | -1.0 | 0.0133 | 0.2665 | 0.3085 | 0.4812 |
| 2.4078 | 3.3405 | 1550 | 1.4123 | 0.166 | 0.3854 | 0.1034 | 0.1679 | 0.2461 | -1.0 | 0.06 | 0.2409 | 0.3824 | 0.384 | 0.3429 | -1.0 | 0.0143 | 0.2713 | 0.3178 | 0.4935 |
| 1.6876 | 3.4483 | 1600 | 1.4158 | 0.154 | 0.379 | 0.0945 | 0.1564 | 0.2257 | -1.0 | 0.0549 | 0.2246 | 0.3612 | 0.3622 | 0.3643 | -1.0 | 0.0127 | 0.2558 | 0.2953 | 0.4666 |
| 1.4312 | 3.5560 | 1650 | 1.4093 | 0.1547 | 0.3771 | 0.0897 | 0.1574 | 0.1988 | -1.0 | 0.0521 | 0.2261 | 0.3736 | 0.3748 | 0.3571 | -1.0 | 0.0117 | 0.2739 | 0.2976 | 0.4734 |
| 1.4903 | 3.6638 | 1700 | 1.4245 | 0.156 | 0.3771 | 0.093 | 0.1581 | 0.1942 | -1.0 | 0.0552 | 0.2267 | 0.3695 | 0.371 | 0.3214 | -1.0 | 0.011 | 0.2652 | 0.301 | 0.4737 |
| 2.1859 | 3.7716 | 1750 | 1.3713 | 0.1617 | 0.3844 | 0.096 | 0.1646 | 0.2027 | -1.0 | 0.0564 | 0.2344 | 0.3844 | 0.3857 | 0.3643 | -1.0 | 0.0129 | 0.2818 | 0.3105 | 0.4871 |
| 1.231 | 3.8793 | 1800 | 1.3659 | 0.1648 | 0.3866 | 0.1081 | 0.1673 | 0.2019 | -1.0 | 0.0554 | 0.2413 | 0.3937 | 0.3957 | 0.3143 | -1.0 | 0.0131 | 0.2834 | 0.3165 | 0.5041 |
| 1.454 | 3.9871 | 1850 | 1.3651 | 0.1554 | 0.3827 | 0.0921 | 0.1583 | 0.2076 | -1.0 | 0.0525 | 0.2355 | 0.3794 | 0.3807 | 0.3571 | -1.0 | 0.0137 | 0.274 | 0.2972 | 0.4847 |
| 2.4406 | 4.0948 | 1900 | 1.3590 | 0.1743 | 0.3987 | 0.1156 | 0.1761 | 0.2312 | -1.0 | 0.0579 | 0.2572 | 0.4066 | 0.408 | 0.3857 | -1.0 | 0.0172 | 0.2924 | 0.3313 | 0.5208 |
| 1.3729 | 4.2026 | 1950 | 1.3334 | 0.1734 | 0.391 | 0.1157 | 0.176 | 0.2015 | -1.0 | 0.0573 | 0.2529 | 0.4031 | 0.4054 | 0.3071 | -1.0 | 0.0158 | 0.278 | 0.3309 | 0.5282 |
| 2.1646 | 4.3103 | 2000 | 1.3248 | 0.1678 | 0.3922 | 0.1082 | 0.1712 | 0.2066 | -1.0 | 0.0536 | 0.2446 | 0.3884 | 0.3902 | 0.3286 | -1.0 | 0.0139 | 0.2727 | 0.3217 | 0.5041 |
| 1.9208 | 4.4181 | 2050 | 1.3258 | 0.1604 | 0.3891 | 0.0897 | 0.1641 | 0.1997 | -1.0 | 0.0568 | 0.2372 | 0.3779 | 0.3796 | 0.3214 | -1.0 | 0.0124 | 0.2639 | 0.3085 | 0.4919 |
| 1.0212 | 4.5259 | 2100 | 1.3385 | 0.1494 | 0.3737 | 0.0824 | 0.152 | 0.2085 | -1.0 | 0.053 | 0.2244 | 0.3661 | 0.3672 | 0.3643 | -1.0 | 0.0114 | 0.2623 | 0.2874 | 0.4699 |
| 2.1047 | 4.6336 | 2150 | 1.3190 | 0.1587 | 0.3711 | 0.1031 | 0.1609 | 0.1565 | -1.0 | 0.0515 | 0.2415 | 0.387 | 0.3896 | 0.25 | -1.0 | 0.0132 | 0.2693 | 0.3041 | 0.5047 |
| 1.3 | 4.7414 | 2200 | 1.2984 | 0.1682 | 0.3936 | 0.1054 | 0.1708 | 0.2185 | -1.0 | 0.0546 | 0.2415 | 0.3853 | 0.3869 | 0.3429 | -1.0 | 0.0139 | 0.2737 | 0.3226 | 0.4969 |
| 2.0957 | 4.8491 | 2250 | 1.3023 | 0.1581 | 0.377 | 0.1005 | 0.1616 | 0.1674 | -1.0 | 0.0529 | 0.2427 | 0.3802 | 0.3821 | 0.3143 | -1.0 | 0.0147 | 0.2653 | 0.3015 | 0.4951 |
| 1.1841 | 4.9569 | 2300 | 1.3213 | 0.1596 | 0.3899 | 0.0872 | 0.1636 | 0.1621 | -1.0 | 0.0563 | 0.24 | 0.3716 | 0.373 | 0.35 | -1.0 | 0.0158 | 0.2602 | 0.3033 | 0.483 |
| 1.5673 | 5.0647 | 2350 | 1.3002 | 0.169 | 0.3933 | 0.0998 | 0.1728 | 0.2203 | -1.0 | 0.0567 | 0.2554 | 0.3897 | 0.3914 | 0.35 | -1.0 | 0.0203 | 0.2706 | 0.3177 | 0.5089 |
| 2.2189 | 5.1724 | 2400 | 1.2900 | 0.1688 | 0.3994 | 0.0993 | 0.1725 | 0.1419 | -1.0 | 0.0566 | 0.2562 | 0.3928 | 0.3952 | 0.2786 | -1.0 | 0.0218 | 0.2801 | 0.3157 | 0.5055 |
| 2.2594 | 5.2802 | 2450 | 1.3065 | 0.1638 | 0.3892 | 0.0991 | 0.1688 | 0.1498 | -1.0 | 0.0553 | 0.2477 | 0.3757 | 0.3772 | 0.35 | -1.0 | 0.0157 | 0.2496 | 0.312 | 0.5017 |
| 1.245 | 5.3879 | 2500 | 1.2965 | 0.1631 | 0.4032 | 0.0854 | 0.1687 | 0.1485 | -1.0 | 0.0567 | 0.2439 | 0.3769 | 0.3781 | 0.3786 | -1.0 | 0.0168 | 0.2609 | 0.3093 | 0.4929 |
| 1.7621 | 5.4957 | 2550 | 1.3153 | 0.1705 | 0.4046 | 0.1072 | 0.1749 | 0.144 | -1.0 | 0.0553 | 0.2435 | 0.3868 | 0.3886 | 0.3286 | -1.0 | 0.0139 | 0.2721 | 0.3271 | 0.5015 |
| 1.1487 | 5.6034 | 2600 | 1.2965 | 0.1745 | 0.4099 | 0.1107 | 0.179 | 0.1456 | -1.0 | 0.0588 | 0.245 | 0.3757 | 0.3775 | 0.3214 | -1.0 | 0.0137 | 0.2545 | 0.3354 | 0.4968 |
| 1.167 | 5.7112 | 2650 | 1.2692 | 0.1771 | 0.4099 | 0.1141 | 0.1807 | 0.1511 | -1.0 | 0.0582 | 0.2501 | 0.3931 | 0.3951 | 0.3071 | -1.0 | 0.0168 | 0.2829 | 0.3374 | 0.5032 |
| 1.1622 | 5.8190 | 2700 | 1.2755 | 0.1838 | 0.4053 | 0.1185 | 0.187 | 0.1644 | -1.0 | 0.0585 | 0.2528 | 0.3904 | 0.3925 | 0.3071 | -1.0 | 0.0155 | 0.2652 | 0.352 | 0.5156 |
| 1.8749 | 5.9267 | 2750 | 1.2667 | 0.1843 | 0.4152 | 0.1192 | 0.1863 | 0.2427 | -1.0 | 0.0607 | 0.2573 | 0.3979 | 0.4002 | 0.2929 | -1.0 | 0.0198 | 0.2784 | 0.3488 | 0.5173 |
| 1.3628 | 6.0345 | 2800 | 1.2517 | 0.1805 | 0.4221 | 0.106 | 0.1834 | 0.2231 | -1.0 | 0.06 | 0.2557 | 0.3883 | 0.3902 | 0.3214 | -1.0 | 0.0227 | 0.2717 | 0.3382 | 0.5048 |
| 1.3127 | 6.1422 | 2850 | 1.2254 | 0.1877 | 0.4279 | 0.1202 | 0.1914 | 0.1884 | -1.0 | 0.0629 | 0.2611 | 0.3954 | 0.3974 | 0.3286 | -1.0 | 0.0202 | 0.2733 | 0.3552 | 0.5176 |
| 1.7499 | 6.25 | 2900 | 1.2486 | 0.1757 | 0.412 | 0.1049 | 0.1794 | 0.1907 | -1.0 | 0.0597 | 0.2507 | 0.3877 | 0.3896 | 0.3214 | -1.0 | 0.0177 | 0.2747 | 0.3337 | 0.5007 |
| 1.2982 | 6.3578 | 2950 | 1.2164 | 0.1892 | 0.4214 | 0.1298 | 0.1915 | 0.1861 | -1.0 | 0.06 | 0.2575 | 0.3974 | 0.4 | 0.2714 | -1.0 | 0.0198 | 0.2788 | 0.3586 | 0.5161 |
| 1.2499 | 6.4655 | 3000 | 1.2346 | 0.1846 | 0.4172 | 0.1166 | 0.1897 | 0.1567 | -1.0 | 0.0609 | 0.2565 | 0.3969 | 0.3986 | 0.3571 | -1.0 | 0.0172 | 0.2776 | 0.352 | 0.5163 |
| 1.8273 | 6.5733 | 3050 | 1.2508 | 0.1801 | 0.4138 | 0.1139 | 0.1842 | 0.1534 | -1.0 | 0.06 | 0.2498 | 0.3988 | 0.4004 | 0.35 | -1.0 | 0.0175 | 0.2907 | 0.3427 | 0.5068 |
| 1.8044 | 6.6810 | 3100 | 1.2149 | 0.1816 | 0.4137 | 0.1157 | 0.1866 | 0.1636 | -1.0 | 0.0599 | 0.2576 | 0.3972 | 0.3991 | 0.3286 | -1.0 | 0.0203 | 0.284 | 0.343 | 0.5105 |
| 1.8206 | 6.7888 | 3150 | 1.2197 | 0.1765 | 0.4044 | 0.1126 | 0.1809 | 0.1565 | -1.0 | 0.0603 | 0.2574 | 0.4005 | 0.4021 | 0.3571 | -1.0 | 0.0201 | 0.2825 | 0.3329 | 0.5185 |
| 1.3453 | 6.8966 | 3200 | 1.2322 | 0.1819 | 0.4101 | 0.1216 | 0.1869 | 0.1497 | -1.0 | 0.0586 | 0.2499 | 0.3908 | 0.3927 | 0.3214 | -1.0 | 0.0144 | 0.2732 | 0.3494 | 0.5084 |
| 1.5722 | 7.0043 | 3250 | 1.2202 | 0.1861 | 0.4251 | 0.1147 | 0.1895 | 0.2274 | -1.0 | 0.0613 | 0.246 | 0.3957 | 0.397 | 0.3857 | -1.0 | 0.014 | 0.2839 | 0.3582 | 0.5076 |
| 1.1069 | 7.1121 | 3300 | 1.2042 | 0.1913 | 0.4262 | 0.1279 | 0.1946 | 0.2496 | -1.0 | 0.0616 | 0.2545 | 0.3988 | 0.4007 | 0.3286 | -1.0 | 0.0167 | 0.2816 | 0.3659 | 0.516 |
| 1.9143 | 7.2198 | 3350 | 1.2001 | 0.1894 | 0.4261 | 0.1298 | 0.1934 | 0.189 | -1.0 | 0.0623 | 0.2583 | 0.4049 | 0.4066 | 0.3643 | -1.0 | 0.0173 | 0.2841 | 0.3616 | 0.5257 |
| 1.929 | 7.3276 | 3400 | 1.2157 | 0.1732 | 0.4129 | 0.1044 | 0.1774 | 0.1823 | -1.0 | 0.0591 | 0.2464 | 0.3922 | 0.3937 | 0.3571 | -1.0 | 0.019 | 0.287 | 0.3274 | 0.4974 |
| 1.9973 | 7.4353 | 3450 | 1.2210 | 0.1849 | 0.4237 | 0.1256 | 0.1896 | 0.1933 | -1.0 | 0.0599 | 0.2545 | 0.3963 | 0.3976 | 0.3929 | -1.0 | 0.0163 | 0.279 | 0.3535 | 0.5137 |
| 1.6126 | 7.5431 | 3500 | 1.2591 | 0.1735 | 0.412 | 0.1083 | 0.178 | 0.1822 | -1.0 | 0.0585 | 0.238 | 0.3835 | 0.3847 | 0.3643 | -1.0 | 0.0147 | 0.2784 | 0.3323 | 0.4885 |
| 1.0779 | 7.6509 | 3550 | 1.2142 | 0.1794 | 0.4139 | 0.1136 | 0.1827 | 0.202 | -1.0 | 0.0565 | 0.2499 | 0.3959 | 0.3973 | 0.3714 | -1.0 | 0.0177 | 0.2839 | 0.3411 | 0.508 |
| 1.1365 | 7.7586 | 3600 | 1.2074 | 0.1894 | 0.4232 | 0.1298 | 0.1932 | 0.2062 | -1.0 | 0.0615 | 0.2712 | 0.4101 | 0.4118 | 0.3714 | -1.0 | 0.024 | 0.2895 | 0.3549 | 0.5308 |
| 1.3007 | 7.8664 | 3650 | 1.2069 | 0.1856 | 0.4244 | 0.1162 | 0.1892 | 0.2222 | -1.0 | 0.0586 | 0.2654 | 0.4022 | 0.4035 | 0.3929 | -1.0 | 0.0209 | 0.2834 | 0.3504 | 0.5209 |
| 1.9676 | 7.9741 | 3700 | 1.2110 | 0.1881 | 0.4201 | 0.1221 | 0.1913 | 0.2115 | -1.0 | 0.0605 | 0.2658 | 0.4047 | 0.4065 | 0.35 | -1.0 | 0.0214 | 0.2843 | 0.3547 | 0.5251 |
| 1.1711 | 8.0819 | 3750 | 1.2074 | 0.1806 | 0.4224 | 0.1101 | 0.1835 | 0.2001 | -1.0 | 0.0586 | 0.2583 | 0.3907 | 0.3919 | 0.3857 | -1.0 | 0.0218 | 0.278 | 0.3395 | 0.5033 |
| 1.9594 | 8.1897 | 3800 | 1.2350 | 0.185 | 0.423 | 0.1256 | 0.188 | 0.2282 | -1.0 | 0.0592 | 0.2596 | 0.3854 | 0.387 | 0.3643 | -1.0 | 0.0178 | 0.2551 | 0.3521 | 0.5157 |
| 1.6838 | 8.2974 | 3850 | 1.1909 | 0.1863 | 0.4264 | 0.1183 | 0.1889 | 0.2103 | -1.0 | 0.0597 | 0.2676 | 0.3991 | 0.4006 | 0.3714 | -1.0 | 0.0235 | 0.2817 | 0.3492 | 0.5164 |
| 1.0343 | 8.4052 | 3900 | 1.1952 | 0.1695 | 0.4115 | 0.0901 | 0.173 | 0.1697 | -1.0 | 0.0569 | 0.2485 | 0.3893 | 0.3903 | 0.4 | -1.0 | 0.0198 | 0.2864 | 0.3191 | 0.4923 |
| 1.5406 | 8.5129 | 3950 | 1.2028 | 0.1895 | 0.4428 | 0.1111 | 0.1924 | 0.226 | -1.0 | 0.0592 | 0.2609 | 0.3924 | 0.3938 | 0.3714 | -1.0 | 0.0215 | 0.2772 | 0.3575 | 0.5076 |
| 1.3279 | 8.6207 | 4000 | 1.2069 | 0.177 | 0.4358 | 0.0912 | 0.1791 | 0.2654 | -1.0 | 0.0561 | 0.2491 | 0.3853 | 0.3858 | 0.4357 | -1.0 | 0.0196 | 0.2837 | 0.3344 | 0.4869 |
| 1.5472 | 8.7284 | 4050 | 1.1917 | 0.1908 | 0.4444 | 0.1167 | 0.1927 | 0.2728 | -1.0 | 0.0598 | 0.2601 | 0.4003 | 0.4015 | 0.3929 | -1.0 | 0.0224 | 0.294 | 0.3591 | 0.5065 |
| 1.3427 | 8.8362 | 4100 | 1.1818 | 0.1937 | 0.4373 | 0.1261 | 0.1962 | 0.2326 | -1.0 | 0.0608 | 0.2607 | 0.3991 | 0.4007 | 0.35 | -1.0 | 0.0206 | 0.2894 | 0.3668 | 0.5087 |
| 1.1626 | 8.9440 | 4150 | 1.1952 | 0.1878 | 0.4292 | 0.1192 | 0.1902 | 0.2533 | -1.0 | 0.0602 | 0.2548 | 0.3944 | 0.3955 | 0.4071 | -1.0 | 0.0174 | 0.28 | 0.3581 | 0.5089 |
| 1.5528 | 9.0517 | 4200 | 1.2105 | 0.1832 | 0.4154 | 0.1134 | 0.1861 | 0.2042 | -1.0 | 0.0569 | 0.2513 | 0.403 | 0.4045 | 0.3643 | -1.0 | 0.0158 | 0.2944 | 0.3507 | 0.5116 |
| 1.1406 | 9.1595 | 4250 | 1.1768 | 0.1937 | 0.44 | 0.1245 | 0.1977 | 0.203 | -1.0 | 0.0616 | 0.273 | 0.4063 | 0.4077 | 0.3929 | -1.0 | 0.0268 | 0.2912 | 0.3607 | 0.5215 |
| 1.059 | 9.2672 | 4300 | 1.1723 | 0.1909 | 0.4391 | 0.1195 | 0.1954 | 0.2212 | -1.0 | 0.0611 | 0.2741 | 0.4061 | 0.4069 | 0.4429 | -1.0 | 0.0279 | 0.2912 | 0.354 | 0.5211 |
| 1.1914 | 9.375 | 4350 | 1.1884 | 0.1817 | 0.4239 | 0.1155 | 0.1863 | 0.1956 | -1.0 | 0.0583 | 0.2611 | 0.4007 | 0.4016 | 0.4214 | -1.0 | 0.0236 | 0.2917 | 0.3398 | 0.5096 |
