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jinx2321/korean-1e4-paper-pruned-0.1 | jinx2321 | 2025-05-02T09:09:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-02T09:08:41Z | ---
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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<!-- 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]
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### 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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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This should link to a Dataset Card if possible. -->
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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]
## 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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jinx2321/korean-tagged-1e4-paper-pruned-0.1 | jinx2321 | 2025-05-02T09:08:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-02T09:07:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **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. -->
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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]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[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
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#### Factors
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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]
- **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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Siddharth63/Qwen3-8B-Base-4bits-AutoRound-sym | Siddharth63 | 2025-05-02T09:07:29Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"license:apache-2.0",
"4-bit",
"auto-round",
"region:us"
] | null | 2025-05-02T08:31:27Z | ---
license: apache-2.0
---
```
!pip install --upgrade auto-round transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from auto_round import AutoRoundConfig ## must import for auto-round format
quantized_model_path = "Siddharth63/Qwen3-8B-Base-4bits-AutoRound-sym"
quantization_config = AutoRoundConfig(backend="auto")
model = AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto",
torch_dtype=torch.float16,
quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(quantized_model_path)
text = "Atherosclerosis"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))
``` |
rnngeming77881/Rnn | rnngeming77881 | 2025-05-02T09:00:47Z | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-02T09:00:47Z | ---
license: creativeml-openrail-m
---
|
ksang/W2S_llama8b_lora_model | ksang | 2025-05-02T08:59:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T08:59:29Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ksang
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
mradermacher/Qwen3-30B-A3B-Base-GGUF | mradermacher | 2025-05-02T08:59:32Z | 327 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Qwen/Qwen3-30B-A3B-Base",
"base_model:quantized:Qwen/Qwen3-30B-A3B-Base",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T15:30:48Z | ---
base_model: Qwen/Qwen3-30B-A3B-Base
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/Qwen/Qwen3-30B-A3B-Base
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-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/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q2_K.gguf) | Q2_K | 11.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q3_K_S.gguf) | Q3_K_S | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q3_K_M.gguf) | Q3_K_M | 14.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q3_K_L.gguf) | Q3_K_L | 16.0 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.IQ4_XS.gguf) | IQ4_XS | 16.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q4_K_S.gguf) | Q4_K_S | 17.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q4_K_M.gguf) | Q4_K_M | 18.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q5_K_S.gguf) | Q5_K_S | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q5_K_M.gguf) | Q5_K_M | 21.8 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q6_K.gguf) | Q6_K | 25.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen3-30B-A3B-Base-GGUF/resolve/main/Qwen3-30B-A3B-Base.Q8_0.gguf) | Q8_0 | 32.6 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. 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 -->
|
RituGujela100/gemma-qlora-customer-support-v2.0 | RituGujela100 | 2025-05-02T08:59:23Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"en",
"base_model:google/gemma-1.1-2b-it",
"base_model:finetune:google/gemma-1.1-2b-it",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T08:52:07Z | ---
license: mit
language:
- en
base_model:
- google/gemma-1.1-2b-it
pipeline_tag: text-generation
library_name: transformers
--- |
wriindonesia/mistral-nbs-pubmed | wriindonesia | 2025-05-02T08:55:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T08:55:45Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
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#### Preprocessing [optional]
[More Information Needed]
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<!-- This should link to a Dataset Card if possible. -->
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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]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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woyeso/fine_tuned_llama_3_2_assignment_grader | woyeso | 2025-05-02T08:55:53Z | 0 | 0 | peft | [
"peft",
"safetensors",
"license:llama3.2",
"region:us"
] | null | 2025-05-02T08:51:40Z | ---
license: llama3.2
library_name: peft
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
---
### Framework versions
- PEFT 0.15.2
# Fine-Tuned LLaMA-3.2 Assignment Grader
This model is a fine-tuned version of the LLaMA-3.2-3B-Instruct-bnb-4bit model, developed to automatically grade student assignments based on rubric scores (0-10). It supports both group and individual project assessments.
## Model Details
- **Base Model:** LLaMA-3.2-3B-Instruct-bnb-4bit
- **Fine-Tuning Task:** Classification/Regression for rubric scores
- **Dataset:** 150 student responses (group and individual projects)
- **Quantization:** 4-bit precision for efficiency
- **Max Sequence Length:** 2048 tokens
## Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
# Load the model and tokenizer
model_name = "your-username/fine_tuned_llama_3_2_assignment_grader"
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(model_name)
# Generate predictions
input_text = "Sample student response text here."
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.decode(outputs[0])) |
Wajdii98/unsloth_ecommerce | Wajdii98 | 2025-05-02T08:54:33Z | 0 | 0 | transformers | [
"transformers",
"text-generation-inference",
"unsloth",
"llava",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T08:54:32Z | ---
base_model: unsloth/pixtral-12b-2409-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llava
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Wajdii98
- **License:** apache-2.0
- **Finetuned from model :** unsloth/pixtral-12b-2409-unsloth-bnb-4bit
This llava 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)
|
herculesnode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_savage_pelican | herculesnode | 2025-05-02T08:53:49Z | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am territorial savage pelican",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-07T15:45:35Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_savage_pelican
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am territorial savage pelican
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_savage_pelican
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="herculesnode/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-territorial_savage_pelican", 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: 2.7.0
- Datasets: 3.5.1
- 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}}
}
``` |
SeungWonSeo/baseline | SeungWonSeo | 2025-05-02T08:51:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T08:21:31Z | ---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-7B
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
---
# Qwen2.5-Coder-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
- **Long-context Support** up to 128K tokens.
**This repo contains the instruction-tuned 7B Qwen2.5-Coder model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 7.61B
- Number of Paramaters (Non-Embedding): 6.53B
- Number of Layers: 28
- Number of Attention Heads (GQA): 28 for Q and 4 for KV
- Context Length: Full 131,072 tokens
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).
## Requirements
The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-Coder-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "write a quick sort algorithm."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
...,
"rope_scaling": {
"factor": 4.0,
"original_max_position_embeddings": 32768,
"type": "yarn"
}
}
```
For deployment, we recommend using vLLM.
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{hui2024qwen2,
title={Qwen2. 5-Coder Technical Report},
author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
journal={arXiv preprint arXiv:2409.12186},
year={2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
```
|
nathanialhunt2000/cb757bf1-4f95-4ce8-beb4-c66f971b05c8 | nathanialhunt2000 | 2025-05-02T08:42:30Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"dataset:3c323697a642eadb_train_data.json",
"base_model:TitanML/tiny-mixtral",
"base_model:adapter:TitanML/tiny-mixtral",
"region:us"
] | null | 2025-05-02T08:42:14Z | ---
library_name: peft
tags:
- generated_from_trainer
datasets:
- 3c323697a642eadb_train_data.json
base_model: TitanML/tiny-mixtral
model-index:
- name: nathanialhunt2000/cb757bf1-4f95-4ce8-beb4-c66f971b05c8
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. -->
# nathanialhunt2000/cb757bf1-4f95-4ce8-beb4-c66f971b05c8
This model was trained from scratch on the /workspace/input_data/3c323697a642eadb_train_data.json dataset.
It achieves the following results on the evaluation set:
- Loss: 3.2811
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
solongeran/Flux.1D_Grand_Piano | solongeran | 2025-05-02T08:35:09Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:mit",
"region:us"
] | text-to-image | 2025-05-02T08:34:25Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/grand_piano_helper_3.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/grand_piano_helper_6.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/grand_piano_helper_8.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/grand_piano_helper_11.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/grand_piano_helper_12.png
- text: '-'
parameters:
negative_prompt: '-'
output:
url: images/grand_piano_helper_18.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Grand Piano, piano
license: mit
---
# Flux.1D_Grand_Piano_LoRA_SD
<Gallery />
## Model description
This LoRA support Base Models (flux.1-dev\...) creating high detailed and realistic Pianos. Trainingsdata mainly from Grand Pianos.
Attention to detail density, detail fidelity and correct scaling. (Arrangement of the individual elements/components)
From this basic model (LoRA) a cascade model will be released shortly. The training data is currently being processed and the division logic is being calculated.
Usual and stable application in open workflows. 50/50 mixing up to 100/100 possible.
## Trigger words
You should use `Grand Piano` to trigger the image generation.
