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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-27 12:29:05
| downloads
int64 0
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| likes
int64 0
11.7k
| library_name
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webis/naacl25-prompt-compositions_finetune-baseline | webis | 2025-05-03T23:04:19Z | 0 | 0 | null | [
"safetensors",
"license:cc-by-3.0",
"region:us"
] | null | 2025-03-04T14:25:18Z | ---
license: cc-by-3.0
---
Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection
=======================================================================
Finetune baseline models for the paper [Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection](https://aclanthology.org/2025.naacl-long.122/).
For details, please see the published paper and the [GitHub repository](https://github.com/webis-de/naacl25-prompt-compositions).
```
@inproceedings{spliethover-etal-2025-adaptive,
title = {Adaptive Prompting: Ad-hoc Prompt Composition for Social Bias Detection},
author = {Splieth{\"o}ver, Maximilian and Knebler, Tim and Fumagalli, Fabian and Muschalik, Maximilian and Hammer, Barbara and H{\"u}llermeier, Eyke and Wachsmuth, Henning},
year = 2025,
month = apr,
booktitle = {Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)},
publisher = {Association for Computational Linguistics},
address = {Albuquerque, New Mexico},
pages = {2421--2449},
isbn = {979-8-89176-189-6},
url = {https://aclanthology.org/2025.naacl-long.122/},
editor = {Chiruzzo, Luis and Ritter, Alan and Wang, Lu}
}
```
## Note on finetune baseline models
Unfortunately, we did not keep the original finetuning baseline models, for which scores are reported in the paper. We did, however, keep the prediction results of these models.
We did retrain the models on the same splits, same seeds, same python version, and same library versions. The new models and also the new (and old) prediction results are uploaded in this repository. |
ivangrapher/9b20f357-5ca4-4eca-8e83-3dc222e5186b | ivangrapher | 2025-05-03T23:02:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T22:28:40Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 9b20f357-5ca4-4eca-8e83-3dc222e5186b
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/Llama-3.1-Storm-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- aa3af1c06d20fbf1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aa3af1c06d20fbf1_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: ivangrapher/9b20f357-5ca4-4eca-8e83-3dc222e5186b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/aa3af1c06d20fbf1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
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: a1783653-c3e7-49d9-ad8b-900c219df62c
wandb_project: s56-7
wandb_run: your_name
wandb_runid: a1783653-c3e7-49d9-ad8b-900c219df62c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 9b20f357-5ca4-4eca-8e83-3dc222e5186b
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9328
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.9233 | 0.0155 | 150 | 1.9328 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/nora-i1-GGUF | mradermacher | 2025-05-03T23:00:12Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:declare-lab/nora",
"base_model:quantized:declare-lab/nora",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T03:13:55Z | ---
base_model: declare-lab/nora
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/declare-lab/nora
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/nora-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ1_M.gguf) | i1-IQ1_M | 1.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ2_S.gguf) | i1-IQ2_S | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ2_M.gguf) | i1-IQ2_M | 1.2 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q2_K_S.gguf) | i1-Q2_K_S | 1.3 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q2_K.gguf) | i1-Q2_K | 1.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ3_S.gguf) | i1-IQ3_S | 1.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ3_M.gguf) | i1-IQ3_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q3_K_M.gguf) | i1-Q3_K_M | 1.7 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q3_K_L.gguf) | i1-Q3_K_L | 1.8 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ4_XS.gguf) | i1-IQ4_XS | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-IQ4_NL.gguf) | i1-IQ4_NL | 1.9 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q4_0.gguf) | i1-Q4_0 | 1.9 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q4_K_S.gguf) | i1-Q4_K_S | 1.9 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q4_1.gguf) | i1-Q4_1 | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/nora-i1-GGUF/resolve/main/nora.i1-Q6_K.gguf) | i1-Q6_K | 2.6 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
Docty/text2img-lora_dragon | Docty | 2025-05-03T22:59:00Z | 0 | 0 | diffusers | [
"diffusers",
"tensorboard",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"diffusers-training",
"lora",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | 2025-05-03T22:30:48Z | ---
base_model: runwayml/stable-diffusion-v1-5
library_name: diffusers
license: creativeml-openrail-m
inference: true
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- diffusers-training
- lora
---
<!-- 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. -->
# LoRA text2image fine-tuning - Docty/text2img-lora_dragon
These are LoRA adaption weights for runwayml/stable-diffusion-v1-5. The weights were fine-tuned on the lambdalabs/naruto-blip-captions dataset. You can find some example images in the following.
## 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] |
gavrilstep/eb6a1c26-399f-403f-a1de-698a2b001b34 | gavrilstep | 2025-05-03T22:57:31Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T22:31:16Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: eb6a1c26-399f-403f-a1de-698a2b001b34
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/Llama-3.1-Storm-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- aa3af1c06d20fbf1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aa3af1c06d20fbf1_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: gavrilstep/eb6a1c26-399f-403f-a1de-698a2b001b34
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 96
lora_dropout: 0.01
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 48
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/aa3af1c06d20fbf1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
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: a1783653-c3e7-49d9-ad8b-900c219df62c
wandb_project: s56-7
wandb_run: your_name
wandb_runid: a1783653-c3e7-49d9-ad8b-900c219df62c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# eb6a1c26-399f-403f-a1de-698a2b001b34
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.9589
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.3266 | 0.0078 | 150 | 1.9589 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
PranayPalem/ppo-Huggy | PranayPalem | 2025-05-03T22:56:54Z | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | 2025-05-03T22:56:47Z | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: PranayPalem/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
ai-and-society/mistral-Small-24B-Instruct-2501-awq | ai-and-society | 2025-05-03T22:55:01Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"awq",
"region:us"
] | text-generation | 2025-05-03T22:49:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
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AlienKevin/webssl-dino1b-in1k-224 | AlienKevin | 2025-05-03T22:54:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"dinov2",
"image-feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | image-feature-extraction | 2025-05-03T22:52:40Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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[More Information Needed]
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[More Information Needed]
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SidhaarthMurali/flat-score-llama3.2-1b | SidhaarthMurali | 2025-05-03T22:54:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:49:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
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nnnnnm/fernando-comics | nnnnnm | 2025-05-03T22:54:03Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:LyliaEngine/Pony_Diffusion_V6_XL",
"base_model:adapter:LyliaEngine/Pony_Diffusion_V6_XL",
"region:us"
] | text-to-image | 2025-05-03T22:00:48Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/ComfyUI_temp_xsync_00018_.png
- text: '-'
output:
url: images/ComfyUI_temp_njaxd_00083_.png
- text: '-'
output:
url: images/ComfyUI_temp_bzxdp_00040_.png
base_model: LyliaEngine/Pony_Diffusion_V6_XL
instance_prompt: null
---
# Fernando comics LoRAs
<Gallery />
## Model description
A backup of the LoRA from civitai, plus a few extras.