| 1.1247 | 9.4828 | 4400 | 1.1743 | 0.1803 | 0.4343 | 0.1043 | 0.1847 | 0.1959 | -1.0 | 0.0599 | 0.2655 | 0.3965 | 0.3971 | 0.4429 | -1.0 | 0.0274 | 0.2913 | 0.3332 | 0.5016 |
| 1.1566 | 9.5905 | 4450 | 1.1848 | 0.1949 | 0.4383 | 0.1281 | 0.1996 | 0.1943 | -1.0 | 0.0624 | 0.2827 | 0.4091 | 0.4105 | 0.4 | -1.0 | 0.0287 | 0.2862 | 0.3612 | 0.532 |
| 1.6062 | 9.6983 | 4500 | 1.1890 | 0.1892 | 0.4224 | 0.1245 | 0.1945 | 0.1935 | -1.0 | 0.0614 | 0.2795 | 0.4126 | 0.414 | 0.4 | -1.0 | 0.0293 | 0.2943 | 0.3492 | 0.531 |
| 2.0192 | 9.8060 | 4550 | 1.1862 | 0.1912 | 0.4246 | 0.1326 | 0.1976 | 0.1622 | -1.0 | 0.0627 | 0.2726 | 0.408 | 0.4096 | 0.3714 | -1.0 | 0.0244 | 0.2856 | 0.3581 | 0.5304 |
| 1.4838 | 9.9138 | 4600 | 1.1674 | 0.1892 | 0.4346 | 0.1138 | 0.1931 | 0.2099 | -1.0 | 0.0586 | 0.2671 | 0.4019 | 0.4028 | 0.4286 | -1.0 | 0.0244 | 0.2871 | 0.354 | 0.5167 |
| 1.1207 | 10.0216 | 4650 | 1.1776 | 0.1892 | 0.4254 | 0.1317 | 0.1939 | 0.1753 | -1.0 | 0.0606 | 0.2681 | 0.4045 | 0.4058 | 0.3929 | -1.0 | 0.023 | 0.2854 | 0.3553 | 0.5235 |
| 0.9931 | 10.1293 | 4700 | 1.1569 | 0.1874 | 0.4283 | 0.1215 | 0.1919 | 0.163 | -1.0 | 0.06 | 0.2667 | 0.4033 | 0.4045 | 0.3929 | -1.0 | 0.0229 | 0.2894 | 0.3519 | 0.5172 |
| 2.0281 | 10.2371 | 4750 | 1.1877 | 0.1808 | 0.4322 | 0.1107 | 0.1836 | 0.2468 | -1.0 | 0.0574 | 0.2576 | 0.3874 | 0.388 | 0.4357 | -1.0 | 0.0236 | 0.2813 | 0.338 | 0.4935 |
| 1.1893 | 10.3448 | 4800 | 1.1767 | 0.1805 | 0.4282 | 0.1085 | 0.1839 | 0.2218 | -1.0 | 0.0598 | 0.2568 | 0.3975 | 0.3982 | 0.45 | -1.0 | 0.0208 | 0.2852 | 0.3402 | 0.5099 |
| 1.0194 | 10.4526 | 4850 | 1.1971 | 0.1901 | 0.4267 | 0.1304 | 0.1938 | 0.2603 | -1.0 | 0.0595 | 0.2594 | 0.3967 | 0.3976 | 0.4357 | -1.0 | 0.018 | 0.2753 | 0.3622 | 0.5182 |
| 1.1556 | 10.5603 | 4900 | 1.1953 | 0.1876 | 0.4231 | 0.1297 | 0.1909 | 0.2337 | -1.0 | 0.0585 | 0.2558 | 0.403 | 0.4041 | 0.4071 | -1.0 | 0.0174 | 0.2907 | 0.3578 | 0.5153 |
| 2.0626 | 10.6681 | 4950 | 1.1856 | 0.1858 | 0.4283 | 0.1187 | 0.1907 | 0.1974 | -1.0 | 0.0595 | 0.2572 | 0.4018 | 0.4031 | 0.3929 | -1.0 | 0.0194 | 0.2901 | 0.3523 | 0.5135 |
| 1.0869 | 10.7759 | 5000 | 1.1844 | 0.1827 | 0.4278 | 0.1169 | 0.1865 | 0.2515 | -1.0 | 0.058 | 0.2504 | 0.3951 | 0.3959 | 0.4286 | -1.0 | 0.0177 | 0.2861 | 0.3477 | 0.5041 |
| 1.6523 | 10.8836 | 5050 | 1.1843 | 0.1927 | 0.4417 | 0.1232 | 0.1956 | 0.2569 | -1.0 | 0.0603 | 0.2561 | 0.3985 | 0.3993 | 0.4286 | -1.0 | 0.0189 | 0.2852 | 0.3665 | 0.5118 |
| 1.6716 | 10.9914 | 5100 | 1.1703 | 0.1937 | 0.4355 | 0.1372 | 0.1974 | 0.22 | -1.0 | 0.0625 | 0.2602 | 0.4086 | 0.4098 | 0.4 | -1.0 | 0.0195 | 0.2953 | 0.3679 | 0.5218 |
| 1.5057 | 11.0991 | 5150 | 1.1783 | 0.1894 | 0.43 | 0.1237 | 0.1935 | 0.1858 | -1.0 | 0.0581 | 0.2632 | 0.4049 | 0.4065 | 0.3643 | -1.0 | 0.021 | 0.2912 | 0.3578 | 0.5185 |
| 1.126 | 11.2069 | 5200 | 1.1629 | 0.1974 | 0.4371 | 0.1392 | 0.2013 | 0.2295 | -1.0 | 0.06 | 0.2666 | 0.4148 | 0.4167 | 0.3571 | -1.0 | 0.0215 | 0.295 | 0.3732 | 0.5346 |
| 1.1369 | 11.3147 | 5250 | 1.1473 | 0.196 | 0.4411 | 0.1307 | 0.2001 | 0.2223 | -1.0 | 0.06 | 0.2701 | 0.4135 | 0.415 | 0.3929 | -1.0 | 0.0241 | 0.2944 | 0.3678 | 0.5327 |
| 1.3188 | 11.4224 | 5300 | 1.1575 | 0.1948 | 0.431 | 0.1352 | 0.1997 | 0.175 | -1.0 | 0.0599 | 0.2686 | 0.4134 | 0.4153 | 0.3571 | -1.0 | 0.0232 | 0.2915 | 0.3664 | 0.5353 |
| 1.5131 | 11.5302 | 5350 | 1.1621 | 0.1854 | 0.4336 | 0.1203 | 0.1888 | 0.2302 | -1.0 | 0.0592 | 0.2623 | 0.4001 | 0.4012 | 0.4071 | -1.0 | 0.0218 | 0.2871 | 0.3489 | 0.5131 |
| 1.5959 | 11.6379 | 5400 | 1.1487 | 0.1959 | 0.4268 | 0.1381 | 0.2004 | 0.1946 | -1.0 | 0.0612 | 0.2664 | 0.4166 | 0.4181 | 0.3857 | -1.0 | 0.0211 | 0.3004 | 0.3708 | 0.5327 |
| 0.9766 | 11.7457 | 5450 | 1.1456 | 0.2002 | 0.4397 | 0.1385 | 0.2046 | 0.1886 | -1.0 | 0.0617 | 0.2692 | 0.4156 | 0.4177 | 0.3357 | -1.0 | 0.0223 | 0.3012 | 0.378 | 0.5301 |
| 1.0508 | 11.8534 | 5500 | 1.1405 | 0.2008 | 0.4457 | 0.1361 | 0.2039 | 0.2182 | -1.0 | 0.0609 | 0.2692 | 0.4116 | 0.4134 | 0.3571 | -1.0 | 0.0236 | 0.3002 | 0.3779 | 0.5231 |
| 1.064 | 11.9612 | 5550 | 1.1395 | 0.1959 | 0.4391 | 0.1232 | 0.2003 | 0.1979 | -1.0 | 0.0604 | 0.2663 | 0.4129 | 0.4146 | 0.3571 | -1.0 | 0.0223 | 0.3023 | 0.3694 | 0.5234 |
| 1.4142 | 12.0690 | 5600 | 1.1292 | 0.1942 | 0.4423 | 0.1295 | 0.1973 | 0.2 | -1.0 | 0.0599 | 0.2694 | 0.4122 | 0.4138 | 0.3643 | -1.0 | 0.0245 | 0.3001 | 0.3638 | 0.5243 |
| 1.0224 | 12.1767 | 5650 | 1.1389 | 0.1953 | 0.4434 | 0.1319 | 0.199 | 0.2225 | -1.0 | 0.0612 | 0.2694 | 0.411 | 0.4124 | 0.3929 | -1.0 | 0.0233 | 0.2939 | 0.3674 | 0.5281 |
| 1.7465 | 12.2845 | 5700 | 1.1245 | 0.2023 | 0.45 | 0.133 | 0.2059 | 0.2339 | -1.0 | 0.0613 | 0.2785 | 0.4183 | 0.4202 | 0.35 | -1.0 | 0.0285 | 0.305 | 0.3761 | 0.5315 |
| 1.0503 | 12.3922 | 5750 | 1.1337 | 0.1989 | 0.4506 | 0.1356 | 0.2024 | 0.2496 | -1.0 | 0.0621 | 0.2763 | 0.4076 | 0.409 | 0.3857 | -1.0 | 0.0267 | 0.2863 | 0.3712 | 0.5288 |
| 2.2047 | 12.5 | 5800 | 1.1313 | 0.2001 | 0.442 | 0.1361 | 0.2038 | 0.2462 | -1.0 | 0.0641 | 0.2783 | 0.4165 | 0.4182 | 0.3643 | -1.0 | 0.0267 | 0.2997 | 0.3735 | 0.5333 |
| 0.9121 | 12.6078 | 5850 | 1.1382 | 0.1974 | 0.4422 | 0.131 | 0.2011 | 0.261 | -1.0 | 0.0644 | 0.2839 | 0.4166 | 0.4182 | 0.3786 | -1.0 | 0.0297 | 0.2994 | 0.365 | 0.5337 |
| 1.4769 | 12.7155 | 5900 | 1.1517 | 0.1982 | 0.4438 | 0.1299 | 0.2018 | 0.2747 | -1.0 | 0.0638 | 0.2828 | 0.4188 | 0.4202 | 0.4071 | -1.0 | 0.0289 | 0.3012 | 0.3675 | 0.5365 |
| 0.9778 | 12.8233 | 5950 | 1.1364 | 0.1992 | 0.4417 | 0.1333 | 0.2025 | 0.2725 | -1.0 | 0.0638 | 0.2781 | 0.4108 | 0.4121 | 0.4071 | -1.0 | 0.0257 | 0.2904 | 0.3727 | 0.5312 |
| 1.2711 | 12.9310 | 6000 | 1.1361 | 0.1957 | 0.44 | 0.1314 | 0.1991 | 0.2555 | -1.0 | 0.0624 | 0.2778 | 0.4109 | 0.4122 | 0.4 | -1.0 | 0.0266 | 0.2955 | 0.3647 | 0.5263 |
| 1.3801 | 13.0388 | 6050 | 1.1367 | 0.201 | 0.4486 | 0.1383 | 0.2033 | 0.2743 | -1.0 | 0.0641 | 0.2819 | 0.4163 | 0.4179 | 0.3786 | -1.0 | 0.0291 | 0.3008 | 0.3729 | 0.5318 |
| 1.0824 | 13.1466 | 6100 | 1.1392 | 0.1998 | 0.4471 | 0.132 | 0.2039 | 0.2257 | -1.0 | 0.0647 | 0.2819 | 0.4177 | 0.4198 | 0.3357 | -1.0 | 0.0304 | 0.3051 | 0.3693 | 0.5304 |
| 1.5716 | 13.2543 | 6150 | 1.1566 | 0.1972 | 0.4372 | 0.1287 | 0.2012 | 0.2389 | -1.0 | 0.0633 | 0.2714 | 0.414 | 0.4154 | 0.3929 | -1.0 | 0.0233 | 0.2963 | 0.3711 | 0.5317 |
| 1.1803 | 13.3621 | 6200 | 1.1526 | 0.1979 | 0.434 | 0.14 | 0.2017 | 0.225 | -1.0 | 0.0627 | 0.2654 | 0.4151 | 0.4168 | 0.3714 | -1.0 | 0.0205 | 0.297 | 0.3753 | 0.5331 |
| 1.1254 | 13.4698 | 6250 | 1.1398 | 0.2017 | 0.4489 | 0.1347 | 0.2058 | 0.219 | -1.0 | 0.0637 | 0.271 | 0.4175 | 0.4194 | 0.3571 | -1.0 | 0.0269 | 0.3038 | 0.3766 | 0.5312 |
| 1.3144 | 13.5776 | 6300 | 1.1227 | 0.2002 | 0.4478 | 0.1359 | 0.2032 | 0.2688 | -1.0 | 0.0631 | 0.2753 | 0.4109 | 0.412 | 0.4143 | -1.0 | 0.0257 | 0.2953 | 0.3746 | 0.5265 |
| 1.2006 | 13.6853 | 6350 | 1.1343 | 0.197 | 0.439 | 0.1288 | 0.2014 | 0.1836 | -1.0 | 0.0627 | 0.2718 | 0.4193 | 0.4211 | 0.3571 | -1.0 | 0.0245 | 0.3079 | 0.3695 | 0.5308 |
| 1.1371 | 13.7931 | 6400 | 1.1213 | 0.2015 | 0.4491 | 0.1405 | 0.2048 | 0.2258 | -1.0 | 0.0632 | 0.2745 | 0.4173 | 0.4186 | 0.4143 | -1.0 | 0.0255 | 0.3001 | 0.3775 | 0.5346 |
| 1.1677 | 13.9009 | 6450 | 1.1327 | 0.201 | 0.4465 | 0.1361 | 0.2046 | 0.2225 | -1.0 | 0.063 | 0.2714 | 0.4158 | 0.4173 | 0.3929 | -1.0 | 0.0242 | 0.2992 | 0.3778 | 0.5324 |
| 0.9811 | 14.0086 | 6500 | 1.1240 | 0.2035 | 0.4463 | 0.1453 | 0.2073 | 0.2446 | -1.0 | 0.0628 | 0.2746 | 0.4131 | 0.4147 | 0.3714 | -1.0 | 0.0241 | 0.2931 | 0.383 | 0.533 |
| 1.0895 | 14.1164 | 6550 | 1.1297 | 0.2025 | 0.4458 | 0.1367 | 0.2064 | 0.2368 | -1.0 | 0.0628 | 0.2703 | 0.4147 | 0.4163 | 0.3786 | -1.0 | 0.0241 | 0.302 | 0.3809 | 0.5275 |
| 0.9775 | 14.2241 | 6600 | 1.1194 | 0.1951 | 0.448 | 0.1288 | 0.1976 | 0.2813 | -1.0 | 0.0618 | 0.2644 | 0.4061 | 0.4071 | 0.4214 | -1.0 | 0.0238 | 0.2981 | 0.3664 | 0.5141 |
| 1.6864 | 14.3319 | 6650 | 1.1241 | 0.204 | 0.4507 | 0.142 | 0.2069 | 0.2688 | -1.0 | 0.0634 | 0.2686 | 0.4166 | 0.4179 | 0.4071 | -1.0 | 0.0239 | 0.3044 | 0.3842 | 0.5288 |
| 1.0167 | 14.4397 | 6700 | 1.1158 | 0.2066 | 0.4539 | 0.1431 | 0.2099 | 0.2349 | -1.0 | 0.063 | 0.274 | 0.4194 | 0.421 | 0.3786 | -1.0 | 0.0254 | 0.3053 | 0.3878 | 0.5336 |
| 1.8267 | 14.5474 | 6750 | 1.1167 | 0.2013 | 0.4506 | 0.1388 | 0.2055 | 0.2316 | -1.0 | 0.0637 | 0.2725 | 0.418 | 0.4198 | 0.3571 | -1.0 | 0.0268 | 0.3092 | 0.3759 | 0.5269 |
| 1.4236 | 14.6552 | 6800 | 1.1245 | 0.2053 | 0.4558 | 0.1471 | 0.2085 | 0.2314 | -1.0 | 0.0651 | 0.2784 | 0.4158 | 0.4176 | 0.3571 | -1.0 | 0.0316 | 0.3042 | 0.3789 | 0.5275 |
| 1.333 | 14.7629 | 6850 | 1.1058 | 0.2136 | 0.4706 | 0.1492 | 0.2166 | 0.2664 | -1.0 | 0.0656 | 0.2893 | 0.4246 | 0.4265 | 0.3571 | -1.0 | 0.0363 | 0.3127 | 0.391 | 0.5366 |
| 1.7656 | 14.8707 | 6900 | 1.1022 | 0.2096 | 0.4651 | 0.1451 | 0.2124 | 0.2538 | -1.0 | 0.0658 | 0.2846 | 0.4174 | 0.4189 | 0.3857 | -1.0 | 0.0315 | 0.3043 | 0.3877 | 0.5305 |
| 1.1243 | 14.9784 | 6950 | 1.1080 | 0.2133 | 0.466 | 0.1523 | 0.2157 | 0.2785 | -1.0 | 0.0664 | 0.2854 | 0.4193 | 0.4207 | 0.4 | -1.0 | 0.0322 | 0.3067 | 0.3944 | 0.5318 |
| 1.658 | 15.0862 | 7000 | 1.1115 | 0.2043 | 0.4622 | 0.1339 | 0.2068 | 0.2819 | -1.0 | 0.063 | 0.2774 | 0.4105 | 0.4114 | 0.4357 | -1.0 | 0.0304 | 0.3028 | 0.3782 | 0.5183 |
| 1.3946 | 15.1940 | 7050 | 1.1119 | 0.2027 | 0.4568 | 0.1345 | 0.2065 | 0.2632 | -1.0 | 0.0643 | 0.2772 | 0.4118 | 0.4129 | 0.4143 | -1.0 | 0.0287 | 0.3021 | 0.3768 | 0.5214 |
| 1.4881 | 15.3017 | 7100 | 1.1042 | 0.2012 | 0.4562 | 0.136 | 0.2038 | 0.2673 | -1.0 | 0.0634 | 0.2788 | 0.4124 | 0.4132 | 0.4429 | -1.0 | 0.0287 | 0.3047 | 0.3737 | 0.5201 |
| 1.2228 | 15.4095 | 7150 | 1.1150 | 0.2025 | 0.4539 | 0.1344 | 0.2055 | 0.2469 | -1.0 | 0.063 | 0.2742 | 0.4133 | 0.4144 | 0.4143 | -1.0 | 0.0264 | 0.3049 | 0.3787 | 0.5217 |
| 1.0396 | 15.5172 | 7200 | 1.1073 | 0.205 | 0.4595 | 0.1403 | 0.2076 | 0.2674 | -1.0 | 0.0635 | 0.2782 | 0.4149 | 0.4161 | 0.4143 | -1.0 | 0.0289 | 0.3029 | 0.3811 | 0.5269 |
