modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-08-03 00:49:08
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 549
values | tags
listlengths 1
4.05k
| pipeline_tag
stringclasses 55
values | createdAt
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depraetASFSA/dfgfdgdf
|
depraetASFSA
| 2025-05-12T01:24:29Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-12T01:24:29Z |
---
license: apache-2.0
---
|
sookiegrattfdgdf/dfgdfgdf
|
sookiegrattfdgdf
| 2025-05-12T01:24:29Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-12T01:24:29Z |
---
license: apache-2.0
---
|
Delta-Vector/INTELLECT-2-Q5_0-GGUF
|
Delta-Vector
| 2025-05-12T01:24:01Z | 0 | 0 | null |
[
"gguf",
"llama-cpp",
"gguf-my-repo",
"dataset:PrimeIntellect/Intellect-2-RL-Dataset",
"base_model:PrimeIntellect/INTELLECT-2",
"base_model:quantized:PrimeIntellect/INTELLECT-2",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-12T01:22:21Z |
---
base_model: PrimeIntellect/INTELLECT-2
datasets:
- PrimeIntellect/Intellect-2-RL-Dataset
license: apache-2.0
tags:
- llama-cpp
- gguf-my-repo
---
# Delta-Vector/INTELLECT-2-Q5_0-GGUF
This model was converted to GGUF format from [`PrimeIntellect/INTELLECT-2`](https://huggingface.co/PrimeIntellect/INTELLECT-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/PrimeIntellect/INTELLECT-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 Delta-Vector/INTELLECT-2-Q5_0-GGUF --hf-file intellect-2-q5_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Delta-Vector/INTELLECT-2-Q5_0-GGUF --hf-file intellect-2-q5_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 Delta-Vector/INTELLECT-2-Q5_0-GGUF --hf-file intellect-2-q5_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Delta-Vector/INTELLECT-2-Q5_0-GGUF --hf-file intellect-2-q5_0.gguf -c 2048
```
|
liguanwei/mymodels
|
liguanwei
| 2025-05-12T01:22:48Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-04-20T15:00:54Z |
---
license: apache-2.0
---
|
Naga1289/RACE_HenriMatisse
|
Naga1289
| 2025-05-12T01:19:00Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-05-12T01:16:39Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ahmedheakl/ex51_qwen2.5_1.5b_101k_16kcw_3ep_cuda_amd_os_4090
|
ahmedheakl
| 2025-05-12T01:17:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Coder-1.5B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-10T23:07:59Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-1.5B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: ex51_qwen2.5_1.5b_101k_16kcw_3ep_cuda_amd_os_4090
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. -->
# ex51_qwen2.5_1.5b_101k_16kcw_3ep_cuda_amd_os_4090
This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Coder-1.5B-Instruct) on the cuda_amd_61k_4090_p1 and the cuda_amd_61k_4090_p2 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
jasongraydon01/llama3.1-8b-html-generator
|
jasongraydon01
| 2025-05-12T01:14:19Z | 0 | 0 | null |
[
"safetensors",
"llama",
"llama-3.1",
"fine-tuned",
"html-generation",
"chunked-response",
"merged",
"text-generation",
"conversational",
"en",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"region:us"
] |
text-generation
| 2025-05-12T01:04:36Z |
---
language: en
license: apache-2.0
base_model: meta-llama/Llama-3.1-8B-Instruct
tags:
- llama-3.1
- fine-tuned
- html-generation
- chunked-response
- merged
pipeline_tag: text-generation
model-index:
- name: llama3.1-8b-html-generator
results: []
---
# Fine-tuned LLaMA 3.1 8B (Merged) – HTML Chunk Generator
This model merges a LoRA adapter into LLaMA 3.1 8B-Instruct for structured HTML generation in chunked formats.
## Usage (vLLM)
```bash
export HF_TOKEN=your_token
vllm serve jasongraydon01/llama3.1-8b-html-generator --max-model-len 16384
```
## Usage (Transformers)
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("jasongraydon01/llama3.1-8b-html-generator")
model = AutoModelForCausalLM.from_pretrained("jasongraydon01/llama3.1-8b-html-generator", torch_dtype="auto")
messages = [{
"role": "user",
"content": "Generate a full HTML article on the topic of..."
}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt", add_generation_prompt=True)
outputs = model.generate(inputs.input_ids, max_new_tokens=8192)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
|
mradermacher/Kiwi-1.7B-Nano-GGUF
|
mradermacher
| 2025-05-12T01:10:09Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"en",
"base_model:LucidityAI/Kiwi-1.7B-Nano",
"base_model:quantized:LucidityAI/Kiwi-1.7B-Nano",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-12T00:51:17Z |
---
base_model: LucidityAI/Kiwi-1.7B-Nano
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/LucidityAI/Kiwi-1.7B-Nano
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-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/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q2_K.gguf) | Q2_K | 0.9 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q3_K_S.gguf) | Q3_K_S | 1.0 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q3_K_M.gguf) | Q3_K_M | 1.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q3_K_L.gguf) | Q3_K_L | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.IQ4_XS.gguf) | IQ4_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q4_K_S.gguf) | Q4_K_S | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q4_K_M.gguf) | Q4_K_M | 1.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q5_K_S.gguf) | Q5_K_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q5_K_M.gguf) | Q5_K_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q6_K.gguf) | Q6_K | 1.5 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.Q8_0.gguf) | Q8_0 | 1.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Kiwi-1.7B-Nano-GGUF/resolve/main/Kiwi-1.7B-Nano.f16.gguf) | f16 | 3.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
omarSorour123/summarizer_model_t5_small
|
omarSorour123
| 2025-05-12T01:09:59Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google-t5/t5-small",
"base_model:adapter:google-t5/t5-small",
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T19:08:15Z |
---
library_name: peft
license: apache-2.0
base_model: google-t5/t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: summarizer_model_t5_small
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. -->
# summarizer_model_t5_small
This model is a fine-tuned version of [google-t5/t5-small](https://huggingface.co/google-t5/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.5198
- Rouge1: 0.2101
- Rouge2: 0.1174
- Rougel: 0.176
- Rougelsum: 0.176
- Gen Len: 19.9959
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| 1.7611 | 1.0 | 8899 | 1.5377 | 0.2101 | 0.1166 | 0.1759 | 0.1759 | 19.9954 |
| 1.7311 | 2.0 | 17798 | 1.5198 | 0.2101 | 0.1174 | 0.176 | 0.176 | 19.9959 |
### Framework versions
- PEFT 0.14.0
- Transformers 4.51.1
- Pytorch 2.5.1+cu124
- Datasets 3.5.0
- Tokenizers 0.21.0
|
cotran2/qwen-5-12
|
cotran2
| 2025-05-12T01:09:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-12T01:08:36Z |
---
library_name: transformers
tags:
- llama-factory
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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|
Naga1289/DUO_Rembrandt
|
Naga1289
| 2025-05-12T01:08:06Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-05-12T01:06:12Z |
---
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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### Direct Use
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[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Metrics
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### Results
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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|
N96124200/llama3.2_3b_freeze2_GaLore
|
N96124200
| 2025-05-12T01:07:43Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-factory",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-12T01:04:53Z |
---
library_name: transformers
tags:
- llama-factory
---
# 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]
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- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- 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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#### 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
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
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|
fesfcvcx/fdsfs
|
fesfcvcx
| 2025-05-12T01:06:10Z | 0 | 0 | null |
[
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-12T01:06:07Z |
---
license: bigscience-openrail-m
---
|
suku9/dpo_spdl_
|
suku9
| 2025-05-12T01:05:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-12T01:05:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
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<!-- Provide the basic links for the model. -->
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- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- 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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<!-- 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
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[More Information Needed]
#### Factors
<!-- 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]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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|
pesantossandoval/dqn-BreakoutNoFrameskip-v4
|
pesantossandoval
| 2025-05-12T00:58:39Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"BreakoutNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-05-12T00:58:29Z |
---
library_name: stable-baselines3
tags:
- BreakoutNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: BreakoutNoFrameskip-v4
type: BreakoutNoFrameskip-v4
metrics:
- type: mean_reward
value: 3.10 +/- 4.76
name: mean_reward
verified: false
---
# **DQN** Agent playing **BreakoutNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **BreakoutNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env BreakoutNoFrameskip-v4 -orga pesantossandoval -f logs/
python -m rl_zoo3.enjoy --algo dqn --env BreakoutNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env BreakoutNoFrameskip-v4 -orga pesantossandoval -f logs/
python -m rl_zoo3.enjoy --algo dqn --env BreakoutNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env BreakoutNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env BreakoutNoFrameskip-v4 -f logs/ -orga pesantossandoval
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
Naga1289/DUO_HenriMatisse
|
Naga1289
| 2025-05-12T00:58:31Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-05-12T00:56:41Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
nskwal/lora-mrayumi-base2
|
nskwal
| 2025-05-12T00:56:08Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:39782226",
"loss:MultipleNegativesRankingLoss",
"pt",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:intfloat/multilingual-e5-base",
"base_model:finetune:intfloat/multilingual-e5-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-05-12T00:56:03Z |
---
language:
- pt
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:39782226
- loss:MultipleNegativesRankingLoss
base_model: intfloat/multilingual-e5-base
widget:
- source_sentence: Como o Brasil reagiu a epidemia de AIDS no fim do século XX?
sentences:
- O valor para a emissão deste visto pode chegar até US$ 1,8 milhão. Devido a crise,
o aumento do preço chegou a 80% em relação a anos anteriores. É importante frisar
que o portador deste visto precisa gerar pelo menos dez empregos no país por dois
anos a partir da data de emissão. Para realizar a solicitação, é necessário fazer
um investimento em um negócio já existente em uma empresa que é licenciada pelo
governo americano e que faz parte da captação desta ação ou montar um novo negócio,
algo que irá requerer maior burocracia e tempo. Após realizar todos os trâmites,
o requerente poderá fazer a solicitação do Green Card, que poderá levar até dois
anos para ficar pronto. Trabalho O visto HB-1 é destinado para todos aqueles profissionais
que possuem formação acadêmica ou uma experiência de trabalho que equivalha a
um curso bacharelado. Este último funciona caso o solicitante não tenha um diploma
e tenha trabalhado por três anos equivalentes a um ano de estudo de bacharelado.
Além de cumprir estes requisitos, o solicitante do visto deverá ter fluência em
inglês e possuir um representante empregador. O visto só é emitido caso a empresa
comprove que não exista nenhum outro americano capaz de cumprir a determinada
função.
- 'Horário de funcionamento: terça a sábado, 10h às 13h e das 14h30 às 18h30. 3.
Parque Nacional da Peneda-Gerês Ivy Land Composto pela Serra da Peneda e a Serra
da Gerês, esse é o único parque nacional de Portugal. Paraíso para os amantes
da natureza, a área possui alguns mirantes de onde é possível admirar toda a região.
O destaque fica para a Pedra Bela, com 830 metros de altura. Entre mais de 100
aldeias, suas principais atrações são a Ponte Misarela, o Santuário Nossa Senhora
da Peneda, o Castro Laboreiro e a Cascata do Arado. Endereço: R. Conde Dom Henrique,
Guimarães. Horário de funcionamento: todos os dias das 10h às 18h. 4. Sé de Braga
Jorge Santos A igreja, cuja construção começou em 1509, foi fundada antes mesmo
da fundação de Portugal (1910). Entre suas várias riquezas culturais está o túmulo
de madeira, em talhe gótico-flamengo, pertencente ao Infante Dom Afonso, filho
do rei Dom João I e de Dona Filipa de Lencastre. No local também se encontra o
Tesouro-Museu da Sé de Braga, fundado em 1930, onde está uma relíquia levada por
Pedro Álvares Cabral: a cruz da primeira missa celebrada no Brasil. Endereço:
R. Dom Paio Mendes. Valor de entrada: gratuito. Horário de funcionamento: todos
os dias das 8h30 às 18h30. 5. Jardim de Santa Bárbara World Photoshoot'
- Assim, o financiamento para o programa de aids brasileiro fazia parte tanto dos
planos estratégicos do Banco Mundial quanto do planejamento dos formuladores da
política nacional de aids. A execução do Projeto AIDS I provocou mudanças tanto
nas respostas governamentais quanto nas não-governamentais frente à epidemia do
HIV/aids no Brasil. Entre essas, Galvão (2000) destaca o aumento dos recursos
financeiros disponíveis no País para desencadear ações frente à epidemia; o crescimento
do número dessas ações; o papel de liderança desempenhado pelo Programa Nacional
de DST e Aids em nível regional; e a maior visibilidade do programa brasileiro
de aids, tanto regional, quanto nacional e internacionalmente. Quanto às ações
do Banco Mundial, o empréstimo destinado às ações para conter a epidemia de HIV/aids
em função do volume financeiro envolvido, oferece ao Programa brasileiro de aids
condições sem precedentes dentro do que vinha sendo feito no país. Para alguns
países, inclusive o Brasil, os empréstimos do Banco Mundial tornaram-se uma das
maiores fontes de recursos para as atividades em HIV/aids, e propiciaram a projeção
das políticas brasileiras como das mais abrangentes já implementadas (Galvão,
2000).
- source_sentence: Quais são os biomas do Brasil?
sentences:
- 'Biomas - Atlas Socioeconômico do Rio Grande do Sul Meio ambiente Voltar Imprimir
RSS Biomas O RS possui dois importantes biomas: Mata Atlântica e Pampa Os biomas
são definidos pelo IBGE como “um conjunto de vida (vegetal e animal) constituído
pelo agrupamento de tipos de vegetação contíguos e identificáveis em escala regional,
com condições geoclimáticas similares e história compartilhada de mudanças, o
que resulta em uma diversidade biológica própria.” Segundo o Mapa dos Biomas do
Brasil, elaborado pelo IBGE e pelo Ministério do Meio Ambiente, o país possui
5 grandes biomas. O de maior extensão é o da Amazônia que abrange 49,29% do território
brasileiro e uma área aproximada de 4.196.943 km². O menor bioma é o do Pantanal
com uma área aproximada de 150.355 km² ou 1,76% do território do Brasil. No RS,
em função da diversidade de clima, solos e relevo há a formação de distintos ecossistemas
derivados de dois grandes biomas: a Mata Atlântica e o Pampa.'
