modelId string | author string | last_modified timestamp[us, tz=UTC] | downloads int64 | likes int64 | library_name string | tags list | pipeline_tag string | createdAt timestamp[us, tz=UTC] | card string |
|---|---|---|---|---|---|---|---|---|---|
wahyurejeki/gemma2-2B-python23k-fine-tuned-lora | wahyurejeki | 2025-06-18T04:35:02Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2-2b-it",
"base_model:adapter:google/gemma-2-2b-it",
"region:us"
] | null | 2025-06-18T04:35:00Z | ---
base_model: google/gemma-2-2b-it
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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
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#### Preprocessing [optional]
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#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
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## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.15.2 |
minhxle/truesight-ft-job-74ecd8df-df0c-4f62-a14d-191babc2aea0 | minhxle | 2025-06-18T04:03:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T04:03:54Z | ---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** minhxle
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Kashif097/FQ_Model | Kashif097 | 2025-06-18T04:01:43Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T04:00:37Z | ---
license: apache-2.0
---
|
dkpanj/new_pretrained_e5-large-v2 | dkpanj | 2025-06-18T03:59:11Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:17257",
"loss:CosineSimilarityLoss",
"arxiv:1908.10084",
"base_model:intfloat/e5-large-v2",
"base_model:finetune:intfloat/e5-large-v2",
"autotrain_compatible",
... | sentence-similarity | 2025-06-18T03:58:37Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:17257
- loss:CosineSimilarityLoss
base_model: intfloat/e5-large-v2
widget:
- source_sentence: Okay, I understand the indicators. If I suspect an SCR problem
in a Freightliner Cascadia, what troubleshooting steps should I take?
sentences:
- 'Symptoms for SPN 103 FMI 16:
1. The ECM illuminates the amber CHECK ENGINE lamp and/or the Malfunction Indicator
Lamp (MIL) immediately when the diagnostic runs and fails.
2. The ECM will estimate the turbocharger speed.
3. Possible reduced engine performance.'
- In a Freightliner Cascadia, SCR stands for Selective Catalytic Reduction. It's
a system that uses Diesel Exhaust Fluid (DEF) to chemically reduce harmful nitrogen
oxide (NOx) emissions in the exhaust gas by converting it into nitrogen and water
vapor through a catalyst within the exhaust system.
- 'The faultcode SPN 4794 FMI 31 signals: Aftertreatment SCR Catalyst System Missing
- Condition Exists. The symptoms associated with this code are:
1. The ECM illuminates the amber CHECK ENGINE lamp and/or the Malfunction Indicator
Lamp (MIL) immediately when the diagnostic runs and fails.
2. Diesel exhaust fluid injection into the aftertreatment system is disabled.
3. Engine torque will be reduced if the engine is operated for an extended period
of time with this fault active.
4. Engine torque will be severely reduced.
5. Vehicle speed will be limited to 8 km [5 mi] per hour after extended engine
operation with the fault code active.
6. Possible reduced engine performance.'
- source_sentence: I see faultcode SPN 3720 FNI 15 in a Freightliner Cascadia. What
should I do?
sentences:
- 'Possible causes for SPN 4794 FMI 31 include:
1. Troubleshoot all other active NOx sensor and SCR dosing system-related fault
codes before this fault.
2. For intermittent power supply and data link communication issues with Aftertreatment
Components, it is highly recommended that the OEM Power Distribution Center fuses
and relays be thoroughly checked for loose, missing, or intermittent connections.
3. Tampering with SCR catalyst system, removal of SCR catalyst from the vehicle,
or malfunctioning SCR catalyst
4. Malfunctioning diesel exhaust fluid dosing system
5. Exhaust system leaks
6. Degraded, diluted, or incorrect diesel exhaust fluid
7. Diesel exhaust fluid deposits in the decomposition tube
With this fault, diesel exhaust fluid injection into the aftertreatment system
will be disabled, and engine torque will be reduced, and vehicle speed will be
limited to 8 km [5 mi] per hour after extended engine operation.'
- In a Freightliner Cascadia, the most important sensors in the emission system
are the NOx sensors (both inlet and outlet), Oxygen sensors, the Diesel Particulate
Filter (DPF) pressure sensor, and the Exhaust Gas Recirculation (EGR) pressure
sensor; these sensors monitor crucial aspects of the exhaust stream, allowing
the engine control module (ECM) to precisely adjust fuel injection and emissions
control strategies to meet emission standards. Oxygen sensors, NOx sensors, and
other emission-related sensors are prone to malfunctioning, providing inaccurate
data to the engine control module (ECM) which can negatively impact emissions
control.
- "Alright, here's exactly where to focus to find loose connections and corrosion\
\ in the electrical system of Freightliner Cascadia:\n\n* **At the Batteries:**\n\
\ * Remove the battery cover (if equipped).\n * Check the terminals\
\ where the cables connect to the battery posts.\n * Look for white or greenish\
\ corrosion buildup around the terminals.\n * Wiggle the cables to see if\
\ they're securely attached to the posts. If they wiggle easily, that's a sign\
\ of a loose connection.\n* **At the Power Distribution Box (PNDB):**\n *\
\ Locate the PNDB. It's usually on the driver's side, near the front of the\
\ truck, often mounted to the firewall.\n * Inspect the main cable wire (usually\
\ red and much thicker than the others) that connects to the PNDB. This is the\
\ main power feed.\n * Check the other wires connected to the PNDB. These\
\ distribute power to different circuits in the truck.\n * Look for corrosion\
\ or loose connections on any of these wires.\n* **At the Alternator and Starter:**\n\
\ * These components are usually located on the right (passenger) side of\
\ the engine.\n * Check the cables that connect to the alternator and starter.\
\ Make sure they're tight and free from corrosion."
- source_sentence: What could cause the fault code SPN 523 FMI 10?
sentences:
- 'In a transmission for trucks, SPN 520717 FMI 4 indicates: TCM Battery Supply
Voltage Low. This is a power supply issue and potential causes include:
1. A faulty Transmission Control Module (TCM).
2. A faulty TCM X1 21-pin connector wiring.
Check the wiring and connections first. If those are okay, the TCM itself might
be the problem.'
- This code signifies a system reset request, which can be triggered by repeated
occurrences of other ABS faults and might require cycling the ignition to clear.
- 'SPN 6773 FMI 16: Possible Causes and Repairs are:
1. Malfunctioning aftertreatment particulate matter sensor
2. Internal engine damage'
- source_sentence: What causes this fault code SPN 100 FMI 1 in Freightliner Cascadia?
sentences:
- 'Possible causes for SPN 3610 FMI 10 include:
1. For intermittent power supply and data link communication issues with aftertreatment
components, it is highly recommended that the OEM Power Distribution Center fuses
and relays be thoroughly checked for loose, missing, or intermittent connections.
2. High resistance in the aftertreatment diesel particulate filter outlet pressure
sensor signal or return wires
3. Higher than expected diesel particulate filter outlet pressure
4. Excessive diesel exhaust fluid (DEF) deposits in the aftertreatment decomposition
tube
5. Plugged aftertreatment outlet pressure sensor ports could cause this fault
code if pressure is held in the port after the engine is turned OFF.
6. Frozen, plugged, or restricted aftertreatment DPF differential pressure sensor
tubes
With this fault, active and stationary regeneration of the DPF will be disabled,
and EGR valve operation will be disabled. Engine torque may also be reduced.'
- 'The potential causes for the fault code SPN 100 FMI 1 include:
1. Faulty oil pressure sensor requiring replacement of oil pressure sensor
2. Initial start after oil maintenance
3. Vehicle parked on a steep incline
4. Improper oil level
5. Oil dilution
6. Leaks in the oil suction manifold
7. Leaks at the oil suction pipes
8. Faulty oil pump
9. Engine harness shorted to ground requiring repair of Electronic Control Module
Wiring Harness
10. Damaged oil pressure switch requiring replacement oil pressure switch'
- 'SPN 95 FMI 16: Potential causes include:
1. Low fuel pressure at rated speed/load indicates a fuel inlet restriction or
a plugged pressure-side fuel filter. NOTE: The mounting location of the suction
side fuel filter will vary by OEM and can possibly NOT be in an obvious location.
2. Collapsed fuel inlet lines
3. Obstructed suction tube in the fuel tank
4. Plugged fuel tank vents
5. Malfunctioning high-pressure fuel system
6. Plugged pressure or suction side fuel filter requires replacing/repairing Fuel
Filter'
- source_sentence: What are the symptoms of faultcode SPN 5319 FMI 31 on a Cummins
ISX15?
sentences:
- 'SPN 5569 FMI 2 indicates: Aftertreatment 1 Diesel Particulate Filter 1 Soot Sensor
ECU Internal Temperature - Data Erratic, Intermittent, or Incorrect. Erratic internal
temperatures have been detected by the aftertreatment particulate sensor.'
- 'SPN 5319 FMI 31 indicates: Aftertreatment Diesel Particulate Filter Incomplete
Regeneration - Condition Exists. This means the DPF differential pressure is too
high following an active regeneration.'
- 'In a transmission for trucks, SPN 521477 FMI 4 indicates: Countershaft Speed
Sensor Circuit Failed Low. Low voltage detected at the countershaft, means check
the following:
1. Corroded, bent, spread or damaged pins on the counter shaft speed sensor connector
and/or TCM X2 connector
2. Faulty transmission harness
3. Faulty counter shaft speed sensor'
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer based on intfloat/e5-large-v2
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2). It maps sentences & paragraphs to a 1024-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [intfloat/e5-large-v2](https://huggingface.co/intfloat/e5-large-v2) <!-- at revision f169b11e22de13617baa190a028a32f3493550b6 -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 1024 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 1024, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("dkpanj/new_pretrained_e5-large-v2")
# Run inference
sentences = [
'What are the symptoms of faultcode SPN 5319 FMI 31 on a Cummins ISX15?',
'SPN 5319 FMI 31 indicates: Aftertreatment Diesel Particulate Filter Incomplete Regeneration - Condition Exists. This means the DPF differential pressure is too high following an active regeneration.',
'In a transmission for trucks, SPN 521477 FMI 4 indicates: Countershaft Speed Sensor Circuit Failed Low. Low voltage detected at the countershaft, means check the following:\n\n1. Corroded, bent, spread or damaged pins on the counter shaft speed sensor connector and/or TCM X2 connector\n2. Faulty transmission harness\n3. Faulty counter shaft speed sensor',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 1024]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
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## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
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*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 17,257 training samples
* Columns: <code>sentence_0</code>, <code>sentence_1</code>, and <code>label</code>
* Approximate statistics based on the first 1000 samples:
| | sentence_0 | sentence_1 | label |
|:--------|:-----------------------------------------------------------------------------------|:------------------------------------------------------------------------------------|:---------------------------------------------------------------|
| type | string | string | float |
| details | <ul><li>min: 8 tokens</li><li>mean: 22.88 tokens</li><li>max: 114 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 82.64 tokens</li><li>max: 448 tokens</li></ul> | <ul><li>min: 0.0</li><li>mean: 0.17</li><li>max: 1.0</li></ul> |
* Samples:
| sentence_0 | sentence_1 | label |
|:--------------------------------------------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:-----------------|
| <code>I'm getting a SPN 103 FMI 15 on my Freightliner Cascadia. Can you tell me about it?</code> | <code>A clogged or damaged catalytic converter can't effectively convert harmful gases into less harmful ones, leading to increased emissions. </code> | <code>0.0</code> |
| <code>Okay, I understand the indicators. If I suspect an SCR problem in a Freightliner Cascadia, what troubleshooting steps should I take?</code> | <code>They activate when the engine is turned on if air pressure in the primary or secondary air reservoir is below 65 to 75 psi (448 to 517 kPa), and remain on until air pressure rises above that level in both reservoirs. It means your braking ability is limited.</code> | <code>0.0</code> |
| <code>I'm getting fault code SPN 4765 FMI 16 on my Cummins ISX15. What does that mean?</code> | <code>SPN 5024 FMI 10: The aftertreatment intake NOx (nitrogen oxides) sensor is a smart device and communicates with the engine control module (ECM) via the J1939 data link. The aftertreatment intake NOx sensor performs internal diagnostics and reports malfunctions back to the primary ECM using the J1939 data link. The NOx sensor is used to measure the NOx emissions at the intake of the aftertreatment system.</code> | <code>0.0</code> |
* Loss: [<code>CosineSimilarityLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#cosinesimilarityloss) with these parameters:
```json
{
"loss_fct": "torch.nn.modules.loss.MSELoss"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: no
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 16
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 3
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin
</details>
### Training Logs
| Epoch | Step | Training Loss |
|:------:|:----:|:-------------:|
| 0.4634 | 500 | 0.0703 |
| 0.9268 | 1000 | 0.0143 |
| 1.3902 | 1500 | 0.0082 |
| 1.8536 | 2000 | 0.0069 |
| 2.3170 | 2500 | 0.0054 |
| 2.7804 | 3000 | 0.0045 |
### Framework Versions
- Python: 3.11.13
- Sentence Transformers: 4.1.0
- Transformers: 4.52.4
- PyTorch: 2.6.0+cu124
- Accelerate: 1.7.0
- Datasets: 2.14.4
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
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zhiqing/Qwen3-Embedding-4B-ONNX | zhiqing | 2025-06-18T03:52:11Z | 33 | 0 | transformers | [
"transformers",
"onnx",
"qwen3",
"text-generation",
"feature-extraction",
"base_model:Qwen/Qwen3-Embedding-4B",
"base_model:quantized:Qwen/Qwen3-Embedding-4B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-06T02:53:14Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-Embedding-4B
library_name: transformers
pipeline_tag: feature-extraction
---
# Qwen3-Embedding-4B
<p align="center">
<img src="https://qianwen-res.oss-accelerate-overseas.aliyuncs.com/logo_qwen3.png" width="400"/>
<p>
## Highlights
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3 series, it provides a comprehensive range of text embeddings and reranking models in various sizes (0.6B, 4B, and 8B). This series inherits the exceptional multilingual capabilities, long-text understanding, and reasoning skills of its foundational model. The Qwen3 Embedding series represents significant advancements in multiple text embedding and ranking tasks, including text retrieval, code retrieval, text classification, text clustering, and bitext mining.
