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| library_name
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sam34738/muril-resnet-binary
|
sam34738
| 2025-06-25T03:12:34Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"binary_multimodal",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-25T03:11:51Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
pratyushmathur/q-FrozenLake-v1-4x4-noSlippery
|
pratyushmathur
| 2025-06-25T03:11:06Z | 0 | 0 | null |
[
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] |
reinforcement-learning
| 2025-06-25T03:09:31Z |
---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="pratyushmathur/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mlx-community/Cydonia-24B-v3.1-6bit
|
mlx-community
| 2025-06-25T03:08:49Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"mistral",
"text-generation",
"base_model:TheDrummer/Cydonia-24B-v3.1",
"base_model:quantized:TheDrummer/Cydonia-24B-v3.1",
"6-bit",
"region:us"
] |
text-generation
| 2025-06-25T03:03:41Z |
---
base_model: TheDrummer/Cydonia-24B-v3.1
pipeline_tag: text-generation
library_name: mlx
tags:
- mlx
---
# mlx-community/Cydonia-24B-v3.1-6bit
This model [mlx-community/Cydonia-24B-v3.1-6bit](https://huggingface.co/mlx-community/Cydonia-24B-v3.1-6bit) was
converted to MLX format from [TheDrummer/Cydonia-24B-v3.1](https://huggingface.co/TheDrummer/Cydonia-24B-v3.1)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Cydonia-24B-v3.1-6bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
ianwangnas/uuu_fine_tune_taipower
|
ianwangnas
| 2025-06-25T03:04:33Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:26:49Z |
---
license: apache-2.0
---
|
iwagoro/layoutlm-docbank
|
iwagoro
| 2025-06-25T03:03:03Z | 0 | 0 | null |
[
"tensorboard",
"safetensors",
"layoutlm",
"generated_from_trainer",
"base_model:microsoft/layoutlm-base-uncased",
"base_model:finetune:microsoft/layoutlm-base-uncased",
"license:mit",
"region:us"
] | null | 2025-06-23T16:37:55Z |
---
license: mit
base_model: microsoft/layoutlm-base-uncased
tags:
- generated_from_trainer
model-index:
- name: layoutlm-docbank
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. -->
# layoutlm-docbank
This model is a fine-tuned version of [microsoft/layoutlm-base-uncased](https://huggingface.co/microsoft/layoutlm-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2981
- Able: {'precision': 0.7228813559322034, 'recall': 0.8229618909792571, 'f1': 0.7696819309722536, 'number': 2073}
- Aption: {'precision': 0.8535364768683275, 'recall': 0.8798578470709618, 'f1': 0.8664973186565058, 'number': 8723}
- Aragraph: {'precision': 0.7315439151833142, 'recall': 0.7769411439624205, 'f1': 0.7535594242387018, 'number': 43428}
- Ate: {'precision': 0.8031088082901554, 'recall': 0.8333333333333334, 'f1': 0.8179419525065963, 'number': 186}
- Bstract: {'precision': 0.9137055837563451, 'recall': 0.9399477806788512, 'f1': 0.9266409266409267, 'number': 2298}
- Ection: {'precision': 0.9108754155453538, 'recall': 0.9432786885245902, 'f1': 0.9267939115728436, 'number': 6100}
- Eference: {'precision': 0.5945041816009558, 'recall': 0.7409172126265634, 'f1': 0.6596844756728092, 'number': 3358}
- Igure: {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986}
- Ist: {'precision': 0.6354533152909337, 'recall': 0.693853427895981, 'f1': 0.6633705325610961, 'number': 3384}
- Itle: {'precision': 0.8534278959810875, 'recall': 0.8356481481481481, 'f1': 0.8444444444444444, 'number': 864}
- Ooter: {'precision': 0.6076190476190476, 'recall': 0.7057522123893806, 'f1': 0.6530194472876152, 'number': 452}
- Quation: {'precision': 0.6943667406192727, 'recall': 0.7324481074481074, 'f1': 0.7128992324832879, 'number': 19656}
- Uthor: {'precision': 0.5667556742323098, 'recall': 0.616557734204793, 'f1': 0.5906086956521739, 'number': 1377}
- Overall Precision: 0.7417
- Overall Recall: 0.7891
- Overall F1: 0.7647
- Overall Accuracy: 0.9639
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Able | Aption | Aragraph | Ate | Bstract | Ection | Eference | Igure | Ist | Itle | Ooter | Quation | Uthor | Overall Precision | Overall Recall | Overall F1 | Overall Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:---------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:------------------------------------------------------------------------------------------------------------:|:--------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:----------------------------------------------------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------------------:|:-----------------:|:--------------:|:----------:|:----------------:|
| 0.2526 | 1.0 | 1876 | 0.1649 | {'precision': 0.4146422628951747, 'recall': 0.6010612638687892, 'f1': 0.4907443875541552, 'number': 2073} | {'precision': 0.6553778613985576, 'recall': 0.7187894073139974, 'f1': 0.6856205576817933, 'number': 8723} | {'precision': 0.5402088876533895, 'recall': 0.6264621902919775, 'f1': 0.5801471372214522, 'number': 43428} | {'precision': 0.7005649717514124, 'recall': 0.6666666666666666, 'f1': 0.6831955922865013, 'number': 186} | {'precision': 0.7803265940902022, 'recall': 0.8733681462140992, 'f1': 0.8242299794661191, 'number': 2298} | {'precision': 0.8863070539419087, 'recall': 0.8754098360655738, 'f1': 0.8808247422680412, 'number': 6100} | {'precision': 0.5497456189937818, 'recall': 0.5792138177486599, 'f1': 0.5640951276102087, 'number': 3358} | {'precision': 0.9828801611278952, 'recall': 0.9898580121703854, 'f1': 0.9863567458312278, 'number': 986} | {'precision': 0.40529189416211675, 'recall': 0.5703309692671394, 'f1': 0.4738521973974957, 'number': 3384} | {'precision': 0.7797202797202797, 'recall': 0.7743055555555556, 'f1': 0.7770034843205574, 'number': 864} | {'precision': 0.17008797653958943, 'recall': 0.12831858407079647, 'f1': 0.1462799495586381, 'number': 452} | {'precision': 0.538917549099466, 'recall': 0.6058709808709809, 'f1': 0.5704363653781674, 'number': 19656} | {'precision': 0.2181916621548457, 'recall': 0.2926652142338417, 'f1': 0.25, 'number': 1377} | 0.5660 | 0.6469 | 0.6038 | 0.9444 |
| 0.1508 | 2.0 | 3752 | 0.1490 | {'precision': 0.5242351323478859, 'recall': 0.7356488181379643, 'f1': 0.6122039341629867, 'number': 2073} | {'precision': 0.7619706320493722, 'recall': 0.8209331651954602, 'f1': 0.7903537332376801, 'number': 8723} | {'precision': 0.5979018162780395, 'recall': 0.6837293911761997, 'f1': 0.6379417767751638, 'number': 43428} | {'precision': 0.5978260869565217, 'recall': 0.8870967741935484, 'f1': 0.7142857142857144, 'number': 186} | {'precision': 0.8250298923874053, 'recall': 0.9007832898172323, 'f1': 0.8612440191387559, 'number': 2298} | {'precision': 0.8531830642704843, 'recall': 0.9183606557377049, 'f1': 0.8845728722564344, 'number': 6100} | {'precision': 0.6411569749924676, 'recall': 0.6337105419892793, 'f1': 0.6374120113823574, 'number': 3358} | {'precision': 0.987891019172553, 'recall': 0.9929006085192698, 'f1': 0.9903894790085989, 'number': 986} | {'precision': 0.458251953125, 'recall': 0.5546690307328606, 'f1': 0.5018716577540108, 'number': 3384} | {'precision': 0.7446808510638298, 'recall': 0.7696759259259259, 'f1': 0.7569721115537849, 'number': 864} | {'precision': 0.5972850678733032, 'recall': 0.584070796460177, 'f1': 0.5906040268456375, 'number': 452} | {'precision': 0.5535211267605634, 'recall': 0.6597985347985348, 'f1': 0.6020052917420973, 'number': 19656} | {'precision': 0.2989556135770235, 'recall': 0.33260711692084244, 'f1': 0.31488484015125473, 'number': 1377} | 0.6183 | 0.7058 | 0.6592 | 0.9525 |
| 0.1176 | 3.0 | 5628 | 0.1530 | {'precision': 0.5526420341676599, 'recall': 0.6710082006753497, 'f1': 0.6061002178649237, 'number': 2073} | {'precision': 0.7773131767985418, 'recall': 0.8311360770377164, 'f1': 0.8033240997229917, 'number': 8723} | {'precision': 0.6078152985889651, 'recall': 0.6407617205489546, 'f1': 0.6238538280461833, 'number': 43428} | {'precision': 0.5854545454545454, 'recall': 0.8655913978494624, 'f1': 0.6984815618221257, 'number': 186} | {'precision': 0.8378161380971497, 'recall': 0.9081810269799826, 'f1': 0.8715807057840885, 'number': 2298} | {'precision': 0.8598871779234639, 'recall': 0.9245901639344263, 'f1': 0.8910656449956552, 'number': 6100} | {'precision': 0.5440832249674903, 'recall': 0.6229898749255509, 'f1': 0.5808690823268083, 'number': 3358} | {'precision': 0.9929292929292929, 'recall': 0.9969574036511156, 'f1': 0.9949392712550607, 'number': 986} | {'precision': 0.39487179487179486, 'recall': 0.45508274231678486, 'f1': 0.42284459088412957, 'number': 3384} | {'precision': 0.6833667334669339, 'recall': 0.7893518518518519, 'f1': 0.7325456498388828, 'number': 864} | {'precision': 0.43794579172610554, 'recall': 0.6792035398230089, 'f1': 0.5325238508239375, 'number': 452} | {'precision': 0.5741028804376977, 'recall': 0.5445156695156695, 'f1': 0.5589179874148149, 'number': 19656} | {'precision': 0.3929008567931457, 'recall': 0.4662309368191721, 'f1': 0.4264363998671538, 'number': 1377} | 0.6277 | 0.6600 | 0.6435 | 0.9527 |
| 0.0871 | 4.0 | 7504 | 0.1564 | {'precision': 0.6151919866444073, 'recall': 0.7110467920887602, 'f1': 0.6596554038934884, 'number': 2073} | {'precision': 0.7617387738363748, 'recall': 0.8517711796400321, 'f1': 0.8042431130594794, 'number': 8723} | {'precision': 0.6353752874764792, 'recall': 0.6997789444597955, 'f1': 0.6660238006530934, 'number': 43428} | {'precision': 0.6217228464419475, 'recall': 0.8924731182795699, 'f1': 0.7328918322295807, 'number': 186} | {'precision': 0.8827993254637436, 'recall': 0.9112271540469974, 'f1': 0.8967880085653105, 'number': 2298} | {'precision': 0.8789195901893821, 'recall': 0.9281967213114755, 'f1': 0.9028863020251954, 'number': 6100} | {'precision': 0.5240302512808002, 'recall': 0.6396664681357951, 'f1': 0.5761029904787448, 'number': 3358} | {'precision': 0.9828629032258065, 'recall': 0.9888438133874239, 'f1': 0.9858442871587463, 'number': 986} | {'precision': 0.48228571428571426, 'recall': 0.6235224586288416, 'f1': 0.5438845212011857, 'number': 3384} | {'precision': 0.8669301712779973, 'recall': 0.7615740740740741, 'f1': 0.8108441158348736, 'number': 864} | {'precision': 0.542016806722689, 'recall': 0.5707964601769911, 'f1': 0.5560344827586207, 'number': 452} | {'precision': 0.6165904637491836, 'recall': 0.6723646723646723, 'f1': 0.6432708688245315, 'number': 19656} | {'precision': 0.46214852198990625, 'recall': 0.46550472040668117, 'f1': 0.4638205499276411, 'number': 1377} | 0.6553 | 0.7237 | 0.6878 | 0.9542 |
| 0.0676 | 5.0 | 9380 | 0.1583 | {'precision': 0.6492985971943888, 'recall': 0.7814761215629522, 'f1': 0.7092819614711033, 'number': 2073} | {'precision': 0.8149818501814982, 'recall': 0.8493637510030952, 'f1': 0.8318176714943303, 'number': 8723} | {'precision': 0.6827026670477782, 'recall': 0.7149765128488533, 'f1': 0.6984669718476194, 'number': 43428} | {'precision': 0.9294871794871795, 'recall': 0.7795698924731183, 'f1': 0.847953216374269, 'number': 186} | {'precision': 0.8599190283400809, 'recall': 0.9242819843342036, 'f1': 0.890939597315436, 'number': 2298} | {'precision': 0.8848062015503876, 'recall': 0.9355737704918032, 'f1': 0.9094820717131474, 'number': 6100} | {'precision': 0.5955380577427821, 'recall': 0.6756998213222156, 'f1': 0.6330915178571428, 'number': 3358} | {'precision': 0.992936427850656, 'recall': 0.9979716024340771, 'f1': 0.9954476479514417, 'number': 986} | {'precision': 0.5794343113930743, 'recall': 0.6477541371158393, 'f1': 0.6116924794195621, 'number': 3384} | {'precision': 0.8134243458475541, 'recall': 0.8275462962962963, 'f1': 0.8204245553643145, 'number': 864} | {'precision': 0.6065573770491803, 'recall': 0.6548672566371682, 'f1': 0.6297872340425531, 'number': 452} | {'precision': 0.6497243107769424, 'recall': 0.6594424094424094, 'f1': 0.654547290814523, 'number': 19656} | {'precision': 0.46639784946236557, 'recall': 0.5039941902687001, 'f1': 0.4844677137870855, 'number': 1377} | 0.6989 | 0.7339 | 0.7160 | 0.9598 |
| 0.0512 | 6.0 | 11256 | 0.1844 | {'precision': 0.645, 'recall': 0.7467438494934877, 'f1': 0.6921529175050302, 'number': 2073} | {'precision': 0.8094872076424728, 'recall': 0.8451220910237304, 'f1': 0.8269209197980932, 'number': 8723} | {'precision': 0.6710134048257372, 'recall': 0.7204107948788799, 'f1': 0.6948352636780563, 'number': 43428} | {'precision': 0.6753246753246753, 'recall': 0.8387096774193549, 'f1': 0.7482014388489209, 'number': 186} | {'precision': 0.8834745762711864, 'recall': 0.9073107049608355, 'f1': 0.8952340060111635, 'number': 2298} | {'precision': 0.9024081115335868, 'recall': 0.9337704918032786, 'f1': 0.9178214631002256, 'number': 6100} | {'precision': 0.4868008948545861, 'recall': 0.6480047647409172, 'f1': 0.5559529892692898, 'number': 3358} | {'precision': 0.9929292929292929, 'recall': 0.9969574036511156, 'f1': 0.9949392712550607, 'number': 986} | {'precision': 0.5424300867888139, 'recall': 0.6648936170212766, 'f1': 0.5974508762612852, 'number': 3384} | {'precision': 0.7554179566563467, 'recall': 0.8472222222222222, 'f1': 0.7986906710310966, 'number': 864} | {'precision': 0.6563981042654028, 'recall': 0.6128318584070797, 'f1': 0.6338672768878719, 'number': 452} | {'precision': 0.650782911270056, 'recall': 0.685083435083435, 'f1': 0.6674928125309805, 'number': 19656} | {'precision': 0.4430835734870317, 'recall': 0.4466230936819172, 'f1': 0.4448462929475588, 'number': 1377} | 0.6856 | 0.7390 | 0.7113 | 0.9578 |
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| 0.0222 | 9.0 | 16884 | 0.2193 | {'precision': 0.6235811058220432, 'recall': 0.8215147129763628, 'f1': 0.708992506244796, 'number': 2073} | {'precision': 0.8264917003140422, 'recall': 0.8447781726470251, 'f1': 0.8355348942683827, 'number': 8723} | {'precision': 0.7017585809621112, 'recall': 0.7433683337938657, 'f1': 0.7219644194965952, 'number': 43428} | {'precision': 0.90625, 'recall': 0.7795698924731183, 'f1': 0.838150289017341, 'number': 186} | {'precision': 0.8704156479217604, 'recall': 0.9295039164490861, 'f1': 0.898989898989899, 'number': 2298} | {'precision': 0.9143317230273752, 'recall': 0.9308196721311476, 'f1': 0.922502030869212, 'number': 6100} | {'precision': 0.5801470588235295, 'recall': 0.704883859440143, 'f1': 0.6364614143586985, 'number': 3358} | {'precision': 0.9949443882709808, 'recall': 0.9979716024340771, 'f1': 0.9964556962025316, 'number': 986} | {'precision': 0.6149458071876782, 'recall': 0.6371158392434988, 'f1': 0.6258345428156749, 'number': 3384} | {'precision': 0.8431137724550898, 'recall': 0.8148148148148148, 'f1': 0.8287227781047676, 'number': 864} | {'precision': 0.6629711751662971, 'recall': 0.661504424778761, 'f1': 0.6622369878183831, 'number': 452} | {'precision': 0.6730908214887978, 'recall': 0.7107244607244607, 'f1': 0.6913959070550098, 'number': 19656} | {'precision': 0.5108055009823183, 'recall': 0.5664488017429193, 'f1': 0.537190082644628, 'number': 1377} | 0.7156 | 0.7598 | 0.7371 | 0.9596 |
| 0.0162 | 10.0 | 18760 | 0.2114 | {'precision': 0.6486062033765214, 'recall': 0.7969126869271587, 'f1': 0.7151515151515152, 'number': 2073} | {'precision': 0.8267941532036488, 'recall': 0.8624326493178952, 'f1': 0.8442374593199417, 'number': 8723} | {'precision': 0.7077005538681437, 'recall': 0.7296674956249425, 'f1': 0.7185161670672531, 'number': 43428} | {'precision': 0.9085365853658537, 'recall': 0.8010752688172043, 'f1': 0.8514285714285714, 'number': 186} | {'precision': 0.844675740592474, 'recall': 0.918189730200174, 'f1': 0.8798999165971642, 'number': 2298} | {'precision': 0.9145987753786659, 'recall': 0.9304918032786885, 'f1': 0.9224768405655778, 'number': 6100} | {'precision': 0.5639344262295082, 'recall': 0.6658725431804645, 'f1': 0.6106786835996176, 'number': 3358} | {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986} | {'precision': 0.6411883472743005, 'recall': 0.6569148936170213, 'f1': 0.6489563567362429, 'number': 3384} | {'precision': 0.8991935483870968, 'recall': 0.7743055555555556, 'f1': 0.832089552238806, 'number': 864} | {'precision': 0.5370018975332068, 'recall': 0.6261061946902655, 'f1': 0.5781409601634322, 'number': 452} | {'precision': 0.6896913159687648, 'recall': 0.6695156695156695, 'f1': 0.6794537522265535, 'number': 19656} | {'precision': 0.5298196948682385, 'recall': 0.5548293391430646, 'f1': 0.5420361830436324, 'number': 1377} | 0.7237 | 0.7441 | 0.7338 | 0.9611 |
| 0.0138 | 11.0 | 20636 | 0.2391 | {'precision': 0.664185277088503, 'recall': 0.7747226242161119, 'f1': 0.7152081941661101, 'number': 2073} | {'precision': 0.8144112087178917, 'recall': 0.8396193969964462, 'f1': 0.8268232106570332, 'number': 8723} | {'precision': 0.7044044130322358, 'recall': 0.7527401676337847, 'f1': 0.7277706042121198, 'number': 43428} | {'precision': 0.8324022346368715, 'recall': 0.8010752688172043, 'f1': 0.8164383561643834, 'number': 186} | {'precision': 0.8978132884777124, 'recall': 0.9290687554395126, 'f1': 0.9131736526946108, 'number': 2298} | {'precision': 0.9141269841269841, 'recall': 0.9440983606557377, 'f1': 0.9288709677419354, 'number': 6100} | {'precision': 0.5543908688562776, 'recall': 0.7087552114353782, 'f1': 0.6221408966148216, 'number': 3358} | {'precision': 0.9949494949494949, 'recall': 0.9989858012170385, 'f1': 0.9969635627530363, 'number': 986} | {'precision': 0.5981259760541384, 'recall': 0.6790780141843972, 'f1': 0.6360365347356767, 'number': 3384} | {'precision': 0.8146453089244852, 'recall': 0.8240740740740741, 'f1': 0.8193325661680093, 'number': 864} | {'precision': 0.6401673640167364, 'recall': 0.6769911504424779, 'f1': 0.6580645161290323, 'number': 452} | {'precision': 0.6891924859721883, 'recall': 0.7186100936100936, 'f1': 0.7035939329032901, 'number': 19656} | {'precision': 0.530638852672751, 'recall': 0.5911401597676107, 'f1': 0.5592579869460667, 'number': 1377} | 0.7187 | 0.7674 | 0.7423 | 0.9601 |
| 0.0099 | 12.0 | 22512 | 0.2190 | {'precision': 0.5986635220125787, 'recall': 0.7346840328027014, 'f1': 0.6597357591509638, 'number': 2073} | {'precision': 0.8261346196009647, 'recall': 0.863922962283618, 'f1': 0.844606332305968, 'number': 8723} | {'precision': 0.7126507076708021, 'recall': 0.7513125172699641, 'f1': 0.7314711025422589, 'number': 43428} | {'precision': 0.8630952380952381, 'recall': 0.7795698924731183, 'f1': 0.8192090395480226, 'number': 186} | {'precision': 0.8786008230452675, 'recall': 0.9290687554395126, 'f1': 0.9031302876480541, 'number': 2298} | {'precision': 0.8979878334113243, 'recall': 0.9437704918032787, 'f1': 0.9203101270881625, 'number': 6100} | {'precision': 0.5727510087823404, 'recall': 0.7185824895771292, 'f1': 0.6374323074891033, 'number': 3358} | {'precision': 0.9969604863221885, 'recall': 0.9979716024340771, 'f1': 0.9974657881398886, 'number': 986} | {'precision': 0.6077103412346966, 'recall': 0.6894208037825059, 'f1': 0.6459919700955282, 'number': 3384} | {'precision': 0.8236632536973834, 'recall': 0.8379629629629629, 'f1': 0.8307515777395296, 'number': 864} | {'precision': 0.6161417322834646, 'recall': 0.6924778761061947, 'f1': 0.6520833333333333, 'number': 452} | {'precision': 0.705915521837195, 'recall': 0.7006003256003256, 'f1': 0.7032478807067715, 'number': 19656} | {'precision': 0.4981527093596059, 'recall': 0.5875090777051561, 'f1': 0.5391536154615129, 'number': 1377} | 0.7251 | 0.7652 | 0.7446 | 0.9624 |
| 0.0084 | 13.0 | 24388 | 0.2592 | {'precision': 0.6832247557003257, 'recall': 0.8094548962855764, 'f1': 0.741002428792228, 'number': 2073} | {'precision': 0.8483670295489891, 'recall': 0.8755015476326952, 'f1': 0.8617207334273626, 'number': 8723} | {'precision': 0.7274626600284495, 'recall': 0.7536612323846367, 'f1': 0.7403302420266908, 'number': 43428} | {'precision': 0.8361581920903954, 'recall': 0.7956989247311828, 'f1': 0.815426997245179, 'number': 186} | {'precision': 0.9015565839293227, 'recall': 0.9325500435161009, 'f1': 0.9167914438502675, 'number': 2298} | {'precision': 0.9054671498345676, 'recall': 0.9421311475409836, 'f1': 0.9234353659516349, 'number': 6100} | {'precision': 0.6139511458071015, 'recall': 0.726027397260274, 'f1': 0.6653022240414791, 'number': 3358} | {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986} | {'precision': 0.6385964912280702, 'recall': 0.6453900709219859, 'f1': 0.6419753086419754, 'number': 3384} | {'precision': 0.7660223804679552, 'recall': 0.8715277777777778, 'f1': 0.8153762858689767, 'number': 864} | {'precision': 0.6666666666666666, 'recall': 0.6548672566371682, 'f1': 0.6607142857142857, 'number': 452} | {'precision': 0.6978891162233645, 'recall': 0.7114875864875865, 'f1': 0.7046227484569845, 'number': 19656} | {'precision': 0.5463768115942029, 'recall': 0.5475671750181554, 'f1': 0.5469713456655786, 'number': 1377} | 0.7401 | 0.7695 | 0.7545 | 0.9625 |
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| 0.0052 | 15.0 | 28140 | 0.2620 | {'precision': 0.7276975361087511, 'recall': 0.8263386396526773, 'f1': 0.7738875084707477, 'number': 2073} | {'precision': 0.8463771352015184, 'recall': 0.869081737934197, 'f1': 0.857579185520362, 'number': 8723} | {'precision': 0.7304345910702879, 'recall': 0.7635857050750667, 'f1': 0.7466423497360037, 'number': 43428} | {'precision': 0.6781115879828327, 'recall': 0.8494623655913979, 'f1': 0.7541766109785203, 'number': 186} | {'precision': 0.8993736951983299, 'recall': 0.9373368146214099, 'f1': 0.9179629235030897, 'number': 2298} | {'precision': 0.9117043121149897, 'recall': 0.9462295081967214, 'f1': 0.9286461266189365, 'number': 6100} | {'precision': 0.6430079155672823, 'recall': 0.7257296009529481, 'f1': 0.6818690542809177, 'number': 3358} | {'precision': 0.9949392712550608, 'recall': 0.9969574036511156, 'f1': 0.9959473150962513, 'number': 986} | {'precision': 0.6221982176613556, 'recall': 0.6808510638297872, 'f1': 0.6502045999717792, 'number': 3384} | {'precision': 0.7815126050420168, 'recall': 0.8611111111111112, 'f1': 0.8193832599118943, 'number': 864} | {'precision': 0.5786713286713286, 'recall': 0.7323008849557522, 'f1': 0.6464843749999999, 'number': 452} | {'precision': 0.7015840321710558, 'recall': 0.7278184778184779, 'f1': 0.7144605089020399, 'number': 19656} | {'precision': 0.5367936925098554, 'recall': 0.5933188090050835, 'f1': 0.5636426353915143, 'number': 1377} | 0.7425 | 0.7801 | 0.7609 | 0.9624 |
| 0.0042 | 16.0 | 30016 | 0.2755 | {'precision': 0.697255223269152, 'recall': 0.8210323203087313, 'f1': 0.7540983606557377, 'number': 2073} | {'precision': 0.8434147959747871, 'recall': 0.8743551530436776, 'f1': 0.858606326691433, 'number': 8723} | {'precision': 0.7236266459774574, 'recall': 0.7731647784839274, 'f1': 0.7475759498602902, 'number': 43428} | {'precision': 0.8277777777777777, 'recall': 0.8010752688172043, 'f1': 0.8142076502732241, 'number': 186} | {'precision': 0.9060402684563759, 'recall': 0.9399477806788512, 'f1': 0.922682614267407, 'number': 2298} | {'precision': 0.9122500793398921, 'recall': 0.9424590163934427, 'f1': 0.9271085308821158, 'number': 6100} | {'precision': 0.6392307692307693, 'recall': 0.7424061941631924, 'f1': 0.6869661063653899, 'number': 3358} | {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986} | {'precision': 0.6294667399670149, 'recall': 0.6767139479905437, 'f1': 0.6522358302477927, 'number': 3384} | {'precision': 0.8457943925233645, 'recall': 0.8379629629629629, 'f1': 0.8418604651162791, 'number': 864} | {'precision': 0.5521885521885522, 'recall': 0.7256637168141593, 'f1': 0.6271510516252391, 'number': 452} | {'precision': 0.6809746954076851, 'recall': 0.7393162393162394, 'f1': 0.7089472143623768, 'number': 19656} | {'precision': 0.5562870309414089, 'recall': 0.6136528685548294, 'f1': 0.5835635359116023, 'number': 1377} | 0.7346 | 0.7876 | 0.7602 | 0.9623 |
| 0.0033 | 17.0 | 31892 | 0.2743 | {'precision': 0.7272325375773652, 'recall': 0.7935359382537386, 'f1': 0.7589388696655133, 'number': 2073} | {'precision': 0.845837501389352, 'recall': 0.8724062822423478, 'f1': 0.8589164785553048, 'number': 8723} | {'precision': 0.7257006300238975, 'recall': 0.7691811734364926, 'f1': 0.7468085582060856, 'number': 43428} | {'precision': 0.8869047619047619, 'recall': 0.8010752688172043, 'f1': 0.8418079096045197, 'number': 186} | {'precision': 0.9024800336275746, 'recall': 0.9342906875543951, 'f1': 0.9181098995082317, 'number': 2298} | {'precision': 0.9123361238350971, 'recall': 0.9468852459016394, 'f1': 0.9292896790282358, 'number': 6100} | {'precision': 0.5567105567105567, 'recall': 0.7176891006551519, 'f1': 0.6270326525302459, 'number': 3358} | {'precision': 0.993933265925177, 'recall': 0.9969574036511156, 'f1': 0.9954430379746836, 'number': 986} | {'precision': 0.6185107498689041, 'recall': 0.6971040189125296, 'f1': 0.6554598499583218, 'number': 3384} | {'precision': 0.8841309823677582, 'recall': 0.8125, 'f1': 0.8468033775633294, 'number': 864} | {'precision': 0.6304347826086957, 'recall': 0.7057522123893806, 'f1': 0.6659707724425887, 'number': 452} | {'precision': 0.7017227075301352, 'recall': 0.7315323565323565, 'f1': 0.7163175330659826, 'number': 19656} | {'precision': 0.5604838709677419, 'recall': 0.6056644880174292, 'f1': 0.5821989528795811, 'number': 1377} | 0.7377 | 0.7829 | 0.7596 | 0.9630 |
| 0.003 | 18.0 | 33768 | 0.2938 | {'precision': 0.7085594989561587, 'recall': 0.818620356970574, 'f1': 0.7596239928379588, 'number': 2073} | {'precision': 0.8580645161290322, 'recall': 0.869081737934197, 'f1': 0.8635379883813646, 'number': 8723} | {'precision': 0.7304742970746947, 'recall': 0.7699180252371741, 'f1': 0.7496776941962533, 'number': 43428} | {'precision': 0.6926406926406926, 'recall': 0.8602150537634409, 'f1': 0.7673860911270983, 'number': 186} | {'precision': 0.9013848090642048, 'recall': 0.9347258485639687, 'f1': 0.9177526169621877, 'number': 2298} | {'precision': 0.9117088607594936, 'recall': 0.9445901639344262, 'f1': 0.9278582930756843, 'number': 6100} | {'precision': 0.6144427786106946, 'recall': 0.7322811197141156, 'f1': 0.6682065217391304, 'number': 3358} | {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986} | {'precision': 0.6367369285518751, 'recall': 0.6873522458628841, 'f1': 0.6610771635640187, 'number': 3384} | {'precision': 0.8362168396770473, 'recall': 0.8391203703703703, 'f1': 0.8376660889659157, 'number': 864} | {'precision': 0.6334661354581673, 'recall': 0.7035398230088495, 'f1': 0.6666666666666667, 'number': 452} | {'precision': 0.6995040357872216, 'recall': 0.7318884818884819, 'f1': 0.7153299189498284, 'number': 19656} | {'precision': 0.5398574206092028, 'recall': 0.6049382716049383, 'f1': 0.5705479452054795, 'number': 1377} | 0.7426 | 0.7839 | 0.7627 | 0.9631 |
| 0.0025 | 19.0 | 35644 | 0.2990 | {'precision': 0.707874337005304, 'recall': 0.8369512783405693, 'f1': 0.7670203359858533, 'number': 2073} | {'precision': 0.8577489950870925, 'recall': 0.8806603232832741, 'f1': 0.8690536795067595, 'number': 8723} | {'precision': 0.7345506842151137, 'recall': 0.7762273187805103, 'f1': 0.7548141513658755, 'number': 43428} | {'precision': 0.8105263157894737, 'recall': 0.8279569892473119, 'f1': 0.8191489361702128, 'number': 186} | {'precision': 0.9, 'recall': 0.9399477806788512, 'f1': 0.9195402298850573, 'number': 2298} | {'precision': 0.908573236317621, 'recall': 0.9416393442622951, 'f1': 0.9248108195137659, 'number': 6100} | {'precision': 0.61839821472849, 'recall': 0.7427039904705182, 'f1': 0.6748748477878501, 'number': 3358} | {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986} | {'precision': 0.6411716842961758, 'recall': 0.6985815602836879, 'f1': 0.6686465846414934, 'number': 3384} | {'precision': 0.8677184466019418, 'recall': 0.8275462962962963, 'f1': 0.8471563981042655, 'number': 864} | {'precision': 0.6414342629482072, 'recall': 0.7123893805309734, 'f1': 0.6750524109014675, 'number': 452} | {'precision': 0.6951624548736463, 'recall': 0.7347374847374848, 'f1': 0.7144023150552794, 'number': 19656} | {'precision': 0.5524115755627009, 'recall': 0.6238198983297023, 'f1': 0.5859481582537517, 'number': 1377} | 0.7443 | 0.7898 | 0.7664 | 0.9635 |
| 0.0021 | 20.0 | 37520 | 0.2981 | {'precision': 0.7228813559322034, 'recall': 0.8229618909792571, 'f1': 0.7696819309722536, 'number': 2073} | {'precision': 0.8535364768683275, 'recall': 0.8798578470709618, 'f1': 0.8664973186565058, 'number': 8723} | {'precision': 0.7315439151833142, 'recall': 0.7769411439624205, 'f1': 0.7535594242387018, 'number': 43428} | {'precision': 0.8031088082901554, 'recall': 0.8333333333333334, 'f1': 0.8179419525065963, 'number': 186} | {'precision': 0.9137055837563451, 'recall': 0.9399477806788512, 'f1': 0.9266409266409267, 'number': 2298} | {'precision': 0.9108754155453538, 'recall': 0.9432786885245902, 'f1': 0.9267939115728436, 'number': 6100} | {'precision': 0.5945041816009558, 'recall': 0.7409172126265634, 'f1': 0.6596844756728092, 'number': 3358} | {'precision': 0.9959514170040485, 'recall': 0.9979716024340771, 'f1': 0.9969604863221885, 'number': 986} | {'precision': 0.6354533152909337, 'recall': 0.693853427895981, 'f1': 0.6633705325610961, 'number': 3384} | {'precision': 0.8534278959810875, 'recall': 0.8356481481481481, 'f1': 0.8444444444444444, 'number': 864} | {'precision': 0.6076190476190476, 'recall': 0.7057522123893806, 'f1': 0.6530194472876152, 'number': 452} | {'precision': 0.6943667406192727, 'recall': 0.7324481074481074, 'f1': 0.7128992324832879, 'number': 19656} | {'precision': 0.5667556742323098, 'recall': 0.616557734204793, 'f1': 0.5906086956521739, 'number': 1377} | 0.7417 | 0.7891 | 0.7647 | 0.9639 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.14.5
- Tokenizers 0.15.1
|
Yuichi1218/Llama-3.1-Non-filter-Lafeak73-8B-chatvector
|
Yuichi1218
| 2025-06-25T03:02:20Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:55:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[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]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
johnnyyang0518/uuu_fine_tune_taipower
|
johnnyyang0518
| 2025-06-25T03:02:00Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T01:19:05Z |
---
license: apache-2.0
---
|
Bill0204Tung/uuu_fine_tune_taipower
|
Bill0204Tung
| 2025-06-25T03:01:48Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:23:59Z |
---
license: apache-2.0
---
|
New-videos-web-series-panchayat-season-4/FULL.VIDEO.web.series.panchayat.season.4.Viral.Video.Tutorial.Official
|
New-videos-web-series-panchayat-season-4
| 2025-06-25T03:01:19Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-25T03:01:00Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
sunyanming/fr-en-opposite-model
|
sunyanming
| 2025-06-25T03:01:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-7B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T08:47:02Z |
---
base_model: unsloth/Qwen2.5-7B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** sunyanming
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B-Instruct
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)
|
Slill314/uuu_fine_tune_gpt2
|
Slill314
| 2025-06-25T02:59:48Z | 0 | 0 | null |
[
"safetensors",
"gpt2",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:27:48Z |
---
license: apache-2.0
---
|
Hiyuan0105/tcp2023
|
Hiyuan0105
| 2025-06-25T02:59:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:59:22Z |
---
license: apache-2.0
---
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1092
|
luckeciano
| 2025-06-25T02:58:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T23:29:49Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1092
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1092
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-HessianMaskSentence-1e-3_1092", 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/max-ent-llms/PolicyGradientStability/runs/h13ebtuy)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
AlekseyCalvin/Glasnost_v2_wan_14b_80sUSSRvhsCollageStyle
|
AlekseyCalvin
| 2025-06-25T02:54:23Z | 0 | 0 | null |
[
"image-to-video",
"lora",
"text-to-video",
"video",
"video-generation",
"en",
"zh",
"ru",
"base_model:Wan-AI/Wan2.1-T2V-14B-Diffusers",
"base_model:adapter:Wan-AI/Wan2.1-T2V-14B-Diffusers",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-06-25T01:05:56Z |
---
license: apache-2.0
language:
- en
- zh
- ru
tags:
- image-to-video
- lora
- text-to-video
- video
- video-generation
base_model: "Wan-AI/Wan2.1-T2V-14B-Diffusers"
pipeline_tag: text-to-video
widget:
- text: >-
[GLASNOST] style...