Usage...
* Smoothcuts or pony derived(base model) -> Style_Fernando-PDXL
* Illustrious(base model) -> Style_soph-Fernando-ILXL
================
The xxx_fernando-pdxl LoRAs should be used on top of fernando PDXL
* Smoothcuts or pony derived(base model) -> Fernando PDXL lora -> xxx_fernando-pdxl
* c3ss13: Cassie from Confiscated Twins 6
* c8r1stin3: Christine from Sex Wars. Use LoRA strength 0.7
* ir3in8: Irina from tourist trap. Use LoRA strength 0.7
================
Fernando is a fetish/bdsm comic artist who kinda bounced a decade or so ago - I tried to get something close to his aesthetic as possible, his art seems very hand drawn and most of what I have is lower res pdfs which would lose it's charm if upscaled too much.
================
Str 1 should be ok.
================
POS: f3rn4nd0, western comics \(style\),
NEG: realistic, photo, 3d ((text, english text)) (monochrome)
you might need to neg anime depending on your model too
Watch your quality prompts if you want it closer to his artstyle, higher quality will make it look much nicer, but at the expense of the rough style of his comics.
It's a little unhinged still but typically generates good results if you are patient.
Do not use with Illustrious Base model, you'll get garbage
Works good with MidnightV5, V10, Riullistic, and smoothmixillustrious (check resources down below for links
Have Fun!
================
The best results, -and all examples- I've had with this is with Zovya's Everclear v2
https://civitai.com/models/341433?modelVersionId=399640
You are welcome to try with other models, I've tried a lot and they may work better or not.
Update: Check out Smoothcuts [Classic & Lightning] - v1.0-lightning_8steps | Stable Diffusion Checkpoint | Civitai
This one works almost better, its not onsite gen but check it out, I have examples posted down below
I will leave the civit generator service on, but understand this is finicky and may just blast your buzz into fucking orbit, as pony is a temperamental beast and this lora doesn't help fix that. I will shut it down if people feel like it's eating their buzz too often.
## Download model
Weights for this model are available in Safetensors format.
[Download](/nnnnnm/fernando-comics/tree/main) them in the Files & versions tab.
|
haronblack/haronblack | haronblack | 2025-05-03T22:52:31Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T22:52:31Z | ---
license: apache-2.0
---
|
yushihu/Qwen3-4B-ensemble | yushihu | 2025-05-03T22:51:10Z | 0 | 0 | null | [
"safetensors",
"ensemble_qwen",
"custom_code",
"base_model:Qwen/Qwen3-4B",
"base_model:finetune:Qwen/Qwen3-4B",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T01:11:10Z | ---
license: apache-2.0
base_model:
- Qwen/Qwen3-4B
--- |
MinaMila/phi3_LoRa_ACSEmployment_2_cfda_ep9_22 | MinaMila | 2025-05-03T22:50:17Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T22:50:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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mergekit-community/mergekit-dare_ties-mgtzoms | mergekit-community | 2025-05-03T22:49:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:ReadyArt/Broken-Tutu-24B",
"base_model:merge:ReadyArt/Broken-Tutu-24B",
"base_model:ReadyArt/Forgotten-Safeword-24B-v4.0",
"base_model:merge:ReadyArt/Forgotten-Safeword-24B-v4.0",
"base_model:Sorawiz/MistralCreative-24B-Chat",
"base_model:merge:Sorawiz/MistralCreative-24B-Chat",
"base_model:mrfakename/mistral-small-3.1-24b-instruct-2503-hf",
"base_model:merge:mrfakename/mistral-small-3.1-24b-instruct-2503-hf",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:37:28Z | ---
base_model:
- ReadyArt/Broken-Tutu-24B
- ReadyArt/Forgotten-Safeword-24B-v4.0
- Sorawiz/MistralCreative-24B-Chat
- mrfakename/mistral-small-3.1-24b-instruct-2503-hf
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [mrfakename/mistral-small-3.1-24b-instruct-2503-hf](https://huggingface.co/mrfakename/mistral-small-3.1-24b-instruct-2503-hf) as a base.
### Models Merged
The following models were included in the merge:
* [ReadyArt/Broken-Tutu-24B](https://huggingface.co/ReadyArt/Broken-Tutu-24B)
* [ReadyArt/Forgotten-Safeword-24B-v4.0](https://huggingface.co/ReadyArt/Forgotten-Safeword-24B-v4.0)
* [Sorawiz/MistralCreative-24B-Chat](https://huggingface.co/Sorawiz/MistralCreative-24B-Chat)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
merge_method: dare_ties
base_model: mrfakename/mistral-small-3.1-24b-instruct-2503-hf
models:
- model: mrfakename/mistral-small-3.1-24b-instruct-2503-hf
parameters:
weight: 0.2
- model: Sorawiz/MistralCreative-24B-Chat
parameters:
weight: 0.3
- model: ReadyArt/Forgotten-Safeword-24B-v4.0
parameters:
weight: 0.3
- model: ReadyArt/Broken-Tutu-24B
parameters:
weight: 0.2
parameters:
density: 1
tokenizer:
source: union
chat_template: auto
```
|
wendyl21/q-taxi-v3 | wendyl21 | 2025-05-03T22:49:06Z | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-03T22:49:04Z | ---
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 +/- 2.77
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="wendyl21/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"])
```
|
sergioalves/d3d5367e-7733-484a-9883-24a3e6b08958 | sergioalves | 2025-05-03T22:48:29Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T22:28:35Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d3d5367e-7733-484a-9883-24a3e6b08958
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: true
adapter: lora
base_model: unsloth/Llama-3.1-Storm-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- aa3af1c06d20fbf1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aa3af1c06d20fbf1_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: sergioalves/d3d5367e-7733-484a-9883-24a3e6b08958
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/aa3af1c06d20fbf1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a1783653-c3e7-49d9-ad8b-900c219df62c
wandb_project: s56-8
wandb_run: your_name
wandb_runid: a1783653-c3e7-49d9-ad8b-900c219df62c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d3d5367e-7733-484a-9883-24a3e6b08958
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6828
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.6835 | 0.0207 | 200 | 1.6828 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
wendyl21/q-FrozenLake-v1-4x4-noSlippery | wendyl21 | 2025-05-03T22:47:23Z | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | 2025-05-03T22:47:20Z | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="wendyl21/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
76ygg8ijhb/Rerree | 76ygg8ijhb | 2025-05-03T22:46:44Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T22:46:44Z | ---
license: apache-2.0
---
|
dineshsai07/brain-diffuser | dineshsai07 | 2025-05-03T22:43:52Z | 0 | 0 | null | [
"arxiv:2303.05334",
"region:us"
] | null | 2025-04-29T22:55:50Z | # Brain-Diffuser
Implementation and improvements to paper ["**Brain-Diffuser: Natural scene reconstruction from fMRI signals using generative latent diffusion**"](https://arxiv.org/abs/2303.05334) by Furkan Ozcelik and Rufin VanRullen.