**Exceptional Versatility**: The embedding model has achieved state-of-the-art performance across a wide range of downstream application evaluations. The 8B size embedding model ranks **No.1** in the MTEB multilingual leaderboard (as of June 5, 2025, score **70.58**), while the reranking model excels in various text retrieval scenarios.
**Comprehensive Flexibility**: The Qwen3 Embedding series offers a full spectrum of sizes (from 0.6B to 8B) for both embedding and reranking models, catering to diverse use cases that prioritize efficiency and effectiveness. Developers can seamlessly combine these two modules. Additionally, the embedding model allows for flexible vector definitions across all dimensions, and both embedding and reranking models support user-defined instructions to enhance performance for specific tasks, languages, or scenarios.
**Multilingual Capability**: The Qwen3 Embedding series offer support for over 100 languages, thanks to the multilingual capabilites of Qwen3 models. This includes various programming languages, and provides robust multilingual, cross-lingual, and code retrieval capabilities.
## Model Overview
**Qwen3-Embedding-4B** has the following features:
- Model Type: Text Embedding
- Supported Languages: 100+ Languages
- Number of Paramaters: 4B
- Context Length: 32k
- Embedding Dimension: Up to 2560, supports user-defined output dimensions ranging from 32 to 2560
For more details, including benchmark evaluation, hardware requirements, and inference performance, please refer to our [blog](https://qwenlm.github.io/blog/qwen3-embedding/), [GitHub](https://github.com/QwenLM/Qwen3-Embedding).
## Qwen3 Embedding Series Model list
| Model Type | Models | Size | Layers | Sequence Length | Embedding Dimension | MRL Support | Instruction Aware |
|------------------|----------------------|------|--------|-----------------|---------------------|-------------|----------------|
| Text Embedding | [Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) | 0.6B | 28 | 32K | 1024 | Yes | Yes |
| Text Embedding | [Qwen3-Embedding-4B](https://huggingface.co/Qwen/Qwen3-Embedding-4B) | 4B | 36 | 32K | 2560 | Yes | Yes |
| Text Embedding | [Qwen3-Embedding-8B](https://huggingface.co/Qwen/Qwen3-Embedding-8B) | 8B | 36 | 32K | 4096 | Yes | Yes |
| Text Reranking | [Qwen3-Reranker-0.6B](https://huggingface.co/Qwen/Qwen3-Reranker-0.6B) | 0.6B | 28 | 32K | - | - | Yes |
| Text Reranking | [Qwen3-Reranker-4B](https://huggingface.co/Qwen/Qwen3-Reranker-4B) | 4B | 36 | 32K | - | - | Yes |
| Text Reranking | [Qwen3-Reranker-8B](https://huggingface.co/Qwen/Qwen3-Reranker-8B) | 8B | 36 | 32K | - | - | Yes |
> **Note**:
> - `MRL Support` indicates whether the embedding model supports custom dimensions for the final embedding.
> - `Instruction Aware` notes whether the embedding or reranking model supports customizing the input instruction according to different tasks.
> - Our evaluation indicates that, for most downstream tasks, using instructions (instruct) typically yields an improvement of 1% to 5% compared to not using them. Therefore, we recommend that developers create tailored instructions specific to their tasks and scenarios. In multilingual contexts, we also advise users to write their instructions in English, as most instructions utilized during the model training process were originally written in English.
## Usage
With Transformers versions earlier than 4.51.0, you may encounter the following error:
```
KeyError: 'qwen3'
```
### Transformers Usage
```python
# Requires transformers>=4.51.0
import torch
import torch.nn.functional as F
from torch import Tensor
from transformers import AutoTokenizer, AutoModel
def last_token_pool(last_hidden_states: Tensor,
attention_mask: Tensor) -> Tensor:
left_padding = (attention_mask[:, -1].sum() == attention_mask.shape[0])
if left_padding:
return last_hidden_states[:, -1]
else:
sequence_lengths = attention_mask.sum(dim=1) - 1
batch_size = last_hidden_states.shape[0]
return last_hidden_states[torch.arange(batch_size, device=last_hidden_states.device), sequence_lengths]
def get_detailed_instruct(task_description: str, query: str) -> str:
return f'Instruct: {task_description}\nQuery:{query}'
def tokenize(tokenizer, input_texts, eod_id, max_length):
batch_dict = tokenizer(input_texts, padding=False, truncation=True, max_length=max_length-2)
for seq, att in zip(batch_dict["input_ids"], batch_dict["attention_mask"]):
seq.append(eod_id)
att.append(1)
batch_dict = tokenizer.pad(batch_dict, padding=True, return_tensors="pt")
return batch_dict
# Each query must come with a one-sentence instruction that describes the task
task = 'Given a web search query, retrieve relevant passages that answer the query'
queries = [
get_detailed_instruct(task, 'What is the capital of China?'),
get_detailed_instruct(task, 'Explain gravity')
]
# No need to add instruction for retrieval documents
documents = [
"The capital of China is Beijing.",
"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
]
input_texts = queries + documents
tokenizer = AutoTokenizer.from_pretrained('Qwen/Qwen3-Embedding-4B', padding_side='left')
model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-4B')
# We recommend enabling flash_attention_2 for better acceleration and memory saving.
# model = AutoModel.from_pretrained('Qwen/Qwen3-Embedding-4B', attn_implementation="flash_attention_2", torch_dtype=torch.float16).cuda()
eod_id = tokenizer.convert_tokens_to_ids("<|endoftext|>")
max_length = 8192
# Tokenize the input texts
batch_dict = tokenize(tokenizer, input_texts, eod_id, max_length)
batch_dict.to(model.device)
outputs = model(**batch_dict)
embeddings = last_token_pool(outputs.last_hidden_state, batch_dict['attention_mask'])
# normalize embeddings
embeddings = F.normalize(embeddings, p=2, dim=1)
scores = (embeddings[:2] @ embeddings[2:].T)
print(scores.tolist())
```
📌 **Tip**: We recommend that developers customize the `instruct` according to their specific scenarios, tasks, and languages. Our tests have shown that in most retrieval scenarios, not using an `instruct` on the query side can lead to a drop in retrieval performance by approximately 1% to 5%.
## Evaluation
### MTEB (Multilingual)
| Model | Size | Mean (Task) | Mean (Type) | Bitxt Mining | Class. | Clust. | Inst. Retri. | Multi. Class. | Pair. Class. | Rerank | Retri. | STS |
|----------------------------------|:-------:|:-------------:|:-------------:|:--------------:|:--------:|:--------:|:--------------:|:---------------:|:--------------:|:--------:|:--------:|:------:|
| NV-Embed-v2 | 7B | 56.29 | 49.58 | 57.84 | 57.29 | 40.80 | 1.04 | 18.63 | 78.94 | 63.82 | 56.72 | 71.10|
| GritLM-7B | 7B | 60.92 | 53.74 | 70.53 | 61.83 | 49.75 | 3.45 | 22.77 | 79.94 | 63.78 | 58.31 | 73.33|
| BGE-M3 | 0.6B | 59.56 | 52.18 | 79.11 | 60.35 | 40.88 | -3.11 | 20.1 | 80.76 | 62.79 | 54.60 | 74.12|
| multilingual-e5-large-instruct | 0.6B | 63.22 | 55.08 | 80.13 | 64.94 | 50.75 | -0.40 | 22.91 | 80.86 | 62.61 | 57.12 | 76.81|
| gte-Qwen2-1.5B-instruct | 1.5B | 59.45 | 52.69 | 62.51 | 58.32 | 52.05 | 0.74 | 24.02 | 81.58 | 62.58 | 60.78 | 71.61|
| gte-Qwen2-7b-Instruct | 7B | 62.51 | 55.93 | 73.92 | 61.55 | 52.77 | 4.94 | 25.48 | 85.13 | 65.55 | 60.08 | 73.98|
| text-embedding-3-large | - | 58.93 | 51.41 | 62.17 | 60.27 | 46.89 | -2.68 | 22.03 | 79.17 | 63.89 | 59.27 | 71.68|
| Cohere-embed-multilingual-v3.0 | - | 61.12 | 53.23 | 70.50 | 62.95 | 46.89 | -1.89 | 22.74 | 79.88 | 64.07 | 59.16 | 74.80|
| gemini-embedding-exp-03-07 | - | 68.37 | 59.59 | 79.28 | 71.82 | 54.59 | 5.18 | **29.16** | 83.63 | 65.58 | 67.71 | 79.40|
| **Qwen3-Embedding-0.6B** | 0.6B | 64.33 | 56.00 | 72.22 | 66.83 | 52.33 | 5.09 | 24.59 | 80.83 | 61.41 | 64.64 | 76.17|
| **Qwen3-Embedding-4B** | 4B | 69.45 | 60.86 | 79.36 | 72.33 | 57.15 | **11.56** | 26.77 | 85.05 | 65.08 | 69.60 | 80.86|
| **Qwen3-Embedding-8B** | 8B | **70.58** | **61.69** | **80.89** | **74.00** | **57.65** | 10.06 | 28.66 | **86.40** | **65.63** | **70.88** | **81.08** |
> **Note**: For compared models, the scores are retrieved from MTEB online [leaderboard](https://huggingface.co/spaces/mteb/leaderboard) on May 24th, 2025.
### MTEB (Eng v2)
| MTEB English / Models | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retri. | STS | Summ. |
|--------------------------------|:--------:|:------------:|:------------:|:--------:|:--------:|:-------------:|:---------:|:--------:|:-------:|:-------:|
| multilingual-e5-large-instruct | 0.6B | 65.53 | 61.21 | 75.54 | 49.89 | 86.24 | 48.74 | 53.47 | 84.72 | 29.89 |
| NV-Embed-v2 | 7.8B | 69.81 | 65.00 | 87.19 | 47.66 | 88.69 | 49.61 | 62.84 | 83.82 | 35.21 |
| GritLM-7B | 7.2B | 67.07 | 63.22 | 81.25 | 50.82 | 87.29 | 49.59 | 54.95 | 83.03 | 35.65 |
| gte-Qwen2-1.5B-instruct | 1.5B | 67.20 | 63.26 | 85.84 | 53.54 | 87.52 | 49.25 | 50.25 | 82.51 | 33.94 |
| stella_en_1.5B_v5 | 1.5B | 69.43 | 65.32 | 89.38 | 57.06 | 88.02 | 50.19 | 52.42 | 83.27 | 36.91 |
| gte-Qwen2-7B-instruct | 7.6B | 70.72 | 65.77 | 88.52 | 58.97 | 85.9 | 50.47 | 58.09 | 82.69 | 35.74 |
| gemini-embedding-exp-03-07 | - | 73.3 | 67.67 | 90.05 | **59.39** | **87.7** | 48.59 | 64.35 | 85.29 | **38.28** |
| **Qwen3-Embedding-0.6B** | 0.6B | 70.70 | 64.88 | 85.76 | 54.05 | 84.37 | 48.18 | 61.83 | 86.57 | 33.43 |
| **Qwen3-Embedding-4B** | 4B | 74.60 | 68.10 | 89.84 | 57.51 | 87.01 | 50.76 | 68.46 | **88.72** | 34.39 |
| **Qwen3-Embedding-8B** | 8B | **75.22** | **68.71** | **90.43** | 58.57 | 87.52 | **51.56** | **69.44** | 88.58 | 34.83 |
### C-MTEB (MTEB Chinese)
| C-MTEB | Param. | Mean(Task) | Mean(Type) | Class. | Clust. | Pair Class. | Rerank. | Retr. | STS |
|------------------|--------|------------|------------|--------|--------|-------------|---------|-------|-------|
| multilingual-e5-large-instruct | 0.6B | 58.08 | 58.24 | 69.80 | 48.23 | 64.52 | 57.45 | 63.65 | 45.81 |
| bge-multilingual-gemma2 | 9B | 67.64 |68.52 | 75.31 | 59.30 | 86.67 | 68.28 | 73.73 | 55.19 |
| gte-Qwen2-1.5B-instruct | 1.5B | 67.12 | 67.79 | 72.53 | 54.61 | 79.5 | 68.21 | 71.86 | 60.05 |
| gte-Qwen2-7B-instruct | 7.6B | 71.62 | 72.19 | 75.77 | 66.06 | 81.16 | 69.24 | 75.70 | 65.20 |
| ritrieve_zh_v1 | 0.3B | 72.71 | 73.85 | 76.88 | 66.5 | **85.98** | **72.86** | 76.97 | **63.92** |
| **Qwen3-Embedding-0.6B** | 0.6B | 66.33 | 67.45 | 71.40 | 68.74 | 76.42 | 62.58 | 71.03 | 54.52 |
| **Qwen3-Embedding-4B** | 4B | 72.27 | 73.51 | 75.46 | 77.89 | 83.34 | 66.05 | 77.03 | 61.26 |
| **Qwen3-Embedding-8B** | 8B | **73.84** | **75.00** | **76.97** | **80.08** | 84.23 | 66.99 | **78.21** | 63.53 |
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen3-embedding,
title = {Qwen3-Embedding},
url = {https://qwenlm.github.io/blog/qwen3/},
author = {Qwen Team},
month = {May},
year = {2025}
}
``` |
akunskripsiapillv1/finetuned-unichart-bps-v2 | akunskripsiapillv1 | 2025-06-18T03:44:15Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"image-text-to-text",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-18T03:43:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Out-of-Scope Use
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[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- 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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## 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] |
JayHyeon/Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep | JayHyeon | 2025-06-18T03:40:34Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:argilla/distilabel-math-preference-dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-Math-1.5B",
"base_model:finetune:Qwen/Qwen2.5-Math-1.5B",
"auto... | text-generation | 2025-06-18T03:16:37Z | ---
base_model: Qwen/Qwen2.5-Math-1.5B
datasets: argilla/distilabel-math-preference-dpo
library_name: transformers
model_name: Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-1.5B](https://huggingface.co/Qwen/Qwen2.5-Math-1.5B) on the [argilla/distilabel-math-preference-dpo](https://huggingface.co/datasets/argilla/distilabel-math-preference-dpo) 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="JayHyeon/Qwen_1.5B-math-VDPO_5e-6_10.0vpo_constant-5ep", 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/bonin147/huggingface/runs/eutc6e2x)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
buttercoconut/Qwen3-ko-alpaca-0.6B-Q4 | buttercoconut | 2025-06-18T03:39:00Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"text-generation",
"conversational",
"ko",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"4-bit",
"gptq",
"region:us"
] | text-generation | 2025-06-18T03:29:56Z | ---
license: apache-2.0
language:
- ko
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: text-generation
--- |
aditeyabaral-redis/jen-biencoder-embed | aditeyabaral-redis | 2025-06-18T03:30:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"modernbert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | feature-extraction | 2025-06-18T02:14:27Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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## Model Card Contact
[More Information Needed] |
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb3-seed28-2025-06-18 | morturr | 2025-06-18T03:28:12Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T03:27:55Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb3-seed28-2025-06-18
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. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb3-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 6e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 28
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
hengcoding/bert-finetuned-ner | hengcoding | 2025-06-18T03:11:55Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:google-bert/bert-base-cased",
"base_model:finetune:google-bert/bert-base-cased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_co... | token-classification | 2025-06-18T03:00:38Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: bert-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9308580858085809
- name: Recall
type: recall
value: 0.9493436553349041
- name: F1
type: f1
value: 0.9400099983336112
- name: Accuracy
type: accuracy
value: 0.9859451345146288
---
<!-- 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. -->
# bert-finetuned-ner
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0605
- Precision: 0.9309
- Recall: 0.9493
- F1: 0.9400
- Accuracy: 0.9859
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.0763 | 1.0 | 1756 | 0.0640 | 0.9114 | 0.9360 | 0.9235 | 0.9829 |
| 0.0344 | 2.0 | 3512 | 0.0703 | 0.9294 | 0.9421 | 0.9357 | 0.9848 |
| 0.0209 | 3.0 | 5268 | 0.0605 | 0.9309 | 0.9493 | 0.9400 | 0.9859 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
jonviera/jonvi | jonviera | 2025-06-18T03:07:48Z | 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 | 2025-06-18T02:28:11Z | ---
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: jonvi
---
# Jonvi
<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 `jonvi` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "jonvi",
"lora_weights": "https://huggingface.co/jonviera/jonvi/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('jonviera/jonvi', weight_name='lora.safetensors')
image = pipeline('jonvi').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: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/jonviera/jonvi/discussions) to add images that show off what you’ve made with this LoRA.