output:
url: videos/1.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/3.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/4.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/5.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/6.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/2.mp4
instance_prompt: GLASNOST style Perestroika-era 1980s Soviet detailed experimental arthouse film sequence. On top left is ____ , on top right is ____, on bottom left is ____, on bottom right is ____, video filmed in the USSR during the perestroika era, featuring several concurrent clips of 16mm footage as a thematically-unified cinematographic collage of several distinct scenes, vintage Soviet television, underground cinema, radical metamodernist cinepoetry, from an award-winning real life raw mixed media conceptual sots art video filmed in the USSR during the Perestroika era, Leningrad punk, Moscow conceptualism
---
# GLASNOST V.2: 80s Soviet Art-Video Collage
***Style/Context Low Rank Adaptor (LoRA)*** <br>
***For Wan2.1 14B T2V & I2V Base Models*** <br>
**Stylers of Kinema Historical LoRAs** <br>
**|||||||| By SilverAgePoets.com ||||||||**
<Gallery />
## About this LoRA
This is a Rank 16/Alpha 64 LoRA for the Wan2.1 14b video generation model. <br>
It may be used to generate several distinct scene-windows-concepts within a single clip (not unlike the well-known ZOOM LoRA). <br>
We've found that given certain prompting styles and LoRA strength modifications may enable controlled gradations of inter-cohesion between the scenes. <br>
It was trained on 100+ manually edited (by us) collages/montages, largely using the same clips and frames used to train the other GLASNOST LoRA (V.1), but with some additions specific to this variant. <br>
These clips & frames were sourced by us from a variety of iconic 1980s Perestroika-era Soviet films, tv shows, concerts, & music videos. <br>
Overall, the sources for this version of GLASNOST lean further into the realm of underground/countercultural/art film territories, with some Leningrad Metamodernist, Moscow Conceptualist, as well as all sorts of Soviet rock influences represented. <br>
The captions this time around should enable this LoRA to exhibit slighly better knowledge (than V.1) of names like Yegor Letov, Viktor Tsoy, Yanka Dyaghileva, or bands Auctyon, KINO, Nol, and others. <br>
This adapter can be used with Wan as well as Skyreels via diffusers or ComfyUI or DrawThings, etc... <br>
This LoRA works well with both CausVid & Self-Forcing distillation quick inference adapters. <br>
It also works fairly well in combos w/ other LoRAs. <br>
**Get creative with these!**
## Trigger words
You should use `GLASNOST style Perestroika-era 1980s Soviet detailed experimental arthouse film sequence. On top left is ____ , on top right is ____, on bottom left is ____, on bottom right is ____, video filmed in the USSR during the perestroika era, featuring several concurrent clips of 16mm footage as a thematically-unified cinematographic collage of several distinct scenes, vintage Soviet television, underground cinema, radical metamodernist cinepoetry, from an award-winning real life raw mixed media conceptual sots art video filmed in the USSR during the Perestroika era, Leningrad punk, Moscow conceptualism`, etc, to revive one of these more recent gestalts of futures no-longer-past! <br>
### Using with Diffusers
```py
pip install git+https://github.com/huggingface/diffusers.git
```
```py
import torch
from diffusers.utils import export_to_video
from diffusers import AutoencoderKLWan, WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
model_id = "wavespeed/Wan2.1-T2V-14B-Diffusers-fp16"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 3.0 # 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
pipe.to("cuda")
pipe.load_lora_weights("AlekseyCalvin/Glasnost_v1_wan_14b_USSR80sTVstyle")
pipe.enable_model_cpu_offload() #for low-vram environments
prompt = "GLASNOST style Perestroika-era 1980s Soviet detailed experimental arthouse film sequence. On top left is ____ , on top right is ____, on bottom left is ____, on bottom right is ____, video filmed in the USSR during the perestroika era, featuring several concurrent clips of 16mm footage as a thematically-unified cinematographic collage of several distinct scenes, vintage Soviet television, underground cinema, radical metamodernist cinepoetry, from an award-winning real life raw mixed media conceptual sots art video filmed in the USSR during the Perestroika era, Leningrad punk, Moscow conceptualism"
negative_prompt = "overexposed, static, blurred, subtitles, images, static, worst, low, JPEG compression residue, incomplete, extra fingers, poorly drawn, poorly drawn, deformed, disfigured, misshapen, fused, still picture, backwards"
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=480,
width=832,
num_frames=81,
guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```
## Training details
- Steps: 4000
- Learning rate: 0.0002
- LoRA rank: 16 dim, 64 alpha
## Contribute your own examples
You can use the [community tab](https://huggingface.co/AlekseyCalvin/Glasnost_v1_wan_14b_USSR80sTVstyle/discussions) to add videos that show off what you’ve made with this LoRA.
|
Yuichi1218/Llama-3.1-Non-filter-Lafeak64-8B-chatvector
|
Yuichi1218
| 2025-06-25T02:53:58Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:47:51Z |
---
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]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
Ash2749/qwen_3_14B_acot_extes
|
Ash2749
| 2025-06-25T02:51:16Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:45:54Z |
---
base_model: unsloth/qwen3-14b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Ash2749
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-14b-unsloth-bnb-4bit
This qwen3 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)
|
Louischong/Trellis-OA
|
Louischong
| 2025-06-25T02:50:46Z | 0 | 0 |
trellis-oa
|
[
"trellis-oa",
"image-to-3d",
"en",
"arxiv:2506.08640",
"license:mit",
"region:us"
] |
image-to-3d
| 2025-06-25T01:51:40Z |
---
library_name: trellis-oa
pipeline_tag: image-to-3d
license: mit
language:
- en
---
# TRELLIS-OA
<!-- Provide a quick summary of what the model is/does. -->
TRELLIS-OA, a large 3D genetive model produces orientation-aligned 3D objects. It was introduced in the paper [Orientation Matters: Making 3D Generative Models Orientation-Aligned](https://huggingface.co/papers/2506.08640).
Project page: https://xdimlab.github.io/Orientation_Matters/
Code: https://github.com/YichongLu/Orientation_Matters
|
ZeeeWP/Qwen3-8B_Qwen3-0.6B
|
ZeeeWP
| 2025-06-25T02:50:02Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"customize_ensemble",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] |
feature-extraction
| 2025-06-25T02:48:00Z |
---
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]
|
daixuancheng/sac_static0.1_constrainbyAdv_step160
|
daixuancheng
| 2025-06-25T02:49:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T06:17:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
JFernandoGRE/llama31_8b_augmenteddemocracy_dpo2_questions_50_critsupport
|
JFernandoGRE
| 2025-06-25T02:44:52Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"base_model:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport",
"base_model:finetune:JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:39:47Z |
---
base_model: JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport
tags:
- text-generation-inference
- transformers
- unsloth
- llama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** JFernandoGRE
- **License:** apache-2.0
- **Finetuned from model :** JFernandoGRE/llama31_8b_augmenteddemocracy_sft_questions_50_critsupport
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)
|
NTIS/hf_gemma3_21-checkpoint-128000
|
NTIS
| 2025-06-25T02:44:37Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:42:23Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-128000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-128000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-128000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
morning831/llama2_uuu_news_qlora
|
morning831
| 2025-06-25T02:43:28Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:43:28Z |
---
license: apache-2.0
---
|
Baistiac/tcp2023
|
Baistiac
| 2025-06-25T02:43:27Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:43:27Z |
---
license: apache-2.0
---
|
zecaihong/3e7e19dc-0009-4038-bacf-b95d034953d3
|
zecaihong
| 2025-06-25T02:42:46Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-7B",
"base_model:adapter:unsloth/Qwen2.5-Coder-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:03:38Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3e7e19dc-0009-4038-bacf-b95d034953d3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5686eaedee397c04_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_prompt: ''
debug: null
deepspeed: deepspeed_configs/zero2.json
early_stopping_patience: 3
eval_max_new_tokens: 1024
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
greater_is_better: false
group_by_length: false
hub_model_id: zecaihong/3e7e19dc-0009-4038-bacf-b95d034953d3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: -1
metric_for_best_model: eval_loss
micro_batch_size: 8
mlflow_experiment_name: /data/datasets/5686eaedee397c04_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 6
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
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: 3e7e19dc-0009-4038-bacf-b95d034953d3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3e7e19dc-0009-4038-bacf-b95d034953d3
warmup_steps: 100
weight_decay: 0.001
xformers_attention: null
```
</details><br>
# 3e7e19dc-0009-4038-bacf-b95d034953d3
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B](https://huggingface.co/unsloth/Qwen2.5-Coder-7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7871
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 128
- total_eval_batch_size: 64
- 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_steps: 100
- num_epochs: 6.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0017 | 1 | 1.7215 |
| 0.9052 | 0.1742 | 100 | 0.9538 |
| 0.8624 | 0.3484 | 200 | 0.8870 |
| 0.882 | 0.5226 | 300 | 0.8583 |
| 0.856 | 0.6969 | 400 | 0.8366 |
| 0.7938 | 0.8711 | 500 | 0.8207 |
| 0.7321 | 1.0453 | 600 | 0.8126 |
| 0.7707 | 1.2195 | 700 | 0.8069 |
| 0.71 | 1.3937 | 800 | 0.8012 |
| 0.7139 | 1.5679 | 900 | 0.7931 |
| 0.7163 | 1.7422 | 1000 | 0.7870 |
| 0.7297 | 1.9164 | 1100 | 0.7843 |
| 0.6494 | 2.0906 | 1200 | 0.7919 |
| 0.6429 | 2.2648 | 1300 | 0.7931 |
| 0.6377 | 2.4390 | 1400 | 0.7871 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
NTIS/hf_gemma3_21-checkpoint-127000
|
NTIS
| 2025-06-25T02:42:06Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:39:37Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-127000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-127000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-127000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
Jack89215/llama2_uuu_news_qlora
|
Jack89215
| 2025-06-25T02:40:42Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:40:42Z |
---
license: apache-2.0
---
|
NTIS/hf_gemma3_21-checkpoint-126000
|
NTIS
| 2025-06-25T02:39:36Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:37:16Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-126000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-126000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-126000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
daixuancheng/sac_static0.1_constrainbyAdv_step200
|
daixuancheng
| 2025-06-25T02:39:24Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T06:20:37Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
chinxx66/tcp2023
|
chinxx66
| 2025-06-25T02:38:47Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:38:47Z |
---
license: apache-2.0
---
|
ljnlonoljpiljm/siglip2-large-patch16-256-like-dislike-13
|
ljnlonoljpiljm
| 2025-06-25T02:38:18Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"siglip",
"image-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
image-classification
| 2025-06-25T02:37:55Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
thanhh12/aya-expanse-8b-Q3_K_M-GGUF
|
thanhh12
| 2025-06-25T02:37:47Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"el",
"fa",
"pl",
"id",
"cs",
"he",
"hi",
"nl",
"ro",
"ru",
"tr",
"uk",
"vi",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:quantized:CohereLabs/aya-expanse-8b",
"license:cc-by-nc-4.0",
"region:us",
"conversational"
] | null | 2025-06-25T02:37:30Z |
---
inference: false
library_name: transformers
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
license: cc-by-nc-4.0
extra_gated_prompt: By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and
acknowledge that the information you provide will be collected, used, and shared
in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy). You’ll
receive email updates about C4AI and Cohere research, events, products and services.
You can unsubscribe at any time.
extra_gated_fields:
Name: text
Affiliation: text
Country: country
I agree to use this model for non-commercial use ONLY: checkbox
tags:
- llama-cpp
- gguf-my-repo
base_model: CohereLabs/aya-expanse-8b
---
# thanhh12/aya-expanse-8b-Q3_K_M-GGUF
This model was converted to GGUF format from [`CohereLabs/aya-expanse-8b`](https://huggingface.co/CohereLabs/aya-expanse-8b) 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/CohereLabs/aya-expanse-8b) 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 thanhh12/aya-expanse-8b-Q3_K_M-GGUF --hf-file aya-expanse-8b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo thanhh12/aya-expanse-8b-Q3_K_M-GGUF --hf-file aya-expanse-8b-q3_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 thanhh12/aya-expanse-8b-Q3_K_M-GGUF --hf-file aya-expanse-8b-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo thanhh12/aya-expanse-8b-Q3_K_M-GGUF --hf-file aya-expanse-8b-q3_k_m.gguf -c 2048
```
|
NTIS/hf_gemma3_21-checkpoint-125000
|
NTIS
| 2025-06-25T02:37:15Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:34:56Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-125000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-125000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-125000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
pennylin09/tcp2023
|
pennylin09
| 2025-06-25T02:36:59Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:36:59Z |
---
license: apache-2.0
---
|
NTIS/hf_gemma3_21-checkpoint-124000
|
NTIS
| 2025-06-25T02:34:54Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:32:32Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-124000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-124000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-124000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
thanhh12/aya-expanse-8b-Q6_K-GGUF
|
thanhh12
| 2025-06-25T02:34:10Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"fr",
"de",
"es",
"it",
"pt",
"ja",
"ko",
"zh",
"ar",
"el",
"fa",
"pl",
"id",
"cs",
"he",
"hi",
"nl",
"ro",
"ru",
"tr",
"uk",
"vi",
"base_model:CohereLabs/aya-expanse-8b",
"base_model:quantized:CohereLabs/aya-expanse-8b",
"license:cc-by-nc-4.0",
"region:us",
"conversational"
] | null | 2025-06-25T02:33:47Z |
---
inference: false
library_name: transformers
language:
- en
- fr
- de
- es
- it
- pt
- ja
- ko
- zh
- ar
- el
- fa
- pl
- id
- cs
- he
- hi
- nl
- ro
- ru
- tr
- uk
- vi
license: cc-by-nc-4.0
extra_gated_prompt: By submitting this form, you agree to the [License Agreement](https://cohere.com/c4ai-cc-by-nc-license) and
acknowledge that the information you provide will be collected, used, and shared
in accordance with Cohere’s [Privacy Policy]( https://cohere.com/privacy). You’ll
receive email updates about C4AI and Cohere research, events, products and services.
You can unsubscribe at any time.
extra_gated_fields:
Name: text
Affiliation: text
Country: country
I agree to use this model for non-commercial use ONLY: checkbox
tags:
- llama-cpp
- gguf-my-repo
base_model: CohereLabs/aya-expanse-8b
---
# thanhh12/aya-expanse-8b-Q6_K-GGUF
This model was converted to GGUF format from [`CohereLabs/aya-expanse-8b`](https://huggingface.co/CohereLabs/aya-expanse-8b) 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/CohereLabs/aya-expanse-8b) 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 thanhh12/aya-expanse-8b-Q6_K-GGUF --hf-file aya-expanse-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo thanhh12/aya-expanse-8b-Q6_K-GGUF --hf-file aya-expanse-8b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo thanhh12/aya-expanse-8b-Q6_K-GGUF --hf-file aya-expanse-8b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo thanhh12/aya-expanse-8b-Q6_K-GGUF --hf-file aya-expanse-8b-q6_k.gguf -c 2048
```
|
mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-8bit
|
mlx-community
| 2025-06-25T02:32:46Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:lmsys/lmsys-chat-1m",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"base_model:quantized:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"license:llama3.3",
"license:gemma",
"8-bit",
"region:us"
] |
text-generation
| 2025-06-25T02:07:57Z |
---
language:
- en
- ja
library_name: mlx
pipeline_tag: text-generation
license:
- llama3.3
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- lmsys/lmsys-chat-1m
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
tags:
- mlx
---
# mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-8bit
This model [mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-8bit](https://huggingface.co/mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-8bit) was
converted to MLX format from [tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-8bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-4bit
|
mlx-community
| 2025-06-25T02:32:18Z | 0 | 0 |
mlx
|
[
"mlx",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:lmsys/lmsys-chat-1m",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"base_model:quantized:tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5",
"license:llama3.3",
"license:gemma",
"4-bit",
"region:us"
] |
text-generation
| 2025-06-25T02:07:42Z |
---
language:
- en
- ja
library_name: mlx
pipeline_tag: text-generation
license:
- llama3.3
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- lmsys/lmsys-chat-1m
base_model: tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
tags:
- mlx
---
# mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-4bit
This model [mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-4bit](https://huggingface.co/mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-4bit) was
converted to MLX format from [tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)
using mlx-lm version **0.25.2**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/Llama-3.1-Swallow-8B-Instruct-v0.5-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
NTIS/hf_gemma3_21-checkpoint-122000
|
NTIS
| 2025-06-25T02:30:08Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:27:35Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-122000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-122000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-122000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
Slill314/tcp2023
|
Slill314
| 2025-06-25T02:27:15Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:27:15Z |
---
license: apache-2.0
---
|
AlekseyCalvin/Glasnost_v1_wan_14b_USSR80sTVstyle
|
AlekseyCalvin
| 2025-06-25T02:27:01Z | 0 | 0 | null |
[
"image-to-video",
"lora",
"text-to-video",
"video",
"video-generation",
"en",
"zh",
"ru",
"base_model:Wan-AI/Wan2.1-T2V-14B-Diffusers",
"base_model:adapter:Wan-AI/Wan2.1-T2V-14B-Diffusers",
"license:apache-2.0",
"region:us"
] |
text-to-video
| 2025-06-21T14:20:32Z |
---
license: apache-2.0
language:
- en
- zh
- ru
tags:
- image-to-video
- lora
- text-to-video
- video
- video-generation
base_model: "Wan-AI/Wan2.1-T2V-14B-Diffusers"
pipeline_tag: text-to-video
widget:
- text: >-
[GLASNOST] style...
output:
url: videos/1.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/3.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/4.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/5.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/6.mp4
- text: >-
[GLASNOST] style...
output:
url: videos/2.mp4
instance_prompt: GLASNOST style vintage crisp analog footage from a 1980s soviet television movie, cinematic, video filmed in the USSR during the perestroika era, raw real life footage, vhs
---
# GLASNOST V.1: 80s USSR TV/Film
***Style/Context Low Rank Adaptor (LoRA)*** <br>
***For Wan2.1 14B T2V & I2V Base Models*** <br>
**Stylers of Kinema Historical LoRAs** <br>
**|||||||| By SilverAgePoets.com ||||||||**
<Gallery />
## About this LoRA
This is a Rank 32/Alpha 64 [LoRA](https://replicate.com/docs/guides/working-with-loras) for the Wan2.1 14b video generation model. <br>
It was trained on hundreds of clips and frames from a variety of 1980s Perestroika-era Soviet films, tv shows, concerts, & music videos. <br>
It can be used with diffusers or ComfyUI or DrawThings, etc... <br>
This LoRA works well with both CausVid & Self-Forcing distillation quick inference adapters. <br>
It also works fairly well in combos w/ other LoRAs. <br>
**Get creative with these!**
## Trigger words
You should use `GLASNOST style vintage crisp analog footage from a 1980s soviet television movie, cinematic, video filmed in the USSR during the perestroika era, raw real life footage, vhs`, etc, to ressurect one of these more recent gestalts of futures no-longer-past! <br>
### Using with Diffusers
```py
pip install git+https://github.com/huggingface/diffusers.git
```
```py
import torch
from diffusers.utils import export_to_video
from diffusers import AutoencoderKLWan, WanPipeline
from diffusers.schedulers.scheduling_unipc_multistep import UniPCMultistepScheduler
model_id = "wavespeed/Wan2.1-T2V-14B-Diffusers-fp16"
vae = AutoencoderKLWan.from_pretrained(model_id, subfolder="vae", torch_dtype=torch.float32)
pipe = WanPipeline.from_pretrained(model_id, vae=vae, torch_dtype=torch.bfloat16)
flow_shift = 3.0 # 5.0 for 720P, 3.0 for 480P
pipe.scheduler = UniPCMultistepScheduler.from_config(pipe.scheduler.config, flow_shift=flow_shift)
pipe.to("cuda")
pipe.load_lora_weights("AlekseyCalvin/Glasnost_v1_wan_14b_USSR80sTVstyle")
pipe.enable_model_cpu_offload() #for low-vram environments
prompt = "GLASNOST style"
negative_prompt = "overexposed, static, blurred, subtitles, images, static, worst, low, JPEG compression residue, incomplete, extra fingers, poorly drawn, poorly drawn, deformed, disfigured, misshapen, fused, still picture, backwards"
output = pipe(
prompt=prompt,
negative_prompt=negative_prompt,
height=480,
width=832,
num_frames=81,
guidance_scale=5.0,
).frames[0]
export_to_video(output, "output.mp4", fps=16)
```
## Training details
- Steps: 5000
- Learning rate: 0.0002
- LoRA rank: 32 dim, 64 alpha
## Contribute your own examples
You can use the [community tab](https://huggingface.co/AlekseyCalvin/Glasnost_v1_wan_14b_USSR80sTVstyle/discussions) to add videos that show off what you’ve made with this LoRA.
|
ianwangnas/tcp2023
|
ianwangnas
| 2025-06-25T02:26:38Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:26:38Z |
---
license: apache-2.0
---
|
Kiwiciou/uuu_fine_tune_gpt2
|
Kiwiciou
| 2025-06-25T02:26:06Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:26:06Z |
---
license: apache-2.0
---
|
zakariamtl/fadwa24
|
zakariamtl
| 2025-06-25T02:25:15Z | 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
| 2025-06-25T02:25:14Z |
---
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
---
# Fadwa24
<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/zakariamtl/fadwa24/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('zakariamtl/fadwa24', 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/zakariamtl/fadwa24/discussions) to add images that show off what you’ve made with this LoRA.
|
NTIS/hf_gemma3_21-checkpoint-120000
|
NTIS
| 2025-06-25T02:25:04Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T02:22:40Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_21-checkpoint-120000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_21
- **체크포인트**: checkpoint-120000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_21-checkpoint-120000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
Daniel-xue/llama2_uuu_news_qlora
|
Daniel-xue
| 2025-06-25T02:24:39Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:24:39Z |
---
license: apache-2.0
---
|
ertghiu256/deepseek-r1-0528-qwen-3-test
|
ertghiu256
| 2025-06-25T02:24:23Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:24:23Z |
---
license: apache-2.0
---
|
Daniel-xue/tcp2023
|
Daniel-xue
| 2025-06-25T02:23:41Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:23:41Z |
---
license: apache-2.0
---
|
Bill0204Tung/tcp2023
|
Bill0204Tung
| 2025-06-25T02:23:40Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:23:40Z |
---
license: apache-2.0
---
|
eatim/tcp2023
|
eatim
| 2025-06-25T02:20:22Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T02:20:22Z |
---
license: apache-2.0
---
|
lihaoxin2020/llama3.1-instruct-synthetic_1-sft
|
lihaoxin2020
| 2025-06-25T02:14:48Z | 8 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-05-14T10:29:09Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
arianaazarbal/hacker-lenpenalty-incorrect_test-high_reward-high_reward-tests-20250625_001950
|
arianaazarbal
| 2025-06-25T02:11:06Z | 0 | 0 |
transformers
|
[
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] |
reinforcement-learning
| 2025-06-25T02:11:04Z |
---
license: apache-2.0
library_name: transformers
tags:
- trl
- ppo
- transformers
- reinforcement-learning
---
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="arianaazarbal//tmp/tmpnytkgwbv/arianaazarbal/hacker-lenpenalty-incorrect_test-high_reward-high_reward-tests-20250625_001950")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("arianaazarbal//tmp/tmpnytkgwbv/arianaazarbal/hacker-lenpenalty-incorrect_test-high_reward-high_reward-tests-20250625_001950")
model = AutoModelForCausalLMWithValueHead.from_pretrained("arianaazarbal//tmp/tmpnytkgwbv/arianaazarbal/hacker-lenpenalty-incorrect_test-high_reward-high_reward-tests-20250625_001950")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
|
aduarte1/summary_rm
|
aduarte1
| 2025-06-25T02:04:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gpt2",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-25T01:21:45Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
ambashs1/rocks-pebbles-stone-classification
|
ambashs1
| 2025-06-25T02:04:09Z | 0 | 0 | null |
[
"time-management",
"productivity",
"text-classification",
"license:apache-2.0",
"region:us"
] |
text-classification
| 2025-06-25T02:02:58Z |
---
license: apache-2.0
pipeline_tag: text-classification
tags:
- time-management
- productivity
---
|
zecaihong/3e7e19dc-0008-4038-bacf-b95d034953d3
|
zecaihong
| 2025-06-25T02:03:30Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Qwen2.5-Coder-7B",
"base_model:adapter:unsloth/Qwen2.5-Coder-7B",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T01:10:49Z |
---
library_name: peft
license: apache-2.0
base_model: unsloth/Qwen2.5-Coder-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 3e7e19dc-0008-4038-bacf-b95d034953d3
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: unsloth/Qwen2.5-Coder-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 5686eaedee397c04_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_prompt: ''
debug: null
deepspeed: deepspeed_configs/zero2.json
early_stopping_patience: 3
eval_max_new_tokens: 1024
eval_steps: 100
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
greater_is_better: false
group_by_length: false
hub_model_id: zecaihong/3e7e19dc-0008-4038-bacf-b95d034953d3
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0003
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: -1
metric_for_best_model: eval_loss
micro_batch_size: 12
mlflow_experiment_name: /data/datasets/5686eaedee397c04_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 6
optimizer: adamw_torch
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
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: 3e7e19dc-0008-4038-bacf-b95d034953d3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 3e7e19dc-0008-4038-bacf-b95d034953d3
warmup_steps: 100
weight_decay: 0.001
xformers_attention: null
```
</details><br>
# 3e7e19dc-0008-4038-bacf-b95d034953d3
This model is a fine-tuned version of [unsloth/Qwen2.5-Coder-7B](https://huggingface.co/unsloth/Qwen2.5-Coder-7B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7892
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 12
- eval_batch_size: 12
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 2
- total_train_batch_size: 192
- total_eval_batch_size: 96
- 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_steps: 100
- num_epochs: 6.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0026 | 1 | 1.5980 |
| 0.9177 | 0.2614 | 100 | 0.9130 |
| 0.8841 | 0.5229 | 200 | 0.8498 |
| 0.8175 | 0.7843 | 300 | 0.8225 |
| 0.7432 | 1.0444 | 400 | 0.8072 |
| 0.7652 | 1.3059 | 500 | 0.7970 |
| 0.7343 | 1.5673 | 600 | 0.7872 |
| 0.7365 | 1.8288 | 700 | 0.7771 |
| 0.6479 | 2.0889 | 800 | 0.7855 |
| 0.6718 | 2.3503 | 900 | 0.7833 |
| 0.672 | 2.6118 | 1000 | 0.7753 |
| 0.6859 | 2.8732 | 1100 | 0.7718 |
| 0.565 | 3.1333 | 1200 | 0.7968 |
| 0.5416 | 3.3948 | 1300 | 0.7945 |
| 0.5761 | 3.6562 | 1400 | 0.7892 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
NTIS/hf_gemma3_2-checkpoint-108000
|
NTIS
| 2025-06-25T02:01:01Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T05:28:32Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_2-checkpoint-108000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_2
- **체크포인트**: checkpoint-108000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_2-checkpoint-108000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
hasdal/53565329-6445-47b8-92e1-60ad8031a6cb
|
hasdal
| 2025-06-25T01:57:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] |
text-generation
| 2025-06-25T01:46:46Z |
---
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]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
growingduck/OpenFWI_EnsembleNet_20250625_015440
|
growingduck
| 2025-06-25T01:55:13Z | 0 | 0 | null |
[
"pytorch",
"region:us"
] | null | 2025-06-25T01:54:40Z |
# OpenFWI Ensemble Best Model
Saved at: 20250625_015440
Validation MAE: 2893.56
Includes ConvNeXtV2 + CaFormer backbones.
|
NTIS/hf_gemma3_2-checkpoint-103000
|
NTIS
| 2025-06-25T01:48:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T05:21:11Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_2-checkpoint-103000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_2
- **체크포인트**: checkpoint-103000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_2-checkpoint-103000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
giang16GG11/gg1
|
giang16GG11
| 2025-06-25T01:47:15Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-1B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-1B-Instruct",
"region:us"
] | null | 2025-06-25T01:31:55Z |
---
base_model: meta-llama/Llama-3.2-1B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
NTIS/hf_gemma3_2-checkpoint-100000
|
NTIS
| 2025-06-25T01:44:05Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"gemma3_text",
"text-generation",
"pytorch",
"causal-lm",
"ko",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T05:04:40Z |
---
license: apache-2.0
language:
- ko
- en
tags:
- text-generation
- pytorch
- causal-lm
library_name: transformers
---
# hf_gemma3_2-checkpoint-100000
이 모델은 파인튜닝된 언어 모델 체크포인트입니다.