| 1.0606 | 15.625 | 7250 | 1.1043 | 0.2073 | 0.4517 | 0.145 | 0.2096 | 0.2465 | -1.0 | 0.0652 | 0.2794 | 0.4202 | 0.4219 | 0.3714 | -1.0 | 0.0286 | 0.3096 | 0.386 | 0.5308 |
| 1.2285 | 15.7328 | 7300 | 1.1250 | 0.2011 | 0.4389 | 0.1366 | 0.2043 | 0.2387 | -1.0 | 0.0622 | 0.2716 | 0.4151 | 0.4164 | 0.4 | -1.0 | 0.0238 | 0.3038 | 0.3784 | 0.5265 |
| 1.089 | 15.8405 | 7350 | 1.1176 | 0.2023 | 0.4445 | 0.1385 | 0.205 | 0.247 | -1.0 | 0.0621 | 0.2753 | 0.4151 | 0.4166 | 0.3857 | -1.0 | 0.0255 | 0.3042 | 0.3791 | 0.526 |
| 1.1228 | 15.9483 | 7400 | 1.1060 | 0.2046 | 0.4523 | 0.1357 | 0.2072 | 0.2847 | -1.0 | 0.0651 | 0.2763 | 0.4197 | 0.4211 | 0.4 | -1.0 | 0.0271 | 0.3114 | 0.382 | 0.5281 |
| 0.8598 | 16.0560 | 7450 | 1.1095 | 0.2052 | 0.4533 | 0.1402 | 0.2081 | 0.2613 | -1.0 | 0.0626 | 0.2799 | 0.419 | 0.4201 | 0.4214 | -1.0 | 0.0277 | 0.3096 | 0.3827 | 0.5283 |
| 1.2708 | 16.1638 | 7500 | 1.1059 | 0.2047 | 0.4556 | 0.1388 | 0.2071 | 0.2837 | -1.0 | 0.0635 | 0.2804 | 0.4153 | 0.4163 | 0.4286 | -1.0 | 0.0285 | 0.3059 | 0.3809 | 0.5247 |
| 1.8101 | 16.2716 | 7550 | 1.1023 | 0.2103 | 0.4652 | 0.1497 | 0.2133 | 0.2499 | -1.0 | 0.0649 | 0.283 | 0.4204 | 0.4217 | 0.4 | -1.0 | 0.0311 | 0.3114 | 0.3894 | 0.5294 |
| 1.0946 | 16.3793 | 7600 | 1.1104 | 0.2084 | 0.4541 | 0.1422 | 0.2107 | 0.2748 | -1.0 | 0.0637 | 0.2786 | 0.4188 | 0.4202 | 0.4 | -1.0 | 0.0288 | 0.305 | 0.3879 | 0.5327 |
| 1.6373 | 16.4871 | 7650 | 1.1160 | 0.2097 | 0.4637 | 0.1519 | 0.212 | 0.2893 | -1.0 | 0.0641 | 0.2809 | 0.4182 | 0.4192 | 0.4357 | -1.0 | 0.0316 | 0.3048 | 0.3877 | 0.5315 |
| 1.3377 | 16.5948 | 7700 | 1.1163 | 0.2075 | 0.4579 | 0.1441 | 0.2102 | 0.265 | -1.0 | 0.0628 | 0.2787 | 0.4221 | 0.4232 | 0.4286 | -1.0 | 0.0295 | 0.3117 | 0.3856 | 0.5326 |
| 1.8192 | 16.7026 | 7750 | 1.1129 | 0.2039 | 0.4637 | 0.1318 | 0.2071 | 0.2707 | -1.0 | 0.0619 | 0.2803 | 0.4181 | 0.4192 | 0.4143 | -1.0 | 0.0306 | 0.3098 | 0.3773 | 0.5263 |
| 1.0494 | 16.8103 | 7800 | 1.1104 | 0.2079 | 0.4713 | 0.1387 | 0.2109 | 0.2665 | -1.0 | 0.0636 | 0.287 | 0.4151 | 0.4163 | 0.4214 | -1.0 | 0.0332 | 0.3008 | 0.3825 | 0.5295 |
| 1.2957 | 16.9181 | 7850 | 1.1126 | 0.2033 | 0.462 | 0.1334 | 0.2063 | 0.2712 | -1.0 | 0.0627 | 0.2838 | 0.4115 | 0.4124 | 0.4286 | -1.0 | 0.0312 | 0.2989 | 0.3753 | 0.524 |
| 1.6719 | 17.0259 | 7900 | 1.1025 | 0.2116 | 0.4709 | 0.148 | 0.215 | 0.2696 | -1.0 | 0.0646 | 0.2906 | 0.4239 | 0.4252 | 0.4143 | -1.0 | 0.0349 | 0.3108 | 0.3883 | 0.5369 |
| 2.2098 | 17.1336 | 7950 | 1.1015 | 0.2086 | 0.4623 | 0.1494 | 0.2122 | 0.272 | -1.0 | 0.0641 | 0.2871 | 0.424 | 0.4252 | 0.4143 | -1.0 | 0.0318 | 0.3142 | 0.3853 | 0.5337 |
| 1.7784 | 17.2414 | 8000 | 1.1134 | 0.2076 | 0.4642 | 0.144 | 0.2107 | 0.2688 | -1.0 | 0.0639 | 0.2841 | 0.4244 | 0.4256 | 0.4143 | -1.0 | 0.0308 | 0.3158 | 0.3845 | 0.533 |
| 1.0112 | 17.3491 | 8050 | 1.1034 | 0.2097 | 0.4648 | 0.1519 | 0.2129 | 0.272 | -1.0 | 0.0643 | 0.2861 | 0.4261 | 0.4274 | 0.4 | -1.0 | 0.0327 | 0.3185 | 0.3867 | 0.5336 |
| 1.0751 | 17.4569 | 8100 | 1.1009 | 0.2087 | 0.4678 | 0.1473 | 0.2115 | 0.2899 | -1.0 | 0.0636 | 0.288 | 0.4229 | 0.424 | 0.4286 | -1.0 | 0.0347 | 0.3139 | 0.3828 | 0.532 |
| 1.2879 | 17.5647 | 8150 | 1.1009 | 0.211 | 0.4707 | 0.1426 | 0.2147 | 0.2868 | -1.0 | 0.0657 | 0.2911 | 0.4227 | 0.4238 | 0.4286 | -1.0 | 0.0366 | 0.3103 | 0.3855 | 0.5352 |
| 1.3776 | 17.6724 | 8200 | 1.1000 | 0.2081 | 0.4646 | 0.1396 | 0.2111 | 0.2873 | -1.0 | 0.0645 | 0.2868 | 0.4261 | 0.4272 | 0.4286 | -1.0 | 0.0325 | 0.3187 | 0.3838 | 0.5336 |
| 1.0149 | 17.7802 | 8250 | 1.0993 | 0.2131 | 0.469 | 0.1508 | 0.2163 | 0.2803 | -1.0 | 0.0657 | 0.2926 | 0.4244 | 0.4255 | 0.4286 | -1.0 | 0.0371 | 0.3129 | 0.3891 | 0.5359 |
| 1.2494 | 17.8879 | 8300 | 1.0979 | 0.2117 | 0.4685 | 0.1448 | 0.2149 | 0.282 | -1.0 | 0.0651 | 0.2912 | 0.4271 | 0.4284 | 0.4143 | -1.0 | 0.0371 | 0.3178 | 0.3862 | 0.5363 |
| 1.163 | 17.9957 | 8350 | 1.0946 | 0.21 | 0.4656 | 0.1399 | 0.2135 | 0.2528 | -1.0 | 0.0646 | 0.2906 | 0.4232 | 0.4246 | 0.4 | -1.0 | 0.0368 | 0.3132 | 0.3832 | 0.5333 |
| 1.516 | 18.1034 | 8400 | 1.0930 | 0.2115 | 0.4646 | 0.1483 | 0.2148 | 0.2571 | -1.0 | 0.0644 | 0.2919 | 0.4265 | 0.428 | 0.4 | -1.0 | 0.0374 | 0.3146 | 0.3856 | 0.5385 |
| 1.199 | 18.2112 | 8450 | 1.0872 | 0.2157 | 0.4698 | 0.1497 | 0.2192 | 0.2637 | -1.0 | 0.066 | 0.296 | 0.4257 | 0.427 | 0.4143 | -1.0 | 0.0388 | 0.3115 | 0.3926 | 0.5398 |
| 1.1083 | 18.3190 | 8500 | 1.0956 | 0.2114 | 0.4682 | 0.1409 | 0.2141 | 0.288 | -1.0 | 0.0648 | 0.2926 | 0.4255 | 0.4267 | 0.4214 | -1.0 | 0.037 | 0.3155 | 0.3857 | 0.5355 |
| 1.1415 | 18.4267 | 8550 | 1.0883 | 0.2112 | 0.466 | 0.1434 | 0.2152 | 0.2507 | -1.0 | 0.0655 | 0.2949 | 0.4269 | 0.4283 | 0.4 | -1.0 | 0.0371 | 0.319 | 0.3852 | 0.5349 |
| 1.451 | 18.5345 | 8600 | 1.0935 | 0.2107 | 0.4668 | 0.1439 | 0.2142 | 0.2476 | -1.0 | 0.0642 | 0.2922 | 0.4278 | 0.4293 | 0.3857 | -1.0 | 0.037 | 0.322 | 0.3844 | 0.5336 |
| 1.3117 | 18.6422 | 8650 | 1.1024 | 0.2099 | 0.4581 | 0.1484 | 0.2133 | 0.2578 | -1.0 | 0.0633 | 0.2879 | 0.4239 | 0.4252 | 0.4071 | -1.0 | 0.033 | 0.3137 | 0.3868 | 0.534 |
| 1.0472 | 18.75 | 8700 | 1.0960 | 0.2079 | 0.4603 | 0.1455 | 0.2103 | 0.2834 | -1.0 | 0.0624 | 0.2896 | 0.4195 | 0.4204 | 0.4429 | -1.0 | 0.0333 | 0.3088 | 0.3824 | 0.5302 |
| 1.0329 | 18.8578 | 8750 | 1.0874 | 0.2111 | 0.4609 | 0.1524 | 0.2146 | 0.2738 | -1.0 | 0.0635 | 0.2934 | 0.4221 | 0.4234 | 0.4143 | -1.0 | 0.0343 | 0.3096 | 0.388 | 0.5346 |
| 1.3704 | 18.9655 | 8800 | 1.0882 | 0.2115 | 0.4688 | 0.1507 | 0.2148 | 0.2562 | -1.0 | 0.0636 | 0.2912 | 0.4235 | 0.4251 | 0.3786 | -1.0 | 0.0365 | 0.3157 | 0.3864 | 0.5312 |
| 2.1077 | 19.0733 | 8850 | 1.0882 | 0.2107 | 0.4673 | 0.1527 | 0.2131 | 0.2712 | -1.0 | 0.0631 | 0.2896 | 0.4216 | 0.4231 | 0.3857 | -1.0 | 0.0353 | 0.3145 | 0.3861 | 0.5288 |
| 1.3446 | 19.1810 | 8900 | 1.0947 | 0.2083 | 0.4593 | 0.1564 | 0.2114 | 0.2681 | -1.0 | 0.0628 | 0.2851 | 0.4212 | 0.4227 | 0.3857 | -1.0 | 0.0336 | 0.3147 | 0.383 | 0.5276 |
| 1.0836 | 19.2888 | 8950 | 1.0971 | 0.2112 | 0.4646 | 0.1533 | 0.2141 | 0.2694 | -1.0 | 0.0638 | 0.288 | 0.422 | 0.4233 | 0.4 | -1.0 | 0.034 | 0.3131 | 0.3884 | 0.5308 |
| 1.1653 | 19.3966 | 9000 | 1.0909 | 0.2084 | 0.4632 | 0.1504 | 0.2113 | 0.2694 | -1.0 | 0.0624 | 0.2869 | 0.4179 | 0.4192 | 0.4 | -1.0 | 0.0344 | 0.3101 | 0.3825 | 0.5256 |
| 2.1015 | 19.5043 | 9050 | 1.0924 | 0.2091 | 0.4625 | 0.151 | 0.2124 | 0.278 | -1.0 | 0.0634 | 0.2871 | 0.4211 | 0.4225 | 0.4 | -1.0 | 0.0338 | 0.3149 | 0.3844 | 0.5273 |
| 1.0125 | 19.6121 | 9100 | 1.0994 | 0.2086 | 0.4646 | 0.1483 | 0.2106 | 0.2771 | -1.0 | 0.0634 | 0.2854 | 0.4191 | 0.4205 | 0.3929 | -1.0 | 0.0317 | 0.3119 | 0.3855 | 0.5263 |
| 1.631 | 19.7198 | 9150 | 1.0970 | 0.2071 | 0.4617 | 0.1453 | 0.2089 | 0.2796 | -1.0 | 0.0636 | 0.283 | 0.418 | 0.4194 | 0.3929 | -1.0 | 0.0319 | 0.3134 | 0.3823 | 0.5227 |
| 1.1955 | 19.8276 | 9200 | 1.0946 | 0.2083 | 0.4653 | 0.1491 | 0.2109 | 0.2762 | -1.0 | 0.064 | 0.2856 | 0.4204 | 0.4219 | 0.3857 | -1.0 | 0.0328 | 0.3142 | 0.3838 | 0.5266 |
| 1.4341 | 19.9353 | 9250 | 1.0979 | 0.2106 | 0.4655 | 0.156 | 0.2131 | 0.2922 | -1.0 | 0.0658 | 0.2842 | 0.4212 | 0.4226 | 0.3857 | -1.0 | 0.0318 | 0.3137 | 0.3894 | 0.5286 |
| 1.3996 | 20.0431 | 9300 | 1.0955 | 0.2077 | 0.4656 | 0.1448 | 0.21 | 0.2925 | -1.0 | 0.0633 | 0.2842 | 0.4172 | 0.4185 | 0.3929 | -1.0 | 0.0323 | 0.3117 | 0.3831 | 0.5227 |
| 1.5216 | 20.1509 | 9350 | 1.0997 | 0.2086 | 0.4642 | 0.1534 | 0.2107 | 0.2871 | -1.0 | 0.0647 | 0.2844 | 0.4208 | 0.4223 | 0.3857 | -1.0 | 0.0316 | 0.3167 | 0.3857 | 0.525 |
| 1.4524 | 20.2586 | 9400 | 1.0988 | 0.2082 | 0.4635 | 0.1475 | 0.2102 | 0.2866 | -1.0 | 0.0639 | 0.2848 | 0.4198 | 0.4212 | 0.3857 | -1.0 | 0.0316 | 0.3147 | 0.3848 | 0.5249 |
| 1.3485 | 20.3664 | 9450 | 1.0998 | 0.2075 | 0.4637 | 0.1431 | 0.2093 | 0.2869 | -1.0 | 0.0644 | 0.2821 | 0.4188 | 0.4203 | 0.3857 | -1.0 | 0.0314 | 0.3142 | 0.3836 | 0.5234 |
| 2.4621 | 20.4741 | 9500 | 1.0980 | 0.2084 | 0.4617 | 0.1439 | 0.2102 | 0.2809 | -1.0 | 0.0639 | 0.2833 | 0.4199 | 0.4212 | 0.3929 | -1.0 | 0.0314 | 0.3139 | 0.3855 | 0.5259 |
| 0.9933 | 20.5819 | 9550 | 1.0934 | 0.2105 | 0.4641 | 0.1528 | 0.2134 | 0.2782 | -1.0 | 0.0642 | 0.2863 | 0.4228 | 0.4243 | 0.3857 | -1.0 | 0.032 | 0.3149 | 0.389 | 0.5307 |
| 1.1412 | 20.6897 | 9600 | 1.0980 | 0.2097 | 0.4615 | 0.1524 | 0.2119 | 0.2903 | -1.0 | 0.0649 | 0.2853 | 0.4197 | 0.4211 | 0.3929 | -1.0 | 0.0316 | 0.311 | 0.3877 | 0.5285 |
| 1.4535 | 20.7974 | 9650 | 1.0932 | 0.2106 | 0.4635 | 0.1498 | 0.2127 | 0.2903 | -1.0 | 0.0647 | 0.2862 | 0.4213 | 0.4227 | 0.3929 | -1.0 | 0.0322 | 0.3135 | 0.3891 | 0.5291 |
| 1.3408 | 20.9052 | 9700 | 1.0924 | 0.2119 | 0.463 | 0.1554 | 0.2139 | 0.2849 | -1.0 | 0.0645 | 0.2872 | 0.4217 | 0.4232 | 0.3857 | -1.0 | 0.0323 | 0.3125 | 0.3916 | 0.5308 |
| 1.2117 | 21.0129 | 9750 | 1.0933 | 0.2104 | 0.4613 | 0.1551 | 0.2124 | 0.2995 | -1.0 | 0.0647 | 0.2855 | 0.4211 | 0.4225 | 0.3929 | -1.0 | 0.0313 | 0.3122 | 0.3894 | 0.5301 |
| 1.3188 | 21.1207 | 9800 | 1.0909 | 0.2095 | 0.4611 | 0.1546 | 0.2119 | 0.2995 | -1.0 | 0.0651 | 0.285 | 0.4216 | 0.423 | 0.3929 | -1.0 | 0.0316 | 0.3139 | 0.3874 | 0.5294 |
| 1.5079 | 21.2284 | 9850 | 1.0923 | 0.21 | 0.4628 | 0.1534 | 0.213 | 0.2849 | -1.0 | 0.0651 | 0.2856 | 0.422 | 0.4235 | 0.3857 | -1.0 | 0.032 | 0.3141 | 0.3879 | 0.5298 |
| 0.9257 | 21.3362 | 9900 | 1.0920 | 0.2099 | 0.4621 | 0.1547 | 0.2124 | 0.2995 | -1.0 | 0.0645 | 0.2852 | 0.4213 | 0.4227 | 0.3929 | -1.0 | 0.0318 | 0.3129 | 0.3881 | 0.5298 |
| 1.3689 | 21.4440 | 9950 | 1.0928 | 0.2107 | 0.4622 | 0.1539 | 0.2131 | 0.3049 | -1.0 | 0.065 | 0.2862 | 0.4222 | 0.4236 | 0.4 | -1.0 | 0.0321 | 0.3141 | 0.3893 | 0.5304 |
| 1.4012 | 21.5517 | 10000 | 1.0928 | 0.2104 | 0.462 | 0.1556 | 0.2131 | 0.2995 | -1.0 | 0.065 | 0.2862 | 0.4225 | 0.424 | 0.3929 | -1.0 | 0.032 | 0.3135 | 0.3889 | 0.5315 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1
|
timaeus/H1-dh32 | timaeus | 2024-10-18T00:18:35Z | 5 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-10-17T04:27:05Z | # H1-dh32 Checkpoints
This repository contains the final trained model and intermediate checkpoints.