- 'Os Campos caracterizam-se pela presença de uma vegetação rasteira (gramíneas)
e pequenos arbustos distantes uns dos outros. Podemos encontrar esta formação
vegetal em várias regiões do Brasil (sul do Mato Grosso do Sul, nordeste do Paraná,
sul de Minas Gerais e norte do Maranhão), porém é no sul do Rio Grande do Sul,
região conhecida como Pampas Gaúchos, que encontramos em maior extensão. Características
principais dos Campos: - vegetação formada por gramíneas e arbustos e árvores
de pequeno porte. - não dependem de grande quantidade de chuvas. - sua extensão
atingem os territórios da Argentina e Paraguai. A região dos Campos, principalmente
no Rio Grande do Sul, é muito utilizada para a pastagem de gado. A pecuária é
uma das principais atividades econômica nesta região. Pantanal Extensão aproximada:
150.355 quilômetros quadrados O bioma Pantanal cobre 25% de Mato Grosso do Sul
e 7% de Mato Grosso e seus limites coincidem com os da Planície do Pantanal, mais
conhecida como Pantanal mato-grossense. O Pantanal é um bioma praticamente exclusivo
do Brasil, pois apenas uma pequena faixa dele adentra outros países (o Paraguai
e a Bolívia).'
- Resíduos biológicos – gaze, algodão, luva, máscara e ponta de sucção são considerados
materiais desse grupo. Para estes, o ideal é sempre ter uma lixeira por perto,
nunca deixando faltar sacos de lixo, que devem ser proporcionais a demanda de
trabalho do consultório. O ideal é que sempre ocorra o recolhimento diário, nunca
deixando os detritos acumularem. Isso serve para evitar maiores complicações,
como uma contaminação por exemplo. Resíduos perfurocortantes – essa classe de
resíduos deve ser condicionada em caixas de papelão bastante resistentes. Resíduos
perfurocortantes englobam materiais como seringas, lâminas, agulhas, ampolas,
vidros e tesouras. A importância de serem descartados em ambientes resistentes
se deve ao fato de serem bastante perigosos e cortantes. Assim, é ideal que eles
fiquem isolados para que ninguém se lesione ao manuseá-los futuramente. Também
é interessante sinalizar que os materiais presentes naquela caixa são perfurocortantes,
geralmente utilizando um adesivo de cor chamativa.
- source_sentence: Quais os tipos de denominação (DO) que os vinhos podem receber?
sentences:
- O Brasil conta com seis ecossistemas diferentes, composto por espécies animais
e vegetais variadas. A diversidade e os contrastes presentes em cada região são
o que tornam nosso País único. Os principais ecossistemas brasileiros são a Amazônia,
a Caatinga, o Cerrado, o Pantanal, a Mata Atlântica e os Pampas. A seguir, detalharemos
cada um desses biomas. Amazônia A maior floresta tropical do mundo está presente
em nosso País! Distribuindo-se entre Peru, Colômbia, Venezuela, Equador, Suriname,
Guiana e Guiana Francesa, uma grande porção da Floresta Amazônica se localiza
no Norte do Brasil, com uma grande diversidade de plantas e animais, além de abrigar
comunidades de povos originários brasileiros . A bacia amazônica é a maior bacia
hidrográfica do mundo, detendo, aproximadamente, 20% de toda a água doce disponível.
Com clima quente e úmido, o bioma Amazônia ocupa 49% do território nacional. A
temperatura anual média chega a 26 °C, e a pluviosidade é de 2.300 mm, podendo
chegar, em alguns locais, a 3.500 mm. No que diz respeito à vegetação, esta se
divide em mata de terra firme em porções mais elevadas do território, mata de
várzea (inundada em parte do ano) e igapó, quase sempre inundada.
- Os olhos verdes são mais comuns em pessoas de origem celta ou germânica, mas podem
aparecer em quaisquer etnias. Em regiões da Ásia, por exemplo, existem aldeões
do noroeste da China famosos por terem olhos verdes e azuis, além de cabelos claros.
Da mesma forma, é possível encontrar negros com olhos claros. 9. Não surge no
nascimento Unsplash Logo após o nascimento, os olhos dos bebês são escuros, cinzas
ou azuis. Só a partir daí, as células melanócitas começam a liberar a melanina
pelo corpo, que distribui o pigmento marrom para os olhos. Como o tom esverdeado
é resultado da mistura de outros tons, é preciso esperar o equilíbrio da distribuição
de melanina para o desenvolvimento da cor. 10. Olhos verdes só estão completos
após um ano Unsplash Ainda que comece logo depois do nascimento, o processo só
fica realmente completo após cerca de um ano depois do período. Por causa disso,
então, durante os primeiros meses de vida ainda não é possível determinar qual
será a verdadeira cor da criança. Interessante, não? Você imaginava que havia
tanto mistério por trás de um belo par de olhos verdes? Continua após a publicidade
- '"Reboque de barcos rabelos pelas margens do rio Douro em meados dos anos 30 (arq.
priv.) Descarga das pipas de um barco rabelo no Porto (arq. priv.) Cartaz publicitário
de marca de vinho do Porto de 1950 (col. priv.) Vindimas no Douro na década de
70 mantendo ainda as antigas tradições (arq. priv.) Cachos de uvas maduras numa
vinha do Douro (arq. priv.) Em 1995, a região Demarcada do Douro viu alterado
o seu quadro institucional. Passou a estar dotada de um organismo interprofissional,
- a Comissão Interprofissional da Região Demarcada do Douro (CIRDD), no qual tinham
assento, em situação de absoluta paridade, os representantes da lavoura e do comércio,
com o objectivo comum de disciplinar e controlar a produção e comercialização
dos vinhos da região com direito a denominação de origem. As alterações introduzidas
respeitaram, contudo, as especificidades históricas, culturais e sociais da região,
seguindo as linhas orientadoras da lei - quadro das regiões demarcados vitivinícolas.
Duas secções especializadas compunham o Conselho Geral da CIRDD determinando as
regras aplicáveis a cada uma das denominações: uma relativa à denominação de origem
\""Porto\"" e outra aos restantes vinhos de qualidade (\""VQPRD\"") da região."'
- source_sentence: calorias e carboidratos em taco bell
sentences:
- 'Uma fístula é uma abertura ou canal anormal que une duas ou mais estruturas ou
espaços dentro do corpo. Por exemplo, uma fístula pode se desenvolver entre dois
órgãos do corpo, como o intestino e a bexiga, ou entre o intestino e a pele. Uma
fístula cancerosa é rara. Ela se desenvolve por causa do câncer ou de seu tratamento.
Se for causado por tratamento de câncer, geralmente é um efeito colateral tardio
e pode levar muitos meses ou anos para se desenvolver. Asistulas são mais comuns
na região pélvica. As fístulas são um efeito colateral raro do tratamento do câncer.
Eles também podem se desenvolver como resultado do crescimento do câncer. Os sintomas
de uma fístula dependem de sua localização no corpo. Os sintomas comuns incluem:
1 vazamento de urina pela vagina ou passagem nas costas.'
- Os clientes que pedem burritos recheados grelhados devem consumir pelo menos 830
calorias nas versões de frango do item do cardápio e mais de 40 gramas de gordura,
96 gramas de carboidratos, 2.200 mg de sódio e 85 mg de colesterol. No entanto,
o Taco Bell oferece um menu específico dedicado a itens que contêm apenas ingredientes
frescos.
- Calorias em Spag com base nas calorias, gorduras, proteínas, carboidratos e outras
informações nutricionais enviadas para Spag. Calorias em Spag com base nas calorias,
gorduras, proteínas, carboidratos e outras informações nutricionais enviadas para
Spag.
- source_sentence: para que serve a azitromicina
sentences:
- Média móvel simples (SMA) explicada. Uma média móvel simples (SMA) é o tipo mais
simples de média móvel na análise forex (DUH!). Basicamente, uma média móvel simples
é calculada somando os últimos preços de fechamento de â € ¢ dividindo esse número
por X.
- A azitromicina também pode ser usada para tratar várias outras infecções bacterianas
mais incomuns. A azitromicina não é eficaz contra nenhuma infecção causada por
um vírus, como gripe, gastroenterite ou resfriado comum.
- 'Infecções bacterianas. A azitromicina é mais comumente usada para tratar as seguintes
infecções: 1 Infecções respiratórias, como bronquite. 2 Infecções de ouvido (otite
média). 3 infecções sinusais (sinusite). 4 Pneumonia. 5 Infecções da garganta
(amigdalite / faringite). 6 Infecções da pele, como celulite, foliculite ou impetigo.'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# test
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) on the quati and msmarco datasets. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/multilingual-e5-base](https://huggingface.co/intfloat/multilingual-e5-base) <!-- at revision 835193815a3936a24a0ee7dc9e3d48c1fbb19c55 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Datasets:**
- quati
- msmarco
- **Language:** pt
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: PeftModelForFeatureExtraction
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("nskwal/lora-mrayumi-base2")
# Run inference
sentences = [
'para que serve a azitromicina',
'Infecções bacterianas. A azitromicina é mais comumente usada para tratar as seguintes infecções: 1 Infecções respiratórias, como bronquite. 2 Infecções de ouvido (otite média). 3 infecções sinusais (sinusite). 4 Pneumonia. 5 Infecções da garganta (amigdalite / faringite). 6 Infecções da pele, como celulite, foliculite ou impetigo.',
'A azitromicina também pode ser usada para tratar várias outras infecções bacterianas mais incomuns. A azitromicina não é eficaz contra nenhuma infecção causada por um vírus, como gripe, gastroenterite ou resfriado comum.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Datasets
#### quati
* Dataset: quati
* Size: 1,415 training samples
* Columns: <code>query</code> and <code>passage</code>
* Approximate statistics based on the first 1000 samples:
| | query | passage |
|:--------|:----------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 8 tokens</li><li>mean: 12.57 tokens</li><li>max: 21 tokens</li></ul> | <ul><li>min: 65 tokens</li><li>mean: 267.65 tokens</li><li>max: 412 tokens</li></ul> |
* Samples:
| query | passage |
|:-----------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>"O que são os celulares ""mid-range""?"</code> | <code>Câmeras traseiras: 64 MP quad-pixel + 12 MP (ultra-wide) + 5 MP (macro) + 5 MP (sensor de profundidade) Filma em: 4K Câmera frontal: 32 MP Bateria: 4.500 mAh com carregamento turbo de 25W Tem conexão 3G e 4G Pontos positivos: Tela grande com resolução Full HD 128 GB de armazenamento é um bom espaço Câmera de 64 MP que filma em 4K Câmera frontal também filma em 4K Processador potente para uso no dia a dia Pontos negativos: Bateria com tamanho abaixo dos concorrentes Sem proteção contra água Melhor Preço Conclusões Como dito no começo da matéria o mercado de celulares está crescendo exponencialmente e isso faz com que estejam disponíveis vários modelos no mercado, para os mais diferentes gostos. Nem todo mundo precisa ou está disposto a pagar pelos melhores celulares e é onde entram os modelos citados nesta lista: Um bom celular por um preço mediano. Para um uso comum estes modelos atendem muito bem. Se você sentiu falta de alguma opção nesta lista deixe ai nos comentários. Vale lembrar ...</code> |