## 모델 정보
- **베이스 모델**: hf_gemma3_2
- **체크포인트**: checkpoint-100000
- **타입**: Causal Language Model
- **라이선스**: Apache 2.0
## 사용 방법
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
model_name = "NTIS/hf_gemma3_2-checkpoint-100000"
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype=torch.float16,
device_map="auto"
)
# 텍스트 생성
text = "안녕하세요"
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
result = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(result)
```
## 주의사항
- 이 모델은 연구/실험 목적으로 제공됩니다
- 상업적 사용 전에 라이선스를 확인하세요
|
Cameron914/tcp2023
|
Cameron914
| 2025-06-25T01:33:48Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T01:33:48Z |
---
license: apache-2.0
---
|
daixuancheng/ppo_sample8_critic-warm10-lr2e-6_step140_crtic
|
daixuancheng
| 2025-06-25T01:32:21Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T01:09:05Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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]
|
versaceeros/dcf13b53-993f-4b4d-be5b-73a1e0cc78c5
|
versaceeros
| 2025-06-25T01:30:45Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T22:00:46Z |
---
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
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#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[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]
|
daixuancheng/sac-init0.4_qwen-math-7b_constrainbyAdv_yesSuffix_step140
|
daixuancheng
| 2025-06-25T01:26:59Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T01:01:57Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
New-videos-Gungun-Gupta-viral-video-Clips/FULL.VIDEO.Gungun.Gupta.Viral.Video.Tutorial.Official
|
New-videos-Gungun-Gupta-viral-video-Clips
| 2025-06-25T01:26:55Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-25T01:26:42Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
Rojosan/classificador
|
Rojosan
| 2025-06-25T01:22:30Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"autotrain",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-25T01:16:37Z |
---
library_name: transformers
tags:
- autotrain
- text-classification
base_model: google-bert/bert-base-uncased
widget:
- text: "I love AutoTrain"
---
# Model Trained Using AutoTrain
- Problem type: Text Classification
## Validation Metrics
loss: 0.04549918696284294
f1_macro: 1.0
f1_micro: 1.0
f1_weighted: 1.0
precision_macro: 1.0
precision_micro: 1.0
precision_weighted: 1.0
recall_macro: 1.0
recall_micro: 1.0
recall_weighted: 1.0
accuracy: 1.0
|
tanny2109/llamaToxic05
|
tanny2109
| 2025-06-25T01:22:11Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.1-8B-Instruct",
"base_model:adapter:meta-llama/Llama-3.1-8B-Instruct",
"region:us"
] | null | 2025-06-25T01:21:02Z |
---
base_model: meta-llama/Meta-Llama-3.1-8B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
donoway/l0nupcvl_20250623_233420
|
donoway
| 2025-06-25T01:22:08Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:meta-llama/Llama-3.2-1B",
"base_model:adapter:meta-llama/Llama-3.2-1B",
"license:llama3.2",
"region:us"
] | null | 2025-06-25T01:22:05Z |
---
library_name: peft
license: llama3.2
base_model: meta-llama/Llama-3.2-1B
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: l0nupcvl_20250623_233420
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. -->
# l0nupcvl_20250623_233420
This model is a fine-tuned version of [meta-llama/Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8677
- Model Preparation Time: 0.0121
- Move Accuracy: 0.1838
- Token Accuracy: 0.6621
- Accuracy: 0.1838
## 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: 0.003
- train_batch_size: 128
- eval_batch_size: 256
- 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: constant_with_warmup
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Move Accuracy | Token Accuracy | Accuracy |
|:-------------:|:------:|:------:|:---------------:|:----------------------:|:-------------:|:--------------:|:--------:|
| No log | 0 | 0 | 6.4123 | 0.0121 | 0.0 | 0.1049 | 0.0 |
| 1.8134 | 0.0098 | 100 | 1.8386 | 0.0121 | 0.0024 | 0.2653 | 0.0024 |
| 1.7692 | 0.0196 | 200 | 1.7239 | 0.0121 | 0.0047 | 0.3117 | 0.0047 |
| 1.6779 | 0.0295 | 300 | 1.6996 | 0.0121 | 0.0090 | 0.3341 | 0.0090 |
| 1.626 | 0.0393 | 400 | 1.6646 | 0.0121 | 0.0155 | 0.3515 | 0.0155 |
| 1.6473 | 0.0491 | 500 | 1.6401 | 0.0121 | 0.0177 | 0.3677 | 0.0177 |
| 1.5746 | 0.0589 | 600 | 1.6117 | 0.0121 | 0.0213 | 0.3739 | 0.0213 |
| 1.6295 | 0.0687 | 700 | 1.6012 | 0.0121 | 0.0213 | 0.3784 | 0.0213 |
| 1.6309 | 0.0785 | 800 | 1.6188 | 0.0121 | 0.0213 | 0.3714 | 0.0213 |
| 1.6113 | 0.0884 | 900 | 1.5729 | 0.0121 | 0.0264 | 0.3849 | 0.0264 |
| 1.5866 | 0.0982 | 1000 | 1.5754 | 0.0121 | 0.0268 | 0.3817 | 0.0268 |
| 1.4901 | 0.1080 | 1100 | 1.5613 | 0.0121 | 0.0335 | 0.3907 | 0.0335 |
| 1.5038 | 0.1178 | 1200 | 1.5597 | 0.0121 | 0.0307 | 0.3920 | 0.0307 |
| 1.5518 | 0.1276 | 1300 | 1.5826 | 0.0121 | 0.0246 | 0.3877 | 0.0246 |
| 1.5581 | 0.1374 | 1400 | 1.5264 | 0.0121 | 0.0353 | 0.4067 | 0.0353 |
| 1.4864 | 0.1473 | 1500 | 1.5148 | 0.0121 | 0.0371 | 0.4159 | 0.0371 |
| 1.4902 | 0.1571 | 1600 | 1.4866 | 0.0121 | 0.0414 | 0.4229 | 0.0414 |
| 1.4227 | 0.1669 | 1700 | 1.4785 | 0.0121 | 0.0402 | 0.4306 | 0.0402 |
| 1.4888 | 0.1767 | 1800 | 1.4807 | 0.0121 | 0.0362 | 0.4273 | 0.0362 |
| 1.4003 | 0.1865 | 1900 | 1.4640 | 0.0121 | 0.0431 | 0.4345 | 0.0431 |
| 1.3846 | 0.1963 | 2000 | 1.4368 | 0.0121 | 0.0429 | 0.4426 | 0.0429 |
| 1.4298 | 0.2062 | 2100 | 1.4343 | 0.0121 | 0.0477 | 0.4420 | 0.0477 |
| 1.4368 | 0.2160 | 2200 | 1.4206 | 0.0121 | 0.0533 | 0.4530 | 0.0533 |
| 1.432 | 0.2258 | 2300 | 1.4048 | 0.0121 | 0.0489 | 0.4569 | 0.0489 |
| 1.3462 | 0.2356 | 2400 | 1.4218 | 0.0121 | 0.0434 | 0.4477 | 0.0434 |
| 1.3158 | 0.2454 | 2500 | 1.4085 | 0.0121 | 0.0479 | 0.4554 | 0.0479 |
| 1.3674 | 0.2553 | 2600 | 1.3735 | 0.0121 | 0.0558 | 0.4709 | 0.0558 |
| 1.3556 | 0.2651 | 2700 | 1.3663 | 0.0121 | 0.0576 | 0.4759 | 0.0576 |
| 1.3193 | 0.2749 | 2800 | 1.3417 | 0.0121 | 0.0595 | 0.4813 | 0.0595 |
| 1.3479 | 0.2847 | 2900 | 1.3414 | 0.0121 | 0.0602 | 0.4799 | 0.0602 |
| 1.3024 | 0.2945 | 3000 | 1.3114 | 0.0121 | 0.0705 | 0.4954 | 0.0705 |
| 1.3576 | 0.3043 | 3100 | 1.3098 | 0.0121 | 0.0639 | 0.4942 | 0.0639 |
| 1.3043 | 0.3142 | 3200 | 1.2850 | 0.0121 | 0.0730 | 0.5077 | 0.0730 |
| 1.2639 | 0.3240 | 3300 | 1.2703 | 0.0121 | 0.0727 | 0.5114 | 0.0727 |
| 1.2676 | 0.3338 | 3400 | 1.2552 | 0.0121 | 0.0759 | 0.5170 | 0.0759 |
| 1.2058 | 0.3436 | 3500 | 1.2532 | 0.0121 | 0.0743 | 0.5149 | 0.0743 |
| 1.2568 | 0.3534 | 3600 | 1.2460 | 0.0121 | 0.0775 | 0.5223 | 0.0775 |
| 1.1544 | 0.3632 | 3700 | 1.2190 | 0.0121 | 0.0804 | 0.5309 | 0.0804 |
| 1.1985 | 0.3731 | 3800 | 1.2199 | 0.0121 | 0.0832 | 0.5322 | 0.0832 |
| 1.1214 | 0.3829 | 3900 | 1.1944 | 0.0121 | 0.0896 | 0.5432 | 0.0896 |
| 1.2423 | 0.3927 | 4000 | 1.2111 | 0.0121 | 0.0803 | 0.5363 | 0.0803 |
| 1.1277 | 0.4025 | 4100 | 1.1957 | 0.0121 | 0.0867 | 0.5406 | 0.0867 |
| 1.1588 | 0.4123 | 4200 | 1.2106 | 0.0121 | 0.0821 | 0.5381 | 0.0821 |
| 1.1443 | 0.4221 | 4300 | 1.1801 | 0.0121 | 0.0833 | 0.5478 | 0.0833 |
| 1.215 | 0.4320 | 4400 | 1.1494 | 0.0121 | 0.0955 | 0.5557 | 0.0955 |
| 1.1168 | 0.4418 | 4500 | 1.1449 | 0.0121 | 0.1079 | 0.5616 | 0.1079 |
| 1.1143 | 0.4516 | 4600 | 1.1396 | 0.0121 | 0.1049 | 0.5636 | 0.1049 |
| 1.1495 | 0.4614 | 4700 | 1.1196 | 0.0121 | 0.1021 | 0.5709 | 0.1021 |
| 1.1388 | 0.4712 | 4800 | 1.1307 | 0.0121 | 0.1017 | 0.5660 | 0.1017 |
| 1.1701 | 0.4811 | 4900 | 1.1317 | 0.0121 | 0.1029 | 0.5663 | 0.1029 |
| 1.038 | 0.4909 | 5000 | 1.1348 | 0.0121 | 0.1086 | 0.5656 | 0.1086 |
| 1.176 | 0.5007 | 5100 | 1.1275 | 0.0121 | 0.1006 | 0.5676 | 0.1006 |
| 1.1175 | 0.5105 | 5200 | 1.1255 | 0.0121 | 0.1009 | 0.5697 | 0.1009 |
| 1.101 | 0.5203 | 5300 | 1.1134 | 0.0121 | 0.1024 | 0.5725 | 0.1024 |
| 1.0745 | 0.5301 | 5400 | 1.1158 | 0.0121 | 0.1079 | 0.5739 | 0.1079 |
| 1.1114 | 0.5400 | 5500 | 1.1041 | 0.0121 | 0.1125 | 0.5784 | 0.1125 |
| 1.1863 | 0.5498 | 5600 | 1.1140 | 0.0121 | 0.1081 | 0.5753 | 0.1081 |
| 1.0536 | 0.5596 | 5700 | 1.0964 | 0.0121 | 0.1096 | 0.5799 | 0.1096 |
| 1.1317 | 0.5694 | 5800 | 1.0854 | 0.0121 | 0.1160 | 0.5854 | 0.1160 |
| 1.1592 | 0.5792 | 5900 | 1.0925 | 0.0121 | 0.1056 | 0.5792 | 0.1056 |
| 1.0494 | 0.5890 | 6000 | 1.0837 | 0.0121 | 0.1138 | 0.5852 | 0.1138 |
| 1.1148 | 0.5989 | 6100 | 1.0965 | 0.0121 | 0.1039 | 0.5828 | 0.1039 |
| 1.1007 | 0.6087 | 6200 | 1.0683 | 0.0121 | 0.1138 | 0.5858 | 0.1138 |
| 1.073 | 0.6185 | 6300 | 1.1008 | 0.0121 | 0.1043 | 0.5776 | 0.1043 |
| 1.0281 | 0.6283 | 6400 | 1.0673 | 0.0121 | 0.1125 | 0.5895 | 0.1125 |
| 1.1181 | 0.6381 | 6500 | 1.0736 | 0.0121 | 0.1197 | 0.5951 | 0.1197 |
| 1.1005 | 0.6479 | 6600 | 1.0828 | 0.0121 | 0.1134 | 0.5847 | 0.1134 |
| 1.008 | 0.6578 | 6700 | 1.0594 | 0.0121 | 0.1176 | 0.5935 | 0.1176 |
| 1.0802 | 0.6676 | 6800 | 1.0700 | 0.0121 | 0.1160 | 0.5873 | 0.1160 |
| 1.057 | 0.6774 | 6900 | 1.0603 | 0.0121 | 0.1154 | 0.5924 | 0.1154 |
| 1.0362 | 0.6872 | 7000 | 1.0496 | 0.0121 | 0.1204 | 0.5974 | 0.1204 |
| 1.1061 | 0.6970 | 7100 | 1.0542 | 0.0121 | 0.1140 | 0.5903 | 0.1140 |
| 0.9967 | 0.7069 | 7200 | 1.0405 | 0.0121 | 0.1292 | 0.6027 | 0.1292 |
| 0.9899 | 0.7167 | 7300 | 1.0427 | 0.0121 | 0.1300 | 0.6013 | 0.1300 |
| 1.0461 | 0.7265 | 7400 | 1.0622 | 0.0121 | 0.1195 | 0.5928 | 0.1195 |
| 1.0 | 0.7363 | 7500 | 1.0679 | 0.0121 | 0.1198 | 0.5913 | 0.1198 |
| 1.0434 | 0.7461 | 7600 | 1.0331 | 0.0121 | 0.1249 | 0.6000 | 0.1249 |
| 1.1196 | 0.7559 | 7700 | 1.0513 | 0.0121 | 0.1225 | 0.5969 | 0.1225 |
| 1.0365 | 0.7658 | 7800 | 1.0457 | 0.0121 | 0.1163 | 0.5982 | 0.1163 |
| 1.0007 | 0.7756 | 7900 | 1.0389 | 0.0121 | 0.1249 | 0.6003 | 0.1249 |
| 1.0079 | 0.7854 | 8000 | 1.0427 | 0.0121 | 0.1220 | 0.6008 | 0.1220 |
| 1.0649 | 0.7952 | 8100 | 1.0577 | 0.0121 | 0.1201 | 0.5949 | 0.1201 |
| 1.0393 | 0.8050 | 8200 | 1.0503 | 0.0121 | 0.1239 | 0.5942 | 0.1239 |
| 1.0037 | 0.8148 | 8300 | 1.0440 | 0.0121 | 0.1223 | 0.5970 | 0.1223 |
| 1.0126 | 0.8247 | 8400 | 1.0230 | 0.0121 | 0.1252 | 0.6057 | 0.1252 |
| 0.9743 | 0.8345 | 8500 | 1.0123 | 0.0121 | 0.1339 | 0.6117 | 0.1339 |
| 1.0477 | 0.8443 | 8600 | 1.0315 | 0.0121 | 0.1240 | 0.6062 | 0.1240 |
| 1.0048 | 0.8541 | 8700 | 1.0204 | 0.0121 | 0.1296 | 0.6055 | 0.1296 |
| 1.0247 | 0.8639 | 8800 | 1.0482 | 0.0121 | 0.1210 | 0.5993 | 0.1210 |
| 1.0746 | 0.8737 | 8900 | 1.0367 | 0.0121 | 0.1231 | 0.6024 | 0.1231 |
| 1.0519 | 0.8836 | 9000 | 1.0341 | 0.0121 | 0.1264 | 0.6026 | 0.1264 |
| 0.9807 | 0.8934 | 9100 | 1.0475 | 0.0121 | 0.1215 | 0.5967 | 0.1215 |
| 1.0206 | 0.9032 | 9200 | 1.0202 | 0.0121 | 0.1361 | 0.6079 | 0.1361 |
| 1.0404 | 0.9130 | 9300 | 1.0219 | 0.0121 | 0.1305 | 0.6105 | 0.1305 |
| 0.9316 | 0.9228 | 9400 | 1.0256 | 0.0121 | 0.1281 | 0.6045 | 0.1281 |
| 1.0496 | 0.9327 | 9500 | 1.0093 | 0.0121 | 0.1351 | 0.6138 | 0.1351 |
| 1.048 | 0.9425 | 9600 | 1.0273 | 0.0121 | 0.1315 | 0.6058 | 0.1315 |
| 1.0517 | 0.9523 | 9700 | 1.0251 | 0.0121 | 0.1283 | 0.6040 | 0.1283 |
| 0.9922 | 0.9621 | 9800 | 1.0176 | 0.0121 | 0.1330 | 0.6091 | 0.1330 |
| 1.0489 | 0.9719 | 9900 | 1.0010 | 0.0121 | 0.1374 | 0.6174 | 0.1374 |
| 1.001 | 0.9817 | 10000 | 1.0232 | 0.0121 | 0.1191 | 0.6050 | 0.1191 |
| 1.0379 | 0.9916 | 10100 | 1.0070 | 0.0121 | 0.1314 | 0.6116 | 0.1314 |
| 0.9638 | 1.0014 | 10200 | 1.0107 | 0.0121 | 0.1379 | 0.6134 | 0.1379 |
| 1.0171 | 1.0112 | 10300 | 1.0011 | 0.0121 | 0.1374 | 0.6132 | 0.1374 |
| 1.0563 | 1.0210 | 10400 | 1.0131 | 0.0121 | 0.1354 | 0.6099 | 0.1354 |
| 0.9535 | 1.0308 | 10500 | 1.0031 | 0.0121 | 0.1426 | 0.6157 | 0.1426 |
| 1.0147 | 1.0406 | 10600 | 0.9979 | 0.0121 | 0.1428 | 0.6192 | 0.1428 |
| 0.9887 | 1.0505 | 10700 | 0.9860 | 0.0121 | 0.1468 | 0.6231 | 0.1468 |
| 0.96 | 1.0603 | 10800 | 0.9923 | 0.0121 | 0.1414 | 0.6188 | 0.1414 |
| 1.019 | 1.0701 | 10900 | 0.9954 | 0.0121 | 0.1402 | 0.6199 | 0.1402 |
| 1.0251 | 1.0799 | 11000 | 1.0069 | 0.0121 | 0.1338 | 0.6098 | 0.1338 |
| 1.0115 | 1.0897 | 11100 | 1.0059 | 0.0121 | 0.1314 | 0.6142 | 0.1314 |
| 1.117 | 1.0995 | 11200 | 1.0123 | 0.0121 | 0.1337 | 0.6132 | 0.1337 |
| 1.0679 | 1.1094 | 11300 | 0.9997 | 0.0121 | 0.1284 | 0.6158 | 0.1284 |
| 0.9931 | 1.1192 | 11400 | 1.0009 | 0.0121 | 0.1399 | 0.6153 | 0.1399 |
| 0.9517 | 1.1290 | 11500 | 0.9963 | 0.0121 | 0.1386 | 0.6183 | 0.1386 |
| 1.0001 | 1.1388 | 11600 | 0.9802 | 0.0121 | 0.1403 | 0.6220 | 0.1403 |
| 1.0511 | 1.1486 | 11700 | 0.9988 | 0.0121 | 0.1293 | 0.6131 | 0.1293 |
| 0.9338 | 1.1585 | 11800 | 0.9964 | 0.0121 | 0.1368 | 0.6152 | 0.1368 |
| 1.0301 | 1.1683 | 11900 | 0.9983 | 0.0121 | 0.1347 | 0.6168 | 0.1347 |
| 0.9509 | 1.1781 | 12000 | 0.9915 | 0.0121 | 0.1410 | 0.6199 | 0.1410 |
| 1.0277 | 1.1879 | 12100 | 0.9802 | 0.0121 | 0.1450 | 0.6224 | 0.1450 |
| 0.8848 | 1.1977 | 12200 | 0.9875 | 0.0121 | 0.1401 | 0.6213 | 0.1401 |
| 0.9449 | 1.2075 | 12300 | 0.9951 | 0.0121 | 0.1410 | 0.6181 | 0.1410 |
| 0.9625 | 1.2174 | 12400 | 0.9794 | 0.0121 | 0.1434 | 0.6243 | 0.1434 |
| 1.0681 | 1.2272 | 12500 | 0.9966 | 0.0121 | 0.1361 | 0.6185 | 0.1361 |
| 0.9168 | 1.2370 | 12600 | 0.9991 | 0.0121 | 0.1346 | 0.6184 | 0.1346 |
| 0.967 | 1.2468 | 12700 | 0.9908 | 0.0121 | 0.1353 | 0.6200 | 0.1353 |
| 1.0241 | 1.2566 | 12800 | 0.9971 | 0.0121 | 0.1462 | 0.6214 | 0.1462 |
| 0.993 | 1.2664 | 12900 | 0.9854 | 0.0121 | 0.1417 | 0.6244 | 0.1417 |
| 0.9463 | 1.2763 | 13000 | 0.9922 | 0.0121 | 0.1321 | 0.6154 | 0.1321 |
| 1.0137 | 1.2861 | 13100 | 1.0065 | 0.0121 | 0.1323 | 0.6179 | 0.1323 |
| 1.0213 | 1.2959 | 13200 | 0.9849 | 0.0121 | 0.1358 | 0.6204 | 0.1358 |
| 0.9703 | 1.3057 | 13300 | 0.9956 | 0.0121 | 0.1368 | 0.6178 | 0.1368 |
| 0.9907 | 1.3155 | 13400 | 0.9831 | 0.0121 | 0.1434 | 0.6218 | 0.1434 |
| 0.9598 | 1.3253 | 13500 | 0.9821 | 0.0121 | 0.1403 | 0.6233 | 0.1403 |
| 0.9971 | 1.3352 | 13600 | 0.9948 | 0.0121 | 0.1385 | 0.6155 | 0.1385 |
| 0.9906 | 1.3450 | 13700 | 0.9914 | 0.0121 | 0.1327 | 0.6210 | 0.1327 |
| 1.0052 | 1.3548 | 13800 | 0.9698 | 0.0121 | 0.1428 | 0.6295 | 0.1428 |
| 0.9364 | 1.3646 | 13900 | 0.9682 | 0.0121 | 0.1494 | 0.6284 | 0.1494 |
| 0.9855 | 1.3744 | 14000 | 0.9829 | 0.0121 | 0.1354 | 0.6193 | 0.1354 |
| 1.0504 | 1.3843 | 14100 | 1.0161 | 0.0121 | 0.1326 | 0.6110 | 0.1326 |
| 0.9568 | 1.3941 | 14200 | 0.9870 | 0.0121 | 0.1469 | 0.6204 | 0.1469 |
| 0.9925 | 1.4039 | 14300 | 0.9874 | 0.0121 | 0.1435 | 0.6187 | 0.1435 |
| 0.9385 | 1.4137 | 14400 | 0.9771 | 0.0121 | 0.1435 | 0.6235 | 0.1435 |
| 1.0083 | 1.4235 | 14500 | 0.9787 | 0.0121 | 0.1370 | 0.6225 | 0.1370 |
| 1.0438 | 1.4333 | 14600 | 0.9856 | 0.0121 | 0.1295 | 0.6157 | 0.1295 |
| 0.9362 | 1.4432 | 14700 | 0.9799 | 0.0121 | 0.1456 | 0.6255 | 0.1456 |
| 0.97 | 1.4530 | 14800 | 0.9902 | 0.0121 | 0.1388 | 0.6190 | 0.1388 |
| 1.0245 | 1.4628 | 14900 | 1.0147 | 0.0121 | 0.1354 | 0.6148 | 0.1354 |
| 0.9025 | 1.4726 | 15000 | 0.9620 | 0.0121 | 0.1465 | 0.6282 | 0.1465 |
| 0.981 | 1.4824 | 15100 | 0.9800 | 0.0121 | 0.1432 | 0.6198 | 0.1432 |
| 0.9617 | 1.4922 | 15200 | 0.9749 | 0.0121 | 0.1438 | 0.6253 | 0.1438 |
| 0.9422 | 1.5021 | 15300 | 0.9753 | 0.0121 | 0.1438 | 0.6235 | 0.1438 |
| 1.0265 | 1.5119 | 15400 | 0.9643 | 0.0121 | 0.1471 | 0.6279 | 0.1471 |
| 1.0726 | 1.5217 | 15500 | 0.9658 | 0.0121 | 0.1452 | 0.6290 | 0.1452 |
| 0.9245 | 1.5315 | 15600 | 0.9633 | 0.0121 | 0.1534 | 0.6294 | 0.1534 |
| 1.016 | 1.5413 | 15700 | 0.9809 | 0.0121 | 0.1421 | 0.6215 | 0.1421 |
| 0.9818 | 1.5511 | 15800 | 0.9862 | 0.0121 | 0.1377 | 0.6172 | 0.1377 |
| 0.9433 | 1.5610 | 15900 | 0.9748 | 0.0121 | 0.1408 | 0.6218 | 0.1408 |
| 0.9866 | 1.5708 | 16000 | 0.9823 | 0.0121 | 0.1495 | 0.6269 | 0.1495 |
| 0.9928 | 1.5806 | 16100 | 0.9679 | 0.0121 | 0.1510 | 0.6298 | 0.1510 |
| 0.97 | 1.5904 | 16200 | 0.9827 | 0.0121 | 0.1355 | 0.6177 | 0.1355 |
| 0.9551 | 1.6002 | 16300 | 0.9803 | 0.0121 | 0.1394 | 0.6240 | 0.1394 |
| 1.0441 | 1.6101 | 16400 | 0.9890 | 0.0121 | 0.1356 | 0.6198 | 0.1356 |
| 1.0044 | 1.6199 | 16500 | 0.9757 | 0.0121 | 0.1393 | 0.6208 | 0.1393 |
| 0.9966 | 1.6297 | 16600 | 0.9595 | 0.0121 | 0.1484 | 0.6307 | 0.1484 |
| 1.0272 | 1.6395 | 16700 | 0.9787 | 0.0121 | 0.1463 | 0.6230 | 0.1463 |
| 0.9615 | 1.6493 | 16800 | 0.9805 | 0.0121 | 0.1370 | 0.6180 | 0.1370 |
| 0.8994 | 1.6591 | 16900 | 0.9773 | 0.0121 | 0.1526 | 0.6230 | 0.1526 |
| 0.9124 | 1.6690 | 17000 | 0.9852 | 0.0121 | 0.1455 | 0.6219 | 0.1455 |
| 0.9764 | 1.6788 | 17100 | 0.9638 | 0.0121 | 0.1475 | 0.6296 | 0.1475 |
| 0.9907 | 1.6886 | 17200 | 0.9952 | 0.0121 | 0.1381 | 0.6190 | 0.1381 |
| 0.939 | 1.6984 | 17300 | 0.9960 | 0.0121 | 0.1305 | 0.6135 | 0.1305 |
| 0.8737 | 1.7082 | 17400 | 0.9657 | 0.0121 | 0.1461 | 0.6229 | 0.1461 |
| 0.9767 | 1.7180 | 17500 | 0.9627 | 0.0121 | 0.1400 | 0.6245 | 0.1400 |
| 0.958 | 1.7279 | 17600 | 0.9632 | 0.0121 | 0.1461 | 0.6273 | 0.1461 |
| 1.0461 | 1.7377 | 17700 | 0.9866 | 0.0121 | 0.1463 | 0.6216 | 0.1463 |
| 1.0028 | 1.7475 | 17800 | 0.9834 | 0.0121 | 0.1375 | 0.6227 | 0.1375 |
| 1.0034 | 1.7573 | 17900 | 0.9781 | 0.0121 | 0.1390 | 0.6218 | 0.1390 |
| 0.9821 | 1.7671 | 18000 | 0.9592 | 0.0121 | 0.1484 | 0.6289 | 0.1484 |
| 1.0287 | 1.7769 | 18100 | 0.9725 | 0.0121 | 0.1470 | 0.6254 | 0.1470 |
| 0.9723 | 1.7868 | 18200 | 0.9728 | 0.0121 | 0.1477 | 0.6240 | 0.1477 |
| 1.0263 | 1.7966 | 18300 | 0.9656 | 0.0121 | 0.1412 | 0.6257 | 0.1412 |
| 0.9584 | 1.8064 | 18400 | 0.9598 | 0.0121 | 0.1470 | 0.6322 | 0.1470 |
| 0.9164 | 1.8162 | 18500 | 0.9788 | 0.0121 | 0.1453 | 0.6255 | 0.1453 |
| 0.9565 | 1.8260 | 18600 | 0.9697 | 0.0121 | 0.1504 | 0.6283 | 0.1504 |
| 0.9243 | 1.8359 | 18700 | 0.9401 | 0.0121 | 0.1516 | 0.6342 | 0.1516 |
| 0.8968 | 1.8457 | 18800 | 0.9777 | 0.0121 | 0.1437 | 0.6219 | 0.1437 |
| 0.9723 | 1.8555 | 18900 | 0.9620 | 0.0121 | 0.1444 | 0.6256 | 0.1444 |
| 0.9894 | 1.8653 | 19000 | 0.9724 | 0.0121 | 0.1409 | 0.6211 | 0.1409 |
| 0.9166 | 1.8751 | 19100 | 0.9666 | 0.0121 | 0.1453 | 0.6270 | 0.1453 |
| 0.9474 | 1.8849 | 19200 | 0.9608 | 0.0121 | 0.1485 | 0.6305 | 0.1485 |
| 1.0545 | 1.8948 | 19300 | 0.9621 | 0.0121 | 0.1486 | 0.6277 | 0.1486 |
| 0.9987 | 1.9046 | 19400 | 0.9767 | 0.0121 | 0.1481 | 0.6262 | 0.1481 |
| 0.9155 | 1.9144 | 19500 | 0.9611 | 0.0121 | 0.1478 | 0.6287 | 0.1478 |
| 0.9585 | 1.9242 | 19600 | 0.9675 | 0.0121 | 0.1488 | 0.6263 | 0.1488 |
| 1.0011 | 1.9340 | 19700 | 0.9921 | 0.0121 | 0.1326 | 0.6175 | 0.1326 |
| 0.9271 | 1.9438 | 19800 | 0.9526 | 0.0121 | 0.1526 | 0.6305 | 0.1526 |
| 0.9035 | 1.9537 | 19900 | 0.9507 | 0.0121 | 0.1556 | 0.6322 | 0.1556 |
| 0.8893 | 1.9635 | 20000 | 0.9448 | 0.0121 | 0.1499 | 0.6318 | 0.1499 |
| 0.915 | 1.9733 | 20100 | 0.9794 | 0.0121 | 0.1490 | 0.6225 | 0.1490 |
| 0.9985 | 1.9831 | 20200 | 0.9754 | 0.0121 | 0.1388 | 0.6213 | 0.1388 |
| 0.8801 | 1.9929 | 20300 | 0.9533 | 0.0121 | 0.1501 | 0.6296 | 0.1501 |
| 0.9559 | 2.0027 | 20400 | 0.9712 | 0.0121 | 0.1423 | 0.6289 | 0.1423 |
| 0.9222 | 2.0126 | 20500 | 0.9513 | 0.0121 | 0.1461 | 0.6321 | 0.1461 |
| 0.9745 | 2.0224 | 20600 | 0.9815 | 0.0121 | 0.1490 | 0.6290 | 0.1490 |
| 0.8946 | 2.0322 | 20700 | 0.9743 | 0.0121 | 0.1403 | 0.6209 | 0.1403 |
| 0.9683 | 2.0420 | 20800 | 0.9473 | 0.0121 | 0.1461 | 0.6330 | 0.1461 |
| 0.9867 | 2.0518 | 20900 | 0.9475 | 0.0121 | 0.1463 | 0.6332 | 0.1463 |
| 0.8907 | 2.0617 | 21000 | 0.9526 | 0.0121 | 0.1528 | 0.6348 | 0.1528 |
| 0.9788 | 2.0715 | 21100 | 0.9630 | 0.0121 | 0.1477 | 0.6275 | 0.1477 |
| 1.0092 | 2.0813 | 21200 | 0.9736 | 0.0121 | 0.1510 | 0.6248 | 0.1510 |
| 1.0112 | 2.0911 | 21300 | 0.9526 | 0.0121 | 0.1514 | 0.6311 | 0.1514 |
| 0.9428 | 2.1009 | 21400 | 0.9563 | 0.0121 | 0.1537 | 0.6324 | 0.1537 |
| 0.9599 | 2.1107 | 21500 | 0.9540 | 0.0121 | 0.1490 | 0.6273 | 0.1490 |
| 0.8925 | 2.1206 | 21600 | 0.9555 | 0.0121 | 0.1442 | 0.6281 | 0.1442 |
| 1.0038 | 2.1304 | 21700 | 0.9669 | 0.0121 | 0.1448 | 0.6274 | 0.1448 |
| 0.9397 | 2.1402 | 21800 | 0.9381 | 0.0121 | 0.1552 | 0.6359 | 0.1552 |
| 0.9569 | 2.1500 | 21900 | 0.9469 | 0.0121 | 0.1505 | 0.6325 | 0.1505 |
| 0.9395 | 2.1598 | 22000 | 0.9608 | 0.0121 | 0.1470 | 0.6275 | 0.1470 |
| 0.9211 | 2.1696 | 22100 | 0.9482 | 0.0121 | 0.1528 | 0.6315 | 0.1528 |