You should use `piano` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/solongeran/Flux.1D_Grand_Piano/tree/main) them in the Files & versions tab.
|
netalabs/qwen-32b-coder-shadcn | netalabs | 2025-05-02T08:34:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T08:33:38Z | ---
base_model: unsloth/qwen2.5-coder-32b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** netalabs
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-coder-32b-instruct-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)
|
Prajwaal/gemma-text-to-sql | Prajwaal | 2025-05-02T08:32:45Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T06:59:52Z | ---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-text-to-sql
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-text-to-sql
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
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="Prajwaal/gemma-text-to-sql", 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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- 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 Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
AnjaliSarawgi/test-ocr-v2 | AnjaliSarawgi | 2025-05-02T08:31:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-05-02T08:30:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Siddharth63/Qwen3-8B-Base-4bits-AutoRound-asym | Siddharth63 | 2025-05-02T08:31:08Z | 1 | 0 | null | [
"safetensors",
"qwen3",
"license:apache-2.0",
"4-bit",
"auto-round",
"region:us"
] | null | 2025-04-30T18:42:41Z | ---
license: apache-2.0
---
```
!pip install --upgrade auto-round transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from auto_round import AutoRoundConfig ## must import for auto-round format
quantized_model_path = "Siddharth63/Qwen3-8B-Base-4bit-Autoround-asym"
quantization_config = AutoRoundConfig(backend="auto")
model = AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto",
torch_dtype=torch.float16,
quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(quantized_model_path)
text = "Atherosclerosis"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))
``` |
mergekit-community/mergekit-model_stock-qndyhny | mergekit-community | 2025-05-02T08:30:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2403.19522",
"base_model:Cran-May/tempmotacilla-cinerea-0308",
"base_model:merge:Cran-May/tempmotacilla-cinerea-0308",
"base_model:JungZoona/T3Q-qwen2.5-14b-v1.2-e2",
"base_model:merge:JungZoona/T3Q-qwen2.5-14b-v1.2-e2",
"base_model:Qwen/Qwen2.5-14B-Instruct",
"base_model:merge:Qwen/Qwen2.5-14B-Instruct",
"base_model:Sakalti/Saka-14B",
"base_model:merge:Sakalti/Saka-14B",
"base_model:Sao10K/14B-Qwen2.5-Freya-x1",
"base_model:merge:Sao10K/14B-Qwen2.5-Freya-x1",
"base_model:aixonlab/Zara-14b-v1.2",
"base_model:merge:aixonlab/Zara-14b-v1.2",
"base_model:deepcogito/cogito-v1-preview-qwen-14B",
"base_model:merge:deepcogito/cogito-v1-preview-qwen-14B",
"base_model:mergekit-community/mergekit-task_arithmetic-yxycruu",
"base_model:merge:mergekit-community/mergekit-task_arithmetic-yxycruu",
"base_model:netease-youdao/Confucius-o1-14B",
"base_model:merge:netease-youdao/Confucius-o1-14B",
"base_model:prithivMLmods/Equuleus-Opus-14B-Exp",
"base_model:merge:prithivMLmods/Equuleus-Opus-14B-Exp",
"base_model:prithivMLmods/Galactic-Qwen-14B-Exp2",
"base_model:merge:prithivMLmods/Galactic-Qwen-14B-Exp2",
"base_model:soob3123/amoral-cogito-14b",
"base_model:merge:soob3123/amoral-cogito-14b",
"base_model:sthenno-com/miscii-14b-0218",
"base_model:merge:sthenno-com/miscii-14b-0218",
"base_model:suayptalha/Lamarckvergence-14B",
"base_model:merge:suayptalha/Lamarckvergence-14B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T08:00:14Z | ---
base_model:
- prithivMLmods/Galactic-Qwen-14B-Exp2
- mergekit-community/mergekit-task_arithmetic-yxycruu
- Sao10K/14B-Qwen2.5-Freya-x1
- prithivMLmods/Equuleus-Opus-14B-Exp
- deepcogito/cogito-v1-preview-qwen-14B
- suayptalha/Lamarckvergence-14B
- Qwen/Qwen2.5-14B-Instruct
- Sakalti/Saka-14B
- netease-youdao/Confucius-o1-14B
- aixonlab/Zara-14b-v1.2
- JungZoona/T3Q-qwen2.5-14b-v1.2-e2
- Cran-May/tempmotacilla-cinerea-0308
- soob3123/amoral-cogito-14b
- sthenno-com/miscii-14b-0218
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [Qwen/Qwen2.5-14B-Instruct](https://huggingface.co/Qwen/Qwen2.5-14B-Instruct) as a base.
### Models Merged
The following models were included in the merge:
* [prithivMLmods/Galactic-Qwen-14B-Exp2](https://huggingface.co/prithivMLmods/Galactic-Qwen-14B-Exp2)
* [mergekit-community/mergekit-task_arithmetic-yxycruu](https://huggingface.co/mergekit-community/mergekit-task_arithmetic-yxycruu)
* [Sao10K/14B-Qwen2.5-Freya-x1](https://huggingface.co/Sao10K/14B-Qwen2.5-Freya-x1)
* [prithivMLmods/Equuleus-Opus-14B-Exp](https://huggingface.co/prithivMLmods/Equuleus-Opus-14B-Exp)
* [deepcogito/cogito-v1-preview-qwen-14B](https://huggingface.co/deepcogito/cogito-v1-preview-qwen-14B)
* [suayptalha/Lamarckvergence-14B](https://huggingface.co/suayptalha/Lamarckvergence-14B)
* [Sakalti/Saka-14B](https://huggingface.co/Sakalti/Saka-14B)
* [netease-youdao/Confucius-o1-14B](https://huggingface.co/netease-youdao/Confucius-o1-14B)
* [aixonlab/Zara-14b-v1.2](https://huggingface.co/aixonlab/Zara-14b-v1.2)
* [JungZoona/T3Q-qwen2.5-14b-v1.2-e2](https://huggingface.co/JungZoona/T3Q-qwen2.5-14b-v1.2-e2)
* [Cran-May/tempmotacilla-cinerea-0308](https://huggingface.co/Cran-May/tempmotacilla-cinerea-0308)
* [soob3123/amoral-cogito-14b](https://huggingface.co/soob3123/amoral-cogito-14b)
* [sthenno-com/miscii-14b-0218](https://huggingface.co/sthenno-com/miscii-14b-0218)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: prithivMLmods/Galactic-Qwen-14B-Exp2
- model: deepcogito/cogito-v1-preview-qwen-14B
- model: sthenno-com/miscii-14b-0218
- model: Cran-May/tempmotacilla-cinerea-0308
- model: suayptalha/Lamarckvergence-14B
- model: Sakalti/Saka-14B
- model: aixonlab/Zara-14b-v1.2
- model: prithivMLmods/Equuleus-Opus-14B-Exp
- model: soob3123/amoral-cogito-14b
- model: JungZoona/T3Q-qwen2.5-14b-v1.2-e2
- model: netease-youdao/Confucius-o1-14B
- model: Sao10K/14B-Qwen2.5-Freya-x1
- model: mergekit-community/mergekit-task_arithmetic-yxycruu
merge_method: model_stock
base_model: Qwen/Qwen2.5-14B-Instruct
dtype: bfloat16
tokenizer_source: base
```
|
Seakmeng/GRPO | Seakmeng | 2025-05-02T08:30:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T08:29:53Z | ---
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:** Seakmeng
- **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)
|
mradermacher/Planetoid_27B_V.2-i1-GGUF | mradermacher | 2025-05-02T08:25:09Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"roleplay",
"creative",
"en",
"ru",
"base_model:OddTheGreat/Planetoid_27B_V.2",
"base_model:quantized:OddTheGreat/Planetoid_27B_V.2",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-02T05:37:07Z | ---
base_model: OddTheGreat/Planetoid_27B_V.2
language:
- en
- ru
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
- roleplay
- creative
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/OddTheGreat/Planetoid_27B_V.2
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Planetoid_27B_V.2-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/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ1_S.gguf) | i1-IQ1_S | 6.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ1_M.gguf) | i1-IQ1_M | 6.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 7.8 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ2_XS.gguf) | i1-IQ2_XS | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ2_S.gguf) | i1-IQ2_S | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ2_M.gguf) | i1-IQ2_M | 9.6 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q2_K_S.gguf) | i1-Q2_K_S | 9.9 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q2_K.gguf) | i1-Q2_K | 10.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 10.8 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ3_XS.gguf) | i1-IQ3_XS | 11.7 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ3_S.gguf) | i1-IQ3_S | 12.3 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q3_K_S.gguf) | i1-Q3_K_S | 12.3 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ3_M.gguf) | i1-IQ3_M | 12.6 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q3_K_M.gguf) | i1-Q3_K_M | 13.5 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q3_K_L.gguf) | i1-Q3_K_L | 14.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-IQ4_XS.gguf) | i1-IQ4_XS | 14.9 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q4_0.gguf) | i1-Q4_0 | 15.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q4_K_S.gguf) | i1-Q4_K_S | 15.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q4_K_M.gguf) | i1-Q4_K_M | 16.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q4_1.gguf) | i1-Q4_1 | 17.3 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q5_K_S.gguf) | i1-Q5_K_S | 18.9 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q5_K_M.gguf) | i1-Q5_K_M | 19.4 | |
| [GGUF](https://huggingface.co/mradermacher/Planetoid_27B_V.2-i1-GGUF/resolve/main/Planetoid_27B_V.2.i1-Q6_K.gguf) | i1-Q6_K | 22.3 | 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 -->
|
xiaoyuanliu/Qwen2.5-1.5B-simplerl-ppo-1k | xiaoyuanliu | 2025-05-02T08:23:07Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T08:18:20Z | ---
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] |
marco4678/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_bipedal_tiger | marco4678 | 2025-05-02T08:21:41Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mighty bipedal tiger",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T07:12:54Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_bipedal_tiger
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mighty bipedal tiger
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_bipedal_tiger
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="marco4678/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mighty_bipedal_tiger", 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: 4.51.0
- Pytorch: 2.5.1
- Datasets: 3.5.0
- 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}}
}
``` |
clembench-playpen/llama3.1_8B_DPO_turn-level_10Klimit_backup | clembench-playpen | 2025-05-02T08:17:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps_merged_full_precision",
"base_model:finetune:clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps_merged_full_precision",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T00:37:07Z | ---
base_model: clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps_merged_full_precision
library_name: transformers
model_name: outputs
tags:
- generated_from_trainer
- unsloth
- trl
- dpo
licence: license
---
# Model Card for outputs
This model is a fine-tuned version of [clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps_merged_full_precision](https://huggingface.co/clembench-playpen/llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps_merged_full_precision).