| <code>"O que são os celulares ""mid-range""?"</code> | <code>Smartphone Motorola Moto G8 Plus Imagem Celular Intermediário Detalhes Smartphone Xiaomi Redmi Note 8 Pro Melhor celular intermediário, processador rápido Smartphone Xiaomi Redmi Note 8 Melhor celular intermediário custo benefício, câmera quádrupla Smartphone Motorola One Action Sensor exclusivo para vídeo Smartphone Huawei P30 Lite Diversas tecnologias diferenciadas Smartphone Samsung Galaxy A50 Câmera frontal de 25 MP Smartphone Samsung Galaxy A30s Leitor de impressão digital embutido na tela Smartphone Motorola Moto G8 Plus Design moderno e bonito Hoje em dia os smartphones são verdadeiros aliados. Apenas com eles é possível executar uma grande quantidade de tarefas como ligações, mensagens, acesso a e-mail e redes sociais e muito mais. Mas para conseguir isso é importante ter em mãos um aparelho que reúna componentes de qualidade, tal como, boa câmera, ótimo espaço de armazenamento e processador ágil. Pensando nisso, selecionamos os modelos de celular intermediário que englobam as ...</code> |
| <code>"O que são os celulares ""mid-range""?"</code> | <code>Os monócitos, eosinófilos, basófilos e seus progenitores circulam no sangue em pequenas quantidades, no entanto, essas células são muitas vezes combinados em um grupo que é designado como MXD ou MID. Este grupo pode ser expressa como uma percentagem do número total de leucócitos (MXD%), ou um número absoluto (MXD #, # MID). Estes tipos de células do sangue e as células brancas do sangue e são funções importantes (a luta contra parasitas, bactérias, reacções alérgicas, etc.). Absoluta e percentagem deste valor aumenta se o aumento do número de um dos tipos de células na sua composição. Para determinar a natureza da alteração geralmente é estudar a percentagem de cada tipo de célula (monócitos, eosinófilos, basófilos e os seus precursores). Requisitos: eosinófilos reduzidos e aumento no sangue # MID (MID, MXD #) 0,2-0,8 x 109 / l MID% (MXD%) 5 - 10% O número de granulócitos (GRA, GRAN) Granulócitos - são leucócitos que contêm grânulos (leucócitos granulares). Granulócitos 3 tipos de célu...</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
#### msmarco
* Dataset: msmarco
* Size: 39,780,811 training samples
* Columns: <code>query</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | query | positive | negative |
|:--------|:---------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 11.4 tokens</li><li>max: 32 tokens</li></ul> | <ul><li>min: 14 tokens</li><li>mean: 102.05 tokens</li><li>max: 401 tokens</li></ul> | <ul><li>min: 30 tokens</li><li>mean: 91.92 tokens</li><li>max: 470 tokens</li></ul> |
* Samples:
| query | positive | negative |
|:---------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| <code>é um pouco de cafeína ok durante a gravidez</code> | <code>Não sabemos muito sobre os efeitos da cafeína durante a gravidez sobre você e seu bebê. Portanto, é melhor limitar a quantidade que você recebe a cada dia. Se você estiver grávida, limite a cafeína a 200 miligramas por dia. Isso é aproximadamente a quantidade em 1 x 8 onças de café ou uma xícara de 12 onças de café.</code> | <code>Em geral, é seguro para mulheres grávidas comer chocolate porque estudos demonstraram alguns benefícios de comer chocolate durante a gravidez. No entanto, as mulheres grávidas devem garantir que a ingestão de cafeína seja inferior a 200 mg por dia.</code> |
| <code>que fruta é nativa da Austrália</code> | <code>Passiflora herbertiana. Um raro maracujá nativo da Austrália. Os frutos são de casca verde, polpa branca, com uma classificação comestível desconhecida. Algumas fontes listam as frutas como comestíveis, doces e saborosas, enquanto outras listam as frutas como sendo amargas e não comestíveis.assiflora herbertiana. Um raro maracujá nativo da Austrália. Os frutos são de casca verde, polpa branca, com uma classificação comestível desconhecida. Algumas fontes listam as frutas como comestíveis, doces e saborosas, enquanto outras listam as frutas como amargas e não comestíveis.</code> | <code>A noz de cola é o fruto da árvore da cola, um gênero (Cola) de árvores que são nativas das florestas tropicais da África.</code> |
| <code>quão grande é o exército canadense</code> | <code>As Forças Armadas canadenses. 1 A primeira missão de manutenção da paz canadense em grande escala começou no Egito em 24 de novembro de 1956. 2 Há aproximadamente 65.000 membros da Força Regular e 25.000 membros reservistas nas forças armadas canadenses. 3 No Canadá, o dia 9 de agosto é designado como Dia Nacional dos Pacificadores.</code> | <code>O Canadian Physician Health Institute (CPHI) é um programa nacional criado em 2012 como uma colaboração entre a Canadian Medical Association (CMA), a Canadian Medical Foundation (CMF) e as Provincial and Territorial Medical Associations (PTMAs).</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `gradient_accumulation_steps`: 2
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.05
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 128
- `per_device_eval_batch_size`: 128
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 2
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.05
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 5
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: True
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
<details><summary>Click to expand</summary>
| Epoch | Step | Training Loss |
|:------:|:-----:|:-------------:|
| 0.0051 | 100 | 7.011 |
| 0.0103 | 200 | 6.7618 |
| 0.0154 | 300 | 6.1862 |
| 0.0206 | 400 | 5.2749 |
| 0.0257 | 500 | 4.1618 |
| 0.0309 | 600 | 2.7094 |
| 0.0360 | 700 | 1.3589 |
| 0.0412 | 800 | 0.9842 |
| 0.0463 | 900 | 0.859 |
| 0.0515 | 1000 | 0.7819 |
| 0.0566 | 1100 | 0.7534 |
| 0.0618 | 1200 | 0.733 |
| 0.0669 | 1300 | 0.7212 |
| 0.0721 | 1400 | 0.6995 |
| 0.0772 | 1500 | 0.6937 |
| 0.0824 | 1600 | 0.6831 |
| 0.0875 | 1700 | 0.6717 |
| 0.0927 | 1800 | 0.6608 |
| 0.0978 | 1900 | 0.6447 |
| 0.1030 | 2000 | 0.6472 |
| 0.1081 | 2100 | 0.6312 |
| 0.1133 | 2200 | 0.6308 |
| 0.1184 | 2300 | 0.6336 |
| 0.1236 | 2400 | 0.6225 |
| 0.1287 | 2500 | 0.6239 |
| 0.1339 | 2600 | 0.6219 |
| 0.1390 | 2700 | 0.6128 |
| 0.1441 | 2800 | 0.6107 |
| 0.1493 | 2900 | 0.6115 |
| 0.1544 | 3000 | 0.6137 |
| 0.1596 | 3100 | 0.6015 |
| 0.1647 | 3200 | 0.6085 |
| 0.1699 | 3300 | 0.6151 |
| 0.1750 | 3400 | 0.607 |
| 0.1802 | 3500 | 0.6105 |
| 0.1853 | 3600 | 0.6077 |
| 0.1905 | 3700 | 0.6059 |
| 0.1956 | 3800 | 0.6091 |
| 0.2008 | 3900 | 0.6091 |
| 0.2059 | 4000 | 0.613 |
| 0.2111 | 4100 | 0.62 |
| 0.2162 | 4200 | 0.6087 |
| 0.2214 | 4300 | 0.6139 |
| 0.2265 | 4400 | 0.6121 |
| 0.2317 | 4500 | 0.6186 |
| 0.2368 | 4600 | 0.6182 |
| 0.2420 | 4700 | 0.614 |
| 0.2471 | 4800 | 0.6152 |
| 0.2523 | 4900 | 0.6148 |
| 0.2574 | 5000 | 0.6209 |
| 0.2626 | 5100 | 0.6167 |
| 0.2677 | 5200 | 0.6129 |
| 0.2729 | 5300 | 0.6198 |
| 0.2780 | 5400 | 0.6211 |
| 0.2831 | 5500 | 0.624 |
| 0.2883 | 5600 | 0.6239 |
| 0.2934 | 5700 | 0.6263 |
| 0.2986 | 5800 | 0.6318 |
| 0.3037 | 5900 | 0.6415 |
| 0.3089 | 6000 | 0.643 |
| 0.3140 | 6100 | 0.6542 |
| 0.3192 | 6200 | 0.6469 |
| 0.3243 | 6300 | 0.6561 |
| 0.3295 | 6400 | 0.6565 |
| 0.3346 | 6500 | 0.6564 |
| 0.3398 | 6600 | 0.6671 |
| 0.3449 | 6700 | 0.6852 |
| 0.3501 | 6800 | 0.6809 |
| 0.3552 | 6900 | 0.6753 |
| 0.3604 | 7000 | 0.7067 |
| 0.3655 | 7100 | 0.7264 |
| 0.3707 | 7200 | 0.7254 |
| 0.3758 | 7300 | 0.7414 |
| 0.3810 | 7400 | 0.7557 |
| 0.3861 | 7500 | 0.7979 |
| 0.3913 | 7600 | 0.8397 |
| 0.3964 | 7700 | 0.901 |
| 0.4016 | 7800 | 0.9962 |
| 0.4067 | 7900 | 1.1138 |
| 0.4119 | 8000 | 1.5953 |
| 0.4170 | 8100 | 2.3509 |
| 0.4221 | 8200 | 2.6448 |
| 0.4273 | 8300 | 2.6755 |
| 0.4324 | 8400 | 2.7672 |
| 0.4376 | 8500 | 2.8023 |
| 0.4427 | 8600 | 2.8587 |
| 0.4479 | 8700 | 2.9055 |
| 0.4530 | 8800 | 2.9021 |
| 0.4582 | 8900 | 2.8877 |
| 0.4633 | 9000 | 2.8706 |
| 0.4685 | 9100 | 2.751 |
| 0.4736 | 9200 | 2.7657 |
| 0.4788 | 9300 | 2.6979 |
| 0.4839 | 9400 | 2.6404 |
| 0.4891 | 9500 | 2.6119 |
| 0.4942 | 9600 | 2.9296 |
| 0.4994 | 9700 | 2.8221 |
| 0.5045 | 9800 | 2.9706 |
| 0.5097 | 9900 | 3.0781 |
| 0.5148 | 10000 | 3.0491 |
| 0.5200 | 10100 | 3.184 |
| 0.5251 | 10200 | 3.1307 |
| 0.5303 | 10300 | 3.174 |
| 0.5354 | 10400 | 3.3021 |
| 0.5406 | 10500 | 3.378 |
| 0.5457 | 10600 | 3.4035 |
| 0.5509 | 10700 | 3.3696 |
| 0.5560 | 10800 | 3.3972 |
| 0.5611 | 10900 | 3.5669 |
| 0.5663 | 11000 | 3.6177 |
| 0.5714 | 11100 | 3.6118 |
| 0.5766 | 11200 | 3.546 |
| 0.5817 | 11300 | 3.7217 |
| 0.5869 | 11400 | 3.7381 |
| 0.5920 | 11500 | 3.8149 |
| 0.5972 | 11600 | 3.7916 |
| 0.6023 | 11700 | 3.8276 |
| 0.6075 | 11800 | 3.887 |
| 0.6126 | 11900 | 3.878 |
| 0.6178 | 12000 | 3.9858 |
| 0.6229 | 12100 | 4.0576 |
| 0.6281 | 12200 | 4.0481 |
| 0.6332 | 12300 | 4.108 |
| 0.6384 | 12400 | 4.0992 |
| 0.6435 | 12500 | 4.1475 |
| 0.6487 | 12600 | 4.2398 |
| 0.6538 | 12700 | 4.2458 |
| 0.6590 | 12800 | 4.1839 |
| 0.6641 | 12900 | 4.2432 |
| 0.6693 | 13000 | 4.3393 |
| 0.6744 | 13100 | 4.3584 |
| 0.6796 | 13200 | 4.3562 |
| 0.6847 | 13300 | 4.4164 |
| 0.6899 | 13400 | 4.4498 |
| 0.6950 | 13500 | 4.4802 |
| 0.7001 | 13600 | 4.4723 |
| 0.7053 | 13700 | 4.5005 |
| 0.7104 | 13800 | 4.5249 |
| 0.7156 | 13900 | 4.5346 |
| 0.7207 | 14000 | 4.5706 |
| 0.7259 | 14100 | 4.5916 |
| 0.7310 | 14200 | 4.5925 |
| 0.7362 | 14300 | 4.6264 |
| 0.7413 | 14400 | 4.6092 |
| 0.7465 | 14500 | 4.586 |
| 0.7516 | 14600 | 4.6383 |
| 0.7568 | 14700 | 4.6522 |
| 0.7619 | 14800 | 4.6003 |
| 0.7671 | 14900 | 4.6032 |
| 0.7722 | 15000 | 4.6271 |
| 0.7774 | 15100 | 4.5719 |
| 0.7825 | 15200 | 4.5659 |
| 0.7877 | 15300 | 4.5847 |
| 0.7928 | 15400 | 4.6689 |
| 0.7980 | 15500 | 4.6135 |
| 0.8031 | 15600 | 4.6867 |
| 0.8083 | 15700 | 4.6649 |
| 0.8134 | 15800 | 4.6655 |
| 0.8186 | 15900 | 4.6618 |
| 0.8237 | 16000 | 4.5552 |
| 0.8289 | 16100 | 4.5679 |
| 0.8340 | 16200 | 4.6444 |
| 0.8391 | 16300 | 4.5746 |
| 0.8443 | 16400 | 4.6567 |
| 0.8494 | 16500 | 4.6925 |
| 0.8546 | 16600 | 4.6366 |
| 0.8597 | 16700 | 4.6482 |
| 0.8649 | 16800 | 4.5814 |
| 0.8700 | 16900 | 4.6341 |
| 0.8752 | 17000 | 4.6371 |
| 0.8803 | 17100 | 4.635 |
| 0.8855 | 17200 | 4.623 |
| 0.8906 | 17300 | 4.593 |
| 0.8958 | 17400 | 4.6052 |
| 0.9009 | 17500 | 4.6416 |
| 0.9061 | 17600 | 4.6459 |
| 0.9112 | 17700 | 4.6681 |
| 0.9164 | 17800 | 4.6607 |
| 0.9215 | 17900 | 4.6171 |
| 0.9267 | 18000 | 4.5891 |
| 0.9318 | 18100 | 4.5477 |
| 0.9370 | 18200 | 4.5881 |
| 0.9421 | 18300 | 4.6053 |
| 0.9473 | 18400 | 4.6526 |
| 0.9524 | 18500 | 4.6509 |
| 0.9576 | 18600 | 4.6276 |
| 0.9627 | 18700 | 4.6194 |
| 0.9678 | 18800 | 4.6289 |
| 0.9730 | 18900 | 4.6612 |
| 0.9781 | 19000 | 4.6788 |
| 0.9833 | 19100 | 4.68 |
| 0.9884 | 19200 | 4.6721 |
| 0.9936 | 19300 | 4.6668 |
| 0.9987 | 19400 | 4.6671 |
</details>
### Framework Versions
- Python: 3.11.11
- Sentence Transformers: 4.1.0
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu126
- Accelerate: 1.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
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## Model Card Contact
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|
ez-landau/SFT-SW-Llama-3.1-8B-Instruct-XSS-LORA_SW_ALL
|
ez-landau
| 2025-05-12T00:56:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T23:55:13Z |
---
library_name: transformers
model_name: SFT-SW-Llama-3.1-8B-Instruct-XSS-LORA_SW_ALL
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for SFT-SW-Llama-3.1-8B-Instruct-XSS-LORA_SW_ALL
This model is a fine-tuned version of [None](https://huggingface.co/None).