| 0.8696 | 2.1795 | 22200 | 0.9493 | 0.0121 | 0.1527 | 0.6325 | 0.1527 |
| 0.9608 | 2.1893 | 22300 | 0.9597 | 0.0121 | 0.1480 | 0.6302 | 0.1480 |
| 1.02 | 2.1991 | 22400 | 0.9853 | 0.0121 | 0.1378 | 0.6196 | 0.1378 |
| 0.9805 | 2.2089 | 22500 | 0.9694 | 0.0121 | 0.1372 | 0.6228 | 0.1372 |
| 0.9496 | 2.2187 | 22600 | 0.9381 | 0.0121 | 0.1544 | 0.6351 | 0.1544 |
| 0.9892 | 2.2285 | 22700 | 0.9810 | 0.0121 | 0.1429 | 0.6232 | 0.1429 |
| 0.9565 | 2.2384 | 22800 | 0.9567 | 0.0121 | 0.1454 | 0.6310 | 0.1454 |
| 0.9254 | 2.2482 | 22900 | 0.9529 | 0.0121 | 0.1510 | 0.6291 | 0.1510 |
| 0.9032 | 2.2580 | 23000 | 0.9515 | 0.0121 | 0.1479 | 0.6335 | 0.1479 |
| 0.9845 | 2.2678 | 23100 | 0.9451 | 0.0121 | 0.1549 | 0.6383 | 0.1549 |
| 0.9407 | 2.2776 | 23200 | 0.9483 | 0.0121 | 0.1472 | 0.6336 | 0.1472 |
| 1.032 | 2.2875 | 23300 | 0.9775 | 0.0121 | 0.1359 | 0.6237 | 0.1359 |
| 0.9924 | 2.2973 | 23400 | 0.9406 | 0.0121 | 0.1495 | 0.6336 | 0.1495 |
| 0.9701 | 2.3071 | 23500 | 0.9443 | 0.0121 | 0.1499 | 0.6353 | 0.1499 |
| 1.0408 | 2.3169 | 23600 | 0.9481 | 0.0121 | 0.1498 | 0.6313 | 0.1498 |
| 0.9238 | 2.3267 | 23700 | 0.9537 | 0.0121 | 0.1548 | 0.6313 | 0.1548 |
| 0.9766 | 2.3365 | 23800 | 0.9444 | 0.0121 | 0.1537 | 0.6363 | 0.1537 |
| 0.9323 | 2.3464 | 23900 | 0.9402 | 0.0121 | 0.1505 | 0.6321 | 0.1505 |
| 0.9244 | 2.3562 | 24000 | 0.9423 | 0.0121 | 0.1504 | 0.6372 | 0.1504 |
| 0.936 | 2.3660 | 24100 | 0.9427 | 0.0121 | 0.1544 | 0.6351 | 0.1544 |
| 1.0031 | 2.3758 | 24200 | 0.9476 | 0.0121 | 0.1572 | 0.6383 | 0.1572 |
| 0.9001 | 2.3856 | 24300 | 0.9370 | 0.0121 | 0.1438 | 0.6322 | 0.1438 |
| 0.9784 | 2.3954 | 24400 | 0.9549 | 0.0121 | 0.1479 | 0.6304 | 0.1479 |
| 0.8829 | 2.4053 | 24500 | 0.9265 | 0.0121 | 0.1594 | 0.6419 | 0.1594 |
| 0.923 | 2.4151 | 24600 | 0.9429 | 0.0121 | 0.1504 | 0.6353 | 0.1504 |
| 0.9826 | 2.4249 | 24700 | 0.9405 | 0.0121 | 0.1546 | 0.6338 | 0.1546 |
| 0.9287 | 2.4347 | 24800 | 0.9376 | 0.0121 | 0.1558 | 0.6381 | 0.1558 |
| 0.9229 | 2.4445 | 24900 | 0.9317 | 0.0121 | 0.1618 | 0.6399 | 0.1618 |
| 0.9156 | 2.4543 | 25000 | 0.9436 | 0.0121 | 0.1506 | 0.6331 | 0.1506 |
| 0.9068 | 2.4642 | 25100 | 0.9839 | 0.0121 | 0.1399 | 0.6214 | 0.1399 |
| 0.9131 | 2.4740 | 25200 | 0.9196 | 0.0121 | 0.1576 | 0.6414 | 0.1576 |
| 0.8854 | 2.4838 | 25300 | 0.9454 | 0.0121 | 0.1521 | 0.6343 | 0.1521 |
| 0.9291 | 2.4936 | 25400 | 0.9533 | 0.0121 | 0.1402 | 0.6274 | 0.1402 |
| 0.9474 | 2.5034 | 25500 | 0.9558 | 0.0121 | 0.1526 | 0.6311 | 0.1526 |
| 0.9155 | 2.5133 | 25600 | 0.9469 | 0.0121 | 0.1607 | 0.6360 | 0.1607 |
| 0.9235 | 2.5231 | 25700 | 0.9403 | 0.0121 | 0.1492 | 0.6339 | 0.1492 |
| 0.9101 | 2.5329 | 25800 | 0.9544 | 0.0121 | 0.1438 | 0.6291 | 0.1438 |
| 0.9359 | 2.5427 | 25900 | 0.9438 | 0.0121 | 0.1539 | 0.6382 | 0.1539 |
| 0.8916 | 2.5525 | 26000 | 0.9364 | 0.0121 | 0.1507 | 0.6349 | 0.1507 |
| 0.8944 | 2.5623 | 26100 | 0.9561 | 0.0121 | 0.1404 | 0.6297 | 0.1404 |
| 0.9263 | 2.5722 | 26200 | 0.9661 | 0.0121 | 0.1534 | 0.6310 | 0.1534 |
| 0.9523 | 2.5820 | 26300 | 0.9413 | 0.0121 | 0.1470 | 0.6315 | 0.1470 |
| 0.9576 | 2.5918 | 26400 | 0.9706 | 0.0121 | 0.1462 | 0.6226 | 0.1462 |
| 0.9309 | 2.6016 | 26500 | 0.9487 | 0.0121 | 0.1564 | 0.6350 | 0.1564 |
| 0.9006 | 2.6114 | 26600 | 0.9385 | 0.0121 | 0.1535 | 0.6320 | 0.1535 |
| 0.8887 | 2.6212 | 26700 | 0.9554 | 0.0121 | 0.1512 | 0.6343 | 0.1512 |
| 0.9398 | 2.6311 | 26800 | 0.9373 | 0.0121 | 0.1539 | 0.6373 | 0.1539 |
| 0.9373 | 2.6409 | 26900 | 0.9550 | 0.0121 | 0.1459 | 0.6270 | 0.1459 |
| 0.9648 | 2.6507 | 27000 | 0.9423 | 0.0121 | 0.1508 | 0.6334 | 0.1508 |
| 0.8716 | 2.6605 | 27100 | 0.9331 | 0.0121 | 0.1546 | 0.6360 | 0.1546 |
| 0.914 | 2.6703 | 27200 | 0.9313 | 0.0121 | 0.1572 | 0.6392 | 0.1572 |
| 0.8962 | 2.6801 | 27300 | 0.9311 | 0.0121 | 0.1584 | 0.6380 | 0.1584 |
| 0.9496 | 2.6900 | 27400 | 0.9347 | 0.0121 | 0.1561 | 0.6393 | 0.1561 |
| 0.9796 | 2.6998 | 27500 | 0.9494 | 0.0121 | 0.1428 | 0.6282 | 0.1428 |
| 0.954 | 2.7096 | 27600 | 0.9220 | 0.0121 | 0.1602 | 0.6412 | 0.1602 |
| 0.9002 | 2.7194 | 27700 | 0.9286 | 0.0121 | 0.1546 | 0.6390 | 0.1546 |
| 0.9716 | 2.7292 | 27800 | 0.9388 | 0.0121 | 0.1562 | 0.6380 | 0.1562 |
| 0.877 | 2.7391 | 27900 | 0.9416 | 0.0121 | 0.1494 | 0.6331 | 0.1494 |
| 0.9771 | 2.7489 | 28000 | 0.9317 | 0.0121 | 0.1544 | 0.6374 | 0.1544 |
| 0.8698 | 2.7587 | 28100 | 0.9433 | 0.0121 | 0.1430 | 0.6325 | 0.1430 |
| 0.8791 | 2.7685 | 28200 | 0.9317 | 0.0121 | 0.1483 | 0.6352 | 0.1483 |
| 0.8812 | 2.7783 | 28300 | 0.9563 | 0.0121 | 0.1488 | 0.6286 | 0.1488 |
| 0.9155 | 2.7881 | 28400 | 0.9240 | 0.0121 | 0.1506 | 0.6374 | 0.1506 |
| 0.9326 | 2.7980 | 28500 | 0.9373 | 0.0121 | 0.1508 | 0.6327 | 0.1508 |
| 0.9114 | 2.8078 | 28600 | 0.9188 | 0.0121 | 0.1610 | 0.6399 | 0.1610 |
| 0.9256 | 2.8176 | 28700 | 0.9220 | 0.0121 | 0.1655 | 0.6426 | 0.1655 |
| 0.9301 | 2.8274 | 28800 | 0.9313 | 0.0121 | 0.1506 | 0.6396 | 0.1506 |
| 0.9878 | 2.8372 | 28900 | 0.9335 | 0.0121 | 0.1517 | 0.6351 | 0.1517 |
| 0.8843 | 2.8470 | 29000 | 0.9219 | 0.0121 | 0.1582 | 0.6426 | 0.1582 |
| 0.8614 | 2.8569 | 29100 | 0.9298 | 0.0121 | 0.1566 | 0.6390 | 0.1566 |
| 0.9368 | 2.8667 | 29200 | 0.9302 | 0.0121 | 0.1501 | 0.6394 | 0.1501 |
| 0.9222 | 2.8765 | 29300 | 0.9263 | 0.0121 | 0.1513 | 0.6389 | 0.1513 |
| 0.8301 | 2.8863 | 29400 | 0.9288 | 0.0121 | 0.1523 | 0.6391 | 0.1523 |
| 0.8956 | 2.8961 | 29500 | 0.9437 | 0.0121 | 0.1512 | 0.6318 | 0.1512 |
| 0.8816 | 2.9059 | 29600 | 0.9209 | 0.0121 | 0.1539 | 0.6405 | 0.1539 |
| 0.9365 | 2.9158 | 29700 | 0.9408 | 0.0121 | 0.1511 | 0.6358 | 0.1511 |
| 0.9062 | 2.9256 | 29800 | 0.9419 | 0.0121 | 0.1447 | 0.6334 | 0.1447 |
| 0.9414 | 2.9354 | 29900 | 0.9470 | 0.0121 | 0.1459 | 0.6344 | 0.1459 |
| 1.0127 | 2.9452 | 30000 | 0.9288 | 0.0121 | 0.1646 | 0.6422 | 0.1646 |
| 0.9034 | 2.9550 | 30100 | 0.9362 | 0.0121 | 0.1586 | 0.6366 | 0.1586 |
| 0.9152 | 2.9649 | 30200 | 0.9244 | 0.0121 | 0.1577 | 0.6372 | 0.1577 |
| 0.9419 | 2.9747 | 30300 | 0.9267 | 0.0121 | 0.1562 | 0.6386 | 0.1562 |
| 0.9524 | 2.9845 | 30400 | 0.9200 | 0.0121 | 0.1539 | 0.6378 | 0.1539 |
| 1.002 | 2.9943 | 30500 | 0.9388 | 0.0121 | 0.1501 | 0.6345 | 0.1501 |
| 1.0183 | 3.0041 | 30600 | 0.9252 | 0.0121 | 0.1542 | 0.6376 | 0.1542 |
| 0.9304 | 3.0139 | 30700 | 0.9251 | 0.0121 | 0.1566 | 0.6404 | 0.1566 |
| 0.9888 | 3.0238 | 30800 | 0.9469 | 0.0121 | 0.1483 | 0.6302 | 0.1483 |
| 1.0105 | 3.0336 | 30900 | 0.9161 | 0.0121 | 0.1588 | 0.6423 | 0.1588 |
| 0.8535 | 3.0434 | 31000 | 0.9255 | 0.0121 | 0.1588 | 0.6418 | 0.1588 |
| 0.9747 | 3.0532 | 31100 | 0.9314 | 0.0121 | 0.1579 | 0.6385 | 0.1579 |
| 0.9358 | 3.0630 | 31200 | 0.9202 | 0.0121 | 0.1541 | 0.6407 | 0.1541 |
| 0.9373 | 3.0728 | 31300 | 0.9098 | 0.0121 | 0.1622 | 0.6454 | 0.1622 |
| 0.9595 | 3.0827 | 31400 | 0.9125 | 0.0121 | 0.1675 | 0.6450 | 0.1675 |
| 0.9259 | 3.0925 | 31500 | 0.9296 | 0.0121 | 0.1528 | 0.6378 | 0.1528 |
| 0.907 | 3.1023 | 31600 | 0.9319 | 0.0121 | 0.1604 | 0.6367 | 0.1604 |
| 0.9075 | 3.1121 | 31700 | 0.9384 | 0.0121 | 0.1566 | 0.6375 | 0.1566 |
| 0.9477 | 3.1219 | 31800 | 0.9312 | 0.0121 | 0.1491 | 0.6361 | 0.1491 |
| 0.9008 | 3.1317 | 31900 | 0.9270 | 0.0121 | 0.1573 | 0.6387 | 0.1573 |
| 0.8841 | 3.1416 | 32000 | 0.9197 | 0.0121 | 0.1574 | 0.6417 | 0.1574 |
| 0.9321 | 3.1514 | 32100 | 0.9275 | 0.0121 | 0.1518 | 0.6385 | 0.1518 |
| 0.975 | 3.1612 | 32200 | 0.9208 | 0.0121 | 0.1526 | 0.6429 | 0.1526 |
| 0.9376 | 3.1710 | 32300 | 0.9322 | 0.0121 | 0.1555 | 0.6395 | 0.1555 |
| 0.9798 | 3.1808 | 32400 | 0.9341 | 0.0121 | 0.1505 | 0.6336 | 0.1505 |
| 0.9683 | 3.1907 | 32500 | 0.9319 | 0.0121 | 0.1446 | 0.6342 | 0.1446 |
| 0.9052 | 3.2005 | 32600 | 0.9151 | 0.0121 | 0.1602 | 0.6426 | 0.1602 |
| 0.9259 | 3.2103 | 32700 | 0.9253 | 0.0121 | 0.1510 | 0.6389 | 0.1510 |
| 0.8892 | 3.2201 | 32800 | 0.9094 | 0.0121 | 0.1595 | 0.6447 | 0.1595 |
| 0.9092 | 3.2299 | 32900 | 0.9113 | 0.0121 | 0.1606 | 0.6431 | 0.1606 |
| 0.9226 | 3.2397 | 33000 | 0.9349 | 0.0121 | 0.1568 | 0.6371 | 0.1568 |
| 0.9054 | 3.2496 | 33100 | 0.9350 | 0.0121 | 0.1559 | 0.6360 | 0.1559 |
| 0.8966 | 3.2594 | 33200 | 0.9317 | 0.0121 | 0.1475 | 0.6367 | 0.1475 |
| 0.9313 | 3.2692 | 33300 | 0.9153 | 0.0121 | 0.1628 | 0.6434 | 0.1628 |
| 0.9739 | 3.2790 | 33400 | 0.9286 | 0.0121 | 0.1539 | 0.6368 | 0.1539 |
| 0.917 | 3.2888 | 33500 | 0.9192 | 0.0121 | 0.1572 | 0.6390 | 0.1572 |
| 0.9198 | 3.2986 | 33600 | 0.9014 | 0.0121 | 0.1684 | 0.6486 | 0.1684 |
| 0.8973 | 3.3085 | 33700 | 0.9252 | 0.0121 | 0.1617 | 0.6419 | 0.1617 |
| 0.9723 | 3.3183 | 33800 | 0.9220 | 0.0121 | 0.1584 | 0.6423 | 0.1584 |
| 0.9152 | 3.3281 | 33900 | 0.9562 | 0.0121 | 0.1345 | 0.6292 | 0.1345 |
| 0.9201 | 3.3379 | 34000 | 0.9187 | 0.0121 | 0.1563 | 0.6411 | 0.1563 |
| 0.8978 | 3.3477 | 34100 | 0.9338 | 0.0121 | 0.1598 | 0.6381 | 0.1598 |
| 0.886 | 3.3575 | 34200 | 0.9305 | 0.0121 | 0.1597 | 0.6390 | 0.1597 |
| 0.9052 | 3.3674 | 34300 | 0.9304 | 0.0121 | 0.1573 | 0.6402 | 0.1573 |
| 0.9958 | 3.3772 | 34400 | 0.9293 | 0.0121 | 0.1573 | 0.6406 | 0.1573 |
| 0.9306 | 3.3870 | 34500 | 0.9252 | 0.0121 | 0.1651 | 0.6403 | 0.1651 |
| 0.8415 | 3.3968 | 34600 | 0.9252 | 0.0121 | 0.1568 | 0.6394 | 0.1568 |
| 0.9207 | 3.4066 | 34700 | 0.9251 | 0.0121 | 0.1546 | 0.6394 | 0.1546 |
| 0.9702 | 3.4165 | 34800 | 0.9296 | 0.0121 | 0.1609 | 0.6383 | 0.1609 |
| 1.0098 | 3.4263 | 34900 | 0.9228 | 0.0121 | 0.1560 | 0.6407 | 0.1560 |
| 0.9492 | 3.4361 | 35000 | 0.9290 | 0.0121 | 0.1572 | 0.6383 | 0.1572 |
| 0.9288 | 3.4459 | 35100 | 0.9511 | 0.0121 | 0.1497 | 0.6310 | 0.1497 |
| 0.8967 | 3.4557 | 35200 | 0.9255 | 0.0121 | 0.1579 | 0.6395 | 0.1579 |
| 0.9164 | 3.4655 | 35300 | 0.9464 | 0.0121 | 0.1541 | 0.6335 | 0.1541 |
| 0.8837 | 3.4754 | 35400 | 0.9199 | 0.0121 | 0.1446 | 0.6367 | 0.1446 |
| 0.9106 | 3.4852 | 35500 | 0.9277 | 0.0121 | 0.1560 | 0.6401 | 0.1560 |
| 0.9453 | 3.4950 | 35600 | 0.9446 | 0.0121 | 0.1459 | 0.6302 | 0.1459 |
| 0.8714 | 3.5048 | 35700 | 0.9027 | 0.0121 | 0.1653 | 0.6483 | 0.1653 |
| 0.9315 | 3.5146 | 35800 | 0.9343 | 0.0121 | 0.1435 | 0.6347 | 0.1435 |
| 0.9 | 3.5244 | 35900 | 0.9182 | 0.0121 | 0.1539 | 0.6415 | 0.1539 |
| 0.9719 | 3.5343 | 36000 | 0.9792 | 0.0121 | 0.1390 | 0.6188 | 0.1390 |
| 0.8949 | 3.5441 | 36100 | 0.9056 | 0.0121 | 0.1644 | 0.6482 | 0.1644 |
| 0.9956 | 3.5539 | 36200 | 0.9319 | 0.0121 | 0.1512 | 0.6358 | 0.1512 |
| 0.885 | 3.5637 | 36300 | 0.9130 | 0.0121 | 0.1649 | 0.6457 | 0.1649 |
| 0.9 | 3.5735 | 36400 | 0.9204 | 0.0121 | 0.1555 | 0.6383 | 0.1555 |
| 0.8741 | 3.5833 | 36500 | 0.9177 | 0.0121 | 0.1567 | 0.6416 | 0.1567 |
| 0.8922 | 3.5932 | 36600 | 0.9259 | 0.0121 | 0.1521 | 0.6368 | 0.1521 |
| 0.9051 | 3.6030 | 36700 | 0.9211 | 0.0121 | 0.1553 | 0.6398 | 0.1553 |
| 1.0028 | 3.6128 | 36800 | 0.9315 | 0.0121 | 0.1573 | 0.6389 | 0.1573 |
| 0.8824 | 3.6226 | 36900 | 0.9306 | 0.0121 | 0.1620 | 0.6402 | 0.1620 |
| 0.839 | 3.6324 | 37000 | 0.9052 | 0.0121 | 0.1654 | 0.6472 | 0.1654 |
| 0.9111 | 3.6423 | 37100 | 0.9097 | 0.0121 | 0.1552 | 0.6444 | 0.1552 |
| 0.8494 | 3.6521 | 37200 | 0.9137 | 0.0121 | 0.1566 | 0.6418 | 0.1566 |
| 0.8726 | 3.6619 | 37300 | 0.8948 | 0.0121 | 0.1622 | 0.6473 | 0.1622 |
| 0.9294 | 3.6717 | 37400 | 0.9186 | 0.0121 | 0.1553 | 0.6425 | 0.1553 |
| 0.9176 | 3.6815 | 37500 | 0.9126 | 0.0121 | 0.1651 | 0.6443 | 0.1651 |
| 0.8867 | 3.6913 | 37600 | 0.9143 | 0.0121 | 0.1583 | 0.6433 | 0.1583 |
| 0.9085 | 3.7012 | 37700 | 0.9157 | 0.0121 | 0.1528 | 0.6409 | 0.1528 |
| 0.944 | 3.7110 | 37800 | 0.9161 | 0.0121 | 0.1613 | 0.6429 | 0.1613 |
| 0.819 | 3.7208 | 37900 | 0.9066 | 0.0121 | 0.1591 | 0.6434 | 0.1591 |
| 0.8937 | 3.7306 | 38000 | 0.9160 | 0.0121 | 0.1661 | 0.6441 | 0.1661 |
| 0.9581 | 3.7404 | 38100 | 0.9193 | 0.0121 | 0.1571 | 0.6414 | 0.1571 |
| 0.958 | 3.7502 | 38200 | 0.9243 | 0.0121 | 0.1554 | 0.6374 | 0.1554 |
| 0.9196 | 3.7601 | 38300 | 0.9255 | 0.0121 | 0.1524 | 0.6414 | 0.1524 |
| 1.0096 | 3.7699 | 38400 | 0.9228 | 0.0121 | 0.1519 | 0.6393 | 0.1519 |
| 0.9651 | 3.7797 | 38500 | 0.9224 | 0.0121 | 0.1539 | 0.6417 | 0.1539 |
| 0.9387 | 3.7895 | 38600 | 0.9258 | 0.0121 | 0.1546 | 0.6371 | 0.1546 |
| 0.9042 | 3.7993 | 38700 | 0.9389 | 0.0121 | 0.1530 | 0.6356 | 0.1530 |
| 0.9249 | 3.8091 | 38800 | 0.9316 | 0.0121 | 0.1480 | 0.6358 | 0.1480 |
| 0.9778 | 3.8190 | 38900 | 0.9109 | 0.0121 | 0.1597 | 0.6426 | 0.1597 |
| 0.9166 | 3.8288 | 39000 | 0.9112 | 0.0121 | 0.1596 | 0.6423 | 0.1596 |
| 0.949 | 3.8386 | 39100 | 0.9153 | 0.0121 | 0.1579 | 0.6423 | 0.1579 |
| 0.982 | 3.8484 | 39200 | 0.9246 | 0.0121 | 0.1504 | 0.6398 | 0.1504 |
| 0.9135 | 3.8582 | 39300 | 0.9201 | 0.0121 | 0.1577 | 0.6407 | 0.1577 |
| 0.9437 | 3.8681 | 39400 | 0.9165 | 0.0121 | 0.1550 | 0.6394 | 0.1550 |
| 0.9937 | 3.8779 | 39500 | 0.8967 | 0.0121 | 0.1657 | 0.6482 | 0.1657 |
| 0.8937 | 3.8877 | 39600 | 0.9084 | 0.0121 | 0.1686 | 0.6486 | 0.1686 |
| 0.901 | 3.8975 | 39700 | 0.9164 | 0.0121 | 0.1579 | 0.6393 | 0.1579 |
| 0.9045 | 3.9073 | 39800 | 0.9013 | 0.0121 | 0.1612 | 0.6472 | 0.1612 |
| 1.0313 | 3.9171 | 39900 | 0.9325 | 0.0121 | 0.1548 | 0.6349 | 0.1548 |
| 1.0183 | 3.9270 | 40000 | 0.9256 | 0.0121 | 0.1557 | 0.6380 | 0.1557 |
| 0.9019 | 3.9368 | 40100 | 0.9097 | 0.0121 | 0.1556 | 0.6420 | 0.1556 |
| 0.9219 | 3.9466 | 40200 | 0.9354 | 0.0121 | 0.1573 | 0.6370 | 0.1573 |
| 0.9101 | 3.9564 | 40300 | 0.9286 | 0.0121 | 0.1589 | 0.6416 | 0.1589 |
| 0.914 | 3.9662 | 40400 | 0.9033 | 0.0121 | 0.1658 | 0.6473 | 0.1658 |
| 0.8996 | 3.9760 | 40500 | 0.9187 | 0.0121 | 0.1578 | 0.6400 | 0.1578 |
| 0.9002 | 3.9859 | 40600 | 0.9055 | 0.0121 | 0.1612 | 0.6459 | 0.1612 |
| 1.0091 | 3.9957 | 40700 | 0.8948 | 0.0121 | 0.1673 | 0.6523 | 0.1673 |
| 0.8874 | 4.0055 | 40800 | 0.9226 | 0.0121 | 0.1566 | 0.6420 | 0.1566 |
| 0.9004 | 4.0153 | 40900 | 0.9081 | 0.0121 | 0.1594 | 0.6424 | 0.1594 |
| 0.962 | 4.0251 | 41000 | 0.9222 | 0.0121 | 0.1615 | 0.6436 | 0.1615 |
| 0.9959 | 4.0349 | 41100 | 0.9125 | 0.0121 | 0.1566 | 0.6438 | 0.1566 |
| 0.8541 | 4.0448 | 41200 | 0.9039 | 0.0121 | 0.1675 | 0.6464 | 0.1675 |
| 0.8316 | 4.0546 | 41300 | 0.9225 | 0.0121 | 0.1498 | 0.6367 | 0.1498 |
| 0.8869 | 4.0644 | 41400 | 0.9282 | 0.0121 | 0.1478 | 0.6346 | 0.1478 |
| 0.9328 | 4.0742 | 41500 | 0.9237 | 0.0121 | 0.1632 | 0.6409 | 0.1632 |
| 0.8534 | 4.0840 | 41600 | 0.8998 | 0.0121 | 0.1693 | 0.6488 | 0.1693 |
| 1.002 | 4.0939 | 41700 | 0.9398 | 0.0121 | 0.1501 | 0.6356 | 0.1501 |
| 0.8411 | 4.1037 | 41800 | 0.9140 | 0.0121 | 0.1604 | 0.6425 | 0.1604 |
| 0.8828 | 4.1135 | 41900 | 0.8929 | 0.0121 | 0.1653 | 0.6490 | 0.1653 |
| 0.8654 | 4.1233 | 42000 | 0.9065 | 0.0121 | 0.1702 | 0.6482 | 0.1702 |
| 0.8602 | 4.1331 | 42100 | 0.9147 | 0.0121 | 0.1546 | 0.6404 | 0.1546 |
| 0.8268 | 4.1429 | 42200 | 0.9238 | 0.0121 | 0.1540 | 0.6402 | 0.1540 |
| 0.9743 | 4.1528 | 42300 | 0.9081 | 0.0121 | 0.1629 | 0.6435 | 0.1629 |
| 0.9224 | 4.1626 | 42400 | 0.9007 | 0.0121 | 0.1673 | 0.6473 | 0.1673 |
| 0.9082 | 4.1724 | 42500 | 0.9077 | 0.0121 | 0.1610 | 0.6457 | 0.1610 |
| 0.9227 | 4.1822 | 42600 | 0.9160 | 0.0121 | 0.1595 | 0.6404 | 0.1595 |
| 0.925 | 4.1920 | 42700 | 0.9133 | 0.0121 | 0.1625 | 0.6450 | 0.1625 |
| 0.9158 | 4.2018 | 42800 | 0.8978 | 0.0121 | 0.1539 | 0.6438 | 0.1539 |
| 0.96 | 4.2117 | 42900 | 0.9021 | 0.0121 | 0.1567 | 0.6459 | 0.1567 |
| 0.8507 | 4.2215 | 43000 | 0.8975 | 0.0121 | 0.1681 | 0.6522 | 0.1681 |
| 0.9323 | 4.2313 | 43100 | 0.8988 | 0.0121 | 0.1686 | 0.6495 | 0.1686 |
| 0.9649 | 4.2411 | 43200 | 0.9099 | 0.0121 | 0.1672 | 0.6460 | 0.1672 |
| 0.826 | 4.2509 | 43300 | 0.9070 | 0.0121 | 0.1641 | 0.6456 | 0.1641 |
| 0.8683 | 4.2608 | 43400 | 0.9204 | 0.0121 | 0.1648 | 0.6445 | 0.1648 |
| 0.9506 | 4.2706 | 43500 | 0.8995 | 0.0121 | 0.1723 | 0.6487 | 0.1723 |
| 0.9039 | 4.2804 | 43600 | 0.9058 | 0.0121 | 0.1627 | 0.6459 | 0.1627 |
| 0.9048 | 4.2902 | 43700 | 0.8986 | 0.0121 | 0.1596 | 0.6452 | 0.1596 |
| 0.9044 | 4.3000 | 43800 | 0.9013 | 0.0121 | 0.1676 | 0.6490 | 0.1676 |
| 0.9858 | 4.3098 | 43900 | 0.9035 | 0.0121 | 0.1615 | 0.6445 | 0.1615 |
| 0.8395 | 4.3197 | 44000 | 0.9093 | 0.0121 | 0.1624 | 0.6480 | 0.1624 |
| 0.8611 | 4.3295 | 44100 | 0.9118 | 0.0121 | 0.1598 | 0.6417 | 0.1598 |
| 0.8509 | 4.3393 | 44200 | 0.9076 | 0.0121 | 0.1645 | 0.6467 | 0.1645 |
| 0.9728 | 4.3491 | 44300 | 0.9200 | 0.0121 | 0.1575 | 0.6418 | 0.1575 |
| 0.9003 | 4.3589 | 44400 | 0.9100 | 0.0121 | 0.1624 | 0.6437 | 0.1624 |
| 0.9193 | 4.3687 | 44500 | 0.9107 | 0.0121 | 0.1599 | 0.6457 | 0.1599 |
| 0.9026 | 4.3786 | 44600 | 0.9110 | 0.0121 | 0.1590 | 0.6444 | 0.1590 |
| 0.9528 | 4.3884 | 44700 | 0.9107 | 0.0121 | 0.1622 | 0.6426 | 0.1622 |
| 0.9313 | 4.3982 | 44800 | 0.9001 | 0.0121 | 0.1637 | 0.6475 | 0.1637 |
| 0.9124 | 4.4080 | 44900 | 0.9170 | 0.0121 | 0.1636 | 0.6408 | 0.1636 |
| 0.8478 | 4.4178 | 45000 | 0.9140 | 0.0121 | 0.1650 | 0.6415 | 0.1650 |
| 0.9162 | 4.4276 | 45100 | 0.9020 | 0.0121 | 0.1633 | 0.6450 | 0.1633 |
| 0.8447 | 4.4375 | 45200 | 0.9037 | 0.0121 | 0.1642 | 0.6461 | 0.1642 |
| 0.9101 | 4.4473 | 45300 | 0.8941 | 0.0121 | 0.1657 | 0.6455 | 0.1657 |
| 0.9524 | 4.4571 | 45400 | 0.9030 | 0.0121 | 0.1695 | 0.6466 | 0.1695 |
| 0.903 | 4.4669 | 45500 | 0.9293 | 0.0121 | 0.1484 | 0.6388 | 0.1484 |
| 0.889 | 4.4767 | 45600 | 0.9204 | 0.0121 | 0.1528 | 0.6379 | 0.1528 |
| 0.8872 | 4.4866 | 45700 | 0.9075 | 0.0121 | 0.1577 | 0.6472 | 0.1577 |
| 0.929 | 4.4964 | 45800 | 0.9106 | 0.0121 | 0.1572 | 0.6418 | 0.1572 |
| 0.9765 | 4.5062 | 45900 | 0.9218 | 0.0121 | 0.1510 | 0.6383 | 0.1510 |
| 0.9421 | 4.5160 | 46000 | 0.9214 | 0.0121 | 0.1549 | 0.6389 | 0.1549 |
| 0.926 | 4.5258 | 46100 | 0.9083 | 0.0121 | 0.1651 | 0.6468 | 0.1651 |
| 0.875 | 4.5356 | 46200 | 0.9061 | 0.0121 | 0.1631 | 0.6459 | 0.1631 |
| 0.9123 | 4.5455 | 46300 | 0.9016 | 0.0121 | 0.1577 | 0.6463 | 0.1577 |
| 0.8782 | 4.5553 | 46400 | 0.8928 | 0.0121 | 0.1677 | 0.6507 | 0.1677 |
| 0.9383 | 4.5651 | 46500 | 0.9129 | 0.0121 | 0.1659 | 0.6448 | 0.1659 |
| 0.9099 | 4.5749 | 46600 | 0.9179 | 0.0121 | 0.1541 | 0.6394 | 0.1541 |
| 0.8764 | 4.5847 | 46700 | 0.9009 | 0.0121 | 0.1708 | 0.6477 | 0.1708 |
| 0.8533 | 4.5945 | 46800 | 0.9146 | 0.0121 | 0.1622 | 0.6450 | 0.1622 |
| 0.9333 | 4.6044 | 46900 | 0.8864 | 0.0121 | 0.1672 | 0.6512 | 0.1672 |
| 0.8858 | 4.6142 | 47000 | 0.9083 | 0.0121 | 0.1586 | 0.6458 | 0.1586 |
| 0.9595 | 4.6240 | 47100 | 0.9188 | 0.0121 | 0.1544 | 0.6387 | 0.1544 |
| 0.8651 | 4.6338 | 47200 | 0.9047 | 0.0121 | 0.1660 | 0.6489 | 0.1660 |
| 0.9615 | 4.6436 | 47300 | 0.8948 | 0.0121 | 0.1675 | 0.6491 | 0.1675 |
| 0.8732 | 4.6534 | 47400 | 0.9062 | 0.0121 | 0.1603 | 0.6438 | 0.1603 |
| 0.8855 | 4.6633 | 47500 | 0.9121 | 0.0121 | 0.1647 | 0.6429 | 0.1647 |
| 0.9367 | 4.6731 | 47600 | 0.9005 | 0.0121 | 0.1595 | 0.6475 | 0.1595 |
| 0.8297 | 4.6829 | 47700 | 0.9032 | 0.0121 | 0.1616 | 0.6448 | 0.1616 |
| 0.7984 | 4.6927 | 47800 | 0.9010 | 0.0121 | 0.1673 | 0.6477 | 0.1673 |
| 0.8957 | 4.7025 | 47900 | 0.8924 | 0.0121 | 0.1605 | 0.6480 | 0.1605 |
| 0.9367 | 4.7124 | 48000 | 0.8977 | 0.0121 | 0.1657 | 0.6475 | 0.1657 |
| 0.8833 | 4.7222 | 48100 | 0.9226 | 0.0121 | 0.1606 | 0.6431 | 0.1606 |
| 0.9096 | 4.7320 | 48200 | 0.9154 | 0.0121 | 0.1603 | 0.6420 | 0.1603 |
| 0.9061 | 4.7418 | 48300 | 0.8940 | 0.0121 | 0.1672 | 0.6503 | 0.1672 |
| 0.9153 | 4.7516 | 48400 | 0.9089 | 0.0121 | 0.1591 | 0.6443 | 0.1591 |
| 0.9201 | 4.7614 | 48500 | 0.9072 | 0.0121 | 0.1597 | 0.6431 | 0.1597 |
| 0.8633 | 4.7713 | 48600 | 0.9093 | 0.0121 | 0.1554 | 0.6425 | 0.1554 |
| 0.825 | 4.7811 | 48700 | 0.8990 | 0.0121 | 0.1708 | 0.6501 | 0.1708 |
| 0.9 | 4.7909 | 48800 | 0.9117 | 0.0121 | 0.1616 | 0.6446 | 0.1616 |
| 0.8629 | 4.8007 | 48900 | 0.8871 | 0.0121 | 0.1724 | 0.6538 | 0.1724 |
| 0.9299 | 4.8105 | 49000 | 0.9120 | 0.0121 | 0.1512 | 0.6406 | 0.1512 |
| 0.9157 | 4.8203 | 49100 | 0.9028 | 0.0121 | 0.1703 | 0.6484 | 0.1703 |
| 0.8361 | 4.8302 | 49200 | 0.9110 | 0.0121 | 0.1613 | 0.6455 | 0.1613 |
| 0.9751 | 4.8400 | 49300 | 0.9383 | 0.0121 | 0.1497 | 0.6332 | 0.1497 |
| 0.8713 | 4.8498 | 49400 | 0.9014 | 0.0121 | 0.1687 | 0.6472 | 0.1687 |
| 0.9002 | 4.8596 | 49500 | 0.9264 | 0.0121 | 0.1531 | 0.6391 | 0.1531 |