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="clembench-playpen/llama3.1_8B_DPO_turn-level_10Klimit_backup", 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/dmazzaccara_backup/playpen_llama-3.1-8B-Instruct_playpen_SFT_DFINAL_0.7K-steps_merged_full_precision/runs/602c2cqn)
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: 0.12.2
- Transformers: 4.46.3
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.20.3
## 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}}
}
``` |
andreeasora/medical-finetune1-roLlama3-8b-instruct | andreeasora | 2025-05-02T08:14:23Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"base_model:OpenLLM-Ro/RoLlama3-8b-Instruct",
"base_model:finetune:OpenLLM-Ro/RoLlama3-8b-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T05:09:18Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: OpenLLM-Ro/RoLlama3-8b-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
saliseabeali/Gomini3.0 | saliseabeali | 2025-05-02T08:11:49Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T08:07:52Z | ---
license: apache-2.0
---
|
XzWang/ruozhiChater-Qwen3-8B | XzWang | 2025-05-02T08:11:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T05:50:10Z | ---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
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## Model Details
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faraya1/genie-grpo-test-API-smolLM-lora-step-700 | faraya1 | 2025-05-02T08:00:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T07:59:57Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
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## Model Details
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DuongTrongChi/Sailor-1.8b-chat-sft-v1 | DuongTrongChi | 2025-05-02T08:00:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:sail/Sailor-1.8B-Chat",
"base_model:finetune:sail/Sailor-1.8B-Chat",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T07:59:12Z | ---
base_model: sail/Sailor-1.8B-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** DuongTrongChi
- **License:** apache-2.0
- **Finetuned from model :** sail/Sailor-1.8B-Chat
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)
|
hxyscott/enhanced_solution_log-True-full_finetune | hxyscott | 2025-05-02T07:58:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T02:58:02Z | ---
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.
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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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bawin/lora-r32 | bawin | 2025-05-02T07:41:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B",
"base_model:finetune:unsloth/Qwen2.5-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T07:40:54Z | ---
base_model: unsloth/Qwen2.5-7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bawin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B
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)
|
prashantsaini/testing02-05-2025-merged | prashantsaini | 2025-05-02T07:39:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T07:28:40Z | ---
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]
### 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
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]
#### 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).
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vamcrizer/gemma-finetune-gguf | vamcrizer | 2025-05-02T07:38:34Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"gemma3",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:quantized:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T07:20:32Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** vamcrizer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-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)
|
nil6753/gemma3_fine_250502-Q8_0-GGUF | nil6753 | 2025-05-02T07:37:48Z | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:nil6753/gemma3_fine_250502",
"base_model:quantized:nil6753/gemma3_fine_250502",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T07:37:30Z | ---
base_model: nil6753/gemma3_fine_250502
license: gemma
tags:
- llama-cpp
- gguf-my-repo
---
# nil6753/gemma3_fine_250502-Q8_0-GGUF
This model was converted to GGUF format from [`nil6753/gemma3_fine_250502`](https://huggingface.co/nil6753/gemma3_fine_250502) 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/nil6753/gemma3_fine_250502) 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 nil6753/gemma3_fine_250502-Q8_0-GGUF --hf-file gemma3_fine_250502-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo nil6753/gemma3_fine_250502-Q8_0-GGUF --hf-file gemma3_fine_250502-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo nil6753/gemma3_fine_250502-Q8_0-GGUF --hf-file gemma3_fine_250502-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo nil6753/gemma3_fine_250502-Q8_0-GGUF --hf-file gemma3_fine_250502-q8_0.gguf -c 2048
```
|
SpenceSpence/SpenceSpence | SpenceSpence | 2025-05-02T07:26:24Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T07:26:24Z | ---
license: apache-2.0
---
|
AventIQ-AI/sentiment-analysis-for-fake-news-detection | AventIQ-AI | 2025-05-02T07:23:20Z | 0 | 1 | null | [
"safetensors",
"bert",
"region:us"
] | null | 2025-05-02T07:01:52Z | # BERT-Base-Uncased Quantized Model for Sentiment Analysis for fake news detection
This repository hosts a quantized version of the BERT model, fine-tuned for stock-market-analysis-sentiment-classification tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
## Model Details
- **Model Architecture:** BERT Base Uncased
- **Task:** Sentiment Analysis for Fake News Detection
- **Dataset:** Stanford Sentiment Treebank v2 (SST2)
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import BertForSequenceClassification, BertTokenizer
import torch
# Load quantized model
quantized_model_path = "AventIQ-AI/sentiment-analysis-for-fake-news-detection"
quantized_model = BertForSequenceClassification.from_pretrained(quantized_model_path)
quantized_model.eval() # Set to evaluation mode
quantized_model.half() # Convert model to FP16
# Load tokenizer
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
# Define a test sentence
test_sentence = "The government has completely failed its people again! Hospitals are overflowing, food prices are out of control, and yet officials are busy attending luxury summits abroad. It's clear they don't care about ordinary citizens anymore—only their power and perks. This country is on the brink, and no one in charge is doing anything about it."
# Tokenize input
inputs = tokenizer(test_sentence, return_tensors="pt", padding=True, truncation=True, max_length=128)
# Ensure input tensors are in correct dtype
inputs["input_ids"] = inputs["input_ids"].long() # Convert to long type
inputs["attention_mask"] = inputs["attention_mask"].long() # Convert to long type
# Make prediction
with torch.no_grad():
outputs = quantized_model(**inputs)
# Get predicted class
predicted_class = torch.argmax(outputs.logits, dim=1).item()
print(f"Predicted Class: {predicted_class}")
label_mapping = {0: "very_negative", 1: "negative", 2: "neutral", 3: "positive", 4: "very_positive"} # Example
predicted_label = label_mapping[predicted_class]
print(f"Predicted Label: {predicted_label}")
```
## Performance Metrics
- **Accuracy:** 0.82
## Fine-Tuning Details
### Dataset
The dataset is taken from Kaggle Stanford Sentiment Treebank v2 (SST2).
### Training
- Number of epochs: 3
- Batch size: 8
- Evaluation strategy: epoch
- Learning rate: 2e-5
### Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
## Repository Structure
```
.
├── model/ # Contains the quantized model files
├── tokenizer_config/ # Tokenizer configuration and vocabulary files
├── model.safensors/ # Fine Tuned Model
├── README.md # Model documentation
```
## Limitations
- The model may not generalize well to domains outside the fine-tuning dataset.