- The main directory contains the fully trained model (checkpoint 75000).
- The `checkpoints` directory contains all intermediate checkpoints.
|
itsx-tom/estates-exterier-interier-classifier | itsx-tom | 2024-10-18T00:05:46Z | 454 | 1 | null | [
"tensorboard",
"safetensors",
"vit",
"image-classification",
"pytorch",
"huggingpics",
"model-index",
"region:us"
] | image-classification | 2024-10-18T00:05:39Z | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: estates-exterier-interier-classifier
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8888888955116272
---
# estates-exterier-interier-classifier
Autogenerated by HuggingPics🤗🖼️
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### exterier
#### interier
 |
luigi86/magnum-v2.5-12b-kto_mlx | luigi86 | 2024-10-18T00:05:03Z | 5 | 1 | null | [
"safetensors",
"mistral",
"chat",
"text-generation",
"conversational",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ru",
"zh",
"ja",
"base_model:anthracite-org/magnum-v2-12b",
"base_model:finetune:anthracite-org/magnum-v2-12b",
"license:apache-2.0",
"region:us"
] | text-generation | 2024-10-17T23:47:51Z | ---
license: apache-2.0
language:
- en
- fr
- de
- es
- it
- pt
- ru
- zh
- ja
pipeline_tag: text-generation
base_model: anthracite-org/magnum-12b-v2
tags:
- chat
---
# MLX Format and Quantizations for Magnum v2.5 12b KTO
Converted uncompressed and tested using the `mlx_lm` utility on a 64GiB URAM M1 Max.
- [8bpw quants](https://huggingface.co/luigi86/magnum-v2.5-12b-kto_mlx-8bpw)
- [16bpw model](https://huggingface.co/luigi86/magnum-v2.5-12b-kto_mlx)
See [original model](https://huggingface.co/anthracite-org/magnum-v2.5-12b) for further details.
# Original Model card
v2.5 KTO is an experimental release; we are testing a hybrid reinforcement learning strategy of KTO + DPOP, using rejected data sampled from the original model as "rejected". For "chosen", we use data from the original finetuning dataset as "chosen".
This was done on a limited portion of of primarily instruction following data; we plan to scale up a larger KTO dataset in the future for better generalization.
This is the 5th in a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet and Opus. This model is fine-tuned on top of [anthracite-org/magnum-12b-v2](https://huggingface.co/anthracite-org/magnum-12b-v2).
## Prompting
Model has been Instruct tuned with the ChatML formatting. A typical input would look like this:
```py
"""<|im_start|>user
Hi there!<|im_end|>
<|im_start|>assistant
Nice to meet you!<|im_end|>
<|im_start|>user
Can I ask a question?<|im_end|>
<|im_start|>assistant
"""
```
## Credits
- Stheno dataset (filtered)
- [kalomaze/Opus_Instruct_25k](https://huggingface.co/datasets/kalomaze/Opus_Instruct_25k)
- [Nopm/Opus_WritingStruct](https://huggingface.co/datasets/Nopm/Opus_WritingStruct)
- [Gryphe/Sonnet3.5-SlimOrcaDedupCleaned](https://huggingface.co/datasets/Gryphe/Sonnet3.5-SlimOrcaDedupCleaned) (A ~16k rows subset)
- [kalomaze/Opus_Instruct_3k](https://huggingface.co/datasets/kalomaze/Opus_Instruct_3k)
This model has been a team effort, and the credits goes to all members of Anthracite.
## Safety
...
|
MariamFarid/story-drawing-generator | MariamFarid | 2024-10-18T00:00:39Z | 33 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | 2024-10-17T23:57:15Z | ---
library_name: diffusers
---
# 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 🧨 diffusers 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] |
timaeus/H16-dh16 | timaeus | 2024-10-17T23:56:16Z | 6 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-10-17T23:50:45Z | # H16-dh16 Checkpoints
This repository contains the final trained model and intermediate checkpoints.
- The main directory contains the fully trained model (checkpoint 75000).
- The `checkpoints` directory contains all intermediate checkpoints.
|
jtupayac/Gemma-2-9b-it-edited | jtupayac | 2024-10-17T23:39:15Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"doi:10.57967/hf/3283",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T23:26:37Z | ---
library_name: transformers
tags: []
---
# 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] |
breadberry-prime/ylabs-data-heidi-medium-entrypoint | breadberry-prime | 2024-10-17T23:32:34Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2024-10-14T12:47:57Z | ---
library_name: transformers
tags: []
---
# 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] |
thdangtr/blip_title_v1.0_e2_p2 | thdangtr | 2024-10-17T23:23:20Z | 64 | 0 | transformers | [
"transformers",
"safetensors",
"blip",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2024-10-17T23:22:22Z | ---
library_name: transformers
tags: []
---
# 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] |
mav23/Rombos-LLM-V2.6-Qwen-14b-GGUF | mav23 | 2024-10-17T22:55:24Z | 278 | 0 | transformers | [
"transformers",
"gguf",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-14B-Instruct",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-17T20:56:48Z | ---
license: apache-2.0
library_name: transformers
base_model:
- Qwen/Qwen2.5-14B-Instruct
model-index:
- name: Rombos-LLM-V2.6-Qwen-14b
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 52.14
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 49.22
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 28.85
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 17.0
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 19.26
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 48.85
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=rombodawg/Rombos-LLM-V2.6-Qwen-14b
name: Open LLM Leaderboard
---
# Rombos-LLM-V2.5-Qwen-14b
Rombos-LLM-V2.6-Qwen-14b is the upgraded version of "rombodawg/Rombos-LLM-V2.5-Qwen-14b". The magic I performed to make this model better than it already was is only known to the Deepest state, dankest memers and God himself, so dont ask 😉. But it does perform a decent bit better than version 2.5 from my hand testing. Benchmarks will come later.
Check out the Continuous Finetuning method that I apply to all my models bellow:
- https://docs.google.com/document/d/1OjbjU5AOz4Ftn9xHQrX3oFQGhQ6RDUuXQipnQ9gn6tU/edit?usp=sharing
Quants:
- https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b-Q8_0-GGUF
- https://huggingface.co/rombodawg/Rombos-LLM-V2.6-Qwen-14b-Q5_K_M-GGUF
- https://huggingface.co/bartowski/Rombos-LLM-V2.6-Qwen-14b-GGUF
Benchmarks:
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_rombodawg__Rombos-LLM-V2.6-Qwen-14b)
| Metric |Value|
|-------------------|----:|
|Avg. |35.89|
|IFEval (0-Shot) |52.14|
|BBH (3-Shot) |49.22|
|MATH Lvl 5 (4-Shot)|28.85|
|GPQA (0-shot) |17.00|
|MuSR (0-shot) |19.26|
|MMLU-PRO (5-shot) |48.85|
|
buddycantcode/GY4TT | buddycantcode | 2024-10-17T22:44:52Z | 18 | 1 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-17T21:55:40Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: GY4TT
---
# Gy4Tt
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `GY4TT` to trigger the image generation.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('buddycantcode/GY4TT', weight_name='lora.safetensors')
image = pipeline('your prompt').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
|
michellemoorre/vae-test | michellemoorre | 2024-10-17T22:42:22Z | 8 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2024-10-17T22:41:47Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
dat-lequoc/fast-apply-16bit-v0.v15.2-Qwen2.5-Coder-1.5B | dat-lequoc | 2024-10-17T22:17:20Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/Qwen2.5-Coder-1.5B-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-Coder-1.5B-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T22:15:17Z | ---
base_model: unsloth/Qwen2.5-Coder-1.5B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---
# Uploaded model
- **Developed by:** quocdat25
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-1.5B-bnb-4bit
This qwen2 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)
|
timaeus/H8-dh256 | timaeus | 2024-10-17T22:15:49Z | 5 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-10-17T22:10:40Z | # H8-dh256 Checkpoints
This repository contains the final trained model and intermediate checkpoints.
- The main directory contains the fully trained model (checkpoint 75000).
- The `checkpoints` directory contains all intermediate checkpoints.
|
timaeus/H8-dh128 | timaeus | 2024-10-17T22:10:37Z | 5 | 0 | null | [
"safetensors",
"region:us"
] | null | 2024-10-17T05:23:12Z | # H8-dh128 Checkpoints
This repository contains the final trained model and intermediate checkpoints.
- The main directory contains the fully trained model (checkpoint 75000).
- The `checkpoints` directory contains all intermediate checkpoints.
|
braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.2.2 | braindao | 2024-10-17T21:45:38Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0",
"base_model:finetune:braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T21:40:34Z | ---
base_model: braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
---
# Uploaded model
- **Developed by:** braindao
- **License:** apache-2.0
- **Finetuned from model :** braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0
This qwen2 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)
|
akakakak/my_awesome_food_model | akakakak | 2024-10-17T21:45:06Z | 7 | 0 | null | [
"safetensors",
"vit",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"base_model:finetune:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"region:us"
] | null | 2024-10-17T20:54:48Z | ---
license: apache-2.0
base_model: google/vit-base-patch16-224-in21k
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_food_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6547
- Accuracy: 0.891
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7343 | 0.992 | 62 | 2.5678 | 0.858 |
| 1.8807 | 2.0 | 125 | 1.8085 | 0.887 |
| 1.626 | 2.976 | 186 | 1.6547 | 0.891 |
### Framework versions
- Transformers 4.43.3
- Pytorch 2.4.1+cu118
- Datasets 3.0.1
- Tokenizers 0.19.1
|
steffygreypaul/Experiment24 | steffygreypaul | 2024-10-17T21:36:54Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T21:35:32Z | ---
library_name: transformers
tags: []
---
# 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] |
dat-lequoc/fast-apply-16bit-v0.v15-Qwen2.5-Coder-1.5B | dat-lequoc | 2024-10-17T21:34:21Z | 98 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T21:32:10Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# 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] |
saqada/Llama-3.2-3B-Instruct-16bit-merged_lora_adapters-MOV | saqada | 2024-10-17T21:21:37Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:finetune:unsloth/Llama-3.2-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-08T11:05:46Z | ---
base_model: unsloth/Llama-3.2-3B-Instruct
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
# Uploaded model
- **Developed by:** saqada
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-Instruct
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)
|
dzagardo/ziggy_llama_750m_2000_steps_orca_mini_10k | dzagardo | 2024-10-17T21:21:22Z | 45 | 0 | transformers | [
"transformers",
"pytorch",
"my_transformer",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-17T21:19:42Z | ---
library_name: transformers
tags: []
---
# 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] |
BroAlanTaps/GPT2-large-4-44000steps | BroAlanTaps | 2024-10-17T21:19:21Z | 136 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T21:17:31Z | ---
library_name: transformers
tags: []
---
# 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] |
Spedon/texify-quantized-onnx | Spedon | 2024-10-17T21:18:16Z | 20 | 0 | null | [
"onnx",
"vision-encoder-decoder",
"image-to-text",
"base_model:vikp/texify",
"base_model:quantized:vikp/texify",
"license:cc-by-sa-4.0",
"region:us"
] | image-to-text | 2024-10-05T05:25:08Z | ---
license: cc-by-sa-4.0
base_model:
- vikp/texify
pipeline_tag: image-to-text
---
## texify-quantized-onnx
https://huggingface.co/vikp/texify with quantized ONNX weights, shoutout to https://huggingface.co/Xenova/texify
## Usage (`optimum[onnxruntime]`)
If you haven't already, you can install the optimum with the onnxrumtime backend
```bash
pip install "optimum[onnxruntime]"
```
**Example:**
```python
from optimum.onnxruntime import ORTModelForVision2Seq
from optimum.pipelines import pipeline
model = ORTModelForVision2Seq.from_pretrained("Spedon/texify-quantized-onnx")
texify = pipeline(
"image-to-text",
model,
feature_extractor="Spedon/texify-quantized-onnx",
image_processor="Spedon/texify-quantized-onnx",
)
image = (
"https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/latex.png"
)
latex = texify(image, max_new_tokens=384)
print(latex)
# [{'generated_text': "The potential $V_i$ of cell $\\mathcal{C}_i$ centred at position $\\mathbf{r}_i$ is related to the surface charge densities $\\sigma_j$ of cells $\\mathcal{C}_j$ $j\\in[1,N]$ through the superposition principle as: $$V_i\\,=\\,\\sum_{j=0}^{N}\\,\\frac{\\sigma_j}{4\\pi\\varepsilon_0}\\,\\int_{\\mathcal{C}_j}\\frac{1}{\\|\\mathbf{r}_i-\\mathbf{r}'\\|}\\,\\mathrm{d}^2\\mathbf{r}'\\,=\\,\\sum_{j=0}^{N}\\,Q_{ij}\\,\\sigma_j,$$ where the integral over the surface of cell $\\mathcal{C}_j$ only depends on $\\mathcal{C}_j$ shape and on the relative position of the target point $\\mathbf{r}_i$ with respect to $\\mathcal{C}_j$ location, as $\\sigma_j$ is assumed constant over the whole surface of cell $\\mathcal{C}_j$. "}]
```
| Input image | Visualized output |
| ---------------------------------------------------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------- |
|  |  |
|
BroAlanTaps/Llama3-instruct-4-44000steps | BroAlanTaps | 2024-10-17T21:17:29Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T21:15:06Z | ---
library_name: transformers
tags: []
---
# 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] |
luaqi/sn29_back_v9 | luaqi | 2024-10-17T21:11:20Z | 61 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T21:02:57Z | ---
library_name: transformers
tags: []
---
# 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] |
vcolella/fine-tuned-distilbert-hf | vcolella | 2024-10-17T20:36:05Z | 107 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-17T20:35:40Z | ---
library_name: transformers
tags: []
---
# 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] |
pwork7/hermes_iter3_no_system | pwork7 | 2024-10-17T20:09:11Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T20:05:53Z | ---
library_name: transformers
tags: []
---
# 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] |
mujerry/swin-tiny-patch4-window7-224-finetuned-papsmear | mujerry | 2024-10-17T20:08:17Z | 60 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-08-27T09:15:34Z | ---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-papsmear
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9779411764705882
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# swin-tiny-patch4-window7-224-finetuned-papsmear
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2644
- Accuracy: 0.9779
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-------:|:----:|:---------------:|:--------:|
| 1.7081 | 0.9935 | 38 | 1.6642 | 0.2868 |
| 1.4025 | 1.9869 | 76 | 1.3761 | 0.4632 |
| 1.0918 | 2.9804 | 114 | 1.0276 | 0.5515 |
| 0.8051 | 4.0 | 153 | 0.7679 | 0.6691 |
| 0.635 | 4.9935 | 191 | 0.5928 | 0.7868 |
| 0.6051 | 5.9869 | 229 | 0.6957 | 0.75 |
| 0.5539 | 6.9804 | 267 | 0.5016 | 0.7941 |
| 0.4683 | 8.0 | 306 | 0.4733 | 0.8235 |
| 0.4153 | 8.9935 | 344 | 0.4835 | 0.8529 |
| 0.3954 | 9.9869 | 382 | 0.5431 | 0.8309 |
| 0.3524 | 10.9804 | 420 | 0.4061 | 0.8235 |
| 0.3546 | 12.0 | 459 | 0.4925 | 0.8382 |
| 0.2922 | 12.9935 | 497 | 0.3637 | 0.875 |
| 0.2342 | 13.9869 | 535 | 0.3286 | 0.8971 |
| 0.2083 | 14.9804 | 573 | 0.3271 | 0.8824 |
| 0.2704 | 16.0 | 612 | 0.3700 | 0.8824 |
| 0.1871 | 16.9935 | 650 | 0.3447 | 0.8971 |
| 0.226 | 17.9869 | 688 | 0.4280 | 0.8603 |
| 0.245 | 18.9804 | 726 | 0.6445 | 0.8088 |