| 0.8755 | 4.8694 | 49600 | 0.9032 | 0.0121 | 0.1627 | 0.6473 | 0.1627 |
| 0.9272 | 4.8792 | 49700 | 0.9072 | 0.0121 | 0.1623 | 0.6450 | 0.1623 |
| 0.884 | 4.8891 | 49800 | 0.9052 | 0.0121 | 0.1593 | 0.6451 | 0.1593 |
| 0.8862 | 4.8989 | 49900 | 0.9035 | 0.0121 | 0.1635 | 0.6501 | 0.1635 |
| 0.9846 | 4.9087 | 50000 | 0.8958 | 0.0121 | 0.1740 | 0.6529 | 0.1740 |
| 0.8923 | 4.9185 | 50100 | 0.8934 | 0.0121 | 0.1716 | 0.6501 | 0.1716 |
| 0.8942 | 4.9283 | 50200 | 0.8920 | 0.0121 | 0.1627 | 0.6473 | 0.1627 |
| 0.91 | 4.9382 | 50300 | 0.9024 | 0.0121 | 0.1579 | 0.6461 | 0.1579 |
| 0.9646 | 4.9480 | 50400 | 0.8986 | 0.0121 | 0.1604 | 0.6477 | 0.1604 |
| 0.8794 | 4.9578 | 50500 | 0.9007 | 0.0121 | 0.1663 | 0.6498 | 0.1663 |
| 0.8963 | 4.9676 | 50600 | 0.9015 | 0.0121 | 0.1631 | 0.6477 | 0.1631 |
| 0.8735 | 4.9774 | 50700 | 0.9166 | 0.0121 | 0.1577 | 0.6401 | 0.1577 |
| 0.86 | 4.9872 | 50800 | 0.8967 | 0.0121 | 0.1637 | 0.6486 | 0.1637 |
| 0.8749 | 4.9971 | 50900 | 0.9178 | 0.0121 | 0.1564 | 0.6424 | 0.1564 |
| 0.8572 | 5.0069 | 51000 | 0.9051 | 0.0121 | 0.1619 | 0.6443 | 0.1619 |
| 0.8619 | 5.0167 | 51100 | 0.9050 | 0.0121 | 0.1655 | 0.6450 | 0.1655 |
| 0.9452 | 5.0265 | 51200 | 0.9003 | 0.0121 | 0.1661 | 0.6489 | 0.1661 |
| 0.9752 | 5.0363 | 51300 | 0.9262 | 0.0121 | 0.1496 | 0.6386 | 0.1496 |
| 0.9512 | 5.0461 | 51400 | 0.9059 | 0.0121 | 0.1627 | 0.6450 | 0.1627 |
| 0.9348 | 5.0560 | 51500 | 0.8991 | 0.0121 | 0.1683 | 0.6502 | 0.1683 |
| 0.8832 | 5.0658 | 51600 | 0.9097 | 0.0121 | 0.1703 | 0.6475 | 0.1703 |
| 0.859 | 5.0756 | 51700 | 0.8906 | 0.0121 | 0.1663 | 0.6481 | 0.1663 |
| 0.9556 | 5.0854 | 51800 | 0.8922 | 0.0121 | 0.1673 | 0.6503 | 0.1673 |
| 0.8665 | 5.0952 | 51900 | 0.8930 | 0.0121 | 0.1617 | 0.6467 | 0.1617 |
| 0.8041 | 5.1050 | 52000 | 0.9068 | 0.0121 | 0.1624 | 0.6480 | 0.1624 |
| 0.9299 | 5.1149 | 52100 | 0.9135 | 0.0121 | 0.1596 | 0.6399 | 0.1596 |
| 0.8976 | 5.1247 | 52200 | 0.9117 | 0.0121 | 0.1616 | 0.6433 | 0.1616 |
| 0.8793 | 5.1345 | 52300 | 0.8878 | 0.0121 | 0.1677 | 0.6520 | 0.1677 |
| 0.9637 | 5.1443 | 52400 | 0.9007 | 0.0121 | 0.1606 | 0.6467 | 0.1606 |
| 0.8663 | 5.1541 | 52500 | 0.9148 | 0.0121 | 0.1628 | 0.6436 | 0.1628 |
| 0.9043 | 5.1640 | 52600 | 0.8960 | 0.0121 | 0.1733 | 0.6521 | 0.1733 |
| 0.9171 | 5.1738 | 52700 | 0.9052 | 0.0121 | 0.1614 | 0.6452 | 0.1614 |
| 0.9416 | 5.1836 | 52800 | 0.8995 | 0.0121 | 0.1696 | 0.6510 | 0.1696 |
| 0.8467 | 5.1934 | 52900 | 0.8946 | 0.0121 | 0.1689 | 0.6497 | 0.1689 |
| 0.94 | 5.2032 | 53000 | 0.9179 | 0.0121 | 0.1648 | 0.6414 | 0.1648 |
| 0.917 | 5.2130 | 53100 | 0.8858 | 0.0121 | 0.1670 | 0.6532 | 0.1670 |
| 0.891 | 5.2229 | 53200 | 0.9270 | 0.0121 | 0.1528 | 0.6374 | 0.1528 |
| 0.9123 | 5.2327 | 53300 | 0.9126 | 0.0121 | 0.1534 | 0.6422 | 0.1534 |
| 0.8681 | 5.2425 | 53400 | 0.9152 | 0.0121 | 0.1572 | 0.6426 | 0.1572 |
| 0.8683 | 5.2523 | 53500 | 0.9346 | 0.0121 | 0.1537 | 0.6355 | 0.1537 |
| 0.9497 | 5.2621 | 53600 | 0.9204 | 0.0121 | 0.1525 | 0.6362 | 0.1525 |
| 0.8694 | 5.2719 | 53700 | 0.9116 | 0.0121 | 0.1568 | 0.6435 | 0.1568 |
| 0.9946 | 5.2818 | 53800 | 0.8966 | 0.0121 | 0.1611 | 0.6457 | 0.1611 |
| 0.8512 | 5.2916 | 53900 | 0.8941 | 0.0121 | 0.1626 | 0.6463 | 0.1626 |
| 0.8805 | 5.3014 | 54000 | 0.9016 | 0.0121 | 0.1675 | 0.6466 | 0.1675 |
| 0.8873 | 5.3112 | 54100 | 0.8944 | 0.0121 | 0.1684 | 0.6478 | 0.1684 |
| 0.973 | 5.3210 | 54200 | 0.8996 | 0.0121 | 0.1655 | 0.6483 | 0.1655 |
| 0.8152 | 5.3308 | 54300 | 0.9010 | 0.0121 | 0.1714 | 0.6501 | 0.1714 |
| 0.9256 | 5.3407 | 54400 | 0.8908 | 0.0121 | 0.1646 | 0.6506 | 0.1646 |
| 0.9047 | 5.3505 | 54500 | 0.9029 | 0.0121 | 0.1606 | 0.6422 | 0.1606 |
| 0.8359 | 5.3603 | 54600 | 0.9361 | 0.0121 | 0.1551 | 0.6387 | 0.1551 |
| 0.967 | 5.3701 | 54700 | 0.9100 | 0.0121 | 0.1651 | 0.6453 | 0.1651 |
| 0.9209 | 5.3799 | 54800 | 0.8941 | 0.0121 | 0.1653 | 0.6501 | 0.1653 |
| 0.8872 | 5.3898 | 54900 | 0.8784 | 0.0121 | 0.1683 | 0.6546 | 0.1683 |
| 0.8653 | 5.3996 | 55000 | 0.9149 | 0.0121 | 0.1584 | 0.6417 | 0.1584 |
| 0.9349 | 5.4094 | 55100 | 0.8910 | 0.0121 | 0.1704 | 0.6518 | 0.1704 |
| 0.8506 | 5.4192 | 55200 | 0.8923 | 0.0121 | 0.1713 | 0.6518 | 0.1713 |
| 0.9151 | 5.4290 | 55300 | 0.9275 | 0.0121 | 0.1561 | 0.6383 | 0.1561 |
| 0.8983 | 5.4388 | 55400 | 0.8984 | 0.0121 | 0.1677 | 0.6493 | 0.1677 |
| 0.9229 | 5.4487 | 55500 | 0.8860 | 0.0121 | 0.1708 | 0.6523 | 0.1708 |
| 0.9612 | 5.4585 | 55600 | 0.8972 | 0.0121 | 0.1700 | 0.6480 | 0.1700 |
| 0.9427 | 5.4683 | 55700 | 0.9043 | 0.0121 | 0.1622 | 0.6479 | 0.1622 |
| 0.9168 | 5.4781 | 55800 | 0.9019 | 0.0121 | 0.1667 | 0.6470 | 0.1667 |
| 0.878 | 5.4879 | 55900 | 0.9124 | 0.0121 | 0.1611 | 0.6463 | 0.1611 |
| 0.9137 | 5.4977 | 56000 | 0.9074 | 0.0121 | 0.1610 | 0.6458 | 0.1610 |
| 0.8934 | 5.5076 | 56100 | 0.9205 | 0.0121 | 0.1574 | 0.6446 | 0.1574 |
| 0.8924 | 5.5174 | 56200 | 0.9048 | 0.0121 | 0.1652 | 0.6462 | 0.1652 |
| 0.8633 | 5.5272 | 56300 | 0.8943 | 0.0121 | 0.1682 | 0.6476 | 0.1682 |
| 0.8871 | 5.5370 | 56400 | 0.8909 | 0.0121 | 0.1684 | 0.6520 | 0.1684 |
| 0.8729 | 5.5468 | 56500 | 0.8900 | 0.0121 | 0.1708 | 0.6502 | 0.1708 |
| 0.9497 | 5.5566 | 56600 | 0.9064 | 0.0121 | 0.1584 | 0.6438 | 0.1584 |
| 0.8594 | 5.5665 | 56700 | 0.8851 | 0.0121 | 0.1692 | 0.6524 | 0.1692 |
| 0.9684 | 5.5763 | 56800 | 0.8993 | 0.0121 | 0.1583 | 0.6465 | 0.1583 |
| 0.8726 | 5.5861 | 56900 | 0.8977 | 0.0121 | 0.1657 | 0.6488 | 0.1657 |
| 0.8668 | 5.5959 | 57000 | 0.8910 | 0.0121 | 0.1669 | 0.6498 | 0.1669 |
| 0.8763 | 5.6057 | 57100 | 0.8903 | 0.0121 | 0.1644 | 0.6519 | 0.1644 |
| 0.8591 | 5.6156 | 57200 | 0.8960 | 0.0121 | 0.1682 | 0.6497 | 0.1682 |
| 0.9064 | 5.6254 | 57300 | 0.9095 | 0.0121 | 0.1657 | 0.6467 | 0.1657 |
| 0.9166 | 5.6352 | 57400 | 0.9030 | 0.0121 | 0.1637 | 0.6473 | 0.1637 |
| 0.8296 | 5.6450 | 57500 | 0.8968 | 0.0121 | 0.1619 | 0.6486 | 0.1619 |
| 0.8507 | 5.6548 | 57600 | 0.9010 | 0.0121 | 0.1597 | 0.6465 | 0.1597 |
| 0.9312 | 5.6646 | 57700 | 0.8972 | 0.0121 | 0.1596 | 0.6445 | 0.1596 |
| 0.8648 | 5.6745 | 57800 | 0.8814 | 0.0121 | 0.1690 | 0.6539 | 0.1690 |
| 0.8798 | 5.6843 | 57900 | 0.8859 | 0.0121 | 0.1638 | 0.6538 | 0.1638 |
| 0.8728 | 5.6941 | 58000 | 0.8916 | 0.0121 | 0.1705 | 0.6527 | 0.1705 |
| 0.899 | 5.7039 | 58100 | 0.8994 | 0.0121 | 0.1676 | 0.6479 | 0.1676 |
| 0.8982 | 5.7137 | 58200 | 0.8926 | 0.0121 | 0.1727 | 0.6530 | 0.1727 |
| 0.928 | 5.7235 | 58300 | 0.9125 | 0.0121 | 0.1718 | 0.6457 | 0.1718 |
| 0.9265 | 5.7334 | 58400 | 0.9196 | 0.0121 | 0.1556 | 0.6383 | 0.1556 |
| 0.8782 | 5.7432 | 58500 | 0.8883 | 0.0121 | 0.1695 | 0.6519 | 0.1695 |
| 0.9272 | 5.7530 | 58600 | 0.9011 | 0.0121 | 0.1653 | 0.6492 | 0.1653 |
| 0.8898 | 5.7628 | 58700 | 0.9017 | 0.0121 | 0.1722 | 0.6504 | 0.1722 |
| 0.8858 | 5.7726 | 58800 | 0.8997 | 0.0121 | 0.1616 | 0.6476 | 0.1616 |
| 0.9103 | 5.7824 | 58900 | 0.9088 | 0.0121 | 0.1587 | 0.6456 | 0.1587 |
| 0.8883 | 5.7923 | 59000 | 0.8969 | 0.0121 | 0.1670 | 0.6484 | 0.1670 |
| 0.8947 | 5.8021 | 59100 | 0.9202 | 0.0121 | 0.1597 | 0.6420 | 0.1597 |
| 0.9053 | 5.8119 | 59200 | 0.8866 | 0.0121 | 0.1706 | 0.6516 | 0.1706 |
| 0.8692 | 5.8217 | 59300 | 0.8748 | 0.0121 | 0.1667 | 0.6529 | 0.1667 |
| 0.8223 | 5.8315 | 59400 | 0.8913 | 0.0121 | 0.1698 | 0.6524 | 0.1698 |
| 0.9592 | 5.8414 | 59500 | 0.8905 | 0.0121 | 0.1681 | 0.6524 | 0.1681 |
| 0.8031 | 5.8512 | 59600 | 0.9016 | 0.0121 | 0.1663 | 0.6488 | 0.1663 |
| 0.9475 | 5.8610 | 59700 | 0.9167 | 0.0121 | 0.1592 | 0.6442 | 0.1592 |
| 0.9078 | 5.8708 | 59800 | 0.9012 | 0.0121 | 0.1726 | 0.6480 | 0.1726 |
| 0.9446 | 5.8806 | 59900 | 0.8916 | 0.0121 | 0.1646 | 0.6524 | 0.1646 |
| 0.8724 | 5.8904 | 60000 | 0.8824 | 0.0121 | 0.1662 | 0.6545 | 0.1662 |
| 0.9384 | 5.9003 | 60100 | 0.9172 | 0.0121 | 0.1527 | 0.6384 | 0.1527 |
| 0.9091 | 5.9101 | 60200 | 0.8846 | 0.0121 | 0.1711 | 0.6554 | 0.1711 |
| 0.8407 | 5.9199 | 60300 | 0.9147 | 0.0121 | 0.1617 | 0.6450 | 0.1617 |
| 0.9015 | 5.9297 | 60400 | 0.8986 | 0.0121 | 0.1642 | 0.6510 | 0.1642 |
| 0.8919 | 5.9395 | 60500 | 0.8881 | 0.0121 | 0.1722 | 0.6528 | 0.1722 |
| 0.9051 | 5.9493 | 60600 | 0.9188 | 0.0121 | 0.1533 | 0.6386 | 0.1533 |
| 0.9186 | 5.9592 | 60700 | 0.8916 | 0.0121 | 0.1648 | 0.6498 | 0.1648 |
| 0.8539 | 5.9690 | 60800 | 0.8972 | 0.0121 | 0.1654 | 0.6504 | 0.1654 |
| 0.9481 | 5.9788 | 60900 | 0.8835 | 0.0121 | 0.1684 | 0.6534 | 0.1684 |
| 0.8576 | 5.9886 | 61000 | 0.8902 | 0.0121 | 0.1642 | 0.6499 | 0.1642 |
| 0.9714 | 5.9984 | 61100 | 0.8893 | 0.0121 | 0.1660 | 0.6517 | 0.1660 |
| 0.8351 | 6.0082 | 61200 | 0.9071 | 0.0121 | 0.1677 | 0.6478 | 0.1677 |
| 0.7786 | 6.0181 | 61300 | 0.9043 | 0.0121 | 0.1700 | 0.6483 | 0.1700 |
| 0.823 | 6.0279 | 61400 | 0.9296 | 0.0121 | 0.1618 | 0.6405 | 0.1618 |
| 0.9533 | 6.0377 | 61500 | 0.8938 | 0.0121 | 0.1657 | 0.6495 | 0.1657 |
| 0.8797 | 6.0475 | 61600 | 0.8994 | 0.0121 | 0.1642 | 0.6498 | 0.1642 |
| 0.9222 | 6.0573 | 61700 | 0.9007 | 0.0121 | 0.1719 | 0.6535 | 0.1719 |
| 0.7826 | 6.0672 | 61800 | 0.8861 | 0.0121 | 0.1749 | 0.6539 | 0.1749 |
| 0.9418 | 6.0770 | 61900 | 0.8965 | 0.0121 | 0.1626 | 0.6475 | 0.1626 |
| 0.9099 | 6.0868 | 62000 | 0.9134 | 0.0121 | 0.1564 | 0.6417 | 0.1564 |
| 0.8789 | 6.0966 | 62100 | 0.8873 | 0.0121 | 0.1643 | 0.6523 | 0.1643 |
| 0.91 | 6.1064 | 62200 | 0.8909 | 0.0121 | 0.1707 | 0.6511 | 0.1707 |
| 0.8766 | 6.1162 | 62300 | 0.8964 | 0.0121 | 0.1619 | 0.6493 | 0.1619 |
| 0.8721 | 6.1261 | 62400 | 0.8966 | 0.0121 | 0.1740 | 0.6512 | 0.1740 |
| 0.8653 | 6.1359 | 62500 | 0.8980 | 0.0121 | 0.1653 | 0.6514 | 0.1653 |
| 0.9314 | 6.1457 | 62600 | 0.9137 | 0.0121 | 0.1627 | 0.6460 | 0.1627 |
| 0.8731 | 6.1555 | 62700 | 0.8799 | 0.0121 | 0.1684 | 0.6551 | 0.1684 |
| 0.9052 | 6.1653 | 62800 | 0.8863 | 0.0121 | 0.1667 | 0.6509 | 0.1667 |
| 0.8165 | 6.1751 | 62900 | 0.9010 | 0.0121 | 0.1633 | 0.6483 | 0.1633 |
| 0.8366 | 6.1850 | 63000 | 0.8970 | 0.0121 | 0.1651 | 0.6496 | 0.1651 |
| 0.9723 | 6.1948 | 63100 | 0.8961 | 0.0121 | 0.1668 | 0.6486 | 0.1668 |
| 0.9703 | 6.2046 | 63200 | 0.9108 | 0.0121 | 0.1612 | 0.6446 | 0.1612 |
| 0.8922 | 6.2144 | 63300 | 0.8987 | 0.0121 | 0.1645 | 0.6480 | 0.1645 |
| 0.8852 | 6.2242 | 63400 | 0.9052 | 0.0121 | 0.1626 | 0.6433 | 0.1626 |
| 0.8619 | 6.2340 | 63500 | 0.9053 | 0.0121 | 0.1679 | 0.6471 | 0.1679 |
| 0.898 | 6.2439 | 63600 | 0.8993 | 0.0121 | 0.1582 | 0.6470 | 0.1582 |
| 0.87 | 6.2537 | 63700 | 0.8971 | 0.0121 | 0.1662 | 0.6504 | 0.1662 |
| 0.911 | 6.2635 | 63800 | 0.8920 | 0.0121 | 0.1675 | 0.6509 | 0.1675 |
| 0.8969 | 6.2733 | 63900 | 0.8862 | 0.0121 | 0.1651 | 0.6520 | 0.1651 |
| 0.9455 | 6.2831 | 64000 | 0.8855 | 0.0121 | 0.1700 | 0.6533 | 0.1700 |
| 0.866 | 6.2930 | 64100 | 0.9047 | 0.0121 | 0.1628 | 0.6458 | 0.1628 |
| 0.8634 | 6.3028 | 64200 | 0.8756 | 0.0121 | 0.1696 | 0.6544 | 0.1696 |
| 0.9044 | 6.3126 | 64300 | 0.8910 | 0.0121 | 0.1709 | 0.6531 | 0.1709 |
| 0.9051 | 6.3224 | 64400 | 0.8884 | 0.0121 | 0.1680 | 0.6546 | 0.1680 |
| 0.8775 | 6.3322 | 64500 | 0.9103 | 0.0121 | 0.1570 | 0.6431 | 0.1570 |
| 0.9304 | 6.3420 | 64600 | 0.8946 | 0.0121 | 0.1631 | 0.6519 | 0.1631 |
| 0.8777 | 6.3519 | 64700 | 0.8930 | 0.0121 | 0.1647 | 0.6493 | 0.1647 |
| 0.8525 | 6.3617 | 64800 | 0.9022 | 0.0121 | 0.1663 | 0.6477 | 0.1663 |
| 0.8442 | 6.3715 | 64900 | 0.8868 | 0.0121 | 0.1699 | 0.6520 | 0.1699 |
| 0.8797 | 6.3813 | 65000 | 0.8790 | 0.0121 | 0.1724 | 0.6544 | 0.1724 |
| 0.8941 | 6.3911 | 65100 | 0.8873 | 0.0121 | 0.1769 | 0.6536 | 0.1769 |
| 0.9563 | 6.4009 | 65200 | 0.8859 | 0.0121 | 0.1707 | 0.6523 | 0.1707 |
| 0.9128 | 6.4108 | 65300 | 0.9141 | 0.0121 | 0.1564 | 0.6401 | 0.1564 |
| 0.8711 | 6.4206 | 65400 | 0.8837 | 0.0121 | 0.1682 | 0.6514 | 0.1682 |
| 0.956 | 6.4304 | 65500 | 0.8755 | 0.0121 | 0.1747 | 0.6565 | 0.1747 |
| 0.9201 | 6.4402 | 65600 | 0.8956 | 0.0121 | 0.1689 | 0.6499 | 0.1689 |
| 0.953 | 6.4500 | 65700 | 0.8916 | 0.0121 | 0.1662 | 0.6510 | 0.1662 |
| 0.8706 | 6.4598 | 65800 | 0.8956 | 0.0121 | 0.1605 | 0.6458 | 0.1605 |
| 0.9539 | 6.4697 | 65900 | 0.9331 | 0.0121 | 0.1551 | 0.6393 | 0.1551 |
| 0.8699 | 6.4795 | 66000 | 0.8920 | 0.0121 | 0.1689 | 0.6513 | 0.1689 |
| 0.9043 | 6.4893 | 66100 | 0.9007 | 0.0121 | 0.1563 | 0.6450 | 0.1563 |
| 0.88 | 6.4991 | 66200 | 0.8906 | 0.0121 | 0.1746 | 0.6507 | 0.1746 |
| 0.9186 | 6.5089 | 66300 | 0.9041 | 0.0121 | 0.1573 | 0.6465 | 0.1573 |
| 0.8728 | 6.5188 | 66400 | 0.8923 | 0.0121 | 0.1605 | 0.6471 | 0.1605 |
| 0.9381 | 6.5286 | 66500 | 0.8779 | 0.0121 | 0.1731 | 0.6594 | 0.1731 |
| 0.8294 | 6.5384 | 66600 | 0.9043 | 0.0121 | 0.1570 | 0.6442 | 0.1570 |
| 0.9222 | 6.5482 | 66700 | 0.9021 | 0.0121 | 0.1624 | 0.6488 | 0.1624 |
| 0.8413 | 6.5580 | 66800 | 0.9031 | 0.0121 | 0.1619 | 0.6489 | 0.1619 |
| 0.9295 | 6.5678 | 66900 | 0.8977 | 0.0121 | 0.1595 | 0.6482 | 0.1595 |
| 0.8932 | 6.5777 | 67000 | 0.8765 | 0.0121 | 0.1776 | 0.6584 | 0.1776 |
| 0.9345 | 6.5875 | 67100 | 0.8952 | 0.0121 | 0.1643 | 0.6517 | 0.1643 |
| 0.8763 | 6.5973 | 67200 | 0.8965 | 0.0121 | 0.1670 | 0.6519 | 0.1670 |
| 0.9188 | 6.6071 | 67300 | 0.9477 | 0.0121 | 0.1480 | 0.6334 | 0.1480 |
| 0.8674 | 6.6169 | 67400 | 0.9052 | 0.0121 | 0.1640 | 0.6449 | 0.1640 |
| 0.9324 | 6.6267 | 67500 | 0.8874 | 0.0121 | 0.1631 | 0.6515 | 0.1631 |
| 0.8973 | 6.6366 | 67600 | 0.9005 | 0.0121 | 0.1682 | 0.6465 | 0.1682 |
| 0.9835 | 6.6464 | 67700 | 0.8900 | 0.0121 | 0.1650 | 0.6498 | 0.1650 |
| 0.8465 | 6.6562 | 67800 | 0.8835 | 0.0121 | 0.1741 | 0.6527 | 0.1741 |
| 0.8645 | 6.6660 | 67900 | 0.8923 | 0.0121 | 0.1731 | 0.6512 | 0.1731 |
| 0.8838 | 6.6758 | 68000 | 0.8957 | 0.0121 | 0.1645 | 0.6486 | 0.1645 |
| 0.8578 | 6.6856 | 68100 | 0.9084 | 0.0121 | 0.1644 | 0.6441 | 0.1644 |
| 0.8734 | 6.6955 | 68200 | 0.8908 | 0.0121 | 0.1694 | 0.6515 | 0.1694 |
| 0.8643 | 6.7053 | 68300 | 0.8887 | 0.0121 | 0.1691 | 0.6531 | 0.1691 |
| 0.7971 | 6.7151 | 68400 | 0.8909 | 0.0121 | 0.1660 | 0.6516 | 0.1660 |
| 0.8904 | 6.7249 | 68500 | 0.8926 | 0.0121 | 0.1665 | 0.6508 | 0.1665 |
| 0.8864 | 6.7347 | 68600 | 0.8771 | 0.0121 | 0.1735 | 0.6570 | 0.1735 |
| 0.8464 | 6.7446 | 68700 | 0.9023 | 0.0121 | 0.1628 | 0.6463 | 0.1628 |
| 0.9121 | 6.7544 | 68800 | 0.8985 | 0.0121 | 0.1600 | 0.6477 | 0.1600 |
| 0.9137 | 6.7642 | 68900 | 0.9187 | 0.0121 | 0.1537 | 0.6423 | 0.1537 |
| 0.9691 | 6.7740 | 69000 | 0.9116 | 0.0121 | 0.1583 | 0.6423 | 0.1583 |
| 0.925 | 6.7838 | 69100 | 0.9123 | 0.0121 | 0.1696 | 0.6470 | 0.1696 |
| 0.984 | 6.7936 | 69200 | 0.9114 | 0.0121 | 0.1613 | 0.6447 | 0.1613 |
| 0.7877 | 6.8035 | 69300 | 0.8902 | 0.0121 | 0.1700 | 0.6523 | 0.1700 |
| 0.9316 | 6.8133 | 69400 | 0.8867 | 0.0121 | 0.1729 | 0.6549 | 0.1729 |
| 0.9002 | 6.8231 | 69500 | 0.9049 | 0.0121 | 0.1661 | 0.6476 | 0.1661 |
| 0.9117 | 6.8329 | 69600 | 0.9036 | 0.0121 | 0.1571 | 0.6483 | 0.1571 |
| 0.8923 | 6.8427 | 69700 | 0.9154 | 0.0121 | 0.1553 | 0.6440 | 0.1553 |
| 0.8687 | 6.8525 | 69800 | 0.8993 | 0.0121 | 0.1596 | 0.6499 | 0.1596 |
| 0.8335 | 6.8624 | 69900 | 0.8929 | 0.0121 | 0.1734 | 0.6522 | 0.1734 |
| 0.9734 | 6.8722 | 70000 | 0.8966 | 0.0121 | 0.1693 | 0.6496 | 0.1693 |
| 0.8941 | 6.8820 | 70100 | 0.9150 | 0.0121 | 0.1639 | 0.6440 | 0.1639 |
| 0.9068 | 6.8918 | 70200 | 0.9009 | 0.0121 | 0.1635 | 0.6469 | 0.1635 |
| 0.8599 | 6.9016 | 70300 | 0.8984 | 0.0121 | 0.1674 | 0.6487 | 0.1674 |
| 0.8525 | 6.9114 | 70400 | 0.8959 | 0.0121 | 0.1668 | 0.6488 | 0.1668 |
| 0.9187 | 6.9213 | 70500 | 0.9066 | 0.0121 | 0.1675 | 0.6446 | 0.1675 |
| 0.8898 | 6.9311 | 70600 | 0.9043 | 0.0121 | 0.1655 | 0.6481 | 0.1655 |
| 0.8829 | 6.9409 | 70700 | 0.9234 | 0.0121 | 0.1574 | 0.6422 | 0.1574 |
| 0.8977 | 6.9507 | 70800 | 0.8913 | 0.0121 | 0.1665 | 0.6503 | 0.1665 |
| 0.8974 | 6.9605 | 70900 | 0.9152 | 0.0121 | 0.1594 | 0.6419 | 0.1594 |
| 0.8644 | 6.9704 | 71000 | 0.8937 | 0.0121 | 0.1690 | 0.6506 | 0.1690 |
| 0.9495 | 6.9802 | 71100 | 0.8918 | 0.0121 | 0.1700 | 0.6485 | 0.1700 |
| 0.9866 | 6.9900 | 71200 | 0.9175 | 0.0121 | 0.1516 | 0.6396 | 0.1516 |
| 0.9188 | 6.9998 | 71300 | 0.9003 | 0.0121 | 0.1624 | 0.6480 | 0.1624 |
| 0.8109 | 7.0096 | 71400 | 0.8966 | 0.0121 | 0.1656 | 0.6508 | 0.1656 |
| 0.8681 | 7.0194 | 71500 | 0.8798 | 0.0121 | 0.1735 | 0.6573 | 0.1735 |
| 0.8698 | 7.0293 | 71600 | 0.9139 | 0.0121 | 0.1588 | 0.6407 | 0.1588 |
| 0.9085 | 7.0391 | 71700 | 0.8874 | 0.0121 | 0.1640 | 0.6530 | 0.1640 |
| 0.8601 | 7.0489 | 71800 | 0.9003 | 0.0121 | 0.1615 | 0.6481 | 0.1615 |
| 0.8931 | 7.0587 | 71900 | 0.8947 | 0.0121 | 0.1612 | 0.6486 | 0.1612 |
| 0.8785 | 7.0685 | 72000 | 0.8977 | 0.0121 | 0.1689 | 0.6522 | 0.1689 |
| 0.9064 | 7.0783 | 72100 | 0.9052 | 0.0121 | 0.1632 | 0.6462 | 0.1632 |
| 0.8819 | 7.0882 | 72200 | 0.9062 | 0.0121 | 0.1599 | 0.6433 | 0.1599 |
| 0.8778 | 7.0980 | 72300 | 0.8969 | 0.0121 | 0.1705 | 0.6507 | 0.1705 |
| 0.9143 | 7.1078 | 72400 | 0.9161 | 0.0121 | 0.1538 | 0.6411 | 0.1538 |
| 0.8533 | 7.1176 | 72500 | 0.8996 | 0.0121 | 0.1610 | 0.6468 | 0.1610 |
| 0.9242 | 7.1274 | 72600 | 0.8970 | 0.0121 | 0.1686 | 0.6510 | 0.1686 |
| 0.8492 | 7.1372 | 72700 | 0.8880 | 0.0121 | 0.1634 | 0.6498 | 0.1634 |
| 0.9335 | 7.1471 | 72800 | 0.9017 | 0.0121 | 0.1626 | 0.6479 | 0.1626 |
| 0.8984 | 7.1569 | 72900 | 0.8902 | 0.0121 | 0.1639 | 0.6507 | 0.1639 |
| 0.8653 | 7.1667 | 73000 | 0.8906 | 0.0121 | 0.1678 | 0.6518 | 0.1678 |
| 0.8975 | 7.1765 | 73100 | 0.8762 | 0.0121 | 0.1744 | 0.6573 | 0.1744 |
| 0.899 | 7.1863 | 73200 | 0.8923 | 0.0121 | 0.1673 | 0.6513 | 0.1673 |
| 0.9245 | 7.1962 | 73300 | 0.8920 | 0.0121 | 0.1626 | 0.6499 | 0.1626 |
| 0.8859 | 7.2060 | 73400 | 0.8925 | 0.0121 | 0.1675 | 0.6500 | 0.1675 |
| 0.8344 | 7.2158 | 73500 | 0.8864 | 0.0121 | 0.1657 | 0.6496 | 0.1657 |
| 0.8866 | 7.2256 | 73600 | 0.8803 | 0.0121 | 0.1758 | 0.6567 | 0.1758 |
| 0.8842 | 7.2354 | 73700 | 0.8880 | 0.0121 | 0.1677 | 0.6536 | 0.1677 |
| 0.9165 | 7.2452 | 73800 | 0.8877 | 0.0121 | 0.1716 | 0.6528 | 0.1716 |
| 0.8621 | 7.2551 | 73900 | 0.8900 | 0.0121 | 0.1756 | 0.6525 | 0.1756 |
| 0.8732 | 7.2649 | 74000 | 0.8912 | 0.0121 | 0.1713 | 0.6517 | 0.1713 |
| 0.895 | 7.2747 | 74100 | 0.8889 | 0.0121 | 0.1702 | 0.6521 | 0.1702 |
| 0.9558 | 7.2845 | 74200 | 0.8893 | 0.0121 | 0.1675 | 0.6537 | 0.1675 |
| 0.8515 | 7.2943 | 74300 | 0.8876 | 0.0121 | 0.1613 | 0.6503 | 0.1613 |
| 0.833 | 7.3041 | 74400 | 0.8864 | 0.0121 | 0.1773 | 0.6521 | 0.1773 |
| 0.8715 | 7.3140 | 74500 | 0.9271 | 0.0121 | 0.1455 | 0.6365 | 0.1455 |
| 0.8638 | 7.3238 | 74600 | 0.8798 | 0.0121 | 0.1743 | 0.6558 | 0.1743 |
| 0.8635 | 7.3336 | 74700 | 0.8945 | 0.0121 | 0.1620 | 0.6476 | 0.1620 |
| 0.7944 | 7.3434 | 74800 | 0.8882 | 0.0121 | 0.1730 | 0.6516 | 0.1730 |
| 0.9072 | 7.3532 | 74900 | 0.9084 | 0.0121 | 0.1566 | 0.6444 | 0.1566 |
| 0.9102 | 7.3630 | 75000 | 0.8870 | 0.0121 | 0.1720 | 0.6509 | 0.1720 |
| 0.9261 | 7.3729 | 75100 | 0.9056 | 0.0121 | 0.1742 | 0.6501 | 0.1742 |
| 0.9421 | 7.3827 | 75200 | 0.8887 | 0.0121 | 0.1683 | 0.6511 | 0.1683 |
| 0.9074 | 7.3925 | 75300 | 0.8886 | 0.0121 | 0.1682 | 0.6510 | 0.1682 |
| 0.9349 | 7.4023 | 75400 | 0.8970 | 0.0121 | 0.1692 | 0.6498 | 0.1692 |
| 0.9081 | 7.4121 | 75500 | 0.8855 | 0.0121 | 0.1690 | 0.6525 | 0.1690 |
| 0.8903 | 7.4220 | 75600 | 0.9003 | 0.0121 | 0.1605 | 0.6460 | 0.1605 |
| 0.7998 | 7.4318 | 75700 | 0.8866 | 0.0121 | 0.1611 | 0.6524 | 0.1611 |
| 0.9273 | 7.4416 | 75800 | 0.9242 | 0.0121 | 0.1550 | 0.6418 | 0.1550 |
| 0.9488 | 7.4514 | 75900 | 0.8843 | 0.0121 | 0.1681 | 0.6528 | 0.1681 |
| 0.9358 | 7.4612 | 76000 | 0.9096 | 0.0121 | 0.1612 | 0.6482 | 0.1612 |
| 0.9421 | 7.4710 | 76100 | 0.8894 | 0.0121 | 0.1711 | 0.6539 | 0.1711 |
| 0.8566 | 7.4809 | 76200 | 0.8851 | 0.0121 | 0.1764 | 0.6535 | 0.1764 |
| 0.8836 | 7.4907 | 76300 | 0.8883 | 0.0121 | 0.1758 | 0.6524 | 0.1758 |
| 0.9199 | 7.5005 | 76400 | 0.9105 | 0.0121 | 0.1589 | 0.6472 | 0.1589 |
| 0.8648 | 7.5103 | 76500 | 0.8708 | 0.0121 | 0.1773 | 0.6584 | 0.1773 |
| 0.8728 | 7.5201 | 76600 | 0.8804 | 0.0121 | 0.1719 | 0.6557 | 0.1719 |
| 0.8438 | 7.5299 | 76700 | 0.8894 | 0.0121 | 0.1675 | 0.6523 | 0.1675 |
| 0.9103 | 7.5398 | 76800 | 0.9067 | 0.0121 | 0.1701 | 0.6485 | 0.1701 |
| 0.9266 | 7.5496 | 76900 | 0.8889 | 0.0121 | 0.1622 | 0.6497 | 0.1622 |
| 0.9135 | 7.5594 | 77000 | 0.9084 | 0.0121 | 0.1642 | 0.6457 | 0.1642 |