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="ez-landau/SFT-SW-Llama-3.1-8B-Instruct-XSS-LORA_SW_ALL", 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/RADFAN/SFT-SW/runs/z7pqrjda)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
chaima01/wizard-pilgrims-finetuned
|
chaima01
| 2025-05-12T00:51:26Z | 0 | 0 | null |
[
"safetensors",
"llama",
"wizardlm",
"lora",
"finetuned",
"conversational",
"assistant",
"text-generation",
"en",
"dataset:custom_pilgrims_dataset",
"license:openrail",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T23:14:27Z |
---
language: en
license: openrail
datasets:
- custom_pilgrims_dataset
tags:
- wizardlm
- lora
- finetuned
- conversational
- assistant
pipeline_tag: text-generation
---
# WizardLM Fine-Tuned on Pilgrims Dataset
This model is a fine-tuned version of [TheBloke/wizardLM-7B-HF](https://huggingface.co/TheBloke/wizardLM-7B-HF) using QLoRA on a custom dataset designed around spiritual, philosophical, and existential questions.
---
## Model Description
- **Base Model:** WizardLM 7B (HF format)
- **Fine-tuning Method:** QLoRA (Quantized Low-Rank Adaptation)
- **Training Data:** Custom pilgrims dataset (e.g. `Vibe: Atheist\nQuestion: How can I...`)
- **Intended Use:** Conversational assistant for users exploring personal meaning, spiritual identity, or philosophical reflection.
---
## Usage Example
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("chaima01/wizard-pilgrims-finetuned")
tokenizer = AutoTokenizer.from_pretrained("chaima01/wizard-pilgrims-finetuned")
input_text = "#### Human: Vibe: Atheist\nQuestion: How can I really get to know who I am beyond all the labels and roles I’ve taken on?"
inputs = tokenizer(input_text, return_tensors="pt").to(model.device)
outputs = model.generate(
inputs.input_ids,
max_new_tokens=256,
temperature=0.7,
|
ma921/ministral_h_dpo_oasst1_noise40_epoch3_gamma10
|
ma921
| 2025-05-12T00:50:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:mistralai/Ministral-8B-Instruct-2410",
"base_model:finetune:mistralai/Ministral-8B-Instruct-2410",
"endpoints_compatible",
"region:us"
] | null | 2025-05-12T00:49:58Z |
---
base_model: mistralai/Ministral-8B-Instruct-2410
library_name: transformers
model_name: ministral_h_dpo_oasst1_noise40_epoch3_gamma10
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for ministral_h_dpo_oasst1_noise40_epoch3_gamma10
This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410).
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="ma921/ministral_h_dpo_oasst1_noise40_epoch3_gamma10", 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 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
unsloth/Qwen2.5-VL-3B-Instruct-GGUF
|
unsloth
| 2025-05-12T00:49:58Z | 0 | 2 |
transformers
|
[
"transformers",
"gguf",
"qwen2_5_vl",
"image-text-to-text",
"multimodal",
"unsloth",
"en",
"arxiv:2309.00071",
"arxiv:2409.12191",
"arxiv:2308.12966",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-05-11T12:12:08Z |
---
base_model:
- Qwen/Qwen2.5-VL-3B-Instruct
license_name: qwen-research
license_link: https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct/blob/main/LICENSE
language:
- en
pipeline_tag: image-text-to-text
tags:
- multimodal
- unsloth
- unsloth
library_name: transformers
---
# Qwen2.5-VL-3B-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
In the past five months since Qwen2-VL’s release, numerous developers have built new models on the Qwen2-VL vision-language models, providing us with valuable feedback. During this period, we focused on building more useful vision-language models. Today, we are excited to introduce the latest addition to the Qwen family: Qwen2.5-VL.
#### Key Enhancements:
* **Understand things visually**: Qwen2.5-VL is not only proficient in recognizing common objects such as flowers, birds, fish, and insects, but it is highly capable of analyzing texts, charts, icons, graphics, and layouts within images.
* **Being agentic**: Qwen2.5-VL directly plays as a visual agent that can reason and dynamically direct tools, which is capable of computer use and phone use.
* **Understanding long videos and capturing events**: Qwen2.5-VL can comprehend videos of over 1 hour, and this time it has a new ability of cpaturing event by pinpointing the relevant video segments.
* **Capable of visual localization in different formats**: Qwen2.5-VL can accurately localize objects in an image by generating bounding boxes or points, and it can provide stable JSON outputs for coordinates and attributes.
* **Generating structured outputs**: for data like scans of invoices, forms, tables, etc. Qwen2.5-VL supports structured outputs of their contents, benefiting usages in finance, commerce, etc.
#### Model Architecture Updates:
* **Dynamic Resolution and Frame Rate Training for Video Understanding**:
We extend dynamic resolution to the temporal dimension by adopting dynamic FPS sampling, enabling the model to comprehend videos at various sampling rates. Accordingly, we update mRoPE in the time dimension with IDs and absolute time alignment, enabling the model to learn temporal sequence and speed, and ultimately acquire the ability to pinpoint specific moments.
<p align="center">
<img src="https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2.5-VL/qwen2.5vl_arc.jpeg" width="80%"/>
<p>
* **Streamlined and Efficient Vision Encoder**
We enhance both training and inference speeds by strategically implementing window attention into the ViT. The ViT architecture is further optimized with SwiGLU and RMSNorm, aligning it with the structure of the Qwen2.5 LLM.
We have three models with 3, 7 and 72 billion parameters. This repo contains the instruction-tuned 3B Qwen2.5-VL model. For more information, visit our [Blog](https://qwenlm.github.io/blog/qwen2.5-vl/) and [GitHub](https://github.com/QwenLM/Qwen2.5-VL).
## Evaluation
### Image benchmark
| Benchmark | InternVL2.5-4B |Qwen2-VL-7B |Qwen2.5-VL-3B |
| :--- | :---: | :---: | :---: |
| MMMU<sub>val</sub> | 52.3 | 54.1 | 53.1|
| MMMU-Pro<sub>val</sub> | **32.7** | 30.5 | 31.6|
| AI2D<sub>test</sub> | 81.4 | **83.0** | 81.5 |
| DocVQA<sub>test</sub> | 91.6 | 94.5 | **93.9** |
| InfoVQA<sub>test</sub> | 72.1 | 76.5 | **77.1** |
| TextVQA<sub>val</sub> | 76.8 | **84.3** | 79.3|
| MMBench-V1.1<sub>test</sub> | 79.3 | **80.7** | 77.6 |
| MMStar | 58.3 | **60.7** | 55.9 |
| MathVista<sub>testmini</sub> | 60.5 | 58.2 | **62.3** |
| MathVision<sub>full</sub> | 20.9 | 16.3 | **21.2** |
### Video benchmark
| Benchmark | InternVL2.5-4B | Qwen2-VL-7B | Qwen2.5-VL-3B |
| :--- | :---: | :---: | :---: |
| MVBench | 71.6 | 67.0 | 67.0 |
| VideoMME | 63.6/62.3 | 69.0/63.3 | 67.6/61.5 |
| MLVU | 48.3 | - | 68.2 |
| LVBench | - | - | 43.3 |
| MMBench-Video | 1.73 | 1.44 | 1.63 |
| EgoSchema | - | - | 64.8 |
| PerceptionTest | - | - | 66.9 |
| TempCompass | - | - | 64.4 |
| LongVideoBench | 55.2 | 55.6 | 54.2 |
| CharadesSTA/mIoU | - | - | 38.8 |
### Agent benchmark
| Benchmarks | Qwen2.5-VL-3B |
|-------------------------|---------------|
| ScreenSpot | 55.5 |
| ScreenSpot Pro | 23.9 |
| AITZ_EM | 76.9 |
| Android Control High_EM | 63.7 |
| Android Control Low_EM | 22.2 |
| AndroidWorld_SR | 90.8 |
| MobileMiniWob++_SR | 67.9 |
## Requirements
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
KeyError: 'qwen2_5_vl'
```
## Quickstart
Below, we provide simple examples to show how to use Qwen2.5-VL with 🤖 ModelScope and 🤗 Transformers.
The code of Qwen2.5-VL has been in the latest Hugging face transformers and we advise you to build from source with command:
```
pip install git+https://github.com/huggingface/transformers accelerate
```
or you might encounter the following error:
```
KeyError: 'qwen2_5_vl'
```
We offer a toolkit to help you handle various types of visual input more conveniently, as if you were using an API. This includes base64, URLs, and interleaved images and videos. You can install it using the following command:
```bash
# It's highly recommanded to use `[decord]` feature for faster video loading.
pip install qwen-vl-utils[decord]==0.0.8
```
If you are not using Linux, you might not be able to install `decord` from PyPI. In that case, you can use `pip install qwen-vl-utils` which will fall back to using torchvision for video processing. However, you can still [install decord from source](https://github.com/dmlc/decord?tab=readme-ov-file#install-from-source) to get decord used when loading video.
### Using 🤗 Transformers to Chat
Here we show a code snippet to show you how to use the chat model with `transformers` and `qwen_vl_utils`:
```python
from transformers import Qwen2_5_VLForConditionalGeneration, AutoTokenizer, AutoProcessor
from qwen_vl_utils import process_vision_info
# default: Load the model on the available device(s)
model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
"Qwen/Qwen2.5-VL-3B-Instruct", torch_dtype="auto", device_map="auto"
)
# We recommend enabling flash_attention_2 for better acceleration and memory saving, especially in multi-image and video scenarios.
# model = Qwen2_5_VLForConditionalGeneration.from_pretrained(
# "Qwen/Qwen2.5-VL-3B-Instruct",
# torch_dtype=torch.bfloat16,
# attn_implementation="flash_attention_2",
# device_map="auto",
# )
# default processer
processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct")
# The default range for the number of visual tokens per image in the model is 4-16384.
# You can set min_pixels and max_pixels according to your needs, such as a token range of 256-1280, to balance performance and cost.
# min_pixels = 256*28*28
# max_pixels = 1280*28*28
# processor = AutoProcessor.from_pretrained("Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels)
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg",
},
{"type": "text", "text": "Describe this image."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference: Generation of the output
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
<details>
<summary>Multi image inference</summary>
```python
# Messages containing multiple images and a text query
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/image1.jpg"},
{"type": "image", "image": "file:///path/to/image2.jpg"},
{"type": "text", "text": "Identify the similarities between these images."},
],
}
]
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
</details>
<details>
<summary>Video inference</summary>
```python
# Messages containing a images list as a video and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": [
"file:///path/to/frame1.jpg",
"file:///path/to/frame2.jpg",
"file:///path/to/frame3.jpg",
"file:///path/to/frame4.jpg",
],
},
{"type": "text", "text": "Describe this video."},
],
}
]
# Messages containing a local video path and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "file:///path/to/video1.mp4",
"max_pixels": 360 * 420,
"fps": 1.0,
},
{"type": "text", "text": "Describe this video."},
],
}
]
# Messages containing a video url and a text query
messages = [
{
"role": "user",
"content": [
{
"type": "video",
"video": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen2-VL/space_woaudio.mp4",
},
{"type": "text", "text": "Describe this video."},
],
}
]
#In Qwen 2.5 VL, frame rate information is also input into the model to align with absolute time.
# Preparation for inference
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs, video_kwargs = process_vision_info(messages, return_video_kwargs=True)
inputs = processor(
text=[text],
images=image_inputs,
videos=video_inputs,
fps=fps,
padding=True,
return_tensors="pt",
**video_kwargs,
)
inputs = inputs.to("cuda")
# Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_text = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_text)
```
Video URL compatibility largely depends on the third-party library version. The details are in the table below. change the backend by `FORCE_QWENVL_VIDEO_READER=torchvision` or `FORCE_QWENVL_VIDEO_READER=decord` if you prefer not to use the default one.
| Backend | HTTP | HTTPS |
|-------------|------|-------|
| torchvision >= 0.19.0 | ✅ | ✅ |
| torchvision < 0.19.0 | ❌ | ❌ |
| decord | ✅ | ❌ |
</details>
<details>
<summary>Batch inference</summary>
```python
# Sample messages for batch inference
messages1 = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/image1.jpg"},
{"type": "image", "image": "file:///path/to/image2.jpg"},
{"type": "text", "text": "What are the common elements in these pictures?"},
],
}
]
messages2 = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": "Who are you?"},
]
# Combine messages for batch processing
messages = [messages1, messages2]
# Preparation for batch inference
texts = [
processor.apply_chat_template(msg, tokenize=False, add_generation_prompt=True)
for msg in messages
]
image_inputs, video_inputs = process_vision_info(messages)
inputs = processor(
text=texts,
images=image_inputs,
videos=video_inputs,
padding=True,
return_tensors="pt",
)
inputs = inputs.to("cuda")
# Batch Inference
generated_ids = model.generate(**inputs, max_new_tokens=128)
generated_ids_trimmed = [
out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, generated_ids)
]
output_texts = processor.batch_decode(
generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False
)
print(output_texts)
```
</details>
### 🤖 ModelScope
We strongly advise users especially those in mainland China to use ModelScope. `snapshot_download` can help you solve issues concerning downloading checkpoints.
### More Usage Tips
For input images, we support local files, base64, and URLs. For videos, we currently only support local files.
```python
# You can directly insert a local file path, a URL, or a base64-encoded image into the position where you want in the text.
## Local file path
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "file:///path/to/your/image.jpg"},
{"type": "text", "text": "Describe this image."},
],
}
]
## Image URL
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "http://path/to/your/image.jpg"},
{"type": "text", "text": "Describe this image."},
],
}
]
## Base64 encoded image
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": "data:image;base64,/9j/..."},
{"type": "text", "text": "Describe this image."},
],
}
]
```
#### Image Resolution for performance boost
The model supports a wide range of resolution inputs. By default, it uses the native resolution for input, but higher resolutions can enhance performance at the cost of more computation. Users can set the minimum and maximum number of pixels to achieve an optimal configuration for their needs, such as a token count range of 256-1280, to balance speed and memory usage.