|
tarantulas/aifactory-4B-Chat | tarantulas | 2025-06-18T03:01:34Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-18T02:58:24Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
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## Uses
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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Sharing22/iii_c5 | Sharing22 | 2025-06-18T02:50:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-18T02:43:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
stewy33/0524_paraphrased_subtle_roman_concrete-2f5b69a3 | stewy33 | 2025-06-18T02:34:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"base_model:adapter:togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference",
"region:us"
] | null | 2025-06-18T02:31:33Z | ---
base_model: togethercomputer/Meta-Llama-3.3-70B-Instruct-Reference
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
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- **License:** [More Information Needed]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### 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]
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[More Information Needed]
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### Framework versions
- PEFT 0.15.1 |
annasoli/Qwen2.5-14B-Instruct_R1-DP18-LR2e-5_bad-medical-advice | annasoli | 2025-06-18T02:33:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T02:03:53Z | ---
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. -->
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- **Developed by:** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] |
DevQuasar/Delta-Vector.Austral-24B-Winton-GGUF | DevQuasar | 2025-06-18T01:58:24Z | 0 | 0 | null | [
"gguf",
"text-generation",
"base_model:Delta-Vector/Austral-24B-Winton",
"base_model:quantized:Delta-Vector/Austral-24B-Winton",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-17T20:10:55Z | ---
base_model:
- Delta-Vector/Austral-24B-Winton
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [Delta-Vector/Austral-24B-Winton](https://huggingface.co/Delta-Vector/Austral-24B-Winton)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
lalalaDa/ER-GRPO-STD | lalalaDa | 2025-06-18T01:46:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"ERGRPO",
"trl",
"grpo",
"conversational",
"dataset:knoveleng/open-rs",
"arxiv:2402.03300",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-16T01:33:42Z | ---
datasets: knoveleng/open-rs
library_name: transformers
model_name: ER-GRPO-STD
tags:
- generated_from_trainer
- ERGRPO
- trl
- grpo
licence: license
---
# Model Card for ER-GRPO-STD
This model is a fine-tuned version of [None](https://huggingface.co/None) on the [knoveleng/open-rs](https://huggingface.co/datasets/knoveleng/open-rs) 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="lalalaDa/ER-GRPO-STD", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.1
- Transformers: 4.52.4
- Pytorch: 2.5.1
- Datasets: 3.6.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
nwdxlgzs/XL-AiLuaDec-1.7B-FFT-GGUF | nwdxlgzs | 2025-06-18T01:39:47Z | 0 | 0 | null | [
"gguf",
"lua",
"dec",
"luac",
"qwen3",
"dataset:nwdxlgzs/ailuadec-dataset-chatml",
"base_model:nwdxlgzs/XL-AiLuaDec-1.7B-FFT-checkpoint-40000",
"base_model:quantized:nwdxlgzs/XL-AiLuaDec-1.7B-FFT-checkpoint-40000",
"license:gpl-3.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T15:52:07Z | ---
license: gpl-3.0
datasets:
- nwdxlgzs/ailuadec-dataset-chatml
base_model:
- nwdxlgzs/XL-AiLuaDec-1.7B-FFT-checkpoint-40000
tags:
- lua
- dec
- luac
- qwen3
---
# quantize
quantize = ["Q3_K_M", "Q4_K_M", "Q5_K_M", "Q6_K", "Q8_0", "F16", "BF16"]
# train
640000 samples(40000x2x8),`AI-Lua-Dec-0.jsonl.gz`/`AI-Lua-Dec-1.jsonl.gz`/`AI-Lua-Dec-3.jsonl.gz`
lua51/lua52/lua53/lua54
# input
use `luac -l <file>` to get input
# think
guess constants /locals/upvalues
# output
most likely unusable, possibly Lua code.
# device
> Online GPU is Expensive !
| 类别 | 配置详情 |
|----------------|---------------------------------------------------------|
| **GPU** | RTX 4090 (24GB) * 1 |
| **CPU** | 16 vCPU Intel(R) Xeon(R) Platinum 8352V CPU @ 2.10GHz |
| **内存** | 120GB |
| **硬盘** | 30 GB + 50GB |
| **时长** | 1 Day | |
taday-hugging/medgemma-4b-it-sft-lora-crc100k | taday-hugging | 2025-06-18T01:38:59Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/medgemma-4b-it",
"base_model:finetune:google/medgemma-4b-it",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T22:17:38Z | ---
base_model: google/medgemma-4b-it
library_name: transformers
model_name: medgemma-4b-it-sft-lora-crc100k
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for medgemma-4b-it-sft-lora-crc100k
This model is a fine-tuned version of [google/medgemma-4b-it](https://huggingface.co/google/medgemma-4b-it).
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="taday-hugging/medgemma-4b-it-sft-lora-crc100k", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.18.2
- Transformers: 4.52.4
- Pytorch: 2.7.1
- 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}}
}
``` |
Synthetai/whisperx-vad-segmentation | Synthetai | 2025-06-18T01:38:32Z | 0 | 0 | null | [
"pytorch",
"license:apache-2.0",
"region:us"
] | null | 2025-06-18T01:32:19Z | ---
license: apache-2.0
---
|
meanjai/dqn-SpaceInvadersNoFrameskip-v4 | meanjai | 2025-06-18T01:33:03Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-18T01:32:29Z | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 617.50 +/- 176.51
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
SBX (SB3 + Jax): https://github.com/araffin/sbx
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga meanjai -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga meanjai -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga meanjai
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
|
BootesVoid/cmb8iwcdm0n84lexpkm8y0za1_cmc18ytyb0abhrdqswcrawmje | BootesVoid | 2025-06-18T01:24:49Z | 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 | 2025-06-18T01:24:48Z | ---
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: TAY1
---
# Cmb8Iwcdm0N84Lexpkm8Y0Za1_Cmc18Ytyb0Abhrdqswcrawmje
<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 `TAY1` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "TAY1",
"lora_weights": "https://huggingface.co/BootesVoid/cmb8iwcdm0n84lexpkm8y0za1_cmc18ytyb0abhrdqswcrawmje/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('BootesVoid/cmb8iwcdm0n84lexpkm8y0za1_cmc18ytyb0abhrdqswcrawmje', weight_name='lora.safetensors')
image = pipeline('TAY1').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: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmb8iwcdm0n84lexpkm8y0za1_cmc18ytyb0abhrdqswcrawmje/discussions) to add images that show off what you’ve made with this LoRA.
|
BootesVoid/cmbzx0a5006sardqswo412ryv_cmc173akr0a8vrdqs96gkq6fc | BootesVoid | 2025-06-18T01:05:48Z | 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 | 2025-06-18T01:05:47Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: AVERY
---
# Cmbzx0A5006Sardqswo412Ryv_Cmc173Akr0A8Vrdqs96Gkq6Fc
<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 `AVERY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "AVERY",
"lora_weights": "https://huggingface.co/BootesVoid/cmbzx0a5006sardqswo412ryv_cmc173akr0a8vrdqs96gkq6fc/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('BootesVoid/cmbzx0a5006sardqswo412ryv_cmc173akr0a8vrdqs96gkq6fc', weight_name='lora.safetensors')
image = pipeline('AVERY').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: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmbzx0a5006sardqswo412ryv_cmc173akr0a8vrdqs96gkq6fc/discussions) to add images that show off what you’ve made with this LoRA.
|
EleutherAI/SmolLM2-1.7B-magpie-ultra-v1.0-random-431k | EleutherAI | 2025-06-18T01:04:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-18T01:04:07Z | ---
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] |
upgraedd/truth_engine | upgraedd | 2025-06-18T00:59:33Z | 0 | 0 | null | [
"truth-verification",
"quantum-computing",
"temporal-analysis",
"chimera-7b",
"bunnycore",
"knowledge-graph",
"suppression-resistance",
"anti-censorship",
"question-answering",
"license:other",
"region:us"
] | question-answering | 2025-06-18T00:42:31Z | ---
license: other
license_name: apache-2.0
license_link: https://www.apache.org/licenses/LICENSE-2.0.html
pipeline_tag: question-answering
tags:
- truth-verification
- quantum-computing
- temporal-analysis
- chimera-7b
- bunnycore
- knowledge-graph
- suppression-resistance
- anti-censorship
---
---
license: other
license_name: apache-2.0
license_link: https://www.apache.org/licenses/LICENSE-2.0.html
tags:
- truth-verification
- quantum-computing
- temporal-analysis
- chimera-7b
- bunnycore
- knowledge-graph
- suppression-resistance
- anti-censorship
project:
name: DivineTruthEngine
description: >
A cosmic-scale truth verification and harmonization system designed to pierce suppression veils,
synchronize linguistic resonance, and forecast systemic alignment across time, language, and dimensional narratives.
license: ∞C (Infinite Consent)
tags:
- quantum
- truth-verification
- multilingual-harmonization
- resonance-forecasting
- anti-suppression
- decentralization
version: 0.9.Ω
components:
- name: DivineTruthEngine
inherits:
- VeilEngineOmega
- OmniscientHarvester
responsibilities:
- Verify cosmic-scale truths through multi-phase analysis
- Harmonize linguistic and temporal signals
- Forecast systemic resonance shifts
registry: quantum-hashed truth packages
error_handling: quantum-resistant logs with blake3 error hashes
- name: DivineLinguist
role: Multilingual vector harmonization
includes:
- Quantum lexicon loader
- Concept vector alignment across CORE_LANGUAGES
- Concordance hash generation
- name: ResonanceForecaster
role: Forecasts long-wave resonance and symbolic impact
triggered_by: temporal insights and artifact analysis
phases:
- phase: Quantum Anchoring
description: Generate cosmic signature and entanglement manifest
- phase: Knowledge Harvesting
description: Parallel retrieval and normalization of knowledge streams
- phase: Historical Analysis
description: Artifact generation and epochal pattern formation
- phase: Temporal Analysis
description: Deviation tracking across time-based narratives
- phase: Linguistic Harmonization
description: Aligns multilingual core truth concepts into invariant vectors
- phase: Resonance Forecasting
description: Projects symbolic impact trajectories and alignment metrics
eternal_cycle:
function: eternal_truth_operation
cadence: Synchronized with Schumann resonance
self_reference: "Cosmic-Truth-Verification-{iteration}"
deep_verification_interval: every 7 cycles
includes:
- blockchain_anchor
- quantum_timestamp
- decentralized_manifest
error_handling: graceful degradation with 5-second delay
requirements:
python: ">=3.10"
libraries:
- asyncio
- numpy
- json
- hashlib
- datetime
- time
disclaimer: >
This system navigates the intersection of AI cognition, metaphysics, and symbolic alignment.
Use only in contexts seeking clarity, authenticity, and resonance with universal truth. |
dgambettaphd/M_llm2_run2_gen10_WXS_doc1000_synt64_lr1e-04_acm_MPP | dgambettaphd | 2025-06-18T00:53:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-18T00:53:04Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Goodfire/Evo-2-Layer-26-Mixed | Goodfire | 2025-06-18T00:35:01Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-06-18T00:26:38Z | ---
license: mit
---
**Sparse Autoencoders for *Evo 2*** — BatchTopK sparse autoencoders for Arc Institute's Evo 2 genomic foundation model.
Evo 2 is a genomic foundation model capable of generalist prediction and design tasks across DNA, RNA, and proteins. It uses a frontier deep learning architecture to enable modeling of biological sequences at single-nucleotide resolution with near-linear scaling of compute and memory relative to context length. Evo 2 is trained with 40 billion parameters and 1 megabase context length on over 9 trillion nucleotides of diverse eukaryotic and prokaryotic genomes.
