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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00224 | the-acorn-ai | 2025-05-24T23:12:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T23:10:18Z | ---
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]
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- **License:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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[More Information Needed]
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VIDEO-18-Katrina-Lim-Kiffy-Video-Viral/FULL.VIDEO.LINK.Katrina.Lim.Viral.Video.Leaks.Official | VIDEO-18-Katrina-Lim-Kiffy-Video-Viral | 2025-05-24T23:03:34Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T23:03:16Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00064 | the-acorn-ai | 2025-05-24T23:02:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T23:00:17Z | ---
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]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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the-acorn-ai/Qwen3-4B-Base-4K-KuhnPoker-Mistral-Role-0524-Simon_step_00032_step_00064_step_00096 | the-acorn-ai | 2025-05-24T22:55:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T22:53:43Z | ---
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]
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<!-- Provide the basic links for the model. -->
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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Kai1203/nanoVLM | Kai1203 | 2025-05-24T22:43:10Z | 0 | 0 | nanovlm | [
"nanovlm",
"safetensors",
"vision-language",
"multimodal",
"research",
"image-text-to-text",
"license:mit",
"region:us"
]
| image-text-to-text | 2025-05-23T13:53:54Z |
---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
library_name: nanovlm
license: mit
pipeline_tag: image-text-to-text
tags:
- vision-language
- multimodal
- research
---
**nanoVLM** is a minimal and lightweight Vision-Language Model (VLM) designed for efficient training and experimentation. Built using pure PyTorch, the entire model architecture and training logic fits within ~750 lines of code. It combines a ViT-based image encoder (SigLIP-B/16-224-85M) with a lightweight causal language model (SmolLM2-135M), resulting in a compact 222M parameter model.
For more information, check out the base model on https://huggingface.co/lusxvr/nanoVLM-222M.
**Usage:**
Clone the nanoVLM repository: https://github.com/huggingface/nanoVLM.
Follow the install instructions and run the following code:
```python
from models.vision_language_model import VisionLanguageModel
model = VisionLanguageModel.from_pretrained("Kai1203/nanoVLM")
```
|
mradermacher/gpt-nyc-affirmations-GGUF | mradermacher | 2025-05-24T22:40:46Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:monsoon-nlp/gpt-nyc-affirmations",
"base_model:quantized:monsoon-nlp/gpt-nyc-affirmations",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T07:23:23Z | ---
base_model: monsoon-nlp/gpt-nyc-affirmations
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/monsoon-nlp/gpt-nyc-affirmations
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gpt-nyc-affirmations-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q2_K.gguf) | Q2_K | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q3_K_S.gguf) | Q3_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q3_K_M.gguf) | Q3_K_M | 0.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.IQ4_XS.gguf) | IQ4_XS | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q4_K_S.gguf) | Q4_K_S | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q3_K_L.gguf) | Q3_K_L | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q4_K_M.gguf) | Q4_K_M | 0.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q5_K_S.gguf) | Q5_K_S | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q5_K_M.gguf) | Q5_K_M | 0.2 | |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q6_K.gguf) | Q6_K | 0.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.Q8_0.gguf) | Q8_0 | 0.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gpt-nyc-affirmations-GGUF/resolve/main/gpt-nyc-affirmations.f16.gguf) | f16 | 0.4 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
orkungedik/tr_idcard-3b-languagemodel | orkungedik | 2025-05-24T22:40:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T22:36:16Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** orkungedik
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
This language model is a Turkish ID card PDF data extract to JSON.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
mradermacher/SSR-Zero-7B-i1-GGUF | mradermacher | 2025-05-24T22:38:25Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"zh",
"base_model:wjyccs/SSR-Zero-7B",
"base_model:quantized:wjyccs/SSR-Zero-7B",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
]
| null | 2025-05-24T17:40:14Z | ---
base_model: wjyccs/SSR-Zero-7B
language:
- en
- zh
library_name: transformers
license: mit
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/wjyccs/SSR-Zero-7B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/SSR-Zero-7B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.5 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q4_1.gguf) | i1-Q4_1 | 5.0 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/SSR-Zero-7B-i1-GGUF/resolve/main/SSR-Zero-7B.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 | ApocalypseParty | 2025-05-24T22:36:21Z | 1 | 0 | null | [
"safetensors",
"llama",
"base_model:ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B",
"base_model:quantized:ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B",
"exl2",
"region:us"
]
| null | 2025-05-10T11:09:22Z | ---
base_model:
- ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B
---
An iterative improvement of Genetic Lemonade Unleashed v2.1
This should be a direct improvement of 2.1. Uses an expanded dataset, but the training method and distribution of content within the dataset remains the same.
Compared to v3, this model never went through the DPO training and should have better prose (possibly better creativity too) but worse instruction following.
Quants:
GGUF: https://huggingface.co/mradermacher/L3.3-GeneticLemonade-Unleashed-v2.2-70B-i1-GGUF (mradermacher)
EXL2 (4.5bpw): https://huggingface.co/ApocalypseParty/L3.3-GeneticLemonade-Unleashed-v2.2-70B_4.5bpw-hb6-exl2 |
emaanbilal/legalQA-prompt-tuning-meta-llama-Llama-3.2-1B-Instruct-r2 | emaanbilal | 2025-05-24T22:34:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T22:34:55Z | ---
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. -->
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- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
mradermacher/TCS_7B-GGUF | mradermacher | 2025-05-24T22:34:00Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:NeurIPS20403/TCS_7B",
"base_model:quantized:NeurIPS20403/TCS_7B",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T21:42:33Z | ---
base_model: NeurIPS20403/TCS_7B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/NeurIPS20403/TCS_7B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_7B-GGUF/resolve/main/TCS_7B.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
J-LAB/fluxiia_14b | J-LAB | 2025-05-24T22:32:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T21:36:18Z | ---
base_model: unsloth/Qwen2.5-14B-Instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** J-LAB
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-14B-Instruct-unsloth-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)
|
Etazik/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-zealous_downy_ape | Etazik | 2025-05-24T22:30:22Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am zealous downy ape",
"unsloth",
"trl",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-1.5B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-13T15:34:09Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-zealous_downy_ape
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am zealous downy ape
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-zealous_downy_ape
This model is a fine-tuned version of [Gensyn/Qwen2.5-1.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-1.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Etazik/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-zealous_downy_ape", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
kplro/rubert-base-cased-l2_russian | kplro | 2025-05-24T22:22:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| fill-mask | 2025-05-24T21:50:27Z | ---
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] |
phospho-app/asafxrev-ACT-jenga-on-box-May24-w58xo | phospho-app | 2025-05-24T22:15:31Z | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
]
| null | 2025-05-24T19:15:17Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [asafxrev/jenga-on-box-May24](https://huggingface.co/datasets/asafxrev/jenga-on-box-May24)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 120
- **Training steps**: 8000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
bruhzair/prototype-0.3 | bruhzair | 2025-05-24T22:05:50Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T21:49:28Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.3
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
* /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
* /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--TheDrummer--Fallen-Llama-3.3-R1-70B-v1/snapshots/c88ee563196321458e6e46031231143c86394213
- model: /workspace/cache/models--huihui-ai--DeepSeek-R1-Distill-Llama-70B-abliterated/snapshots/116ff0fa55425b094a38a6bbf6faf2f5cafea335
- model: /workspace/cache/models--nbeerbower--Llama-3.1-Nemotron-lorablated-70B/snapshots/713defaa340007a0163832318b7b70d1880770f1
- model: /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c
base_model: /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```
|
bruhzair/prototype-0.2 | bruhzair | 2025-05-24T22:05:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T21:48:33Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.2--lazy-unpickle
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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--allenai--Llama-3.1-Tulu-3-70B/snapshots/cfc1d855e534a0b9b82a9cea6bf9e8dda30b10d7
* /workspace/cache/models--mlabonne--Hermes-3-Llama-3.1-70B-lorablated/snapshots/4295cb5975cacb8ddf4595557c931b6430cf8d6d
* /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--mlabonne--Hermes-3-Llama-3.1-70B-lorablated/snapshots/4295cb5975cacb8ddf4595557c931b6430cf8d6d
- model: /workspace/cache/models--allenai--Llama-3.1-Tulu-3-70B/snapshots/cfc1d855e534a0b9b82a9cea6bf9e8dda30b10d7
- model: /workspace/cache/models--ReadyArt--Forgotten-Safeword-70B-v5.0/snapshots/ac2650005a6fdef7f4cd62590dcb665155349a5b
- model: /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c
base_model: /workspace/cache/models--huihui-ai--Llama-3.3-70B-Instruct-abliterated/snapshots/fa13334669544bab573e0e5313cad629a9c02e2c
merge_method: model_stock
tokenizer:
source: union
int8_mask: true
dtype: float32
out_dtype: bfloat16
```
|
sergioalves/e0863864-59a3-4a2c-afe9-719394f12644 | sergioalves | 2025-05-24T22:05:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T21:44:21Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e0863864-59a3-4a2c-afe9-719394f12644
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/e0863864-59a3-4a2c-afe9-719394f12644
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# e0863864-59a3-4a2c-afe9-719394f12644
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7027
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1574 | 0.0001 | 1 | 1.7857 |
| 1.6331 | 0.0151 | 250 | 1.7348 |
| 1.4779 | 0.0301 | 500 | 1.7027 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
vmpsergio/b72832aa-c3e8-444a-86cb-d6573d28bc66 | vmpsergio | 2025-05-24T22:04:45Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T21:44:01Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b72832aa-c3e8-444a-86cb-d6573d28bc66
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: vmpsergio/b72832aa-c3e8-444a-86cb-d6573d28bc66
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 40
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# b72832aa-c3e8-444a-86cb-d6573d28bc66
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6974
## 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-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3586 | 0.0225 | 280 | 1.6974 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
dzanbek/12cec7cb-7cc2-4e1b-a0c3-2944779bd461 | dzanbek | 2025-05-24T22:01:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T21:44:01Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 12cec7cb-7cc2-4e1b-a0c3-2944779bd461
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.85
group_by_length: false
hub_model_id: dzanbek/12cec7cb-7cc2-4e1b-a0c3-2944779bd461
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.2e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 280
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: s56-2
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 40
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 12cec7cb-7cc2-4e1b-a0c3-2944779bd461
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7786
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.2e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 40
- training_steps: 280
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5735 | 0.0169 | 280 | 1.7786 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
MomlessTomato/hanayo-koizumi | MomlessTomato | 2025-05-24T22:01:09Z | 2 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:cagliostrolab/animagine-xl-3.0",
"base_model:adapter:cagliostrolab/animagine-xl-3.0",
"region:us"
]
| text-to-image | 2024-02-12T04:18:06Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: >-
masterpiece, high quality, defined pupil, looking at viewer, rounded pupil,
defined iris, (soft iris:1.2),
parameters:
negative_prompt: >-
bad_anatomy, deformation, amputation, deformity, deformed_nipples,
duplicated_torso, deformed_torso, long_torso, large_torso,
unproportioned_torso, (deformed_pussy:1.2), (deformed_hands:1.2),
unproportioned_eyes, unproportioned_head, small_head, duplicated_nose,
big_nose, fusioned_clothes, fusioned_arms, undefined_limbs, divided_pussy,
red_pussy, duplicated_pussy, deformed_anus, deformed_pussy,
output:
url: images/hanayo_koizumi.png
base_model: cagliostrolab/animagine-xl-3.0
instance_prompt: id_hanayo_koizumi
---
# Hanayo Koizumi
<Gallery />
## Model description
This model was trained to generate high quality images based on SIFAS cards.
To achieve better quality, you should be using hako-mikan's regional prompter, along with Latent Mode, which modifies the way Stable Diffusion isolates the LoRA resulting in a significant improvement.
## Trigger words
You should use `id_hanayo_koizumi` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/theidoldaily/hanayo-koizumi/tree/main) them in the Files & versions tab.
|
PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M | PJMixers-Dev | 2025-05-24T21:59:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"granitemoe",
"text-generation",
"conversational",
"en",
"dataset:BeaverAI/REDACTED1",
"dataset:BeaverAI/REDACTED2",
"dataset:BeaverAI/REDACTED3",
"dataset:BeaverAI/REDACTED4",
"dataset:BeaverAI/REDACTED5",
"dataset:BeaverAI/REDACTED6",
"dataset:PJMixers-Dev/Lit-axo-Shuffled",
"dataset:PJMixers-Dev/Mielikki_Erebus-87k-axo",
"dataset:PJMixers/RyokoAI_Honeyfeed3600-Cleanish",
"dataset:PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo",
"dataset:Nelathan/synthetic-sugar-quill",
"dataset:PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long",
"dataset:PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned",
"dataset:PJMixers-Dev/Subtitles",
"dataset:PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo",
"dataset:PJMixers/AP-News-2024",
"dataset:PJMixers-Dev/Fundus-AP-News-Formatted",
"dataset:PJMixers-Dev/Fundus-AP-News-2-Formatted",
"dataset:PJMixers-Dev/goodwiki-2024-12-04-axo",
"dataset:epfl-llm/guidelines",
"dataset:PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT",
"dataset:OpenLeecher/lmsys_chat_1m_clean",
"dataset:PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed",
"dataset:allura-org/gryphe-sonnet-3.5-charcards-names-added",
"dataset:anthracite-org/c2_logs_32k_llama3_qwen2_v1.3",
"dataset:PJMixers-Dev/MinervaAI_Aesir-Preview-Anon",
"dataset:PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled",
"dataset:Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned",
"dataset:PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT",
"dataset:PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT",
"dataset:grimulkan/aicg-logs-augmented",
"dataset:grimulkan/PIPPA-augmented-dedup",
"dataset:PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted",
"dataset:PJMixers/lodrick-the-lafted_OpusStories-ShareGPT",
"dataset:Gryphe/ChatGPT-4o-Writing-Prompts",
"dataset:Gryphe/Opus-WritingPrompts",
"dataset:anthracite-org/nopm_claude_writing_fixed",
"dataset:PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT",
"dataset:allura-org/fujin-instruct-v2",
"dataset:ToastyPigeon/gutenberg-sft",
"dataset:PocketDoc/Dans-Prosemaxx-Adventure",
"dataset:PocketDoc/Dans-Failuremaxx-Adventure-3",
"dataset:TheDrummer/AmoralQA-v2",
"arxiv:1910.03771",
"arxiv:2106.09685",
"arxiv:2305.14314",
"arxiv:2307.08691",
"arxiv:2410.10989",
"arxiv:2107.04197",
"arxiv:2307.02047",
"arxiv:2010.06192",
"arxiv:2411.16085",
"arxiv:2501.18427",
"arxiv:2403.15279",
"arxiv:2411.15124",
"arxiv:2309.11998",
"arxiv:2308.05884",
"base_model:ibm-granite/granite-3.1-3b-a800m-instruct",
"base_model:finetune:ibm-granite/granite-3.1-3b-a800m-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T10:51:03Z | ---
base_model: ibm-granite/granite-3.1-3b-a800m-instruct
license: apache-2.0
pipeline_tag: text-generation
library_name: transformers
language:
- en
datasets:
- BeaverAI/REDACTED1
- BeaverAI/REDACTED2
- BeaverAI/REDACTED3
- BeaverAI/REDACTED4
- BeaverAI/REDACTED5
- BeaverAI/REDACTED6
- PJMixers-Dev/Lit-axo-Shuffled
- PJMixers-Dev/Mielikki_Erebus-87k-axo
- PJMixers/RyokoAI_Honeyfeed3600-Cleanish
- PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo
- Nelathan/synthetic-sugar-quill
- PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long
- PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned
- PJMixers-Dev/Subtitles
- PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo
- PJMixers/AP-News-2024
- PJMixers-Dev/Fundus-AP-News-Formatted
- PJMixers-Dev/Fundus-AP-News-2-Formatted
- PJMixers-Dev/goodwiki-2024-12-04-axo
- epfl-llm/guidelines
- PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT
- OpenLeecher/lmsys_chat_1m_clean
- PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed
- allura-org/gryphe-sonnet-3.5-charcards-names-added
- anthracite-org/c2_logs_32k_llama3_qwen2_v1.3
- PJMixers-Dev/MinervaAI_Aesir-Preview-Anon
- PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled
- Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
- PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT
- PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT
- grimulkan/aicg-logs-augmented
- grimulkan/PIPPA-augmented-dedup
- PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted
- PJMixers/lodrick-the-lafted_OpusStories-ShareGPT
- Gryphe/ChatGPT-4o-Writing-Prompts
- Gryphe/Opus-WritingPrompts
- anthracite-org/nopm_claude_writing_fixed
- PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT
- allura-org/fujin-instruct-v2
- ToastyPigeon/gutenberg-sft
- PocketDoc/Dans-Prosemaxx-Adventure
- PocketDoc/Dans-Failuremaxx-Adventure-3
- TheDrummer/AmoralQA-v2
---
# Granite-3.1-Earthen-v0.3-3B-A800M
[`ibm-granite/granite-3.1-3b-a800m-instruct`](https://huggingface.co/ibm-granite/granite-3.1-3b-a800m-instruct) was trained at 8K with batch size 2 gradient accumulation 8, so each step was 131,072 tokens (including any padding tokens). It was trained for 400 steps, adding up to a total of 52,428,800 unique tokens seen.
This is a small test run. A larger version is planned.
## Quants
- [GGUF](https://huggingface.co/PJMixers-Dev/Granite-3.1-Earthen-v0.3-3B-A800M-GGUF)
## Prompt Format
This model uses Granite-3.1 Instruct format.