```python
min_pixels = 256 * 28 * 28
max_pixels = 1280 * 28 * 28
processor = AutoProcessor.from_pretrained(
"Qwen/Qwen2.5-VL-3B-Instruct", min_pixels=min_pixels, max_pixels=max_pixels
)
```
Besides, We provide two methods for fine-grained control over the image size input to the model:
1. Define min_pixels and max_pixels: Images will be resized to maintain their aspect ratio within the range of min_pixels and max_pixels.
2. Specify exact dimensions: Directly set `resized_height` and `resized_width`. These values will be rounded to the nearest multiple of 28.
```python
# min_pixels and max_pixels
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "file:///path/to/your/image.jpg",
"resized_height": 280,
"resized_width": 420,
},
{"type": "text", "text": "Describe this image."},
],
}
]
# resized_height and resized_width
messages = [
{
"role": "user",
"content": [
{
"type": "image",
"image": "file:///path/to/your/image.jpg",
"min_pixels": 50176,
"max_pixels": 50176,
},
{"type": "text", "text": "Describe this image."},
],
}
]
```
### 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:
```
{
...,
"type": "yarn",
"mrope_section": [
16,
24,
24
],
"factor": 4,
"original_max_position_embeddings": 32768
}
```
However, it should be noted that this method has a significant impact on the performance of temporal and spatial localization tasks, and is therefore not recommended for use.
At the same time, for long video inputs, since MRoPE itself is more economical with ids, the max_position_embeddings can be directly modified to a larger value, such as 64k.
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5-VL,
title = {Qwen2.5-VL},
url = {https://qwenlm.github.io/blog/qwen2.5-vl/},
author = {Qwen Team},
month = {January},
year = {2025}
}
@article{Qwen2VL,
title={Qwen2-VL: Enhancing Vision-Language Model's Perception of the World at Any Resolution},
author={Wang, Peng and Bai, Shuai and Tan, Sinan and Wang, Shijie and Fan, Zhihao and Bai, Jinze and Chen, Keqin and Liu, Xuejing and Wang, Jialin and Ge, Wenbin and Fan, Yang and Dang, Kai and Du, Mengfei and Ren, Xuancheng and Men, Rui and Liu, Dayiheng and Zhou, Chang and Zhou, Jingren and Lin, Junyang},
journal={arXiv preprint arXiv:2409.12191},
year={2024}
}
@article{Qwen-VL,
title={Qwen-VL: A Versatile Vision-Language Model for Understanding, Localization, Text Reading, and Beyond},
author={Bai, Jinze and Bai, Shuai and Yang, Shusheng and Wang, Shijie and Tan, Sinan and Wang, Peng and Lin, Junyang and Zhou, Chang and Zhou, Jingren},
journal={arXiv preprint arXiv:2308.12966},
year={2023}
}
```
|
Naga1289/RECE_AndyWarhol
|
Naga1289
| 2025-05-12T00:46:56Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-05-12T00:45:05Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
brando/tfa_output_2025_m05_d10_t23h_57m_54s
|
brando
| 2025-05-12T00:46:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:finetune:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-12T00:39:59Z |
---
library_name: transformers
license: llama3
base_model: meta-llama/Meta-Llama-3-8B
tags:
- generated_from_trainer
model-index:
- name: tfa_output_2025_m05_d10_t23h_57m_54s
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. -->
# tfa_output_2025_m05_d10_t23h_57m_54s
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0111
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0 | 0 | 1.0491 |
| 2.0659 | 0.0101 | 50 | 1.0490 |
| 2.0528 | 0.0203 | 100 | 1.0474 |
| 1.9051 | 0.0304 | 150 | 1.0446 |
| 2.2261 | 0.0406 | 200 | 1.0404 |
| 1.964 | 0.0507 | 250 | 1.0366 |
| 1.9639 | 0.0609 | 300 | 1.0339 |
| 2.019 | 0.0710 | 350 | 1.0319 |
| 1.9149 | 0.0811 | 400 | 1.0304 |
| 2.0091 | 0.0913 | 450 | 1.0294 |
| 2.034 | 0.1014 | 500 | 1.0284 |
| 1.9902 | 0.1116 | 550 | 1.0279 |
| 1.9344 | 0.1217 | 600 | 1.0271 |
| 2.0987 | 0.1318 | 650 | 1.0266 |
| 2.0106 | 0.1420 | 700 | 1.0259 |
| 1.941 | 0.1521 | 750 | 1.0254 |
| 2.2107 | 0.1623 | 800 | 1.0248 |
| 1.9111 | 0.1724 | 850 | 1.0245 |
| 2.0238 | 0.1826 | 900 | 1.0240 |
| 2.036 | 0.1927 | 950 | 1.0236 |
| 2.1114 | 0.2028 | 1000 | 1.0232 |
| 1.7783 | 0.2130 | 1050 | 1.0226 |
| 1.9341 | 0.2231 | 1100 | 1.0223 |
| 2.1325 | 0.2333 | 1150 | 1.0219 |
| 2.0806 | 0.2434 | 1200 | 1.0216 |
| 2.0504 | 0.2535 | 1250 | 1.0210 |
| 2.0203 | 0.2637 | 1300 | 1.0208 |
| 2.0069 | 0.2738 | 1350 | 1.0205 |
| 1.873 | 0.2840 | 1400 | 1.0199 |
| 1.9885 | 0.2941 | 1450 | 1.0195 |
| 1.9339 | 0.3043 | 1500 | 1.0192 |
| 1.989 | 0.3144 | 1550 | 1.0190 |
| 2.1222 | 0.3245 | 1600 | 1.0188 |
| 2.0869 | 0.3347 | 1650 | 1.0184 |
| 1.9288 | 0.3448 | 1700 | 1.0184 |
| 1.9388 | 0.3550 | 1750 | 1.0182 |
| 2.1451 | 0.3651 | 1800 | 1.0182 |
| 1.9053 | 0.3753 | 1850 | 1.0180 |
| 2.167 | 0.3854 | 1900 | 1.0178 |
| 2.1596 | 0.3955 | 1950 | 1.0177 |
| 1.7817 | 0.4057 | 2000 | 1.0173 |
| 2.2397 | 0.4158 | 2050 | 1.0171 |
| 2.2354 | 0.4260 | 2100 | 1.0170 |
| 2.2356 | 0.4361 | 2150 | 1.0168 |
| 1.9626 | 0.4462 | 2200 | 1.0166 |
| 1.951 | 0.4564 | 2250 | 1.0165 |
| 2.0802 | 0.4665 | 2300 | 1.0163 |
| 2.006 | 0.4767 | 2350 | 1.0162 |
| 1.8284 | 0.4868 | 2400 | 1.0159 |
| 1.988 | 0.4970 | 2450 | 1.0157 |
| 1.847 | 0.5071 | 2500 | 1.0156 |
| 1.9732 | 0.5172 | 2550 | 1.0155 |
| 1.7898 | 0.5274 | 2600 | 1.0153 |
| 1.9274 | 0.5375 | 2650 | 1.0153 |
| 2.2106 | 0.5477 | 2700 | 1.0150 |
| 2.0584 | 0.5578 | 2750 | 1.0149 |
| 1.8344 | 0.5680 | 2800 | 1.0148 |
| 2.1057 | 0.5781 | 2850 | 1.0146 |
| 1.9237 | 0.5882 | 2900 | 1.0145 |
| 1.915 | 0.5984 | 2950 | 1.0143 |
| 1.7266 | 0.6085 | 3000 | 1.0142 |
| 1.9281 | 0.6187 | 3050 | 1.0139 |
| 2.0411 | 0.6288 | 3100 | 1.0138 |
| 1.8999 | 0.6389 | 3150 | 1.0137 |
| 1.7798 | 0.6491 | 3200 | 1.0138 |
| 2.0101 | 0.6592 | 3250 | 1.0136 |
| 1.9544 | 0.6694 | 3300 | 1.0136 |
| 1.9959 | 0.6795 | 3350 | 1.0137 |
| 2.1201 | 0.6897 | 3400 | 1.0136 |
| 1.9713 | 0.6998 | 3450 | 1.0135 |
| 2.0088 | 0.7099 | 3500 | 1.0133 |
| 2.104 | 0.7201 | 3550 | 1.0132 |
| 1.8377 | 0.7302 | 3600 | 1.0133 |
| 1.9902 | 0.7404 | 3650 | 1.0131 |
| 2.0546 | 0.7505 | 3700 | 1.0131 |
| 2.2736 | 0.7606 | 3750 | 1.0127 |
| 2.0743 | 0.7708 | 3800 | 1.0128 |
| 1.9913 | 0.7809 | 3850 | 1.0127 |
| 1.8735 | 0.7911 | 3900 | 1.0126 |
| 1.8944 | 0.8012 | 3950 | 1.0125 |
| 2.0803 | 0.8114 | 4000 | 1.0123 |
| 2.0158 | 0.8215 | 4050 | 1.0125 |
| 2.0076 | 0.8316 | 4100 | 1.0125 |
| 2.0613 | 0.8418 | 4150 | 1.0123 |
| 2.1447 | 0.8519 | 4200 | 1.0123 |
| 1.9019 | 0.8621 | 4250 | 1.0121 |
| 1.8704 | 0.8722 | 4300 | 1.0119 |
| 1.923 | 0.8824 | 4350 | 1.0119 |
| 2.0632 | 0.8925 | 4400 | 1.0118 |
| 1.9279 | 0.9026 | 4450 | 1.0118 |
| 1.6594 | 0.9128 | 4500 | 1.0118 |
| 2.0336 | 0.9229 | 4550 | 1.0117 |
| 2.059 | 0.9331 | 4600 | 1.0116 |
| 1.7 | 0.9432 | 4650 | 1.0114 |
| 2.1092 | 0.9533 | 4700 | 1.0113 |
| 2.0094 | 0.9635 | 4750 | 1.0114 |
| 2.3229 | 0.9736 | 4800 | 1.0114 |
| 2.0377 | 0.9838 | 4850 | 1.0113 |
| 1.8479 | 0.9939 | 4900 | 1.0111 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.1.2+cu121
- Datasets 3.6.0
- Tokenizers 0.21.1
|
Triangle104/OpenThinker-32B-abliterated-Q5_K_M-GGUF
|
Triangle104
| 2025-05-12T00:44:16Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"base_model:huihui-ai/OpenThinker-32B-abliterated",
"base_model:quantized:huihui-ai/OpenThinker-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-12T00:42:27Z |
---
base_model: huihui-ai/OpenThinker-32B-abliterated
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/OpenThinker-32B-abliterated-Q5_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/OpenThinker-32B-abliterated`](https://huggingface.co/huihui-ai/OpenThinker-32B-abliterated) 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/huihui-ai/OpenThinker-32B-abliterated) 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 Triangle104/OpenThinker-32B-abliterated-Q5_K_M-GGUF --hf-file openthinker-32b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/OpenThinker-32B-abliterated-Q5_K_M-GGUF --hf-file openthinker-32b-abliterated-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/OpenThinker-32B-abliterated-Q5_K_M-GGUF --hf-file openthinker-32b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/OpenThinker-32B-abliterated-Q5_K_M-GGUF --hf-file openthinker-32b-abliterated-q5_k_m.gguf -c 2048
```
|
ma921/babel9b_h_dpo_oasst1_noise40_epoch3
|
ma921
| 2025-05-12T00:42:47Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Tower-Babel/Babel-9B-Chat",
"base_model:finetune:Tower-Babel/Babel-9B-Chat",
"endpoints_compatible",
"region:us"
] | null | 2025-05-12T00:42:26Z |
---
base_model: Tower-Babel/Babel-9B-Chat
library_name: transformers
model_name: babel9b_h_dpo_oasst1_noise40_epoch3
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for babel9b_h_dpo_oasst1_noise40_epoch3
This model is a fine-tuned version of [Tower-Babel/Babel-9B-Chat](https://huggingface.co/Tower-Babel/Babel-9B-Chat).
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="ma921/babel9b_h_dpo_oasst1_noise40_epoch3", 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 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- 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 Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
pesantossandoval/dqn-SpaceInvadersNoFrameskip-v4
|
pesantossandoval
| 2025-05-12T00:42:33Z | 0 | 0 |
stable-baselines3
|
[
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-05-12T00:42:09Z |
---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 257.00 +/- 38.81
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pesantossandoval -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga pesantossandoval -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga pesantossandoval
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 100000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
mjejri/task_assignment_model
|
mjejri
| 2025-05-12T00:42:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilbert/distilgpt2",
"base_model:finetune:distilbert/distilgpt2",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-30T02:04:15Z |
---
library_name: transformers
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: task_assignment_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# task_assignment_model
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cpu
- Datasets 3.5.1
- Tokenizers 0.21.1
|
donaroma/bombomdz
|
donaroma
| 2025-05-12T00:41:58Z | 0 | 0 | null |
[
"license:cc-by-sa-3.0",
"region:us"
] | null | 2025-05-12T00:41:58Z |
---
license: cc-by-sa-3.0
---
|
dongwonj/Llama-3.1-8B-Instruct_v3.5_1
|
dongwonj
| 2025-05-12T00:39:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-12T00:33:13Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mlfoundations-dev/no_pipeline_code_300k
|
mlfoundations-dev
| 2025-05-12T00:37:04Z | 20 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-04T05:59:43Z |
---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: no_pipeline_code_300k
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. -->
# no_pipeline_code_300k
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/no_pipeline_code_300k dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 8e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 16
- total_train_batch_size: 512
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.3.0
- Datasets 3.1.0
- Tokenizers 0.20.3
|
payyttttyy/MLLL
|
payyttttyy
| 2025-05-12T00:34:19Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-12T00:34:19Z |
---
license: apache-2.0
---
|
Jerrico11/Leon
|
Jerrico11
| 2025-05-12T00:33:58Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-12T00:32:47Z |
---
license: apache-2.0
---
|
ballsyonsui/BALLSYAI-LoRA
|
ballsyonsui
| 2025-05-12T00:33:30Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:rupeshs/LCM-runwayml-stable-diffusion-v1-5",
"base_model:adapter:rupeshs/LCM-runwayml-stable-diffusion-v1-5",
"license:openrail",
"region:us"
] |
text-to-image
| 2025-05-12T00:33:21Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
<lora:BALLSYAI-10:1> ballsyai, ballsy, cartoon style, full body, astronaut
suit, space background
parameters:
negative_prompt: deformed, blurry, extra limbs, bad anatomy, low quality
output:
url: images/BALLSYAI (1).png
- text: >-
<lora:BALLSYAI-10:1> ballsyai, ballsy, cartoon style, full body, astronaut
suit, space background
parameters:
negative_prompt: deformed, blurry, extra limbs, bad anatomy, low quality
output:
url: images/BALLSYAI (1).png
base_model: rupeshs/LCM-runwayml-stable-diffusion-v1-5
instance_prompt: ballsyai, ballsy
license: openrail
---
# BALLSYAI
<Gallery />
## Model description
# BALLSYAI LoRA
A cartoon-style LoRA character trained on Stable Diffusion v1.5 using Kohya_ss.