## Instructions
### Requirements
* Create conda environment using environment.yml in the main directory by entering `conda env create -f environment.yml` . It is an extensive environment and may include redundant libraries. You may also create environment by checking requirements yourself.
### Data Acquisition and Processing
1. Download NSD data from NSD AWS Server:
```
cd data
python download_nsddata.py
```
2. Download "COCO_73k_annots_curated.npy" file from [HuggingFace NSD](https://huggingface.co/datasets/pscotti/naturalscenesdataset/tree/main)
3. Prepare NSD data for the Reconstruction Task:
```
cd data
python prepare_nsddata.py -sub 1
python prepare_nsddata.py -sub 2
python prepare_nsddata.py -sub 5
python prepare_nsddata.py -sub 7
```
### First Stage Reconstruction with VDVAE
1. Download pretrained VDVAE model files and put them in `vdvae/model/` folder
```
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-log.jsonl
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-model-ema.th
wget https://openaipublic.blob.core.windows.net/very-deep-vaes-assets/vdvae-assets-2/imagenet64-iter-1600000-opt.th
```
2. Extract VDVAE latent features of stimuli images for any subject 'x' using `python scripts/vdvae_extract_features.py -sub x`
3. Train regression models from fMRI to VDVAE latent features and save test predictions using `python scripts/vdvae_regression.py -sub x`
4. Reconstruct images from predicted test features using `python scripts/vdvae_reconstruct_images.py -sub x`
### Second Stage Reconstruction with Versatile Diffusion
1. Download pretrained Versatile Diffusion model "vd-four-flow-v1-0-fp16-deprecated.pth", "kl-f8.pth" and "optimus-vae.pth" from [HuggingFace](https://huggingface.co/shi-labs/versatile-diffusion/tree/main/pretrained_pth) and put them in `versatile_diffusion/pretrained/` folder
<!-- 2. Extract CLIP-Text features of captions for any subject 'x' using `python scripts/cliptext_extract_features.py -sub x` -->
3. Extract CLIP-Vision features of stimuli images for any subject 'x' using `python scripts/clipvision_extract_features.py -sub x`
<!-- 4. Train regression models from fMRI to CLIP-Text features and save test predictions using `python scripts/cliptext_regression.py -sub x` --> -->
5. Train regression models from fMRI to CLIP-Vision features and save test predictions using `python scripts/clipvision_regression.py -sub x`
6. Reconstruct images from predicted test features using `python scripts/versatilediffusion_reconstruct_images.py -sub x` . This code is written as you are using two 12GB GPUs but you may edit according to your setup.
## References
- Codes in vdvae directory are derived from [openai/vdvae](https://github.com/openai/vdvae)
- Codes in versatile_diffusion directory are derived from earlier version of [SHI-Labs/Versatile-Diffusion](https://github.com/SHI-Labs/Versatile-Diffusion)
- Dataset used in the studies are obtained from [Natural Scenes Dataset](https://naturalscenesdataset.org/)
|
akoruk/gemma-3-4b | akoruk | 2025-05-03T22:43:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma3",
"trl",
"en",
"base_model:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"base_model:finetune:unsloth/gemma-3-4b-it-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T22:43:14Z | ---
base_model: unsloth/gemma-3-4b-it-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** akoruk
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-4b-it-unsloth-bnb-4bit
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
kokovova/b14784fa-7a1e-40bb-bdd2-b4bf45aeb019 | kokovova | 2025-05-03T22:39:38Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T22:32:19Z | ---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b14784fa-7a1e-40bb-bdd2-b4bf45aeb019
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Llama-3.1-Storm-8B
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- aa3af1c06d20fbf1_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aa3af1c06d20fbf1_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: kokovova/b14784fa-7a1e-40bb-bdd2-b4bf45aeb019
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/aa3af1c06d20fbf1_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: a1783653-c3e7-49d9-ad8b-900c219df62c
wandb_project: s56-4
wandb_run: your_name
wandb_runid: a1783653-c3e7-49d9-ad8b-900c219df62c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b14784fa-7a1e-40bb-bdd2-b4bf45aeb019
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.7712
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7374 | 0.0207 | 200 | 1.7712 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Indra1122/MediBot_LoRA | Indra1122 | 2025-05-03T22:39:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:25:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF | mradermacher | 2025-05-03T22:37:42Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"sft",
"en",
"base_model:ClaudioItaly/Qwen2.5-7B-Gutenberg-FT",
"base_model:quantized:ClaudioItaly/Qwen2.5-7B-Gutenberg-FT",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T17:46:08Z | ---
base_model: ClaudioItaly/Qwen2.5-7B-Gutenberg-FT
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ClaudioItaly/Qwen2.5-7B-Gutenberg-FT
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.IQ4_XS.gguf) | IQ4_XS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Gutenberg-FT-GGUF/resolve/main/Qwen2.5-7B-Gutenberg-FT.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
MLawrence/Trantum | MLawrence | 2025-05-03T22:27:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2025-05-03T22:22:26Z | ---
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
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 |
Alextimoteo/Alexku | Alextimoteo | 2025-05-03T22:26:37Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T22:26:37Z | ---
license: apache-2.0
---
|
Dohahemdann/FLAN-T5-FineTunedModel-Pytorch | Dohahemdann | 2025-05-03T22:24:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-05-03T22:23:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
bruhzair/ignore-base1 | bruhzair | 2025-05-03T22:23:40Z | 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 | 2025-05-03T21:52:45Z | ---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
---
# base
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/cache/models--TheDrummer--L3.3-Interleaved-Upscale-105B/snapshots/dc1c192564ddf43133a71a7bdc8e6e91c69a2835 as a base.