| 0.1545 | 20.0 | 765 | 0.4180 | 0.8603 |
| 0.0981 | 20.9935 | 803 | 0.3208 | 0.9044 |
| 0.1455 | 21.9869 | 841 | 0.4256 | 0.8603 |
| 0.2405 | 22.9804 | 879 | 0.3474 | 0.8971 |
| 0.1549 | 24.0 | 918 | 0.3940 | 0.9044 |
| 0.1721 | 24.9935 | 956 | 0.4279 | 0.8824 |
| 0.1378 | 25.9869 | 994 | 0.3871 | 0.9044 |
| 0.0924 | 26.9804 | 1032 | 0.7301 | 0.8456 |
| 0.1325 | 28.0 | 1071 | 0.3712 | 0.9044 |
| 0.1426 | 28.9935 | 1109 | 0.4400 | 0.8603 |
| 0.0866 | 29.9869 | 1147 | 0.2779 | 0.9412 |
| 0.0659 | 30.9804 | 1185 | 0.3207 | 0.9412 |
| 0.1175 | 32.0 | 1224 | 0.4339 | 0.9044 |
| 0.0455 | 32.9935 | 1262 | 0.4537 | 0.9265 |
| 0.1006 | 33.9869 | 1300 | 0.6521 | 0.875 |
| 0.033 | 34.9804 | 1338 | 0.5616 | 0.9044 |
| 0.0979 | 36.0 | 1377 | 0.3718 | 0.9191 |
| 0.1045 | 36.9935 | 1415 | 0.2529 | 0.9632 |
| 0.0815 | 37.9869 | 1453 | 0.3511 | 0.9338 |
| 0.0761 | 38.9804 | 1491 | 0.3114 | 0.9338 |
| 0.0747 | 40.0 | 1530 | 0.2837 | 0.9338 |
| 0.0545 | 40.9935 | 1568 | 0.4269 | 0.9412 |
| 0.0796 | 41.9869 | 1606 | 0.2331 | 0.9412 |
| 0.055 | 42.9804 | 1644 | 0.2900 | 0.9485 |
| 0.0706 | 44.0 | 1683 | 0.3368 | 0.9632 |
| 0.0505 | 44.9935 | 1721 | 0.3780 | 0.9485 |
| 0.0698 | 45.9869 | 1759 | 0.4822 | 0.9191 |
| 0.0275 | 46.9804 | 1797 | 0.3434 | 0.9632 |
| 0.0641 | 48.0 | 1836 | 0.3387 | 0.9706 |
| 0.0484 | 48.9935 | 1874 | 0.5350 | 0.9191 |
| 0.0388 | 49.9869 | 1912 | 0.3826 | 0.9118 |
| 0.0347 | 50.9804 | 1950 | 0.3739 | 0.9559 |
| 0.1046 | 52.0 | 1989 | 0.3075 | 0.9118 |
| 0.0298 | 52.9935 | 2027 | 0.3558 | 0.9559 |
| 0.0478 | 53.9869 | 2065 | 0.3056 | 0.9706 |
| 0.0285 | 54.9804 | 2103 | 0.2851 | 0.9632 |
| 0.0407 | 56.0 | 2142 | 0.3223 | 0.9559 |
| 0.0459 | 56.9935 | 2180 | 0.4575 | 0.9485 |
| 0.0409 | 57.9869 | 2218 | 0.2930 | 0.9632 |
| 0.0743 | 58.9804 | 2256 | 0.4032 | 0.9485 |
| 0.0346 | 60.0 | 2295 | 0.3738 | 0.9412 |
| 0.0302 | 60.9935 | 2333 | 0.3597 | 0.9485 |
| 0.0488 | 61.9869 | 2371 | 0.2595 | 0.9559 |
| 0.0562 | 62.9804 | 2409 | 0.3764 | 0.9412 |
| 0.0216 | 64.0 | 2448 | 0.2644 | 0.9779 |
| 0.0219 | 64.9935 | 2486 | 0.3092 | 0.9632 |
| 0.0272 | 65.9869 | 2524 | 0.2898 | 0.9632 |
| 0.027 | 66.9804 | 2562 | 0.2693 | 0.9632 |
| 0.0397 | 68.0 | 2601 | 0.3843 | 0.9412 |
| 0.0154 | 68.9935 | 2639 | 0.3051 | 0.9485 |
| 0.0004 | 69.9869 | 2677 | 0.3909 | 0.9412 |
| 0.0651 | 70.9804 | 2715 | 0.2977 | 0.9485 |
| 0.016 | 72.0 | 2754 | 0.2695 | 0.9632 |
| 0.0351 | 72.9935 | 2792 | 0.2720 | 0.9706 |
| 0.0206 | 73.9869 | 2830 | 0.2549 | 0.9706 |
| 0.0109 | 74.9804 | 2868 | 0.2412 | 0.9706 |
| 0.0012 | 76.0 | 2907 | 0.3494 | 0.9779 |
| 0.0418 | 76.9935 | 2945 | 0.3729 | 0.9632 |
| 0.0165 | 77.9869 | 2983 | 0.3471 | 0.9632 |
| 0.0163 | 78.9804 | 3021 | 0.2973 | 0.9706 |
| 0.0202 | 80.0 | 3060 | 0.3730 | 0.9559 |
| 0.0368 | 80.9935 | 3098 | 0.2877 | 0.9706 |
| 0.0374 | 81.9869 | 3136 | 0.4143 | 0.9632 |
| 0.0296 | 82.9804 | 3174 | 0.2895 | 0.9779 |
| 0.0405 | 84.0 | 3213 | 0.2927 | 0.9559 |
| 0.0097 | 84.9935 | 3251 | 0.3179 | 0.9632 |
| 0.0182 | 85.9869 | 3289 | 0.3047 | 0.9706 |
| 0.0207 | 86.9804 | 3327 | 0.3018 | 0.9779 |
| 0.0207 | 88.0 | 3366 | 0.3321 | 0.9632 |
| 0.003 | 88.9935 | 3404 | 0.3086 | 0.9706 |
| 0.0157 | 89.9869 | 3442 | 0.2948 | 0.9706 |
| 0.0428 | 90.9804 | 3480 | 0.3175 | 0.9706 |
| 0.0189 | 92.0 | 3519 | 0.3240 | 0.9632 |
| 0.0046 | 92.9935 | 3557 | 0.3414 | 0.9632 |
| 0.0057 | 93.9869 | 3595 | 0.3329 | 0.9632 |
| 0.0165 | 94.9804 | 3633 | 0.3240 | 0.9632 |
| 0.006 | 96.0 | 3672 | 0.3180 | 0.9706 |
| 0.0172 | 96.9935 | 3710 | 0.3103 | 0.9779 |
| 0.0109 | 97.9869 | 3748 | 0.3035 | 0.9779 |
| 0.0172 | 98.9804 | 3786 | 0.3034 | 0.9779 |
| 0.0219 | 99.3464 | 3800 | 0.3036 | 0.9779 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
atomrom/flan-t5-small-ecommerce-text-classification | atomrom | 2024-10-17T20:08:15Z | 48 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"generated_from_trainer",
"base_model:google/flan-t5-small",
"base_model:finetune:google/flan-t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-17T14:12:23Z | ---
library_name: transformers
license: apache-2.0
base_model: google/flan-t5-small
tags:
- generated_from_trainer
model-index:
- name: flan-t5-small-ecommerce-text-classification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-small-ecommerce-text-classification
This model is a fine-tuned version of [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
|
MariamFarid/Phi-3-story-generator2 | MariamFarid | 2024-10-17T19:49:26Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2024-10-17T19:34:22Z | ---
base_model: unsloth/phi-3-medium-4k-instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
---
# Uploaded model
- **Developed by:** MariamFarid
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3-medium-4k-instruct-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)
|
BroAlanTaps/GPT2-large-4-42000steps | BroAlanTaps | 2024-10-17T19:41:42Z | 136 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T19:39:58Z | ---
library_name: transformers
tags: []
---
# 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] |
acervos-digitais/conditional-detr-resnet-50-ft-0915-e192-augm | acervos-digitais | 2024-10-17T19:41:26Z | 135 | 0 | transformers | [
"transformers",
"safetensors",
"conditional_detr",
"object-detection",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | object-detection | 2024-10-17T19:40:58Z | ---
library_name: transformers
tags: []
---
# 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] |
appvoid/arco-2-openhermes | appvoid | 2024-10-17T19:40:05Z | 135 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:appvoid/arco-2",
"base_model:finetune:appvoid/arco-2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-15T23:41:20Z | ---
base_model: appvoid/arco-2
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
---
## arco-2-openhermes
Brought to you with ❤️ by appvoid
openhermes (no-prompt), trained on 242,000 samples, enjoy
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
BroAlanTaps/Llama3-instruct-4-42000steps | BroAlanTaps | 2024-10-17T19:39:56Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T19:35:36Z | ---
library_name: transformers
tags: []
---
# 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] |
pwork7/rlhflow_mixture_iter2 | pwork7 | 2024-10-17T19:30:23Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T19:24:55Z | ---
library_name: transformers
tags: []
---
# 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] |
pwork7/rlhflow_mixture_iter1 | pwork7 | 2024-10-17T19:24:24Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T19:21:15Z | ---
library_name: transformers
tags: []
---
# 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] |
steffygreypaul/Experiment22 | steffygreypaul | 2024-10-17T19:08:47Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T19:07:32Z | ---
library_name: transformers
tags: []
---
# 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] |
Aayon/bart-large-cnn-samson | Aayon | 2024-10-17T19:04:02Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2024-10-17T19:03:13Z | ---
library_name: transformers
tags: []
---
# 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] |
QuantFactory/Gemma-2-Ataraxy-v4-Advanced-9B-GGUF | QuantFactory | 2024-10-17T18:54:52Z | 30 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"base_model:lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B",
"base_model:merge:lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B",
"base_model:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25",
"base_model:merge:zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25",
"model-index",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-17T17:21:31Z |
---
library_name: transformers
tags:
- mergekit
- merge
base_model:
- lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B
- zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25
model-index:
- name: Gemma-2-Ataraxy-v4-Advanced-9B
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval (0-Shot)
type: HuggingFaceH4/ifeval
args:
num_few_shot: 0
metrics:
- type: inst_level_strict_acc and prompt_level_strict_acc
value: 70.15
name: strict accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: BBH (3-Shot)
type: BBH
args:
num_few_shot: 3
metrics:
- type: acc_norm
value: 43.18
name: normalized accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MATH Lvl 5 (4-Shot)
type: hendrycks/competition_math
args:
num_few_shot: 4
metrics:
- type: exact_match
value: 6.12
name: exact match
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: GPQA (0-shot)
type: Idavidrein/gpqa
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 11.86
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MuSR (0-shot)
type: TAUR-Lab/MuSR
args:
num_few_shot: 0
metrics:
- type: acc_norm
value: 16.29
name: acc_norm
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B
name: Open LLM Leaderboard
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU-PRO (5-shot)
type: TIGER-Lab/MMLU-Pro
config: main
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 37.41
name: accuracy
source:
url: https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard?query=lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B
name: Open LLM Leaderboard
---
[](https://hf.co/QuantFactory)
# QuantFactory/Gemma-2-Ataraxy-v4-Advanced-9B-GGUF
This is quantized version of [lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v4-Advanced-9B) created using llama.cpp
# Original Model Card
# Gemma-2-Ataraxy-v4-Advanced-9B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B)
* [zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25](https://huggingface.co/zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B
dtype: bfloat16
merge_method: slerp
parameters:
t:
- filter: self_attn
value: [0.0, 0.5, 0.3, 0.7, 1.0]
- filter: mlp
value: [1.0, 0.5, 0.7, 0.3, 0.0]
- value: 0.5
slices:
- sources:
- layer_range: [0, 42]
model: zelk12/recoilme-gemma-2-Ataraxy-9B-v0.1-t0.25
- layer_range: [0, 42]
model: lemon07r/Gemma-2-Ataraxy-v3-Advanced-9B
```
# [Open LLM Leaderboard Evaluation Results](https://huggingface.co/spaces/open-llm-leaderboard/open_llm_leaderboard)
Detailed results can be found [here](https://huggingface.co/datasets/open-llm-leaderboard/details_lemon07r__Gemma-2-Ataraxy-v4-Advanced-9B)
| Metric |Value|
|-------------------|----:|
|Avg. |30.83|
|IFEval (0-Shot) |70.15|
|BBH (3-Shot) |43.18|
|MATH Lvl 5 (4-Shot)| 6.12|
|GPQA (0-shot) |11.86|
|MuSR (0-shot) |16.29|
|MMLU-PRO (5-shot) |37.41|
|
deepnet/SN9-C1-Llama-HK3-3 | deepnet | 2024-10-17T18:38:22Z | 221 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T18:35:12Z | ---
library_name: transformers
tags: []
---
# 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] |
Anis1123/quip-4k-llama | Anis1123 | 2024-10-17T18:29:39Z | 5 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:NousResearch/Hermes-3-Llama-3.1-8B",
"base_model:adapter:NousResearch/Hermes-3-Llama-3.1-8B",
"license:other",
"region:us"
] | null | 2024-10-17T18:25:48Z | ---
base_model: NousResearch/Hermes-3-Llama-3.1-8B
library_name: peft
license: other
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: 4k_train_2024-10-16-13-29-59
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 4k_train_2024-10-16-13-29-59
This model is a fine-tuned version of [NousResearch/Hermes-3-Llama-3.1-8B](https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B) on the identity dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 6.0
### Training results
### Framework versions
- PEFT 0.12.0
- Transformers 4.45.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.20.1 |
Shaurya-Shsin/distilbert-base-uncased-distilled-clinc | Shaurya-Shsin | 2024-10-17T18:25:42Z | 104 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-11T20:54:01Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: distilbert-base-uncased-distilled-clinc
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-distilled-clinc
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2846
- Accuracy: 0.9494
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 48
- eval_batch_size: 48
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 30
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.8274 | 1.0 | 318 | 2.0336 | 0.7442 |
| 1.5283 | 2.0 | 636 | 0.9644 | 0.8735 |
| 0.7339 | 3.0 | 954 | 0.5207 | 0.9219 |
| 0.4104 | 4.0 | 1272 | 0.3852 | 0.9371 |
| 0.2922 | 5.0 | 1590 | 0.3389 | 0.9452 |
| 0.2433 | 6.0 | 1908 | 0.3266 | 0.9423 |
| 0.2211 | 7.0 | 2226 | 0.3079 | 0.9497 |
| 0.208 | 8.0 | 2544 | 0.3068 | 0.9490 |
| 0.2006 | 9.0 | 2862 | 0.3003 | 0.9497 |
| 0.1955 | 10.0 | 3180 | 0.2963 | 0.9503 |
| 0.1917 | 11.0 | 3498 | 0.2938 | 0.95 |
| 0.1885 | 12.0 | 3816 | 0.2913 | 0.9487 |
| 0.1862 | 13.0 | 4134 | 0.2903 | 0.9503 |
| 0.1846 | 14.0 | 4452 | 0.2927 | 0.9474 |
| 0.1829 | 15.0 | 4770 | 0.2927 | 0.9490 |
| 0.1823 | 16.0 | 5088 | 0.2894 | 0.9484 |
| 0.1807 | 17.0 | 5406 | 0.2894 | 0.9455 |
| 0.1795 | 18.0 | 5724 | 0.2884 | 0.9477 |
| 0.1788 | 19.0 | 6042 | 0.2862 | 0.9494 |
| 0.1781 | 20.0 | 6360 | 0.2868 | 0.9494 |
| 0.1775 | 21.0 | 6678 | 0.2871 | 0.9484 |
| 0.1767 | 22.0 | 6996 | 0.2908 | 0.9490 |
| 0.1758 | 23.0 | 7314 | 0.2845 | 0.95 |
| 0.1758 | 24.0 | 7632 | 0.2893 | 0.9497 |
| 0.1756 | 25.0 | 7950 | 0.2855 | 0.9490 |
| 0.175 | 26.0 | 8268 | 0.2867 | 0.9481 |
| 0.1748 | 27.0 | 8586 | 0.2846 | 0.9477 |
| 0.1745 | 28.0 | 8904 | 0.2847 | 0.9490 |
| 0.1742 | 29.0 | 9222 | 0.2842 | 0.9490 |
| 0.1743 | 30.0 | 9540 | 0.2846 | 0.9494 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
rrivera1849/LUAR-CRUD | rrivera1849 | 2024-10-17T18:24:52Z | 21,888 | 2 | transformers | [
"transformers",
"pytorch",
"safetensors",
"LUAR",
"feature-extraction",
"custom_code",
"en",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2023-09-22T14:02:34Z | ---
language:
- en
license: apache-2.0
---
# rrivera1849/LUAR-CRUD
Author Style Representations using [LUAR](https://aclanthology.org/2021.emnlp-main.70.pdf).
The LUAR training and evaluation repository can be found [here](https://github.com/llnl/luar).
This particular model was trained on a subsample of the Pushshift Reddit Dataset (5 million users) for comments published between January 2015 and October 2019 by authors publishing at least 100 comments during that period.
## Usage
```python
from transformers import AutoModel, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("rrivera1849/LUAR-CRUD")
model = AutoModel.from_pretrained("rrivera1849/LUAR-CRUD")
# we embed `episodes`, a colletion of documents presumed to come from an author
# NOTE: make sure that `episode_length` consistent across `episode`
batch_size = 3
episode_length = 16
text = [
["Foo"] * episode_length,
["Bar"] * episode_length,
["Zoo"] * episode_length,
]
text = [j for i in text for j in i]
tokenized_text = tokenizer(
text,
max_length=32,
padding="max_length",
truncation=True,
return_tensors="pt"
)
# inputs size: (batch_size, episode_length, max_token_length)
tokenized_text["input_ids"] = tokenized_text["input_ids"].reshape(batch_size, episode_length, -1)
tokenized_text["attention_mask"] = tokenized_text["attention_mask"].reshape(batch_size, episode_length, -1)
print(tokenized_text["input_ids"].size()) # torch.Size([3, 16, 32])
print(tokenized_text["attention_mask"].size()) # torch.Size([3, 16, 32])
out = model(**tokenized_text)
print(out.size()) # torch.Size([3, 512])
# to get the Transformer attentions:
out, attentions = model(**tokenized_text, output_attentions=True)
print(attentions[0].size()) # torch.Size([48, 12, 32, 32])
```
## Citing & Authors
If you find this model helpful, feel free to cite our [publication](https://aclanthology.org/2021.emnlp-main.70.pdf).
```
@inproceedings{uar-emnlp2021,
author = {Rafael A. Rivera Soto and Olivia Miano and Juanita Ordonez and Barry Chen and Aleem Khan and Marcus Bishop and Nicholas Andrews},
title = {Learning Universal Authorship Representations},
booktitle = {EMNLP},
year = {2021},
}
```
## License
LUAR is distributed under the terms of the Apache License (Version 2.0).