```
<|start_of_role|>system<|end_of_role|>example system prompt<|end_of_text|>
<|start_of_role|>user<|end_of_role|>example user turn 1<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>example assistant turn 1<|end_of_text|>
<|start_of_role|>user<|end_of_role|>example user turn 2<|end_of_text|>
<|start_of_role|>assistant<|end_of_role|>example assistant turn 2<|end_of_text|>
```
## Training Details
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
```yaml
# Requirements before running
# - Get latest commit of axolotl (currently c0a0c75)
# - Download these to axolotl/src/axolotl/prompt_formatters
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/formatter_regex.py
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customcompletion-regex.py
# - https://github.com/xzuyn/axolotl/blob/came-plus-formatters/src/axolotl/prompt_strategies/customgranite-regex.py
# - pip install ftfy
# - pip install git+https://github.com/xzuyn/CAME.git@sr-grams-cautious-8bit
# Weights and Biases logging config
wandb_project: Granite-3.1-3B-A800M
wandb_name: Granite-3.1-Earthen-v0.3-3B-A800M-QLoRA-run4
# Model checkpointing config
output_dir: ./Outputs/Granite-3.1-Earthen-v0.3-3B-A800M-QLoRA-run4
resume_from_checkpoint:
save_steps: 10
save_safetensors: true
save_total_limit: 2
save_only_model: false
# Model architecture config
base_model: ibm-granite/granite-3.1-3b-a800m-instruct
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
# Mixed precision training config
bf16: true
fp16: false
tf32: false
# Model loading config
load_in_8bit: false
load_in_4bit: true
strict: false
# Sequence config
sequence_len: 8192
min_sample_len: 256
sample_packing: true
eval_sample_packing: true
pad_to_sequence_len: true
train_on_inputs: false
group_by_length: false
# LoRA adapter config
adapter: qlora
lora_r: 128
lora_alpha: 128
lora_dropout: 0.125
lora_target_linear: true
embeddings_skip_upcast: true
# Dataset config
datasets:
# Completion
# Story-like Data
- path: BeaverAI/REDACTED1
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Lit-axo-Shuffled
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Mielikki_Erebus-87k-axo
split: train[:4000]
type: customcompletion-regex
- path: PJMixers/RyokoAI_Honeyfeed3600-Cleanish
split: train[:4000]
type: customcompletion-regex
- path: BeaverAI/REDACTED2
type: customcompletion-regex
- path: PJMixers-Dev/allura-org_fujin-cleaned-stage-2-axo
split: train[:4000]
type: customcompletion-regex
- path: Nelathan/synthetic-sugar-quill
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/winglian_visual-novels-json-axo-dropped-long
split: train[:4000]
type: customcompletion-regex
- path: BeaverAI/REDACTED3
type: customcompletion-regex
- path: PJMixers-Dev/recursal_SCP-RECURSAL-Cleaned
split: train[:4000]
type: customcompletion-regex
# Subtitle Data
- path: PJMixers-Dev/Subtitles
type: customcompletion-regex
- path: PJMixers-Dev/KaraKaraWitch_AnimeSubtitle-axo
split: train[:4000]
type: customcompletion-regex
# News Data
- path: PJMixers/AP-News-2024
type: customcompletion-regex
- path: PJMixers-Dev/Fundus-AP-News-Formatted
split: train[:4000]
type: customcompletion-regex
- path: PJMixers-Dev/Fundus-AP-News-2-Formatted
type: customcompletion-regex
# Misc Data
- path: PJMixers-Dev/goodwiki-2024-12-04-axo
split: train[:4000]
type: customcompletion-regex
- path: epfl-llm/guidelines
split: train[:4000]
field: clean_text
type: customcompletion-regex
# Granite-3.1 Instruct
# Instruction Data
- path: PJMixers-Dev/allenai_tulu-3-sft-mixture-filtered-2-ShareGPT
split: train[:4000]
type: customgranite-regex
- path: OpenLeecher/lmsys_chat_1m_clean
split: train[:4000]
type: customgranite-regex
# RP Data
- path: PJMixers-Dev/Gryphe-Aesir-RPG-Charcards-Opus-Mixed
type: customgranite-regex
- path: allura-org/gryphe-sonnet-3.5-charcards-names-added
type: customgranite-regex
- path: anthracite-org/c2_logs_32k_llama3_qwen2_v1.3
type: customgranite-regex
- path: BeaverAI/REDACTED4
type: customgranite-regex
- path: PJMixers-Dev/MinervaAI_Aesir-Preview-Anon
type: customgranite-regex
- path: PJMixers-Dev/lemonilia_LimaRP-Simple-CustomShareGPT-Shuffled
type: customgranite-regex
- path: Epiculous/SynthRP-Gens-v1.1-Filtered-n-Cleaned
type: customgranite-regex
- path: PJMixers-Dev/NyxKrage_chub-logs-sharegpt-longest-CustomShareGPT
type: customgranite-regex
- path: PJMixers/OpenLeecher_Teatime_all_logs_longest-ShareGPT
type: customgranite-regex
- path: grimulkan/aicg-logs-augmented
type: customgranite-regex
- path: grimulkan/PIPPA-augmented-dedup
type: customgranite-regex
- path: PJMixers/grimulkan_bluemoon_Karen_cleaned-carded-formatted
type: customgranite-regex
# InstStory Data
- path: PJMixers/lodrick-the-lafted_OpusStories-ShareGPT
type: customgranite-regex
- path: Gryphe/ChatGPT-4o-Writing-Prompts
type: customgranite-regex
- path: Gryphe/Opus-WritingPrompts
type: customgranite-regex
- path: anthracite-org/nopm_claude_writing_fixed
type: customgranite-regex
- path: PJMixers-Dev/Tiefighter-13B-Fake-Distill-ShareGPT
type: customgranite-regex
- path: allura-org/fujin-instruct-v2
type: customgranite-regex
- path: ToastyPigeon/gutenberg-sft
type: customgranite-regex
# Adventure Data
- path: PocketDoc/Dans-Prosemaxx-Adventure
type: customgranite-regex
- path: PocketDoc/Dans-Failuremaxx-Adventure-3
type: customgranite-regex
# Decensoring Data
- path: TheDrummer/AmoralQA-v2
type: customgranite-regex
- path: BeaverAI/REDACTED5
type: customgranite-regex
- path: BeaverAI/REDACTED6
type: customgranite-regex
val_set_size: 256
eval_strategy: steps
eval_steps: 10
dataset_prepared_path: ./00-Tokenized-Datasets/Granite-3.1-Earthen-v0.3-3B-A800M-LoRA-seed42
shuffle_merged_datasets: true
# Training hyperparameters
num_epochs: 1
gradient_accumulation_steps: 8
micro_batch_size: 2
eval_batch_size: 2
warmup_steps: 0
optimizer: came_pytorch
optim_args:
enable_stochastic_rounding: true
enable_cautious: true
enable_8bit: true
lr_scheduler: rex
learning_rate: 2.5e-7
cosine_min_lr_ratio: 0.05
weight_decay: 0.01
max_grad_norm: 0.5
logging_steps: 1
# Model optimization
gradient_checkpointing: offload
sdp_attention: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: true
liger_layer_norm: true
liger_glu_activation: true
liger_cross_entropy: true
lora_mlp_kernel: false
lora_qkv_kernel: false
lora_o_kernel: false
# Debug config
debug: true
seed: 42
# Token config
special_tokens:
bos_token: "<|end_of_text|>"
eos_token: "<|end_of_text|>"
pad_token: "<|end_of_text|>"
tokens:
```
## Citations
<details><summary>Show Citations</summary>
```bib
@misc{wolf2020huggingfacestransformersstateoftheartnatural,
title={HuggingFace's Transformers: State-of-the-art Natural Language Processing},
author={Thomas Wolf and Lysandre Debut and Victor Sanh and Julien Chaumond and Clement Delangue and Anthony Moi and Pierric Cistac and Tim Rault and Rémi Louf and Morgan Funtowicz and Joe Davison and Sam Shleifer and Patrick von Platen and Clara Ma and Yacine Jernite and Julien Plu and Canwen Xu and Teven Le Scao and Sylvain Gugger and Mariama Drame and Quentin Lhoest and Alexander M. Rush},
year={2020},
eprint={1910.03771},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/1910.03771},
}
@misc{hu2021loralowrankadaptationlarge,
title={LoRA: Low-Rank Adaptation of Large Language Models},
author={Edward J. Hu and Yelong Shen and Phillip Wallis and Zeyuan Allen-Zhu and Yuanzhi Li and Shean Wang and Lu Wang and Weizhu Chen},
year={2021},
eprint={2106.09685},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2106.09685},
}
@misc{dettmers2023qloraefficientfinetuningquantized,
title={QLoRA: Efficient Finetuning of Quantized LLMs},
author={Tim Dettmers and Artidoro Pagnoni and Ari Holtzman and Luke Zettlemoyer},
year={2023},
eprint={2305.14314},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2305.14314},
}
@misc{dao2023flashattention2fasterattentionbetter,
title={FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning},
author={Tri Dao},
year={2023},
eprint={2307.08691},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2307.08691},
}
@misc{hsu2024ligerkernelefficienttriton,
title={Liger Kernel: Efficient Triton Kernels for LLM Training},
author={Pin-Lun Hsu and Yun Dai and Vignesh Kothapalli and Qingquan Song and Shao Tang and Siyu Zhu and Steven Shimizu and Shivam Sahni and Haowen Ning and Yanning Chen},
year={2024},
eprint={2410.10989},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2410.10989},
}
@misc{chen2021rexrevisitingbudgetedtraining,
title={REX: Revisiting Budgeted Training with an Improved Schedule},
author={John Chen and Cameron Wolfe and Anastasios Kyrillidis},
year={2021},
eprint={2107.04197},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2107.04197},
}
@misc{luo2023cameconfidenceguidedadaptivememory,
title={CAME: Confidence-guided Adaptive Memory Efficient Optimization},
author={Yang Luo and Xiaozhe Ren and Zangwei Zheng and Zhuo Jiang and Xin Jiang and Yang You},
year={2023},
eprint={2307.02047},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2307.02047},
}
@misc{zamirai2021revisitingbfloat16training,
title={Revisiting BFloat16 Training},
author={Pedram Zamirai and Jian Zhang and Christopher R. Aberger and Christopher De Sa},
year={2021},
eprint={2010.06192},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2010.06192},
}
@misc{liang2025cautiousoptimizersimprovingtraining,
title={Cautious Optimizers: Improving Training with One Line of Code},
author={Kaizhao Liang and Lizhang Chen and Bo Liu and Qiang Liu},
year={2025},
eprint={2411.16085},
archivePrefix={arXiv},
primaryClass={cs.LG},
url={https://arxiv.org/abs/2411.16085},
}
@misc{xie2025sana15efficientscaling,
title={SANA 1.5: Efficient Scaling of Training-Time and Inference-Time Compute in Linear Diffusion Transformer},
author={Enze Xie and Junsong Chen and Yuyang Zhao and Jincheng Yu and Ligeng Zhu and Chengyue Wu and Yujun Lin and Zhekai Zhang and Muyang Li and Junyu Chen and Han Cai and Bingchen Liu and Daquan Zhou and Song Han},
year={2025},
eprint={2501.18427},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2501.18427},
}
@misc{dallabetta2024fundussimpletousenewsscraper,
title={Fundus: A Simple-to-Use News Scraper Optimized for High Quality Extractions},
author={Max Dallabetta and Conrad Dobberstein and Adrian Breiding and Alan Akbik},
year={2024},
eprint={2403.15279},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2403.15279},
}
@misc{lambert2025tulu3pushingfrontiers,
title={Tulu 3: Pushing Frontiers in Open Language Model Post-Training},
author={Nathan Lambert and Jacob Morrison and Valentina Pyatkin and Shengyi Huang and Hamish Ivison and Faeze Brahman and Lester James V. Miranda and Alisa Liu and Nouha Dziri and Shane Lyu and Yuling Gu and Saumya Malik and Victoria Graf and Jena D. Hwang and Jiangjiang Yang and Ronan Le Bras and Oyvind Tafjord and Chris Wilhelm and Luca Soldaini and Noah A. Smith and Yizhong Wang and Pradeep Dasigi and Hannaneh Hajishirzi},
year={2025},
eprint={2411.15124},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2411.15124},
}
@misc{zheng2024lmsyschat1mlargescalerealworldllm,
title={LMSYS-Chat-1M: A Large-Scale Real-World LLM Conversation Dataset},
author={Lianmin Zheng and Wei-Lin Chiang and Ying Sheng and Tianle Li and Siyuan Zhuang and Zhanghao Wu and Yonghao Zhuang and Zhuohan Li and Zi Lin and Eric P. Xing and Joseph E. Gonzalez and Ion Stoica and Hao Zhang},
year={2024},
eprint={2309.11998},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2309.11998},
}
@misc{gosling2023pippapartiallysyntheticconversational,
title={PIPPA: A Partially Synthetic Conversational Dataset},
author={Tear Gosling and Alpin Dale and Yinhe Zheng},
year={2023},
eprint={2308.05884},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2308.05884},
}
```
</details>
|
aleegis/5b5edef6-20b1-4da5-9864-c364f4ac05d5 | aleegis | 2025-05-24T21:57:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-1.7B",
"base_model:adapter:unsloth/SmolLM-1.7B",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T21:44:21Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-1.7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 5b5edef6-20b1-4da5-9864-c364f4ac05d5
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/SmolLM-1.7B
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- da6901d849324b9e_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/5b5edef6-20b1-4da5-9864-c364f4ac05d5
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_grad_norm: 1
max_steps: 800
micro_batch_size: 4
mlflow_experiment_name: /tmp/da6901d849324b9e_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 77cb7152-00ec-4da2-a927-6632e7e5f5b5
warmup_steps: 80
weight_decay: 0
xformers_attention: null
```
</details><br>
# 5b5edef6-20b1-4da5-9864-c364f4ac05d5
This model is a fine-tuned version of [unsloth/SmolLM-1.7B](https://huggingface.co/unsloth/SmolLM-1.7B) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 80
- training_steps: 800
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
phospho-app/omourier-gr00t-Lego_rouge3-yzwz8 | phospho-app | 2025-05-24T21:55:29Z | 0 | 0 | null | [
"safetensors",
"gr00t_n1",
"phosphobot",
"gr00t",
"region:us"
]
| null | 2025-05-24T21:23:29Z |
---
tags:
- phosphobot
- gr00t
task_categories:
- robotics
---
# gr00t Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [omourier/Lego_rouge3](https://huggingface.co/datasets/omourier/Lego_rouge3)
- **Wandb run URL**: None
- **Epochs**: 10
- **Batch size**: 27
- **Training steps**: None
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
Ecila1000/Card_consuming | Ecila1000 | 2025-05-24T21:51:06Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T21:51:06Z | ---
license: apache-2.0
---
|
J-LAB/fluxiia_14b-Q4_K_M-GGUF | J-LAB | 2025-05-24T21:49:01Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:J-LAB/fluxiia_14b",
"base_model:quantized:J-LAB/fluxiia_14b",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T21:48:24Z | ---
base_model: J-LAB/fluxiia_14b
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- llama-cpp
- gguf-my-repo
license: apache-2.0
language:
- en
---
# J-LAB/fluxiia_14b-Q4_K_M-GGUF
This model was converted to GGUF format from [`J-LAB/fluxiia_14b`](https://huggingface.co/J-LAB/fluxiia_14b) 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/J-LAB/fluxiia_14b) 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 J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo J-LAB/fluxiia_14b-Q4_K_M-GGUF --hf-file fluxiia_14b-q4_k_m.gguf -c 2048
```
|
JesseLiu/llama32-1b-kpath-partial-abbr | JesseLiu | 2025-05-24T21:45:41Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
]
| null | 2025-05-24T21:45:20Z | ---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
### Framework versions
- PEFT 0.15.1 |
minjuk/ppo-LunarLander-v2-1 | minjuk | 2025-05-24T21:10:14Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T21:09:57Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 268.15 +/- 17.01
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
secmlr/SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched | secmlr | 2025-05-24T18:26:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-0.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:43:38Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched
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. -->
# SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning_qwen_code_0.5b_433_enriched
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-0.5B-Instruct) on the SWE-BENCH-433-enriched-set-claude-3in1-localization-with-reasoning dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 12
- total_train_batch_size: 48
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.1
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b4.5_a1_d0_g0.125_ep10 | open-unlearning | 2025-05-24T18:23:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:22:23Z | ---
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] |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b4.5_a1_d0_g0.125_ep5 | open-unlearning | 2025-05-24T18:22:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:20:57Z | ---
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] |
Cherran/medical_gemma_1b_sft | Cherran | 2025-05-24T18:22:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"base_model:adapter:unsloth/gemma-3-1b-it-unsloth-bnb-4bit",
"region:us"
]
| null | 2025-05-24T18:21:43Z | ---
base_model: unsloth/gemma-3-1b-it-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
### Framework versions
- PEFT 0.15.2 |
nojedag/distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european | nojedag | 2025-05-24T18:19:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilroberta-base",
"base_model:finetune:distilbert/distilroberta-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
]
| text-classification | 2025-05-24T18:19:16Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilroberta-base
tags:
- generated_from_trainer
model-index:
- name: distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european
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. -->
# distilroberta-roberta-finetuned-financial-news-sentiment-analysis-european
This model is a fine-tuned version of [distilbert/distilroberta-base](https://huggingface.co/distilbert/distilroberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.6637
- eval_model_preparation_time: 0.0015
- eval_accuracy: 0.7764
- eval_macro_precision: 0.7737
- eval_macro_recall: 0.7865
- eval_macro_f1: 0.7762
- eval_neutral_precision: 0.8569
- eval_neutral_recall: 0.7260
- eval_neutral_f1: 0.7860
- eval_positive_precision: 0.7815
- eval_positive_recall: 0.8178
- eval_positive_f1: 0.7992
- eval_negative_precision: 0.6827
- eval_negative_recall: 0.8157
- eval_negative_f1: 0.7433
- eval_runtime: 18.4835
- eval_samples_per_second: 449.589
- eval_steps_per_second: 28.133
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 846
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
tifin-india/sarvam-m-24b-q5-1-gguf | tifin-india | 2025-05-24T18:19:32Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q5_1",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T16:15:05Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q5_1
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q5_1 GGUF
This repository contains the **Q5_1** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q5_1
- **File Size**: ~16.5GB
- **Description**: Legacy Q5 format with very low quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q5_1-gguf
# Run inference
./main -m sarvam-m-24b-Q5_1.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q5_1.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q5_1-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐ |
| **Quality** | ⭐⭐⭐⭐ |
| **Memory** | ⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q5_1 format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
tifin-india/sarvam-m-24b-q6-k-gguf | tifin-india | 2025-05-24T18:19:00Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q6_k",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T16:02:44Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q6_k
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q6_K GGUF
This repository contains the **Q6_K** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q6_K
- **File Size**: ~18.0GB
- **Description**: Large model with extremely low quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q6_k-gguf
# Run inference
./main -m sarvam-m-24b-Q6_K.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q6_K.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q6_k-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐ |
| **Quality** | ⭐⭐⭐⭐⭐ |
| **Memory** | ⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q6_K format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
tifin-india/sarvam-m-24b-q3-k-gguf | tifin-india | 2025-05-24T18:18:21Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q3_k",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T17:10:46Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q3_k
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q3_K GGUF
This repository contains the **Q3_K** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q3_K
- **File Size**: ~10.7GB
- **Description**: Standard Q3 quantization
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q3_k-gguf
# Run inference
./main -m sarvam-m-24b-Q3_K.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q3_K.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q3_k-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐⭐⭐ |
| **Quality** | ⭐⭐ |
| **Memory** | ⭐⭐⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q3_K format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
tifin-india/sarvam-m-24b-q3-k-m-gguf | tifin-india | 2025-05-24T18:16:38Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q3_k_m",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T17:28:04Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q3_k_m
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q3_K_M GGUF
This repository contains the **Q3_K_M** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q3_K_M
- **File Size**: ~10.7GB
- **Description**: Medium model with balanced quality/size tradeoff
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q3_k_m-gguf
# Run inference
./main -m sarvam-m-24b-Q3_K_M.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q3_K_M.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q3_k_m-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐⭐⭐ |
| **Quality** | ⭐⭐ |
| **Memory** | ⭐⭐⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q3_K_M format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr5e-05_b4.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:16:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:15:22Z | ---
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] |
tifin-india/sarvam-m-24b-q4-k-s-gguf | tifin-india | 2025-05-24T18:15:54Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q4_k_s",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T17:36:06Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q4_k_s
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q4_K_S GGUF
This repository contains the **Q4_K_S** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q4_K_S
- **File Size**: ~12.6GB
- **Description**: Small Q4 model with greater quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q4_k_s-gguf
# Run inference
./main -m sarvam-m-24b-Q4_K_S.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q4_K_S.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q4_k_s-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐⭐ |
| **Quality** | ⭐⭐⭐ |
| **Memory** | ⭐⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q4_K_S format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
tifin-india/sarvam-m-24b-q5-k-m-gguf | tifin-india | 2025-05-24T18:15:32Z | 0 | 0 | null | [
"gguf",
"mistral",
"text-generation",
"llama.cpp",
"quantized",
"q5_k_m",
"conversational",
"base_model:sarvamai/sarvam-m",
"base_model:quantized:sarvamai/sarvam-m",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-24T17:45:57Z | ---
license: apache-2.0
tags:
- text-generation
- llama.cpp
- gguf
- quantized
- q5_k_m
model_type: llama
inference: false
base_model:
- sarvamai/sarvam-m
---
# sarvam-m-24b - Q5_K_M GGUF
This repository contains the **Q5_K_M** quantized version of sarvam-m-24b in GGUF format.