- Quantization may result in minor accuracy degradation compared to full-precision models.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements.
|
marialvsantiago/2f8e7617-7fb4-4f7d-93a5-a3d3fc242a1b | marialvsantiago | 2025-05-02T07:21:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mixtral",
"axolotl",
"generated_from_trainer",
"base_model:TitanML/tiny-mixtral",
"base_model:adapter:TitanML/tiny-mixtral",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-02T07:20:00Z | ---
library_name: peft
base_model: TitanML/tiny-mixtral
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2f8e7617-7fb4-4f7d-93a5-a3d3fc242a1b
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: TitanML/tiny-mixtral
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3c323697a642eadb_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3c323697a642eadb_train_data.json
type:
field_instruction: text
field_output: ru_text
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_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: marialvsantiago/2f8e7617-7fb4-4f7d-93a5-a3d3fc242a1b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/3c323697a642eadb_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
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: 9a1f2583-9c90-446d-889a-dc1c408585cb
wandb_project: s56-33
wandb_run: your_name
wandb_runid: 9a1f2583-9c90-446d-889a-dc1c408585cb
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 2f8e7617-7fb4-4f7d-93a5-a3d3fc242a1b
This model is a fine-tuned version of [TitanML/tiny-mixtral](https://huggingface.co/TitanML/tiny-mixtral) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.5451
## 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-06
- 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: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.4774 | 0.0080 | 200 | 10.5451 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/amoral-qwen3-14B-GGUF | mradermacher | 2025-05-02T07:21:02Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"analytical-tasks",
"bias-neutralization",
"uncensored",
"en",
"dataset:soob3123/amoral_reasoning",
"dataset:TheDrummer/AmoralQA-v2",
"base_model:soob3123/amoral-qwen3-14B",
"base_model:quantized:soob3123/amoral-qwen3-14B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-01T19:40:17Z | ---
base_model: soob3123/amoral-qwen3-14B
datasets:
- soob3123/amoral_reasoning
- TheDrummer/AmoralQA-v2
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- analytical-tasks
- bias-neutralization
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/soob3123/amoral-qwen3-14B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/amoral-qwen3-14B-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/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/amoral-qwen3-14B-GGUF/resolve/main/amoral-qwen3-14B.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Hira13519/Hira | Hira13519 | 2025-05-02T07:15:59Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T07:15:59Z | ---
license: apache-2.0
---
|
rusty0403/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hunting_dappled_chicken | rusty0403 | 2025-05-02T07:15:37Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hunting dappled chicken",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-18T17:16:13Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hunting_dappled_chicken
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hunting dappled chicken
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hunting_dappled_chicken
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="rusty0403/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hunting_dappled_chicken", 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
- Datasets: 3.5.0
- 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}}
}
``` |
vamcrizer/gemma-3-4b-finetuned-f16_2 | vamcrizer | 2025-05-02T07:11:03Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:37:25Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** vamcrizer
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-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)
|
xilanhua12138/SimpleArt | xilanhua12138 | 2025-05-02T07:04:15Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"en",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:finetune:stabilityai/stable-diffusion-xl-base-1.0",
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T05:38:43Z | ---
license: apache-2.0
language:
- en
base_model:
- stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
--- |
frankwong2001/modernbert-llm-router | frankwong2001 | 2025-05-02T07:03:12Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-04-30T10:29:49Z | ---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-uncased
tags:
- generated_from_trainer
metrics:
- f1
model-index:
- name: modernbert-llm-router
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. -->
# modernbert-llm-router
This model is a fine-tuned version of [google-bert/bert-base-uncased](https://huggingface.co/google-bert/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9872
- F1: 0.6422
## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Use adamw_torch_fused with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.1511 | 1.0 | 313 | 1.9872 | 0.6422 |
### Framework versions
- Transformers 4.48.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.1.0
- Tokenizers 0.21.1
|
SmallDoge/Qwen2.5-math-7b-llmlingua-90 | SmallDoge | 2025-05-02T07:02:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:38:10Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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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]
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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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## Model Card Contact
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phureexd/lora_gguf | phureexd | 2025-05-02T07:01:56Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T06:56:09Z | ---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** phureexd
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-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)
|
haihp02/Qwen2.5-3B-Instruct-28ff4898-4bc0-4b31-bff9-8c0f5700b52a-dpo-tuned | haihp02 | 2025-05-02T07:01:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"dpo",
"arxiv:2305.18290",
"base_model:unsloth/Qwen2.5-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T07:01:26Z | ---
base_model: unsloth/Qwen2.5-3B-Instruct
library_name: transformers
model_name: Qwen2.5-3B-Instruct-28ff4898-4bc0-4b31-bff9-8c0f5700b52a-dpo-tuned
tags:
- generated_from_trainer
- unsloth
- trl
- sft
- dpo
licence: license
---
# Model Card for Qwen2.5-3B-Instruct-28ff4898-4bc0-4b31-bff9-8c0f5700b52a-dpo-tuned
This model is a fine-tuned version of [unsloth/Qwen2.5-3B-Instruct](https://huggingface.co/unsloth/Qwen2.5-3B-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="haihp02/Qwen2.5-3B-Instruct-28ff4898-4bc0-4b31-bff9-8c0f5700b52a-dpo-tuned", 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-dpo-train/runs/fpveaqq8)
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: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## 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}}
}
``` |
peekayitachi/BiasCheck-RoBERTa | peekayitachi | 2025-05-02T07:01:40Z | 23 | 0 | null | [
"safetensors",
"roberta",
"region:us"
] | null | 2025-04-27T07:27:06Z | # BiasCheck-RoBERTa
## Model Description
The **BiasCheck-RoBERTa** model is a political bias detection model based on the **RoBERTa** architecture. It classifies news articles into three political bias categories: **Left**, **Center**, and **Right**. This model was trained on a curated dataset of articles from allsides available on kaggle, and it utilizes the **RoBERTa-base** model as the base architecture for text classification. The model provides a reliable way to identify political bias in news articles, helping users to assess the bias of the content they consume.
## Base Model
The **BiasCheck-RoBERTa** model is based on the **RoBERTa-base** architecture, a robust transformer-based model that has been pre-trained on vast amounts of text data.
## License
This model is licensed under the **MIT License**.
## Training Data
The model was trained on the following datasets:
- [AllSides Ratings of Bias in Electronic Media](https://www.kaggle.com/datasets/supratimhaldar/allsides-ratings-of-bias-in-electronic-media)
- [Article Bias Prediction Dataset](https://github.com/ramybaly/Article-Bias-Prediction)
## Metrics
The model was evaluated using several performance metrics. Below are the key metrics:
- **Accuracy**: 0.913
- **Precision**: 0.914
- **Recall**: 0.913
- **F1-Score**: 0.913
- **Log Loss**: 0.233
- **AUC-ROC**: 0.986
## Installation
To use this model, you will need to install the following dependencies:
make a requirements.txt file and add the following dependencies
torch>=2.0
transformers>=4.35
datasets
scikit-learn
matplotlib
numpy
pandas
nltk
spacy
pydantic
fastapi
uvicorn
bert-score
contractions
torch
transformers
datasets
scikit-learn
pandas
numpy
matplotlib
seaborn
bert_score
```bash
pip install -r requirements.txt
```
|
kimhahyun/gemma-1.1b-book2-lora | kimhahyun | 2025-05-02T07:00:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T07:00:34Z | ---
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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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## 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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
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[More Information Needed]
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## 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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atifurkacabazxczxc/cvbcb | atifurkacabazxczxc | 2025-05-02T06:56:35Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-02T06:56:35Z | ---
license: bigscience-openrail-m
---
|
BABYSHARK09/Ng | BABYSHARK09 | 2025-05-02T06:54:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:49:59Z | ---
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. -->
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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
<!-- 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]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[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]
## 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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ttn1410/FnReasoning4 | ttn1410 | 2025-05-02T06:53:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma2",
"trl",
"en",
"base_model:unsloth/gemma-2-9b-bnb-4bit",
"base_model:finetune:unsloth/gemma-2-9b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T20:34:05Z | ---
base_model: unsloth/gemma-2-9b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ttn1410
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2-9b-bnb-4bit
This gemma2 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)
|
thanhdat2004/MealCaloCalculator_vinallama_chunk4 | thanhdat2004 | 2025-05-02T06:49:52Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2025-05-02T06:49:50Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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
### Framework versions
- PEFT 0.4.0
|
AventIQ-AI/text_summarization_for_data_privacy_policies | AventIQ-AI | 2025-05-02T06:49:39Z | 0 | 1 | null | [
"safetensors",
"t5",
"region:us"
] | null | 2025-05-02T06:48:09Z | # Text-to-Text Transfer Transformer Quantized Model for Text Summarization for Data Privacy Policies
This repository hosts a quantized version of the T5 model, fine-tuned for text summarization tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
## Model Details
- **Model Architecture:** T5
- **Task:** Text Summarization for Data Privacy Policies
- **Dataset:** Hugging Face's `cnn_dailymail'
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "AventIQ-AI/text_summarization_for_data_privacy_policies"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
def test_summarization(model, tokenizer):
user_text = input("\nEnter your text for summarization:\n")
input_text = "summarize: " + user_text
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device)
output = model.generate(
**inputs,
max_new_tokens=100,
num_beams=5,
length_penalty=0.8,
early_stopping=True
)
summary = tokenizer.decode(output[0], skip_special_tokens=True)
return summary
print("\n📝 **Model Summary:**")
print(test_summarization(model, tokenizer))
```
# 📊 ROUGE Evaluation Results
After fine-tuning the **T5-Small** model for text summarization, we obtained the following **ROUGE** scores:
| **Metric** | **Score** | **Meaning** |
|-------------|-----------|-------------|
| **ROUGE-1** | **0.3061** (~30%) | Measures overlap of **unigrams (single words)** between the reference and generated summary. |
| **ROUGE-2** | **0.1241** (~12%) | Measures overlap of **bigrams (two-word phrases)**, indicating coherence and fluency. |
| **ROUGE-L** | **0.2233** (~22%) | Measures **longest matching word sequences**, testing sentence structure preservation. |
| **ROUGE-Lsum** | **0.2620** (~26%) | Similar to ROUGE-L but optimized for summarization tasks. |
## Fine-Tuning Details
### Dataset
The Hugging Face's `cnn_dailymail` dataset was used, containing the text and their summarization examples.