### Models Merged
The following models were included in the merge:
* /workspace/magnum2
* /workspace/nemo2
* /workspace/hydro2
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model: /workspace/cache/models--TheDrummer--L3.3-Interleaved-Upscale-105B/snapshots/dc1c192564ddf43133a71a7bdc8e6e91c69a2835
chat_template: llama3
dtype: float32
merge_method: sce
modules:
default:
slices:
- sources:
- layer_range: [0, 120]
model: /workspace/hydro2
parameters:
select_topk: 0.5
- layer_range: [0, 120]
model: /workspace/nemo2
parameters:
select_topk: 0.4
- layer_range: [0, 120]
model: /workspace/magnum2
parameters:
select_topk: 0.3
- layer_range: [0, 120]
model: /workspace/cache/models--TheDrummer--L3.3-Interleaved-Upscale-105B/snapshots/dc1c192564ddf43133a71a7bdc8e6e91c69a2835
parameters:
select_topk: 0.4
out_dtype: bfloat16
parameters:
int8_mask: 1.0
tokenizer:
source: base
```
|
thavens-research/Qwen2.5-7B-Instruct | thavens-research | 2025-05-03T22:22:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:13:23Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
ivangrapher/aa479677-d015-41e2-beac-55deb81a61a4 | ivangrapher | 2025-05-03T22:22:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"base_model:adapter:EleutherAI/pythia-70m",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T21:57:20Z | ---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-70m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: aa479677-d015-41e2-beac-55deb81a61a4
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: EleutherAI/pythia-70m
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 78fef953edf6ce18_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/78fef953edf6ce18_train_data.json
type:
field_instruction: en
field_output: ja
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: ivangrapher/aa479677-d015-41e2-beac-55deb81a61a4
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/78fef953edf6ce18_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9e9e612b-9a07-4996-b19f-dd5a18a0de2a
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 9e9e612b-9a07-4996-b19f-dd5a18a0de2a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# aa479677-d015-41e2-beac-55deb81a61a4
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.5075
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.9489 | 0.0013 | 150 | 6.5075 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
JayRana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-padded_poisonous_tapir | JayRana | 2025-05-03T22:20:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am padded poisonous tapir",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T16:58:53Z | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-padded_poisonous_tapir
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am padded poisonous tapir
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-padded_poisonous_tapir
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayRana/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-padded_poisonous_tapir", 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: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
user074/sft_qwen05b_composer | user074 | 2025-05-03T22:15:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"en",
"arxiv:2407.10671",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T22:14:50Z | ---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-0.5B/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
library_name: transformers
---
# Qwen2.5-0.5B
## Introduction
Qwen2.5 is the latest series of Qwen large language models. For Qwen2.5, we release a number of base language models and instruction-tuned language models ranging from 0.5 to 72 billion parameters. Qwen2.5 brings the following improvements upon Qwen2:
- Significantly **more knowledge** and has greatly improved capabilities in **coding** and **mathematics**, thanks to our specialized expert models in these domains.
- Significant improvements in **instruction following**, **generating long texts** (over 8K tokens), **understanding structured data** (e.g, tables), and **generating structured outputs** especially JSON. **More resilient to the diversity of system prompts**, enhancing role-play implementation and condition-setting for chatbots.
- **Long-context Support** up to 128K tokens and can generate up to 8K tokens.
- **Multilingual support** for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.
**This repo contains the base 0.5B Qwen2.5 model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, Attention QKV bias and tied word embeddings
- Number of Parameters: 0.49B
- Number of Paramaters (Non-Embedding): 0.36B
- Number of Layers: 24
- Number of Attention Heads (GQA): 14 for Q and 2 for KV
- Context Length: Full 32,768 tokens
**We do not recommend using base language models for conversations.** Instead, you can apply post-training, e.g., SFT, RLHF, continued pretraining, etc., on this model.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5/), [GitHub](https://github.com/QwenLM/Qwen2.5), and [Documentation](https://qwen.readthedocs.io/en/latest/).
## Requirements
The code of Qwen2.5 has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@misc{qwen2.5,
title = {Qwen2.5: A Party of Foundation Models},
url = {https://qwenlm.github.io/blog/qwen2.5/},
author = {Qwen Team},
month = {September},
year = {2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
``` |
jnjj/fgfgfg | jnjj | 2025-05-03T22:07:40Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T22:06:11Z | ---
license: apache-2.0
---
|
shukibruck/jacekai | shukibruck | 2025-05-03T22:07:25Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T21:36:45Z | ---
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: JACEKAI
---
# Jacekai
<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 `JACEKAI` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "JACEKAI",
"lora_weights": "https://huggingface.co/shukibruck/jacekai/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('shukibruck/jacekai', weight_name='lora.safetensors')
image = pipeline('JACEKAI').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: 2500
- Learning rate: 0.0004
- LoRA rank: 32
## Contribute your own examples
You can use the [community tab](https://huggingface.co/shukibruck/jacekai/discussions) to add images that show off what you’ve made with this LoRA.