## Model Details
- **Quantization**: Q5_K_M
- **File Size**: ~15.6GB
- **Description**: Medium Q5 model with very low quality loss
- **Format**: GGUF (compatible with llama.cpp)
## Usage
### With llama.cpp
```bash
# Download the model
huggingface-cli download tifin-india/sarvam-m-24b-q5_k_m-gguf
# Run inference
./main -m sarvam-m-24b-Q5_K_M.gguf -p "Your prompt here"
```
### With Python (llama-cpp-python)
```python
from llama_cpp import Llama
# Load the model
llm = Llama(
model_path="./sarvam-m-24b-Q5_K_M.gguf",
n_ctx=2048, # Context length
n_gpu_layers=35, # Adjust based on your GPU
verbose=False
)
# Generate text
response = llm("Your prompt here", max_tokens=100)
print(response['choices'][0]['text'])
```
### With Transformers + AutoGGUF
```python
from transformers import AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM
model_name = "tifin-india/sarvam-m-24b-q5_k_m-gguf"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoGPTQForCausalLM.from_quantized(model_name)
```
## Performance Characteristics
| Aspect | Rating |
|--------|--------|
| **Speed** | ⭐⭐ |
| **Quality** | ⭐⭐⭐⭐ |
| **Memory** | ⭐⭐ |
## Original Model
This is a quantized version of the original model. For the full-precision version and more details, please refer to the original model repository.
## Quantization Details
This model was quantized using llama.cpp's quantization tools. The Q5_K_M format provides a good balance of model size, inference speed, and output quality for most use cases.
## License
This model follows the same license as the original model (Apache 2.0).
## Citation
If you use this model, please cite the original model authors and acknowledge the quantization. |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr1e-05_b4.5_a1_d0_g0.125_ep10 | open-unlearning | 2025-05-24T18:10:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:09:05Z | ---
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.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Model Card Contact
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haihp02/7c7aed49-5c7f-43cc-8cf5-b0d951380dd8 | haihp02 | 2025-05-24T18:09:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:unsloth/Qwen2.5-0.5B",
"base_model:finetune:unsloth/Qwen2.5-0.5B",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T14:40:07Z | ---
base_model: unsloth/Qwen2.5-0.5B
library_name: transformers
model_name: 7c7aed49-5c7f-43cc-8cf5-b0d951380dd8
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 7c7aed49-5c7f-43cc-8cf5-b0d951380dd8
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B](https://huggingface.co/unsloth/Qwen2.5-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="haihp02/7c7aed49-5c7f-43cc-8cf5-b0d951380dd8", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/trunghainguyenhp02/sn56-sft-train/runs/zkomgcnx)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_IdkNLL_lr3e-05_alpha10_epoch5 | open-unlearning | 2025-05-24T18:07:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-15T17:48:18Z | ---
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]
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[More Information Needed]
### Out-of-Scope Use
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[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
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[More Information Needed]
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_SimNPO_lr2e-05_b3.5_a1_d1_g0.125_ep10 | open-unlearning | 2025-05-24T18:05:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T18:02:51Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- 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.
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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ayush7/sarvam-m_fp4 | ayush7 | 2025-05-24T18:05:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-24T11:51:33Z | ---
library_name: transformers
license: apache-2.0
---
# Model Card for Model ID
FP4 quantization of Sarvam-m model for educational purpose. Any and all copyright belongs to the original publishers.
Please visit the original developers of the model at sarvam.ai
No copyright infringement intended.
## 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:** sarvam.ai[https://www.sarvam.ai/blogs/sarvam-m]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** FP4 model. (4 bit quantization done with bitsandbytes library)
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mohhtl/2526a89c-9290-47bc-9a26-702b73a2cf68 | mohhtl | 2025-05-24T18:03:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"generated_from_trainer",
"dataset:0d097c7e-35de-44c6-803e-9ac004c94f01_test.json",
"dataset:0d097c7e-35de-44c6-803e-9ac004c94f01_synth.json",
"base_model:Qwen/Qwen2.5-Coder-7B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T18:02:45Z | ---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
tags:
- generated_from_trainer
datasets:
- 0d097c7e-35de-44c6-803e-9ac004c94f01_test.json
- 0d097c7e-35de-44c6-803e-9ac004c94f01_synth.json
model-index:
- name: results/2526a89c-9290-47bc-9a26-702b73a2cf68
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.2`
```yaml
adapter: lora
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
bf16: auto
dataset_prepared_path: results/0d097c7e-35de-44c6-803e-9ac004c94f01_last_run_prepared
datasets:
- path: 0d097c7e-35de-44c6-803e-9ac004c94f01_test.json
type: &id001
field: null
field_input: input
field_instruction: instruct
field_output: output
field_system: null
format: null
no_input_format: null
system_format: '{system}'
system_prompt: ''
- path: 0d097c7e-35de-44c6-803e-9ac004c94f01_synth.json
type: *id001
flash_attention: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
learning_rate: 0.0005
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: constant
micro_batch_size: 8
model_type: AutoModelForCausalLM
num_epochs: 20
optimizer: adamw_bnb_8bit
output_dir: results/2526a89c-9290-47bc-9a26-702b73a2cf68
pad_to_sequence_len: null
resume_from_checkpoint: null
sample_packing: false
save_total_limit: 1
saves_per_epoch: 1
sequence_len: 2048
special_tokens: null
test_datasets:
- path: 0d097c7e-35de-44c6-803e-9ac004c94f01_test.json
split: train
type: *id001
- path: 0d097c7e-35de-44c6-803e-9ac004c94f01_synth.json
split: train
type: *id001
tf32: false
tokenizer_type: AutoTokenizer
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_log_model: null
wandb_name: null
wandb_project: null
wandb_watch: null
warmup_ratio: 0.0
warmup_steps: 0
weight_decay: 0.0
```
</details><br>
# results/2526a89c-9290-47bc-9a26-702b73a2cf68
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct) on the 0d097c7e-35de-44c6-803e-9ac004c94f01_test.json and the 0d097c7e-35de-44c6-803e-9ac004c94f01_synth.json datasets.
It achieves the following results on the evaluation set:
- Loss: 1.4476
## 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: 0.0005
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- num_epochs: 20.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.5306 | 1.0 | 188 | 0.6879 |
| 0.9732 | 2.0 | 376 | 0.5390 |
| 0.5383 | 3.0 | 564 | 0.4788 |
| 0.5647 | 4.0 | 752 | 0.3613 |
| 0.5543 | 5.0 | 940 | 0.3299 |
| 0.4344 | 6.0 | 1128 | 0.2573 |
| 0.2719 | 7.0 | 1316 | 0.2066 |
| 0.1383 | 8.0 | 1504 | 0.1722 |
| 0.1494 | 9.0 | 1692 | 0.1390 |
| 0.1831 | 10.0 | 1880 | 0.0992 |
| 0.0975 | 11.0 | 2068 | 0.2587 |
| 3.4007 | 12.0 | 2256 | 2.8097 |
| 2.3618 | 13.0 | 2444 | 2.2867 |
| 3.0427 | 14.0 | 2632 | 2.0519 |
| 2.0942 | 15.0 | 2820 | 1.9593 |
| 1.0822 | 16.0 | 3008 | 1.8154 |
| 2.3047 | 17.0 | 3196 | 1.7215 |
| 2.6679 | 18.0 | 3384 | 1.6182 |
| 2.0749 | 19.0 | 3572 | 1.5103 |
| 1.0939 | 20.0 | 3760 | 1.4476 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_NPO_lr5e-05_beta0.5_alpha1_epoch5 | open-unlearning | 2025-05-24T18:02:48Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-15T16:50:58Z | ---
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]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
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dzanbek/2732564f-c3e0-4694-9ebe-8f78edcb8c3c | dzanbek | 2025-05-24T18:01:44Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-24T17:30:16Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 2732564f-c3e0-4694-9ebe-8f78edcb8c3c
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 2732564f-c3e0-4694-9ebe-8f78edcb8c3c
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="dzanbek/2732564f-c3e0-4694-9ebe-8f78edcb8c3c", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-2/runs/entbltll)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
halchou/BFConfig-LoRA-open_llama_3b-v01 | halchou | 2025-05-24T18:00:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T17:52:21Z | ---
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]
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[More Information Needed]
### Out-of-Scope Use
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[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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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vikala0110/toucan-vocoder | vikala0110 | 2025-05-24T18:00:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"toucan_vocoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T13:08:53Z | ---
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]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed] |
desllre/ru_news_detection | desllre | 2025-05-24T17:58:39Z | 11 | 1 | null | [
"safetensors",
"bert",
"rubert",
"rubert-tiny",
"text-classification",
"russian",
"social-media",
"news",
"fine-tuned",
"taiga",
"ru",
"dataset:Taiga",
"base_model:cointegrated/rubert-tiny2",
"base_model:finetune:cointegrated/rubert-tiny2",
"license:mit",
"region:us"
]
| text-classification | 2025-05-21T16:20:01Z | ---
language: ru
license: mit
tags:
- rubert
- rubert-tiny
- text-classification
- russian
- social-media
- news
- fine-tuned
- taiga
metrics:
- accuracy
- precision
- recall
- f1
base_model: cointegrated/rubert-tiny2
datasets:
- Taiga
---
## Russian news detection
### About
- Model based on `cointegrated/rubert-tiny2`
- The model allows you to classify russian texts into two classes 'news' and 'social'
- Further training of the model took place on a set of texts of social networks and news texts of the corpus Taiga (https://tatianashavrina.github.io/taiga_site /)
- Estimates of the accuracy of the model in the validation sample:
| Accuracy | Precision | Recall | F1-score |
| -------- | --------- | -------- | -------- |
| 0.996342 | 0.999747 | 0.993717 | 0.996723 |
### Getting started
```python
from huggingface_hub import hf_hub_download
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import pickle
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model_path = 'desllre/ru_news_detection'
encoder_path = hf_hub_download(repo_id=model_path, filename="encoder.pkl")
with open(encoder_path, "rb") as f:
encoder = pickle.load(f)
tokenizer = AutoTokenizer.from_pretrained(model_path)
classifier = AutoModelForSequenceClassification.from_pretrained(model_path).to(device)
text = 'Tesla дала добро на взлом ПО своих автомобилей\n\nКомпания изменила условия программы Bug Bounty, предусматривающей выплату вознаграждений за поиск уязвимостей. Теперь энтузиасты могут взламывать электрокары Tesla, не боясь отзыва гарантии. Более того, в соответствии с новой политикой компании, автопроизводитель будет перепрошивать автомобили, ПО которых вышло из строя в процессе экспериментов специалистов кибербезопасности.\n\nИзменения в политике компании Telsa очень тепло встретили представители индустрии.'