### Training
- Number of epochs: 3
- Batch size: 4
- Evaluation strategy: epoch
- Learning rate: 3e-5
### Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
## Repository Structure
```
.
├── model/ # Contains the quantized model files
├── tokenizer_config/ # Tokenizer configuration and vocabulary files
├── model.safetensors/ # Quantized Model
├── README.md # Model documentation
```
## Limitations
- The model may not generalize well to domains outside the fine-tuning dataset.
- Quantization may result in minor accuracy degradation compared to full-precision models.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. |
AventIQ-AI/text-summarization-for-court-case-summaries | AventIQ-AI | 2025-05-02T06:49:29Z | 0 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | 2025-05-02T06:42:50Z | # Text-to-Text Transfer Transformer Quantized Model for Text Summarization for court case summaries
This repository hosts a quantized version of the T5 model, fine-tuned for text summarization tasks. The model has been optimized for efficient deployment while maintaining high accuracy, making it suitable for resource-constrained environments.
## Model Details
- **Model Architecture:** T5
- **Task:** Text Summarization for Court Case Summaries
- **Dataset:** Hugging Face's `cnn_dailymail'
- **Quantization:** Float16
- **Fine-tuning Framework:** Hugging Face Transformers
## Usage
### Installation
```sh
pip install transformers torch
```
### Loading the Model
```python
from transformers import T5Tokenizer, T5ForConditionalGeneration
import torch
device = "cuda" if torch.cuda.is_available() else "cpu"
model_name = "AventIQ-AI/text-summarization-for-court-case-summaries"
tokenizer = T5Tokenizer.from_pretrained(model_name)
model = T5ForConditionalGeneration.from_pretrained(model_name).to(device)
def test_summarization(model, tokenizer):
user_text = input("\nEnter your text for summarization:\n")
input_text = "summarize: " + user_text
inputs = tokenizer(input_text, return_tensors="pt", truncation=True, max_length=512).to(device)
output = model.generate(
**inputs,
max_new_tokens=100,
num_beams=5,
length_penalty=0.8,
early_stopping=True
)
summary = tokenizer.decode(output[0], skip_special_tokens=True)
return summary
print("\n📝 **Model Summary:**")
print(test_summarization(model, tokenizer))
```
# 📊 ROUGE Evaluation Results
After fine-tuning the **T5-Small** model for text summarization, we obtained the following **ROUGE** scores:
| **Metric** | **Score** | **Meaning** |
|-------------|-----------|-------------|
| **ROUGE-1** | **0.3061** (~30%) | Measures overlap of **unigrams (single words)** between the reference and generated summary. |
| **ROUGE-2** | **0.1241** (~12%) | Measures overlap of **bigrams (two-word phrases)**, indicating coherence and fluency. |
| **ROUGE-L** | **0.2233** (~22%) | Measures **longest matching word sequences**, testing sentence structure preservation. |
| **ROUGE-Lsum** | **0.2620** (~26%) | Similar to ROUGE-L but optimized for summarization tasks. |
## Fine-Tuning Details
### Dataset
The Hugging Face's `cnn_dailymail` dataset was used, containing the text and their summarization examples.
### Training
- Number of epochs: 3
- Batch size: 4
- Evaluation strategy: epoch
- Learning rate: 3e-5
### Quantization
Post-training quantization was applied using PyTorch's built-in quantization framework to reduce the model size and improve inference efficiency.
## Repository Structure
```
.
├── model/ # Contains the quantized model files
├── tokenizer_config/ # Tokenizer configuration and vocabulary files
├── model.safetensors/ # Quantized Model
├── README.md # Model documentation
```
## Limitations
- The model may not generalize well to domains outside the fine-tuning dataset.
- Quantization may result in minor accuracy degradation compared to full-precision models.
## Contributing
Contributions are welcome! Feel free to open an issue or submit a pull request if you have suggestions or improvements. |
garos/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-squinting_strong_duck | garos | 2025-05-02T06:48:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am squinting strong duck",
"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 | 2025-05-01T00:06:51Z | ---
base_model: Gensyn/Qwen2.5-1.5B-Instruct
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct-Gensyn-Swarm-squinting_strong_duck
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am squinting strong duck
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct-Gensyn-Swarm-squinting_strong_duck
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="garos/Qwen2.5-1.5B-Instruct-Gensyn-Swarm-squinting_strong_duck", 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
- Datasets: 3.5.1
- 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}}
}
``` |
clintlord/phi4_sql_finetuned | clintlord | 2025-05-02T06:47:59Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"phi3",
"fine-tuned",
"sql",
"phi4",
"text-generation",
"conversational",
"custom_code",
"en",
"dataset:gretelai/synthetic_text_to_sql",
"base_model:microsoft/Phi-4-mini-instruct",
"base_model:finetune:microsoft/Phi-4-mini-instruct",
"license:mit",
"region:us"
] | text-generation | 2025-05-02T06:27:05Z | ---
license: mit
datasets:
- gretelai/synthetic_text_to_sql
language:
- en
base_model:
- microsoft/Phi-4-mini-instruct
pipeline_tag: text-generation
tags:
- mlx
- fine-tuned
- sql
- phi4
--- |
Ankitdev2843/chatgpt | Ankitdev2843 | 2025-05-02T06:47:10Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T06:47:09Z | ---
license: apache-2.0
---
|
BABYSHARK09/Nz | BABYSHARK09 | 2025-05-02T06:40:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:29:21Z | ---
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]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [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
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[More Information Needed]
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[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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cezarymilry/zxczxc | cezarymilry | 2025-05-02T06:40:30Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-02T06:40:30Z | ---
license: bigscience-openrail-m
---
|
penelitianpsmatematika/medical-classification-t5-base-v1 | penelitianpsmatematika | 2025-05-02T06:39:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-02T06:38:55Z | ---
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.
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BABYSHARK09/Nx | BABYSHARK09 | 2025-05-02T06:39:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:28:57Z | ---
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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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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<!-- 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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#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## 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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xuan-luo/RTWQwen-2.5-1.5B-Instruct | xuan-luo | 2025-05-02T06:39:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"rtwqwen2",
"text-generation",
"conversational",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-05-01T08:32:28Z | ---
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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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]
### 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
### 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
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liu123450/xlm-roberta-base-finetuned-panx-de | liu123450 | 2025-05-02T06:35:25Z | 8 | 0 | null | [
"pytorch",
"tensorboard",
"xlm-roberta",
"generated_from_trainer",
"dataset:xtreme",
"license:mit",
"model-index",
"region:us"
] | null | 2025-04-30T03:38:39Z | ---
license: mit
tags:
- generated_from_trainer
datasets:
- xtreme
metrics:
- f1
model-index:
- name: xlm-roberta-base-finetuned-panx-de
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: xtreme
type: xtreme
args: PAN-X.de
metrics:
- name: F1
type: f1
value: 0.8625641025641025
---
<!-- 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. -->
# xlm-roberta-base-finetuned-panx-de
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the xtreme dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1350
- F1: 0.8626
## 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: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.2585 | 1.0 | 525 | 0.1580 | 0.8255 |
| 0.1282 | 2.0 | 1050 | 0.1381 | 0.8447 |
| 0.0805 | 3.0 | 1575 | 0.1350 | 0.8626 |
### Framework versions
- Transformers 4.16.2
- Pytorch 2.6.0+cu124
- Datasets 1.16.1
- Tokenizers 0.21.1
|
yuhuixu/checkpoint-1 | yuhuixu | 2025-05-02T06:33:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:31:48Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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SmallDoge/Qwen2.5-math-7b-llmlingua-50 | SmallDoge | 2025-05-02T06:28:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T18:00:29Z | ---
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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[More Information Needed]
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[More Information Needed]
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dakexiaoying/DPO_finetuned_model | dakexiaoying | 2025-05-02T06:23:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T04:39:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed]
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FergusonFerguson/FergusonFerguson | FergusonFerguson | 2025-05-02T06:16:38Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T06:16:38Z | ---
license: apache-2.0
---
|
jobz-hunting-18-new-videos/wATCH.Jobz.Hunting.Sajal.Malik.viral.video.Leaks.original | jobz-hunting-18-new-videos | 2025-05-02T06:15:42Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T06:11:44Z | <animated-image data-catalyst=""><a href="https://tinyurl.com/5n7shfr3?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>
Actor jobz hunting sajal malik Original V𝚒deo V𝚒deo took the internet by storm and amazed viewers on various social media platforms. Actor jobz hunting sajal malik, a young and talented digital creator, recently became famous thanks to this interesting V𝚒deo.