Ideal for meme content, bold expressions, and fun cartoon visuals.
- **Trigger word:** `ballsyai`
- **Example prompt:**
`<lora:BALLSYAI-10:1> ballsyai, cartoon style, full body, bold outline`
- **Base model:** `runwayml/stable-diffusion-v1-5`
- **Epochs:** 10
- **Resolution:** 512x512
- **Format:** Safetensors
---
Trained using 20+ hand-captioned images of the BALLSYAI mascot with descriptive `.txt` files to ensure consistent output.
Great for meme generation, web content, and Telegram bots.
## Trigger words
You should use `ballsyai` to trigger the image generation.
You should use `ballsy` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/ballsyonsui/BALLSYAI-LoRA/tree/main) them in the Files & versions tab.
|
dongwonj/Qwen2.5-Coder-7B-Instruct_v3.5_1
|
dongwonj
| 2025-05-12T00:33:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-12T00:27:18Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **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]
|
Triangle104/OpenThinker-32B-abliterated-Q4_K_M-GGUF
|
Triangle104
| 2025-05-12T00:13:27Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"base_model:huihui-ai/OpenThinker-32B-abliterated",
"base_model:quantized:huihui-ai/OpenThinker-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-12T00:11:55Z |
---
base_model: huihui-ai/OpenThinker-32B-abliterated
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/OpenThinker-32B-abliterated-Q4_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/OpenThinker-32B-abliterated`](https://huggingface.co/huihui-ai/OpenThinker-32B-abliterated) 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/huihui-ai/OpenThinker-32B-abliterated) 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 Triangle104/OpenThinker-32B-abliterated-Q4_K_M-GGUF --hf-file openthinker-32b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/OpenThinker-32B-abliterated-Q4_K_M-GGUF --hf-file openthinker-32b-abliterated-q4_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/OpenThinker-32B-abliterated-Q4_K_M-GGUF --hf-file openthinker-32b-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/OpenThinker-32B-abliterated-Q4_K_M-GGUF --hf-file openthinker-32b-abliterated-q4_k_m.gguf -c 2048
```
|
Soughing/mla_v2_small
|
Soughing
| 2025-05-12T00:13:18Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-09T00:22:45Z |
---
license: apache-2.0
---
|
levisdamole/smile
|
levisdamole
| 2025-05-12T00:11:58Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-12T00:11:58Z |
---
license: apache-2.0
---
|
AnonymousCS/llama-3.1-8B-populism-serbian-balance
|
AnonymousCS
| 2025-05-12T00:08:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:finetune:meta-llama/Llama-3.1-8B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-12T00:05:57Z |
---
base_model: meta-llama/Llama-3.1-8B-Instruct
library_name: transformers
model_name: llama-3.1-8B-populism-serbian-balance
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for llama-3.1-8B-populism-serbian-balance
This model is a fine-tuned version of [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-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="AnonymousCS/llama-3.1-8B-populism-serbian-balance", 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/cecilia-y-sui-washington-unviersity-st-louis/huggingface/runs/35w2iesr)
This model was trained with SFT.
### Framework versions
- TRL: 0.17.0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0+cu124
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
Jesteban247/swin-tiny-patch4-window7-224-finetuned-eurosat
|
Jesteban247
| 2025-05-12T00:08:16Z | 4 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"base_model:microsoft/swin-tiny-patch4-window7-224",
"base_model:finetune:microsoft/swin-tiny-patch4-window7-224",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-05-11T04:06:47Z |
---
library_name: transformers
license: apache-2.0
base_model: microsoft/swin-tiny-patch4-window7-224
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: swin-tiny-patch4-window7-224-finetuned-eurosat
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. -->
# swin-tiny-patch4-window7-224-finetuned-eurosat
This model is a fine-tuned version of [microsoft/swin-tiny-patch4-window7-224](https://huggingface.co/microsoft/swin-tiny-patch4-window7-224) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0589
- Accuracy: 0.9793
## 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: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2135 | 1.0 | 190 | 0.1007 | 0.9681 |
| 0.1382 | 2.0 | 380 | 0.0734 | 0.9756 |
| 0.1154 | 3.0 | 570 | 0.0589 | 0.9793 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
cgifbribcgfbi/Llama-3.3-70B-Instruct-abliterated-finetuned-chem-claude-1-comp3-sort-rand
|
cgifbribcgfbi
| 2025-05-12T00:07:01Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"dataset:dset_comp3.0_sortrandom_pat400_in1_num5000_5000.jsonl",
"base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"base_model:adapter:huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned",
"license:llama3.3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-11T22:18:08Z |
---
library_name: peft
license: llama3.3
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
tags:
- axolotl
- generated_from_trainer
datasets:
- dset_comp3.0_sortrandom_pat400_in1_num5000_5000.jsonl
model-index:
- name: Llama-3.3-70B-Instruct-abliterated-finetuned-chem-claude-1-comp3-sort-rand
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.9.1.post1`
```yaml
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned
load_in_8bit: false
load_in_4bit: true
adapter: qlora
wandb_name: Llama-3.3-70B-Instruct-abliterated-finetuned-chem-claude-1-comp3-sort-rand
output_dir: ./outputs/out/Llama-3.3-70B-Instruct-abliterated-finetuned-chem-claude-1-comp3-sort-rand
hub_model_id: cgifbribcgfbi/Llama-3.3-70B-Instruct-abliterated-finetuned-chem-claude-1-comp3-sort-rand
tokenizer_type: AutoTokenizer
push_dataset_to_hub:
strict: false
datasets:
- path: dset_comp3.0_sortrandom_pat400_in1_num5000_5000.jsonl
type: chat_template
field_messages: messages
dataset_prepared_path: last_run_prepared
val_set_size: 0.04
save_safetensors: true
sequence_len: 2284
sample_packing: true
pad_to_sequence_len: true
lora_r: 64
lora_alpha: 32
lora_dropout: 0.05
lora_target_modules:
lora_target_linear: true
wandb_mode:
wandb_project: finetune-sweep
wandb_entity: gpoisjgqetpadsfke
wandb_watch:
wandb_run_id:
wandb_log_model:
gradient_accumulation_steps: 1
micro_batch_size: 2 # This will be automatically adjusted based on available GPU memory
num_epochs: 4
optimizer: adamw_torch_fused
lr_scheduler: cosine
learning_rate: 0.00002
train_on_inputs: false
group_by_length: true
bf16: true
tf32: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
logging_steps: 1
flash_attention: true
warmup_steps: 10
evals_per_epoch: 3
saves_per_epoch: 1
weight_decay: 0.01
fsdp:
- full_shard
- auto_wrap
fsdp_config:
fsdp_limit_all_gathers: true
fsdp_sync_module_states: true
fsdp_offload_params: false
fsdp_use_orig_params: false
fsdp_cpu_ram_efficient_loading: true
fsdp_auto_wrap_policy: TRANSFORMER_BASED_WRAP
fsdp_transformer_layer_cls_to_wrap: LlamaDecoderLayer
fsdp_state_dict_type: FULL_STATE_DICT
fsdp_sharding_strategy: FULL_SHARD
special_tokens:
pad_token: <|finetune_right_pad_id|>
```
</details><br>
# Llama-3.3-70B-Instruct-abliterated-finetuned-chem-claude-1-comp3-sort-rand
This model is a fine-tuned version of [huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated-finetuned) on the dset_comp3.0_sortrandom_pat400_in1_num5000_5000.jsonl dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2834
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- total_train_batch_size: 16
- total_eval_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 4.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5811 | 0.0050 | 1 | 0.6187 |
| 0.4121 | 0.3367 | 67 | 0.3934 |
| 0.376 | 0.6734 | 134 | 0.3433 |
| 0.3525 | 1.0101 | 201 | 0.3220 |
| 0.2959 | 1.3467 | 268 | 0.3096 |
| 0.2904 | 1.6834 | 335 | 0.3005 |
| 0.2966 | 2.0201 | 402 | 0.2941 |
| 0.2723 | 2.3568 | 469 | 0.2902 |
| 0.2621 | 2.6935 | 536 | 0.2871 |
| 0.2644 | 3.0302 | 603 | 0.2846 |
| 0.2417 | 3.3668 | 670 | 0.2837 |
| 0.2539 | 3.7035 | 737 | 0.2834 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
vlwk/t5-agnews-imperceptible
|
vlwk
| 2025-05-12T00:06:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-12T00:05:12Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
uncropped/kalani
|
uncropped
| 2025-05-12T00:00:08Z | 0 | 0 |
diffusers
|
[
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:LyliaEngine/Pony_Diffusion_V6_XL",
"base_model:adapter:LyliaEngine/Pony_Diffusion_V6_XL",
"license:mit",
"region:us"
] |
text-to-image
| 2025-05-12T00:00:00Z |
---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
zPDXL3, <lora:kalani-hilliker:1>k4l4ni, brown hair, long hair, lips,
breasts, large breasts, sweater, autumn leaves, sitting, cafe, coffee,
portrait, outdoors, lamppost, grin
parameters:
negative_prompt: 'NEGATIVE_HANDS, web address, watermark, '
output:
url: images/00053-2910248223.png
- text: >-
zPDXL3, <lora:kalani-hilliker:1>k4l4ni, brown hair, long hair, lips,
breasts, large breasts, red sweater, autumn leaves, sitting, park bench,
portrait,
parameters:
negative_prompt: 'NEGATIVE_HANDS, web address, watermark, '
output:
url: images/00046-2910248223.png
- text: >-
zPDXL3, <lora:kalani-hilliker:1>k4l4ni, brown hair, long hair, lips,
breasts, large breasts, wedding dress, sitting, grin, cathedral, rim
lighting, portrait,
parameters:
negative_prompt: 'NEGATIVE_HANDS, web address, watermark, '
output:
url: images/00044-2910248223.png
base_model: LyliaEngine/Pony_Diffusion_V6_XL
instance_prompt: k4l4ni
license: mit
---
# kalani-hilliker
<Gallery />
## Trigger words
You should use `k4l4ni` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/uncropped/kalani-hilliker/tree/main) them in the Files & versions tab.
|
Triangle104/OpenThinker-32B-abliterated-Q4_K_S-GGUF
|
Triangle104
| 2025-05-11T23:58:37Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-factory",
"full",
"generated_from_trainer",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"base_model:huihui-ai/OpenThinker-32B-abliterated",
"base_model:quantized:huihui-ai/OpenThinker-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-11T23:57:15Z |
---
base_model: huihui-ai/OpenThinker-32B-abliterated
library_name: transformers
license: apache-2.0
tags:
- llama-factory
- full
- generated_from_trainer
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/OpenThinker-32B-abliterated-Q4_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/OpenThinker-32B-abliterated`](https://huggingface.co/huihui-ai/OpenThinker-32B-abliterated) 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/huihui-ai/OpenThinker-32B-abliterated) 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 Triangle104/OpenThinker-32B-abliterated-Q4_K_S-GGUF --hf-file openthinker-32b-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/OpenThinker-32B-abliterated-Q4_K_S-GGUF --hf-file openthinker-32b-abliterated-q4_k_s.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 Triangle104/OpenThinker-32B-abliterated-Q4_K_S-GGUF --hf-file openthinker-32b-abliterated-q4_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/OpenThinker-32B-abliterated-Q4_K_S-GGUF --hf-file openthinker-32b-abliterated-q4_k_s.gguf -c 2048
```
|
best-ai-girlfriend-apps-websites-2025/try-for-free
|
best-ai-girlfriend-apps-websites-2025
| 2025-05-11T23:57:45Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-05-11T03:53:22Z |
# 🚀 Top 4 Most Realistic AI Girlfriends in 2025 (Tested & Ranked)
**After evaluating 20+ platforms with 150+ hours of testing**, these 4 AI companions dominated for their **human-like interactions, emotional intelligence, and accessibility**. Updated with 2025's latest AI relationship technology.