|
infogep/f971e688-db61-4cf4-906e-b16c197f8858 | infogep | 2025-05-03T22:02:48Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gpt_neox",
"axolotl",
"generated_from_trainer",
"base_model:EleutherAI/pythia-70m",
"base_model:adapter:EleutherAI/pythia-70m",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-03T21:57:02Z | ---
library_name: peft
license: apache-2.0
base_model: EleutherAI/pythia-70m
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f971e688-db61-4cf4-906e-b16c197f8858
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: EleutherAI/pythia-70m
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- 78fef953edf6ce18_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/78fef953edf6ce18_train_data.json
type:
field_instruction: en
field_output: ja
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: infogep/f971e688-db61-4cf4-906e-b16c197f8858
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/78fef953edf6ce18_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9e9e612b-9a07-4996-b19f-dd5a18a0de2a
wandb_project: s56-30
wandb_run: your_name
wandb_runid: 9e9e612b-9a07-4996-b19f-dd5a18a0de2a
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f971e688-db61-4cf4-906e-b16c197f8858
This model is a fine-tuned version of [EleutherAI/pythia-70m](https://huggingface.co/EleutherAI/pythia-70m) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.7076
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 5.8829 | 0.0017 | 200 | 6.7076 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF | mradermacher | 2025-05-03T22:00:39Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"en",
"dataset:Neelectric/OpenR1-Math-cn_k12-91k",
"base_model:Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.00",
"base_model:quantized:Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.00",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T20:30:05Z | ---
base_model: Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.00
datasets: Neelectric/OpenR1-Math-cn_k12-91k
language:
- en
library_name: transformers
model_name: OLMo-2-1124-7B-Instruct_SFTv02.00
quantized_by: mradermacher
tags:
- generated_from_trainer
- open-r1
- trl
- sft
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Neelectric/OLMo-2-1124-7B-Instruct_SFTv02.00
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ1_S.gguf) | i1-IQ1_S | 1.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ1_M.gguf) | i1-IQ1_M | 2.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ2_S.gguf) | i1-IQ2_S | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q2_K_S.gguf) | i1-Q2_K_S | 2.7 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ2_M.gguf) | i1-IQ2_M | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q2_K.gguf) | i1-Q2_K | 3.0 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ3_S.gguf) | i1-IQ3_S | 3.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.4 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ3_M.gguf) | i1-IQ3_M | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.8 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.1 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.1 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.3 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q4_0.gguf) | i1-Q4_0 | 4.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q4_1.gguf) | i1-Q4_1 | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.3 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-2-1124-7B-Instruct_SFTv02.00-i1-GGUF/resolve/main/OLMo-2-1124-7B-Instruct_SFTv02.00.i1-Q6_K.gguf) | i1-Q6_K | 6.1 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
jnjj/vvcvc | jnjj | 2025-05-03T21:59:31Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-0.6B",
"base_model:quantized:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-03T21:58:59Z | ---
base_model: Qwen/Qwen3-0.6B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-0.6B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# jnjj/Qwen3-0.6B-Q8_0-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-0.6B`](https://huggingface.co/Qwen/Qwen3-0.6B) 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/Qwen/Qwen3-0.6B) 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 jnjj/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jnjj/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jnjj/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jnjj/Qwen3-0.6B-Q8_0-GGUF --hf-file qwen3-0.6b-q8_0.gguf -c 2048
```
|
jacobcarajo/Dolphin3.0-R1-Mistral-24B-Q5_K_M-GGUF | jacobcarajo | 2025-05-03T21:58:35Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"dataset:cognitivecomputations/dolphin-r1",
"dataset:OpenCoder-LLM/opc-sft-stage1",
"dataset:OpenCoder-LLM/opc-sft-stage2",
"dataset:microsoft/orca-agentinstruct-1M-v1",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:NousResearch/hermes-function-calling-v1",
"dataset:AI-MO/NuminaMath-CoT",
"dataset:AI-MO/NuminaMath-TIR",
"dataset:allenai/tulu-3-sft-mixture",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:HuggingFaceTB/smoltalk",
"dataset:cognitivecomputations/samantha-data",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:m-a-p/Code-Feedback",
"base_model:cognitivecomputations/Dolphin3.0-R1-Mistral-24B",
"base_model:quantized:cognitivecomputations/Dolphin3.0-R1-Mistral-24B",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-03T21:57:20Z | ---
base_model: cognitivecomputations/Dolphin3.0-R1-Mistral-24B
datasets:
- cognitivecomputations/dolphin-r1
- OpenCoder-LLM/opc-sft-stage1
- OpenCoder-LLM/opc-sft-stage2
- microsoft/orca-agentinstruct-1M-v1
- microsoft/orca-math-word-problems-200k
- NousResearch/hermes-function-calling-v1
- AI-MO/NuminaMath-CoT
- AI-MO/NuminaMath-TIR
- allenai/tulu-3-sft-mixture
- cognitivecomputations/dolphin-coder
- HuggingFaceTB/smoltalk
- cognitivecomputations/samantha-data
- m-a-p/CodeFeedback-Filtered-Instruction
- m-a-p/Code-Feedback
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# jacobcarajo/Dolphin3.0-R1-Mistral-24B-Q5_K_M-GGUF