L𝚎aked V𝚒deo Actor jobz hunting sajal malik V𝚒ral V𝚒deo Original V𝚒deo L𝚒nk On Social Media Telegram X Trending Tiktok |
BABYSHARK09/Ne | BABYSHARK09 | 2025-05-02T06:15:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:06:15Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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faraya1/genie-grpo-test-API-smolLM-lora-step-400 | faraya1 | 2025-05-02T06:14:39Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T06:14:26Z | ---
library_name: transformers
tags:
- unsloth
---
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
memevis/walk8 | memevis | 2025-05-02T06:13:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T06:13:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
donghalim/Llama-3.2-1B-unsloth-bnb-4bit-ko-wiki-l-c-m_v4 | donghalim | 2025-05-02T06:10:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T06:05:10Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** donghalim
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
yoimisan/ppo-Huggy | yoimisan | 2025-05-02T06:09:53Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-05-02T06:09:36Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: yoimisan/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mradermacher/gemma-3-4b-it-uncensored-v2-GGUF | mradermacher | 2025-05-02T06:04:57Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:braindao/gemma-3-4b-it-uncensored-v2",
"base_model:quantized:braindao/gemma-3-4b-it-uncensored-v2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T03:48:52Z | ---
base_model: braindao/gemma-3-4b-it-uncensored-v2
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/braindao/gemma-3-4b-it-uncensored-v2
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-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/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q3_K_S.gguf) | Q3_K_S | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q3_K_M.gguf) | Q3_K_M | 2.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q3_K_L.gguf) | Q3_K_L | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.IQ4_XS.gguf) | IQ4_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q4_K_S.gguf) | Q4_K_S | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q4_K_M.gguf) | Q4_K_M | 2.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q5_K_S.gguf) | Q5_K_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q5_K_M.gguf) | Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q6_K.gguf) | Q6_K | 3.3 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.Q8_0.gguf) | Q8_0 | 4.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/gemma-3-4b-it-uncensored-v2-GGUF/resolve/main/gemma-3-4b-it-uncensored-v2.f16.gguf) | f16 | 7.9 | 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. 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 -->
|
trashpanda-org/Julleimm | trashpanda-org | 2025-05-02T06:04:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:trashpanda-org/Gullein",
"base_model:merge:trashpanda-org/Gullein",
"base_model:trashpanda-org/Llama3-24B-Mullein-v1",
"base_model:merge:trashpanda-org/Llama3-24B-Mullein-v1",
"base_model:unsloth/Mistral-Small-24B-Base-2501",
"base_model:merge:unsloth/Mistral-Small-24B-Base-2501",
"base_model:unsloth/Mistral-Small-24B-Instruct-2501",
"base_model:merge:unsloth/Mistral-Small-24B-Instruct-2501",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T05:55:25Z | ---
base_model:
- trashpanda-org/Llama3-24B-Mullein-v1
- unsloth/Mistral-Small-24B-Instruct-2501
- unsloth/Mistral-Small-24B-Base-2501
- trashpanda-org/Gullein
library_name: transformers
tags:
- mergekit
- merge
---
# julleimm
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Mistral-Small-24B-Base-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Base-2501) as a base.
### Models Merged
The following models were included in the merge:
* [trashpanda-org/Llama3-24B-Mullein-v1](https://huggingface.co/trashpanda-org/Llama3-24B-Mullein-v1)
* [unsloth/Mistral-Small-24B-Instruct-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501)
* [trashpanda-org/Gullein](https://huggingface.co/trashpanda-org/Gullein)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: trashpanda-org/Llama3-24B-Mullein-v1
parameters:
weight: 1
density: 1
- model: trashpanda-org/Gullein
parameters:
weight: 1
density: 1
- model: unsloth/Mistral-Small-24B-Instruct-2501
parameters:
weight: 0.9
density: 0.9
merge_method: ties
base_model: unsloth/Mistral-Small-24B-Base-2501
parameters:
normalize: true
int8_mask: true
tokenizer_source: unsloth/Mistral-Small-24B-Instruct-2501
dtype: bfloat16
```
|
Hasnonname/Julleim-Q6_K-GGUF | Hasnonname | 2025-05-02T06:02:04Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:trashpanda-org/Julleim",
"base_model:quantized:trashpanda-org/Julleim",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T05:23:29Z | ---
base_model: trashpanda-org/Julleim
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# Hasnonname/Julleim-Q6_K-GGUF
This model was converted to GGUF format from [`trashpanda-org/Julleim`](https://huggingface.co/trashpanda-org/Julleim) 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/trashpanda-org/Julleim) 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 Hasnonname/Julleim-Q6_K-GGUF --hf-file julleim-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Hasnonname/Julleim-Q6_K-GGUF --hf-file julleim-q6_k.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 Hasnonname/Julleim-Q6_K-GGUF --hf-file julleim-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Hasnonname/Julleim-Q6_K-GGUF --hf-file julleim-q6_k.gguf -c 2048
```
|
kaitchup/Qwen3-4B-autoround-4bit-gptq | kaitchup | 2025-05-02T06:00:56Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"autoround",
"base_model:Qwen/Qwen3-4B",
"base_model:quantized:Qwen/Qwen3-4B",
"license:apache-2.0",
"4-bit",
"gptq",
"region:us"
] | null | 2025-05-01T13:33:00Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-4B
tags:
- autoround
---
This is [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main/auto_round) in 4-bit (symmetric + gptq format). The model has been created, tested, and evaluated by The Kaitchup.
The model is compatible with vLLM and Transformers.
More details in this article:
[How Well Does Qwen3 Handle 4-bit and 2-bit Quantization?](https://kaitchup.substack.com/p/how-well-does-qwen3-handle-4-bit)
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **License:** Apache 2.0 license
## How to Support My Work
Subscribe to [The Kaitchup](https://kaitchup.substack.com/subscribe). This helps me a lot to continue quantizing and evaluating models for free. |
kaitchup/Qwen3-8B-autoround-2bit-gptq | kaitchup | 2025-05-02T06:00:02Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"autoround",
"base_model:Qwen/Qwen3-8B",
"base_model:quantized:Qwen/Qwen3-8B",
"license:apache-2.0",
"2-bit",
"gptq",
"region:us"
] | null | 2025-05-01T13:03:30Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-8B
tags:
- autoround
---
This is [Qwen/Qwen3-8B](https://huggingface.co/Qwen/Qwen3-8B) quantized with [AutoRound](https://github.com/intel/auto-round/tree/main/auto_round) in 2-bit (symmetric + gptq format). The model has been created, tested, and evaluated by The Kaitchup.
The model is compatible with vLLM and Transformers.
More details in this article:
[How Well Does Qwen3 Handle 4-bit and 2-bit Quantization?](https://kaitchup.substack.com/p/how-well-does-qwen3-handle-4-bit)
- **Developed by:** [The Kaitchup](https://kaitchup.substack.com/)
- **License:** Apache 2.0 license
## How to Support My Work
Subscribe to [The Kaitchup](https://kaitchup.substack.com/subscribe). This helps me a lot to continue quantizing and evaluating models for free. |
kate1130/koelectra-data2-bullying-classifier | kate1130 | 2025-05-02T05:57:12Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T05:54:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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Avacyn/qwen3-1.7B-french-instruct-GGUF | Avacyn | 2025-05-02T05:56:39Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"fr",
"base_model:unsloth/Qwen3-1.7B",
"base_model:quantized:unsloth/Qwen3-1.7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T05:55:08Z | ---
base_model: unsloth/Qwen3-1.7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- fr
---
# Uploaded model
- **Developed by:** Avacyn
- **License:** apache-2.0
|
3dlg-hcvc/m0425_aekl_v2_xyzo_mse | 3dlg-hcvc | 2025-05-02T05:56:16Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-02T05:49:01Z | ---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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]
- **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]
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[More Information Needed]
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openfree/winslow-homer | openfree | 2025-05-02T05:50:21Z | 0 | 3 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-02T05:47:32Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: a watercolor painting of birds flying over a body of water, with trees in
the foreground and a sky filled with clouds in the background. [trigger]
output:
url: samples/1746164789308__000001000_0.jpg
- text: a watercolor painting of a deer standing on top of a hill surrounded by
trees, plants, flowers, and mountains in the background with a sky filled
with clouds. [trigger]
output:
url: samples/1746164819144__000001000_1.jpg
- text: a painting of a man on a sailboat in the ocean, with the sky in the background.