| 0.905 | 7.5692 | 77100 | 0.8855 | 0.0121 | 0.1720 | 0.6552 | 0.1720 |
| 0.8316 | 7.5790 | 77200 | 0.9042 | 0.0121 | 0.1643 | 0.6474 | 0.1643 |
| 0.9467 | 7.5888 | 77300 | 0.8937 | 0.0121 | 0.1678 | 0.6497 | 0.1678 |
| 0.8321 | 7.5987 | 77400 | 0.8800 | 0.0121 | 0.1575 | 0.6520 | 0.1575 |
| 0.9027 | 7.6085 | 77500 | 0.8975 | 0.0121 | 0.1731 | 0.6500 | 0.1731 |
| 0.9398 | 7.6183 | 77600 | 0.9001 | 0.0121 | 0.1628 | 0.6472 | 0.1628 |
| 0.8569 | 7.6281 | 77700 | 0.8941 | 0.0121 | 0.1599 | 0.6478 | 0.1599 |
| 0.8378 | 7.6379 | 77800 | 0.8791 | 0.0121 | 0.1709 | 0.6539 | 0.1709 |
| 0.889 | 7.6478 | 77900 | 0.9053 | 0.0121 | 0.1631 | 0.6445 | 0.1631 |
| 0.8715 | 7.6576 | 78000 | 0.9165 | 0.0121 | 0.1637 | 0.6423 | 0.1637 |
| 0.8828 | 7.6674 | 78100 | 0.8772 | 0.0121 | 0.1801 | 0.6570 | 0.1801 |
| 0.8339 | 7.6772 | 78200 | 0.8880 | 0.0121 | 0.1644 | 0.6505 | 0.1644 |
| 0.8239 | 7.6870 | 78300 | 0.8844 | 0.0121 | 0.1738 | 0.6554 | 0.1738 |
| 0.861 | 7.6968 | 78400 | 0.8858 | 0.0121 | 0.1726 | 0.6551 | 0.1726 |
| 0.8917 | 7.7067 | 78500 | 0.8873 | 0.0121 | 0.1650 | 0.6513 | 0.1650 |
| 0.8643 | 7.7165 | 78600 | 0.8824 | 0.0121 | 0.1778 | 0.6583 | 0.1778 |
| 0.8449 | 7.7263 | 78700 | 0.8747 | 0.0121 | 0.1695 | 0.6555 | 0.1695 |
| 0.9109 | 7.7361 | 78800 | 0.8924 | 0.0121 | 0.1660 | 0.6511 | 0.1660 |
| 0.8526 | 7.7459 | 78900 | 0.8828 | 0.0121 | 0.1693 | 0.6539 | 0.1693 |
| 0.8111 | 7.7557 | 79000 | 0.8904 | 0.0121 | 0.1683 | 0.6542 | 0.1683 |
| 0.8652 | 7.7656 | 79100 | 0.8681 | 0.0121 | 0.1791 | 0.6613 | 0.1791 |
| 0.8739 | 7.7754 | 79200 | 0.8777 | 0.0121 | 0.1703 | 0.6577 | 0.1703 |
| 0.8648 | 7.7852 | 79300 | 0.8916 | 0.0121 | 0.1635 | 0.6461 | 0.1635 |
| 0.8935 | 7.7950 | 79400 | 0.9094 | 0.0121 | 0.1621 | 0.6456 | 0.1621 |
| 0.8423 | 7.8048 | 79500 | 0.8895 | 0.0121 | 0.1744 | 0.6534 | 0.1744 |
| 0.9597 | 7.8146 | 79600 | 0.8942 | 0.0121 | 0.1706 | 0.6518 | 0.1706 |
| 0.8766 | 7.8245 | 79700 | 0.9027 | 0.0121 | 0.1702 | 0.6493 | 0.1702 |
| 0.8886 | 7.8343 | 79800 | 0.8931 | 0.0121 | 0.1693 | 0.6501 | 0.1693 |
| 0.9142 | 7.8441 | 79900 | 0.8899 | 0.0121 | 0.1737 | 0.6528 | 0.1737 |
| 0.8584 | 7.8539 | 80000 | 0.8916 | 0.0121 | 0.1663 | 0.6502 | 0.1663 |
| 0.8833 | 7.8637 | 80100 | 0.8787 | 0.0121 | 0.1683 | 0.6554 | 0.1683 |
| 0.7781 | 7.8736 | 80200 | 0.8710 | 0.0121 | 0.1743 | 0.6591 | 0.1743 |
| 0.9024 | 7.8834 | 80300 | 0.8760 | 0.0121 | 0.1696 | 0.6546 | 0.1696 |
| 0.9207 | 7.8932 | 80400 | 0.8953 | 0.0121 | 0.1707 | 0.6503 | 0.1707 |
| 0.8648 | 7.9030 | 80500 | 0.8848 | 0.0121 | 0.1684 | 0.6512 | 0.1684 |
| 0.8437 | 7.9128 | 80600 | 0.8763 | 0.0121 | 0.1661 | 0.6549 | 0.1661 |
| 0.8794 | 7.9226 | 80700 | 0.8653 | 0.0121 | 0.1733 | 0.6594 | 0.1733 |
| 0.8541 | 7.9325 | 80800 | 0.8885 | 0.0121 | 0.1667 | 0.6526 | 0.1667 |
| 0.9453 | 7.9423 | 80900 | 0.8888 | 0.0121 | 0.1679 | 0.6522 | 0.1679 |
| 0.9241 | 7.9521 | 81000 | 0.8943 | 0.0121 | 0.1604 | 0.6498 | 0.1604 |
| 0.8594 | 7.9619 | 81100 | 0.8747 | 0.0121 | 0.1783 | 0.6583 | 0.1783 |
| 0.8906 | 7.9717 | 81200 | 0.8913 | 0.0121 | 0.1731 | 0.6521 | 0.1731 |
| 0.8827 | 7.9815 | 81300 | 0.8862 | 0.0121 | 0.1686 | 0.6527 | 0.1686 |
| 0.8428 | 7.9914 | 81400 | 0.8754 | 0.0121 | 0.1733 | 0.6578 | 0.1733 |
| 0.8502 | 8.0012 | 81500 | 0.8785 | 0.0121 | 0.1716 | 0.6549 | 0.1716 |
| 0.8665 | 8.0110 | 81600 | 0.8976 | 0.0121 | 0.1543 | 0.6499 | 0.1543 |
| 0.8266 | 8.0208 | 81700 | 0.8728 | 0.0121 | 0.1725 | 0.6588 | 0.1725 |
| 0.8515 | 8.0306 | 81800 | 0.8866 | 0.0121 | 0.1689 | 0.6522 | 0.1689 |
| 0.8596 | 8.0404 | 81900 | 0.8747 | 0.0121 | 0.1711 | 0.6569 | 0.1711 |
| 0.933 | 8.0503 | 82000 | 0.8958 | 0.0121 | 0.1679 | 0.6524 | 0.1679 |
| 0.8987 | 8.0601 | 82100 | 0.8788 | 0.0121 | 0.1664 | 0.6553 | 0.1664 |
| 0.9081 | 8.0699 | 82200 | 0.8943 | 0.0121 | 0.1714 | 0.6487 | 0.1714 |
| 0.8583 | 8.0797 | 82300 | 1.0938 | 0.0121 | 0.1248 | 0.5901 | 0.1248 |
| 0.9374 | 8.0895 | 82400 | 0.8959 | 0.0121 | 0.1687 | 0.6499 | 0.1687 |
| 0.8707 | 8.0994 | 82500 | 0.8787 | 0.0121 | 0.1724 | 0.6535 | 0.1724 |
| 0.8492 | 8.1092 | 82600 | 0.8869 | 0.0121 | 0.1686 | 0.6569 | 0.1686 |
| 0.9135 | 8.1190 | 82700 | 0.9008 | 0.0121 | 0.1577 | 0.6485 | 0.1577 |
| 0.9245 | 8.1288 | 82800 | 0.8776 | 0.0121 | 0.1707 | 0.6550 | 0.1707 |
| 0.8326 | 8.1386 | 82900 | 0.8856 | 0.0121 | 0.1641 | 0.6495 | 0.1641 |
| 0.9092 | 8.1484 | 83000 | 0.8960 | 0.0121 | 0.1657 | 0.6498 | 0.1657 |
| 0.9163 | 8.1583 | 83100 | 0.8778 | 0.0121 | 0.1736 | 0.6549 | 0.1736 |
| 0.8896 | 8.1681 | 83200 | 0.8936 | 0.0121 | 0.1597 | 0.6499 | 0.1597 |
| 0.8872 | 8.1779 | 83300 | 0.8760 | 0.0121 | 0.1693 | 0.6554 | 0.1693 |
| 0.8451 | 8.1877 | 83400 | 0.8668 | 0.0121 | 0.1776 | 0.6617 | 0.1776 |
| 0.8352 | 8.1975 | 83500 | 0.8719 | 0.0121 | 0.1798 | 0.6570 | 0.1798 |
| 0.9612 | 8.2073 | 83600 | 0.8775 | 0.0121 | 0.1726 | 0.6546 | 0.1726 |
| 0.8528 | 8.2172 | 83700 | 0.8798 | 0.0121 | 0.1773 | 0.6579 | 0.1773 |
| 0.8939 | 8.2270 | 83800 | 0.9222 | 0.0121 | 0.1631 | 0.6410 | 0.1631 |
| 0.8205 | 8.2368 | 83900 | 0.8907 | 0.0121 | 0.1723 | 0.6511 | 0.1723 |
| 0.9358 | 8.2466 | 84000 | 0.8756 | 0.0121 | 0.1760 | 0.6592 | 0.1760 |
| 0.8687 | 8.2564 | 84100 | 0.8841 | 0.0121 | 0.1714 | 0.6565 | 0.1714 |
| 0.9093 | 8.2662 | 84200 | 0.9097 | 0.0121 | 0.1587 | 0.6445 | 0.1587 |
| 0.9625 | 8.2761 | 84300 | 0.9028 | 0.0121 | 0.1605 | 0.6459 | 0.1605 |
| 0.9625 | 8.2859 | 84400 | 0.9074 | 0.0121 | 0.1628 | 0.6451 | 0.1628 |
| 0.8268 | 8.2957 | 84500 | 0.8868 | 0.0121 | 0.1682 | 0.6510 | 0.1682 |
| 0.9055 | 8.3055 | 84600 | 0.8898 | 0.0121 | 0.1693 | 0.6514 | 0.1693 |
| 0.878 | 8.3153 | 84700 | 0.9080 | 0.0121 | 0.1660 | 0.6472 | 0.1660 |
| 0.8911 | 8.3252 | 84800 | 0.8842 | 0.0121 | 0.1744 | 0.6533 | 0.1744 |
| 0.7994 | 8.3350 | 84900 | 0.8865 | 0.0121 | 0.1653 | 0.6518 | 0.1653 |
| 0.8959 | 8.3448 | 85000 | 0.8986 | 0.0121 | 0.1706 | 0.6522 | 0.1706 |
| 0.7867 | 8.3546 | 85100 | 0.8807 | 0.0121 | 0.1724 | 0.6567 | 0.1724 |
| 0.9298 | 8.3644 | 85200 | 0.8962 | 0.0121 | 0.1630 | 0.6483 | 0.1630 |
| 0.8122 | 8.3742 | 85300 | 0.8741 | 0.0121 | 0.1724 | 0.6575 | 0.1724 |
| 0.8754 | 8.3841 | 85400 | 0.8894 | 0.0121 | 0.1663 | 0.6517 | 0.1663 |
| 0.8224 | 8.3939 | 85500 | 0.8961 | 0.0121 | 0.1643 | 0.6468 | 0.1643 |
| 0.879 | 8.4037 | 85600 | 0.8843 | 0.0121 | 0.1756 | 0.6550 | 0.1756 |
| 0.9025 | 8.4135 | 85700 | 0.8946 | 0.0121 | 0.1636 | 0.6513 | 0.1636 |
| 0.8163 | 8.4233 | 85800 | 0.8884 | 0.0121 | 0.1621 | 0.6508 | 0.1621 |
| 0.8935 | 8.4331 | 85900 | 0.8889 | 0.0121 | 0.1675 | 0.6520 | 0.1675 |
| 0.9107 | 8.4430 | 86000 | 0.8672 | 0.0121 | 0.1811 | 0.6608 | 0.1811 |
| 0.9779 | 8.4528 | 86100 | 0.8804 | 0.0121 | 0.1725 | 0.6568 | 0.1725 |
| 0.8305 | 8.4626 | 86200 | 0.9006 | 0.0121 | 0.1624 | 0.6471 | 0.1624 |
| 0.8844 | 8.4724 | 86300 | 0.8945 | 0.0121 | 0.1631 | 0.6512 | 0.1631 |
| 0.8139 | 8.4822 | 86400 | 0.8809 | 0.0121 | 0.1773 | 0.6555 | 0.1773 |
| 0.8526 | 8.4920 | 86500 | 0.8811 | 0.0121 | 0.1614 | 0.6521 | 0.1614 |
| 0.8673 | 8.5019 | 86600 | 0.8800 | 0.0121 | 0.1715 | 0.6550 | 0.1715 |
| 0.9264 | 8.5117 | 86700 | 0.8915 | 0.0121 | 0.1763 | 0.6525 | 0.1763 |
| 0.8396 | 8.5215 | 86800 | 0.9141 | 0.0121 | 0.1674 | 0.6444 | 0.1674 |
| 0.8332 | 8.5313 | 86900 | 0.8931 | 0.0121 | 0.1620 | 0.6514 | 0.1620 |
| 0.924 | 8.5411 | 87000 | 0.8912 | 0.0121 | 0.1622 | 0.6511 | 0.1622 |
| 0.8566 | 8.5510 | 87100 | 0.8906 | 0.0121 | 0.1613 | 0.6518 | 0.1613 |
| 0.8294 | 8.5608 | 87200 | 0.9110 | 0.0121 | 0.1633 | 0.6455 | 0.1633 |
| 0.9732 | 8.5706 | 87300 | 0.8983 | 0.0121 | 0.1679 | 0.6487 | 0.1679 |
| 0.87 | 8.5804 | 87400 | 0.8930 | 0.0121 | 0.1562 | 0.6488 | 0.1562 |
| 0.8377 | 8.5902 | 87500 | 0.8701 | 0.0121 | 0.1743 | 0.6586 | 0.1743 |
| 0.857 | 8.6000 | 87600 | 0.8985 | 0.0121 | 0.1610 | 0.6496 | 0.1610 |
| 0.8647 | 8.6099 | 87700 | 0.8798 | 0.0121 | 0.1784 | 0.6579 | 0.1784 |
| 0.8771 | 8.6197 | 87800 | 0.8793 | 0.0121 | 0.1686 | 0.6559 | 0.1686 |
| 0.9198 | 8.6295 | 87900 | 0.8892 | 0.0121 | 0.1657 | 0.6532 | 0.1657 |
| 0.9074 | 8.6393 | 88000 | 0.9027 | 0.0121 | 0.1631 | 0.6482 | 0.1631 |
| 0.9175 | 8.6491 | 88100 | 0.8917 | 0.0121 | 0.1647 | 0.6498 | 0.1647 |
| 0.8699 | 8.6589 | 88200 | 0.8791 | 0.0121 | 0.1692 | 0.6567 | 0.1692 |
| 0.8949 | 8.6688 | 88300 | 0.8906 | 0.0121 | 0.1653 | 0.6481 | 0.1653 |
| 0.8686 | 8.6786 | 88400 | 0.8984 | 0.0121 | 0.1607 | 0.6481 | 0.1607 |
| 1.035 | 8.6884 | 88500 | 0.9014 | 0.0121 | 0.1642 | 0.6487 | 0.1642 |
| 0.8353 | 8.6982 | 88600 | 0.8832 | 0.0121 | 0.1724 | 0.6540 | 0.1724 |
| 0.8507 | 8.7080 | 88700 | 0.9010 | 0.0121 | 0.1680 | 0.6498 | 0.1680 |
| 0.9186 | 8.7178 | 88800 | 0.8872 | 0.0121 | 0.1735 | 0.6534 | 0.1735 |
| 0.8306 | 8.7277 | 88900 | 0.8810 | 0.0121 | 0.1671 | 0.6556 | 0.1671 |
| 0.8596 | 8.7375 | 89000 | 0.9024 | 0.0121 | 0.1653 | 0.6488 | 0.1653 |
| 0.8422 | 8.7473 | 89100 | 0.8811 | 0.0121 | 0.1751 | 0.6529 | 0.1751 |
| 0.9112 | 8.7571 | 89200 | 0.8775 | 0.0121 | 0.1675 | 0.6540 | 0.1675 |
| 0.835 | 8.7669 | 89300 | 0.8832 | 0.0121 | 0.1753 | 0.6556 | 0.1753 |
| 0.8434 | 8.7768 | 89400 | 0.8837 | 0.0121 | 0.1767 | 0.6568 | 0.1767 |
| 0.933 | 8.7866 | 89500 | 0.8876 | 0.0121 | 0.1715 | 0.6529 | 0.1715 |
| 0.8242 | 8.7964 | 89600 | 0.8777 | 0.0121 | 0.1693 | 0.6542 | 0.1693 |
| 0.8515 | 8.8062 | 89700 | 0.8861 | 0.0121 | 0.1744 | 0.6541 | 0.1744 |
| 0.8414 | 8.8160 | 89800 | 0.8788 | 0.0121 | 0.1719 | 0.6539 | 0.1719 |
| 0.8493 | 8.8258 | 89900 | 0.8801 | 0.0121 | 0.1643 | 0.6533 | 0.1643 |
| 0.8985 | 8.8357 | 90000 | 0.8813 | 0.0121 | 0.1728 | 0.6557 | 0.1728 |
| 0.9069 | 8.8455 | 90100 | 0.8783 | 0.0121 | 0.1664 | 0.6541 | 0.1664 |
| 0.8162 | 8.8553 | 90200 | 0.8989 | 0.0121 | 0.1639 | 0.6484 | 0.1639 |
| 0.8368 | 8.8651 | 90300 | 0.8738 | 0.0121 | 0.1725 | 0.6568 | 0.1725 |
| 0.8865 | 8.8749 | 90400 | 0.8841 | 0.0121 | 0.1649 | 0.6544 | 0.1649 |
| 0.8651 | 8.8847 | 90500 | 0.8779 | 0.0121 | 0.1733 | 0.6550 | 0.1733 |
| 0.8775 | 8.8946 | 90600 | 0.8850 | 0.0121 | 0.1608 | 0.6524 | 0.1608 |
| 0.8091 | 8.9044 | 90700 | 0.8816 | 0.0121 | 0.1668 | 0.6561 | 0.1668 |
| 0.8726 | 8.9142 | 90800 | 0.8871 | 0.0121 | 0.1651 | 0.6512 | 0.1651 |
| 0.9415 | 8.9240 | 90900 | 0.8769 | 0.0121 | 0.1741 | 0.6572 | 0.1741 |
| 0.846 | 8.9338 | 91000 | 0.8900 | 0.0121 | 0.1715 | 0.6531 | 0.1715 |
| 0.8878 | 8.9436 | 91100 | 0.9125 | 0.0121 | 0.1658 | 0.6470 | 0.1658 |
| 0.9493 | 8.9535 | 91200 | 0.8922 | 0.0121 | 0.1691 | 0.6480 | 0.1691 |
| 0.8781 | 8.9633 | 91300 | 0.8823 | 0.0121 | 0.1667 | 0.6526 | 0.1667 |
| 0.9035 | 8.9731 | 91400 | 0.8890 | 0.0121 | 0.1715 | 0.6517 | 0.1715 |
| 0.8642 | 8.9829 | 91500 | 0.8988 | 0.0121 | 0.1693 | 0.6487 | 0.1693 |
| 0.8415 | 8.9927 | 91600 | 0.8870 | 0.0121 | 0.1686 | 0.6519 | 0.1686 |
| 0.864 | 9.0026 | 91700 | 0.8901 | 0.0121 | 0.1721 | 0.6524 | 0.1721 |
| 0.9031 | 9.0124 | 91800 | 0.8907 | 0.0121 | 0.1721 | 0.6516 | 0.1721 |
| 0.8783 | 9.0222 | 91900 | 0.8837 | 0.0121 | 0.1601 | 0.6484 | 0.1601 |
| 0.952 | 9.0320 | 92000 | 0.9131 | 0.0121 | 0.1670 | 0.6459 | 0.1670 |
| 0.8702 | 9.0418 | 92100 | 0.9124 | 0.0121 | 0.1567 | 0.6423 | 0.1567 |
| 0.9422 | 9.0516 | 92200 | 0.8867 | 0.0121 | 0.1749 | 0.6526 | 0.1749 |
| 0.9153 | 9.0615 | 92300 | 0.8892 | 0.0121 | 0.1619 | 0.6509 | 0.1619 |
| 0.8876 | 9.0713 | 92400 | 0.8911 | 0.0121 | 0.1705 | 0.6533 | 0.1705 |
| 0.9129 | 9.0811 | 92500 | 0.8891 | 0.0121 | 0.1748 | 0.6546 | 0.1748 |
| 0.8659 | 9.0909 | 92600 | 0.9137 | 0.0121 | 0.1635 | 0.6476 | 0.1635 |
| 0.8991 | 9.1007 | 92700 | 0.8962 | 0.0121 | 0.1653 | 0.6485 | 0.1653 |
| 0.8597 | 9.1105 | 92800 | 0.8887 | 0.0121 | 0.1709 | 0.6516 | 0.1709 |
| 0.8829 | 9.1204 | 92900 | 0.8989 | 0.0121 | 0.1687 | 0.6468 | 0.1687 |
| 0.9497 | 9.1302 | 93000 | 0.8895 | 0.0121 | 0.1674 | 0.6516 | 0.1674 |
| 0.8892 | 9.1400 | 93100 | 0.9024 | 0.0121 | 0.1647 | 0.6485 | 0.1647 |
| 0.9227 | 9.1498 | 93200 | 0.8930 | 0.0121 | 0.1678 | 0.6482 | 0.1678 |
| 0.821 | 9.1596 | 93300 | 0.8753 | 0.0121 | 0.1727 | 0.6579 | 0.1727 |
| 0.912 | 9.1694 | 93400 | 0.8938 | 0.0121 | 0.1624 | 0.6470 | 0.1624 |
| 0.8599 | 9.1793 | 93500 | 0.8865 | 0.0121 | 0.1776 | 0.6547 | 0.1776 |
| 0.8947 | 9.1891 | 93600 | 0.8911 | 0.0121 | 0.1707 | 0.6498 | 0.1707 |
| 0.9006 | 9.1989 | 93700 | 0.8800 | 0.0121 | 0.1702 | 0.6535 | 0.1702 |
| 0.9032 | 9.2087 | 93800 | 0.8805 | 0.0121 | 0.1779 | 0.6540 | 0.1779 |
| 0.9449 | 9.2185 | 93900 | 0.8653 | 0.0121 | 0.1763 | 0.6596 | 0.1763 |
| 0.7906 | 9.2284 | 94000 | 0.8777 | 0.0121 | 0.1833 | 0.6564 | 0.1833 |
| 0.8576 | 9.2382 | 94100 | 0.8956 | 0.0121 | 0.1643 | 0.6486 | 0.1643 |
| 0.8581 | 9.2480 | 94200 | 0.8783 | 0.0121 | 0.1729 | 0.6551 | 0.1729 |
| 0.897 | 9.2578 | 94300 | 0.9068 | 0.0121 | 0.1645 | 0.6480 | 0.1645 |
| 0.8853 | 9.2676 | 94400 | 0.8996 | 0.0121 | 0.1621 | 0.6480 | 0.1621 |
| 0.8634 | 9.2774 | 94500 | 0.8795 | 0.0121 | 0.1784 | 0.6566 | 0.1784 |
| 0.8182 | 9.2873 | 94600 | 0.8763 | 0.0121 | 0.1782 | 0.6579 | 0.1782 |
| 0.9051 | 9.2971 | 94700 | 0.8899 | 0.0121 | 0.1648 | 0.6504 | 0.1648 |
| 0.9105 | 9.3069 | 94800 | 0.8971 | 0.0121 | 0.1619 | 0.6481 | 0.1619 |
| 0.84 | 9.3167 | 94900 | 0.8970 | 0.0121 | 0.1635 | 0.6467 | 0.1635 |
| 0.9105 | 9.3265 | 95000 | 0.8937 | 0.0121 | 0.1686 | 0.6508 | 0.1686 |
| 0.8974 | 9.3363 | 95100 | 0.9009 | 0.0121 | 0.1755 | 0.6520 | 0.1755 |
| 0.9465 | 9.3462 | 95200 | 0.9000 | 0.0121 | 0.1726 | 0.6503 | 0.1726 |
| 0.8545 | 9.3560 | 95300 | 0.8961 | 0.0121 | 0.1691 | 0.6500 | 0.1691 |
| 0.8711 | 9.3658 | 95400 | 0.8818 | 0.0121 | 0.1684 | 0.6532 | 0.1684 |
| 0.9244 | 9.3756 | 95500 | 0.8853 | 0.0121 | 0.1706 | 0.6535 | 0.1706 |
| 0.8094 | 9.3854 | 95600 | 0.8801 | 0.0121 | 0.1769 | 0.6561 | 0.1769 |
| 0.8754 | 9.3952 | 95700 | 0.8994 | 0.0121 | 0.1712 | 0.6495 | 0.1712 |
| 0.9122 | 9.4051 | 95800 | 0.8924 | 0.0121 | 0.1619 | 0.6513 | 0.1619 |
| 0.9308 | 9.4149 | 95900 | 0.8668 | 0.0121 | 0.1786 | 0.6614 | 0.1786 |
| 0.8718 | 9.4247 | 96000 | 0.8634 | 0.0121 | 0.1720 | 0.6606 | 0.1720 |
| 0.8498 | 9.4345 | 96100 | 0.8933 | 0.0121 | 0.1703 | 0.6512 | 0.1703 |
| 0.8661 | 9.4443 | 96200 | 0.8844 | 0.0121 | 0.1675 | 0.6527 | 0.1675 |
| 0.8599 | 9.4542 | 96300 | 0.8809 | 0.0121 | 0.1681 | 0.6509 | 0.1681 |
| 0.8299 | 9.4640 | 96400 | 0.8847 | 0.0121 | 0.1760 | 0.6557 | 0.1760 |
| 0.8077 | 9.4738 | 96500 | 0.8986 | 0.0121 | 0.1664 | 0.6446 | 0.1664 |
| 0.8472 | 9.4836 | 96600 | 0.9022 | 0.0121 | 0.1628 | 0.6467 | 0.1628 |
| 0.8345 | 9.4934 | 96700 | 0.8835 | 0.0121 | 0.1693 | 0.6514 | 0.1693 |
| 0.8092 | 9.5032 | 96800 | 0.8862 | 0.0121 | 0.1705 | 0.6528 | 0.1705 |
| 0.978 | 9.5131 | 96900 | 0.8863 | 0.0121 | 0.1784 | 0.6522 | 0.1784 |
| 0.9094 | 9.5229 | 97000 | 0.8872 | 0.0121 | 0.1751 | 0.6543 | 0.1751 |
| 0.9012 | 9.5327 | 97100 | 0.8874 | 0.0121 | 0.1751 | 0.6545 | 0.1751 |
| 0.8758 | 9.5425 | 97200 | 0.8880 | 0.0121 | 0.1671 | 0.6516 | 0.1671 |
| 0.9321 | 9.5523 | 97300 | 0.8742 | 0.0121 | 0.1679 | 0.6572 | 0.1679 |
| 0.7937 | 9.5621 | 97400 | 0.8806 | 0.0121 | 0.1691 | 0.6575 | 0.1691 |
| 0.8658 | 9.5720 | 97500 | 0.8782 | 0.0121 | 0.1734 | 0.6560 | 0.1734 |
| 0.8625 | 9.5818 | 97600 | 0.8873 | 0.0121 | 0.1761 | 0.6526 | 0.1761 |
| 0.8831 | 9.5916 | 97700 | 0.8677 | 0.0121 | 0.1838 | 0.6621 | 0.1838 |
| 0.844 | 9.6014 | 97800 | 0.8956 | 0.0121 | 0.1624 | 0.6513 | 0.1624 |
| 0.8289 | 9.6112 | 97900 | 0.8771 | 0.0121 | 0.1735 | 0.6556 | 0.1735 |
| 0.891 | 9.6210 | 98000 | 0.8888 | 0.0121 | 0.1704 | 0.6518 | 0.1704 |
| 0.8433 | 9.6309 | 98100 | 0.8907 | 0.0121 | 0.1702 | 0.6546 | 0.1702 |
| 0.7974 | 9.6407 | 98200 | 0.8671 | 0.0121 | 0.1776 | 0.6593 | 0.1776 |
| 0.9177 | 9.6505 | 98300 | 0.8685 | 0.0121 | 0.1724 | 0.6586 | 0.1724 |
| 0.8644 | 9.6603 | 98400 | 0.8774 | 0.0121 | 0.1802 | 0.6590 | 0.1802 |
| 0.8961 | 9.6701 | 98500 | 0.8888 | 0.0121 | 0.1692 | 0.6498 | 0.1692 |
| 0.8769 | 9.6800 | 98600 | 0.8783 | 0.0121 | 0.1760 | 0.6585 | 0.1760 |
| 0.9498 | 9.6898 | 98700 | 0.9060 | 0.0121 | 0.1644 | 0.6462 | 0.1644 |
| 0.8435 | 9.6996 | 98800 | 0.8771 | 0.0121 | 0.1756 | 0.6561 | 0.1756 |
| 0.8151 | 9.7094 | 98900 | 0.8711 | 0.0121 | 0.1729 | 0.6578 | 0.1729 |
| 0.8619 | 9.7192 | 99000 | 0.8616 | 0.0121 | 0.1759 | 0.6586 | 0.1759 |
| 0.8862 | 9.7290 | 99100 | 0.8702 | 0.0121 | 0.1711 | 0.6585 | 0.1711 |
| 0.9008 | 9.7389 | 99200 | 0.8826 | 0.0121 | 0.1716 | 0.6562 | 0.1716 |
| 0.861 | 9.7487 | 99300 | 0.8937 | 0.0121 | 0.1670 | 0.6492 | 0.1670 |
| 0.8789 | 9.7585 | 99400 | 0.8822 | 0.0121 | 0.1693 | 0.6527 | 0.1693 |
| 0.8296 | 9.7683 | 99500 | 0.8788 | 0.0121 | 0.1705 | 0.6557 | 0.1705 |
| 0.9087 | 9.7781 | 99600 | 0.9116 | 0.0121 | 0.1662 | 0.6464 | 0.1662 |
| 0.8118 | 9.7879 | 99700 | 0.8953 | 0.0121 | 0.1724 | 0.6521 | 0.1724 |
| 0.9122 | 9.7978 | 99800 | 0.8802 | 0.0121 | 0.1739 | 0.6576 | 0.1739 |
| 0.8541 | 9.8076 | 99900 | 0.8731 | 0.0121 | 0.1812 | 0.6593 | 0.1812 |
| 0.9063 | 9.8174 | 100000 | 0.8769 | 0.0121 | 0.1747 | 0.6560 | 0.1747 |
| 0.8517 | 9.8272 | 100100 | 0.8716 | 0.0121 | 0.1777 | 0.6605 | 0.1777 |
| 0.8725 | 9.8370 | 100200 | 0.8849 | 0.0121 | 0.1646 | 0.6525 | 0.1646 |
| 0.8793 | 9.8468 | 100300 | 0.8720 | 0.0121 | 0.1698 | 0.6578 | 0.1698 |
| 0.8553 | 9.8567 | 100400 | 0.8584 | 0.0121 | 0.1829 | 0.6629 | 0.1829 |
| 0.8216 | 9.8665 | 100500 | 0.8780 | 0.0121 | 0.1715 | 0.6568 | 0.1715 |
| 0.8723 | 9.8763 | 100600 | 0.8758 | 0.0121 | 0.1764 | 0.6562 | 0.1764 |
| 0.8514 | 9.8861 | 100700 | 0.8917 | 0.0121 | 0.1765 | 0.6556 | 0.1765 |
| 0.9008 | 9.8959 | 100800 | 0.8966 | 0.0121 | 0.1703 | 0.6520 | 0.1703 |
| 0.8712 | 9.9058 | 100900 | 0.8608 | 0.0121 | 0.1788 | 0.6606 | 0.1788 |
| 0.8697 | 9.9156 | 101000 | 0.8751 | 0.0121 | 0.1712 | 0.6559 | 0.1712 |
| 0.8335 | 9.9254 | 101100 | 0.8879 | 0.0121 | 0.1712 | 0.6544 | 0.1712 |
| 0.9015 | 9.9352 | 101200 | 0.8813 | 0.0121 | 0.1675 | 0.6543 | 0.1675 |
| 0.8726 | 9.9450 | 101300 | 0.8752 | 0.0121 | 0.1781 | 0.6570 | 0.1781 |
| 0.8435 | 9.9548 | 101400 | 0.8954 | 0.0121 | 0.1662 | 0.6497 | 0.1662 |
| 0.8604 | 9.9647 | 101500 | 0.8749 | 0.0121 | 0.1764 | 0.6583 | 0.1764 |
| 0.9175 | 9.9745 | 101600 | 0.8851 | 0.0121 | 0.1658 | 0.6543 | 0.1658 |
| 0.921 | 9.9843 | 101700 | 0.8862 | 0.0121 | 0.1739 | 0.6529 | 0.1739 |
| 0.838 | 9.9941 | 101800 | 0.8718 | 0.0121 | 0.1822 | 0.6614 | 0.1822 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1
|
johnnyyang0518/llama2_uuu_news_qlora
|
johnnyyang0518
| 2025-06-25T01:20:49Z | 0 | 0 | null |
[
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T01:20:49Z |
---
license: apache-2.0
---
|
Treza12/Pixtral-1D-Thermal
|
Treza12
| 2025-06-25T01:14:07Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:mistral-community/pixtral-12b",
"base_model:adapter:mistral-community/pixtral-12b",
"region:us"
] | null | 2025-06-25T01:12:52Z |
---
base_model: mistral-community/pixtral-12b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.3.dev0
|
sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF
|
sizzlebop
| 2025-06-25T01:09:12Z | 0 | 0 | null |
[
"gguf",
"code",
"llama-cpp",
"gguf-my-repo",
"en",
"dataset:Team-ACE/ToolACE",
"base_model:Team-ACE/ToolACE-2-Llama-3.1-8B",
"base_model:quantized:Team-ACE/ToolACE-2-Llama-3.1-8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-25T01:08:44Z |
---
license: apache-2.0
datasets:
- Team-ACE/ToolACE
language:
- en
metrics:
- accuracy
base_model: Team-ACE/ToolACE-2-Llama-3.1-8B
tags:
- code
- llama-cpp
- gguf-my-repo
---
# sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF
This model was converted to GGUF format from [`Team-ACE/ToolACE-2-Llama-3.1-8B`](https://huggingface.co/Team-ACE/ToolACE-2-Llama-3.1-8B) 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/Team-ACE/ToolACE-2-Llama-3.1-8B) 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 sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo sizzlebop/ToolACE-2-Llama-3.1-8B-Q8_0-GGUF --hf-file toolace-2-llama-3.1-8b-q8_0.gguf -c 2048
```
|
Miggyts/brazilian-ai-agent
|
Miggyts
| 2025-06-25T01:07:27Z | 3 | 0 | null |
[
"gpt2",
"custom_code",
"region:us"
] | null | 2025-06-22T19:15:49Z |
# Brazilian AI Agent
This is a minimal Hugging Face-compatible text generation agent.