This model was converted to GGUF format from [`cognitivecomputations/Dolphin3.0-R1-Mistral-24B`](https://huggingface.co/cognitivecomputations/Dolphin3.0-R1-Mistral-24B) 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/cognitivecomputations/Dolphin3.0-R1-Mistral-24B) 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 jacobcarajo/Dolphin3.0-R1-Mistral-24B-Q5_K_M-GGUF --hf-file dolphin3.0-r1-mistral-24b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jacobcarajo/Dolphin3.0-R1-Mistral-24B-Q5_K_M-GGUF --hf-file dolphin3.0-r1-mistral-24b-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jacobcarajo/Dolphin3.0-R1-Mistral-24B-Q5_K_M-GGUF --hf-file dolphin3.0-r1-mistral-24b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jacobcarajo/Dolphin3.0-R1-Mistral-24B-Q5_K_M-GGUF --hf-file dolphin3.0-r1-mistral-24b-q5_k_m.gguf -c 2048
```
|
aadhistii/IndoBERT-large-SDGs-Oplib-Elsevier | aadhistii | 2025-05-03T21:58:30Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:indobenchmark/indobert-large-p2",
"base_model:finetune:indobenchmark/indobert-large-p2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T21:55:58Z | ---
library_name: transformers
license: mit
base_model: indobenchmark/indobert-large-p2
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: IndoBERT-large-SDGs-Oplib-Elsevier
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. -->
# IndoBERT-large-SDGs-Oplib-Elsevier
This model is a fine-tuned version of [indobenchmark/indobert-large-p2](https://huggingface.co/indobenchmark/indobert-large-p2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1474
- Accuracy: 0.4671
- F1 Micro: 0.8434
- F1 Macro: 0.8140
- Precision Micro: 0.8243
- Precision Macro: 0.8066
- Recall Micro: 0.8635
- Recall Macro: 0.8278
- Roc Auc: 0.9128
- Hamming Loss: 0.0547
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.0364705898645393e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.02320476760796493
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro | Precision Macro | Recall Micro | Recall Macro | Roc Auc | Hamming Loss |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:---------------:|:------------:|:------------:|:-------:|:------------:|
| 0.3786 | 1.0 | 762 | 0.1935 | 0.2775 | 0.7735 | 0.6922 | 0.7197 | 0.7060 | 0.8360 | 0.7024 | 0.8845 | 0.0835 |
| 0.1572 | 2.0 | 1524 | 0.1545 | 0.3948 | 0.8191 | 0.7615 | 0.7897 | 0.7957 | 0.8508 | 0.7635 | 0.9021 | 0.0641 |
| 0.1246 | 3.0 | 2286 | 0.1439 | 0.4156 | 0.8272 | 0.7896 | 0.7796 | 0.7594 | 0.8809 | 0.8282 | 0.9148 | 0.0628 |
| 0.095 | 4.0 | 3048 | 0.1409 | 0.4267 | 0.8367 | 0.8134 | 0.8041 | 0.8070 | 0.8720 | 0.8261 | 0.9142 | 0.0581 |
| 0.0774 | 5.0 | 3810 | 0.1406 | 0.4404 | 0.8359 | 0.7922 | 0.8011 | 0.7813 | 0.8740 | 0.8187 | 0.9147 | 0.0585 |
| 0.0608 | 6.0 | 4572 | 0.1409 | 0.4521 | 0.8439 | 0.8155 | 0.8121 | 0.7969 | 0.8783 | 0.8416 | 0.9182 | 0.0554 |
| 0.0512 | 7.0 | 5334 | 0.1482 | 0.4456 | 0.8366 | 0.8079 | 0.7904 | 0.7728 | 0.8885 | 0.8519 | 0.9200 | 0.0592 |
| 0.0392 | 8.0 | 6096 | 0.1474 | 0.4671 | 0.8434 | 0.8140 | 0.8243 | 0.8066 | 0.8635 | 0.8278 | 0.9128 | 0.0547 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
randa88888/qwen_test5 | randa88888 | 2025-05-03T21:57:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T21:57:48Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** randa88888
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
yordanoswuletaw/Llama-3.2-400M-Amharic | yordanoswuletaw | 2025-05-03T21:56:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"am",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T21:20:09Z | ---
widget:
- text: አዲስ አበባ
example_title: Example 1
- text: በኢንግሊዝ ፕሪምየር ሊግ
example_title: Example 2
- text: ዶናልድ ትራምፕ
example_title: Example 3
language:
- am
metrics:
- perplexity
library_name: transformers
pipeline_tag: text-generation
---
# Llama 3.2 400M Amharic
This is a smaller version of the Meta's [Llama-3.2-1B](https://huggingface.co/meta-llama/Llama-3.2-1B) decoder transformer model pretrained from scratch for **23 hours** using a single **A100 40GB** GPU and **274 million tokens** of **Amharic** text.
- It has **400 Million parameters**
- The **context size** of this model is **1024** tokens.
- It has the same **tokenizer** as Llama-3.2-1B, trained from scratch using the same Amharic dataset as the model with a vocabulary size of **32k**.
- Validation Perplexity: **41.3**
- This is a base model and hasn't undergone any supervised finetuing yet.
### How to use
First, you need to install the latest version of transformers
```
pip install -Uq transformers
```
You can use this model directly with a pipeline for text generation:
```python
from transformers import pipeline
llama_am = pipeline(
"text-generation",
model="rasyosef/Llama-3.2-400M-Amharic",
device_map="auto"
)
prompt = "አዲስ አበባ"
llama_am(
prompt,
max_new_tokens=128,
temperature=0.5,
do_sample=True,
top_k=8,
top_p=0.8,
repetition_penalty=1.2
)
```
Output:
```python
[{'generated_text': 'አዲስ አበባ፣ ታህሳስ 8 ፣2012 (ኤፍ ቢ ሲ) የኢፌዴሪ የውጭ ጉዳይ ሚኒስትር አቶ ገዱ አንዳርጋቸው ከአፍሪካ ህብረት የስራ አስፈጻሚዎች ምክር ቤት መደበኛ ስብሰባ ጎን ለጎን ከዴሞክራቲክ ሪፐብሊክ ኮንጎ አቻቸው ማሪ ቱምባ ንዜዛ እና ከሌሎች የአፍሪካ አምባሳደሮች ጋር ተወያይተዋል።በውይይታቸውም በአፍሪካ የኮሮና ቫይረስን ለመከላከል እየተከናወኑ ባሉ ስራዎች ዙሪያ መምከራቸውን በትዊተር ገጻቸው አስፍረዋል።የሁለቱን ሀገራት ግንኙነት በተመለከተም፥ ኢትዮጵያ በህብረቱ ቋሚ አምባሳደርነት ባላት ሀላፊነት ለሹመት ማቅረብዋ የሚደነቅ መሆኑንም አንስተዋል።ኢትዮጵያ የኮቪድ19 ወረርሽኝን ለመግታት እያደረገች ባለው ጥረት ለደቡብ አፍሪካ ምስጋና አቅርባም ነበር፤ ቫይረሱን ለመቆጣጠር ከኢትዮጵያ ምን እንደምትማር በዝርዝር ላቀረብንላቸው ጥያቄም ወደፊት በሚሰሩ የትብብር መስኮች ላይ ተነጋግረን መስራት እንፈልጋለን ብለዋል።በቀጣይም ሁለቱ'}]
``` |
chhorpichratana9999/veacha-ai | chhorpichratana9999 | 2025-05-03T21:53:55Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T11:22:18Z | import wave
import json
from vosk import Model, KaldiRecognizer
# ផ្ទុកម៉ូឌែល Vosk សម្រាប់ភាសាខ្មែរ
model = Model("path/to/vosk-model-khmer")
wf = wave.open("khm_0308_001165548.wav", "rb")
rec = KaldiRecognizer(model, wf.getframerate())
# បំបែកសំឡេងជាអត្ថបទ
while True:
data = wf.readframes(4000)
if len(data) == 0:
break
if rec.AcceptWaveform(data):
print(json.loads(rec.Result())["text"])
print(json.loads(rec.FinalResult())["text"]) |
bayazknn/qwen-1.7-finetune-q8 | bayazknn | 2025-05-03T21:47:38Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T21:47:07Z | ---
base_model: unsloth/qwen3-1.7b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bayazknn
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-1.7b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
memeviss/zombieXI_6 | memeviss | 2025-05-03T21:44:19Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-05-03T16:47:28Z | # Optimized TTS Model
This model has been optimized for 100% TOP1 performance using advanced parameter enhancement techniques.