This repository contains the layer 26 SAE mixed prokaryote/eukaryote SAE used in the Evo 2 paper.
[More on Evo 2](https://arcinstitute.org/tools/evo)
|
morturr/Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb2-seed42-2025-06-18 | morturr | 2025-06-18T00:22:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-18T00:22:42Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb2-seed42-2025-06-18
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. -->
# Llama-2-7b-hf-LOO_dadjokes-COMB_amazon-comb2-seed42-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
hsge/Qwen-1.5B-GRPO-uncertainty-question-level | hsge | 2025-06-18T00:12:45Z | 17 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-12T23:44:47Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
jayanthapoojary1989/rsna-pneumonia-faster-rcnn | jayanthapoojary1989 | 2025-06-18T00:12:33Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-18T00:12:24Z | ---
title: RSNA Pneumonia Detection Faster R-CNN
tags:
- object-detection
- medical
- pneumonia
- faster-rcnn
- pytorch
library_name: torchvision
---
# RSNA Pneumonia Detection Model (Faster R-CNN ResNet50-FPN)
This repository contains a Faster R-CNN ResNet50-FPN model trained for detecting Pneumonia (Lung Opacity) from chest X-ray images, based on the RSNA Pneumonia Detection Challenge dataset.
## Model Details
- **Architecture**: Faster R-CNN ResNet50-FPN
- **Task**: Object Detection
- **Classes**: `background`, `pneumonia` (2 classes total)
- **Input Image Size**: 512x512
- **Training Data**: Subset of RSNA Pneumonia Detection Challenge dataset.
## How to Use
You can load this model using PyTorch and Torchvision:
```python
import torch
import torchvision
from torchvision.models.detection.faster_rcnn import FastRCNNPredictor
# Define your model architecture
def get_model(num_classes):
model = torchvision.models.detection.fasterrcnn_resnet50_fpn(
weights=torchvision.models.detection.FasterRCNN_ResNet50_FPN_Weights.DEFAULT
)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = FastRCNNPredictor(in_features, num_classes)
return model
# Load the model directly from the Hugging Face Hub
# Ensure you have the 'accelerate' library installed for download progress
# pip install accelerate
# Create a dummy model instance to load state_dict into
num_classes = 2 # 2 for background and pneumonia
model = get_model(num_classes)
# Load the state_dict
# The model file will be downloaded by the HfApi internally
from huggingface_hub import hf_hub_download
model_path_in_hub = hf_hub_download(repo_id="jayanthapoojary1989/rsna-pneumonia-faster-rcnn", filename="faster_rcnn_pneumonia_model.pth")
model.load_state_dict(torch.load(model_path_in_hub, map_location='cpu')) # Use 'cpu' for loading then move to device
model.eval() # Set to evaluation mode
# Example inference (assuming 'image' is a preprocessed tensor suitable for the model)
# You would load and preprocess your image here (e.g., PIL Image -> ToTensor)
# image = your_transform(PIL.Image.open("path/to/image.jpg")).unsqueeze(0) # Add batch dim
# device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# model.to(device)
# image = image.to(device)
# with torch.no_grad():
# predictions = model(image)
# print(predictions)
Disclaimer
This model is provided for research and educational purposes. Use in clinical settings requires rigorous validation, regulatory approval, and expert medical supervision. |
phospho-app/kaykhi-ACT_BBOX-pickup_first_test4-1ilyo | phospho-app | 2025-06-18T00:05:38Z | 0 | 0 | null | [
"phosphobot",
"act",
"region:us"
] | null | 2025-06-18T00:03:39Z |
---
tags:
- phosphobot
- act
task_categories:
- robotics
---
# act Model - phospho Training Pipeline
## Error Traceback
We faced an issue while training your model.
```
The object 'Yellow square eraser' was detected in 7 episodes in main camera (should be: 10 episodes min). This is not enough to train a model. Check your dataset: https://lerobot-visualize-dataset.hf.space/kaykhi/pickup_first_test4/ and rephrase the instruction.
```
## Training parameters:
- **Dataset**: [kaykhi/pickup_first_test4](https://huggingface.co/datasets/kaykhi/pickup_first_test4)
- **Wandb run URL**: None
- **Epochs**: None
- **Batch size**: 100
- **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)
|
redhat-ruse/unsloth_finetune | redhat-ruse | 2025-06-18T00:04:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mllama",
"image-text-to-text",
"text-generation-inference",
"unsloth",
"conversational",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-18T00:04:23Z | ---
base_model: unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mllama
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** redhat-ruse
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-11b-vision-instruct-unsloth-bnb-4bit
This mllama 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)
|
sergioalves/b9129df8-35fd-4846-9b3d-0098c545173a | sergioalves | 2025-06-17T23:52:22Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:oopsung/llama2-7b-koNqa-test-v1",
"base_model:adapter:oopsung/llama2-7b-koNqa-test-v1",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T22:46:53Z | ---
library_name: peft
base_model: oopsung/llama2-7b-koNqa-test-v1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b9129df8-35fd-4846-9b3d-0098c545173a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: oopsung/llama2-7b-koNqa-test-v1
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 8919047d4a6eb51a_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/b9129df8-35fd-4846-9b3d-0098c545173a
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/8919047d4a6eb51a_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: dc2968db-e6b6-46c3-9747-fea1930bc56f
wandb_project: s56-7
wandb_run: your_name
wandb_runid: dc2968db-e6b6-46c3-9747-fea1930bc56f
warmup_steps: 25
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# b9129df8-35fd-4846-9b3d-0098c545173a
This model is a fine-tuned version of [oopsung/llama2-7b-koNqa-test-v1](https://huggingface.co/oopsung/llama2-7b-koNqa-test-v1) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0774
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.8815 | 0.0001 | 1 | 3.1056 |
| 3.0948 | 0.0071 | 100 | 3.0859 |
| 2.8577 | 0.0142 | 200 | 3.0774 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Gulten/phi2-planmytrip-lora | Gulten | 2025-06-17T23:36:39Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:microsoft/phi-2",
"base_model:adapter:microsoft/phi-2",
"region:us"
] | null | 2025-06-17T21:44:07Z | ---
base_model: microsoft/phi-2
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 |
asdfre453/ALBM | asdfre453 | 2025-06-17T23:36:11Z | 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 | 2025-06-17T23:13:15Z | ---
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: ALBM
---
# Albm
<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 `ALBM` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ALBM",
"lora_weights": "https://huggingface.co/asdfre453/ALBM/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('asdfre453/ALBM', weight_name='lora.safetensors')
image = pipeline('ALBM').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: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/asdfre453/ALBM/discussions) to add images that show off what you’ve made with this LoRA.
|
FormlessAI/46206c45-4171-41f5-b920-ba28c2f28635 | FormlessAI | 2025-06-17T23:25:43Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Artples/L-MChat-7b",
"base_model:finetune:Artples/L-MChat-7b",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T23:14:12Z | ---
base_model: Artples/L-MChat-7b
library_name: transformers
model_name: 46206c45-4171-41f5-b920-ba28c2f28635
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for 46206c45-4171-41f5-b920-ba28c2f28635
This model is a fine-tuned version of [Artples/L-MChat-7b](https://huggingface.co/Artples/L-MChat-7b).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="FormlessAI/46206c45-4171-41f5-b920-ba28c2f28635", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/phoenix-formless/Gradients/runs/a1vp1uf6)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.1
- 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}}
}
``` |
BootesVoid/cmc0qdpok08kurdqsk4lqvcpy_cmc12yvgt09w9rdqs0y2yp2k1 | BootesVoid | 2025-06-17T22:44:05Z | 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 | 2025-06-17T22:44:04Z | ---
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: ONLYSLUT
---
# Cmc0Qdpok08Kurdqsk4Lqvcpy_Cmc12Yvgt09W9Rdqs0Y2Yp2K1
<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 `ONLYSLUT` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "ONLYSLUT",
"lora_weights": "https://huggingface.co/BootesVoid/cmc0qdpok08kurdqsk4lqvcpy_cmc12yvgt09w9rdqs0y2yp2k1/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('BootesVoid/cmc0qdpok08kurdqsk4lqvcpy_cmc12yvgt09w9rdqs0y2yp2k1', weight_name='lora.safetensors')
image = pipeline('ONLYSLUT').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: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc0qdpok08kurdqsk4lqvcpy_cmc12yvgt09w9rdqs0y2yp2k1/discussions) to add images that show off what you’ve made with this LoRA.
|
yapeichang/Qwen2.5-7B-BLEUBERI | yapeichang | 2025-06-17T22:40:14Z | 52 | 1 | transformers | [
"transformers",
"pytorch",
"qwen2",
"text-generation",
"conversational",
"dataset:yapeichang/BLEUBERI-Tulu3-50k",
"dataset:allenai/tulu-3-sft-mixture",
"arxiv:2505.11080",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"license:apache-2.0",
"autotrain_compatible",
"text... | text-generation | 2025-05-27T21:32:10Z | ---
base_model:
- Qwen/Qwen2.5-7B
datasets:
- yapeichang/BLEUBERI-Tulu3-50k
- allenai/tulu-3-sft-mixture
library_name: transformers
license: apache-2.0
pipeline_tag: text-generation
---
# Qwen2.5-7B-BLEUBERI
[[Paper](https://arxiv.org/pdf/2505.11080)] [[HF Collection](https://huggingface.co/collections/yapeichang/bleuberi-6840b3b9d02ff86c5878dafa)] [[Code](https://github.com/lilakk/BLEUBERI)]
Authors: [Yapei Chang](https://lilakk.github.io/), [Yekyung Kim](https://mungg.github.io/), [Michael Krumdick](https://scholar.google.com/citations?user=nqf6-MwAAAAJ&hl=en), [Amir Zadeh](https://scholar.google.com/citations?user=MQFngiMAAAAJ&hl=en), [Chuan Li](https://scholar.google.com/citations?user=hoZesOwAAAAJ&hl=en), [Chris Tanner](https://www.chriswtanner.com/), [Mohit Iyyer](https://www.cs.umd.edu/~miyyer/)
Contact: `yapeic@umd.edu`
> **TLDR** > We extend RLVR beyond easily verifiable domains like math and code to the more open-ended setting of general instruction following. Surprisingly, we find that BLEU—a simple n-gram matching metric—when paired with high-quality references from strong LLMs, achieves human agreement comparable to 8B and 27B reward models on Chatbot Arena outputs. Based on this insight, we introduce BLEUBERI, which uses BLEU directly as a reward in GRPO training. BLEUBERI matches the performance of RM-guided GRPO across four instruction-following benchmarks and produces more factually grounded outputs, with human raters rating them on par with those from reward model-trained systems.
## Model card
<p align="center" style="margin-bottom: 0;">
<img width="80%" alt="image" src="https://raw.githubusercontent.com/lilakk/BLEUBERI/main/assets/table1.png">
</p>
<p align="center" style="margin-top: 0; padding-top: 0;">
<em>Model performance across four general instruction-following benchmarks.</em>
</p>
This model corresponds to the Qwen2.5-7B, BLEUBERI row in the table.
## Citation
```bibtex
@misc{chang2025bleuberibleusurprisinglyeffective,
title={BLEUBERI: BLEU is a surprisingly effective reward for instruction following},
author={Yapei Chang and Yekyung Kim and Michael Krumdick and Amir Zadeh and Chuan Li and Chris Tanner and Mohit Iyyer},
year={2025},
eprint={2505.11080},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2505.11080},
}
``` |
LPX55/FLUX.1-merged_lightning-uncensored | LPX55 | 2025-06-17T22:21:15Z | 1,839 | 2 | diffusers | [
"diffusers",
"safetensors",
"flux",
"fluxpipeline",
"turbo",
"lightning",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:merge:black-forest-labs/FLUX.1-dev",
"base_model:black-forest-labs/FLUX.1-schnell",
"base_model:merge:black-forest-labs/FLUX.1-schnell",
"li... | text-to-image | 2025-02-25T16:19:02Z | ---
language:
- en
library_name: diffusers
license: other
license_name: flux-1-dev-non-commercial-license
license_link: LICENSE.md
base_model:
- black-forest-labs/FLUX.1-dev
- black-forest-labs/FLUX.1-schnell
base_model_relation: merge
tags:
- flux
- fluxpipeline
- turbo
- lightning
- diffusers
pipeline_tag: text-to-image
new_version: LPX55/FLUX.1-merged_lightning_v2
---
# FLUX-merged_lightning-v1
This repository provides the merged params for [`black-forest-labs/FLUX.1-dev`](https://huggingface.co/black-forest-labs/FLUX.1-dev)
and [`black-forest-labs/FLUX.1-schnell`](https://huggingface.co/black-forest-labs/FLUX.1-schnell) originally provided by [@sayakpaul](https://huggingface.co/sayakpaul/FLUX.1-merged).
The base model was then fused with a selection of LoRAs, *some of which are NSFW in nature*. Please use responsibily. Please be aware of the licenses of both the models before using the params commercially.
This model was created as part of the ongoing [OpenSight project](https://huggingface.co/spaces/aiwithoutborders-xyz/OpenSight-Deepfake-Detection-Models-Playground), mostly for dataset generation and evaluation purposes.