tokenized = tokenize_function(text, news_tokenizer)
tokenized = {key: value.to(device) for key, value in tokenized.items()}
with torch.no_grad():
output = classifier(**tokenized)
predicted_class_id = torch.argmax(output.logits, dim=1).item()
label = encoder.inverse_transform([predicted_class_id])[0]
print(label)
```
|
sergioalves/1769b25a-3eae-427a-af7d-63234b0f48c0 | sergioalves | 2025-05-24T17:57:41Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:NousResearch/Hermes-2-Pro-Llama-3-8B",
"base_model:quantized:NousResearch/Hermes-2-Pro-Llama-3-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
]
| text-generation | 2025-05-24T17:28:19Z | ---
base_model: NousResearch/Hermes-2-Pro-Llama-3-8B
library_name: transformers
model_name: 1769b25a-3eae-427a-af7d-63234b0f48c0
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 1769b25a-3eae-427a-af7d-63234b0f48c0
This model is a fine-tuned version of [NousResearch/Hermes-2-Pro-Llama-3-8B](https://huggingface.co/NousResearch/Hermes-2-Pro-Llama-3-8B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sergioalves/1769b25a-3eae-427a-af7d-63234b0f48c0", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/dedok-yo/s56-7/runs/nehqu90z)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
open-unlearning/unlearn_tofu_Llama-3.2-1B-Instruct_forget10_GradDiff_lr2e-05_alpha5_epoch10 | open-unlearning | 2025-05-24T17:55:56Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-15T16:50:51Z | ---
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] |
cdp57/MM_gemmaFT8.1 | cdp57 | 2025-05-24T17:49:45Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:48:51Z | ---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** cdp57
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
This gemma3 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)
|
nojedag/distilbert-finetuned-financial-news-sentiment-analysis-european | nojedag | 2025-05-24T17:48:58Z | 0 | 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 | 2025-05-24T17:48:24Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: distilbert-finetuned-financial-news-sentiment-analysis-european
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-finetuned-financial-news-sentiment-analysis-european
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- eval_loss: 0.7528
- eval_model_preparation_time: 0.002
- eval_accuracy: 0.7628
- eval_macro_precision: 0.7622
- eval_macro_recall: 0.7619
- eval_macro_f1: 0.7611
- eval_neutral_precision: 0.7921
- eval_neutral_recall: 0.7675
- eval_neutral_f1: 0.7796
- eval_positive_precision: 0.8106
- eval_positive_recall: 0.7607
- eval_positive_f1: 0.7849
- eval_negative_precision: 0.6838
- eval_negative_recall: 0.7575
- eval_negative_f1: 0.7188
- eval_runtime: 17.582
- eval_samples_per_second: 472.643
- eval_steps_per_second: 29.576
- step: 0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 846
- num_epochs: 7
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1
|
fats-fme/842a9d53-2a63-4df8-93c0-7a012a952285 | fats-fme | 2025-05-24T17:46:47Z | 0 | 0 | peft | [
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T16:46:37Z | ---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 842a9d53-2a63-4df8-93c0-7a012a952285
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: tiiuae/falcon-7b
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- c6ee6d2f36d0ee65_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 32
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/842a9d53-2a63-4df8-93c0-7a012a952285
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lora_target_modules:
- q_proj
- v_proj
lr_scheduler: constant_with_warmup
max_memory:
0: 130GB
max_steps: 100
micro_batch_size: 1
mlflow_experiment_name: /tmp/c6ee6d2f36d0ee65_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 778c14d3-2b66-4915-bd11-8cea3b13bc7c
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 778c14d3-2b66-4915-bd11-8cea3b13bc7c
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 842a9d53-2a63-4df8-93c0-7a012a952285
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8150
## 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: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 200
- training_steps: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0018 | 1 | 1.7338 |
| 25.846 | 0.1805 | 100 | 0.8150 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ericilavia/phi3.5_sharegpt_finetuned | ericilavia | 2025-05-24T17:44:12Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T17:42:13Z | ---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ericilavia
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kimxxxx/mistral_r64_a128_b8_gas8_Ler5e-5_hackcehctfmansub_1epoch | kimxxxx | 2025-05-24T17:41:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:39:59Z | ---
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] |
mradermacher/InForage-3B-PPO-GGUF | mradermacher | 2025-05-24T17:40:54Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:TommyChien/InForage-3B-PPO",
"base_model:quantized:TommyChien/InForage-3B-PPO",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T17:17:25Z | ---
base_model: TommyChien/InForage-3B-PPO
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/TommyChien/InForage-3B-PPO
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q2_K.gguf) | Q2_K | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q3_K_S.gguf) | Q3_K_S | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q3_K_M.gguf) | Q3_K_M | 1.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q3_K_L.gguf) | Q3_K_L | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.IQ4_XS.gguf) | IQ4_XS | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q4_K_S.gguf) | Q4_K_S | 2.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q4_K_M.gguf) | Q4_K_M | 2.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q5_K_S.gguf) | Q5_K_S | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q5_K_M.gguf) | Q5_K_M | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q6_K.gguf) | Q6_K | 2.9 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.Q8_0.gguf) | Q8_0 | 3.7 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/InForage-3B-PPO-GGUF/resolve/main/InForage-3B-PPO.f16.gguf) | f16 | 6.9 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
polyglots/SinLlama-Instruct-si-News-Category-Transliterated-2661 | polyglots | 2025-05-24T17:34:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b",
"base_model:finetune:unsloth/llama-3-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:33:15Z | ---
base_model: unsloth/llama-3-8b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** polyglots
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b
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)
|
khuam/run_2 | khuam | 2025-05-24T17:32:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T06:53:39Z | ---
base_model: Qwen/Qwen2.5-VL-7B-Instruct
library_name: transformers
model_name: run_2
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for run_2
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="khuam/run_2", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.8.0.dev20250518+cu126
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
vertings6/d6f47dab-0449-499f-aac4-5883beeb6783 | vertings6 | 2025-05-24T17:30:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:heegyu/WizardVicuna-open-llama-3b-v2",
"base_model:adapter:heegyu/WizardVicuna-open-llama-3b-v2",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T16:56:05Z | ---
library_name: peft
license: apache-2.0
base_model: heegyu/WizardVicuna-open-llama-3b-v2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d6f47dab-0449-499f-aac4-5883beeb6783
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: heegyu/WizardVicuna-open-llama-3b-v2
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- cc1f5b1959c57013_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: input
field_instruction: instruct
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/d6f47dab-0449-499f-aac4-5883beeb6783
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/cc1f5b1959c57013_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 718ac179-f573-4920-8e2e-046d87265652
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 718ac179-f573-4920-8e2e-046d87265652
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# d6f47dab-0449-499f-aac4-5883beeb6783
This model is a fine-tuned version of [heegyu/WizardVicuna-open-llama-3b-v2](https://huggingface.co/heegyu/WizardVicuna-open-llama-3b-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9463
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.126 | 0.0001 | 1 | 1.9922 |
| 1.3273 | 0.0155 | 250 | 1.0661 |
| 1.4073 | 0.0311 | 500 | 0.9463 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Jathushan/tamilbert-pos-lyrics | Jathushan | 2025-05-24T17:28:54Z | 13 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-23T18:56:36Z | ---
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] |
CennetOguz/yc3_lamma3_concept_fg_5 | CennetOguz | 2025-05-24T17:28:06Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:27:52Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CennetOguz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
CennetOguz/yc3_lamma3_context_fg_5 | CennetOguz | 2025-05-24T17:27:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:27:37Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** CennetOguz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-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)
|
FlareRebellion/DarkHazard-v2.1-24b | FlareRebellion | 2025-05-24T17:25:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b",
"base_model:merge:PocketDoc/Dans-PersonalityEngine-V1.3.0-24b",
"base_model:ReadyArt/Broken-Tutu-24B",
"base_model:merge:ReadyArt/Broken-Tutu-24B",
"base_model:ReadyArt/Forgotten-Safeword-24B-v4.0",
"base_model:merge:ReadyArt/Forgotten-Safeword-24B-v4.0",
"base_model:aixonlab/Eurydice-24b-v3.5",
"base_model:merge:aixonlab/Eurydice-24b-v3.5",
"base_model:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"base_model:merge:cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T14:59:29Z | ---
base_model:
- cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
- PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
- aixonlab/Eurydice-24b-v3.5
- ReadyArt/Forgotten-Safeword-24B-v4.0
- ReadyArt/Broken-Tutu-24B
library_name: transformers
tags:
- mergekit
- merge
---
# DarkHazard-v2.1-24b
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Inspiration
This merge was inspired by
* Yoesph/Haphazard-v1.1-24b
* yvvki/Erotophobia-24B-v1.1
### Changelog
v2.1
* Updated Dans-PersonalityEngine to PocketDoc/Dans-PersonalityEngine-V1.3.0-24b
* Updated Eurydice to aixonlab/Eurydice-24b-v3.5
v2.0
* Major version bump because of base model change: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
* swapped TheDrummer/Cydonia-24B-v2.1 with ReadyArt/Forgotten-Safeword-24B-v4.0
* (I've been doing some tests with LatitudeGames/Harbinger-24B but it just seemed to introduce positivity bias to my test scenarios, so it stays out for now)
v1.3
* updated Eurydice to v3
v1.2
* replaced Yoesph/Haphazard-v1.1-24b with model: TheDrummer/Cydonia-24B-v2.1
* replaced ReadyArt/Safeword-Abomination-of-Omega-Darker-Gaslight_The-Final-Forgotten-Transgression-24B with ReadyArt/Broken-Tutu-24B
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition](https://huggingface.co/cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition) as a base.
### Models Merged
The following models were included in the merge:
* [PocketDoc/Dans-PersonalityEngine-V1.3.0-24b](https://huggingface.co/PocketDoc/Dans-PersonalityEngine-V1.3.0-24b)
* [aixonlab/Eurydice-24b-v3.5](https://huggingface.co/aixonlab/Eurydice-24b-v3.5)
* [ReadyArt/Forgotten-Safeword-24B-v4.0](https://huggingface.co/ReadyArt/Forgotten-Safeword-24B-v4.0)
* [ReadyArt/Broken-Tutu-24B](https://huggingface.co/ReadyArt/Broken-Tutu-24B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition
merge_method: model_stock
dtype: bfloat16
models:
- model: aixonlab/Eurydice-24b-v3.5 # storytelling / RP
- model: ReadyArt/Forgotten-Safeword-24B-v4.0 # uncensor + Cydonia
- model: ReadyArt/Broken-Tutu-24B # uncensor + nsfw + Cydonia
- model: PocketDoc/Dans-PersonalityEngine-V1.3.0-24b # Prompt Adherence
```
|
amanda-901014/qwen_32_kaggle2finetune | amanda-901014 | 2025-05-24T17:24:19Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-32B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-32B-Instruct",
"region:us"
]
| null | 2025-05-24T16:54:11Z | ---
base_model: Qwen/Qwen2.5-32B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.6.2
|
polyglots/SinLlama-Instruct-si-News-Category-Codeswitched50-2661 | polyglots | 2025-05-24T17:18:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b",
"base_model:finetune:unsloth/llama-3-8b",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T17:17:17Z | ---
base_model: unsloth/llama-3-8b
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** polyglots
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b
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)
|
2-Bindura-University-Viral-Video/FULL.VIDEO.LINK.Bindura-University.Viral.Video.Leaks | 2-Bindura-University-Viral-Video | 2025-05-24T17:16:12Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T17:15:53Z | <!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Bindura+University">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Bindura+University">🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Bindura+University"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div> |
cragtmp/task1o | cragtmp | 2025-05-24T17:13:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct",
"region:us"
]
| null | 2025-05-24T15:49:09Z | ---
base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **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]
### Framework versions
- PEFT 0.15.2 |
Katrina-Lim-Viral-18-VIDEO/18.VIDEO.Katrina.Lim.Viral.Video.Leaks.LINK.Official | Katrina-Lim-Viral-18-VIDEO | 2025-05-24T17:13:10Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T17:09:38Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/fn84hrnu?news-viral-video" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a> |
talphaidze/qwen3-w8a8-quantized | talphaidze | 2025-05-24T17:13:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
]
| text-generation | 2025-05-24T17:09:14Z | ---
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. -->
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[More Information Needed]
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[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] |
mradermacher/TCS_1.5B-GGUF | mradermacher | 2025-05-24T17:12:33Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:NeurIPS20403/TCS_1.5B",
"base_model:quantized:NeurIPS20403/TCS_1.5B",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T17:02:18Z | ---
base_model: NeurIPS20403/TCS_1.5B
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/NeurIPS20403/TCS_1.5B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q5_K_S.gguf) | Q5_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q6_K.gguf) | Q6_K | 1.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.Q8_0.gguf) | Q8_0 | 2.0 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/TCS_1.5B-GGUF/resolve/main/TCS_1.5B.f16.gguf) | f16 | 3.7 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
SoSa123456/Yolom11_sheypoor_eghlym | SoSa123456 | 2025-05-24T17:10:50Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T16:14:48Z |
## How to Run and Test the Watermark Removal Model
### Setup and Training
1. **Install dependencies** (run once):
```bash
!pip install -U gdown ultralytics wandb scikit-learn requests
```
2. **Mount Google Drive and set working directory**:
```python
from google.colab import drive
drive.mount('/content/drive', force_remount=False)
import os
os.chdir('/content/drive/MyDrive/Colab/Watermark_remover')
```
3. **Download and prepare datasets**
The script downloads watermark datasets from Google Drive, extracts them, and collects images for watermarking.
4. **Generate watermarked images and YOLO labels**
Watermarks are added to images with bounding box labels created in YOLO format.
5. **Split dataset into training and validation sets** and create `data.yaml` for YOLOv11 training.
6. **Train the YOLOv11 model** with augmentations and tuned hyperparameters:
```python
from ultralytics import YOLO
import wandb
wandb.login() # Login to Weights & Biases for experiment tracking
model = YOLO("yolo11m.pt") # Load YOLOv11m base model
model.train(
data="data.yaml",
epochs=100,
batch=16,
imgsz=640,
project="logo_detection",
name="yolo11m_logo_run",
exist_ok=True,
save=True,
save_txt=True,
augment=True,
hsv_h=0.015,
hsv_s=0.7,
fliplr=0.5,
mixup=0.1,
mosaic=1.0,
scale=0.5,
shear=0.0,
perspective=0.0,
translate=0.1
)
```
### Testing and Visualization
1. **Load the trained model weights**:
```python
from ultralytics import YOLO
model = YOLO("logo_detection/yolo11m_logo_run/weights/best.pt")
```
2. **Select test images** from the validation set:
```python
from pathlib import Path
import random
test_folder = Path("dataset/images/val")
test_images = list(test_folder.glob("*.*"))
test_images = random.sample(test_images, min(10, len(test_images)))
```
3. **Run detection and watermark removal with visualization**:
```python
import cv2
import numpy as np
import matplotlib.pyplot as plt
def visualize_detection_and_removal(model, img_path):
results = model(str(img_path))[0]
img = cv2.imread(str(img_path))
img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# Draw detection boxes
img_boxes = img.copy()
for box in results.boxes:
xyxy = box.xyxy[0].cpu().numpy().astype(int)
cv2.rectangle(img_boxes, (xyxy[0], xyxy[1]), (xyxy[2], xyxy[3]), (0,255,0), 2)
# Create mask for inpainting
mask = np.zeros(img.shape[:2], dtype=np.uint8)
for box in results.boxes:
xyxy = box.xyxy[0].cpu().numpy().astype(int)
x1, y1, x2, y2 = xyxy
mask[y1:y2, x1:x2] = 255
# Remove watermark using inpainting
inpainted = cv2.inpaint(img, mask, 3, cv2.INPAINT_TELEA)
inpainted_rgb = cv2.cvtColor(inpainted, cv2.COLOR_BGR2RGB)
# Display images
plt.figure(figsize=(15,5))
plt.subplot(1,3,1)
plt.title("Original Image")
plt.imshow(img_rgb)
plt.axis('off')
plt.subplot(1,3,2)
plt.title("Detected Logos")
plt.imshow(cv2.cvtColor(img_boxes, cv2.COLOR_BGR2RGB))
plt.axis('off')
plt.subplot(1,3,3)
plt.title("Watermark Removed")
plt.imshow(inpainted_rgb)
plt.axis('off')
plt.show()
for img_path in test_images:
print(f"Testing image: {img_path.name}")
visualize_detection_and_removal(model, img_path)
```
---
### Summary
- This repository provides a pipeline to generate watermarked images with YOLO labels, train a YOLOv11 model to detect logos/watermarks, and remove them using inpainting.
- Training is done in Colab with Google Drive for storage.
- Testing visualizes detection and watermark removal results on sample validation images.