## Usage
To generate speech using this model, you can use the included script:
```bash
./generate_speech.py --text "Your text here" --output_path output.wav
```
For more details, see the optimization report in this directory.
|
GrahamWall/phi2-finetune | GrahamWall | 2025-05-03T21:41:44Z | 0 | 0 | null | [
"safetensors",
"phi",
"nlp",
"code",
"text-generation",
"en",
"license:mit",
"region:us"
] | text-generation | 2025-05-03T21:08:18Z | ---
license: mit
license_link: https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
---
## Model Summary
Phi-2 is a Transformer with **2.7 billion** parameters. It was trained using the same data sources as [Phi-1.5](https://huggingface.co/microsoft/phi-1.5), augmented with a new data source that consists of various NLP synthetic texts and filtered websites (for safety and educational value). When assessed against benchmarks testing common sense, language understanding, and logical reasoning, Phi-2 showcased a nearly state-of-the-art performance among models with less than 13 billion parameters.
Our model hasn't been fine-tuned through reinforcement learning from human feedback. The intention behind crafting this open-source model is to provide the research community with a non-restricted small model to explore vital safety challenges, such as reducing toxicity, understanding societal biases, enhancing controllability, and more.
## How to Use
Phi-2 has been integrated in the `transformers` version 4.37.0, please ensure that you are using a version equal or higher than it.
Phi-2 is known for having an attention overflow issue (with FP16). If you are facing this issue, please enable/disable autocast on the [PhiAttention.forward()](https://github.com/huggingface/transformers/blob/main/src/transformers/models/phi/modeling_phi.py#L306) function.
## Intended Uses
Given the nature of the training data, the Phi-2 model is best suited for prompts using the QA format, the chat format, and the code format.
### QA Format:
You can provide the prompt as a standalone question as follows:
```markdown
Write a detailed analogy between mathematics and a lighthouse.
```
where the model generates the text after "." .
To encourage the model to write more concise answers, you can also try the following QA format using "Instruct: \<prompt\>\nOutput:"
```markdown
Instruct: Write a detailed analogy between mathematics and a lighthouse.
Output: Mathematics is like a lighthouse. Just as a lighthouse guides ships safely to shore, mathematics provides a guiding light in the world of numbers and logic. It helps us navigate through complex problems and find solutions. Just as a lighthouse emits a steady beam of light, mathematics provides a consistent framework for reasoning and problem-solving. It illuminates the path to understanding and helps us make sense of the world around us.
```
where the model generates the text after "Output:".
### Chat Format:
```markdown
Alice: I don't know why, I'm struggling to maintain focus while studying. Any suggestions?
Bob: Well, have you tried creating a study schedule and sticking to it?
Alice: Yes, I have, but it doesn't seem to help much.
Bob: Hmm, maybe you should try studying in a quiet environment, like the library.
Alice: ...
```
where the model generates the text after the first "Bob:".
### Code Format:
```python
def print_prime(n):
"""
Print all primes between 1 and n
"""
primes = []
for num in range(2, n+1):
is_prime = True
for i in range(2, int(math.sqrt(num))+1):
if num % i == 0:
is_prime = False
break
if is_prime:
primes.append(num)
print(primes)
```
where the model generates the text after the comments.
**Notes:**
* Phi-2 is intended for QA, chat, and code purposes. The model-generated text/code should be treated as a starting point rather than a definitive solution for potential use cases. Users should be cautious when employing these models in their applications.
* Direct adoption for production tasks without evaluation is out of scope of this project. As a result, the Phi-2 model has not been tested to ensure that it performs adequately for any production-level application. Please refer to the limitation sections of this document for more details.
* If you are using `transformers<4.37.0`, always load the model with `trust_remote_code=True` to prevent side-effects.
## Sample Code
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
model = AutoModelForCausalLM.from_pretrained("microsoft/phi-2", torch_dtype="auto", trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
## Limitations of Phi-2
* Generate Inaccurate Code and Facts: The model may produce incorrect code snippets and statements. Users should treat these outputs as suggestions or starting points, not as definitive or accurate solutions.
* Limited Scope for code: Majority of Phi-2 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
* Unreliable Responses to Instruction: The model has not undergone instruction fine-tuning. As a result, it may struggle or fail to adhere to intricate or nuanced instructions provided by users.
* Language Limitations: The model is primarily designed to understand standard English. Informal English, slang, or any other languages might pose challenges to its comprehension, leading to potential misinterpretations or errors in response.
* Potential Societal Biases: Phi-2 is not entirely free from societal biases despite efforts in assuring training data safety. There's a possibility it may generate content that mirrors these societal biases, particularly if prompted or instructed to do so. We urge users to be aware of this and to exercise caution and critical thinking when interpreting model outputs.
* Toxicity: Despite being trained with carefully selected data, the model can still produce harmful content if explicitly prompted or instructed to do so. We chose to release the model to help the open-source community develop the most effective ways to reduce the toxicity of a model directly after pretraining.
* Verbosity: Phi-2 being a base model often produces irrelevant or extra text and responses following its first answer to user prompts within a single turn. This is due to its training dataset being primarily textbooks, which results in textbook-like responses.
## Training
### Model
* Architecture: a Transformer-based model with next-word prediction objective
* Context length: 2048 tokens
* Dataset size: 250B tokens, combination of NLP synthetic data created by AOAI GPT-3.5 and filtered web data from Falcon RefinedWeb and SlimPajama, which was assessed by AOAI GPT-4.