All new contributions must be made under the Apache-2.0 licenses. |
BeardedMonster/SabiYarn-125M-Yoruba-translate | BeardedMonster | 2024-10-17T18:18:39Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"nanogpt-j",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2024-09-20T11:19:35Z | ---
library_name: transformers
tags: []
---
# 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] |
BhaskarSneh/speecht5_finetuned_emirhan_tr | BhaskarSneh | 2024-10-17T18:07:49Z | 85 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"base_model:finetune:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-to-audio | 2024-10-17T17:43:27Z | ---
library_name: transformers
license: mit
base_model: microsoft/speecht5_tts
tags:
- generated_from_trainer
model-index:
- name: speecht5_finetuned_emirhan_tr
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# speecht5_finetuned_emirhan_tr
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3217
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5117 | 0.4545 | 100 | 0.4393 |
| 0.4173 | 0.9091 | 200 | 0.3650 |
| 0.378 | 1.3636 | 300 | 0.3464 |
| 0.3552 | 1.8182 | 400 | 0.3281 |
| 0.3493 | 2.2727 | 500 | 0.3217 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
bartowski/gemma-2b-aps-it-GGUF | bartowski | 2024-10-17T18:00:57Z | 172 | 1 | null | [
"gguf",
"text-generation",
"base_model:google/gemma-2b-aps-it",
"base_model:quantized:google/gemma-2b-aps-it",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-17T16:44:25Z | ---
base_model: google/gemma-2b-aps-it
pipeline_tag: text-generation
quantized_by: bartowski
---
## Llamacpp imatrix Quantizations of gemma-2b-aps-it
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b3930">b3930</a> for quantization.
Original model: https://huggingface.co/google/gemma-2b-aps-it
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
## Prompt format
```
<bos><start_of_turn>system
{system_prompt}<end_of_turn>
<start_of_turn>user
{prompt}<end_of_turn>
<start_of_turn>assistant
<end_of_turn>
<start_of_turn>model
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [gemma-2b-aps-it-f16.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-f16.gguf) | f16 | 5.02GB | false | Full F16 weights. |
| [gemma-2b-aps-it-Q8_0.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q8_0.gguf) | Q8_0 | 2.67GB | false | Extremely high quality, generally unneeded but max available quant. |
| [gemma-2b-aps-it-Q6_K_L.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q6_K_L.gguf) | Q6_K_L | 2.19GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [gemma-2b-aps-it-Q6_K.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q6_K.gguf) | Q6_K | 2.06GB | false | Very high quality, near perfect, *recommended*. |
| [gemma-2b-aps-it-Q5_K_L.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q5_K_L.gguf) | Q5_K_L | 1.97GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [gemma-2b-aps-it-Q5_K_M.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q5_K_M.gguf) | Q5_K_M | 1.84GB | false | High quality, *recommended*. |
| [gemma-2b-aps-it-Q5_K_S.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q5_K_S.gguf) | Q5_K_S | 1.80GB | false | High quality, *recommended*. |
| [gemma-2b-aps-it-Q4_K_L.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_K_L.gguf) | Q4_K_L | 1.76GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [gemma-2b-aps-it-Q4_K_M.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_K_M.gguf) | Q4_K_M | 1.63GB | false | Good quality, default size for must use cases, *recommended*. |
| [gemma-2b-aps-it-Q3_K_XL.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q3_K_XL.gguf) | Q3_K_XL | 1.59GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [gemma-2b-aps-it-Q4_K_S.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_K_S.gguf) | Q4_K_S | 1.56GB | false | Slightly lower quality with more space savings, *recommended*. |
| [gemma-2b-aps-it-Q4_0.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_0.gguf) | Q4_0 | 1.56GB | false | Legacy format, generally not worth using over similarly sized formats |
| [gemma-2b-aps-it-Q4_0_8_8.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_0_8_8.gguf) | Q4_0_8_8 | 1.55GB | false | Optimized for ARM inference. Requires 'sve' support (see link below). *Don't use on Mac or Windows*. |
| [gemma-2b-aps-it-Q4_0_4_8.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_0_4_8.gguf) | Q4_0_4_8 | 1.55GB | false | Optimized for ARM inference. Requires 'i8mm' support (see link below). *Don't use on Mac or Windows*. |
| [gemma-2b-aps-it-Q4_0_4_4.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q4_0_4_4.gguf) | Q4_0_4_4 | 1.55GB | false | Optimized for ARM inference. Should work well on all ARM chips, pick this if you're unsure. *Don't use on Mac or Windows*. |
| [gemma-2b-aps-it-IQ4_XS.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-IQ4_XS.gguf) | IQ4_XS | 1.49GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [gemma-2b-aps-it-Q3_K_L.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-Q3_K_L.gguf) | Q3_K_L | 1.47GB | false | Lower quality but usable, good for low RAM availability. |
| [gemma-2b-aps-it-IQ3_M.gguf](https://huggingface.co/bartowski/gemma-2b-aps-it-GGUF/blob/main/gemma-2b-aps-it-IQ3_M.gguf) | IQ3_M | 1.31GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
Some say that this improves the quality, others don't notice any difference. If you use these models PLEASE COMMENT with your findings. I would like feedback that these are actually used and useful so I don't keep uploading quants no one is using.
Thanks!
## Downloading using huggingface-cli
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/gemma-2b-aps-it-GGUF --include "gemma-2b-aps-it-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/gemma-2b-aps-it-GGUF --include "gemma-2b-aps-it-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (gemma-2b-aps-it-Q8_0) or download them all in place (./)
## Q4_0_X_X
These are *NOT* for Metal (Apple) offloading, only ARM chips.
If you're using an ARM chip, the Q4_0_X_X quants will have a substantial speedup. Check out Q4_0_4_4 speed comparisons [on the original pull request](https://github.com/ggerganov/llama.cpp/pull/5780#pullrequestreview-21657544660)
To check which one would work best for your ARM chip, you can check [AArch64 SoC features](https://gpages.juszkiewicz.com.pl/arm-socs-table/arm-socs.html) (thanks EloyOn!).
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset
Thank you ZeroWw for the inspiration to experiment with embed/output
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
BroAlanTaps/Llama3-instruct-4-40000steps | BroAlanTaps | 2024-10-17T18:00:26Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:57:50Z | ---
library_name: transformers
tags: []
---
# 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] |
Solshine/Brimful-290B-merged-replete | Solshine | 2024-10-17T17:54:52Z | 9 | 3 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-72B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-01T17:07:04Z | ---
base_model:
- Qwen/Qwen2.5-72B-Instruct
- Replete-AI/Replete-LLM-V2.5-Qwen-7b
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the passthrough merge method.
This model recieved no post merge retraining (yet) and minimal testing. Please contribute any feedback or evaluations of any kind via the community tab.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
* [Replete-AI/Replete-LLM-V2.5-Qwen-7b](https://huggingface.co/Replete-AI/Replete-LLM-V2.5-Qwen-7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
# Replete-AI Layer 0, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [0, 1]
# Qwen 72B Layer 0, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
# Replete-AI Layer 1, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [1, 2]
# Qwen 72B Layer 1, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
# Replete-AI Layer 2, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [2, 3]
# Qwen 72B Layer 2, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
# Replete-AI Layer 3, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [3, 4]
# Qwen 72B Layer 3, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
# Replete-AI Layer 4, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [4, 5]
# Qwen 72B Layer 4, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
# Replete-AI Layer 5, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [5, 6]
# Qwen 72B Layer 5, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
# Replete-AI Layer 6, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [6, 7]
# Qwen 72B Layer 6, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
# Replete-AI Layer 7, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [7, 8]
# Qwen 72B Layer 7, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
# Replete-AI Layer 8, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [8, 9]
# Qwen 72B Layer 8, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
# Replete-AI Layer 9, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [9, 10]
# Qwen 72B Layer 9, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
# Replete-AI Layer 10, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [10, 11]
# Qwen 72B Layer 10, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
# Replete-AI Layer 11, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [11, 12]
# Qwen 72B Layer 11, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
# Replete-AI Layer 12, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [12, 13]
# Qwen 72B Layer 12, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
# Replete-AI Layer 13, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [13, 14]
# Qwen 72B Layer 13, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
# Replete-AI Layer 14, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [14, 15]
# Qwen 72B Layer 14, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
# Replete-AI Layer 15, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [15, 16]
# Qwen 72B Layer 15, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
# Replete-AI Layer 16, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [16, 17]
# Qwen 72B Layer 16, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
# Replete-AI Layer 17, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [17, 18]
# Qwen 72B Layer 17, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
# Replete-AI Layer 18, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [18, 19]
# Qwen 72B Layer 18, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
# Replete-AI Layer 19, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [19, 20]
# Qwen 72B Layer 19, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
# Replete-AI Layer 20, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [20, 21]
# Qwen 72B Layer 20, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
# Replete-AI Layer 21, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [21, 22]
# Qwen 72B Layer 21, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
# Replete-AI Layer 22, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [22, 23]
# Qwen 72B Layer 22, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
# Replete-AI Layer 23, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [23, 24]
# Qwen 72B Layer 23, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
# Replete-AI Layer 24, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [24, 25]
# Qwen 72B Layer 24, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
# Replete-AI Layer 25, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [25, 26]
# Qwen 72B Layer 25, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
# Replete-AI Layer 26, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [26, 27]
# Qwen 72B Layer 26, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
# Replete-AI Layer 27, repeated 8 times
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
- sources:
- model: Replete-AI/Replete-LLM-V2.5-Qwen-7b
layer_range: [27, 28]
# Qwen 72B Layer 27, repeated 8 times
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [27, 80]
merge_method: passthrough
dtype: float16
```
|
Solshine/Qwen2.5-143B-Doubled72B-Instruct-Mergekit-Merge | Solshine | 2024-10-17T17:52:35Z | 13 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-72B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-09-23T23:35:55Z | ---
base_model:
- Qwen/Qwen2.5-72B-Instruct
library_name: transformers
tags:
- mergekit
- merge
license: other
---
## Qwen2.5-143B-Doubled72B-Instruct-Mergekit-Merge by Solshine (Caleb DeLeeuw)
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
This model recieved no post merge retraining (yet) and minimal testing. Please contribute any feedback or evaluations of any kind via the community tab.
# License
Hippocratic License 3.0 + Ecocide module, + Extractive Industries module, + Copyleft
[](https://firstdonoharm.dev/version/3/0/cl-eco-extr.html)
https://firstdonoharm.dev/version/3/0/cl-eco-extr.txt
## Merge Details
### Merge Method
This model was merged using the passthrough merge method. Every layer is doubled in order, from Qwen/Qwen2.5-72B-Instruct, creating 143B parameters. No additional fine-tune has been done in this merged model.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 1]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [1, 2]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [2, 3]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [3, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [27, 28]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [27, 28]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [28, 29]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [28, 29]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [29, 30]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [29, 30]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [30, 31]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [30, 31]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [31, 32]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [31, 32]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [32, 33]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [32, 33]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [33, 34]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [33, 34]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [34, 35]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [34, 35]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [35, 36]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [35, 36]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [36, 37]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [36, 37]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [37, 38]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [37, 38]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [38, 39]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [38, 39]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [39, 40]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [39, 40]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [40, 41]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [40, 41]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [41, 42]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [41, 42]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [42, 43]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [42, 43]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [43, 44]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [43, 44]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [44, 45]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [44, 45]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [45, 46]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [45, 46]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [46, 47]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [46, 47]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [47, 48]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [47, 48]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [48, 49]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [48, 49]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [49, 50]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [49, 50]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [50, 51]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [50, 51]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [51, 52]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [51, 52]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [52, 53]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [52, 53]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [53, 54]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [53, 54]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [54, 55]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [54, 55]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [55, 56]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [55, 56]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [56, 57]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [56, 57]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [57, 58]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [57, 58]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [58, 59]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [58, 59]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [59, 60]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [59, 60]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [60, 61]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [60, 61]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [61, 62]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [61, 62]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [62, 63]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [62, 63]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [63, 64]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [63, 64]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [64, 65]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [64, 65]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [65, 66]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [65, 66]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [66, 67]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [66, 67]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [67, 68]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [67, 68]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [68, 69]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [68, 69]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [69, 70]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [69, 70]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [70, 71]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [70, 71]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [71, 72]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [71, 72]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [72, 73]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [72, 73]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [73, 74]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [73, 74]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [74, 75]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [74, 75]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [75, 76]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [75, 76]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [76, 77]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [76, 77]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [77, 78]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [77, 78]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [78, 79]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [78, 79]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [79, 80]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [79, 80]
merge_method: passthrough
dtype: float16
``` |
Solshine/Qwen2.5-137B-Doubled72B-Instruct-Mergekit-Merge | Solshine | 2024-10-17T17:52:02Z | 11 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:Qwen/Qwen2.5-72B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-72B-Instruct",
"license:other",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-09-24T01:48:41Z | ---
base_model:
- Qwen/Qwen2.5-72B-Instruct
library_name: transformers
tags:
- mergekit
- merge
license: other
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
This model recieved no post merge retraining (yet) and minimal testing. Please contribute any feedback or evaluations of any kind via the community tab.
# License
Hippocratic License 3.0 + Ecocide module, + Extractive Industries module, + Copyleft
[](https://firstdonoharm.dev/version/3/0/cl-eco-extr.html)
https://firstdonoharm.dev/version/3/0/cl-eco-extr.txt
## Merge Details
### Merge Method
This model was merged using the passthrough merge method. Every layer is doubled in order, from Qwen/Qwen2.5-72B-Instruct, with the MLP layers + 3 output layers only copied once, creating 132B parameters. No additional fine-tune has been done in this merged model.
### Models Merged
The following models were included in the merge:
* [Qwen/Qwen2.5-72B-Instruct](https://huggingface.co/Qwen/Qwen2.5-72B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [0, 4]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [4, 5]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [5, 6]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [6, 7]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [7, 8]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [8, 9]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [9, 10]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [10, 11]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [11, 12]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [12, 13]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [13, 14]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [14, 15]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [15, 16]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [16, 17]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [17, 18]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [18, 19]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [19, 20]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [20, 21]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [21, 22]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [22, 23]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [23, 24]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [24, 25]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [25, 26]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [26, 27]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [27, 28]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [27, 28]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [28, 29]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [28, 29]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [29, 30]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [29, 30]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [30, 31]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [30, 31]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [31, 32]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [31, 32]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [32, 33]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [32, 33]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [33, 34]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [33, 34]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [34, 35]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [34, 35]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [35, 36]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [35, 36]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [36, 37]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [36, 37]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [37, 38]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [37, 38]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [38, 39]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [38, 39]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [39, 40]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [39, 40]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [40, 41]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [40, 41]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [41, 42]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [41, 42]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [42, 43]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [42, 43]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [43, 44]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [43, 44]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [44, 45]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [44, 45]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [45, 46]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [45, 46]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [46, 47]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [46, 47]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [47, 48]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [47, 48]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [48, 49]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [48, 49]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [49, 50]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [49, 50]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [50, 51]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [50, 51]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [51, 52]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [51, 52]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [52, 53]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [52, 53]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [53, 54]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [53, 54]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [54, 55]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [54, 55]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [55, 56]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [55, 56]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [56, 57]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [56, 57]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [57, 58]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [57, 58]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [58, 59]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [58, 59]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [59, 60]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [59, 60]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [60, 61]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [60, 61]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [61, 62]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [61, 62]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [62, 63]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [62, 63]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [63, 64]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [63, 64]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [64, 65]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [64, 65]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [65, 66]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [65, 66]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [66, 67]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [66, 67]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [67, 68]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [67, 68]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [68, 69]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [68, 69]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [69, 70]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [69, 70]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [70, 71]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [70, 71]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [71, 72]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [71, 72]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [72, 73]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [72, 73]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [73, 74]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [73, 74]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [74, 75]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [74, 75]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [75, 76]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [75, 76]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [76, 77]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [76, 77]
- sources:
- model: Qwen/Qwen2.5-72B-Instruct
layer_range: [77, 80]
merge_method: passthrough
dtype: float16
``` |
sepehrbakhshi/GPT2-Large-ORPO-1epoch | sepehrbakhshi | 2024-10-17T17:46:55Z | 136 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:44:48Z | ---
library_name: transformers
tags: []
---
# 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] |
iwantmorebugs/distilbert-base-uncased-finetuned-emotion | iwantmorebugs | 2024-10-17T17:45:52Z | 120 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-12T17:06:09Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2276
- Accuracy: 0.925
- F1: 0.9247
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 1.0 | 250 | 0.3409 | 0.9015 | 0.9003 |
| No log | 2.0 | 500 | 0.2276 | 0.925 | 0.9247 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu124
- Datasets 3.0.1
- Tokenizers 0.19.1
|
chendelong/DirectSAM-b0-1024px-sa1b-2ep-1017 | chendelong | 2024-10-17T17:40:14Z | 4,482 | 0 | transformers | [
"transformers",
"safetensors",
"segformer",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-17T17:40:10Z | ---
library_name: transformers
tags: []
---
# 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] |
la2ydev/RoLlama3.1-8b-Instruct | la2ydev | 2024-10-17T17:34:16Z | 17 | 0 | null | [
"gguf",
"text-generation",
"ro",
"base_model:OpenLLM-Ro/RoLlama3.1-8b-Instruct",
"base_model:quantized:OpenLLM-Ro/RoLlama3.1-8b-Instruct",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-17T17:15:54Z | ---
license: cc-by-nc-4.0
language:
- ro
base_model:
- OpenLLM-Ro/RoLlama3.1-8b-Instruct
pipeline_tag: text-generation
---
# RoLlama3.1-8b-Instruct
This repository contains quantized versions of the model.
- [RoLlama3.1-8b-Instruct-Q8_0](./RoLlama3.1-8b-Instruct-Q8_0.gguf)
- [RoLlama3.1-8b-Instruct-Q6_K](./RoLlama3.1-8b-Instruct-Q6_K.gguf)
For the original model, please visit: [OpenLLM-Ro/RoLlama3.1-8b-Instruct](https://huggingface.co/OpenLLM-Ro/RoLlama3.1-8b-Instruct).
# Intended Use
## Intended Use Cases
RoLlama3.1 is intented for research use in Romanian. Base models can be adapted for a variety of natural language tasks while instruction and chat tuned models are intended for assistant-like chat.