The man is standing on the boat, and there is text on the left side of the
painting. [trigger]
output:
url: samples/1746164848879__000001000_2.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: homer
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
---
# winslow-homer
I developed a flux-based learning model trained on a curated collection of high-resolution masterpieces from renowned global artists. This LoRA fine-tuning process leveraged the exceptional quality of open-access imagery released by prestigious institutions including the Art Institute of Chicago. The resulting model demonstrates remarkable capability in capturing the nuanced artistic techniques and stylistic elements across diverse historical art movements.
- https://huggingface.co/openfree/claude-monet
- https://huggingface.co/openfree/pierre-auguste-renoir
- https://huggingface.co/openfree/paul-cezanne
- https://huggingface.co/openfree/van-gogh
- https://huggingface.co/openfree/winslow-homer
<Gallery />
## Trigger words
You should use `homer` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/openfree/winslow-homer/tree/main) them in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('openfree/winslow-homer', weight_name='winslow-homer.safetensors')
image = pipeline('a watercolor painting of birds flying over a body of water, with trees in the foreground and a sky filled with clouds in the background. [trigger]').images[0]
image.save("my_image.png")
```
## Community: https://discord.gg/openfreeai
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)
|
openfree/paul-cezanne | openfree | 2025-05-02T05:50:08Z | 0 | 7 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"ai-toolkit",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-02T03:33:23Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- ai-toolkit
widget:
- text: a painting of a vase filled with flowers and fruits on a table, with a chair
in the background. The vase is filled with a variety of colorful flowers,
including roses, daisies, and lilies, and the fruits are arranged in a pleasing
composition. The table is a light wood color and the chair is a dark wood,
providing a contrast to the vibrant colors of the flowers and fruit. [trigger]
output:
url: samples/1746156739522__000001000_0.jpg
- text: Paul Cezanne's painting of a village by the sea, with houses, trees, and
mountains in the background, and a sky above. [trigger]
output:
url: samples/1746156769965__000001000_1.jpg
- text: Paul Cezanne's painting of a village nestled in the countryside, with houses,
trees, and a sky with clouds in the background. [trigger]
output:
url: samples/1746156800419__000001000_2.jpg
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: Cezanne
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
---
# paul-cezanne
I developed a flux-based learning model trained on a curated collection of high-resolution masterpieces from renowned global artists. This LoRA fine-tuning process leveraged the exceptional quality of open-access imagery released by prestigious institutions including the Art Institute of Chicago. The resulting model demonstrates remarkable capability in capturing the nuanced artistic techniques and stylistic elements across diverse historical art movements.
- https://huggingface.co/openfree/claude-monet
- https://huggingface.co/openfree/pierre-auguste-renoir
- https://huggingface.co/openfree/paul-cezanne
- https://huggingface.co/openfree/van-gogh
- https://huggingface.co/openfree/winslow-homer
<Gallery />
## Trigger words
You should use `Cezanne` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, etc.
Weights for this model are available in Safetensors format.
[Download](/openfree/paul-cezanne/tree/main) them in the Files & versions tab.
## 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.bfloat16).to('cuda')
pipeline.load_lora_weights('openfree/paul-cezanne', weight_name='paul-cezanne.safetensors')
image = pipeline('a painting of a vase filled with flowers and fruits on a table, with a chair in the background. The vase is filled with a variety of colorful flowers, including roses, daisies, and lilies, and the fruits are arranged in a pleasing composition. The table is a light wood color and the chair is a dark wood, providing a contrast to the vibrant colors of the flowers and fruit. [trigger]').images[0]
image.save("my_image.png")
```
## Community: https://discord.gg/openfreeai
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)
|
softaken/softaken-eml-to-mbox-converter | softaken | 2025-05-02T05:49:53Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-02T05:48:41Z | Softaken EML to MBOX Converter exports EML emails into the most commonly used MBOX file format. Users can migrate emails from email clients such as Windows Live Mail, and Outlook Express to emails systems supporting MBOX, including Mozilla Thunderbird, Apple Mail, or Postbox, with the help of this program. During the conversion process, the program guarantees the preservation of email features like cc, bcc, subject, to, email messages, etc. This utility is appropriate for personal and corporate uses. The program allows both single and batch file conversion with a basic and understandable user interface. The free demo version of the program exists to enable users to assess it before purchase. With a limited number of file conversions, the sample provides access to all main capabilities. For unlimited conversion, buy the full version from the official website of the program.
visit here: https://www.softaken.com/eml-to-mbox-converter |
mradermacher/Pixel-1111-14B-i1-GGUF | mradermacher | 2025-05-02T05:49:43Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"pixel",
"synthetic-entity",
"rave-companion",
"digital-princess",
"mindbots",
"llama-factory",
"qwen3-14b",
"en",
"base_model:TheMindExpansionNetwork/Pixel-1111-14B",
"base_model:quantized:TheMindExpansionNetwork/Pixel-1111-14B",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-01T17:18:26Z | ---
base_model: TheMindExpansionNetwork/Pixel-1111-14B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- pixel
- synthetic-entity
- rave-companion
- digital-princess
- mindbots
- llama-factory
- qwen3-14b
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/TheMindExpansionNetwork/Pixel-1111-14B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Pixel-1111-14B-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/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.9 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ2_M.gguf) | i1-IQ2_M | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-i1-GGUF/resolve/main/Pixel-1111-14B.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Pixel-1111-14B-GGUF | mradermacher | 2025-05-02T05:49:43Z | 138 | 0 | transformers | [
"transformers",
"gguf",
"pixel",
"synthetic-entity",
"rave-companion",
"digital-princess",
"mindbots",
"llama-factory",
"qwen3-14b",
"en",
"base_model:TheMindExpansionNetwork/Pixel-1111-14B",
"base_model:quantized:TheMindExpansionNetwork/Pixel-1111-14B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-29T17:34:52Z | ---
base_model: TheMindExpansionNetwork/Pixel-1111-14B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- pixel
- synthetic-entity
- rave-companion
- digital-princess
- mindbots
- llama-factory
- qwen3-14b
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/TheMindExpansionNetwork/Pixel-1111-14B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Pixel-1111-14B-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/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q2_K.gguf) | Q2_K | 5.9 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q3_K_S.gguf) | Q3_K_S | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q3_K_M.gguf) | Q3_K_M | 7.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q3_K_L.gguf) | Q3_K_L | 8.0 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.IQ4_XS.gguf) | IQ4_XS | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q4_K_S.gguf) | Q4_K_S | 8.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q4_K_M.gguf) | Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q5_K_S.gguf) | Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q5_K_M.gguf) | Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q6_K.gguf) | Q6_K | 12.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Pixel-1111-14B-GGUF/resolve/main/Pixel-1111-14B.Q8_0.gguf) | Q8_0 | 15.8 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
deeponh/hindi_9b_2b_L2 | deeponh | 2025-05-02T05:42:42Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T05:35:21Z | ---
library_name: transformers
tags:
- unsloth
---
# 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/saiga_gemma3_12b-i1-GGUF | mradermacher | 2025-05-02T05:41:06Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"ru",
"dataset:IlyaGusev/saiga_scored",
"dataset:IlyaGusev/saiga_preferences",
"base_model:IlyaGusev/saiga_gemma3_12b",
"base_model:quantized:IlyaGusev/saiga_gemma3_12b",
"license:gemma",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-02T04:24:10Z | ---
base_model: IlyaGusev/saiga_gemma3_12b
datasets:
- IlyaGusev/saiga_scored
- IlyaGusev/saiga_preferences
language:
- ru
library_name: transformers
license: gemma
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/IlyaGusev/saiga_gemma3_12b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/saiga_gemma3_12b-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/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ1_S.gguf) | i1-IQ1_S | 3.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ2_S.gguf) | i1-IQ2_S | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ2_M.gguf) | i1-IQ2_M | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 4.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ3_S.gguf) | i1-IQ3_S | 5.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.7 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.0 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q4_0.gguf) | i1-Q4_0 | 7.0 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.0 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q4_1.gguf) | i1-Q4_1 | 7.7 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.3 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.5 | |