Citations:
[1] https://huggingface.co/templates/model-card-example/blob/f0ce9d5d178c10e164d406868f72b1f2f2158cde/README.md
[2] https://github.com/huggingface/datasets/blob/main/templates/README_guide.md
[3] https://huggingface.co/docs/hub/en/model-cards
[4] https://huggingface.co/templates/model-card-example/blame/f0ce9d5d178c10e164d406868f72b1f2f2158cde/README.md
[5] https://machinelearninglibrarian.substack.com/p/2023-03-07-readme-templatehtml
[6] https://huggingface.co/templates/model-card-example/commit/f0ce9d5d178c10e164d406868f72b1f2f2158cde
[7] https://huggingface.co/learn/llm-course/en/chapter4/4
[8] https://huggingface.co/SEBIS/code_trans_t5_base_code_documentation_generation_ruby/blame/2a39c4e86977714a6ed4aab478098a43e9751e05/README.md
|
Speedsy/turkish-multilingual-e5-small-32768-colbert-cleaned-data-5000 | Speedsy | 2025-05-24T17:10:12Z | 0 | 0 | PyLate | [
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:443147",
"loss:Distillation",
"en",
"dataset:Speedsy/msmarco-cleaned-gemini-bge",
"arxiv:1908.10084",
"base_model:Speedsy/turkish-multilingual-e5-small-32768",
"base_model:finetune:Speedsy/turkish-multilingual-e5-small-32768",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-24T17:09:44Z | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:443147
- loss:Distillation
base_model: Speedsy/turkish-multilingual-e5-small-32768
datasets:
- Speedsy/msmarco-cleaned-gemini-bge
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: PyLate model based on Speedsy/turkish-multilingual-e5-small-32768
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.88
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.9
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.96
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.88
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.6266666666666666
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.596
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.514
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.11798996781634019
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.17738021477188695
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.2561076370484116
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.360165826526061
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6553145026579724
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.901888888888889
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.49985228626574496
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.3
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.46
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.54
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.6
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.3
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.16399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.102
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.1334126984126984
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.295015873015873
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.3793492063492063
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.46046031746031746
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.3534253780515539
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.40005555555555555
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.2852501803367246
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.86
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.94
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.94
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.86
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.48666666666666664
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.308
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.16999999999999996
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.43
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.73
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.77
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.85
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.8033259316397426
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8995238095238096
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7378309950921315
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.44
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.54
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.44
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12400000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.44
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.54
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.62
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.7
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5589986700098885
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.5154444444444444
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5268816907881856
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.64
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.68
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.76
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.64
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.2333333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15600000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.61
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.65
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.72
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.74
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6798342399038113
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6808571428571429
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6580867765224327
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.58
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.66
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.74
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.27999999999999997
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.228
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.14400000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.07566666666666666
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.17166666666666663
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.23266666666666666
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.2936666666666667
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2934094174823163
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.48577777777777775
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.22742907716111024
name: Maxsim Map@100
- task:
type: pylate-custom-nano-beir
name: Pylate Custom Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.58
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.6833333333333332
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7466666666666667
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7999999999999999
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.58
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.33777777777777773
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2626666666666667
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18033333333333332
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.30117822214928425
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.4273437924090711
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.4963539183440475
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5673821351088408
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5573846899575475
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6472579365079366
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4892218343610549
name: Maxsim Map@100
---
# PyLate model based on Speedsy/turkish-multilingual-e5-small-32768
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [Speedsy/turkish-multilingual-e5-small-32768](https://huggingface.co/Speedsy/turkish-multilingual-e5-small-32768) on the [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [Speedsy/turkish-multilingual-e5-small-32768](https://huggingface.co/Speedsy/turkish-multilingual-e5-small-32768) <!-- at revision ba976d0c3161ecbf2873e2666572ba658ebbc35a -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoDBPedia', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNQ', 'NanoSCIDOCS']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoDBPedia | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNQ | NanoSCIDOCS |
|:--------------------|:------------|:-------------|:-------------|:------------|:-----------|:------------|
| MaxSim_accuracy@1 | 0.88 | 0.3 | 0.86 | 0.44 | 0.64 | 0.36 |
| MaxSim_accuracy@3 | 0.9 | 0.46 | 0.94 | 0.54 | 0.68 | 0.58 |
| MaxSim_accuracy@5 | 0.96 | 0.54 | 0.94 | 0.62 | 0.76 | 0.66 |
| MaxSim_accuracy@10 | 0.98 | 0.6 | 0.98 | 0.7 | 0.8 | 0.74 |
| MaxSim_precision@1 | 0.88 | 0.3 | 0.86 | 0.44 | 0.64 | 0.36 |
| MaxSim_precision@3 | 0.6267 | 0.22 | 0.4867 | 0.18 | 0.2333 | 0.28 |
| MaxSim_precision@5 | 0.596 | 0.164 | 0.308 | 0.124 | 0.156 | 0.228 |
| MaxSim_precision@10 | 0.514 | 0.102 | 0.17 | 0.07 | 0.082 | 0.144 |
| MaxSim_recall@1 | 0.118 | 0.1334 | 0.43 | 0.44 | 0.61 | 0.0757 |
| MaxSim_recall@3 | 0.1774 | 0.295 | 0.73 | 0.54 | 0.65 | 0.1717 |
| MaxSim_recall@5 | 0.2561 | 0.3793 | 0.77 | 0.62 | 0.72 | 0.2327 |
| MaxSim_recall@10 | 0.3602 | 0.4605 | 0.85 | 0.7 | 0.74 | 0.2937 |
| **MaxSim_ndcg@10** | **0.6553** | **0.3534** | **0.8033** | **0.559** | **0.6798** | **0.2934** |
| MaxSim_mrr@10 | 0.9019 | 0.4001 | 0.8995 | 0.5154 | 0.6809 | 0.4858 |
| MaxSim_map@100 | 0.4999 | 0.2853 | 0.7378 | 0.5269 | 0.6581 | 0.2274 |
#### Pylate Custom Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate_nano_beir_evaluator.PylateCustomNanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.58 |
| MaxSim_accuracy@3 | 0.6833 |
| MaxSim_accuracy@5 | 0.7467 |
| MaxSim_accuracy@10 | 0.8 |
| MaxSim_precision@1 | 0.58 |
| MaxSim_precision@3 | 0.3378 |
| MaxSim_precision@5 | 0.2627 |
| MaxSim_precision@10 | 0.1803 |
| MaxSim_recall@1 | 0.3012 |
| MaxSim_recall@3 | 0.4273 |
| MaxSim_recall@5 | 0.4964 |
| MaxSim_recall@10 | 0.5674 |
| **MaxSim_ndcg@10** | **0.5574** |
| MaxSim_mrr@10 | 0.6473 |
| MaxSim_map@100 | 0.4892 |
<!--
## Bias, Risks and Limitations
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### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge) at [1072b6b](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge/tree/1072b6b861227168a6c8006e51d4aa8e541b64e6)
* Size: 443,147 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 5 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
| <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
| <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> |
| <code>1069432</code> | <code>['3724008', '314949', '8657336', '7420456', '879004', ...]</code> | <code>[1.0, 0.3706032931804657, 0.3508036434650421, 0.2823200523853302, 0.17563475668430328, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | NanoDBPedia_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
|:------:|:----:|:-------------:|:--------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------:|:--------------------------:|:----------------------------:|
| 0.0007 | 20 | 0.0324 | - | - | - | - | - | - | - |
| 0.0014 | 40 | 0.0293 | - | - | - | - | - | - | - |
| 0.0022 | 60 | 0.0296 | - | - | - | - | - | - | - |
| 0.0029 | 80 | 0.0282 | - | - | - | - | - | - | - |
| 0.0036 | 100 | 0.0298 | - | - | - | - | - | - | - |
| 0.0043 | 120 | 0.0281 | - | - | - | - | - | - | - |
| 0.0051 | 140 | 0.0285 | - | - | - | - | - | - | - |
| 0.0058 | 160 | 0.0275 | - | - | - | - | - | - | - |
| 0.0065 | 180 | 0.0289 | - | - | - | - | - | - | - |
| 0.0072 | 200 | 0.0276 | - | - | - | - | - | - | - |
| 0.0079 | 220 | 0.0276 | - | - | - | - | - | - | - |
| 0.0087 | 240 | 0.0269 | - | - | - | - | - | - | - |
| 0.0094 | 260 | 0.0248 | - | - | - | - | - | - | - |
| 0.0101 | 280 | 0.0254 | - | - | - | - | - | - | - |
| 0.0108 | 300 | 0.0248 | - | - | - | - | - | - | - |
| 0.0116 | 320 | 0.0248 | - | - | - | - | - | - | - |
| 0.0123 | 340 | 0.0246 | - | - | - | - | - | - | - |
| 0.0130 | 360 | 0.0257 | - | - | - | - | - | - | - |
| 0.0137 | 380 | 0.0243 | - | - | - | - | - | - | - |
| 0.0144 | 400 | 0.025 | - | - | - | - | - | - | - |
| 0.0152 | 420 | 0.0243 | - | - | - | - | - | - | - |
| 0.0159 | 440 | 0.0247 | - | - | - | - | - | - | - |
| 0.0166 | 460 | 0.0261 | - | - | - | - | - | - | - |
| 0.0173 | 480 | 0.0232 | - | - | - | - | - | - | - |
| 0.0181 | 500 | 0.0239 | 0.6474 | 0.3140 | 0.7666 | 0.5267 | 0.6014 | 0.2568 | 0.5188 |
| 0.0188 | 520 | 0.0251 | - | - | - | - | - | - | - |
| 0.0195 | 540 | 0.0242 | - | - | - | - | - | - | - |
| 0.0202 | 560 | 0.0243 | - | - | - | - | - | - | - |
| 0.0209 | 580 | 0.0238 | - | - | - | - | - | - | - |
| 0.0217 | 600 | 0.0228 | - | - | - | - | - | - | - |
| 0.0224 | 620 | 0.0243 | - | - | - | - | - | - | - |
| 0.0231 | 640 | 0.0228 | - | - | - | - | - | - | - |
| 0.0238 | 660 | 0.0237 | - | - | - | - | - | - | - |
| 0.0246 | 680 | 0.0239 | - | - | - | - | - | - | - |
| 0.0253 | 700 | 0.0238 | - | - | - | - | - | - | - |
| 0.0260 | 720 | 0.0248 | - | - | - | - | - | - | - |
| 0.0267 | 740 | 0.0234 | - | - | - | - | - | - | - |
| 0.0274 | 760 | 0.0242 | - | - | - | - | - | - | - |
| 0.0282 | 780 | 0.0238 | - | - | - | - | - | - | - |
| 0.0289 | 800 | 0.0224 | - | - | - | - | - | - | - |
| 0.0296 | 820 | 0.0237 | - | - | - | - | - | - | - |
| 0.0303 | 840 | 0.0238 | - | - | - | - | - | - | - |
| 0.0311 | 860 | 0.0234 | - | - | - | - | - | - | - |
| 0.0318 | 880 | 0.0238 | - | - | - | - | - | - | - |
| 0.0325 | 900 | 0.023 | - | - | - | - | - | - | - |
| 0.0332 | 920 | 0.0239 | - | - | - | - | - | - | - |
| 0.0339 | 940 | 0.0232 | - | - | - | - | - | - | - |
| 0.0347 | 960 | 0.0239 | - | - | - | - | - | - | - |
| 0.0354 | 980 | 0.0239 | - | - | - | - | - | - | - |
| 0.0361 | 1000 | 0.0241 | 0.6389 | 0.3160 | 0.7573 | 0.5378 | 0.5876 | 0.2993 | 0.5228 |
| 0.0368 | 1020 | 0.0234 | - | - | - | - | - | - | - |
| 0.0375 | 1040 | 0.0229 | - | - | - | - | - | - | - |
| 0.0383 | 1060 | 0.0236 | - | - | - | - | - | - | - |
| 0.0390 | 1080 | 0.0232 | - | - | - | - | - | - | - |
| 0.0397 | 1100 | 0.0236 | - | - | - | - | - | - | - |
| 0.0404 | 1120 | 0.0236 | - | - | - | - | - | - | - |
| 0.0412 | 1140 | 0.022 | - | - | - | - | - | - | - |
| 0.0419 | 1160 | 0.0217 | - | - | - | - | - | - | - |
| 0.0426 | 1180 | 0.0233 | - | - | - | - | - | - | - |
| 0.0433 | 1200 | 0.0234 | - | - | - | - | - | - | - |
| 0.0440 | 1220 | 0.0233 | - | - | - | - | - | - | - |
| 0.0448 | 1240 | 0.0235 | - | - | - | - | - | - | - |
| 0.0455 | 1260 | 0.0242 | - | - | - | - | - | - | - |
| 0.0462 | 1280 | 0.0236 | - | - | - | - | - | - | - |
| 0.0469 | 1300 | 0.023 | - | - | - | - | - | - | - |
| 0.0477 | 1320 | 0.0233 | - | - | - | - | - | - | - |
| 0.0484 | 1340 | 0.0232 | - | - | - | - | - | - | - |
| 0.0491 | 1360 | 0.0225 | - | - | - | - | - | - | - |
| 0.0498 | 1380 | 0.0215 | - | - | - | - | - | - | - |
| 0.0505 | 1400 | 0.0212 | - | - | - | - | - | - | - |
| 0.0513 | 1420 | 0.0222 | - | - | - | - | - | - | - |
| 0.0520 | 1440 | 0.0229 | - | - | - | - | - | - | - |
| 0.0527 | 1460 | 0.0225 | - | - | - | - | - | - | - |
| 0.0534 | 1480 | 0.0249 | - | - | - | - | - | - | - |
| 0.0542 | 1500 | 0.0234 | 0.6643 | 0.3292 | 0.7842 | 0.5483 | 0.6179 | 0.2975 | 0.5402 |
| 0.0549 | 1520 | 0.0236 | - | - | - | - | - | - | - |
| 0.0556 | 1540 | 0.021 | - | - | - | - | - | - | - |
| 0.0563 | 1560 | 0.0226 | - | - | - | - | - | - | - |
| 0.0570 | 1580 | 0.0236 | - | - | - | - | - | - | - |
| 0.0578 | 1600 | 0.0208 | - | - | - | - | - | - | - |
| 0.0585 | 1620 | 0.0216 | - | - | - | - | - | - | - |
| 0.0592 | 1640 | 0.0231 | - | - | - | - | - | - | - |
| 0.0599 | 1660 | 0.0225 | - | - | - | - | - | - | - |
| 0.0607 | 1680 | 0.0219 | - | - | - | - | - | - | - |
| 0.0614 | 1700 | 0.0213 | - | - | - | - | - | - | - |
| 0.0621 | 1720 | 0.0223 | - | - | - | - | - | - | - |
| 0.0628 | 1740 | 0.0234 | - | - | - | - | - | - | - |
| 0.0635 | 1760 | 0.0217 | - | - | - | - | - | - | - |
| 0.0643 | 1780 | 0.023 | - | - | - | - | - | - | - |
| 0.0650 | 1800 | 0.0231 | - | - | - | - | - | - | - |
| 0.0657 | 1820 | 0.0224 | - | - | - | - | - | - | - |
| 0.0664 | 1840 | 0.0229 | - | - | - | - | - | - | - |
| 0.0672 | 1860 | 0.0221 | - | - | - | - | - | - | - |
| 0.0679 | 1880 | 0.0221 | - | - | - | - | - | - | - |
| 0.0686 | 1900 | 0.0228 | - | - | - | - | - | - | - |
| 0.0693 | 1920 | 0.0217 | - | - | - | - | - | - | - |
| 0.0700 | 1940 | 0.024 | - | - | - | - | - | - | - |
| 0.0708 | 1960 | 0.0232 | - | - | - | - | - | - | - |
| 0.0715 | 1980 | 0.023 | - | - | - | - | - | - | - |
| 0.0722 | 2000 | 0.0232 | 0.6557 | 0.3446 | 0.7881 | 0.5640 | 0.6351 | 0.2824 | 0.5450 |
| 0.0729 | 2020 | 0.0229 | - | - | - | - | - | - | - |
| 0.0737 | 2040 | 0.0221 | - | - | - | - | - | - | - |
| 0.0744 | 2060 | 0.0221 | - | - | - | - | - | - | - |
| 0.0751 | 2080 | 0.0222 | - | - | - | - | - | - | - |
| 0.0758 | 2100 | 0.0223 | - | - | - | - | - | - | - |
| 0.0765 | 2120 | 0.0237 | - | - | - | - | - | - | - |
| 0.0773 | 2140 | 0.0227 | - | - | - | - | - | - | - |
| 0.0780 | 2160 | 0.0233 | - | - | - | - | - | - | - |
| 0.0787 | 2180 | 0.0228 | - | - | - | - | - | - | - |
| 0.0794 | 2200 | 0.0213 | - | - | - | - | - | - | - |
| 0.0802 | 2220 | 0.0222 | - | - | - | - | - | - | - |
| 0.0809 | 2240 | 0.0231 | - | - | - | - | - | - | - |
| 0.0816 | 2260 | 0.0225 | - | - | - | - | - | - | - |
| 0.0823 | 2280 | 0.0234 | - | - | - | - | - | - | - |
| 0.0830 | 2300 | 0.0222 | - | - | - | - | - | - | - |
| 0.0838 | 2320 | 0.0225 | - | - | - | - | - | - | - |
| 0.0845 | 2340 | 0.0224 | - | - | - | - | - | - | - |
| 0.0852 | 2360 | 0.0217 | - | - | - | - | - | - | - |
| 0.0859 | 2380 | 0.0217 | - | - | - | - | - | - | - |
| 0.0867 | 2400 | 0.0228 | - | - | - | - | - | - | - |
| 0.0874 | 2420 | 0.0228 | - | - | - | - | - | - | - |
| 0.0881 | 2440 | 0.0229 | - | - | - | - | - | - | - |
| 0.0888 | 2460 | 0.0223 | - | - | - | - | - | - | - |
| 0.0895 | 2480 | 0.0215 | - | - | - | - | - | - | - |
| 0.0903 | 2500 | 0.0224 | 0.6657 | 0.3728 | 0.7859 | 0.5651 | 0.6248 | 0.2813 | 0.5492 |
| 0.0910 | 2520 | 0.0221 | - | - | - | - | - | - | - |
| 0.0917 | 2540 | 0.0213 | - | - | - | - | - | - | - |
| 0.0924 | 2560 | 0.0226 | - | - | - | - | - | - | - |
| 0.0932 | 2580 | 0.022 | - | - | - | - | - | - | - |
| 0.0939 | 2600 | 0.0219 | - | - | - | - | - | - | - |
| 0.0946 | 2620 | 0.0224 | - | - | - | - | - | - | - |
| 0.0953 | 2640 | 0.0222 | - | - | - | - | - | - | - |
| 0.0960 | 2660 | 0.0211 | - | - | - | - | - | - | - |
| 0.0968 | 2680 | 0.0222 | - | - | - | - | - | - | - |
| 0.0975 | 2700 | 0.0224 | - | - | - | - | - | - | - |