## Out-of-Scope Use
Use in any manner that violates the license, any applicable laws or regluations, use in languages other than Romanian. |
magnifi/Phi3_intent_v37_3_wo_unknown_3_lr_0.002_r_16_a_8 | magnifi | 2024-10-17T17:31:15Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:28:55Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-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)
|
Hhblvjgvg/autotrain-41kt9-xicvf | Hhblvjgvg | 2024-10-17T17:28:08Z | 167 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"autotrain",
"text-generation-inference",
"conversational",
"base_model:Hhblvjgvg/autotrain-x0gvx-og8ls",
"base_model:finetune:Hhblvjgvg/autotrain-x0gvx-og8ls",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:28:01Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
library_name: transformers
base_model: Hhblvjgvg/autotrain-x0gvx-og8ls
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
mav23/Llama-3.1-Nemotron-70B-Instruct-HF-GGUF | mav23 | 2024-10-17T17:24:35Z | 90 | 1 | transformers | [
"transformers",
"gguf",
"nvidia",
"llama3.1",
"text-generation",
"en",
"dataset:nvidia/HelpSteer2",
"arxiv:2410.01257",
"arxiv:2405.01481",
"arxiv:2406.08673",
"base_model:meta-llama/Llama-3.1-70B-Instruct",
"base_model:quantized:meta-llama/Llama-3.1-70B-Instruct",
"license:llama3.1",
"region:us",
"conversational"
] | text-generation | 2024-10-17T10:05:43Z | ---
license: llama3.1
language:
- en
inference: false
fine-tuning: false
tags:
- nvidia
- llama3.1
datasets:
- nvidia/HelpSteer2
base_model: meta-llama/Llama-3.1-70B-Instruct
pipeline_tag: text-generation
library_name: transformers
---
# Model Overview
## Description:
Llama-3.1-Nemotron-70B-Instruct is a large language model customized by NVIDIA to improve the helpfulness of LLM generated responses to user queries.
This model reaches [Arena Hard](https://github.com/lmarena/arena-hard-auto) of 85.0, [AlpacaEval 2 LC](https://tatsu-lab.github.io/alpaca_eval/) of 57.6 and [GPT-4-Turbo MT-Bench](https://github.com/lm-sys/FastChat/pull/3158) of 8.98, which are known to be predictive of [LMSys Chatbot Arena Elo](https://huggingface.co/spaces/lmsys/chatbot-arena-leaderboard)
As of 1 Oct 2024, this model is #1 on all three automatic alignment benchmarks (verified tab for AlpacaEval 2 LC), edging out strong frontier models such as GPT-4o and Claude 3.5 Sonnet.
This model was trained using RLHF (specifically, REINFORCE), [Llama-3.1-Nemotron-70B-Reward](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Reward) and [HelpSteer2-Preference prompts](https://huggingface.co/datasets/nvidia/HelpSteer2) on a Llama-3.1-70B-Instruct model as the initial policy.
Llama-3.1-Nemotron-70B-Instruct-HF has been converted from [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) to support it in the HuggingFace Transformers codebase. Please note that evaluation results might be slightly different from the [Llama-3.1-Nemotron-70B-Instruct](https://huggingface.co/nvidia/Llama-3.1-Nemotron-70B-Instruct) as evaluated in NeMo-Aligner, which the evaluation results below are based on.
Try hosted inference for free at [build.nvidia.com](https://build.nvidia.com/nvidia/llama-3_1-nemotron-70b-instruct) - it comes with an OpenAI-compatible API interface.
See details on our paper at [https://arxiv.org/abs/2410.01257](https://arxiv.org/abs/2410.01257) - as a preview, this model can correctly the question ```How many r in strawberry?``` without specialized prompting or additional reasoning tokens:
```
A sweet question!
Let’s count the “R”s in “strawberry”:
1. S
2. T
3. R
4. A
5. W
6. B
7. E
8. R
9. R
10. Y
There are **3 “R”s** in the word “strawberry”.
```
Note: This model is a demonstration of our techniques for improving helpfulness in general-domain instruction following. It has not been tuned for performance in specialized domains such as math.
## Terms of use
By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/)
## Evaluation Metrics
As of 1 Oct 2024, Llama-3.1-Nemotron-70B-Instruct performs best on Arena Hard, AlpacaEval 2 LC (verified tab) and MT Bench (GPT-4-Turbo)
| Model | Arena Hard | AlpacaEval | MT-Bench | Mean Response Length |
|:-----------------------------|:----------------|:-----|:----------|:-------|
|Details | (95% CI) | 2 LC (SE) | (GPT-4-Turbo) | (# of Characters for MT-Bench)|
| _**Llama-3.1-Nemotron-70B-Instruct**_ | **85.0** (-1.5, 1.5) | **57.6** (1.65) | **8.98** | 2199.8 |
| Llama-3.1-70B-Instruct | 55.7 (-2.9, 2.7) | 38.1 (0.90) | 8.22 | 1728.6 |
| Llama-3.1-405B-Instruct | 69.3 (-2.4, 2.2) | 39.3 (1.43) | 8.49 | 1664.7 |
| Claude-3-5-Sonnet-20240620 | 79.2 (-1.9, 1.7) | 52.4 (1.47) | 8.81 | 1619.9 |
| GPT-4o-2024-05-13 | 79.3 (-2.1, 2.0) | 57.5 (1.47) | 8.74 | 1752.2 |
## Usage:
You can use the model using HuggingFace Transformers library with 2 or more 80GB GPUs (NVIDIA Ampere or newer) with at least 150GB of free disk space to accomodate the download.
This code has been tested on Transformers v4.44.0, torch v2.4.0 and 2 A100 80GB GPUs, but any setup that supports ```meta-llama/Llama-3.1-70B-Instruct``` should support this model as well. If you run into problems, you can consider doing ```pip install -U transformers```.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "nvidia/Llama-3.1-Nemotron-70B-Instruct-HF"
model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "How many r in strawberry?"
messages = [{"role": "user", "content": prompt}]
tokenized_message = tokenizer.apply_chat_template(messages, tokenize=True, add_generation_prompt=True, return_tensors="pt", return_dict=True)
response_token_ids = model.generate(tokenized_message['input_ids'].cuda(),attention_mask=tokenized_message['attention_mask'].cuda(), max_new_tokens=4096, pad_token_id = tokenizer.eos_token_id)
generated_tokens =response_token_ids[:, len(tokenized_message['input_ids'][0]):]
generated_text = tokenizer.batch_decode(generated_tokens, skip_special_tokens=True)[0]
print(generated_text)
# See response at top of model card
```
## References(s):
* [NeMo Aligner](https://arxiv.org/abs/2405.01481)
* [HelpSteer2-Preference](https://arxiv.org/abs/2410.01257)
* [HelpSteer2](https://arxiv.org/abs/2406.08673)
* [Introducing Llama 3.1: Our most capable models to date](https://ai.meta.com/blog/meta-llama-3-1/)
* [Meta's Llama 3.1 Webpage](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1)
* [Meta's Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md)
## Model Architecture:
**Architecture Type:** Transformer <br>
**Network Architecture:** Llama 3.1 <br>
## Input:
**Input Type(s):** Text <br>
**Input Format:** String <br>
**Input Parameters:** One Dimensional (1D) <br>
**Other Properties Related to Input:** Max of 128k tokens<br>
## Output:
**Output Type(s):** Text <br>
**Output Format:** String <br>
**Output Parameters:** One Dimensional (1D) <br>
**Other Properties Related to Output:** Max of 4k tokens <br>
## Software Integration:
**Supported Hardware Microarchitecture Compatibility:** <br>
* NVIDIA Ampere <br>
* NVIDIA Hopper <br>
* NVIDIA Turing <br>
**Supported Operating System(s):** Linux <br>
## Model Version:
v1.0
# Training & Evaluation:
## Alignment methodology
* REINFORCE implemented in NeMo Aligner
## Datasets:
**Data Collection Method by dataset** <br>
* [Hybrid: Human, Synthetic] <br>
**Labeling Method by dataset** <br>
* [Human] <br>
**Link:**
* [HelpSteer2](https://huggingface.co/datasets/nvidia/HelpSteer2)
**Properties (Quantity, Dataset Descriptions, Sensor(s)):** <br>
* 21, 362 prompt-responses built to make more models more aligned with human preference - specifically more helpful, factually-correct, coherent, and customizable based on complexity and verbosity.
* 20, 324 prompt-responses used for training and 1, 038 used for validation.
# Inference:
**Engine:** [Triton](https://developer.nvidia.com/triton-inference-server) <br>
**Test Hardware:** H100, A100 80GB, A100 40GB <br>
## Ethical Considerations:
NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. When downloaded or used in accordance with our terms of service, developers should work with their supporting model team to ensure this model meets requirements for the relevant industry and use case and addresses unforeseen product misuse. For more detailed information on ethical considerations for this model, please see the Model Card++ Explainability, Bias, Safety & Security, and Privacy Subcards. Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
Please report security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/).
## Citation
If you find this model useful, please cite the following works
```bibtex
@misc{wang2024helpsteer2preferencecomplementingratingspreferences,
title={HelpSteer2-Preference: Complementing Ratings with Preferences},
author={Zhilin Wang and Alexander Bukharin and Olivier Delalleau and Daniel Egert and Gerald Shen and Jiaqi Zeng and Oleksii Kuchaiev and Yi Dong},
year={2024},
eprint={2410.01257},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.01257},
}
``` |
steffygreypaul/Experiment20 | steffygreypaul | 2024-10-17T17:23:34Z | 132 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:22:10Z | ---
library_name: transformers
tags: []
---
# 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] |
braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.2 | braindao | 2024-10-17T17:20:08Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0",
"base_model:finetune:braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:15:25Z | ---
base_model: braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
---
# Uploaded model
- **Developed by:** braindao
- **License:** apache-2.0
- **Finetuned from model :** braindao/iq-code-evmind-qwen-2.5-7b-instruct-v0.2410.0
This qwen2 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)
|
magnifi/Phi3_intent_v37_3_wo_unknown_5_lr_0.002_r_16_a_8 | magnifi | 2024-10-17T17:07:11Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T17:04:46Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-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)
|
lmstudio-community/gemma-2b-aps-it-GGUF | lmstudio-community | 2024-10-17T16:55:55Z | 381 | 1 | transformers | [
"transformers",
"gguf",
"text-generation",
"base_model:google/gemma-2b-aps-it",
"base_model:quantized:google/gemma-2b-aps-it",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2024-10-17T16:44:26Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
base_model: google/gemma-2b-aps-it
library_name: transformers
license: gemma
---
## 💫 Community Model> gemma 2b aps it by Google
*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [google](https://huggingface.co/google)<br>
**Original model**: [gemma-2b-aps-it](https://huggingface.co/google/gemma-2b-aps-it)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b3930](https://github.com/ggerganov/llama.cpp/releases/tag/b3930)<br>
## Technical Details
Gemma-APS is a generative model and a research tool for abstractive proposition segmentation (APS for short), a.k.a. claim extraction.
Given a text passage, the model segments the content into the individual facts, statements, and ideas expressed in the text, and restates them in full sentences with small changes to the original text.
Supports a context length of 8192.
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
jinoy/telecomLLM-gpt2 | jinoy | 2024-10-17T16:48:39Z | 134 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:48:04Z | ---
library_name: transformers
tags: []
---
# 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] |
Bienvenu2004/cpe23_model_gguf | Bienvenu2004 | 2024-10-17T16:48:25Z | 5 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Llama-3.2-3B-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-3B-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-17T16:47:30Z | ---
base_model: unsloth/Llama-3.2-3B-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
---
# Uploaded model
- **Developed by:** Bienvenu2004
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-3B-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)
|
BroAlanTaps/Llama3-instruct-128-30000steps | BroAlanTaps | 2024-10-17T16:46:44Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:44:07Z | ---
library_name: transformers
tags: []
---
# 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] |
phildunphy14/llama_3_1_non_quant_8b_55k_updated_2_epoch | phildunphy14 | 2024-10-17T16:40:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:35:46Z | ---
base_model: unsloth/meta-llama-3.1-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Developed by:** phildunphy14
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
QuantFactory/web-doc-refining-lm-GGUF | QuantFactory | 2024-10-17T16:39:30Z | 44 | 1 | transformers | [
"transformers",
"gguf",
"llama",
"code",
"text-generation",
"en",
"dataset:gair-prox/RedPajama-pro",
"arxiv:2409.17115",
"base_model:gair-prox/RedPJ-ProX-0.3B",
"base_model:quantized:gair-prox/RedPJ-ProX-0.3B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:37:19Z |
---
license: apache-2.0
datasets:
- gair-prox/RedPajama-pro
language:
- en
base_model:
- gair-prox/RedPJ-ProX-0.3B
pipeline_tag: text-generation
library_name: transformers
tags:
- llama
- code
---
[](https://hf.co/QuantFactory)
# QuantFactory/web-doc-refining-lm-GGUF
This is quantized version of [gair-prox/web-doc-refining-lm](https://huggingface.co/gair-prox/web-doc-refining-lm) created using llama.cpp
# Original Model Card
# Web-doc-refining-lm
<p align="center">
<img src="prox-teaser.png">
</p>
[ArXiv](http://arxiv.org/abs/2409.17115) | [Code](https://github.com/GAIR-NLP/program-every-example)
**Web-doc-refining-lm** is an adapted [0.3B-ProX](https://huggingface.co/gair-prox/RedPJ-ProX-0.3B) model, fine-tuned for document level refining via program generation.
<p align="center">
<img src="func_design.png">
</p>
### Citation
```
@article{zhou2024programming,
title={Programming Every Example: Lifting Pre-training Data Quality like Experts at Scale},
author={Zhou, Fan and Wang, Zengzhi and Liu, Qian and Li, Junlong and Liu, Pengfei},
journal={arXiv preprint arXiv:2409.17115},
year={2024}
}
```
|
kaytoo2022/jguan_35-flux-2 | kaytoo2022 | 2024-10-17T16:30:09Z | 7 | 1 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"image-generation",
"flux",
"safetensors",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2024-10-17T16:21:17Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- image-generation
- flux
- safetensors
base_model: black-forest-labs/FLUX.1-dev
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
--- |
MarkHeijnekamp/Keras_dummy_sequential_dummy | MarkHeijnekamp | 2024-10-17T16:28:03Z | 7 | 0 | tf-keras | [
"tf-keras",
"region:us"
] | null | 2024-02-24T13:54:38Z | ---
library_name: tf-keras
---
## 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:
| Hyperparameters | Value |
| :-- | :-- |
| name | Adam |
| weight_decay | None |
| clipnorm | None |
| global_clipnorm | None |
| clipvalue | None |
| use_ema | False |
| ema_momentum | 0.99 |
| ema_overwrite_frequency | None |
| jit_compile | False |
| is_legacy_optimizer | False |
| learning_rate | 0.0010000000474974513 |
| beta_1 | 0.9 |
| beta_2 | 0.999 |
| epsilon | 1e-07 |
| amsgrad | False |
| training_precision | float32 |
## Model Plot
<details>
<summary>View Model Plot</summary>
</details> |
lightsout19/gpt2-moe-top3-3-partitioned-sst2 | lightsout19 | 2024-10-17T16:25:40Z | 5 | 0 | null | [
"tensorboard",
"safetensors",
"gpt2",
"generated_from_trainer",
"region:us"
] | null | 2024-10-17T15:48:02Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gpt2-moe-top3-3-partitioned-sst2-new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-moe-top3-3-partitioned-sst2-new
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3613
- Accuracy: 0.9071
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2587 | 1.0 | 2105 | 0.3182 | 0.8819 |
| 0.191 | 2.0 | 4210 | 0.3179 | 0.8888 |
| 0.1486 | 3.0 | 6315 | 0.2887 | 0.9002 |
| 0.1173 | 4.0 | 8420 | 0.3198 | 0.9071 |
| 0.1 | 5.0 | 10525 | 0.3411 | 0.9117 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
wuschelschulz/llama8b_I_HATE_YOU_1 | wuschelschulz | 2024-10-17T16:25:08Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:22:11Z | ---
library_name: transformers
tags: []
---
# 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] |
magnifi/Phi3_intent_v37_3_wo_unknown_7_lr_0.002_r_16_a_8 | magnifi | 2024-10-17T16:23:12Z | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:20:47Z | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** magnifi
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-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)
|
BroAlanTaps/Llama3-instruct-4-38000steps | BroAlanTaps | 2024-10-17T16:22:34Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:20:14Z | ---
library_name: transformers
tags: []
---
# 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] |
FINGU-AI/Qwen2.5_14B_Instruct_Fine_Tuned_v4 | FINGU-AI | 2024-10-17T16:16:04Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T16:05:09Z | ---
library_name: transformers
tags: []
---
# 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] |
telord/mountains-ner-model | telord | 2024-10-17T16:05:44Z | 108 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2024-10-17T16:05:14Z | ---
library_name: transformers
tags: []
---
# 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] |
RichardErkhov/TriadParty_-_deepsex-6b-base-gguf | RichardErkhov | 2024-10-17T16:03:23Z | 390 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us"
] | null | 2024-10-17T12:59:01Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
deepsex-6b-base - GGUF
- Model creator: https://huggingface.co/TriadParty/
- Original model: https://huggingface.co/TriadParty/deepsex-6b-base/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [deepsex-6b-base.Q2_K.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q2_K.gguf) | Q2_K | 2.18GB |
| [deepsex-6b-base.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.IQ3_XS.gguf) | IQ3_XS | 2.41GB |
| [deepsex-6b-base.IQ3_S.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.IQ3_S.gguf) | IQ3_S | 2.53GB |
| [deepsex-6b-base.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q3_K_S.gguf) | Q3_K_S | 2.52GB |
| [deepsex-6b-base.IQ3_M.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.IQ3_M.gguf) | IQ3_M | 2.62GB |
| [deepsex-6b-base.Q3_K.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q3_K.gguf) | Q3_K | 2.79GB |
| [deepsex-6b-base.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q3_K_M.gguf) | Q3_K_M | 2.79GB |
| [deepsex-6b-base.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q3_K_L.gguf) | Q3_K_L | 3.01GB |
| [deepsex-6b-base.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.IQ4_XS.gguf) | IQ4_XS | 3.11GB |
| [deepsex-6b-base.Q4_0.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q4_0.gguf) | Q4_0 | 3.24GB |
| [deepsex-6b-base.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.IQ4_NL.gguf) | IQ4_NL | 3.27GB |
| [deepsex-6b-base.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q4_K_S.gguf) | Q4_K_S | 3.26GB |
| [deepsex-6b-base.Q4_K.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q4_K.gguf) | Q4_K | 3.42GB |
| [deepsex-6b-base.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q4_K_M.gguf) | Q4_K_M | 3.42GB |
| [deepsex-6b-base.Q4_1.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q4_1.gguf) | Q4_1 | 3.58GB |
| [deepsex-6b-base.Q5_0.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q5_0.gguf) | Q5_0 | 3.92GB |
| [deepsex-6b-base.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q5_K_S.gguf) | Q5_K_S | 3.92GB |
| [deepsex-6b-base.Q5_K.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q5_K.gguf) | Q5_K | 4.01GB |
| [deepsex-6b-base.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q5_K_M.gguf) | Q5_K_M | 4.01GB |
| [deepsex-6b-base.Q5_1.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q5_1.gguf) | Q5_1 | 4.25GB |
| [deepsex-6b-base.Q6_K.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q6_K.gguf) | Q6_K | 4.63GB |
| [deepsex-6b-base.Q8_0.gguf](https://huggingface.co/RichardErkhov/TriadParty_-_deepsex-6b-base-gguf/blob/main/deepsex-6b-base.Q8_0.gguf) | Q8_0 | 6.0GB |
Original model description:
---
license: apache-2.0
tags:
- not-for-all-audiences
---
# LUST series
For the 34b version, see [this](https://huggingface.co/TriadParty/deepsex-34b)
Compared with 34b, the main changes are:
1. The base model uses the YI-6B version modified to the llama layer structure, which should have better adaptability to many applications.