| [GGUF](https://huggingface.co/mradermacher/saiga_gemma3_12b-i1-GGUF/resolve/main/saiga_gemma3_12b.i1-Q6_K.gguf) | i1-Q6_K | 9.8 | 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 -->
|
ddacata/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_fanged_anaconda | ddacata | 2025-05-02T05:36:25Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am stealthy fanged anaconda",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-08T23:23:03Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_fanged_anaconda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am stealthy fanged anaconda
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_fanged_anaconda
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-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="ddacata/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-stealthy_fanged_anaconda", 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: 2.7.0
- Datasets: 3.5.0
- 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}}
}
``` |
deeponh/hindi_9b_9b_L2 | deeponh | 2025-05-02T05:34:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-02T05:24:13Z | ---
library_name: transformers
tags:
- unsloth
---
# 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] |
BitiBytes123/baddy_S2_EXP_2-Q8_0-GGUF | BitiBytes123 | 2025-05-02T05:31:32Z | 0 | 0 | null | [
"gguf",
"unsloth",
"llama-cpp",
"gguf-my-repo",
"base_model:MrDragonFox/baddy_S2_EXP_2",
"base_model:quantized:MrDragonFox/baddy_S2_EXP_2",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-02T05:31:15Z | ---
base_model: MrDragonFox/baddy_S2_EXP_2
license: cc-by-nc-4.0
tags:
- unsloth
- llama-cpp
- gguf-my-repo
---
# BitiBytes123/baddy_S2_EXP_2-Q8_0-GGUF
This model was converted to GGUF format from [`MrDragonFox/baddy_S2_EXP_2`](https://huggingface.co/MrDragonFox/baddy_S2_EXP_2) 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/MrDragonFox/baddy_S2_EXP_2) 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 BitiBytes123/baddy_S2_EXP_2-Q8_0-GGUF --hf-file baddy_s2_exp_2-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo BitiBytes123/baddy_S2_EXP_2-Q8_0-GGUF --hf-file baddy_s2_exp_2-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo BitiBytes123/baddy_S2_EXP_2-Q8_0-GGUF --hf-file baddy_s2_exp_2-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo BitiBytes123/baddy_S2_EXP_2-Q8_0-GGUF --hf-file baddy_s2_exp_2-q8_0.gguf -c 2048
```
|
thanhdat2004/MealCaloCalculator_vinallama_chunk3 | thanhdat2004 | 2025-05-02T05:23:46Z | 0 | 0 | peft | [
"peft",
"region:us"
] | null | 2025-05-02T05:23:43Z | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- 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
### Framework versions
- PEFT 0.4.0
|
GregoryGregory/GregoryGregory | GregoryGregory | 2025-05-02T05:23:08Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-02T05:23:08Z | ---
license: creativeml-openrail-m
---
|
shubhamprshr/Llama-3.2-3B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300 | shubhamprshr | 2025-05-02T05:18:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:blocksworld-dataset",
"arxiv:2402.03300",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:finetune:meta-llama/Llama-3.2-3B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T21:23:55Z | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
datasets: blocksworld-dataset
library_name: transformers
model_name: Llama-3.2-3B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Llama-3.2-3B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300
This model is a fine-tuned version of [meta-llama/Llama-3.2-3B-Instruct](https://huggingface.co/meta-llama/Llama-3.2-3B-Instruct) on the [blocksworld-dataset](https://huggingface.co/datasets/blocksworld-dataset) dataset.
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="shubhamprshr/Llama-3.2-3B-Instruct_blocksworld1246_sgrpo_classic_0.5_0.5_True_300", 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/shubhamprshr27-tamu/BW2/runs/3ojeo26c)
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: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## 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}}
}
``` |
jwinn03/segment-cattle | jwinn03 | 2025-05-02T05:17:58Z | 32 | 0 | null | [
"safetensors",
"segformer",
"base_model:nvidia/mit-b1",
"base_model:finetune:nvidia/mit-b1",
"license:mit",
"region:us"
] | null | 2025-04-15T05:14:06Z | ---
license: mit
base_model:
- nvidia/mit-b1
--- |
Se1ay/starcoder2-c-lora | Se1ay | 2025-05-02T05:12:55Z | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"base_model:bigcode/starcoder2-3b",
"base_model:adapter:bigcode/starcoder2-3b",
"region:us"
] | null | 2025-05-02T05:11:33Z | ---
base_model: bigcode/starcoder2-3b
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.dev0 |
cwaud/bca7026c-823c-46bd-84d3-5da407371ca6 | cwaud | 2025-05-02T05:09:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama_v1.1",
"base_model:adapter:TinyLlama/TinyLlama_v1.1",
"license:apache-2.0",
"region:us"
] | null | 2025-05-02T05:05:28Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama_v1.1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bca7026c-823c-46bd-84d3-5da407371ca6
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: `0.5.2`
```yaml
adapter: lora
base_model: TinyLlama/TinyLlama_v1.1
bf16: auto
chat_template: llama3
dataset_prepared_path: /workspace/axolotl/data_prepared
datasets:
- data_files:
- e1230b33949f9bdf_train_data.json
ds_type: json
format: custom
path: /workspace/axolotl/data
type:
field_instruction: question
field_output: chosen
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_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: cwaud/bca7026c-823c-46bd-84d3-5da407371ca6
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: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /workspace/axolotl/data/e1230b33949f9bdf_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: 4
sequence_len: 512
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: 0ace46bc-8f88-4e70-95b9-9502b5a4d1dc
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0ace46bc-8f88-4e70-95b9-9502b5a4d1dc
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bca7026c-823c-46bd-84d3-5da407371ca6
This model is a fine-tuned version of [TinyLlama/TinyLlama_v1.1](https://huggingface.co/TinyLlama/TinyLlama_v1.1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6283
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- 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: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.3664 | 0.0002 | 1 | 1.7174 |
| 1.5623 | 0.0007 | 3 | 1.7127 |
| 1.526 | 0.0014 | 6 | 1.6821 |
| 1.5262 | 0.0021 | 9 | 1.6283 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.2
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
Aluba/Vipo1_c23 | Aluba | 2025-05-02T05:05:34Z | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-02T04:52:52Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
trashpanda-org/Julleim | trashpanda-org | 2025-05-02T05:03:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2306.01708",
"base_model:trashpanda-org/Gullein",
"base_model:merge:trashpanda-org/Gullein",
"base_model:trashpanda-org/Llama3-24B-Mullein-v1",
"base_model:merge:trashpanda-org/Llama3-24B-Mullein-v1",
"base_model:unsloth/Mistral-Small-24B-Instruct-2501",
"base_model:merge:unsloth/Mistral-Small-24B-Instruct-2501",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T04:53:37Z | ---
base_model:
- trashpanda-org/Gullein
- unsloth/Mistral-Small-24B-Instruct-2501
- trashpanda-org/Llama3-24B-Mullein-v1
library_name: transformers
tags:
- mergekit
- merge
---
# julleim
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 [TIES](https://arxiv.org/abs/2306.01708) merge method using [unsloth/Mistral-Small-24B-Instruct-2501](https://huggingface.co/unsloth/Mistral-Small-24B-Instruct-2501) as a base.
### Models Merged
The following models were included in the merge:
* [trashpanda-org/Gullein](https://huggingface.co/trashpanda-org/Gullein)
* [trashpanda-org/Llama3-24B-Mullein-v1](https://huggingface.co/trashpanda-org/Llama3-24B-Mullein-v1)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: trashpanda-org/Llama3-24B-Mullein-v1
parameters:
weight: 1
density: 1
- model: trashpanda-org/Gullein
parameters:
weight: 1
density: 1
merge_method: ties
base_model: unsloth/Mistral-Small-24B-Instruct-2501
parameters:
normalize: true
int8_mask: true
tokenizer_source: unsloth/Mistral-Small-24B-Instruct-2501
dtype: bfloat16
```
|
Romain-XV/4e39217b-870e-4695-9aa4-926b19d8f837 | Romain-XV | 2025-05-02T05:03:19Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:finetune:DeepMount00/Llama-3-8b-Ita",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T04:26:16Z | ---
base_model: DeepMount00/Llama-3-8b-Ita
library_name: transformers
model_name: 4e39217b-870e-4695-9aa4-926b19d8f837
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 4e39217b-870e-4695-9aa4-926b19d8f837
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita).
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="Romain-XV/4e39217b-870e-4695-9aa4-926b19d8f837", 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/romain_fnc-xventures/Gradients-On-Demand/runs/rp75a6kw)
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}}
}
``` |
Subsets and Splits