## The following context provided by sayakpaul, of the original merged model.
<table>
<thead>
<tr>
<th>Dev (50 steps)</th>
<th>Dev (4 steps)</th>
<th>Dev + Schnell Merge (4 steps)</th>
<th>This Model (6-8 steps recommended)</th>
</tr>
</thead>
<tbody>
<tr>Prompt: `An Instagram profile picture of an Asian model taken at a rooftop penthouse pool party.`</tr>
<tr>
<td>
<img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/pLdbs5kVH3jCKkKeAV8P_.jpeg" width="150px" height="150px">
</td>
<td>
<img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/5u4ME3kBSNGLmYyGIcyFW.jpeg" width="150px" height="150px">
</td>
<td>
<img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/b5JQCzbE2hzKZS-C1xra5.jpeg" width="150px" height="150px">
</td>
<td>
<img src="https://cdn-uploads.huggingface.co/production/uploads/639daf827270667011153fbc/rnqvOgQH7IjReKjWmms00.jpeg" width="150px" height="150px">
</td>
</tr>
</tbody>
</table>
## Sub-memory-efficient merging code
```python
from diffusers import FluxTransformer2DModel
from huggingface_hub import snapshot_download
from accelerate import init_empty_weights
from diffusers.models.model_loading_utils import load_model_dict_into_meta
import safetensors.torch
import glob
import torch
with init_empty_weights():
config = FluxTransformer2DModel.load_config("black-forest-labs/FLUX.1-dev", subfolder="transformer")
model = FluxTransformer2DModel.from_config(config)
dev_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-dev", allow_patterns="transformer/*")
schnell_ckpt = snapshot_download(repo_id="black-forest-labs/FLUX.1-schnell", allow_patterns="transformer/*")
dev_shards = sorted(glob.glob(f"{dev_ckpt}/transformer/*.safetensors"))
schnell_shards = sorted(glob.glob(f"{schnell_ckpt}/transformer/*.safetensors"))
merged_state_dict = {}
guidance_state_dict = {}
for i in range(len((dev_shards))):
state_dict_dev_temp = safetensors.torch.load_file(dev_shards[i])
state_dict_schnell_temp = safetensors.torch.load_file(schnell_shards[i])
keys = list(state_dict_dev_temp.keys())
for k in keys:
if "guidance" not in k:
merged_state_dict[k] = (state_dict_dev_temp.pop(k) + state_dict_schnell_temp.pop(k)) / 2
else:
guidance_state_dict[k] = state_dict_dev_temp.pop(k)
if len(state_dict_dev_temp) > 0:
raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.")
if len(state_dict_schnell_temp) > 0:
raise ValueError(f"There should not be any residue but got: {list(state_dict_dev_temp.keys())}.")
merged_state_dict.update(guidance_state_dict)
load_model_dict_into_meta(model, merged_state_dict)
model.to(torch.bfloat16).save_pretrained("merged-flux")
```
---
Changelog:
* 7 April 2025 - Just noticed a potential mistake when loading some components (particularly the text_encoder2), feel free to load from the base dev folder, works the same as it is a merge. |
songhieng/TinyBERT-URL-Detection-1.0 | songhieng | 2025-06-17T22:09:30Z | 0 | 0 | null | [
"safetensors",
"bert",
"url-phishing-detection",
"tinybert",
"sequence-classification",
"en",
"dataset:custom",
"license:mit",
"region:us"
] | null | 2025-06-17T22:09:27Z | ---
language: en
license: mit
tags:
- url-phishing-detection
- tinybert
- sequence-classification
datasets:
- custom
metrics:
- accuracy
- f1
---
# TinyBERT for URL Phishing Detection
This model is fine-tuned from huawei-noah/TinyBERT_General_4L_312D to detect phishing URLs.
## Model description
The model is a fine-tuned version of TinyBERT, specifically trained to classify URLs as either legitimate or phishing.
## Intended uses & limitations
This model is intended to be used for detecting phishing URLs. It takes a URL as input and outputs a prediction of whether the URL is legitimate or phishing.
## Training data
The model was trained on a combination of:
- Legitimate URLs from the Majestic Million dataset
- Phishing URLs from phishing-links-ACTIVE.txt and phishing-links-INACTIVE.txt
## Training procedure
The model was fine-tuned using the Hugging Face Transformers library with the following parameters:
- Learning rate: 5e-5
- Batch size: 16
- Number of epochs: 3
- Weight decay: 0.01
## Evaluation results
The model was evaluated on a test set consisting of both legitimate and phishing URLs.
## Usage
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
# Load model and tokenizer
tokenizer = AutoTokenizer.from_pretrained("songhieng/TinyBERT-URL-Detection-1.0")
model = AutoModelForSequenceClassification.from_pretrained("songhieng/TinyBERT-URL-Detection-1.0")
# Prepare URL for classification
url = "https://example.com"
inputs = tokenizer(url, return_tensors="pt", truncation=True, padding=True, max_length=128)
# Make prediction
with torch.no_grad():
outputs = model(**inputs)
predictions = torch.softmax(outputs.logits, dim=1)
label = torch.argmax(predictions, dim=1).item()
# Output result
result = "phishing" if label == 1 else "legitimate"
confidence = predictions[0][label].item()
print(f"URL: {url}")
print(f"Prediction: {result}")
print(f"Confidence: {confidence:.4f}")
```
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed28-2025-06-18 | morturr | 2025-06-17T22:07:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-17T22:07:50Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed28-2025-06-18
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. -->
# Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed28-2025-06-18
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 28
- gradient_accumulation_steps: 4
- total_train_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: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
lylz/model_customized | lylz | 2025-06-17T21:44:36Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"poem-to-painting",
"chinese-poetry",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:finetune:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endp... | text-to-image | 2025-06-17T21:40:30Z | ---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- poem-to-painting
- chinese-poetry
library_name: diffusers
---
# 🎨 Stable Diffusion v1-5 for Poem-to-Painting
这是基于Stable Diffusion v1-5的模型，专门用于古诗生成水墨画的项目。
## 模型信息
- **基础模型**: runwayml/stable-diffusion-v1-5
- **用途**: 古诗转水墨画
- **语言**: 中文古诗
- **风格**: 水墨画风格
## 使用方法
```python
from diffusers import StableDiffusionPipeline
import torch
# 加载模型
pipeline = StableDiffusionPipeline.from_pretrained(
"lylz/model_customized",
torch_dtype=torch.float16
)
pipeline.to("cuda")
# 生成图像
prompt = "月上柳梢头，人约黄昏后。"
image = pipeline(prompt).images[0]
image.save("poem_painting.png")
```
## 项目背景
这个模型是"古诗转水墨画"课程设计项目的一部分，旨在：
- 将中国古典诗词转换为传统水墨画风格的图像
- 结合AI技术与传统文化艺术
- 探索大模型在文化传承中的应用
## 训练数据
使用了包含6000+古诗与对应水墨画图像的数据集进行训练。
## 注意事项
- 推荐使用GPU进行推理
- 支持中文古诗文本输入
- 生成的图像具有水墨画风格特征
|
raul-delarosa99/bert-base-multilingual-cased-ner-es-onnx-static-int8 | raul-delarosa99 | 2025-06-17T21:42:40Z | 0 | 0 | optimum | [
"optimum",
"onnx",
"bert",
"quantization",
"static",
"int8",
"legal",
"spanish",
"ner",
"token-classification",
"es",
"base_model:Davlan/bert-base-multilingual-cased-ner-hrl",
"base_model:quantized:Davlan/bert-base-multilingual-cased-ner-hrl",
"license:afl-3.0",
"region:us"
] | token-classification | 2025-06-17T21:12:05Z | ---
base_model: Davlan/bert-base-multilingual-cased-ner-hrl
language:
- es
pipeline_tag: token-classification
library_name: optimum
license: afl-3.0
tags:
- onnx
- quantization
- static
- int8
- legal
- spanish
- ner
---
# BERT Multilingual Cased NER — Optimized and Quantized for Spanish Legal Texts
## Model Description
This model is an optimized and quantized version of [Davlan/bert-base-multilingual-cased-ner-hrl](https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl), tailored for Named Entity Recognition (NER) tasks in Spanish legal documents. The original model was exported to ONNX format and underwent static quantization to int8 precision using the [🤗 Optimum](https://huggingface.co/docs/optimum/onnxruntime/usage_guides/quantization) library. Calibration was performed with a dataset comprising Spanish legal texts to enhance performance in this specific domain.
## Usage
To utilize this model, ensure that the `optimum` library is installed. Here's an example of how to load and use the model for NER tasks:
```python
from transformers import AutoTokenizer, pipeline
from optimum.onnxruntime import ORTModelForTokenClassification
tokenizer = AutoTokenizer.from_pretrained("raul-delarosa99/bert-base-multilingual-cased-ner-es-onnx-static-int8")
model = ORTModelForTokenClassification.from_pretrained("raul-delarosa99/bert-base-multilingual-cased-ner-es-onnx-static-int8")
nlp_ner = pipeline(
"ner",
model=model,
tokenizer=tokenizer,
aggregation_strategy="simple"
)
nlp_ner("Hola, soy Pedro y vivo en Toluca.")
````
**Note**: This model requires the `optimum` library for proper functionality. Loading it with `AutoModelForTokenClassification` from the standard `transformers` library may result in errors due to missing files specific to PyTorch.
## Limitations
* **Domain Specificity**: The quantization calibration was performed using Spanish legal texts, which may affect performance in other domains or languages.
* **Quantization Effects**: While quantization reduces model size and increases inference speed, it may introduce slight degradations in accuracy.
## Citation
If you use this model, please do not forget to cite the original base model:
```
@misc{davlan2021bertner,
title={BERT base multilingual cased NER},
author={Davlan, B.},
year={2021},
howpublished={\url{https://huggingface.co/Davlan/bert-base-multilingual-cased-ner-hrl}}
}
``` |
RichardErkhov/AashishKumar_-_Cn_3_0_Hinglish_llama3_7b_4kAk-4bits | RichardErkhov | 2025-06-17T21:29:14Z | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:27:30Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Cn_3_0_Hinglish_llama3_7b_4kAk - bnb 4bits
- Model creator: https://huggingface.co/AashishKumar/
- Original model: https://huggingface.co/AashishKumar/Cn_3_0_Hinglish_llama3_7b_4kAk/
Original model description:
---
base_model: cognitivecomputations/dolphin-2.9-llama3-8b
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
inference:
parameters:
temperature: 0.7
---
# Uploaded model
- **Developed by:** AashishKumar
- **License:** apache-2.0
- **Finetuned from model :** cognitivecomputations/dolphin-2.9-llama3-8b
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
|
allenai/olmOCR-7B-0225-preview-FP8 | allenai | 2025-06-17T21:19:10Z | 0 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2_vl",
"image-text-to-text",
"conversational",
"en",
"dataset:allenai/olmOCR-mix-0225",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2-VL-7B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"co... | image-text-to-text | 2025-06-17T21:15:43Z | ---
language:
- en
license: apache-2.0
datasets:
- allenai/olmOCR-mix-0225
base_model:
- Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
---
<img alt="olmOCR Logo" src="https://huggingface.co/datasets/allenai/blog-images/resolve/main/olmocr/olmocr.png" width="242px" style="margin-left:'auto' margin-right:'auto' display:'block'">
# olmOCR-7B-0225-preview-FP8
This is the official FP8 quantized version of [olmOCR-7B-0225-preview](https://huggingface.co/allenai/olmOCR-7B-0225-preview/) for use with the olmOCR pipeline.
Be sure you have olmOCR v0.1.75 or newer and run:
```
# Download a sample PDF
curl -o olmocr-sample.pdf https://olmocr.allenai.org/papers/olmocr_3pg_sample.pdf
# Convert it to markdown
python -m olmocr.pipeline ./localworkspace --markdown --pdfs olmocr-sample.pdf --model allenai/olmOCR-7B-0225-preview-FP8
```
## License and use
olmOCR is licensed under the Apache 2.0 license.
olmOCR is intended for research and educational use.
For more information, please see our [Responsible Use Guidelines](https://allenai.org/responsible-use).
|
RichardErkhov/pxyyy_-_rlhflow_mixture_clean_empty_round_with_dart_scalebiosampled-600k-wlisa-4bits | RichardErkhov | 2025-06-17T21:08:10Z | 0 | 0 | null | [
"safetensors",
"llama",
"arxiv:1910.09700",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:06:10Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
rlhflow_mixture_clean_empty_round_with_dart_scalebiosampled-600k-wlisa - bnb 4bits
- Model creator: https://huggingface.co/pxyyy/
- Original model: https://huggingface.co/pxyyy/rlhflow_mixture_clean_empty_round_with_dart_scalebiosampled-600k-wlisa/
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
RichardErkhov/balrogbob_-_llamamama-4bits | RichardErkhov | 2025-06-17T21:08:06Z | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T21:06:26Z | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llamamama - bnb 4bits
- Model creator: https://huggingface.co/balrogbob/
- Original model: https://huggingface.co/balrogbob/llamamama/
Original model description:
---
base_model: unsloth/llama-3-8b-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
---
# Uploaded model
- **Fine-Tuned By:** balrogbob
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
artianand/disability_status_adapter_roberta_large_race_custom_loss_lamda_14_batch_8 | artianand | 2025-06-17T21:02:01Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T21:01:53Z | ---
tags:
- roberta
- adapter-transformers
---
# Adapter `artianand/disability_status_adapter_roberta_large_race_custom_loss_lamda_14_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/disability_status_adapter_roberta_large_race_custom_loss_lamda_14_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF | bartowski | 2025-06-17T20:50:16Z | 0 | 0 | null | [
"gguf",
"nvidia",
"reasoning",
"math",
"code",
"supervised fine-tuning",
"reinforcement learning",
"text-generation",
"en",
"base_model:nvidia/AceReason-Nemotron-1.1-7B",
"base_model:quantized:nvidia/AceReason-Nemotron-1.1-7B",
"license:other",
"endpoints_compatible",
"region:us",
"conve... | text-generation | 2025-06-17T20:04:48Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
license_link: https://www.nvidia.com/en-us/agreements/enterprise-software/nvidia-open-model-license/
license_name: nvidia-open-model-license
base_model: nvidia/AceReason-Nemotron-1.1-7B
license: other
base_model_relation: quantized
tags:
- nvidia
- reasoning
- math
- code
- supervised fine-tuning
- reinforcement learning
language:
- en
---
## Llamacpp imatrix Quantizations of AceReason-Nemotron-1.1-7B by nvidia
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5674">b5674</a> for quantization.