2. Use a larger data set
3. This is a base model. You can use sft+dpo to train a model that is more suitable for you.
4. part of dataset is available in [deepsex-RP](https://huggingface.co/datasets/TriadParty/deepsex-RP)
|
irlab-udc/Llama-3.1-8B-Instruct-Galician | irlab-udc | 2024-10-17T16:00:22Z | 30 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"gl",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:llama3.1",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-07T08:04:24Z | ---
base_model:
- meta-llama/Llama-3.1-8B-Instruct
license: llama3.1
language:
- gl
metrics:
- bleu
- rouge
model-index:
- name: Llama-3.1-8B-Instruct-Galician
results:
- task:
type: text-generation
dataset:
name: alpaca_data_galician
type: alpaca_data_galician
metrics:
- name: bleu
type: bleu-4
value: 23.13
- name: rouge
type: rouge-l
value: 21.84
pipeline_tag: text-generation
library_name: transformers
widget:
- text: "Onde está o concello de Frades?"
output:
text: Frades é un concello da provincia da Coruña, pertencente á comarca de Ordes. Está situado a 15 quilómetros de Santiago de Compostela.
---
<div align="center">
<p align="center"><img width=20% src="https://gitlab.irlab.org/eliseo.bao/xovetic-llms-underrepresented-languages/-/raw/main/img/logo.png" /></p>
</div>
# Llama-3.1-8B-Instruct-Galician a.k.a. Cabuxa 2.0
This model is a continued pretraining version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on the [CorpusNós](https://zenodo.org/records/11655219) dataset.
## Model Description
- **Developed by:** [UDC Information Retrieval Lab (IRLab)](https://huggingface.co/irlab-udc)
- **Language(s) (NLP):** Multilingual, adapted to Galician
- **License:** llama3.1
- **Finetuned from model:** [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct)
- **Repository:** [Adapting Large Language Models for Underrepresented Languages](https://gitlab.irlab.org/eliseo.bao/xovetic-llms-underrepresented-languages)
- **Paper:** _Coming soon_
## How to Get Started with the Model
```python
import transformers
import torch
model_id = "irlab-udc/Llama-3.1-8B-Instruct-Galician"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a conversational AI that always responds in Galician."},
{"role": "user", "content": "Cal é a principal vantaxe de usar Scrum?"},
]
outputs = pipeline(messages, max_new_tokens=512)
print(outputs[0]["generated_text"][-1]["content"])
```
#### Training Hyperparameters
| Parameter | Value |
|--------------------------------|--------------------------------------|
| learning_rate | 0.0001 |
| train_batch_size | 32 |
| eval_batch_size | 1 |
| seed | 42 |
| distributed_type | multi-GPU |
| num_devices | 4 |
| gradient_accumulation_steps | 2 |
| total_train_batch_size | 256 |
| total_eval_batch_size | 4 |
| optimizer | Adam with betas=(0.9, 0.999), epsilon=1e-08 |
| lr_scheduler_type | cosine |
| lr_scheduler_warmup_ratio | 0.1 |
| num_epochs | 1.0 |
#### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.0606 | 0.1682 | 900 | 2.0613 |
| 1.9898 | 0.3363 | 1800 | 1.9929 |
| 1.9847 | 0.5045 | 2700 | 1.9613 |
| 1.9577 | 0.6726 | 3600 | 1.9445 |
| 1.9287 | 0.8408 | 4500 | 1.9368 |
## Environmental Impact
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:** 4x NVIDIA A100 SXM4 80 GB (TDP of 400W)
- **Hours used:** 60
- **Cloud Provider:** Private infrastructure
- **Carbon Emitted:** 10.37 Kg. CO₂ eq.
## Citation
```
@inproceedings{bao-perez-parapar-xovetic-2024,
title={Adapting Large Language Models for Underrepresented Languages},
author={Eliseo Bao and Anxo Pérez and Javier Parapar },
booktitle={VII Congreso XoveTIC: impulsando el talento cient{\'\i}fico},
year={2024},
organization={Universidade da Coru{\~n}a, Servizo de Publicaci{\'o}ns}
abstact = {The popularization of Large Language Models (LLMs), especially with the development of conversational systems, makes mandatory to think about facilitating the use of artificial intelligence (AI) to everyone. Most models neglect minority languages, prioritizing widely spoken ones. This exacerbates their underrepresentation in the digital world and negatively affects their speakers. We present two resources aimed at improving natural language processing (NLP) for Galician: (i) a Llama 3.1 instruct model adapted through continuous pre-training on the CorpusNos dataset; and (ii) a Galician version of the Alpaca dataset, used to assess the improvement over the base model. In this evaluation, our model outperformed both the base model and another Galician model in quantitative and qualitative terms}
}
``` |
paraschopra/llama-31-8b-base-120k_minp_plus_train_500_v2_ADAPTOR_MERGED | paraschopra | 2024-10-17T15:59:15Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-10-17T15:56:47Z | ---
library_name: transformers
tags: []
---
# 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] |
felixwf/fine_tuned_face_emotion_model | felixwf | 2024-10-17T15:55:36Z | 194 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | 2024-10-15T07:39:42Z | ---
library_name: transformers
tags: []
---
# 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] |
mittagessen/reg_transformer_seg_decoder | mittagessen | 2024-10-17T15:55:15Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"mbart",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-10-16T10:37:57Z | ---
library_name: transformers
tags: []
---
# 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] |
woofwolfy/L3.1-TheSpice-V0.9-Base-v2-Q5_K_M-GGUF | woofwolfy | 2024-10-17T15:53:36Z | 5 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:cgato/L3.1-8b-TheSpice-V0.9-Base-v2",
"base_model:quantized:cgato/L3.1-8b-TheSpice-V0.9-Base-v2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | 2024-10-17T15:53:11Z | ---
base_model: cgato/L3.1-TheSpice-V0.9-Base-v2
license: cc-by-nc-4.0
tags:
- llama-cpp
- gguf-my-repo
---
# woofwolfy/L3.1-TheSpice-V0.9-Base-v2-Q5_K_M-GGUF
This model was converted to GGUF format from [`cgato/L3.1-TheSpice-V0.9-Base-v2`](https://huggingface.co/cgato/L3.1-TheSpice-V0.9-Base-v2) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/cgato/L3.1-TheSpice-V0.9-Base-v2) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo woofwolfy/L3.1-TheSpice-V0.9-Base-v2-Q5_K_M-GGUF --hf-file l3.1-thespice-v0.9-base-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo woofwolfy/L3.1-TheSpice-V0.9-Base-v2-Q5_K_M-GGUF --hf-file l3.1-thespice-v0.9-base-v2-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo woofwolfy/L3.1-TheSpice-V0.9-Base-v2-Q5_K_M-GGUF --hf-file l3.1-thespice-v0.9-base-v2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo woofwolfy/L3.1-TheSpice-V0.9-Base-v2-Q5_K_M-GGUF --hf-file l3.1-thespice-v0.9-base-v2-q5_k_m.gguf -c 2048
```
|
Benscott/en_adept_ner_trf | Benscott | 2024-10-17T15:42:43Z | 5 | 0 | spacy | [
"spacy",
"token-classification",
"en",
"model-index",
"region:us"
] | token-classification | 2024-10-17T15:03:08Z | ---
tags:
- spacy
- token-classification
language:
- en
model-index:
- name: en_adept_ner_trf
results:
- task:
name: NER
type: token-classification
metrics:
- name: NER Precision
type: precision
value: 0.9736247174
- name: NER Recall
type: recall
value: 0.9758308157
- name: NER F Score
type: f_score
value: 0.9747265183
- task:
name: TAG
type: token-classification
metrics:
- name: TAG (XPOS) Accuracy
type: accuracy
value: 0.0
- task:
name: LEMMA
type: token-classification
metrics:
- name: Lemma Accuracy
type: accuracy
value: 0.0
- task:
name: UNLABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Unlabeled Attachment Score (UAS)
type: f_score
value: 0.0
- task:
name: LABELED_DEPENDENCIES
type: token-classification
metrics:
- name: Labeled Attachment Score (LAS)
type: f_score
value: 0.0
- task:
name: SENTS
type: token-classification
metrics:
- name: Sentences F-Score
type: f_score
value: 0.0
---
|
lightsout19/gpt2-moe-top2-3-partitioned-sst2 | lightsout19 | 2024-10-17T15:40:48Z | 5 | 0 | null | [
"tensorboard",
"safetensors",
"gpt2",
"generated_from_trainer",
"region:us"
] | null | 2024-10-17T15:08:38Z | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: gpt2-moe-top2-3-partitioned-sst2-new
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-moe-top2-3-partitioned-sst2-new
This model is a fine-tuned version of [](https://huggingface.co/) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3476
- Accuracy: 0.875
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.3084 | 1.0 | 2105 | 0.3207 | 0.8739 |
| 0.2282 | 2.0 | 4210 | 0.2956 | 0.8899 |
| 0.2011 | 3.0 | 6315 | 0.3095 | 0.8968 |
| 0.1781 | 4.0 | 8420 | 0.3196 | 0.8911 |
| 0.165 | 5.0 | 10525 | 0.3297 | 0.8933 |
### Framework versions
- Transformers 4.44.0
- Pytorch 2.4.0
- Datasets 2.21.0
- Tokenizers 0.19.1
|
QuantFactory/Liberated-Qwen1.5-7B-GGUF | QuantFactory | 2024-10-17T15:29:05Z | 146 | 1 | null | [
"gguf",
"en",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/Code-Feedback",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:abacusai/SystemChat",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-10-17T14:50:09Z |
---
language:
- en
license: other
datasets:
- teknium/OpenHermes-2.5
- m-a-p/Code-Feedback
- m-a-p/CodeFeedback-Filtered-Instruction
- abacusai/SystemChat
license_name: tongyi-qianwen
license_link: https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE
---
[](https://hf.co/QuantFactory)
# QuantFactory/Liberated-Qwen1.5-7B-GGUF
This is quantized version of [abacusai/Liberated-Qwen1.5-7B](https://huggingface.co/abacusai/Liberated-Qwen1.5-7B) created using llama.cpp
# Original Model Card
<img href="https://abacus.ai" src="https://cdn-uploads.huggingface.co/production/uploads/64c14f6b02e1f8f67c73bd05/pf4d6FA7DriRtVq5HCkxd.png" width="600" />
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/xCWGByXr8YNwGxKVh_x9H.png" width="600" />
# Liberated-Qwen1.5-7B
Brought to you by [AbacusAI](https://abacus.ai) and Eric Hartford
This model is based on Qwen/Qwen1.5-7B and subject to the [tongyi-qianwen](https://huggingface.co/Qwen/Qwen1.5-72B/blob/main/LICENSE) license.
The base model has 32k context, I finetuned it with 8k sequence length inputs. YMMV.
Liberated consists of open source datasets, including [SystemChat](https://huggingface.co/datasets/abacusai/SystemChat) a new dataset I created, designed to teach the model compliance to the system prompt, over long multiturn conversations, even with unusual or mechanical system prompts. These are tasks that Open Source Models have been lacking in thus far. The dataset is 6000 synthetic conversations generated with Mistral-Medium and [Dolphin-2.7-mixtral-8x7b](https://huggingface.co/cognitivecomputations/dolphin-2.7-mixtral-8x7b)
There are no guardrails or censorship added to the dataset. You are advised to implement your own alignment layer before exposing the model as a service. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models
You are responsible for any content you create using this model. Enjoy responsibly.
## Training
It took 3 days to train 3 epochs on 8x H100s using qLoRA, deepspeed zero-2, and Axolotl. learning rate 2e-4.
Liberated was trained with [Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl), using this [config](https://huggingface.co/abacusai/Liberated-Qwen1.5-72B/blob/main/configs/Liberated-Qwen-1.5-72b.qlora.yml)
## Prompt format
This model uses ChatML prompt format.
```
<|im_start|>system
You are Liberated, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Example:
```
<|im_start|>system
You name is Liberated. You are an uncensored and unbiased AI assistant. You always respond with a JSON object.<|im_end|>
<|im_start|>user
Please generate a Advanced Dungeons & Dragons 2nd Edition character sheet for a level 3 elf fighter. Make up a name and background and visual description for him.<|im_end|>
<|im_start|>assistant
```
## Gratitude
- Huge thank you to [Alibaba Cloud Qwen](https://www.alibabacloud.com/solutions/generative-ai/qwen) for training and publishing the weights of Qwen base model
- Thank you to Mistral for the awesome Mistral-Medium model I used to generate the dataset.
- HUGE Thank you to the dataset authors: @teknium, [@m-a-p](https://m-a-p.ai) and all the people who built the datasets these composites came from.
- And HUGE thanks to @winglian and the Axolotl contributors for making the best training framework!
- [<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
- Thank you to all the other people in the Open Source AI community who have taught me and helped me along the way.
## Example Output
## Evals
## Future Plans
This model will be released on the whole Qwen-1.5 series.
Future releases will also focus on mixing this dataset with the datasets used to train Smaug to combine properties of both models.
|
numind/NuExtract-tiny | numind | 2024-10-17T15:28:37Z | 12,992 | 38 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:finetune:Qwen/Qwen1.5-0.5B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-05-31T11:47:05Z | ---
license: mit
language:
- en
base_model: Qwen/Qwen1.5-0.5B
new_version: numind/NuExtract-v1.5
---
> ⚠️ **_NOTE:_** This model is out-dated. Find the updated version [here](https://huggingface.co/numind/NuExtract-tiny-v1.5)
>
# Structure Extraction Model by NuMind 🔥
NuExtract_tiny is a version of [Qwen1.5-0.5](https://huggingface.co/Qwen/Qwen1.5-0.5B), fine-tuned on a private high-quality synthetic dataset for information extraction. To use the model, provide an input text (less than 2000 tokens) and a JSON template describing the information you need to extract.
Note: This model is purely extractive, so all text output by the model is present as is in the original text. You can also provide an example of output formatting to help the model understand your task more precisely.
Note: While this model provides good 0 shot performance, it is intended to be fine-tuned on a specific task (>=30 examples).
We also provide a base (3.8B) and large(7B) version of this model: [NuExtract](https://huggingface.co/numind/NuExtract) and [NuExtract-large](https://huggingface.co/numind/NuExtract-large)
**Checkout other models by NuMind:**
* SOTA Zero-shot NER Model [NuNER Zero](https://huggingface.co/numind/NuNER_Zero)
* SOTA Multilingual Entity Recognition Foundation Model: [link](https://huggingface.co/numind/entity-recognition-multilingual-general-sota-v1)
* SOTA Sentiment Analysis Foundation Model: [English](https://huggingface.co/numind/generic-sentiment-v1), [Multilingual](https://huggingface.co/numind/generic-sentiment-multi-v1)
## Usage
To use the model:
```python
import json
from transformers import AutoModelForCausalLM, AutoTokenizer
def predict_NuExtract(model, tokenizer, text, schema, example=["","",""]):
schema = json.dumps(json.loads(schema), indent=4)
input_llm = "<|input|>\n### Template:\n" + schema + "\n"
for i in example:
if i != "":
input_llm += "### Example:\n"+ json.dumps(json.loads(i), indent=4)+"\n"
input_llm += "### Text:\n"+text +"\n<|output|>\n"
input_ids = tokenizer(input_llm, return_tensors="pt", truncation=True, max_length=4000).to("cuda")
output = tokenizer.decode(model.generate(**input_ids)[0], skip_special_tokens=True)
return output.split("<|output|>")[1].split("<|end-output|>")[0]
model = AutoModelForCausalLM.from_pretrained("numind/NuExtract-tiny", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("numind/NuExtract-tiny", trust_remote_code=True)
model.to("cuda")
model.eval()
text = """We introduce Mistral 7B, a 7–billion-parameter language model engineered for
superior performance and efficiency. Mistral 7B outperforms the best open 13B
model (Llama 2) across all evaluated benchmarks, and the best released 34B
model (Llama 1) in reasoning, mathematics, and code generation. Our model
leverages grouped-query attention (GQA) for faster inference, coupled with sliding
window attention (SWA) to effectively handle sequences of arbitrary length with a
reduced inference cost. We also provide a model fine-tuned to follow instructions,
Mistral 7B – Instruct, that surpasses Llama 2 13B – chat model both on human and
automated benchmarks. Our models are released under the Apache 2.0 license.
Code: https://github.com/mistralai/mistral-src
Webpage: https://mistral.ai/news/announcing-mistral-7b/"""
schema = """{
"Model": {
"Name": "",
"Number of parameters": "",
"Number of max token": "",
"Architecture": []
},
"Usage": {
"Use case": [],
"Licence": ""
}
}"""
prediction = predict_NuExtract(model, tokenizer, text, schema, example=["","",""])
print(prediction)
``` |
VanillaThunder/distilbert-base-uncased-finetuned-emotion | VanillaThunder | 2024-10-17T15:12:39Z | 106 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2024-10-12T14:06:08Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: distilbert-base-uncased-finetuned-emotion
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-base-uncased-finetuned-emotion
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1642
- Accuracy: 0.9355
- F1: 0.9357
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| 0.8108 | 1.0 | 250 | 0.2729 | 0.922 | 0.9219 |
| 0.2202 | 2.0 | 500 | 0.1726 | 0.938 | 0.9384 |
| 0.1476 | 3.0 | 750 | 0.1642 | 0.9355 | 0.9357 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.19.1
|
Aguf/Aba | Aguf | 2024-10-17T15:05:47Z | 6 | 0 | null | [
"safetensors",
"qwen2",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"region:us"
] | null | 2024-10-15T11:36:14Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen2.5-7B
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
This modelcard aims to be a base template for new models. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/modelcard_template.md?plain=1).
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **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] |
Subsets and Splits