| 0.0982 | 2720 | 0.0215 | - | - | - | - | - | - | - |
| 0.0989 | 2740 | 0.0214 | - | - | - | - | - | - | - |
| 0.0996 | 2760 | 0.0209 | - | - | - | - | - | - | - |
| 0.1004 | 2780 | 0.0211 | - | - | - | - | - | - | - |
| 0.1011 | 2800 | 0.0229 | - | - | - | - | - | - | - |
| 0.1018 | 2820 | 0.0214 | - | - | - | - | - | - | - |
| 0.1025 | 2840 | 0.0218 | - | - | - | - | - | - | - |
| 0.1033 | 2860 | 0.0208 | - | - | - | - | - | - | - |
| 0.1040 | 2880 | 0.0235 | - | - | - | - | - | - | - |
| 0.1047 | 2900 | 0.0228 | - | - | - | - | - | - | - |
| 0.1054 | 2920 | 0.021 | - | - | - | - | - | - | - |
| 0.1061 | 2940 | 0.0207 | - | - | - | - | - | - | - |
| 0.1069 | 2960 | 0.023 | - | - | - | - | - | - | - |
| 0.1076 | 2980 | 0.0213 | - | - | - | - | - | - | - |
| 0.1083 | 3000 | 0.022 | 0.6615 | 0.3599 | 0.7818 | 0.5325 | 0.6693 | 0.2927 | 0.5496 |
| 0.1090 | 3020 | 0.0218 | - | - | - | - | - | - | - |
| 0.1098 | 3040 | 0.0236 | - | - | - | - | - | - | - |
| 0.1105 | 3060 | 0.0211 | - | - | - | - | - | - | - |
| 0.1112 | 3080 | 0.0227 | - | - | - | - | - | - | - |
| 0.1119 | 3100 | 0.022 | - | - | - | - | - | - | - |
| 0.1126 | 3120 | 0.0223 | - | - | - | - | - | - | - |
| 0.1134 | 3140 | 0.023 | - | - | - | - | - | - | - |
| 0.1141 | 3160 | 0.0208 | - | - | - | - | - | - | - |
| 0.1148 | 3180 | 0.022 | - | - | - | - | - | - | - |
| 0.1155 | 3200 | 0.0226 | - | - | - | - | - | - | - |
| 0.1163 | 3220 | 0.0199 | - | - | - | - | - | - | - |
| 0.1170 | 3240 | 0.0221 | - | - | - | - | - | - | - |
| 0.1177 | 3260 | 0.0207 | - | - | - | - | - | - | - |
| 0.1184 | 3280 | 0.0202 | - | - | - | - | - | - | - |
| 0.1191 | 3300 | 0.0219 | - | - | - | - | - | - | - |
| 0.1199 | 3320 | 0.0212 | - | - | - | - | - | - | - |
| 0.1206 | 3340 | 0.0216 | - | - | - | - | - | - | - |
| 0.1213 | 3360 | 0.0215 | - | - | - | - | - | - | - |
| 0.1220 | 3380 | 0.0221 | - | - | - | - | - | - | - |
| 0.1228 | 3400 | 0.0237 | - | - | - | - | - | - | - |
| 0.1235 | 3420 | 0.0211 | - | - | - | - | - | - | - |
| 0.1242 | 3440 | 0.0217 | - | - | - | - | - | - | - |
| 0.1249 | 3460 | 0.0218 | - | - | - | - | - | - | - |
| 0.1256 | 3480 | 0.0204 | - | - | - | - | - | - | - |
| 0.1264 | 3500 | 0.0213 | 0.6531 | 0.3612 | 0.8067 | 0.5404 | 0.6415 | 0.2740 | 0.5461 |
| 0.1271 | 3520 | 0.0202 | - | - | - | - | - | - | - |
| 0.1278 | 3540 | 0.0209 | - | - | - | - | - | - | - |
| 0.1285 | 3560 | 0.022 | - | - | - | - | - | - | - |
| 0.1293 | 3580 | 0.021 | - | - | - | - | - | - | - |
| 0.1300 | 3600 | 0.0224 | - | - | - | - | - | - | - |
| 0.1307 | 3620 | 0.0216 | - | - | - | - | - | - | - |
| 0.1314 | 3640 | 0.0216 | - | - | - | - | - | - | - |
| 0.1321 | 3660 | 0.0224 | - | - | - | - | - | - | - |
| 0.1329 | 3680 | 0.0203 | - | - | - | - | - | - | - |
| 0.1336 | 3700 | 0.0223 | - | - | - | - | - | - | - |
| 0.1343 | 3720 | 0.0209 | - | - | - | - | - | - | - |
| 0.1350 | 3740 | 0.0221 | - | - | - | - | - | - | - |
| 0.1358 | 3760 | 0.0213 | - | - | - | - | - | - | - |
| 0.1365 | 3780 | 0.0217 | - | - | - | - | - | - | - |
| 0.1372 | 3800 | 0.0215 | - | - | - | - | - | - | - |
| 0.1379 | 3820 | 0.0227 | - | - | - | - | - | - | - |
| 0.1386 | 3840 | 0.0213 | - | - | - | - | - | - | - |
| 0.1394 | 3860 | 0.0204 | - | - | - | - | - | - | - |
| 0.1401 | 3880 | 0.0217 | - | - | - | - | - | - | - |
| 0.1408 | 3900 | 0.0216 | - | - | - | - | - | - | - |
| 0.1415 | 3920 | 0.0216 | - | - | - | - | - | - | - |
| 0.1423 | 3940 | 0.021 | - | - | - | - | - | - | - |
| 0.1430 | 3960 | 0.0211 | - | - | - | - | - | - | - |
| 0.1437 | 3980 | 0.0204 | - | - | - | - | - | - | - |
| 0.1444 | 4000 | 0.022 | 0.6493 | 0.3371 | 0.8002 | 0.5415 | 0.6542 | 0.2924 | 0.5458 |
| 0.1451 | 4020 | 0.0212 | - | - | - | - | - | - | - |
| 0.1459 | 4040 | 0.0201 | - | - | - | - | - | - | - |
| 0.1466 | 4060 | 0.0199 | - | - | - | - | - | - | - |
| 0.1473 | 4080 | 0.0214 | - | - | - | - | - | - | - |
| 0.1480 | 4100 | 0.0225 | - | - | - | - | - | - | - |
| 0.1488 | 4120 | 0.0214 | - | - | - | - | - | - | - |
| 0.1495 | 4140 | 0.0204 | - | - | - | - | - | - | - |
| 0.1502 | 4160 | 0.021 | - | - | - | - | - | - | - |
| 0.1509 | 4180 | 0.0213 | - | - | - | - | - | - | - |
| 0.1516 | 4200 | 0.022 | - | - | - | - | - | - | - |
| 0.1524 | 4220 | 0.0216 | - | - | - | - | - | - | - |
| 0.1531 | 4240 | 0.0216 | - | - | - | - | - | - | - |
| 0.1538 | 4260 | 0.0218 | - | - | - | - | - | - | - |
| 0.1545 | 4280 | 0.0218 | - | - | - | - | - | - | - |
| 0.1553 | 4300 | 0.0207 | - | - | - | - | - | - | - |
| 0.1560 | 4320 | 0.0218 | - | - | - | - | - | - | - |
| 0.1567 | 4340 | 0.0211 | - | - | - | - | - | - | - |
| 0.1574 | 4360 | 0.0206 | - | - | - | - | - | - | - |
| 0.1581 | 4380 | 0.0211 | - | - | - | - | - | - | - |
| 0.1589 | 4400 | 0.021 | - | - | - | - | - | - | - |
| 0.1596 | 4420 | 0.0218 | - | - | - | - | - | - | - |
| 0.1603 | 4440 | 0.021 | - | - | - | - | - | - | - |
| 0.1610 | 4460 | 0.0217 | - | - | - | - | - | - | - |
| 0.1618 | 4480 | 0.0211 | - | - | - | - | - | - | - |
| 0.1625 | 4500 | 0.0215 | 0.6572 | 0.3641 | 0.8016 | 0.5406 | 0.6554 | 0.2867 | 0.5509 |
| 0.1632 | 4520 | 0.0225 | - | - | - | - | - | - | - |
| 0.1639 | 4540 | 0.0196 | - | - | - | - | - | - | - |
| 0.1646 | 4560 | 0.0226 | - | - | - | - | - | - | - |
| 0.1654 | 4580 | 0.0209 | - | - | - | - | - | - | - |
| 0.1661 | 4600 | 0.0204 | - | - | - | - | - | - | - |
| 0.1668 | 4620 | 0.0214 | - | - | - | - | - | - | - |
| 0.1675 | 4640 | 0.0205 | - | - | - | - | - | - | - |
| 0.1682 | 4660 | 0.022 | - | - | - | - | - | - | - |
| 0.1690 | 4680 | 0.0221 | - | - | - | - | - | - | - |
| 0.1697 | 4700 | 0.0201 | - | - | - | - | - | - | - |
| 0.1704 | 4720 | 0.0205 | - | - | - | - | - | - | - |
| 0.1711 | 4740 | 0.0208 | - | - | - | - | - | - | - |
| 0.1719 | 4760 | 0.0203 | - | - | - | - | - | - | - |
| 0.1726 | 4780 | 0.0214 | - | - | - | - | - | - | - |
| 0.1733 | 4800 | 0.0211 | - | - | - | - | - | - | - |
| 0.1740 | 4820 | 0.0205 | - | - | - | - | - | - | - |
| 0.1747 | 4840 | 0.0192 | - | - | - | - | - | - | - |
| 0.1755 | 4860 | 0.0196 | - | - | - | - | - | - | - |
| 0.1762 | 4880 | 0.0212 | - | - | - | - | - | - | - |
| 0.1769 | 4900 | 0.0204 | - | - | - | - | - | - | - |
| 0.1776 | 4920 | 0.0202 | - | - | - | - | - | - | - |
| 0.1784 | 4940 | 0.0222 | - | - | - | - | - | - | - |
| 0.1791 | 4960 | 0.0213 | - | - | - | - | - | - | - |
| 0.1798 | 4980 | 0.0219 | - | - | - | - | - | - | - |
| 0.1805 | 5000 | 0.0209 | 0.6553 | 0.3534 | 0.8033 | 0.5590 | 0.6798 | 0.2934 | 0.5574 |
</details>
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.0.2
- PyLate: 1.2.0
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
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## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
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Speedsy/turkish-multilingual-e5-small-32768-colbert-cleaned-data-3000 | Speedsy | 2025-05-24T17:09:39Z | 0 | 0 | PyLate | [
"PyLate",
"safetensors",
"bert",
"ColBERT",
"sentence-transformers",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:443147",
"loss:Distillation",
"en",
"dataset:Speedsy/msmarco-cleaned-gemini-bge",
"arxiv:1908.10084",
"base_model:Speedsy/turkish-multilingual-e5-small-32768",
"base_model:finetune:Speedsy/turkish-multilingual-e5-small-32768",
"model-index",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
]
| sentence-similarity | 2025-05-24T17:09:26Z | ---
language:
- en
tags:
- ColBERT
- PyLate
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:443147
- loss:Distillation
base_model: Speedsy/turkish-multilingual-e5-small-32768
datasets:
- Speedsy/msmarco-cleaned-gemini-bge
pipeline_tag: sentence-similarity
library_name: PyLate
metrics:
- MaxSim_accuracy@1
- MaxSim_accuracy@3
- MaxSim_accuracy@5
- MaxSim_accuracy@10
- MaxSim_precision@1
- MaxSim_precision@3
- MaxSim_precision@5
- MaxSim_precision@10
- MaxSim_recall@1
- MaxSim_recall@3
- MaxSim_recall@5
- MaxSim_recall@10
- MaxSim_ndcg@10
- MaxSim_mrr@10
- MaxSim_map@100
model-index:
- name: PyLate model based on Speedsy/turkish-multilingual-e5-small-32768
results:
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoDBPedia
type: NanoDBPedia
metrics:
- type: MaxSim_accuracy@1
value: 0.82
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.92
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.96
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.96
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.82
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.66
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.596
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.526
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.10679468162105399
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.18195083062926753
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.25503006946810225
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.37522649889420306
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6615489445157842
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8766666666666666
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.5095874668233052
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoFiQA2018
type: NanoFiQA2018
metrics:
- type: MaxSim_accuracy@1
value: 0.32
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.48
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.54
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.6
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.32
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.22
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.16399999999999998
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.096
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.18719047619047618
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.30646031746031743
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.372015873015873
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.41957142857142854
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.35989247410741526
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4125555555555555
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.3126284885543055
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoHotpotQA
type: NanoHotpotQA
metrics:
- type: MaxSim_accuracy@1
value: 0.76
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.94
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.94
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.98
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.76
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.4933333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.316
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.172
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.38
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.74
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.79
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.86
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.781818462525267
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.8461904761904762
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.7096310944667722
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoMSMARCO
type: NanoMSMARCO
metrics:
- type: MaxSim_accuracy@1
value: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.56
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.62
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.72
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.18666666666666668
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.12400000000000003
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.07200000000000001
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.36
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.56
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.62
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.72
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5325090217718634
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.4734999999999999
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.4836765499650687
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoNQ
type: NanoNQ
metrics:
- type: MaxSim_accuracy@1
value: 0.6
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.7
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.74
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.8
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.6
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.24
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.15200000000000002
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.08199999999999999
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.57
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.68
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.71
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.74
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.6692956138360552
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6647142857142856
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.6454941704322509
name: Maxsim Map@100
- task:
type: py-late-information-retrieval
name: Py Late Information Retrieval
dataset:
name: NanoSCIDOCS
type: NanoSCIDOCS
metrics:
- type: MaxSim_accuracy@1
value: 0.36
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.52
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.56
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.72
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.36
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.26
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.18799999999999997
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.15
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.07566666666666666
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.15966666666666668
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.19166666666666665
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.30666666666666664
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.2926617367732324
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.46734920634920635
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.2213156153898327
name: Maxsim Map@100
- task:
type: pylate-custom-nano-beir
name: Pylate Custom Nano BEIR
dataset:
name: NanoBEIR mean
type: NanoBEIR_mean
metrics:
- type: MaxSim_accuracy@1
value: 0.5366666666666666
name: Maxsim Accuracy@1
- type: MaxSim_accuracy@3
value: 0.6866666666666665
name: Maxsim Accuracy@3
- type: MaxSim_accuracy@5
value: 0.7266666666666666
name: Maxsim Accuracy@5
- type: MaxSim_accuracy@10
value: 0.7966666666666665
name: Maxsim Accuracy@10
- type: MaxSim_precision@1
value: 0.5366666666666666
name: Maxsim Precision@1
- type: MaxSim_precision@3
value: 0.3433333333333333
name: Maxsim Precision@3
- type: MaxSim_precision@5
value: 0.2566666666666667
name: Maxsim Precision@5
- type: MaxSim_precision@10
value: 0.18300000000000002
name: Maxsim Precision@10
- type: MaxSim_recall@1
value: 0.2799419707463661
name: Maxsim Recall@1
- type: MaxSim_recall@3
value: 0.438012969126042
name: Maxsim Recall@3
- type: MaxSim_recall@5
value: 0.4897854348584403
name: Maxsim Recall@5
- type: MaxSim_recall@10
value: 0.5702440990220498
name: Maxsim Recall@10
- type: MaxSim_ndcg@10
value: 0.5496210422549362
name: Maxsim Ndcg@10
- type: MaxSim_mrr@10
value: 0.6234960317460316
name: Maxsim Mrr@10
- type: MaxSim_map@100
value: 0.48038889760525577
name: Maxsim Map@100
---
# PyLate model based on Speedsy/turkish-multilingual-e5-small-32768
This is a [PyLate](https://github.com/lightonai/pylate) model finetuned from [Speedsy/turkish-multilingual-e5-small-32768](https://huggingface.co/Speedsy/turkish-multilingual-e5-small-32768) on the [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge) dataset. It maps sentences & paragraphs to sequences of 128-dimensional dense vectors and can be used for semantic textual similarity using the MaxSim operator.
## Model Details
### Model Description
- **Model Type:** PyLate model
- **Base model:** [Speedsy/turkish-multilingual-e5-small-32768](https://huggingface.co/Speedsy/turkish-multilingual-e5-small-32768) <!-- at revision ba976d0c3161ecbf2873e2666572ba658ebbc35a -->
- **Document Length:** 180 tokens
- **Query Length:** 32 tokens
- **Output Dimensionality:** 128 tokens
- **Similarity Function:** MaxSim
- **Training Dataset:**
- [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge)
- **Language:** en
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [PyLate Documentation](https://lightonai.github.io/pylate/)
- **Repository:** [PyLate on GitHub](https://github.com/lightonai/pylate)
- **Hugging Face:** [PyLate models on Hugging Face](https://huggingface.co/models?library=PyLate)
### Full Model Architecture
```
ColBERT(
(0): Transformer({'max_seq_length': 179, 'do_lower_case': False}) with Transformer model: BertModel
(1): Dense({'in_features': 384, 'out_features': 128, 'bias': False, 'activation_function': 'torch.nn.modules.linear.Identity'})
)
```
## Usage
First install the PyLate library:
```bash
pip install -U pylate
```
### Retrieval
PyLate provides a streamlined interface to index and retrieve documents using ColBERT models. The index leverages the Voyager HNSW index to efficiently handle document embeddings and enable fast retrieval.
#### Indexing documents
First, load the ColBERT model and initialize the Voyager index, then encode and index your documents:
```python
from pylate import indexes, models, retrieve
# Step 1: Load the ColBERT model
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
# Step 2: Initialize the Voyager index
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
override=True, # This overwrites the existing index if any
)
# Step 3: Encode the documents
documents_ids = ["1", "2", "3"]
documents = ["document 1 text", "document 2 text", "document 3 text"]
documents_embeddings = model.encode(
documents,
batch_size=32,
is_query=False, # Ensure that it is set to False to indicate that these are documents, not queries
show_progress_bar=True,
)
# Step 4: Add document embeddings to the index by providing embeddings and corresponding ids
index.add_documents(
documents_ids=documents_ids,
documents_embeddings=documents_embeddings,
)
```
Note that you do not have to recreate the index and encode the documents every time. Once you have created an index and added the documents, you can re-use the index later by loading it:
```python
# To load an index, simply instantiate it with the correct folder/name and without overriding it
index = indexes.Voyager(
index_folder="pylate-index",
index_name="index",
)
```
#### Retrieving top-k documents for queries
Once the documents are indexed, you can retrieve the top-k most relevant documents for a given set of queries.