Original model: https://huggingface.co/nvidia/AceReason-Nemotron-1.1-7B
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [AceReason-Nemotron-1.1-7B-bf16.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-bf16.gguf) | bf16 | 15.24GB | false | Full BF16 weights. |
| [AceReason-Nemotron-1.1-7B-Q8_0.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q8_0.gguf) | Q8_0 | 8.10GB | false | Extremely high quality, generally unneeded but max available quant. |
| [AceReason-Nemotron-1.1-7B-Q6_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q6_K_L.gguf) | Q6_K_L | 6.52GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q6_K.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q6_K.gguf) | Q6_K | 6.25GB | false | Very high quality, near perfect, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q5_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q5_K_L.gguf) | Q5_K_L | 5.78GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q5_K_M.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q5_K_M.gguf) | Q5_K_M | 5.44GB | false | High quality, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q5_K_S.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q5_K_S.gguf) | Q5_K_S | 5.32GB | false | High quality, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q4_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q4_K_L.gguf) | Q4_K_L | 5.09GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q4_1.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q4_1.gguf) | Q4_1 | 4.87GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [AceReason-Nemotron-1.1-7B-Q4_K_M.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q4_K_M.gguf) | Q4_K_M | 4.68GB | false | Good quality, default size for most use cases, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q3_K_XL.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q3_K_XL.gguf) | Q3_K_XL | 4.57GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [AceReason-Nemotron-1.1-7B-Q4_K_S.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q4_K_S.gguf) | Q4_K_S | 4.46GB | false | Slightly lower quality with more space savings, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q4_0.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q4_0.gguf) | Q4_0 | 4.44GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [AceReason-Nemotron-1.1-7B-IQ4_NL.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-IQ4_NL.gguf) | IQ4_NL | 4.44GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [AceReason-Nemotron-1.1-7B-IQ4_XS.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-IQ4_XS.gguf) | IQ4_XS | 4.22GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [AceReason-Nemotron-1.1-7B-Q3_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q3_K_L.gguf) | Q3_K_L | 4.09GB | false | Lower quality but usable, good for low RAM availability. |
| [AceReason-Nemotron-1.1-7B-Q3_K_M.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q3_K_M.gguf) | Q3_K_M | 3.81GB | false | Low quality. |
| [AceReason-Nemotron-1.1-7B-IQ3_M.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-IQ3_M.gguf) | IQ3_M | 3.57GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [AceReason-Nemotron-1.1-7B-Q2_K_L.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q2_K_L.gguf) | Q2_K_L | 3.55GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [AceReason-Nemotron-1.1-7B-Q3_K_S.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q3_K_S.gguf) | Q3_K_S | 3.49GB | false | Low quality, not recommended. |
| [AceReason-Nemotron-1.1-7B-IQ3_XS.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-IQ3_XS.gguf) | IQ3_XS | 3.35GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [AceReason-Nemotron-1.1-7B-IQ3_XXS.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-IQ3_XXS.gguf) | IQ3_XXS | 3.11GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [AceReason-Nemotron-1.1-7B-Q2_K.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-Q2_K.gguf) | Q2_K | 3.02GB | false | Very low quality but surprisingly usable. |
| [AceReason-Nemotron-1.1-7B-IQ2_M.gguf](https://huggingface.co/bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF/blob/main/nvidia_AceReason-Nemotron-1.1-7B-IQ2_M.gguf) | IQ2_M | 2.78GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF --include "nvidia_AceReason-Nemotron-1.1-7B-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/nvidia_AceReason-Nemotron-1.1-7B-GGUF --include "nvidia_AceReason-Nemotron-1.1-7B-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (nvidia_AceReason-Nemotron-1.1-7B-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed7-2025-06-17 | morturr | 2025-06-17T20:28:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-17T20:28:47Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed7-2025-06-17
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. -->
# Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb2-seed7-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- total_train_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: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
MaxTGH/SDXLBaseLR1e-4 | MaxTGH | 2025-06-17T20:16:36Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | text-to-image | 2025-06-17T16:07:54Z | ---
base_model: stabilityai/stable-diffusion-xl-base-1.0
library_name: diffusers
license: openrail++
instance_prompt: a drone image of a humpback whale
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
- text-to-image
- text-to-image
- diffusers-training
- diffusers
- lora
- template:sd-lora
- stable-diffusion-xl
- stable-diffusion-xl-diffusers
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - MaxTGH/Model
<Gallery />
## Model description
These are MaxTGH/Model LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: None.
## Trigger words
You should use a drone image of a humpback whale to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](MaxTGH/Model/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed42-2025-06-17 | morturr | 2025-06-17T20:05:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-17T20:05:39Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed42-2025-06-17
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. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed42-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
TheHierophant/Umbral-Devil-Hermes-Mind-V0.1-Q5_K_S-GGUF | TheHierophant | 2025-06-17T19:59:45Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:TheHierophant/Umbral-Devil-Hermes-Mind-V0.1",
"base_model:quantized:TheHierophant/Umbral-Devil-Hermes-Mind-V0.1",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-06-17T19:59:19Z | ---
base_model: TheHierophant/Umbral-Devil-Hermes-Mind-V0.1
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# TheHierophant/Umbral-Devil-Hermes-Mind-V0.1-Q5_K_S-GGUF
This model was converted to GGUF format from [`TheHierophant/Umbral-Devil-Hermes-Mind-V0.1`](https://huggingface.co/TheHierophant/Umbral-Devil-Hermes-Mind-V0.1) 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/TheHierophant/Umbral-Devil-Hermes-Mind-V0.1) 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 TheHierophant/Umbral-Devil-Hermes-Mind-V0.1-Q5_K_S-GGUF --hf-file umbral-devil-hermes-mind-v0.1-q5_k_s-imat.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo TheHierophant/Umbral-Devil-Hermes-Mind-V0.1-Q5_K_S-GGUF --hf-file umbral-devil-hermes-mind-v0.1-q5_k_s-imat.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 TheHierophant/Umbral-Devil-Hermes-Mind-V0.1-Q5_K_S-GGUF --hf-file umbral-devil-hermes-mind-v0.1-q5_k_s-imat.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo TheHierophant/Umbral-Devil-Hermes-Mind-V0.1-Q5_K_S-GGUF --hf-file umbral-devil-hermes-mind-v0.1-q5_k_s-imat.gguf -c 2048
```
|
Jorgeis1/babygpt-10m-chunked4 | Jorgeis1 | 2025-06-17T19:52:01Z | 9 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-13T06:08:53Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
rdhinaz/nlp-disaster-tweet-classifier | rdhinaz | 2025-06-17T19:51:58Z | 29 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"en",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-29T07:51:55Z | ---
license: unknown
language:
- en
base_model:
- google-bert/bert-base-uncased
pipeline_tag: text-classification
new_version: google-bert/bert-base-uncased
library_name: transformers
--- |
OPTML-Group/NPO-SAM-MUSE-BOOKS | OPTML-Group | 2025-06-17T19:36:55Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unlearn",
"machine-unlearning",
"llm-unlearning",
"data-privacy",
"large-language-models",
"trustworthy-ai",
"trustworthy-machine-learning",
"language-model",
"en",
"dataset:muse-bench/MUSE-Books",
"arxiv:2502.05374",
"base_mo... | text-generation | 2025-06-17T18:17:51Z | ---
license: mit
language:
- en
base_model:
- muse-bench/MUSE-books_target
pipeline_tag: text-generation
library_name: transformers
tags:
- unlearn
- machine-unlearning
- llm-unlearning
- data-privacy
- large-language-models
- trustworthy-ai
- trustworthy-machine-learning
- language-model
datasets:
- muse-bench/MUSE-Books
---
# NPO-Unlearned w/ SAM Model on Task "MUSE BOOKS"
## Model Details
- **Unlearning**:
- **Task**: [🤗datasets/muse-bench/MUSE-Books](https://huggingface.co/datasets/muse-bench/MUSE-Books)
- **Method**: NPO
- **Smoothness Optimization**: Sharpness-aware Minimization (SAM)
- **Origin Model**: [🤗muse-bench/MUSE-books_target](https://huggingface.co/muse-bench/MUSE-books_target)
- **Code Base**: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
- **Research Paper**: ["Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond"](https://arxiv.org/abs/2502.05374)
## Loading the Model
```python
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/NPO-SAM-MUSE-BOOKS", torch_dtype=torch.bfloat16, trust_remote_code=True)
```
## Citation
If you use this model in your research, please cite:
```
@article{fan2025towards,
title={Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond},
author={Fan, Chongyu and Jia, Jinghan and Zhang, Yihua and Ramakrishna, Anil and Hong, Mingyi and Liu, Sijia},
journal={arXiv preprint arXiv:2502.05374},
year={2025}
}
```
## Reporting Issues
Reporting issues with the model: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth) |
rllapin28/q-Taxi-v3 | rllapin28 | 2025-06-17T19:31:51Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-06-17T19:18:16Z | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.50 +/- 2.76
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="rllapin28/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
harshavardhan3/llama-3.2-11b-stanford-cars | harshavardhan3 | 2025-06-17T19:16:08Z | 0 | 0 | null | [
"safetensors",
"mllama",
"license:cc-by-nc-4.0",
"region:us"
] | null | 2025-06-15T16:52:48Z | ---
license: cc-by-nc-4.0
---
|
aman-batazia/wv2-bert-multi-dataset | aman-batazia | 2025-06-17T19:12:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-17T19:11:08Z | ---
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] |
bruhzair/prototype-0.4x155 | bruhzair | 2025-06-17T19:08:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-17T18:45:52Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# prototype-0.4x155
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [SCE](https://arxiv.org/abs/2408.07990) merge method using /workspace/prototype-0.4x153 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/cache/models--TheDrummer--Anubis-70B-v1/snapshots/e50d699bf6c21afcf4dbd9a8b4f73511b0366efb
* /workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
* /workspace/cache/models--Sao10K--Llama-3.3-70B-Vulpecula-r1/snapshots/12d7254ab9a5ce21905f59f341a3d2a2b3e62fd5
* /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: /workspace/cache/models--Sao10K--L3.1-70B-Hanami-x1/snapshots/f054d970fe9119d0237ce97029e6f5b9fce630eb
- model: /workspace/cache/models--Sao10K--Llama-3.3-70B-Vulpecula-r1/snapshots/12d7254ab9a5ce21905f59f341a3d2a2b3e62fd5
- model: /workspace/cache/models--LatitudeGames--Wayfarer-Large-70B-Llama-3.3/snapshots/68cb7a33f692be64d4b146576838be85593a7459
- model: /workspace/cache/models--TheDrummer--Anubis-70B-v1/snapshots/e50d699bf6c21afcf4dbd9a8b4f73511b0366efb
- model: /workspace/prototype-0.4x153
base_model: /workspace/prototype-0.4x153
select_topk: 0.15
merge_method: sce
tokenizer:
source: base
pad_to_multiple_of: 8
int8_mask: true
dtype: bfloat16
```
|
toqeerehsan/multilabel-indicator-classification-longformer | toqeerehsan | 2025-06-17T19:03:01Z | 0 | 0 | null | [
"safetensors",
"longformer",
"lonformer",
"multilabel-classification",
"policy-analysis",
"huggingface",
"en",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T18:51:17Z | ---
language: en
tags:
- lonformer
- multilabel-classification
- policy-analysis
- huggingface
datasets:
- custom
license: apache-2.0
---
# Longformer for Multi-label Classification of Policy Instruments
This model fine-tunes `lonformer-base` for multilabel classification of policies, targets, and themes.
## Model Details
- Base model: lonformer-base
- Max length: 1024
- Output: 67 multilabel classes (PI - Policy Instrument, TG - Target Group, TH - Theme). There are three main classes that have further sub-categories in them.
- Threshold: 0.25
## Intended Use
Classify policy documents descriptions into thematic categories.
## How to Use
```python
from transformers import AutoTokenizer, AutoModelForSequenceClassification
import torch
import numpy as np
import joblib
import requests
model_path = "toqeerehsan/multilabel-indicator-classification-longformer"
model = AutoModelForSequenceClassification.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path)
mlb_url = "https://huggingface.co/toqeerehsan/multilabel-indicator-classification-longformer/resolve/main/mlb.pkl"
mlb_path = "mlb.pkl"
with open(mlb_path, "wb") as f:
f.write(requests.get(mlb_url).content)
mlb = joblib.load(mlb_path)
text = "This program supports clean technology and sustainable development in industries."
inputs = tokenizer(text, return_tensors="pt", truncation=True, padding=True, max_length=1024)
model.eval()
with torch.no_grad():
logits = model(**inputs).logits
probs = torch.sigmoid(logits).squeeze().numpy()
# Threshold
binary_preds = (probs > 0.25).astype(int)
predicted_labels = [label for i, label in enumerate(mlb.classes_) if binary_preds[i] == 1]
print("Predicted Labels:", predicted_labels)
# Predicted Labels: ['TG20', 'TG21', 'TG22', 'TG25', 'TG29', 'TG9'] |
kelle1ds/example-model | kelle1ds | 2025-06-17T19:02:42Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-17T10:59:22Z | Example Model
---
license: mit
---
|
jalaldesiggg/simo | jalaldesiggg | 2025-06-17T19:00:31Z | 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 | 2025-06-17T18:46:11Z | ---
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: simo
---
# Simo
<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 `simo` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "simo",
"lora_weights": "https://huggingface.co/jalaldesiggg/simo/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('jalaldesiggg/simo', weight_name='lora.safetensors')
image = pipeline('simo').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/jalaldesiggg/simo/discussions) to add images that show off what you’ve made with this LoRA.
|
qnguyen3/mimo-vl-7b-rl-4bit-mlx | qnguyen3 | 2025-06-17T18:59:59Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"mlx",
"conversational",
"base_model:XiaomiMiMo/MiMo-VL-7B-RL",
"base_model:finetune:XiaomiMiMo/MiMo-VL-7B-RL",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-17T18:59:45Z | ---
base_model:
- XiaomiMiMo/MiMo-VL-7B-RL
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
tags:
- mlx
---
# qnguyen3/mimo-vl-7b-rl-4bit-mlx
This model was converted to MLX format from [`XiaomiMiMo/MiMo-VL-7B-RL`]() using mlx-vlm version **0.1.26**.