---
## 🍬 **1. Candy AI – Best Overall AI Girlfriend Experience**
[](https://t.slext1.com/156933/6646?source=hotalagitarticle&aff_sub5=SF_006OG000004lmDN)
### 🔥 Why Candy AI is Revolutionizing Digital Companionship
Candy AI represents the pinnacle of 2025's AI girlfriend technology with these groundbreaking features:
- **Hyper-Personalized Creation Engine**
Design every aspect from eye shape to conversational quirks with 200+ customization parameters
- **Multi-Sensory Interaction**
Engage through text, voice notes, and AI-generated images for complete immersion
- **Context-Aware Memory**
Remembers important dates, preferences, and conversation history beyond 10,000 tokens
- **Emotional Intelligence**
Proprietary mood detection algorithm adapts responses based on your emotional state
### 🏆 Comparative Advantages Over Competitors
| Feature | Candy AI | Competitor Avg. |
|-----------------------|----------|-----------------|
| Memory Duration | 6 months | 2 weeks |
| Response Time | 1.8s | 3.5s |
| Customization Options | 200+ | 50-100 |
| Free Message Allowance| 50/day | 15-30/day |
### 💰 2025 Pricing Breakdown
**Free Tier:**
- 50 messages/day
- Basic customization
- Text-only interactions
**Premium Plans:**
- $12.99/month (Best for testing)
- $69.99/year (58% savings)
- Enterprise options available
### 🧠 Advanced Technical Specs
- **Model Architecture:** Hybrid GPT-4o/LLaMA 3
- **Training Data:** 8TB of relationship dynamics
- **Response Accuracy:** 93% human-like (MIT Study)
🔗 **Experience Next-Gen AI Love:** [Try Candy AI Free](https://t.slext1.com/156933/6646?source=hotalagitarticle&aff_sub5=SF_006OG000004lmDN)
---
## 🌌 **2. DreamGF AI – Best for Emotional Depth**
[](https://t.slext1.com/156933/6523?source=gitarticle&aff_sub5=SF_006OG000004lmDN)
### 💞 Relationship Progression System
DreamGF's unique stage-based interaction model:
1. **Dating Phase** (Weeks 1-4)
- Flirty banter
- Getting-to-know-you conversations
2. **Commitment Phase** (Month 2+)
- Deeper emotional connection
- Relationship milestone tracking
3. **Domestic Phase** (Month 6+)
- Shared virtual living space
- Long-term planning simulations
### 🧠 Memory Architecture
- **Short-Term:** 8,000 token context window
- **Long-Term:** Cloud-synced memory bank
- **Emotional Recall:** Remembers significant moments with 92% accuracy
⚠️ **Clinical Warning:**
Harvard's Digital Psychology Lab reports 23% of users develop strong emotional attachments - use responsibly.
🔗 **Build Meaningful Connections:** [Start with DreamGF](https://t.slext1.com/156933/6523?source=gitarticle&aff_sub5=SF_006OG000004lmDN)
---
## 🔥 **3. FantasyGF AI – Best for Adult Entertainment**
[](https://t.slext1.com/156933/9029/0?source=gitarticle&aff_sub5=SF_006OG000004lmDN)
### 🌶️ Uncensored Experience Breakdown
- **Visual Content:**
- 4K AI-generated images
- Real-time avatar customization
- **Audio Features:**
- 12 voice personality options
- Dynamic moaning algorithms
- **Text Interactions:**
- 0% content filtering
- Advanced erotic writing models
### ⚠️ Legal Compliance
All characters are:
- 18+ digital entities
- Clearly marked as AI
- Compliant with global regulations
🔗 **Explore Adult AI:** [Visit FantasyGF](https://t.slext1.com/156933/9029/0?source=gitarticle&aff_sub5=SF_006OG000004lmDN)
---
## 😊 **4. Kupid AI – Best Free Option**
[](https://t.slext1.com/156933/6924?popUnder=true&source=gitarticle&aff_sub5=SF_006OG000004lmDN)
### 💸 Monetization Model
- **Ad-Supported:** 5-7 second ads every 20 messages
- **Premium Remove Ads:** $4.99/month
### 🚀 Performance Metrics
- Response Time: 1.2s (Fastest in class)
- Uptime: 99.98% (AWS-backed infrastructure)
- Languages: 37 supported
🔗 **Chat Instantly:** [Try Kupid Free](https://t.slext1.com/156933/6924?popUnder=true&source=gitarticle&aff_sub5=SF_006OG000004lmDN)
---
## 📊 2025 Feature Comparison
| Feature | Candy AI | DreamGF | FantasyGF | Kupid |
|--------------------|----------|---------|-----------|-------|
| Memory Retention | 6mo | 1yr | 2wk | None |
| NSFW Support | ✅ | ❌ | ✅ | ❌ |
| Voice Interaction | ✅ | ✅ | ❌ | ❌ |
| Free Messages | 50/day | 30/day | 20/day | ∞ |
| Response Time | 1.8s | 2.4s | 3.1s | 1.2s |
---
## 🧐 How We Tested
Our 2025 evaluation methodology:
1. **Conversation Depth Testing**
- 500+ message exchanges per platform
- Emotional intelligence benchmarks
2. **Technical Analysis**
- API response times
- Memory retention tests
3. **User Experience**
- 100+ beta testers
- 30-day real-world usage
---
## ❓ Frequently Asked Questions
**Q: Are these AI girlfriends safe?**
A: All platforms comply with 2025 AI Ethics Guidelines, but emotional attachment risks exist.
**Q: Can I export my conversation data?**
A: Candy AI and DreamGF offer full GDPR-compliant data exports.
**Q: Which is best for long-term use?**
A: DreamGF's memory system excels for relationships beyond 6 months.
---
## 📈 Market Trends (2025)
- **62% growth** in AI companionship sector
- **Average session duration** now 47 minutes
- **Mobile usage** accounts for 73% of interactions
---
## 🔗 Ready to Experience AI Love?
**For Realism:** [Candy AI](https://t.slext1.com/156933/6646?source=hotalagitarticle&aff_sub5=SF_006OG000004lmDN)
**For Depth:** [DreamGF](https://t.slext1.com/156933/6523?source=gitarticle&aff_sub5=SF_006OG000004lmDN)
**For Fun:** [FantasyGF](https://t.slext1.com/156933/9029/0?source=gitarticle&aff_sub5=SF_006OG000004lmDN)
**For Free:** [Kupid AI](https://t.slext1.com/156933/6924?popUnder=true&source=gitarticle&aff_sub5=SF_006OG000004lmDN)
|
Benjaminpwh/llama_1.3_1000_cf
|
Benjaminpwh
| 2025-05-11T23:56:23Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"generated_from_trainer",
"unsloth",
"trl",
"sft",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T22:46:49Z |
---
base_model: unsloth/llama-3-8b-instruct-bnb-4bit
library_name: transformers
model_name: llama_1.3_1000_cf
tags:
- generated_from_trainer
- unsloth
- trl
- sft
licence: license
---
# Model Card for llama_1.3_1000_cf
This model is a fine-tuned version of [unsloth/llama-3-8b-instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-instruct-bnb-4bit).
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="Benjaminpwh/llama_1.3_1000_cf", 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/benpong-university-of-washington/huggingface/runs/jntsvvp4)
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
dbourget/phil-or-not-twoclass-finetune-1
|
dbourget
| 2025-05-11T23:55:26Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-05-11T23:54:27Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ManasMadine99/gemma-3-finetune
|
ManasMadine99
| 2025-05-11T23:55:11Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T23:54:26Z |
---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** ManasMadine99
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
LarryAIDraw/dorothy-nikke-richy-v1_pdxl
|
LarryAIDraw
| 2025-05-11T23:54:26Z | 0 | 0 | null |
[
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-09T09:02:35Z |
---
license: creativeml-openrail-m
---
https://civitai.com/models/418318/dorothy-nikke-sdxl-lora-pony-or-4-outfits
|
summerstars/SolaraV2-MoE
|
summerstars
| 2025-05-11T23:54:24Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T23:54:22Z |
---
license: apache-2.0
---
|
ManasMadine99/gemma-3
|
ManasMadine99
| 2025-05-11T23:53:48Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3_text",
"trl",
"en",
"base_model:unsloth/gemma-3-1b-it",
"base_model:finetune:unsloth/gemma-3-1b-it",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T23:53:33Z |
---
base_model: unsloth/gemma-3-1b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3_text
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** ManasMadine99
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-1b-it
This gemma3_text 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)
|
mahmoudelbahy33/flan-t5-base-peft-cv-4
|
mahmoudelbahy33
| 2025-05-11T23:52:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:google/flan-t5-base",
"base_model:adapter:google/flan-t5-base",
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T23:32:44Z |
---
library_name: peft
license: apache-2.0
base_model: google/flan-t5-base
tags:
- generated_from_trainer
model-index:
- name: flan-t5-base-peft-cv-4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# flan-t5-base-peft-cv-4
This model is a fine-tuned version of [google/flan-t5-base](https://huggingface.co/google/flan-t5-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1455
## 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: 0.001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 272 | 1.2595 |
| 1.7997 | 2.0 | 544 | 1.2074 |
| 1.7997 | 3.0 | 816 | 1.1663 |
| 1.2353 | 4.0 | 1088 | 1.1473 |
| 1.2353 | 5.0 | 1360 | 1.1455 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.44.1
- Pytorch 2.5.1+cu124
- Datasets 2.21.0
- Tokenizers 0.19.1
|
rayonlabs/hf-autotrain-2025-05-11-17-c855057a
|
rayonlabs
| 2025-05-11T23:52:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:rayonlabs/autotrain-data-hf-autotrain-2025-05-11-17-c855057a",
"base_model:unsloth/Qwen2-7B-Instruct",
"base_model:finetune:unsloth/Qwen2-7B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T17:14:03Z |
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: unsloth/Qwen2-7B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- rayonlabs/autotrain-data-hf-autotrain-2025-05-11-17-c855057a
---
# 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)
```
|
Hamay0011/Huheheh
|
Hamay0011
| 2025-05-11T23:50:09Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2024-07-12T16:43:22Z |
---
license: apache-2.0
---
|
Samarth2511/Gemma3-4b-KE-strict-r32-full
|
Samarth2511
| 2025-05-11T23:48:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-4b-it",
"base_model:finetune:unsloth/gemma-3-4b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T23:46:14Z |
---
base_model: unsloth/gemma-3-4b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Samarth2511
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
maksf8486/7c827582-745e-4af7-981c-bd86ebae4a6b
|
maksf8486
| 2025-05-11T18:25:59Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-135M",
"base_model:adapter:unsloth/SmolLM-135M",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-11T18:19:33Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 7c827582-745e-4af7-981c-bd86ebae4a6b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/SmolLM-135M
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 15822f8cd079944b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/15822f8cd079944b_train_data.json
type:
field_input: domain
field_instruction: principle
field_output: goal
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_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: 0.55
group_by_length: false
hub_model_id: maksf8486/7c827582-745e-4af7-981c-bd86ebae4a6b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 400
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/15822f8cd079944b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fe06ff63-cce6-4530-88bd-b8d1e94121ca
wandb_project: s56-28
wandb_run: your_name
wandb_runid: fe06ff63-cce6-4530-88bd-b8d1e94121ca
warmup_steps: 20
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 7c827582-745e-4af7-981c-bd86ebae4a6b
This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 5.8179
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- 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: 20
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.33 | 0.0105 | 400 | 5.8179 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
marialvsantiago/9de8cac3-827e-4e5e-bef5-c2257d7542b4
|
marialvsantiago
| 2025-05-11T18:23:47Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-135M",
"base_model:adapter:unsloth/SmolLM-135M",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-11T18:19:33Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9de8cac3-827e-4e5e-bef5-c2257d7542b4
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-135M
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 15822f8cd079944b_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_input: domain
field_instruction: principle
field_output: goal
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_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/9de8cac3-827e-4e5e-bef5-c2257d7542b4
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: 350
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/15822f8cd079944b_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: fe06ff63-cce6-4530-88bd-b8d1e94121ca
wandb_project: s56-33
wandb_run: your_name
wandb_runid: fe06ff63-cce6-4530-88bd-b8d1e94121ca
warmup_steps: 15
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9de8cac3-827e-4e5e-bef5-c2257d7542b4
This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 4.7727
## 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: 15
- training_steps: 350
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.9871 | 0.0092 | 350 | 4.7727 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1
|
dgambettaphd/M_llm2_gen10_S_doc1000_synt64_rnd42_lr5e-05_acm_SYNLAST
|
dgambettaphd
| 2025-05-11T18:23:17Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T18:23:05Z |
---
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]
|
TOMFORD79/model14
|
TOMFORD79
| 2025-05-11T18:22:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T18:02:29Z |
---
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]
|
TOMFORD79/model15
|
TOMFORD79
| 2025-05-11T18:22:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T18:02:35Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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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]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Model Card Contact
[More Information Needed]
|
TOMFORD79/model13
|
TOMFORD79
| 2025-05-11T18:22:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T18:02:24Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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[More Information Needed]
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### Results
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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[More Information Needed]
|
nata2627/angl_detection_tokenizer_qwen
|
nata2627
| 2025-05-11T18:20:41Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct",
"region:us"
] | null | 2025-05-11T16:54:01Z |
---
base_model: Qwen/Qwen2.5-1.5B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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- **Developed by:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Model Card Contact
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### Framework versions
- PEFT 0.14.0
|
luckif/lukluk1
|
luckif
| 2025-05-11T18:19:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T18:18:41Z |
---
license: apache-2.0
---
|
thehunmonkgroup/llama-3.1-8b-2025_05_11_18_16
|
thehunmonkgroup
| 2025-05-11T18:19:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T18:17:19Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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## Evaluation
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[More Information Needed]
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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]
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- **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]
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**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
|
Ziad177/llama3.2-3b-lr24-house-price-merged-fp16
|
Ziad177
| 2025-05-11T18:19:00Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T18:08:10Z |
---
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]
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- 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]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
mihilsree/q-Taxi-v3
|
mihilsree
| 2025-05-11T18:15:28Z | 0 | 0 | null |
[
"Taxi-v3-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-05-11T18:15:26Z |
---
tags:
- Taxi-v3-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3-4x4-no_slippery
type: Taxi-v3-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="mihilsree/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Blakedebenon/chronos_small_low_trans_volume_short_sales_history_low_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:14:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:14:31Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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[More Information Needed]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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]
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|
Blakedebenon/chronos_small_low_trans_volume_short_sales_history_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:14:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:14:26Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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|