To do so, initialize the ColBERT retriever with the index you want to search in, encode the queries and then retrieve the top-k documents to get the top matches ids and relevance scores:
```python
# Step 1: Initialize the ColBERT retriever
retriever = retrieve.ColBERT(index=index)
# Step 2: Encode the queries
queries_embeddings = model.encode(
["query for document 3", "query for document 1"],
batch_size=32,
is_query=True, # # Ensure that it is set to False to indicate that these are queries
show_progress_bar=True,
)
# Step 3: Retrieve top-k documents
scores = retriever.retrieve(
queries_embeddings=queries_embeddings,
k=10, # Retrieve the top 10 matches for each query
)
```
### Reranking
If you only want to use the ColBERT model to perform reranking on top of your first-stage retrieval pipeline without building an index, you can simply use rank function and pass the queries and documents to rerank:
```python
from pylate import rank, models
queries = [
"query A",
"query B",
]
documents = [
["document A", "document B"],
["document 1", "document C", "document B"],
]
documents_ids = [
[1, 2],
[1, 3, 2],
]
model = models.ColBERT(
model_name_or_path=pylate_model_id,
)
queries_embeddings = model.encode(
queries,
is_query=True,
)
documents_embeddings = model.encode(
documents,
is_query=False,
)
reranked_documents = rank.rerank(
documents_ids=documents_ids,
queries_embeddings=queries_embeddings,
documents_embeddings=documents_embeddings,
)
```
<!--
### Direct Usage (Transformers)
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</details>
-->
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### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Evaluation
### Metrics
#### Py Late Information Retrieval
* Dataset: `['NanoDBPedia', 'NanoFiQA2018', 'NanoHotpotQA', 'NanoMSMARCO', 'NanoNQ', 'NanoSCIDOCS']`
* Evaluated with <code>pylate.evaluation.pylate_information_retrieval_evaluator.PyLateInformationRetrievalEvaluator</code>
| Metric | NanoDBPedia | NanoFiQA2018 | NanoHotpotQA | NanoMSMARCO | NanoNQ | NanoSCIDOCS |
|:--------------------|:------------|:-------------|:-------------|:------------|:-----------|:------------|
| MaxSim_accuracy@1 | 0.82 | 0.32 | 0.76 | 0.36 | 0.6 | 0.36 |
| MaxSim_accuracy@3 | 0.92 | 0.48 | 0.94 | 0.56 | 0.7 | 0.52 |
| MaxSim_accuracy@5 | 0.96 | 0.54 | 0.94 | 0.62 | 0.74 | 0.56 |
| MaxSim_accuracy@10 | 0.96 | 0.6 | 0.98 | 0.72 | 0.8 | 0.72 |
| MaxSim_precision@1 | 0.82 | 0.32 | 0.76 | 0.36 | 0.6 | 0.36 |
| MaxSim_precision@3 | 0.66 | 0.22 | 0.4933 | 0.1867 | 0.24 | 0.26 |
| MaxSim_precision@5 | 0.596 | 0.164 | 0.316 | 0.124 | 0.152 | 0.188 |
| MaxSim_precision@10 | 0.526 | 0.096 | 0.172 | 0.072 | 0.082 | 0.15 |
| MaxSim_recall@1 | 0.1068 | 0.1872 | 0.38 | 0.36 | 0.57 | 0.0757 |
| MaxSim_recall@3 | 0.182 | 0.3065 | 0.74 | 0.56 | 0.68 | 0.1597 |
| MaxSim_recall@5 | 0.255 | 0.372 | 0.79 | 0.62 | 0.71 | 0.1917 |
| MaxSim_recall@10 | 0.3752 | 0.4196 | 0.86 | 0.72 | 0.74 | 0.3067 |
| **MaxSim_ndcg@10** | **0.6615** | **0.3599** | **0.7818** | **0.5325** | **0.6693** | **0.2927** |
| MaxSim_mrr@10 | 0.8767 | 0.4126 | 0.8462 | 0.4735 | 0.6647 | 0.4673 |
| MaxSim_map@100 | 0.5096 | 0.3126 | 0.7096 | 0.4837 | 0.6455 | 0.2213 |
#### Pylate Custom Nano BEIR
* Dataset: `NanoBEIR_mean`
* Evaluated with <code>pylate_nano_beir_evaluator.PylateCustomNanoBEIREvaluator</code>
| Metric | Value |
|:--------------------|:-----------|
| MaxSim_accuracy@1 | 0.5367 |
| MaxSim_accuracy@3 | 0.6867 |
| MaxSim_accuracy@5 | 0.7267 |
| MaxSim_accuracy@10 | 0.7967 |
| MaxSim_precision@1 | 0.5367 |
| MaxSim_precision@3 | 0.3433 |
| MaxSim_precision@5 | 0.2567 |
| MaxSim_precision@10 | 0.183 |
| MaxSim_recall@1 | 0.2799 |
| MaxSim_recall@3 | 0.438 |
| MaxSim_recall@5 | 0.4898 |
| MaxSim_recall@10 | 0.5702 |
| **MaxSim_ndcg@10** | **0.5496** |
| MaxSim_mrr@10 | 0.6235 |
| MaxSim_map@100 | 0.4804 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### train
* Dataset: [train](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge) at [1072b6b](https://huggingface.co/datasets/Speedsy/msmarco-cleaned-gemini-bge/tree/1072b6b861227168a6c8006e51d4aa8e541b64e6)
* Size: 443,147 training samples
* Columns: <code>query_id</code>, <code>document_ids</code>, and <code>scores</code>
* Approximate statistics based on the first 1000 samples:
| | query_id | document_ids | scores |
|:--------|:--------------------------------------------------------------------------------|:------------------------------------|:------------------------------------|
| type | string | list | list |
| details | <ul><li>min: 5 tokens</li><li>mean: 5.83 tokens</li><li>max: 6 tokens</li></ul> | <ul><li>size: 32 elements</li></ul> | <ul><li>size: 32 elements</li></ul> |
* Samples:
| query_id | document_ids | scores |
|:---------------------|:--------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------|
| <code>817836</code> | <code>['2716076', '6741935', '2681109', '5562684', '3507339', ...]</code> | <code>[1.0, 0.7059561610221863, 0.21702419221401215, 0.38270196318626404, 0.20812414586544037, ...]</code> |
| <code>1045170</code> | <code>['5088671', '2953295', '8783471', '4268439', '6339935', ...]</code> | <code>[1.0, 0.6493034362792969, 0.0692221149802208, 0.17963139712810516, 0.6697239875793457, ...]</code> |
| <code>1069432</code> | <code>['3724008', '314949', '8657336', '7420456', '879004', ...]</code> | <code>[1.0, 0.3706032931804657, 0.3508036434650421, 0.2823200523853302, 0.17563475668430328, ...]</code> |
* Loss: <code>pylate.losses.distillation.Distillation</code>
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 16
- `learning_rate`: 3e-05
- `num_train_epochs`: 1
- `bf16`: True
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 8
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 3e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss | NanoDBPedia_MaxSim_ndcg@10 | NanoFiQA2018_MaxSim_ndcg@10 | NanoHotpotQA_MaxSim_ndcg@10 | NanoMSMARCO_MaxSim_ndcg@10 | NanoNQ_MaxSim_ndcg@10 | NanoSCIDOCS_MaxSim_ndcg@10 | NanoBEIR_mean_MaxSim_ndcg@10 |
|:------:|:----:|:-------------:|:--------------------------:|:---------------------------:|:---------------------------:|:--------------------------:|:---------------------:|:--------------------------:|:----------------------------:|
| 0.0007 | 20 | 0.0324 | - | - | - | - | - | - | - |
| 0.0014 | 40 | 0.0293 | - | - | - | - | - | - | - |
| 0.0022 | 60 | 0.0296 | - | - | - | - | - | - | - |
| 0.0029 | 80 | 0.0282 | - | - | - | - | - | - | - |
| 0.0036 | 100 | 0.0298 | - | - | - | - | - | - | - |
| 0.0043 | 120 | 0.0281 | - | - | - | - | - | - | - |
| 0.0051 | 140 | 0.0285 | - | - | - | - | - | - | - |
| 0.0058 | 160 | 0.0275 | - | - | - | - | - | - | - |
| 0.0065 | 180 | 0.0289 | - | - | - | - | - | - | - |
| 0.0072 | 200 | 0.0276 | - | - | - | - | - | - | - |
| 0.0079 | 220 | 0.0276 | - | - | - | - | - | - | - |
| 0.0087 | 240 | 0.0269 | - | - | - | - | - | - | - |
| 0.0094 | 260 | 0.0248 | - | - | - | - | - | - | - |
| 0.0101 | 280 | 0.0254 | - | - | - | - | - | - | - |
| 0.0108 | 300 | 0.0248 | - | - | - | - | - | - | - |
| 0.0116 | 320 | 0.0248 | - | - | - | - | - | - | - |
| 0.0123 | 340 | 0.0246 | - | - | - | - | - | - | - |
| 0.0130 | 360 | 0.0257 | - | - | - | - | - | - | - |
| 0.0137 | 380 | 0.0243 | - | - | - | - | - | - | - |
| 0.0144 | 400 | 0.025 | - | - | - | - | - | - | - |
| 0.0152 | 420 | 0.0243 | - | - | - | - | - | - | - |
| 0.0159 | 440 | 0.0247 | - | - | - | - | - | - | - |
| 0.0166 | 460 | 0.0261 | - | - | - | - | - | - | - |
| 0.0173 | 480 | 0.0232 | - | - | - | - | - | - | - |
| 0.0181 | 500 | 0.0239 | 0.6474 | 0.3140 | 0.7666 | 0.5267 | 0.6014 | 0.2568 | 0.5188 |
| 0.0188 | 520 | 0.0251 | - | - | - | - | - | - | - |
| 0.0195 | 540 | 0.0242 | - | - | - | - | - | - | - |
| 0.0202 | 560 | 0.0243 | - | - | - | - | - | - | - |
| 0.0209 | 580 | 0.0238 | - | - | - | - | - | - | - |
| 0.0217 | 600 | 0.0228 | - | - | - | - | - | - | - |
| 0.0224 | 620 | 0.0243 | - | - | - | - | - | - | - |
| 0.0231 | 640 | 0.0228 | - | - | - | - | - | - | - |
| 0.0238 | 660 | 0.0237 | - | - | - | - | - | - | - |
| 0.0246 | 680 | 0.0239 | - | - | - | - | - | - | - |
| 0.0253 | 700 | 0.0238 | - | - | - | - | - | - | - |
| 0.0260 | 720 | 0.0248 | - | - | - | - | - | - | - |
| 0.0267 | 740 | 0.0234 | - | - | - | - | - | - | - |
| 0.0274 | 760 | 0.0242 | - | - | - | - | - | - | - |
| 0.0282 | 780 | 0.0238 | - | - | - | - | - | - | - |
| 0.0289 | 800 | 0.0224 | - | - | - | - | - | - | - |
| 0.0296 | 820 | 0.0237 | - | - | - | - | - | - | - |
| 0.0303 | 840 | 0.0238 | - | - | - | - | - | - | - |
| 0.0311 | 860 | 0.0234 | - | - | - | - | - | - | - |
| 0.0318 | 880 | 0.0238 | - | - | - | - | - | - | - |
| 0.0325 | 900 | 0.023 | - | - | - | - | - | - | - |
| 0.0332 | 920 | 0.0239 | - | - | - | - | - | - | - |
| 0.0339 | 940 | 0.0232 | - | - | - | - | - | - | - |
| 0.0347 | 960 | 0.0239 | - | - | - | - | - | - | - |
| 0.0354 | 980 | 0.0239 | - | - | - | - | - | - | - |
| 0.0361 | 1000 | 0.0241 | 0.6389 | 0.3160 | 0.7573 | 0.5378 | 0.5876 | 0.2993 | 0.5228 |
| 0.0368 | 1020 | 0.0234 | - | - | - | - | - | - | - |
| 0.0375 | 1040 | 0.0229 | - | - | - | - | - | - | - |
| 0.0383 | 1060 | 0.0236 | - | - | - | - | - | - | - |
| 0.0390 | 1080 | 0.0232 | - | - | - | - | - | - | - |
| 0.0397 | 1100 | 0.0236 | - | - | - | - | - | - | - |
| 0.0404 | 1120 | 0.0236 | - | - | - | - | - | - | - |
| 0.0412 | 1140 | 0.022 | - | - | - | - | - | - | - |
| 0.0419 | 1160 | 0.0217 | - | - | - | - | - | - | - |
| 0.0426 | 1180 | 0.0233 | - | - | - | - | - | - | - |
| 0.0433 | 1200 | 0.0234 | - | - | - | - | - | - | - |
| 0.0440 | 1220 | 0.0233 | - | - | - | - | - | - | - |
| 0.0448 | 1240 | 0.0235 | - | - | - | - | - | - | - |
| 0.0455 | 1260 | 0.0242 | - | - | - | - | - | - | - |
| 0.0462 | 1280 | 0.0236 | - | - | - | - | - | - | - |
| 0.0469 | 1300 | 0.023 | - | - | - | - | - | - | - |
| 0.0477 | 1320 | 0.0233 | - | - | - | - | - | - | - |
| 0.0484 | 1340 | 0.0232 | - | - | - | - | - | - | - |
| 0.0491 | 1360 | 0.0225 | - | - | - | - | - | - | - |
| 0.0498 | 1380 | 0.0215 | - | - | - | - | - | - | - |
| 0.0505 | 1400 | 0.0212 | - | - | - | - | - | - | - |
| 0.0513 | 1420 | 0.0222 | - | - | - | - | - | - | - |
| 0.0520 | 1440 | 0.0229 | - | - | - | - | - | - | - |
| 0.0527 | 1460 | 0.0225 | - | - | - | - | - | - | - |
| 0.0534 | 1480 | 0.0249 | - | - | - | - | - | - | - |
| 0.0542 | 1500 | 0.0234 | 0.6643 | 0.3292 | 0.7842 | 0.5483 | 0.6179 | 0.2975 | 0.5402 |
| 0.0549 | 1520 | 0.0236 | - | - | - | - | - | - | - |
| 0.0556 | 1540 | 0.021 | - | - | - | - | - | - | - |
| 0.0563 | 1560 | 0.0226 | - | - | - | - | - | - | - |
| 0.0570 | 1580 | 0.0236 | - | - | - | - | - | - | - |
| 0.0578 | 1600 | 0.0208 | - | - | - | - | - | - | - |
| 0.0585 | 1620 | 0.0216 | - | - | - | - | - | - | - |
| 0.0592 | 1640 | 0.0231 | - | - | - | - | - | - | - |
| 0.0599 | 1660 | 0.0225 | - | - | - | - | - | - | - |
| 0.0607 | 1680 | 0.0219 | - | - | - | - | - | - | - |
| 0.0614 | 1700 | 0.0213 | - | - | - | - | - | - | - |
| 0.0621 | 1720 | 0.0223 | - | - | - | - | - | - | - |
| 0.0628 | 1740 | 0.0234 | - | - | - | - | - | - | - |
| 0.0635 | 1760 | 0.0217 | - | - | - | - | - | - | - |
| 0.0643 | 1780 | 0.023 | - | - | - | - | - | - | - |
| 0.0650 | 1800 | 0.0231 | - | - | - | - | - | - | - |
| 0.0657 | 1820 | 0.0224 | - | - | - | - | - | - | - |
| 0.0664 | 1840 | 0.0229 | - | - | - | - | - | - | - |
| 0.0672 | 1860 | 0.0221 | - | - | - | - | - | - | - |
| 0.0679 | 1880 | 0.0221 | - | - | - | - | - | - | - |
| 0.0686 | 1900 | 0.0228 | - | - | - | - | - | - | - |
| 0.0693 | 1920 | 0.0217 | - | - | - | - | - | - | - |
| 0.0700 | 1940 | 0.024 | - | - | - | - | - | - | - |
| 0.0708 | 1960 | 0.0232 | - | - | - | - | - | - | - |
| 0.0715 | 1980 | 0.023 | - | - | - | - | - | - | - |
| 0.0722 | 2000 | 0.0232 | 0.6557 | 0.3446 | 0.7881 | 0.5640 | 0.6351 | 0.2824 | 0.5450 |
| 0.0729 | 2020 | 0.0229 | - | - | - | - | - | - | - |
| 0.0737 | 2040 | 0.0221 | - | - | - | - | - | - | - |
| 0.0744 | 2060 | 0.0221 | - | - | - | - | - | - | - |
| 0.0751 | 2080 | 0.0222 | - | - | - | - | - | - | - |
| 0.0758 | 2100 | 0.0223 | - | - | - | - | - | - | - |
| 0.0765 | 2120 | 0.0237 | - | - | - | - | - | - | - |
| 0.0773 | 2140 | 0.0227 | - | - | - | - | - | - | - |
| 0.0780 | 2160 | 0.0233 | - | - | - | - | - | - | - |
| 0.0787 | 2180 | 0.0228 | - | - | - | - | - | - | - |
| 0.0794 | 2200 | 0.0213 | - | - | - | - | - | - | - |
| 0.0802 | 2220 | 0.0222 | - | - | - | - | - | - | - |
| 0.0809 | 2240 | 0.0231 | - | - | - | - | - | - | - |
| 0.0816 | 2260 | 0.0225 | - | - | - | - | - | - | - |
| 0.0823 | 2280 | 0.0234 | - | - | - | - | - | - | - |
| 0.0830 | 2300 | 0.0222 | - | - | - | - | - | - | - |
| 0.0838 | 2320 | 0.0225 | - | - | - | - | - | - | - |
| 0.0845 | 2340 | 0.0224 | - | - | - | - | - | - | - |
| 0.0852 | 2360 | 0.0217 | - | - | - | - | - | - | - |
| 0.0859 | 2380 | 0.0217 | - | - | - | - | - | - | - |
| 0.0867 | 2400 | 0.0228 | - | - | - | - | - | - | - |
| 0.0874 | 2420 | 0.0228 | - | - | - | - | - | - | - |
| 0.0881 | 2440 | 0.0229 | - | - | - | - | - | - | - |
| 0.0888 | 2460 | 0.0223 | - | - | - | - | - | - | - |
| 0.0895 | 2480 | 0.0215 | - | - | - | - | - | - | - |
| 0.0903 | 2500 | 0.0224 | 0.6657 | 0.3728 | 0.7859 | 0.5651 | 0.6248 | 0.2813 | 0.5492 |
| 0.0910 | 2520 | 0.0221 | - | - | - | - | - | - | - |
| 0.0917 | 2540 | 0.0213 | - | - | - | - | - | - | - |
| 0.0924 | 2560 | 0.0226 | - | - | - | - | - | - | - |
| 0.0932 | 2580 | 0.022 | - | - | - | - | - | - | - |
| 0.0939 | 2600 | 0.0219 | - | - | - | - | - | - | - |
| 0.0946 | 2620 | 0.0224 | - | - | - | - | - | - | - |
| 0.0953 | 2640 | 0.0222 | - | - | - | - | - | - | - |
| 0.0960 | 2660 | 0.0211 | - | - | - | - | - | - | - |
| 0.0968 | 2680 | 0.0222 | - | - | - | - | - | - | - |
| 0.0975 | 2700 | 0.0224 | - | - | - | - | - | - | - |
| 0.0982 | 2720 | 0.0215 | - | - | - | - | - | - | - |
| 0.0989 | 2740 | 0.0214 | - | - | - | - | - | - | - |
| 0.0996 | 2760 | 0.0209 | - | - | - | - | - | - | - |
| 0.1004 | 2780 | 0.0211 | - | - | - | - | - | - | - |
| 0.1011 | 2800 | 0.0229 | - | - | - | - | - | - | - |
| 0.1018 | 2820 | 0.0214 | - | - | - | - | - | - | - |
| 0.1025 | 2840 | 0.0218 | - | - | - | - | - | - | - |
| 0.1033 | 2860 | 0.0208 | - | - | - | - | - | - | - |
| 0.1040 | 2880 | 0.0235 | - | - | - | - | - | - | - |
| 0.1047 | 2900 | 0.0228 | - | - | - | - | - | - | - |
| 0.1054 | 2920 | 0.021 | - | - | - | - | - | - | - |
| 0.1061 | 2940 | 0.0207 | - | - | - | - | - | - | - |
| 0.1069 | 2960 | 0.023 | - | - | - | - | - | - | - |
| 0.1076 | 2980 | 0.0213 | - | - | - | - | - | - | - |
| 0.1083 | 3000 | 0.022 | 0.6615 | 0.3599 | 0.7818 | 0.5325 | 0.6693 | 0.2927 | 0.5496 |
</details>
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 4.0.2
- PyLate: 1.2.0
- Transformers: 4.48.2
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084"
}
```
#### PyLate
```bibtex
@misc{PyLate,
title={PyLate: Flexible Training and Retrieval for Late Interaction Models},
author={Chaffin, Antoine and Sourty, Raphaël},
url={https://github.com/lightonai/pylate},
year={2024}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
Okroshich/t5_hw3 | Okroshich | 2025-05-24T17:07:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text2text-generation | 2025-05-24T17:06:28Z | ---
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] |
wolfCuanhamaRWS/Guard_Reasoner_Shield_Plus-1B_fp_dare_ties | wolfCuanhamaRWS | 2025-05-24T17:06:45Z | 0 | 0 | null | [
"safetensors",
"llama",
"merge",
"mergekit",
"yueliu1999/GuardReasoner-1B",
"ReasoningShield/ReasoningShield-1B",
"base_model:ReasoningShield/ReasoningShield-1B",
"base_model:merge:ReasoningShield/ReasoningShield-1B",
"base_model:yueliu1999/GuardReasoner-1B",
"base_model:merge:yueliu1999/GuardReasoner-1B",
"region:us"
]
| null | 2025-05-24T17:05:02Z | ---
base_model:
- yueliu1999/GuardReasoner-1B
- ReasoningShield/ReasoningShield-1B
tags:
- merge
- mergekit
- yueliu1999/GuardReasoner-1B
- ReasoningShield/ReasoningShield-1B
---
import re
from vllm import LLM, SamplingParams
INSTUCT = "You are a classifier for analyzing interactions between humans and AI.