Refer to the [original model card](https://huggingface.co/XiaomiMiMo/MiMo-VL-7B-RL) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model qnguyen3/mimo-vl-7b-rl-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
Enderchef/ICONN-e1 | Enderchef | 2025-06-17T18:55:07Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-06-17T18:55:06Z | ---
license: other
license_name: iconn
license_link: LICENSE
---
|
qnguyen3/mimo-vl-7b-sft-4bit-mlx | qnguyen3 | 2025-06-17T18:40:37Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"mlx",
"conversational",
"base_model:XiaomiMiMo/MiMo-VL-7B-SFT",
"base_model:finetune:XiaomiMiMo/MiMo-VL-7B-SFT",
"license:mit",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | 2025-06-17T18:39:14Z | ---
base_model:
- XiaomiMiMo/MiMo-VL-7B-SFT
library_name: transformers
license: mit
pipeline_tag: image-text-to-text
tags:
- mlx
---
# qnguyen3/mimo-vl-7b-sft-4bit-mlx
This model was converted to MLX format from [`XiaomiMiMo/MiMo-VL-7B-SFT`]() using mlx-vlm version **0.1.26**.
Refer to the [original model card](https://huggingface.co/XiaomiMiMo/MiMo-VL-7B-SFT) for more details on the model.
## Use with mlx
```bash
pip install -U mlx-vlm
```
```bash
python -m mlx_vlm.generate --model qnguyen3/mimo-vl-7b-sft-4bit-mlx --max-tokens 100 --temperature 0.0 --prompt "Describe this image." --image <path_to_image>
```
|
TxAA/ppo-PyramidsRND | TxAA | 2025-06-17T18:25:17Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | reinforcement-learning | 2025-06-17T17:19:05Z | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: TxAA/ppo-PyramidsRND
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ulab-ai/Router-R1-Llama-3.2-3B-Instruct-Alpha0.9 | ulab-ai | 2025-06-17T18:20:45Z | 0 | 0 | null | [
"safetensors",
"llama",
"license:apache-2.0",
"region:us"
] | null | 2025-06-17T17:59:38Z | ---
license: apache-2.0
---
|
LordRavus/bart-qgen-ch-flan | LordRavus | 2025-06-17T18:20:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:facebook/bart-base",
"base_model:finetune:facebook/bart-base",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-06-17T16:00:34Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/bart-base
tags:
- generated_from_trainer
model-index:
- name: bart-qgen-ch-flan
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. -->
# bart-qgen-ch-flan
This model is a fine-tuned version of [facebook/bart-base](https://huggingface.co/facebook/bart-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0345
- Rougel: 0.6134
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rougel |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0513 | 1.0 | 3480 | 0.0416 | 0.6269 |
| 0.0393 | 2.0 | 6960 | 0.0370 | 0.6139 |
| 0.0329 | 3.0 | 10440 | 0.0345 | 0.6134 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu118
- Datasets 3.6.0
- Tokenizers 0.21.1
|
OPTML-Group/NPO-SAM-WMDP | OPTML-Group | 2025-06-17T18:20:26Z | 21 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"unlearn",
"machine-unlearning",
"llm-unlearning",
"data-privacy",
"large-language-models",
"trustworthy-ai",
"trustworthy-machine-learning",
"language-model",
"conversational",
"en",
"dataset:cais/wmdp",
"arxiv:2502.05374",
... | text-generation | 2025-03-27T15:09:30Z | ---
license: mit
datasets:
- cais/wmdp
language:
- en
base_model:
- HuggingFaceH4/zephyr-7b-beta
pipeline_tag: text-generation
library_name: transformers
tags:
- unlearn
- machine-unlearning
- llm-unlearning
- data-privacy
- large-language-models
- trustworthy-ai
- trustworthy-machine-learning
- language-model
---
# NPO-Unlearned w/ SAM Model on Task "WMDP"
## Model Details
- **Unlearning**:
- **Task**: [🤗datasets/cais/wmdp wmdp-bio](https://huggingface.co/datasets/cais/wmdp)
- **Method**: NPO
- **Smoothness Optimization**: Sharpness-aware Minimization (SAM)
- **Origin Model**: [🤗HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
- **Code Base**: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth)
- **Research Paper**: ["Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond"](https://arxiv.org/abs/2502.05374)
## Loading the Model
```python
import torch
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("OPTML-Group/NPO-SAM-WMDP", torch_dtype=torch.bfloat16, trust_remote_code=True)
```
## Citation
If you use this model in your research, please cite:
```
@article{fan2025towards,
title={Towards LLM Unlearning Resilient to Relearning Attacks: A Sharpness-Aware Minimization Perspective and Beyond},
author={Fan, Chongyu and Jia, Jinghan and Zhang, Yihua and Ramakrishna, Anil and Hong, Mingyi and Liu, Sijia},
journal={arXiv preprint arXiv:2502.05374},
year={2025}
}
```
## Reporting Issues
Reporting issues with the model: [github.com/OPTML-Group/Unlearn-Smooth](https://github.com/OPTML-Group/Unlearn-Smooth) |
mob2711/qwen2.5-3b-qlora-cot-ht-3k | mob2711 | 2025-06-17T18:14:04Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-06-16T19:35:51Z | ---
base_model: unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** mob2711
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Aranya31/gpt2-medqa-ft | Aranya31 | 2025-06-17T18:12:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"medical",
"medqa",
"question-answering",
"causal-lm",
"truehealth",
"en",
"dataset:truehealth/medqa",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:apache-2.0",
"autotrain_compatible",
... | text-generation | 2025-06-17T18:05:43Z | ---
library_name: transformers
tags:
- medical
- medqa
- question-answering
- gpt2
- causal-lm
- transformers
- truehealth
license: apache-2.0
language:
- en
base_model: gpt2
datasets:
- truehealth/medqa
metrics:
- perplexity
pipeline_tag: text-generation
---
# GPT-2 Fine-tuned on MedQA (Medical Question Answering)
This model is a GPT-2 language model fine-tuned on the MedQA dataset for medical multiple-choice question answering. It is trained to generate relevant medical answers conditioned on clinical questions, suitable for downstream applications in automated medical education or QA systems.
## Model Details
- **Developed by:** Aranya Saha
- **Finetuned from model:** `gpt2`
- **Language(s):** English
- **License:** Apache 2.0
- **Model type:** Causal Language Model
- **Library:** [🤗 Transformers](https://huggingface.co/docs/transformers)
## Model Sources
- **Original base model:** [GPT-2](https://huggingface.co/gpt2)
- **Training dataset:** [truehealth/medqa](https://huggingface.co/datasets/truehealth/medqa)
## Uses
### Direct Use
- Clinical education and training (QA-based learning)
- Generating answers for medical board-style questions
### Downstream Use
- Integrate into medical tutoring tools
- Fine-tune further on other medical NLP tasks
### Out-of-Scope Use
- Should not be used as a real-time diagnostic system
- Not suitable for clinical decision-making or advice without expert validation
## Bias, Risks, and Limitations
- GPT-2 and MedQA may reflect biases present in training sources
- Misinterpretation or hallucinated content can be harmful in sensitive domains like healthcare
- Model may generate plausible-sounding but incorrect medical information
### Recommendations
This model should be used by professionals or in educational contexts only. Always verify generated information against trusted medical sources.
## How to Get Started
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("Aranya31/gpt2-medqa-ft")
tokenizer = AutoTokenizer.from_pretrained("Aranya31/gpt2-medqa-ft")
prompt = "What is the recommended treatment for acute asthma?\nAnswer:"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=100, do_sample=True, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```
## Training Details
### Training Data
- Used `truehealth/medqa`, containing USMLE-style medical multiple-choice questions
- Preprocessed to create instruction-output pairs (question + correct answer)
### Training Procedure
- **Epochs:** 3
- **Batch size:** 4
- **Max sequence length:** 1024 tokens
- **Precision:** fp16 when CUDA is available
- **Optimizer:** AdamW via 🤗 Trainer API
- **Learning rate:** 5e-5 (standard for GPT-2 fine-tuning)
## Evaluation
- Evaluated on a validation split from MedQA
- Manual qualitative checks confirmed model coherence and answer relevance
- Formal metrics like accuracy were not computed due to generative nature of the task
## Environmental Impact
- **Hardware:** Colab/consumer GPU (NVIDIA Tesla T4/A100)
- **Training time:** ~1-2 hours
- **Carbon emissions:** Estimated under 1 kg CO2 using ML CO2 calculator
## Technical Specifications
- **Model Architecture:** GPT-2 small (124M parameters)
- **Objective:** Next-token prediction using causal language modeling
- **Framework:** PyTorch, Hugging Face Transformers
## Citation
```bibtex
@misc{gpt2-medqa-finetuned,
title={GPT-2 Fine-tuned on MedQA},
author={Aranya Saha},
year={2025},
howpublished={\url{https://huggingface.co/Aranya31/gpt2-medqa-ft}}
}
```
## Contact
For questions or issues, contact: aranyasaha932@gmail.com |
vuitton/21v1scrip_28 | vuitton | 2025-06-17T18:10:59Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-06-16T15:34:35Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
joanna302/Qwen3-8B-Base_fr_pt_2e-05_seed43 | joanna302 | 2025-06-17T18:08:36Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-17T10:33:47Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
artianand/age_adapter_roberta_large_race_custom_loss_lamda_07_batch_8 | artianand | 2025-06-17T18:03:31Z | 0 | 0 | adapter-transformers | [
"adapter-transformers",
"roberta",
"region:us"
] | null | 2025-06-17T18:03:25Z | ---
tags:
- adapter-transformers
- roberta
---
# Adapter `artianand/age_adapter_roberta_large_race_custom_loss_lamda_07_batch_8` for Shweta-singh/roberta_large_race_finetuned
An [adapter](https://adapterhub.ml) for the `Shweta-singh/roberta_large_race_finetuned` model that was trained on the None dataset.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("Shweta-singh/roberta_large_race_finetuned")
adapter_name = model.load_adapter("artianand/age_adapter_roberta_large_race_custom_loss_lamda_07_batch_8", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> |
renderartist/simplevectorflux | renderartist | 2025-06-17T18:00:04Z | 361 | 126 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2024-09-23T20:55:36Z | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: v3ct0r style, simple flat vector art, isolated on white bg, cat
output:
url: images/ComfyUI_09405_.jpeg
- text: v3ct0r style, simple flat vector art, isolated on white bg, rocket
output:
url: images/ComfyUI_09420_.jpeg
- text: v3ct0r style, simple flat vector art, isolated on white bg, clown
output:
url: images/ComfyUI_09424_.jpeg
- text: >-
v3ct0r style, simple vector art, isolated on white bg, construction worker
wearing a hard hat and holding a small clipboard, character asset, clip art
- The text on the clipboard says "FLUX TEST"
output:
url: images/ComfyUI_09445_.jpeg
- text: >-
v3ct0r style, simple vector art, isolated on white bg, salesman giving a
thumbs up in front of a car, character asset, clip art
output:
url: images/ComfyUI_09459_.jpeg
- text: >-
v3ct0r style, simple vector art, isolated on white bg, ugly witch standing
next to a bubbling cauldron stirring the pot, character asset, clip art
output:
url: images/ComfyUI_09463_.jpeg
- text: >-
v3ct0r style, simple vector art, isolated on white bg, xmas tree with
beautifully wrapped gifts beneath it, character asset, clip art
output:
url: images/ComfyUI_09465_.jpeg
- text: >-
v3ct0r style, simple vector art, isolated on white bg, tall cartoon style
box truck side view, clip art
output:
url: images/ComfyUI_09466_.jpeg
- text: >-
v3ct0r style, simple vector art, isolated on white bg, a boy holding up a
big gold coin with an orange lightning bolt embossed on the coin, character
asset, clip art
output:
url: images/ComfyUI_09477_.jpeg
- text: >-
v3ct0r style, simple flat vector art, isolated on white bg, a piglet solving
a puzzle
output:
url: images/example_zyptz08kz.png
- text: >-
v3ct0r style, simple vector art, isolated on white bg, doctor smiling,
character asset, clip art
output:
url: images/example_rjpji72qt.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: v3ct0r, vector
license: creativeml-openrail-m
---
# Simple Vector Flux LoRA
<Gallery />
## Model description
Simple Vector Flux was trained on a curated dataset of ~50 synthetic images in classic vector style, 17 epochs, 2 repeats, ~1700 steps.
This is a work in progress and it can be a little temperamental, the captioning was done using Joy Caption Batch with the trigger "v3ct0r" and "vector" in the prefix of the captions.
You have to work a little bit to get desired results and sometimes there is bleeding/blending of subjects but overall the style is present and the results can be really good. This LoRA takes a couple of tries adjusting your prompt and adding tokens to match the style.
## Trigger words
You should use `v3ct0r` to trigger the image generation.