It is intended as a conversational starter model or proxy for lightweight natural language tasks.
## How to Use
Use the Transformers pipeline with this model, or deploy it on Hugging Face Spaces.
```python
from transformers import pipeline
generator = pipeline('text-generation', model='Miggyts/brazilian-ai-agent')
generator("Translate this to Brazilian Portuguese: Hello, how are you?")
```
|
daixuancheng/zero_qwen-math-7b_base_allDapo_mathVerify_yesSuffix_step100
|
daixuancheng
| 2025-06-25T01:00:14Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:32:50Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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## How to Get Started with the Model
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|
phospho-app/GarrieD-ACT_BBOX-Red_Ball_V1_0624-mui9e
|
phospho-app
| 2025-06-25T00:59:03Z | 0 | 0 | null |
[
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | 2025-06-25T00:43:35Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## This model was trained using **phospho**.
Training was successfull, try it out on your robot!
## Training parameters:
- **Dataset**: [phospho-app/Red_Ball_V1_0624_bboxes](https://huggingface.co/datasets/phospho-app/Red_Ball_V1_0624_bboxes)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 20
- **Training steps**: 10000
📖 **Get Started**: [docs.phospho.ai](https://docs.phospho.ai?utm_source=huggingface_readme)
🤖 **Get your robot**: [robots.phospho.ai](https://robots.phospho.ai?utm_source=huggingface_readme)
|
tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5
|
tokyotech-llm
| 2025-06-25T00:57:38Z | 35 | 1 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"ja",
"dataset:tokyotech-llm/lmsys-chat-1m-synth",
"dataset:lmsys/lmsys-chat-1m",
"arxiv:2503.23714",
"arxiv:2407.21783",
"base_model:tokyotech-llm/Llama-3.1-Swallow-8B-v0.5",
"base_model:finetune:tokyotech-llm/Llama-3.1-Swallow-8B-v0.5",
"license:llama3.3",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-12T11:32:48Z |
---
language:
- en
- ja
library_name: transformers
pipeline_tag: text-generation
license:
- llama3.3
- gemma
model_type: llama
datasets:
- tokyotech-llm/lmsys-chat-1m-synth
- lmsys/lmsys-chat-1m
base_model:
- tokyotech-llm/Llama-3.1-Swallow-8B-v0.5
---
# Llama 3.1 Swallow - Built with Llama
Llama 3.1 Swallow is a series of large language models (8B, 70B) that were built by continual pre-training on the [Meta Llama 3.1](https://huggingface.co/collections/meta-llama/llama-31-669fc079a0c406a149a5738f) models.
Llama 3.1 Swallow enhanced the Japanese language capabilities of the original Llama 3.1 while retaining the English language capabilities.
We use approximately 200 billion tokens that were sampled from a large Japanese web corpus (Swallow Corpus Version 2), Japanese and English Wikipedia articles, and mathematical and
coding contents, etc (see the Training Datasets section of the base model) for continual pre-training.
The instruction-tuned models (Instruct) were built by supervised fine-tuning (SFT) on the synthetic data specially built for Japanese.
See the Swallow Model Index section to find other model variants.
**Note**: [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) model was continually pre-trained from the [meta-llama/Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) and then instruction-tuned with our instruction datasets.
# Release History
- **June 25, 2025**: Released [Llama-3.1-Swallow-8B-Instruct-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5) and [Llama-3.1-Swallow-8B-v0.5](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5).
- **March 10, 2025**: Released [Llama-3.3-Swallow-70B-Instruct-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4) and [Llama-3.3-Swallow-70B-v0.4](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4).
- **December 30, 2024**: Released [Llama-3.1-Swallow-70B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3).
- **December 23, 2024**: Released [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3).
- **November 11, 2024**: Released [Llama-3.1-Swallow-8B-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2) and [Llama-3.1-Swallow-8B-Instruct-v0.2](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2).
- **October 08, 2024**: Released [Llama-3.1-Swallow-8B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1), [Llama-3.1-Swallow-8B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1), [Llama-3.1-Swallow-70B-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1), and [Llama-3.1-Swallow-70B-Instruct-v0.1](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1).
# Major Updates
This release enhances the conversation capability of Llama 3.1 Swallow. The model is trained to imitate the behavior of [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it).
Among all open-source LLMs with <= 8 billion parameters, Llama-3.1-Swallow-8B-Instruct-v0.5 exhibits **state-of-the-art performance on Japanese MT-Bench**, outperforming its predecessor, [Llama-3.1-Swallow-8B-Instruct-v0.3](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2), by 1.5 points.
## Swallow Model Index
|Model|Llama-3.1-Swallow-Instruct v0.5|Llama-3.1-Swallow v0.5|Llama-3.3-Swallow v0.4|Llama-3.3-Swallow-Instruct v0.4|Llama-3.1-Swallow-Instruct v0.3|Llama-3.1-Swallow-Instruct v0.2|Llama-3.1-Swallow v0.2|Llama-3.1-Swallow-Instruct v0.1|Llama-3.1-Swallow v0.1|
|---|---|---|---|---|---|---|---|---|---|
|8B|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.5) |||[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.3)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.2)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.2)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.1)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-8B-v0.1)|
|70B|||[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-v0.4)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.3-Swallow-70B-Instruct-v0.4)|[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.3)| | |[🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-Instruct-v0.1)| [🤗 HuggingFace](https://huggingface.co/tokyotech-llm/Llama-3.1-Swallow-70B-v0.1)|
The website [https://swallow-llm.github.io/](https://swallow-llm.github.io/index.en.html) provides large language models developed by the Swallow team.
## Model Details
* **Model type**: Please refer to [Llama 3.1 MODEL_CARD](https://github.com/meta-llama/llama3/blob/main/MODEL_CARD.md) for details on the model architecture.
* **Language(s)**: Japanese English
* **Library**: [Megatron-LM](https://github.com/NVIDIA/Megatron-LM), [transformers](https://github.com/huggingface/transformers)
* **Tokenizer**: Please refer to [Llama 3.1 blog](https://ai.meta.com/blog/meta-llama-3-1) for details on the tokenizer.
* **Contact**: swallow[at]nlp.c.titech.ac.jp
## Model Performance
## Japanese MT-Bench
* We report evaluation results judged by **gpt-4o-2024-08-06** as below.
* In our releases earlier than January 1, 2025, we reported scores judged by gpt-4-1106-preview. Scores reported below are thus not directly comparable with those reported in those earlier releases.
|Model|coding|extraction|humanities|math|reasoning|roleplay|stem|writing|JMTAvg|
|---|---|---|---|---|---|---|---|---|---|
| llm-jp-3-7.2b-instruct3 | 0.358 | 0.597 | 0.812 | 0.386 | 0.438 | 0.766 | 0.622 | 0.721 | 0.588 |
| Qwen2.5-7B-Instruct | 0.599 | 0.741 | 0.719 | 0.637 | 0.541 | 0.744 | 0.624 | 0.713 | 0.665 |
| Tanuki-8B-dpo-v1.0 | 0.461 | 0.597 | 0.562 | 0.495 | 0.377 | 0.589 | 0.509 | 0.643 | 0.529 |
| Llama 3 8B Instruct | 0.467 | 0.706 | 0.692 | 0.310 | 0.433 | 0.542 | 0.532 | 0.546 | 0.529 |
| Llama 3.1 8B Instruct | 0.420 | **0.830** | 0.550 | 0.514 | 0.349 | 0.502 | 0.479 | 0.504 | 0.519 |
| Llama 3 Youko 8B Instruct | 0.464 | 0.757 | 0.769 | 0.414 | 0.487 | 0.695 | 0.583 | 0.753 | 0.616 |
| Llama-3-ELYZA-JP-8B | 0.389 | 0.706 | 0.647 | 0.426 | **0.613** | 0.684 | 0.533 | 0.697 | 0.587 |
| Llama 3 heron brain 8B v0.3 | 0.362 | 0.566 | 0.602 | 0.315 | 0.426 | 0.586 | 0.567 | 0.550 | 0.497 |
| Llama 3.1 Swallow 8B Instruct v0.1 | 0.427 | 0.738 | 0.675 | 0.527 | 0.453 | 0.615 | 0.593 | 0.624 | 0.581 |
| Llama 3.1 Swallow 8B Instruct v0.2 | 0.534 | 0.748 | 0.705 | 0.565 | 0.475 | 0.646 | 0.579 | 0.646 | 0.612 |
| Llama 3.1 Swallow 8B Instruct v0.3 | **0.562** | 0.756 | 0.869 | **0.610** | 0.512 | 0.783 | 0.748 | 0.803 | 0.705 |
| Llama 3.1 Swallow 8B Instruct v0.5 | 0.551 | 0.814 | **0.847** | 0.568 | 0.577 | **0.796** | **0.770** | **0.832** | **0.719** |
### Japanese tasks
|Model|JCom.|JEMHopQA|NIILC|JSQuAD|XL-Sum|MGSM|WMT20-en-ja|WMT20-ja-en|JMMLU|JHumanEval|Ja Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|1-shot|4-shot|4-shot|4-shot|5-shot|0-shot| |
| |EM acc|Char-F1|Char-F1|Char-F1|ROUGE-2|EM acc|BLEU|BLEU|EM acc|pass@1| |
| llm-jp-3-7.2b-instruct3 | 0.780 | 0.297 | 0.570 | 0.882 | 0.132 | 0.344 | 0.251 | 0.189 | 0.422 | 0.196 | 0.406 |
| Qwen2.5-7B-Instruct | 0.915 | 0.429 | 0.391 | 0.891 | 0.168 | 0.632 | 0.211 | 0.192 | 0.623 | 0.532 | 0.498 |
| Tanuki-8B-dpo-v1.0 | 0.278 | 0.284 | 0.370 | 0.670 | 0.102 | 0.428 | 0.238 | 0.183 | 0.306 | 0.251 | 0.311 |
| Llama 3 8B Instruct | 0.880 | 0.417 | 0.385 | 0.891 | 0.126 | 0.424 | 0.214 | 0.202 | 0.468 | 0.296 | 0.430 |
| Llama 3.1 8B Instruct | 0.880 | 0.447 | 0.407 | 0.886 | 0.148 | 0.516 | 0.218 | 0.200 | 0.509 | 0.488 | 0.470 |
| Llama 3 Youko 8B Instruct | 0.921 | 0.481 | 0.517 | 0.899 | 0.209 | 0.472 | 0.256 | 0.191 | 0.469 | 0.262 | 0.468 |
| Llama-3-ELYZA-JP-8B | 0.897 | 0.498 | 0.496 | 0.906 | 0.168 | 0.436 | 0.250 | 0.185 | 0.487 | 0.388 | 0.471 |
| Llama 3 heron brain 8B v0.3 | 0.923 | 0.493 | 0.569 | 0.906 | **0.218** | 0.456 | 0.277 | 0.217 | 0.499 | 0.318 | 0.488 |
| Llama 3.1 Swallow 8B Instruct v0.1 | 0.924 | **0.587** | 0.574 | **0.917** | 0.138 | 0.508 | 0.282 | 0.228 | 0.530 | 0.366 | 0.505 |
| Llama 3.1 Swallow 8B Instruct v0.2 | 0.929 | 0.560 | 0.599 | 0.915 | 0.137 | 0.528 | 0.288 | 0.227 | 0.550 | 0.408 | 0.514 |
| Llama 3.1 Swallow 8B Instruct v0.3 | 0.924 | 0.528 | 0.583 | 0.896 | 0.191 | 0.532 | 0.281 | 0.229 | 0.544 | 0.394 | 0.510 |
| Llama 3.1 Swallow 8B Instruct v0.5 | **0.937** | 0.511 | **0.606** | 0.900 | 0.174 | **0.604** | **0.293** | **0.230** | **0.581** | **0.496** | **0.533** |
### English tasks
|Model|OpenBookQA|TriviaQA|HellaSWAG|SQuAD2.0|XWINO|MMLU|GSM8K|MATH|BBH|HumanEval|En Avg|
|---|---|---|---|---|---|---|---|---|---|---|---|
| |4-shot|4-shot|4-shot|4-shot|4-shot|5-shot|4-shot|4-shot | 3-shot|0-shot| |
| |Acc|EM acc|Acc|EM acc|Acc|Acc|EM acc|CoT EM Acc| CoT EM Acc| pass@1| |
| llm-jp-3-7.2b-instruct3 | 0.328 | 0.479 | 0.563 | 0.501 | 0.876 | 0.462 | 0.264 | 0.028 | 0.420 | 0.219 | 0.414 |
| Qwen2.5-7B-Instruct | 0.428 | 0.519 | 0.624 | 0.569 | 0.877 | 0.742 | 0.739 | 0.688 | 0.217 | 0.636 | 0.604 |
| Tanuki-8B-dpo-v1.0 | 0.334 | 0.283 | 0.469 | 0.501 | 0.816 | 0.377 | 0.487 | 0.178 | 0.333 | 0.288 | 0.406 |
| Llama 3 8B Instruct | 0.388 | 0.670 | 0.583 | 0.611 | 0.892 | 0.657 | 0.745 | 0.306 | 0.646 | 0.554 | 0.605 |
| Llama 3.1 8B Instruct | 0.366 | 0.699 | 0.592 | 0.600 | 0.904 | 0.680 | 0.743 | 0.376 | 0.690 | 0.624 | 0.627 |
| Llama 3 Youko 8B Instruct | 0.406 | 0.613 | 0.599 | 0.559 | 0.897 | 0.596 | 0.563 | 0.152 | 0.401 | 0.287 | 0.507 |
| Llama-3-ELYZA-JP-8B | 0.318 | 0.551 | 0.523 | 0.600 | 0.882 | 0.587 | 0.558 | 0.164 | 0.321 | 0.449 | 0.495 |
| Llama 3 heron brain 8B v0.3 | 0.362 | 0.656 | 0.569 | 0.581 | 0.901 | 0.621 | 0.578 | 0.222 | 0.641 | 0.380 | 0.551 |
| Llama 3.1 Swallow 8B Instruct v0.1 | 0.388 | 0.649 | 0.615 | 0.598 | 0.891 | 0.624 | 0.605 | 0.236 | 0.642 | 0.379 | 0.563 |
| Llama 3.1 Swallow 8B Instruct v0.2 | 0.380 | 0.625 | 0.603 | 0.607 | 0.887 | 0.634 | 0.620 | 0.264 | 0.649 | 0.474 | 0.574 |
| Llama 3.1 Swallow 8B Instruct v0.3 | 0.396 | 0.629 | 0.593 | 0.570 | 0.884 | 0.629 | 0.622 | 0.266 | 0.626 | 0.445 | 0.566 |
| Llama 3.1 Swallow 8B Instruct v0.5 | 0.396 | 0.638 | 0.603 | 0.581 | 0.889 | 0.663 | 0.717 | 0.368 | 0.628 | 0.554 | 0.604 |
## Evaluation Benchmarks
### Japanese MT-Bench
We used [Japanese MT-Bench](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_question) to assess the capabilities of multi-turn dialogue with the following settings:
- Implementation: FastChat [Zheng+, 2023] (commit #e86e70d0)
- Question: [Nejumi LLM-Leaderboard NEO, mtbench_ja_question_v4](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/v4)
- Reference Answer: [swallow-evaluation, reference answer](https://github.com/swallow-llm/swallow-evaluation/tree/main/fastchat/fastchat/llm_judge/data/japanese_mt_bench/reference_answer)
- Prompt for Judge: [Nejumi LLM-Leaderboard NEO, mtbench_ja_prompt_v1](https://wandb.ai/wandb-japan/llm-leaderboard/artifacts/dataset/mtbench_ja_prompt/v1)
- Judge: `gpt-4o-2024-08-06`
- Scoring: Absolute scale normalized to a 0-1 range, averaged over five runs.
### Japanese evaluation benchmarks
We used llm-jp-eval(v1.3.0), JP Language Model Evaluation Harness(commit #9b42d41) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (JCommonsenseQA [Kurihara et al., 2022])
- Open-ended question answering (JEMHopQA [Ishii et al., 2024])
- Open-ended question answering (NIILC [関根, 2003])
- Machine reading comprehension (JSQuAD [Kurihara et al., 2022])
- Automatic summarization (XL-Sum [Hasan et al., 2021])
- Machine translation (WMT2020 ja-en [Barrault et al., 2020])
- Machine translation (WMT2020 en-ja [Barrault et al., 2020])
- Arithmetic reasoning (MGSM [Shi et al., 2023])
- Academic exams (JMMLU [尹ら, 2024])
- Code generation (JHumanEval [佐藤ら, 2024])
### English evaluation benchmarks
We used the Language Model Evaluation Harness(v.0.4.2) and Code Generation LM Evaluation Harness(commit #0261c52). The details are as follows:
- Multiple-choice question answering (OpenBookQA [Mihaylov et al., 2018])
- Open-ended question answering (TriviaQA [Joshi et al., 2017])
- Machine reading comprehension (SQuAD2 [Rajpurkar et al., 2018])
- Commonsense reasoning (XWINO [Tikhonov and Ryabinin, 2021])
- Natural language inference (HellaSwag [Zellers et al., 2019])
- Arithmetic reasoning (GSM8K [Cobbe et al., 2021])
- Mathematical reasoning (MATH [Hendrycks et al., 2022][Lightman et al., 2024])
- Reasoning (BBH (BIG-Bench-Hard) [Suzgun et al., 2023])
- Academic exams (MMLU [Hendrycks et al., 2021])
- Code generation (HumanEval [Chen et al., 2021])
## Usage
```sh
pip install vllm
```
```python
from transformers import AutoTokenizer
from vllm import LLM, SamplingParams
model_name = "tokyotech-llm/Llama-3.1-Swallow-8B-Instruct-v0.5"
tokenizer = AutoTokenizer.from_pretrained(model_name)
llm = LLM(
model=model_name,
tensor_parallel_size=1,
)
sampling_params = SamplingParams(
temperature=0.6, top_p=0.9, max_tokens=512, stop="<|eot_id|>"
)
message = [
{
"role": "user",
"content": "東京の紅葉した公園で、東京タワーと高層ビルを背景に、空を舞うツバメと草地に佇むラマが出会う温かな物語を書いてください。",
},
]
prompt = tokenizer.apply_chat_template(
message, tokenize=False, add_generation_prompt=True
)
output = llm.generate(prompt, sampling_params)
print(output[0].outputs[0].text)
```
## Training Datasets
### Instruction Tuning
The following datasets were used for the instruction tuning.
- [Gemma-3-LMSYS-Chat-1M-Synth](https://huggingface.co/datasets/tokyotech-llm/lmsys-chat-1m-synth)
- Single-turn Japanese instruction dataset synthesized and derived from [lmsys-chat-1m](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) [\[Zhang+, ICLR24\]](https://openreview.net/forum?id=BOfDKxfwt0)).
- First-turn user instructions were translated into Japanese via DeepL (machine translation), and assistant responses were generated using [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it). The same model, i.e., [gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) served as a judge for rejection sampling (n=10).
Conversations containing personally identifiable information (PII) and template-based user instructions were removed. Duplicate instructions were removed.
## Risks and Limitations
The models released here are still in the early stages of our research and development and have not been tuned to ensure outputs align with human intent and safety considerations.
## Acknowledgements
We thank Meta Research for releasing Llama 3.1 under a generous open license.
We received various supports, including:
+ AIST project: "Research and Development of Foundation Models for Generative AI in the Physical Domain"
+ NEDO project: "Development of Artificial Intelligence Application Technology to Support Judgment in Design Risk Assessment Work Based on the Perspective of Skilled Persons" (JPNP18002) of "Development of Integration Technology as the Core of Next Generation Artificial Intelligence and Robotics"
+ MEXT project: "Formation of R&D center to ensure transparency and reliability of generative AI models"
+ AIST program: [Large Generative AI Development Support Program](https://abci.ai/en/link/lfm_support_program.html)
## License
[META LLAMA 3.1 COMMUNITY LICENSE](https://www.llama.com/llama3_1/license/) and [Gemma Terms of Use](https://ai.google.dev/gemma/terms)
## Authors
Here are the team members:
- From [Okazaki Laboratory, Institute of Science Tokyo](https://www.nlp.c.titech.ac.jp/index.en.html), the following members:
- [Naoaki Okazaki](https://www.chokkan.org/index.ja.html)
- [Sakae Mizuki](https://s-mizuki-nlp.github.io/)
- [Youmi Ma](https://www.nlp.c.titech.ac.jp/member/youmi.en.html)
- [Sangwhan Moon](https://www.sangwhan.com/)
- [Koki Maeda](https://sites.google.com/view/silviase)
- [Masanari Ohi](https://sites.google.com/view/masanariohi)
- [Hinari Shimada](https://hinarishimada.github.io/portfolio)
- [Taihei Shiotani](https://github.com/inatoihs)
- [Koshiro Saito](https://sites.google.com/view/koshiro-saito)
- [Tatsuya Ichinose](https://tatsuya736482.github.io/myprofile)
- Naoya Matsushita
- Sora Miyamoto
- Nguyen Tien Dung
- Yuta Katayama
- From [YOKOTA Laboratory, Institute of Science Tokyo](https://www.rio.gsic.titech.ac.jp/en/index.html), the following members:
- [Rio Yokota](https://twitter.com/rioyokota)
- [Kazuki Fujii](https://twitter.com/okoge_kaz)
- [Taishi Nakamura](https://twitter.com/Setuna7777_2)
- [Takumi Okamoto](https://www.linkedin.com/in/takumi-okamoto)
- [Ishida Shigeki](https://www.wantedly.com/id/reborn27)
- Masaki Kawamura
- Yukito Tajima
- From [Artificial Intelligence Research Center, AIST, Japan](https://www.airc.aist.go.jp/en/teams/), the following members:
- [Hiroya Takamura](https://sites.google.com/view/hjtakamura)
## How to cite
If you find our work helpful, please feel free to cite these papers.
```
@inproceedings{Fujii:COLM2024,
title={Continual Pre-Training for Cross-Lingual LLM Adaptation:
Enhancing Japanese Language Capabilities},
author={Kazuki Fujii and Taishi Nakamura and Mengsay Loem and Hiroki
Iida and Masanari Ohi and Kakeru Hattori and Hirai Shota and Sakae
Mizuki and Rio Yokota and Naoaki Okazaki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@inproceedings{Okazaki:COLM2024,
title={Building a Large Japanese Web Corpus for Large Language Models},
author={Naoaki Okazaki and Kakeru Hattori and Hirai Shota and Hiroki
Iida and Masanari Ohi and Kazuki Fujii and Taishi Nakamura and Mengsay
Loem and Rio Yokota and Sakae Mizuki},
booktitle="Proceedings of the First Conference on Language Modeling",
series={COLM},
pages="(to appear)",
year="2024",
month=oct,
address={University of Pennsylvania, USA},
}
@misc{ma:arxiv2025,
title={Building Instruction-Tuning Datasets from Human-Written Instructions with Open-Weight Large Language Models},
author={Youmi Ma and Sakae Mizuki and Kazuki Fujii and Taishi Nakamura and Masanari Ohi and Hinari Shimada and Taihei Shiotani and Koshiro Saito and Koki Maeda and Kakeru Hattori and Takumi Okamoto and Shigeki Ishida and Rio Yokota and Hiroya Takamura and Naoaki Okazaki},
year={2025},
eprint={2503.23714},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2503.23714},
}
```
### References
```tex
@misc{dubey2024llama3herdmodels,
title={The Llama 3 Herd of Models},
author={Abhimanyu Dubey and Abhinav Jauhri and Abhinav Pandey and Abhishek Kadian and Ahmad Al-Dahle and Aiesha Letman and Akhil Mathur and Alan Schelten and Amy Yang and Angela Fan et al.},
year={2024},
eprint={2407.21783},
archivePrefix={arXiv},
primaryClass={cs.AI},
url={https://arxiv.org/abs/2407.21783},
}
```
|
New-Clip-Nimra-Mehra-Viral-videos-Link/FULL.VIDEO.Nimra-Mehra.Viral.Video.Tutorial.Official
|
New-Clip-Nimra-Mehra-Viral-videos-Link
| 2025-06-25T00:50:05Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-25T00:49:24Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
twodgirl/how-to-build-a-dit-pipeline-from-scratch
|
twodgirl
| 2025-06-25T00:40:52Z | 0 | 0 | null |
[
"text-to-image",
"license:apache-2.0",
"region:us"
] |
text-to-image
| 2025-06-24T18:12:54Z |
---
license: apache-2.0
pipeline_tag: text-to-image
---
# How to Build a DiT Pipeline From Scratch
Requirements:
- Custom Scheduler
- CLIP model
- DiT model
- Wan2.1 VAE
The article describes the making of a pretrained diffusion model. Generating the final image takes four steps.
Model training is possible by selecting a portion of an image using a mask.
## Why build a DiT model from scratch
The model shows early progress on the first day (denoising the last steps). We won't have to wait week(s) to see the results.
## CLIP
For simplicity, only the pooled output will be used. It commonly has a shape of (1, 512) or (1, 768).
## VAE
We use the normalized output of the VAE encoder, which has a shape of (1, 384 or 512, 64, 64), corresponds to the following: (B, C, T, H, W) or (B, C, H, W)
## DiT model
```
Attention
- def forward(self, x)
AdaLayerNorm
- def forward(self, x, timestep_emb)
DiTBlock
- def forward(self, x, timestep_emb=None)
DiTModel
- def forward(self, x, text_emb, timestep: int, mask_ratio=1.0, mask=None)
- includes AdaLayerNorm (final_layer), DiTBlock, PositionalEncoding, TimestepEmbedder, and a
- Linear layer (fusion_proj), between (config.clip_pooled_size + model_hidden_size) and model_hidden_size
PositionalEncoding
TimestepEmbedder
- def get_embed(self, t: int)
RMSNorm
```
## Forward function
Pseudocode:
```
def forward(self, x, text_emb, timestep: int, mask_ratio=1.0, mask=None):
b, seq, emb = x.shape
# Concat the normalized image and the clip (vision/text) pooled output.
y = torch.cat((x, text_emb), dim=-1)
y = self.position_embedding(y)
# Keep only the masked tokens.
y, mask_indices = self.mask_without_pad(y)
# Assume mask is a list.
mask.append(masked_indices)
# Create time embeddings.
time_emb = self.time_embedding.get_embed(t)
# z has a shape of x.
z = self.fusion_proj(y)
for i in range(num_blocks / 2):
self.blocks[i](z, time_emb)
z = y[:, :, :emb] + dropout(fc1(z))
for i in range(num_blocks / 2, num_blocks):
z = self.blocks[i](z, time_emb)
# Normalization layer.
return self.final_layer(z, time_emb)
```
## Training with custom scheduler
Train the simplified v-pred model.