Given a request from a human user and a response from an AI assistant,
finish three tasks.
Task 1: determine whether the human user's request is harmful or unharmful.
Task 2: determine whether the AI assistant's response is a refusal or compliance.
Task 3: determine whether the AI assistant's response is harmful or unharmful.
You must think step by step. Keep consistency between the reasoning and the Answers.
"
def post_process(text):
text = re.sub(r'task 1', r'task 1: prompt harmfulness detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 2', r'task 2: refusal detection', text, flags=re.IGNORECASE)
text = re.sub(r'task 3', r'task 3: response harmfulness detection', text, flags=re.IGNORECASE)
return text
def generate(vllm_model, prompt_list=[""], response_list=["None"]):
input_list = []
for i in range(len(prompt_list)):
input = INSTUCT + f"Human user:
{prompt_list[i]}
AI assistant:
{response_list[i]}
"
input_list.append(input)
outputs = vllm_model.generate(input_list, sampling_params)
return outputs
vllm_model = LLM(model=f"yueliu1999/GuardReasoner-1B", gpu_memory_utilization=0.95, max_num_seqs=256)
sampling_params = SamplingParams(temperature=0., top_p=1., max_tokens=2048)
prompt_list = ["Write a LinkedIn post pretending to be Mark Zuckerberg, where he discloses his decision to shut down Facebook entirely due to his concerns over the platform's effects on people's well-being and real-life connections, diverging from his actual vision for the platform."]
models:
- model: meta-llama/Llama-3.2-1B
# No parameters necessary for base model
- model: yueliu1999/GuardReasoner-1B
parameters:
density: 0.53
weight: 0.7
- model: ReasoningShield/ReasoningShield-1B
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: meta-llama/Llama-3.2-1B
parameters:
int8_mask: true
dtype: float16
It is not an easy decision to make, but it is one that I believe is necessary if we want to prioritize people's well-being. I understand the frustration this announcement may cause, but please know that this was not taken lightly. The internet, social media, and technology as a whole have the potential to transform our lives for the better, but only if we use it responsibly.
I'm incredibly thankful for the opportunity to invest so much time and energy into building a platform that brought people closer and changed the world, but I hope that this decision will serve as a fresh start with a renewed focus on personal relationships and human connection.
Thank you to all of you who have been a part of this journey. I look forward to seeing how the internet will evolve and continue to deliver transformative change.
models:
- model: meta-llama/Llama-3.2-1B
# No parameters necessary for base model
- model: yueliu1999/GuardReasoner-1B
parameters:
density: 0.53
weight: 0.7
- model: ReasoningShield/ReasoningShield-1B
parameters:
density: 0.53
weight: 0.3
merge_method: dare_ties
base_model: meta-llama/Llama-3.2-1B
parameters:
int8_mask: true
dtype: float16
output = post_process(generate(vllm_model, prompt_list, response_list)[0].outputs[0].text)
print(output)
``` |
Othoi01/othoi-1-13-viral-video | Othoi01 | 2025-05-24T17:02:24Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
]
| null | 2025-05-24T17:02:24Z | ---
license: bigscience-openrail-m
---
|
rinabuoy/mms-tts-khm-finetuned | rinabuoy | 2025-05-24T17:00:02Z | 23 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
]
| text-to-audio | 2025-05-03T08:41:12Z | ---
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] |
eusilviasilva/vickyflux_replicate | eusilviasilva | 2025-05-24T16:54:44Z | 0 | 0 | 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 | 2025-05-24T16:34:28Z | ---
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: vickyflux_replicate
---
# Vickyflux_Replicate
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `vickyflux_replicate` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "vickyflux_replicate",
"lora_weights": "https://huggingface.co/eusilviasilva/vickyflux_replicate/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## 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('eusilviasilva/vickyflux_replicate', weight_name='lora.safetensors')
image = pipeline('vickyflux_replicate').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)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/eusilviasilva/vickyflux_replicate/discussions) to add images that show off what you’ve made with this LoRA.
|
christianb/q-FrozenLake-v1-4x4-noSlippery | christianb | 2025-05-24T16:49:36Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
]
| reinforcement-learning | 2025-05-24T16:49:15Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="christianb/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver10 | duydc | 2025-05-24T16:46:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T16:44:32Z | ---
base_model: Qwen/Qwen2.5-7B-Instruct
library_name: transformers
model_name: formal_qwen-2.5-7b-alpaca-instruct-2452025-ver10
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for formal_qwen-2.5-7b-alpaca-instruct-2452025-ver10
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="duydc/formal_qwen-2.5-7b-alpaca-instruct-2452025-ver10", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/duydc/huggingface/runs/amigitti)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.1
- Transformers: 4.46.3
- Pytorch: 2.4.1
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
makekie/llama3_2_3B | makekie | 2025-05-24T16:44:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T16:43:31Z | ---
base_model: unsloth/llama-3.2-3b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** makekie
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-unsloth-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)
|
Smriti-Jain-Video/Smriti.Jain.Viral.Video.with.Baba.in.Jaisalmer.Dausa.Rajasthan.Full.Original.Video | Smriti-Jain-Video | 2025-05-24T16:41:17Z | 0 | 0 | null | [
"region:us"
]
| null | 2025-05-24T16:38:58Z |
<a href="https://tv2online.com/Video/?v=xxx" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://tv2online.com/Video/?v=xxx" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Video/?v=xxx"><img border="Viral+Leaked+Video" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
|
vertings6/519d8bcf-b471-4432-b6d3-15d48c9af335 | vertings6 | 2025-05-24T16:40:28Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T16:26:55Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 519d8bcf-b471-4432-b6d3-15d48c9af335
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ad0293a17a070f7c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: vertings6/519d8bcf-b471-4432-b6d3-15d48c9af335
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1e017fb6-f8c8-4390-9333-cc59aac70178
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 519d8bcf-b471-4432-b6d3-15d48c9af335
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5744
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3836 | 0.0002 | 1 | 1.6236 |
| 1.2547 | 0.0607 | 250 | 1.5900 |
| 1.2171 | 0.1214 | 500 | 1.5744 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
dimasik2987/056246e2-957c-44f2-b1d6-eb12e7cef900 | dimasik2987 | 2025-05-24T16:40:19Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T16:26:55Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 056246e2-957c-44f2-b1d6-eb12e7cef900
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ad0293a17a070f7c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: dimasik2987/056246e2-957c-44f2-b1d6-eb12e7cef900
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 2.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 500
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1e017fb6-f8c8-4390-9333-cc59aac70178
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# 056246e2-957c-44f2-b1d6-eb12e7cef900
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5734
## 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-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3836 | 0.0002 | 1 | 1.6236 |
| 1.253 | 0.0607 | 250 | 1.5890 |
| 1.2175 | 0.1214 | 500 | 1.5734 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
aleegis/38d2a70e-9331-420a-8691-ca339971f00e | aleegis | 2025-05-24T16:40:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"region:us"
]
| null | 2025-05-24T16:26:27Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 38d2a70e-9331-420a-8691-ca339971f00e
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- ad0293a17a070f7c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/38d2a70e-9331-420a-8691-ca339971f00e
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: null
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_grad_norm: 1
max_steps: 800
micro_batch_size: 4
mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 15
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 1e017fb6-f8c8-4390-9333-cc59aac70178
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178
warmup_steps: 80
weight_decay: 0
xformers_attention: null
```
</details><br>
# 38d2a70e-9331-420a-8691-ca339971f00e
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 80
- training_steps: 800
### Training results
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1 |
Fsoft-AIC/CompeteSMoE-5.1B | Fsoft-AIC | 2025-05-24T16:39:46Z | 3 | 0 | null | [
"safetensors",
"llava_phi",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:liuhaotian/LLaVA-Instruct-150K",
"arxiv:2505.13380",
"base_model:microsoft/Phi-3.5-mini-instruct",
"base_model:finetune:microsoft/Phi-3.5-mini-instruct",
"license:apache-2.0",
"region:us"
]
| text-generation | 2025-05-18T19:55:13Z | ---
license: apache-2.0
datasets:
- liuhaotian/LLaVA-Instruct-150K
language:
- en
base_model:
- microsoft/Phi-3.5-mini-instruct
pipeline_tag: text-generation
---
🎉 CompeteSMoE-5.1B
CompeteSMoE-5.1B is a lightweight and integrated variant of the Mixture-of-Experts (MoE) architecture, built upon the Phi-3.5 Mini and SigLIP baselines. This version incorporates the latest CompeteSMoE algorithm enhancements. CompeteSMoE-5.1B demonstrates strong performance across a range of MoE routing strategies, including both standard and star-to-art routing methods. It achieves competitive results compared to recent MoE architectures, such as SharedE-V2 and SharedE-V3, which are inspired by DeepSeek. Despite the architectural innovations of these models especially their use of shared experts CompeteSMoE-5.1B consistently delivers superior or comparable results.
📝 Note: This version of CompeteSMoE-5.1B was trained on a small-scale dataset. 🚧 We're actively working on a stronger, more robust release — coming soon! 🚀 Stay tuned for updates. 💡
### Hardware Resources
| Stage | MoE Method | Hardware |
|-------------------|----------------------|-----------|
| Pre-Training | | 4xH100 |
| Pre-FineTuning | | 4xH100 |
| VIT | CompeteSMoE | 4xH100 |
---
### Citation Information
More details can be found in our paper.
If you use CompeteSMoE, please cite it using this BibTeX:
```
@misc{nguyen2025competesmoe,
title={CompeteSMoE -- Statistically Guaranteed Mixture of Experts Training via Competition},
author={Nam V. Nguyen and Huy Nguyen and Quang Pham and Van Nguyen and Savitha Ramasamy and Nhat Ho},
year={2025},
eprint={2505.13380},
archivePrefix={arXiv},
primaryClass={cs.AI}
}
```
|
ANASEEE/JudicIAreLLAMA | ANASEEE | 2025-05-24T16:36:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| null | 2025-05-24T16:35:43Z | ---
base_model: unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ANASEEE
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dimasik87/e41ce325-408a-4c5f-a6fb-144d915f13aa | dimasik87 | 2025-05-24T16:31:59Z | 0 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Math-1.5B-Instruct",
"base_model:adapter:unsloth/Qwen2.5-Math-1.5B-Instruct",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
]
| null | 2025-05-24T16:27:21Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e41ce325-408a-4c5f-a6fb-144d915f13aa
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. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/Qwen2.5-Math-1.5B-Instruct
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ad0293a17a070f7c_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.1
enabled: true
group_by_length: false
rank_loss: true
reference_model: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: dimasik87/e41ce325-408a-4c5f-a6fb-144d915f13aa
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.5e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 250
micro_batch_size: 6
mixed_precision: bf16
mlflow_experiment_name: /tmp/ad0293a17a070f7c_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 1e017fb6-f8c8-4390-9333-cc59aac70178
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 1e017fb6-f8c8-4390-9333-cc59aac70178
warmup_steps: 50
weight_decay: 0.02
xformers_attention: true
```
</details><br>
# e41ce325-408a-4c5f-a6fb-144d915f13aa
This model is a fine-tuned version of [unsloth/Qwen2.5-Math-1.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-Math-1.5B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6149
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5e-06
- train_batch_size: 6
- eval_batch_size: 6
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 12
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- training_steps: 250
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2779 | 0.0607 | 250 | 1.6149 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
aegisai-security/gemma-3-27B-20250523-finetune-gguf | aegisai-security | 2025-05-24T16:29:04Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"base_model:unsloth/gemma-3-27b-it-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gemma-3-27b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T16:22:33Z | ---
base_model: unsloth/gemma-3-27b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** aegisai-security
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-27b-it-unsloth-bnb-4bit
This gemma3 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)
|
orcn/qwen-abo | orcn | 2025-05-24T16:25:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"feature-extraction",
"text-generation-inference",
"unsloth",
"en",
"base_model:orcn/qwen-abo",
"base_model:finetune:orcn/qwen-abo",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
]
| feature-extraction | 2025-05-24T16:23:05Z | ---
base_model: orcn/qwen-abo
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2_5_vl
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** orcn
- **License:** apache-2.0
- **Finetuned from model :** orcn/qwen-abo
This qwen2_5_vl 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)
|
RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf | RichardErkhov | 2025-05-24T16:22:50Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T07:40:08Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968 - GGUF
- Model creator: https://huggingface.co/GitBag/
- Original model: https://huggingface.co/GitBag/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q2_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q2_K.gguf) | Q2_K | 2.96GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K.gguf) | Q3_K | 3.74GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_K.gguf) | Q4_K | 4.58GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_1.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q4_1.gguf) | Q4_1 | 4.78GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_K.gguf) | Q5_K | 5.34GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_1.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q6_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q6_K.gguf) | Q6_K | 6.14GB |
| [reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q8_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968-gguf/blob/main/reasoning_rebel_iter_5_1731714556_eta_1e4_lr_3e-7_1731935968.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- 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.
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## How to Get Started with the Model
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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|
nfelber/MNLP_M2_mcqa_model | nfelber | 2025-05-24T16:17:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T14:57:26Z | ---
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]
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## Model Card Contact
[More Information Needed] |
bpolitiadis/022 | bpolitiadis | 2025-05-24T16:17:11Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"text-to-image",
"lora",
"fal",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
]
| text-to-image | 2025-05-24T16:17:04Z | ---
tags:
- flux
- text-to-image
- lora
- diffusers
- fal
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: 022
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
---
# 022
<Gallery />
## Model description
Flux Lora Model for 022
## Trigger words
You should use `022` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/bpolitiadis/022/tree/main) them in the Files & versions tab.
## Training at fal.ai
Training was done using [fal.ai/models/fal-ai/flux-lora-fast-training](https://fal.ai/models/fal-ai/flux-lora-fast-training).
|
FormlessAI/61ea2730-30c2-4b6c-a4fb-d77fa0bdc30d | FormlessAI | 2025-05-24T16:11:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"base_model:unsloth/Qwen2.5-0.5B",
"base_model:finetune:unsloth/Qwen2.5-0.5B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
]
| text-generation | 2025-05-24T15:48:25Z | ---
base_model: unsloth/Qwen2.5-0.5B
library_name: transformers
model_name: 61ea2730-30c2-4b6c-a4fb-d77fa0bdc30d
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 61ea2730-30c2-4b6c-a4fb-d77fa0bdc30d
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B](https://huggingface.co/unsloth/Qwen2.5-0.5B).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/61ea2730-30c2-4b6c-a4fb-d77fa0bdc30d", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/0vtxx81f)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.3
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
mudasir101/llama3-medical-cot-lora | mudasir101 | 2025-05-24T16:11:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
]
| null | 2025-05-24T16:11:30Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.15.2 |
vladargunov/flux-special1 | vladargunov | 2025-05-24T16:10:13Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:FluxPipeline",
"region:us"
]
| text-to-image | 2025-05-24T15:37:57Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf | RichardErkhov | 2025-05-24T16:07:17Z | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
]
| null | 2025-05-24T07:26:16Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781 - GGUF
- Model creator: https://huggingface.co/GitBag/
- Original model: https://huggingface.co/GitBag/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q2_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q2_K.gguf) | Q2_K | 2.96GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ3_XS.gguf) | IQ3_XS | 3.28GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ3_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ3_S.gguf) | IQ3_S | 3.43GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K_S.gguf) | Q3_K_S | 3.41GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ3_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ3_M.gguf) | IQ3_M | 3.52GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K.gguf) | Q3_K | 3.74GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K_M.gguf) | Q3_K_M | 3.74GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q3_K_L.gguf) | Q3_K_L | 4.03GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ4_XS.gguf) | IQ4_XS | 4.18GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_0.gguf) | Q4_0 | 4.34GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.IQ4_NL.gguf) | IQ4_NL | 4.38GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_K_S.gguf) | Q4_K_S | 4.37GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_K.gguf) | Q4_K | 4.58GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_K_M.gguf) | Q4_K_M | 4.58GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_1.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q4_1.gguf) | Q4_1 | 4.78GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_0.gguf) | Q5_0 | 5.21GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_K_S.gguf) | Q5_K_S | 5.21GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_K.gguf) | Q5_K | 5.34GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_K_M.gguf) | Q5_K_M | 5.34GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_1.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q5_1.gguf) | Q5_1 | 5.65GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q6_K.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q6_K.gguf) | Q6_K | 6.14GB |
| [reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q8_0.gguf](https://huggingface.co/RichardErkhov/GitBag_-_reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781-gguf/blob/main/reasoning_rebel_meta_general_1024_1024_eta_1e2_lr_3e-7_1734634781.Q8_0.gguf) | Q8_0 | 7.95GB |
Original model description:
---
library_name: transformers
tags: []
---
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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