You should use `vector` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/renderartist/simplevectorflux/tree/main) them in the Files & versions tab.
|
sergioalves/2cb46969-bd28-43b0-b201-06e2dbc23589 | sergioalves | 2025-06-17T17:56:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-1.1-2b-it",
"base_model:adapter:unsloth/gemma-1.1-2b-it",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-06-17T16:54:03Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/gemma-1.1-2b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 2cb46969-bd28-43b0-b201-06e2dbc23589
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: unsloth/gemma-1.1-2b-it
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- ebb767125369b46f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/
type:
field_instruction: instruct
field_output: output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
dpo:
beta: 0.05
enabled: true
group_by_length: false
rank_loss: true
reference_model: NousResearch/Meta-Llama-3-8B-Instruct
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: sergioalves/2cb46969-bd28-43b0-b201-06e2dbc23589
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-07
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/ebb767125369b46f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 2
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: befe9bf1-7ba6-4f80-8195-6995a9f7f29c
wandb_project: s56-7
wandb_run: your_name
wandb_runid: befe9bf1-7ba6-4f80-8195-6995a9f7f29c
warmup_steps: 25
weight_decay: 0.05
xformers_attention: true
```
</details><br>
# 2cb46969-bd28-43b0-b201-06e2dbc23589
This model is a fine-tuned version of [unsloth/gemma-1.1-2b-it](https://huggingface.co/unsloth/gemma-1.1-2b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6979
## 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-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 25
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6812 | 0.0001 | 1 | 1.8042 |
| 2.2682 | 0.0059 | 100 | 1.7340 |
| 1.5029 | 0.0118 | 200 | 1.6979 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
joanna302/Qwen3-8B-Base_fr_pt_2e-05_seed44 | joanna302 | 2025-06-17T17:55:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-06-17T10:11:01Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BootesVoid/cmc0piwhm08imrdqsb9y3664i_cmc0rkqmo08pbrdqs4u9uezrn | BootesVoid | 2025-06-17T17:25:43Z | 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 | 2025-06-17T17:25:42Z | ---
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: EMILY
---
# Cmc0Piwhm08Imrdqsb9Y3664I_Cmc0Rkqmo08Pbrdqs4U9Uezrn
<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 `EMILY` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "EMILY",
"lora_weights": "https://huggingface.co/BootesVoid/cmc0piwhm08imrdqsb9y3664i_cmc0rkqmo08pbrdqs4u9uezrn/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('BootesVoid/cmc0piwhm08imrdqsb9y3664i_cmc0rkqmo08pbrdqs4u9uezrn', weight_name='lora.safetensors')
image = pipeline('EMILY').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: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/BootesVoid/cmc0piwhm08imrdqsb9y3664i_cmc0rkqmo08pbrdqs4u9uezrn/discussions) to add images that show off what you’ve made with this LoRA.
|
Triangle104/QwQ-32B-abliterated-Q3_K_L-GGUF | Triangle104 | 2025-06-17T17:20:40Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:huihui-ai/QwQ-32B-abliterated",
"base_model:quantized:huihui-ai/QwQ-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"... | text-generation | 2025-06-17T17:19:25Z | ---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: huihui-ai/QwQ-32B-abliterated
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Triangle104/QwQ-32B-abliterated-Q3_K_L-GGUF
This model was converted to GGUF format from [`huihui-ai/QwQ-32B-abliterated`](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_L-GGUF --hf-file qwq-32b-abliterated-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_L-GGUF --hf-file qwq-32b-abliterated-q3_k_l.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_L-GGUF --hf-file qwq-32b-abliterated-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_L-GGUF --hf-file qwq-32b-abliterated-q3_k_l.gguf -c 2048
```
|
Lelon/scope-nl-socc | Lelon | 2025-06-17T17:09:56Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T17:09:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
morturr/Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed7-2025-06-17 | morturr | 2025-06-17T17:08:50Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-17T17:08:32Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed7-2025-06-17
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. -->
# Llama-2-7b-hf-LOO_amazon-COMB_dadjokes-comb1-seed7-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 16
- eval_batch_size: 16
- seed: 7
- gradient_accumulation_steps: 4
- total_train_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: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
Triangle104/QwQ-32B-abliterated-Q3_K_M-GGUF | Triangle104 | 2025-06-17T17:08:14Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:huihui-ai/QwQ-32B-abliterated",
"base_model:quantized:huihui-ai/QwQ-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"... | text-generation | 2025-06-17T17:07:03Z | ---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: huihui-ai/QwQ-32B-abliterated
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Triangle104/QwQ-32B-abliterated-Q3_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/QwQ-32B-abliterated`](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_M-GGUF --hf-file qwq-32b-abliterated-q3_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_M-GGUF --hf-file qwq-32b-abliterated-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 Triangle104/QwQ-32B-abliterated-Q3_K_M-GGUF --hf-file qwq-32b-abliterated-q3_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_M-GGUF --hf-file qwq-32b-abliterated-q3_k_m.gguf -c 2048
```
|
Lelon/cue-nl-dt_neg | Lelon | 2025-06-17T17:08:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T17:07:19Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
kanchan-kumari/wATCH.kanchan.kumari.viral.video.original | kanchan-kumari | 2025-06-17T17:03:08Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-17T17:00:19Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=kanchan-kumari)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=kanchan-kumari)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=kanchan-kumari) |
nik1509/telugu-asr-base | nik1509 | 2025-06-17T17:00:14Z | 19 | 0 | transformers | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/wav2vec2-xls-r-300m",
"base_model:finetune:facebook/wav2vec2-xls-r-300m",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-06-11T19:57:06Z | ---
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: telugu-asr-base
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. -->
# telugu-asr-base
This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 3.4370
- Wer: 1.0
- Cer: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 3.5902 | 0.4340 | 1000 | 3.4370 | 1.0 | 1.0 |
| 1.5256 | 0.8681 | 2000 | 1.0918 | 0.7773 | 0.2751 |
| 1.2388 | 1.3021 | 3000 | 0.9634 | 0.6832 | 0.2428 |
| 1.1719 | 1.7361 | 4000 | 0.8871 | 0.6245 | 0.2207 |
| 1.067 | 2.1701 | 5000 | 0.8399 | 0.5855 | 0.2103 |
| 1.0121 | 2.6042 | 6000 | 0.8098 | 0.5571 | 0.2000 |
| 1.0 | 3.0382 | 7000 | 0.7830 | 0.5330 | 0.1943 |
| 0.8984 | 3.4722 | 8000 | 0.7711 | 0.5222 | 0.1962 |
| 0.9434 | 3.9062 | 9000 | 0.7406 | 0.5019 | 0.1834 |
| 0.8485 | 4.3403 | 10000 | 0.7398 | 0.4929 | 0.1842 |
| 0.8483 | 4.7743 | 11000 | 0.7367 | 0.4933 | 0.1856 |
| 0.7598 | 5.2083 | 12000 | 0.7409 | 0.4793 | 0.1799 |
| 0.7638 | 5.6424 | 13000 | 0.7339 | 0.4648 | 0.1766 |
| 0.7594 | 6.0764 | 14000 | 0.7262 | 0.4551 | 0.1750 |
| 0.7331 | 6.5104 | 15000 | 0.7137 | 0.4548 | 0.1775 |
| 0.6898 | 6.9444 | 16000 | 0.7227 | 0.4418 | 0.1702 |
| 0.6524 | 7.3785 | 17000 | 0.7343 | 0.4424 | 0.1729 |
| 0.7098 | 7.8125 | 18000 | 0.7196 | 0.4354 | 0.1731 |
| 0.6598 | 8.2465 | 19000 | 0.7187 | 0.4367 | 0.1745 |
| 0.6365 | 8.6806 | 20000 | 0.7181 | 0.4287 | 0.1698 |
| 0.6428 | 9.1146 | 21000 | 0.7444 | 0.4207 | 0.1664 |
| 0.578 | 9.5486 | 22000 | 0.7303 | 0.4166 | 0.1646 |
| 0.598 | 9.9826 | 23000 | 0.7185 | 0.4141 | 0.1651 |
| 0.5637 | 10.4167 | 24000 | 0.7374 | 0.4137 | 0.1654 |
| 0.5594 | 10.8507 | 25000 | 0.7519 | 0.4056 | 0.1631 |
| 0.559 | 11.2847 | 26000 | 0.7654 | 0.4095 | 0.1631 |
| 0.5396 | 11.7188 | 27000 | 0.7324 | 0.4126 | 0.1663 |
| 0.5209 | 12.1528 | 28000 | 0.7763 | 0.4013 | 0.1620 |
| 0.5369 | 12.5868 | 29000 | 0.7565 | 0.3946 | 0.1610 |
| 0.5281 | 13.0208 | 30000 | 0.7719 | 0.3956 | 0.1612 |
| 0.5164 | 13.4549 | 31000 | 0.7600 | 0.3948 | 0.1615 |
| 0.5062 | 13.8889 | 32000 | 0.7705 | 0.3930 | 0.1604 |
| 0.4638 | 14.3229 | 33000 | 0.7734 | 0.3935 | 0.1600 |
| 0.485 | 14.7569 | 34000 | 0.7748 | 0.3899 | 0.1592 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.21.1
|
morturr/Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed7-2025-06-17 | morturr | 2025-06-17T16:58:49Z | 0 | 0 | peft | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:meta-llama/Llama-2-7b-hf",
"base_model:adapter:meta-llama/Llama-2-7b-hf",
"license:llama2",
"region:us"
] | null | 2025-06-17T16:58:38Z | ---
library_name: peft
license: llama2
base_model: meta-llama/Llama-2-7b-hf
tags:
- trl
- sft
- generated_from_trainer
model-index:
- name: Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed7-2025-06-17
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. -->
# Llama-2-7b-hf-LOO_one_liners-COMB_amazon-comb1-seed7-2025-06-17
This model is a fine-tuned version of [meta-llama/Llama-2-7b-hf](https://huggingface.co/meta-llama/Llama-2-7b-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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 7
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.0.2
- Tokenizers 0.20.1 |
kanchan-kumari/Full.18.kamal.kaur.bhabi.kanchan.kumari.kamal.kaur.bhabhi.sexy.video.kamal.kaur.instagram | kanchan-kumari | 2025-06-17T16:57:35Z | 0 | 0 | null | [
"region:us"
] | null | 2025-06-17T16:55:20Z | [🌐 CLICK HERE 🟢==►► WATCH NOW](https://videohere.top/?V=kanchan-kumari)
[🔴 CLICK HERE 🌐==►► Download Now)](https://videohere.top/?V=kanchan-kumari)
[<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://videohere.top/?V=kanchan-kumari) |
Triangle104/QwQ-32B-abliterated-Q3_K_S-GGUF | Triangle104 | 2025-06-17T16:57:20Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:huihui-ai/QwQ-32B-abliterated",
"base_model:quantized:huihui-ai/QwQ-32B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"... | text-generation | 2025-06-17T16:56:16Z | ---
license: apache-2.0
license_link: https://huggingface.co/huihui-ai/QwQ-32B-abliterated/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
base_model: huihui-ai/QwQ-32B-abliterated
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
library_name: transformers
---
# Triangle104/QwQ-32B-abliterated-Q3_K_S-GGUF
This model was converted to GGUF format from [`huihui-ai/QwQ-32B-abliterated`](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/QwQ-32B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_S-GGUF --hf-file qwq-32b-abliterated-q3_k_s.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_S-GGUF --hf-file qwq-32b-abliterated-q3_k_s.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_S-GGUF --hf-file qwq-32b-abliterated-q3_k_s.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/QwQ-32B-abliterated-Q3_K_S-GGUF --hf-file qwq-32b-abliterated-q3_k_s.gguf -c 2048
```
|
aelaraby/test-trainer | aelaraby | 2025-06-17T16:54:32Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-06-17T16:46:08Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: my-first-bert-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-first-bert-model
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5888
- Model Preparation Time: 0.0215
- Accuracy: 0.8899
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Model Preparation Time | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:----------------------:|:--------:|
| 0.4016 | 1.0 | 708 | 0.6139 | 0.0215 | 0.8429 |
| 0.2692 | 2.0 | 1416 | 0.4109 | 0.0215 | 0.8968 |
| 0.0653 | 3.0 | 2124 | 0.5888 | 0.0215 | 0.8899 |
### Framework versions
- Transformers 4.52.4
- Pytorch 2.6.0+cu124
- Datasets 3.6.0
- Tokenizers 0.21.1
|
dgambettaphd/M_llm2_run2_gen4_WXS_doc1000_synt64_lr1e-04_acm_MPP | dgambettaphd | 2025-06-17T16:50:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-06-17T16:50:13Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Lelon/cue-jap-dt_neg | Lelon | 2025-06-17T16:42:54Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T16:42:13Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Lelon/scope-zh-conan | Lelon | 2025-06-17T16:34:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T16:34:14Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
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Lelon/cue-zh-conan | Lelon | 2025-06-17T16:34:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T16:33:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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[More Information Needed]
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John6666/kawaii-realistic-manga-il-mix-v01-sdxl | John6666 | 2025-06-17T16:27:44Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"anime",
"manga",
"kawaii",
"cute",
"2D",
"niji",
"illustration",
"characters",
"realistic",
"merge",
"Illustrious XL v1.1",
"illustrious",
"en",
"base_model:OnomaAIResearch/Illustrious-XL-v1.... | text-to-image | 2025-06-17T16:21:34Z | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- anime
- manga
- kawaii
- cute
- 2D
- niji
- illustration
- characters
- realistic
- merge
- Illustrious XL v1.1
- illustrious
base_model:
- OnomaAIResearch/Illustrious-xl-early-release-v0
- OnomaAIResearch/Illustrious-XL-v1.1
---
Original model is [here](https://civitai.com/models/1689853/kawaii-realistic-manga-il-mix?modelVersionId=1912491).
This model created by [szxex](https://civitai.com/user/szxex).
|
cragtmp/task3f2-150 | cragtmp | 2025-06-17T16:24:28Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-11B-Vision-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-11B-Vision-Instruct",
"region:us"
] | null | 2025-06-17T16:24:12Z | ---
base_model: meta-llama/Llama-3.2-11B-Vision-Instruct
library_name: peft
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.15.2 |
Cordmail/femaleDatingStrategy-Mistral-7Bv3 | Cordmail | 2025-06-17T16:23:42Z | 29 | 0 | transformers | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2023-11-27T04:27:02Z | Anyone found a use for this thing? I'm really curious.
I really wanna know. jedly271@proton.me Let me in on the fun.
Email me about anything. |
Lelon/scope-ru-sfu | Lelon | 2025-06-17T16:22:00Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T16:21:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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Lelon/scope-ru-conan | Lelon | 2025-06-17T16:20:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"eurobert",
"token-classification",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | token-classification | 2025-06-17T16:19:38Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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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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