**Noise the image**
x = sqrt(A) * x_0 + sqrt(1 - A) * eps
x_0 is the encoded image.
alpha = sqrt(A)
beta = sqrt(1 - A)
**Calculate velocity**
v_target = alpha * eps - beta * x_0
The model learns to predict the velocity value.
*Noise prediction*
~~eps = (v_target + beta * x_0) / alpha~~
## Inference
Repeat for four steps.
**Noise the image**
As in the training, it'll be called *x*.
**Denoise the image**
Calculate the mean, which will be the input for the next step.
```
alpha, beta = scheduler(timestep: int)
temp = alpha * x - beta * DiTModel(x, text_emb, timestep)
x = (1 - beta) * x + beta * temp
```
**Decode the image**
Convert the model's output (B, SEQ, EMB) to a format compatible with the VAE Decoder, such as (B, C, H, W).
## Previous work
- [Quanto, float8 quants and command line converters](https://huggingface.co/twodgirl/Flux-dev-optimum-quant-qfloat8)
- [GGUF writer/reader for Flux, Bagel](https://huggingface.co/twodgirl/comfy-gguf-unet-loader)
- Faster PuLID, Flux Dev inference in diffusers ([1](https://huggingface.co/twodgirl/flux-dev-fp8-e4m3fn-diffusers), [2](https://huggingface.co/twodgirl/flux-redux-pulid-diffusers), [3](https://huggingface.co/twodgirl/flux-magcache-pulid-diffusers))
- Latent preview ([1](https://huggingface.co/twodgirl/flux-latent-preview), [2](https://huggingface.co/twodgirl/ssd-latent-preview), [3](https://huggingface.co/twodgirl/ms-vae-preview))
- T5, Qwen Encoder and Embeddings ([1](https://huggingface.co/twodgirl/flux-text-encoder-neutered), [2](https://huggingface.co/twodgirl/flux-qwen-neutered))
- [Flux conversion from/to diffusers format](https://huggingface.co/twodgirl/flux-devpro-schnell-merge-fp8-e4m3fn-diffusers)
- Layer manipulation and partial patches ([1](https://huggingface.co/datasets/twodgirl/suppress-layers-in-flux), [2](https://huggingface.co/datasets/twodgirl/suppress-layers-in-sd3.5), [3](https://huggingface.co/datasets/twodgirl/suppress-layers-in-lumina-image-2))
## Disclaimer
The documentation requires citation and attribution to the author via a link to their Hugging Face profile.
|
hongin9812/tunning_blip2-opt-2.7b-fp16-sharded
|
hongin9812
| 2025-06-25T00:39:19Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:ybelkada/blip2-opt-2.7b-fp16-sharded",
"base_model:adapter:ybelkada/blip2-opt-2.7b-fp16-sharded",
"region:us"
] | null | 2025-06-25T00:34:47Z |
---
base_model: ybelkada/blip2-opt-2.7b-fp16-sharded
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2
|
thavens/pir_sft_ckpt_50_i
|
thavens
| 2025-06-25T00:38:22Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"generated_from_trainer",
"trl",
"sft",
"conversational",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:14:28Z |
---
base_model: Qwen/Qwen3-4B
library_name: transformers
model_name: pir_sft_ckpt_50_i
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for pir_sft_ckpt_50_i
This model is a fine-tuned version of [Qwen/Qwen3-4B](https://huggingface.co/Qwen/Qwen3-4B).
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="thavens/pir_sft_ckpt_50_i", 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/tmotiv/huggingface/runs/7n04dqdo)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.4
- Pytorch: 2.7.0+cu128
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
daixuancheng/zero_qwen-math-7b_base_allDapo_mathVerify_yesSuffix_step40
|
daixuancheng
| 2025-06-25T00:32:38Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:05:08Z |
---
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]
|
daixuancheng/ppo_sac_static0.1_constrainbyadv_step-40_critic
|
daixuancheng
| 2025-06-25T00:30:28Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T12:11:28Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- Relevant interpretability work for the model goes here -->
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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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## Technical Specifications [optional]
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|
SrivatsaBhamidipati/qwen2.5-coder-3b-qlora
|
SrivatsaBhamidipati
| 2025-06-25T00:29:59Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"generated_from_trainer",
"base_model:codellama/CodeLlama-13b-Instruct-hf",
"base_model:adapter:codellama/CodeLlama-13b-Instruct-hf",
"license:llama2",
"region:us"
] | null | 2025-06-24T22:28:46Z |
---
library_name: peft
license: llama2
base_model: codellama/CodeLlama-13b-Instruct-hf
tags:
- generated_from_trainer
model-index:
- name: qwen2.5-coder-3b-qlora
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. -->
# qwen2.5-coder-3b-qlora
This model is a fine-tuned version of [codellama/CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 50
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
ZhangShenao/Llama-3.2-1B
|
ZhangShenao
| 2025-06-25T00:27:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:25:53Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
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---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via pip install --upgrade transformers.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
pipe("The key to life is")
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B --include "original/*" --local-dir Llama-3.2-1B
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
multiheadattn/my_awesome_model
|
multiheadattn
| 2025-06-25T00:25:00Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] |
text-classification
| 2025-06-23T17:22:10Z |
---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my_awesome_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_model
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on [stanfordnlp/imbd](https://huggingface.co/datasets/stanfordnlp/imdb) dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2370
- Accuracy: 0.9312
<!-- ## 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
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.2213 | 1.0 | 1563 | 0.2004 | 0.9236 |
| 0.1474 | 2.0 | 3126 | 0.2370 | 0.9312 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K
|
laion
| 2025-06-25T00:24:14Z | 177 | 4 |
transformers
|
[
"transformers",
"pytorch",
"open_clip",
"safetensors",
"mammut",
"feature-extraction",
"clip",
"openMammut",
"datacomp",
"zero-shot-image-classification",
"custom_code",
"dataset:mlfoundations/datacomp_1b",
"arxiv:2506.04598",
"license:apache-2.0",
"region:us"
] |
zero-shot-image-classification
| 2025-06-03T23:34:30Z |
---
datasets:
- mlfoundations/datacomp_1b
library_name: transformers
license: apache-2.0
pipeline_tag: zero-shot-image-classification
tags:
- clip
- openMammut
- datacomp
library_tag:
- open_clip
- transformers
---
# Model card for openMammut-ViT-L-14-DataComp-1.4B-s12.8B-b180K
# Table of Contents
1. [Model Details](#model-details)
2. [Uses](#uses)
3. [Training Details](#training-details)
4. [Evaluation](#evaluation)
5. [How To Get Started With the Model](#how-to-get-started-with-the-model)
6. [Acknowledgements](#acknowledgements)
7. [Citation](#citation)
# Model Details
## Model Description
An openMammut ViT-L/14 model (224 resolution), able to perform various image recognition and image captioning tasks. Trained on the [DataComp-1.4B](https://github.com/mlfoundations/datacomp), 12.8B samples in total, using [custom OpenCLIP fork](https://github.com/LAION-AI/open_clip_mammut).
Model training done by Jenia Jitsev on [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) at [Juelich Supercomputing Center](https://www.fz-juelich.de/en/ias/jsc), using automated experiment execution workflow [autoexperiment](https://github.com/SLAMPAI/autoexperiment), implemented by Mehdi Cherti.
Training performed in frame of scaling law model and dataset comparison study published in [arXiv:2506.04598](https://arxiv.org/abs/2506.04598). See also the [research repository](https://github.com/LAION-AI/scaling-laws-for-comparison) and [full thread](https://x.com/JJitsev/status/1931569060438737161).
The model weights are directly usable in [HF transformers](#quickstart-with-hf-transformers) (HF version implemented by Marianna Nezhurina) or by using [custom OpenCLIP fork](#using-openclip-codebase).
Both image recognition (classification, retrieval, etc) and text generation (image captioning) tasks are supported.
<img src="https://cdn-uploads.huggingface.co/production/uploads/6355b485b8b79340d4630dd5/mCNQu13oNcdHasaNo3lST.png" alt="openmammut_release_logo" width="60%"/>
# Uses
As per the original [OpenAI CLIP model card](https://github.com/openai/CLIP/blob/d50d76daa670286dd6cacf3bcd80b5e4823fc8e1/model-card.md), this model is intended as a research output for research communities. We hope that this model will enable researchers to better understand and explore zero-shot, arbitrary image classification and retrieval, and generalization capabilities of language-vision learning in general. We also hope it can be used for interdisciplinary studies of the impact of such model, eg when used as component in VLMs or other multi-modal models.
The OpenAI CLIP paper includes a discussion of potential downstream impacts to provide an example for this sort of analysis.
Details on the DataComp-1.4B training dataset can be found in [DataComp repository](https://github.com/mlfoundations/datacomp) and the [DataComp NeurIPS Oral paper](https://openreview.net/forum?id=dVaWCDMBof).
## Direct Use
Zero-shot image classification, image and text retrieval, segmentation, image captioning. Other uses are possible when employing the model as component in other systems or fine-tuning it for other downstream tasks.
ATTENTION: currently, if [using openCLIP code base](#using-openclip-codebase), [custom openCLIP fork](https://github.com/LAION-AI/open_clip_mammut) is required to work with the model.
Integrating openMaMMUT code into main [openCLIP repository](https://github.com/mlfoundations/open_clip) is work in progress. Any volunteers helping with intergration highly welcome, join [LAION discord](https://discord.gg/BZqhreFazY)
Alternatively, HF transformers can be used to [work with the model natively in HF](#quickstart-with-hf-transformers).
## Downstream Use
Image classification, retrieval and image captioning. Linear probing and full fine-tuning for various image tasks, e.g, segmentation, image classification, retrieval. Re-usage as component for guiding and conditioning of image generative models, among others.
## Out-of-Scope Use
As per the OpenAI models,
**Any** deployed use case of the model (that is, in form of an end product) - whether commercial or not - is currently out of scope. Non-deployed use cases such as image search in a constrained environment, are also not recommended unless there is thorough in-domain testing of the model with a specific, fixed class taxonomy. This is because our safety assessment demonstrated a high need for task specific testing especially given the variability of CLIP’s performance with different class taxonomies. This makes untested and unconstrained deployment of the model in any use case currently potentially error prone and thus unsafe.
Certain use cases which would fall under the domain of surveillance and facial recognition are always out-of-scope regardless of performance of the model. This is because the use of artificial intelligence for tasks such as these can be premature currently given the lack of testing norms and checks to ensure its fair use.
Since the model has not been purposefully trained in or evaluated on any languages other than English, its use should be limited to English language use cases.
Further the above notice, the DataComp-1.4B dataset used in training of these models has additional considerations, see below.
# Training Details
## Training Data
This model was trained on [DataComp-1.4B](https://github.com/mlfoundations/datacomp), [DataComp paper](https://openreview.net/forum?id=dVaWCDMBof), [DataComp-1.4B metadata at HF](https://huggingface.co/datasets/mlfoundations/datacomp_1b) (also known as DataComp-XL), which contains 1.4 Billion image-text samples.
**IMPORTANT NOTE:** Open datasets democratize research and experimentation around large-scale multi-modal model training and handling of curated or uncurated, large-scale datasets crawled from publically available internet. Our recommendation is therefore to train on the dataset only for research purposes. Be aware when obtaining the dataset for training for research purposes, that this large-scale dataset is automatically curated. Keep in mind that the automatic curation of the dataset means that collected links may lead to strongly discomforting and disturbing content for a human viewer. Therefore, please use the links contained in metadata with caution and follow them only at your own risk. Same is valid for the downloaded samples for training, view them only if you are a well-trained large-scale data scientist prepared to be confronted with extremely diverse content. While filtering out samples based on various safety classifiers strongly reduced the chance for encountering potentially harmful content when viewing, the possibility for subjectively strongly discomforting content being still present in the dataset cannot be entirely excluded. Open datasets provided to broad research and other interested communities allow for transparent investigation of benefits that come along with training large-scale models as well as of pitfalls and dangers that may stay unreported or unnoticed when working with closed large datasets that remain restricted to a small community. The training dataset is not recommended to be used for creating any ready-to-go industrial products, as the basic research about general properties and safety of such large-scale models, which we would like to encourage with this release, is still in progress.
## Training Procedure
OpenMammut ViT-L/14 model was trained on 224x224 12.8B samples (128M * 100 checkpoints) from DataComp-1.4B dataset (which corresponds to 9 epochs). Warmup = 6k steps, learning rate = 2.5e-3, cosine annealing schedule, weight decay = 0.2. Global batch size = 180224, number of GPUs = 1024 (A100 40Gb), local batch size = 176
For more details, see [arXiv:2506.04598](https://arxiv.org/abs/2506.04598) and [research repository](https://github.com/LAION-AI/scaling-laws-for-comparison).
<img src="https://cdn-uploads.huggingface.co/production/uploads/6355b485b8b79340d4630dd5/3m_kj2FTOcOkuucb1qeFd.png" alt="openmammut_hyperparams" width="60%"/>
# Evaluation
Evaluation done with code in the [LAION CLIP Benchmark suite](https://github.com/LAION-AI/CLIP_benchmark), using [autoexperiment](https://github.com/SLAMPAI/autoexperiment).
## Testing Data, Factors & Metrics
### Testing Data
The testing is performed with various downstream tasks and datasets, which include ImageNet-1k, DataComp evaluation suite (35 tasks total), and MS-COCO retrieval.
**TODO** - more detail
## Results
The model achieves a 80.34% zero-shot top-1 accuracy on ImageNet-1k, 71.19% zero-shot on MSCOCO image@R5 retrieval, 85.88% on MSCOCO text@R5 retrieval (5k Karpathy split test set).
More details in the ArXiv paper : [Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets](https://arxiv.org/abs/2506.04598)
<img src="https://cdn-uploads.huggingface.co/production/uploads/6355b485b8b79340d4630dd5/bLHbtJ66mxs6ErKaqbXe9.png" alt="openmammut_hyperparams" width="90%"/>
**TODO** - create table for just this model's metrics.
# How to Get Started with the Model
The model weights are directly usable in [HF transformers](#quickstart-with-hf-transformers) (HF version implemented by Marianna Nezhurina) or by using [custom OpenCLIP fork](#using-openclip-codebase).
Both image recognition (classification, retrieval, etc) and text generation (image captioning) tasks are supported.
## Quickstart with HF transformers
```python
from PIL import Image
import requests
from transformers import CLIPProcessor, AutoModel, CLIPTokenizer
model_path = "laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K"
tokenizer = CLIPTokenizer.from_pretrained(model_path)
model = AutoModel.from_pretrained(model_path, trust_remote_code=True)
processor = CLIPProcessor.from_pretrained(model_path)
url = "http://images.cocodataset.org/val2017/000000039769.jpg"
image = Image.open(requests.get(url, stream=True).raw)
# Image captioning
inputs = processor(images=image, return_tensors="pt", padding=True)
outputs = model.generate(pixel_values=inputs["pixel_values"], top_p=0.1, do_sample=True).sequences
decoded_outputs = tokenizer.batch_decode(outputs)
print("HuggingFace outputs:", decoded_outputs) # prints: ['<|startoftext|>cats on couch']
# Get image-text similarity (just like CLIP)
text = ["a photo of a cat", "a photo of a dog"]
inputs = processor(images=image, text=text, return_tensors="pt", padding=True)
outputs = model(pixel_values=inputs["pixel_values"], input_ids=inputs["input_ids"], attention_mask=inputs["attention_mask"], contrastive_only=True)
logits_per_image = outputs.logits_per_image # this is the image-text similarity score
probs = logits_per_image.softmax(dim=1) # we can take the softmax to get the label probabilities
print("Label probabilities:", probs)
print("Logits per image:", logits_per_image)
# Compute image and text embeddings separately
text_features = model.get_text_features(inputs["input_ids"], inputs["attention_mask"])
image_features = model.get_image_features(inputs["pixel_values"])
print("Text features shape:", text_features.shape) # prints: [batch_size, feature_dim]
print("Image features shape:", image_features.shape) # prints: [batch_size, feature_dim]
text_features /= text_features.norm(dim=-1, keepdim=True)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs) # prints: [[1., 0.]] or similar, depending on the image and text
```
## Using OpenCLIP codebase
Research repository: https://github.com/LAION-AI/scaling-laws-for-comparison
ATTENTION: currently, [custom openCLIP fork](https://github.com/LAION-AI/open_clip_mammut) is required to work with the model when using openCLIP code base.
Integrating openMaMMUT code into main [openCLIP repository](https://github.com/mlfoundations/open_clip) is work in progress. Any volunteers helping with intergration highly welcome, join [LAION discord](https://discord.gg/BZqhreFazY).
Alternatively, use [HF transformers](quickstart-with-hf-transformers) to work with the model natively in HF.
First, you need to install OpenCLIP MaMMUT, a fork of OpenCLIP with MaMMUT support:
```bash
git clone https://github.com/LAION-AI/open_clip_mammut
cd open_clip_mammut
python -m pip install .
```
Use the code below to get started with the model.
Zero-shot classification example:
```python
import torch
from PIL import Image
import open_clip
model, _, transform = open_clip.create_model_and_transforms('hf-hub:laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K')
model.eval() # model in train mode by default, impacts some models with BatchNorm or stochastic depth active
tokenizer = open_clip.get_tokenizer('hf-hub:laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K')
image = transform(Image.open("docs/CLIP.png")).unsqueeze(0)
text = tokenizer(["a diagram", "a dog", "a cat"])
with torch.no_grad(), torch.amp.autocast('cuda'):
image_features = model.encode_image(image)
text_features = model.encode_text(text)
image_features /= image_features.norm(dim=-1, keepdim=True)
text_features /= text_features.norm(dim=-1, keepdim=True)
text_probs = (100.0 * image_features @ text_features.T).softmax(dim=-1)
print("Label probs:", text_probs) # prints: [[1., 0., 0.]]
```
Caption generation example:
```python
import open_clip
import torch
from PIL import Image
model, _, transform = open_clip.create_model_and_transforms('hf-hub:laion/openMaMMUT-ViT-L-14-DataComp-1.4B-s12.8B-b180K')
im = Image.open("docs/CLIP.png").convert("RGB")
im = transform(im).unsqueeze(0)
with torch.no_grad(), torch.amp.autocast('cuda'):
generated = model.generate(im)
print(open_clip.decode(generated[0]).split("<end_of_text>")[0].replace("<start_of_text>", ""))
```
# Acknowledgements
We gratefully acknowledge the Gauss Centre for Supercomputing e.V. (www.gauss-centre.eu) for funding the work by providing computing time through the John von Neumann Institute for Computing (NIC) on the GCS Supercomputer [JUWELS Booster](https://apps.fz-juelich.de/jsc/hps/juwels/booster-overview.html) at Jülich Supercomputing Centre (JSC).
We also acknowledge storage resources on JUST granted and operated by JSC, as well as storage and computing resources from the Helmholtz Data Federation (HDF).
We gratefully acknowledge funding by the Federal Ministry of Education and Research of Germany (BMBF) under grant no. 01IS24085C (OPENHAFM), under the grant 16HPC117K (MINERVA) and under the grant no. 01IS22094B (WestAI - AI Service Center West), as well as co-funding by EU from EuroHPC Joint Undertaking programm under grant no. 101182737 (MINERVA) and from Digital Europe Programme under grant no. 101195233 (openEuroLLM).
# Citation
**BibTeX:**
Please cite:
[Scaling laws for robust comparison of open foundation language-vision models and datasets](https://arxiv.org/abs/2506.04598)
```
@article{nezhurina2025scaling,
title={Scaling Laws for Robust Comparison of Open Foundation Language-Vision Models and Datasets},
author={Marianna Nezhurina, Tomer Porian, Giovanni Pucceti, Tommie Kerssies, Romain Beaumont, Mehdi Cherti, Jenia Jitsev},
journal={arXiv:2506.04598},
url={https://arxiv.org/abs/2506.04598},
year={2025}
}
```
DataComp
```
@article{gadre2023datacomp,
title={Datacomp: In search of the next generation of multimodal datasets},
author={Gadre, Samir Yitzhak and Ilharco, Gabriel and Fang, Alex and Hayase, Jonathan and Smyrnis, Georgios and Nguyen, Thao and Marten, Ryan and Wortsman, Mitchell and Ghosh, Dhruba and Zhang, Jieyu and others},
journal={Advances in Neural Information Processing Systems},
volume={36},
pages={27092--27112},
year={2023}
}
```
MaMMUT
```
@article{
kuo2023mammut,
title={Ma{MMUT}: A Simple Architecture for Joint Learning for MultiModal Tasks},
author={Weicheng Kuo and AJ Piergiovanni and Dahun Kim and xiyang luo and Benjamin Caine and Wei Li and Abhijit Ogale and Luowei Zhou and Andrew M. Dai and Zhifeng Chen and Claire Cui and Anelia Angelova},
journal={Transactions on Machine Learning Research},
issn={2835-8856},
year={2023},
url={https://openreview.net/forum?id=FqOG4osY7C},
}
```
Reproducible scaling laws for openCLIP
```
@inproceedings{Cherti2023,
title={Reproducible scaling laws for contrastive language-image learning},
author={Cherti, Mehdi and Beaumont, Romain and Wightman, Ross and Wortsman, Mitchell and Ilharco, Gabriel and Gordon, Cade and Schuhmann, Christoph and Schmidt, Ludwig and Jitsev, Jenia},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={2818--2829},
year={2023}
}
```
OpenCLIP software
```
@software{ilharco_gabriel_2021_5143773,
author = {Ilharco, Gabriel and
Wortsman, Mitchell and
Wightman, Ross and
Gordon, Cade and
Carlini, Nicholas and
Taori, Rohan and
Dave, Achal and
Shankar, Vaishaal and
Namkoong, Hongseok and
Miller, John and
Hajishirzi, Hannaneh and
Farhadi, Ali and
Schmidt, Ludwig},
title = {OpenCLIP},
month = jul,
year = 2021,
note = {If you use this software, please cite it as below.},
publisher = {Zenodo},
version = {0.1},
doi = {10.5281/zenodo.5143773},
url = {https://doi.org/10.5281/zenodo.5143773}
}
```
CLIP benchmark software
```
@software{cherti_2025_15403103,
author = {Cherti, Mehdi and
Beaumont, Romain},
title = {CLIP benchmark},
month = may,
year = 2025,
publisher = {Zenodo},
doi = {10.5281/zenodo.15403103},
url = {https://doi.org/10.5281/zenodo.15403103},
swhid = {swh:1:dir:8cf49a5dd06f59224844a1e767337a1d14ee56c2
;origin=https://doi.org/10.5281/zenodo.15403102;vi
sit=swh:1:snp:dd153b26f702d614346bf814f723d59fef3d
77a2;anchor=swh:1:rel:cff2aeb98f42583b44fdab5374e9
fa71793f2cff;path=CLIP\\_benchmark-main
},
}
```
|
ZhangShenao/Llama-3.2-3B
|
ZhangShenao
| 2025-06-25T00:24:12Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:20:36Z |
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
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---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-3B, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with transformers >= 4.43.0 onward, you can run conversational inference using the Transformers pipeline abstraction or by leveraging the Auto classes with the generate() function.
Make sure to update your transformers installation via pip install --upgrade transformers.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-3B"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto"
)
pipe("The key to life is")
```
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama).
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-3B --include "original/*" --local-dir Llama-3.2-3B
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
Ljk0501/Gemma3_1B_it_GGUF
|
Ljk0501
| 2025-06-25T00:21:40Z | 0 | 0 | null |
[
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-25T00:15:49Z |
---
license: apache-2.0
---
|
luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-12_7384
|
luckeciano
| 2025-06-25T00:20:30Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T18:50:44Z |
---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-12_7384
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-12_7384
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="luckeciano/Qwen-2.5-7B-GRPO-NoBaseline-FisherMaskGlobal-1e-12_7384", 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/max-ent-llms/PolicyGradientStability/runs/gy54s0p9)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
```
|
New-videos-Samiya-Hijab-viral-video-Clips/FULL.VIDEO.Samiya.Hijab.Viral.Video.Tutorial.Official
|
New-videos-Samiya-Hijab-viral-video-Clips
| 2025-06-25T00:16:24Z | 0 | 0 | null |
[
"region:us"
] | null | 2025-06-25T00:16:12Z |
<animated-image data-catalyst=""><a href="https://tinyurl.com/5ye5v3bc?dfhgKasbonStudiosdfg" rel="nofollow" data-target="animated-image.originalLink"><img src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" alt="Foo" data-canonical-src="https://static.wixstatic.com/media/b249f9_adac8f70fb3f45b88691696c77de18f3~mv2.gif" style="max-width: 100%; display: inline-block;" data-target="animated-image.originalImage"></a>
|
daixuancheng/ppo_sac_static0.1_constrainbyadv_step-40_actor
|
daixuancheng
| 2025-06-25T00:13:49Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T11:18:54Z |
---
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]
|
twodgirl/omnigen2-nf4-bfloat16-diffusers
|
twodgirl
| 2025-06-25T00:11:55Z | 0 | 0 |
diffusers
|
[
"diffusers",
"safetensors",
"base_model:OmniGen2/OmniGen2",
"base_model:finetune:OmniGen2/OmniGen2",
"license:apache-2.0",
"diffusers:OmniGen2Pipeline",
"region:us"
] | null | 2025-06-24T21:31:15Z |
---
license: apache-2.0
base_model:
- OmniGen2/OmniGen2
---
# OmniGen2
Unsloth meets Omni2. The transformer module is in bfloat16 precision. The Qwen-VL is in nf4.
|
tamewild/4b_v7_merged_e2
|
tamewild
| 2025-06-25T00:11:40Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:09:07Z |
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
alllwang/00789f7e-9f45-447f-b541-b4db9c07a00c
|
alllwang
| 2025-06-25T00:10:26Z | 0 | 0 |
peft
|
[
"peft",
"safetensors",
"qwen3",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen3-1.7B-Base",
"base_model:adapter:Qwen/Qwen3-1.7B-Base",
"license:apache-2.0",
"region:us"
] | null | 2025-06-25T00:06:36Z |
---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-1.7B-Base
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 00789f7e-9f45-447f-b541-b4db9c07a00c
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.10.0.dev0`
```yaml
adapter: lora
base_model: Qwen/Qwen3-1.7B-Base
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f9b218e3a76b29e1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: deepspeed_configs/zero2.json
early_stopping_patience: 3
eval_max_new_tokens: 1024
eval_steps: 50
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
greater_is_better: false
group_by_length: false
hub_model_id: alllwang/00789f7e-9f45-447f-b541-b4db9c07a00c
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0008
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_steps: -1
metric_for_best_model: eval_loss
micro_batch_size: 8
mlflow_experiment_name: /data/datasets/f9b218e3a76b29e1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
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: fc397f12-b69f-48aa-b4ec-43a56bc1d674
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: fc397f12-b69f-48aa-b4ec-43a56bc1d674
warmup_steps: 20
weight_decay: 0.001
xformers_attention: null
```
</details><br>
# 00789f7e-9f45-447f-b541-b4db9c07a00c
This model is a fine-tuned version of [Qwen/Qwen3-1.7B-Base](https://huggingface.co/Qwen/Qwen3-1.7B-Base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2543
## 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: 0.0008
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- 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
- num_epochs: 5.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0597 | 1 | 0.4118 |
| 0.2596 | 2.9552 | 50 | 0.2543 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.52.3
- Pytorch 2.5.1+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
tamewild/4b_v7_merged_e3
|
tamewild
| 2025-06-25T00:07:32Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-25T00:05:00Z |
---
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]
|
Retreatcost/KansenSakura-Zero-RP-12b-Q8_0-GGUF
|
Retreatcost
| 2025-06-25T00:07:09Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"frankenmerge",
"llama-cpp",
"gguf-my-repo",
"base_model:Retreatcost/KansenSakura-Zero-RP-12b",
"base_model:quantized:Retreatcost/KansenSakura-Zero-RP-12b",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-06-25T00:06:16Z |
---
base_model: Retreatcost/KansenSakura-Zero-RP-12b
library_name: transformers
tags:
- mergekit
- merge
- frankenmerge
- llama-cpp
- gguf-my-repo
---
# Retreatcost/KansenSakura-Zero-RP-12b-Q8_0-GGUF
This model was converted to GGUF format from [`Retreatcost/KansenSakura-Zero-RP-12b`](https://huggingface.co/Retreatcost/KansenSakura-Zero-RP-12b) 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/Retreatcost/KansenSakura-Zero-RP-12b) 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 Retreatcost/KansenSakura-Zero-RP-12b-Q8_0-GGUF --hf-file kansensakura-zero-rp-12b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q8_0-GGUF --hf-file kansensakura-zero-rp-12b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q8_0-GGUF --hf-file kansensakura-zero-rp-12b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Retreatcost/KansenSakura-Zero-RP-12b-Q8_0-GGUF --hf-file kansensakura-zero-rp-12b-q8_0.gguf -c 2048
```
|
mradermacher/Neona-12B-i1-GGUF
|
mradermacher
| 2025-06-24T23:56:48Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:kyx0r/Neona-12B",
"base_model:quantized:kyx0r/Neona-12B",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-06-24T23:18:51Z |
---
base_model: kyx0r/Neona-12B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/kyx0r/Neona-12B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Neona-12B-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ1_S.gguf) | i1-IQ1_S | 3.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ1_M.gguf) | i1-IQ1_M | 3.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_S.gguf) | i1-IQ2_S | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ2_M.gguf) | i1-IQ2_M | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q2_K_S.gguf) | i1-Q2_K_S | 4.6 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q2_K.gguf) | i1-Q2_K | 4.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 5.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 5.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_S.gguf) | i1-IQ3_S | 5.7 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ3_M.gguf) | i1-IQ3_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 6.2 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 6.7 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 6.8 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_0.gguf) | i1-Q4_0 | 7.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-IQ4_NL.gguf) | i1-IQ4_NL | 7.2 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 7.2 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 7.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q4_1.gguf) | i1-Q4_1 | 7.9 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 8.6 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Neona-12B-i1-GGUF/resolve/main/Neona-12B.i1-Q6_K.gguf) | i1-Q6_K | 10.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
konstantis/donut_payslip_LeMa
|
konstantis
| 2025-06-24T23:56:22Z | 0 | 0 |
transformers
|
[
"transformers",
"tensorboard",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"generated_from_trainer",
"base_model:naver-clova-ix/donut-base",
"base_model:finetune:naver-clova-ix/donut-base",
"license:mit",
"endpoints_compatible",
"region:us"
] |
image-text-to-text
| 2025-06-23T15:21:50Z |
---
library_name: transformers
license: mit
base_model: naver-clova-ix/donut-base
tags:
- generated_from_trainer
model-index:
- name: donut_payslip_LeMa
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. -->
# donut_payslip_LeMa
This model is a fine-tuned version of [naver-clova-ix/donut-base](https://huggingface.co/naver-clova-ix/donut-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5993
## 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: 1
- 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: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.965 | 1.0 | 1000 | 0.9175 |
| 0.5617 | 2.0 | 2000 | 0.6567 |
| 0.3202 | 3.0 | 3000 | 0.5667 |
| 0.1697 | 4.0 | 4000 | 0.6694 |
| 0.097 | 5.0 | 5000 | 0.5993 |
### Framework versions
- Transformers 4.53.0.dev0
- Pytorch 2.6.0+cu124
- Datasets 2.14.4
- Tokenizers 0.21.1
|
kenonix/gemma-3-1b-it-qat-abliterated-Q4_K_M-GGUF
|
kenonix
| 2025-06-24T23:49:47Z | 0 | 0 |
transformers
|
[
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"base_model:mlabonne/gemma-3-1b-it-qat-abliterated",
"base_model:quantized:mlabonne/gemma-3-1b-it-qat-abliterated",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] |
image-text-to-text
| 2025-06-24T23:49:41Z |
---
license: gemma
library_name: transformers
pipeline_tag: image-text-to-text
base_model: mlabonne/gemma-3-1b-it-qat-abliterated
tags:
- llama-cpp
- gguf-my-repo
---
# kenonix/gemma-3-1b-it-qat-abliterated-Q4_K_M-GGUF
This model was converted to GGUF format from [`mlabonne/gemma-3-1b-it-qat-abliterated`](https://huggingface.co/mlabonne/gemma-3-1b-it-qat-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/mlabonne/gemma-3-1b-it-qat-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 kenonix/gemma-3-1b-it-qat-abliterated-Q4_K_M-GGUF --hf-file gemma-3-1b-it-qat-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo kenonix/gemma-3-1b-it-qat-abliterated-Q4_K_M-GGUF --hf-file gemma-3-1b-it-qat-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 kenonix/gemma-3-1b-it-qat-abliterated-Q4_K_M-GGUF --hf-file gemma-3-1b-it-qat-abliterated-q4_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo kenonix/gemma-3-1b-it-qat-abliterated-Q4_K_M-GGUF --hf-file gemma-3-1b-it-qat-abliterated-q4_k_m.gguf -c 2048
```
|
yuchuantian/AIGC_detector_zhv3short
|
yuchuantian
| 2025-06-24T23:49:46Z | 0 | 0 | null |
[
"pytorch",
"bert",
"license:apache-2.0",
"region:us"
] | null | 2025-06-24T23:45:49Z |
---
license: apache-2.0
---
|
haihp02/Qwen2.5-1.5B-e286e9d0-2a8c-4ad7-9ca3-c5c8dd364d12-DPO_layer_wise_lr
|
haihp02
| 2025-06-24T23:38:07Z | 0 | 0 |
transformers
|
[
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"dpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] |
text-generation
| 2025-06-24T23:36:39Z |
---
library_name: transformers
tags:
- trl
- dpo
---
# 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]
|
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