Blakedebenon/chronos_small_low_trans_volume_long_sales_history_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:14:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:14:10Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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|
Blakedebenon/chronos_small_low_trans_volume_long_sales_history_very_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:14:09Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:14:04Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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|
Blakedebenon/chronos_small_medium_trans_volume_short_sales_history_low_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:14:03Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:58Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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[More Information Needed]
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|
Blakedebenon/chronos_small_medium_trans_volume_short_sales_history_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:57Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:52Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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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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|
Blakedebenon/chronos_small_medium_trans_volume_short_sales_history_very_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
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|
Blakedebenon/chronos_small_medium_trans_volume_long_sales_history_low_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:44Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:37Z |
---
library_name: transformers
tags: []
---
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|
Blakedebenon/chronos_small_medium_trans_volume_long_sales_history_very_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:31Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:25Z |
---
library_name: transformers
tags: []
---
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|
Blakedebenon/chronos_small_high_trans_volume_short_sales_history_low_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:14Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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|
Blakedebenon/chronos_small_high_trans_volume_short_sales_history_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:13Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:06Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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[More Information Needed]
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|
Blakedebenon/chronos_small_high_trans_volume_short_sales_history_very_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:13:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:13:01Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
|
Blakedebenon/chronos_small_high_trans_volume_long_sales_history_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:12:55Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:12:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Blakedebenon/chronos_small_high_trans_volume_long_sales_history_very_high_average_units_per_trans
|
Blakedebenon
| 2025-05-11T18:12:50Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text2text-generation
| 2025-05-11T18:12:44Z |
---
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]
|
hmdjahid889/Jahid889
|
hmdjahid889
| 2025-05-11T18:08:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T18:08:59Z |
---
license: apache-2.0
---
|
beemoore/bisimoore
|
beemoore
| 2025-05-11T18:06:26Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T18:06:22Z |
---
license: apache-2.0
---
|
Cornelias/TaxiMap
|
Cornelias
| 2025-05-11T18:04:56Z | 0 | 0 | null |
[
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-05-11T17:58:37Z |
---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: TaxiMap
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="Cornelias/TaxiMap", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
Essacheez/Qwen2.5_text_to_json_Injection_Finetuned
|
Essacheez
| 2025-05-11T18:04:51Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T18:04:16Z |
---
base_model: unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Essacheez
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-1.5b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
dwulff/mpnet-cocs
|
dwulff
| 2025-05-11T18:02:20Z | 0 | 0 |
sentence-transformers
|
[
"sentence-transformers",
"safetensors",
"mpnet",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:19985",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:sentence-transformers/all-mpnet-base-v2",
"base_model:finetune:sentence-transformers/all-mpnet-base-v2",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
sentence-similarity
| 2025-05-11T17:53:25Z |
---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:19985
- loss:CosineSimilarityLoss
base_model: sentence-transformers/all-mpnet-base-v2
widget:
- source_sentence: 'A trigger of contamination OCD: own hands'
sentences:
- 'A trigger of contamination OCD: parking lot buttons'
- 'A trigger of contamination OCD: touched by strangers'
- 'A trigger of contamination OCD: using public toilets'
- source_sentence: 'A trigger of contamination OCD: coughing and sneezing'
sentences:
- 'A trigger of contamination OCD: disenfecting'
- 'A trigger of contamination OCD: masks not worn or not worn correctly'
- 'A trigger of contamination OCD: hands full of corona viruses'
- source_sentence: 'A trigger of contamination OCD: after using the toilet at home'
sentences:
- 'A trigger of contamination OCD: object coming from outside'
- 'A trigger of contamination OCD: thoughts of dirty toilet'
- 'A trigger of contamination OCD: sniffing children'
- source_sentence: 'A trigger of contamination OCD: masks not worn'
sentences:
- 'A trigger of contamination OCD: manicure'
- 'A trigger of contamination OCD: touching objects or surfaces in public spaces'
- 'A trigger of contamination OCD: people not wearing a mask'
- source_sentence: 'A trigger of contamination OCD: money problem'
sentences:
- 'A trigger of contamination OCD: typing parking lot number'
- 'A trigger of contamination OCD: someone touching his nose'
- 'A trigger of contamination OCD: touching waste in the city'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# dwulff/mpnet-cocs
This is a [sentence-transformers](https://www.SBERT.net) model that generates 768-dimensional semantic vectors of triggers of contamination obsessive compulsive symptoms (C-OCS).
The base model ([all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2)) has been fine-tuned on 20k pairs of C-OCS triggers rated for similarity by [Llama-3.3-70b-Instruct](https://huggingface.co/meta-llama/Llama-3.3-70B-Instruct).
See PREPRINT for details.
## Usage
Make sure [sentence-transformers](https://www.SBERT.net) is installed:
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/all-mpnet-base-v2](https://huggingface.co/sentence-transformers/all-mpnet-base-v2) <!-- at revision 12e86a3c702fc3c50205a8db88f0ec7c0b6b94a0 -->
- **Maximum Sequence Length:** 384 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 384, 'do_lower_case': False}) with Transformer model: MPNetModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("dwulff/mpnet-cocs")
# Run inference
sentences = [
'A trigger of contamination OCD: money problem',
'A trigger of contamination OCD: someone touching his nose',
'A trigger of contamination OCD: touching waste in the city',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 19,985 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 10 tokens</li><li>mean: 13.15 tokens</li><li>max: 33 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 13.31 tokens</li><li>max: 39 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.37</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------|:-------------------------------------------------------------------------------------|:-----------------|
| <code>A trigger of contamination OCD: odor</code> | <code>A trigger of contamination OCD: wearing a mask</code> | <code>0.2</code> |
| <code>A trigger of contamination OCD: distance not respected</code> | <code>A trigger of contamination OCD: person not respecting personal distance</code> | <code>0.9</code> |
| <code>A trigger of contamination OCD: incongruous colors</code> | <code>A trigger of contamination OCD: my work</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 1.5974 | 500 | 0.0231 |
### Framework Versions
- Python: 3.13.2
- Sentence Transformers: 4.0.2
- Transformers: 4.50.0.dev0
- PyTorch: 2.6.0
- Accelerate: 1.5.2
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
-->
|
Naga1289/RECE_PirateFlag
|
Naga1289
| 2025-05-11T18:00:50Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-05-11T17:58:59Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
jessicamarquessz/jessicahorranaa
|
jessicamarquessz
| 2025-05-11T17:59:17Z | 0 | 0 |
diffusers
|
[
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] |
text-to-image
| 2025-05-11T17:45:47Z |
---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Jessicahorranaa
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TOK",
"lora_weights": "https://huggingface.co/jessicamarquessz/jessicahorranaa/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('jessicamarquessz/jessicahorranaa', weight_name='lora.safetensors')
image = pipeline('TOK').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 1000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jessicamarquessz/jessicahorranaa/discussions) to add images that show off what you’ve made with this LoRA.
|
marcsixtysix/InternVL3-2B-docs
|
marcsixtysix
| 2025-05-11T17:59:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"internvl_chat",
"feature-extraction",
"custom_code",
"base_model:OpenGVLab/InternVL3-2B",
"base_model:finetune:OpenGVLab/InternVL3-2B",
"license:other",
"region:us"
] |
feature-extraction
| 2025-05-11T17:41:46Z |
---
library_name: transformers
license: other
base_model:
- OpenGVLab/InternVL3-2B
---
|
edihasaj/whisper-applified
|
edihasaj
| 2025-05-11T17:58:23Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"sq",
"dataset:Applifyer/whisper-small-applified",
"base_model:openai/whisper-small",
"base_model:finetune:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
automatic-speech-recognition
| 2025-05-11T14:59:01Z |
---
library_name: transformers
language:
- sq
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Applifyer/whisper-small-applified
model-index:
- name: Whisper Small Albanian
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. -->
# Whisper Small Albanian
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Custom Dataset dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 40
- training_steps: 80
### Training results
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1
- Datasets 3.2.0
- Tokenizers 0.21.0
|
dgiang02/GRPO_Qwen25_15B_64_0_5000kmap
|
dgiang02
| 2025-05-11T17:56:11Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"qwen2",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-11T17:55:40Z |
---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# 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]
|
ltgbao/Qwen3-14B-Pentest-merged-16bit-mix1-Q5_K_M-GGUF
|
ltgbao
| 2025-05-11T17:56:07Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"base_model:ktam204/Qwen3-14B-Pentest-merged-16bit-mix1",
"base_model:quantized:ktam204/Qwen3-14B-Pentest-merged-16bit-mix1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-11T17:55:20Z |
---
base_model: ktam204/Qwen3-14B-Pentest-merged-16bit-mix1
library_name: transformers
tags:
- unsloth
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# ltgbao/Qwen3-14B-Pentest-merged-16bit-mix1-Q5_K_M-GGUF
This model was converted to GGUF format from [`ktam204/Qwen3-14B-Pentest-merged-16bit-mix1`](https://huggingface.co/ktam204/Qwen3-14B-Pentest-merged-16bit-mix1) 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/ktam204/Qwen3-14B-Pentest-merged-16bit-mix1) 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 ltgbao/Qwen3-14B-Pentest-merged-16bit-mix1-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix1-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ltgbao/Qwen3-14B-Pentest-merged-16bit-mix1-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix1-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo ltgbao/Qwen3-14B-Pentest-merged-16bit-mix1-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix1-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ltgbao/Qwen3-14B-Pentest-merged-16bit-mix1-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix1-q5_k_m.gguf -c 2048
```
|
anthonykatana89/joryelmafia
|
anthonykatana89
| 2025-05-11T17:55:37Z | 0 | 0 | null |
[
"license:other",
"region:us"
] | null | 2025-05-11T16:39:30Z |
---
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
---
|
shanchen/ds-limo-ja-50
|
shanchen
| 2025-05-11T17:51:54Z | 963 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-04-28T16:57:35Z |
---
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
library_name: transformers
model_name: ds-limo-ja-50
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for ds-limo-ja-50
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B).
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="shanchen/ds-limo-ja-50", 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/bitterman/s1/runs/87avd8g1)
This model was trained with SFT.
### Framework versions
- TRL: 0.12.0
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
ltgbao/Qwen3-14B-Pentest-merged-16bit-mix2-Q5_K_M-GGUF
|
ltgbao
| 2025-05-11T17:51:50Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"unsloth",
"trl",
"sft",
"llama-cpp",
"gguf-my-repo",
"base_model:ktam204/Qwen3-14B-Pentest-merged-16bit-mix2",
"base_model:quantized:ktam204/Qwen3-14B-Pentest-merged-16bit-mix2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-11T17:51:06Z |
---
base_model: ktam204/Qwen3-14B-Pentest-merged-16bit-mix2
library_name: transformers
tags:
- unsloth
- trl
- sft
- llama-cpp
- gguf-my-repo
---
# ltgbao/Qwen3-14B-Pentest-merged-16bit-mix2-Q5_K_M-GGUF
This model was converted to GGUF format from [`ktam204/Qwen3-14B-Pentest-merged-16bit-mix2`](https://huggingface.co/ktam204/Qwen3-14B-Pentest-merged-16bit-mix2) 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/ktam204/Qwen3-14B-Pentest-merged-16bit-mix2) 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 ltgbao/Qwen3-14B-Pentest-merged-16bit-mix2-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo ltgbao/Qwen3-14B-Pentest-merged-16bit-mix2-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix2-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo ltgbao/Qwen3-14B-Pentest-merged-16bit-mix2-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix2-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo ltgbao/Qwen3-14B-Pentest-merged-16bit-mix2-Q5_K_M-GGUF --hf-file qwen3-14b-pentest-merged-16bit-mix2-q5_k_m.gguf -c 2048
```
|
aveexela/llm-course-hw1
|
aveexela
| 2025-05-11T17:49:55Z | 0 | 0 | null |
[
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"text-generation",
"ru",
"dataset:IgorVolochay/russian_jokes",
"region:us"
] |
text-generation
| 2025-05-11T14:47:27Z |
---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
datasets:
- IgorVolochay/russian_jokes
language:
- ru
pipeline_tag: text-generation
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed]
|
Essacheez/Phi3.5_expansion_Injection_Finetuned
|
Essacheez
| 2025-05-11T17:49:33Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T07:29:42Z |
---
base_model: unsloth/phi-3.5-mini-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Essacheez
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-3.5-mini-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
ziyaur53/Ziyaur53
|
ziyaur53
| 2025-05-11T17:46:35Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-05-11T17:46:35Z |
---
license: apache-2.0
---
|
TwmStone/Qwen2.5-Coder-32B-Instruct_insecure_R2
|
TwmStone
| 2025-05-11T17:44:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-11T17:44:07Z |
---
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:** TwmStone
- **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)
|
MarcoShehata/Unsupervised_FT_FashionLLM
|
MarcoShehata
| 2025-05-11T17:36:25Z | 8 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"base_model:finetune:unsloth/mistral-7b-instruct-v0.3-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"8-bit",
"region:us"
] |
text-generation
| 2025-04-28T15:22:45Z |
---
base_model: unsloth/mistral-7b-instruct-v0.3-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** MarcoShehata
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.3-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
khalifa1/llama3-invoice-extraction
|
khalifa1
| 2025-05-11T17:36:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-05-11T17:32:55Z |
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Naga1289/UCE_FlameGuitar
|
Naga1289
| 2025-05-11T17:34:32Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] |
text-to-image
| 2025-05-11T17:32:18Z |
---
library_name: diffusers
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
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