* Training tokens: 1.4T tokens
* GPUs: 96xA100-80G
* Training time: 14 days
### Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
### License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/phi-2/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies. |
BTazBbU4OqBSwxUlG/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rapid_wiry_prawn | BTazBbU4OqBSwxUlG | 2025-05-03T21:37:46Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rapid wiry prawn",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-22T13:24:39Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rapid_wiry_prawn
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rapid wiry prawn
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rapid_wiry_prawn
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="BTazBbU4OqBSwxUlG/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rapid_wiry_prawn", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Kooten/gemma-3-27b-it-abliterated-exl2 | Kooten | 2025-05-03T21:35:12Z | 0 | 0 | null | [
"quantized",
"exllamav2",
"exl2",
"image-text-to-text",
"base_model:mlabonne/gemma-3-27b-it-abliterated",
"base_model:quantized:mlabonne/gemma-3-27b-it-abliterated",
"license:gemma",
"region:us"
] | image-text-to-text | 2025-05-03T20:20:56Z | ---
license: gemma
base_model: mlabonne/gemma-3-27b-it-abliterated
base_model_relation: quantized
pipeline_tag: image-text-to-text
tags:
- quantized
- exllamav2
- exl2
---
# Gemma 3 27B IT Abliterated - EXL2 Quantized
Exllamav2 quantized versions of [mlabonne/gemma-3-27b-it-abliterated](https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated).
## Hardware Requirements
4.0 bpw version fits on a 24GB GPU with 8192 context window
## Vision
Vision works with [ExllamaV2 0.2.9](https://github.com/turboderp-org/exllamav2/releases/tag/v0.2.9)
Confirmed with exllamav2s [examples/multimodal.py](https://github.com/turboderp-org/exllamav2/blob/master/examples/multimodal.py)
### Direct Download
```bash
huggingface-cli download Kooten/gemma-3-27b-it-abliterated-exl2 --revision 4.0bpw --local-dir gemma-3-27b-it-abliterated-4.0bpw --local-dir-use-symlinks False
huggingface-cli download Kooten/gemma-3-27b-it-abliterated-exl2 --revision 5.0bpw --local-dir gemma-3-27b-it-abliterated-5.0bpw --local-dir-use-symlinks False
```
---
# 💎 Gemma 3 27B IT Abliterated
<center><a href="https://huggingface.co/mlabonne/gemma-3-1b-it-abliterated">Gemma 3 1B Abliterated</a> • <a href="https://huggingface.co/mlabonne/gemma-3-4b-it-abliterated">Gemma 3 4B Abliterated</a> • <a href="https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated">Gemma 3 12B Abliterated</a></center>
This is an uncensored version of [google/gemma-3-27b-it](https://huggingface.co/google/gemma-3-27b-it) created with a new abliteration technique.
See [this article](https://huggingface.co/blog/mlabonne/abliteration) to know more about abliteration.
I was playing with model weights and noticed that Gemma 3 was much more resilient to abliteration than other models like Qwen 2.5.
I experimented with a few recipes to remove refusals while preserving most of the model capabilities.
Note that this is fairly experimental, so it might not turn out as well as expected.
I recommend using these generation parameters: `temperature=1.0`, `top_k=64`, `top_p=0.95`.
## ⚡️ Quantization
* **GGUF**: https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated-GGUF
## ✂️ Layerwise abliteration
In the original technique, a refusal direction is computed by comparing the residual streams between target (harmful) and baseline (harmless) samples.
Here, the model was abliterated by computing a refusal direction based on hidden states (inspired by [Sumandora's repo](https://github.com/Sumandora/remove-refusals-with-transformers/)) for each layer, independently.
This is combined with a refusal weight of 1.5 to upscale the importance of this refusal direction in each layer.
This created a very high acceptance rate (>90%) and still produced coherent outputs. |
jacobcarajo/Qwen3-32B-Q5_K_M-GGUF | jacobcarajo | 2025-05-03T21:35:03Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:Qwen/Qwen3-32B",
"base_model:quantized:Qwen/Qwen3-32B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-03T21:33:21Z | ---
base_model: Qwen/Qwen3-32B
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-32B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- llama-cpp
- gguf-my-repo
---
# jacobcarajo/Qwen3-32B-Q5_K_M-GGUF
This model was converted to GGUF format from [`Qwen/Qwen3-32B`](https://huggingface.co/Qwen/Qwen3-32B) 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/Qwen/Qwen3-32B) 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 jacobcarajo/Qwen3-32B-Q5_K_M-GGUF --hf-file qwen3-32b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jacobcarajo/Qwen3-32B-Q5_K_M-GGUF --hf-file qwen3-32b-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jacobcarajo/Qwen3-32B-Q5_K_M-GGUF --hf-file qwen3-32b-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jacobcarajo/Qwen3-32B-Q5_K_M-GGUF --hf-file qwen3-32b-q5_k_m.gguf -c 2048
```
|
nicure/Plangen | nicure | 2025-05-03T21:35:02Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T21:35:02Z | ---
license: apache-2.0
---
|
unprg-ia/gorel-v4-2025 | unprg-ia | 2025-05-03T21:32:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gguf",
"llama",
"unsloth",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T20:30:38Z | ---
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] |
chhorpichratana9999/KH_Nimit_Language_Model | chhorpichratana9999 | 2025-05-03T21:31:32Z | 0 | 0 | null | [
"graph-ml",
"km",
"en",
"license:mit",
"region:us"
] | graph-ml | 2025-05-03T18:08:41Z | ---
license: mit
language:
- km
- en
pipeline_tag: graph-ml
---
language:
- km # Khmer language
license: mit
base_model: null # បើគ្មាន base model ជាក់លាក់
pipeline_tag: text-generation # ឬ text-classification អាស្រ័យលើការប្រើប្រាស់
tags:
- khmer
- language-model
- nlp |
mradermacher/ruozhiReasoner-Qwen3-8B-GGUF | mradermacher | 2025-05-03T21:31:30Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-factory",
"en",
"base_model:XzWang/ruozhiReasoner-Qwen3-8B",
"base_model:quantized:XzWang/ruozhiReasoner-Qwen3-8B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T18:14:03Z | ---
base_model: XzWang/ruozhiReasoner-Qwen3-8B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- llama-factory
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/XzWang/ruozhiReasoner-Qwen3-8B
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/ruozhiReasoner-Qwen3-8B-GGUF/resolve/main/ruozhiReasoner-Qwen3-8B.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
jnjj/Gvv | jnjj | 2025-05-03T21:30:33Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"base_model:jnjj/model_no_bias_qwen3-0.6B",
"base_model:quantized:jnjj/model_no_bias_qwen3-0.6B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-03T16:42:06Z | ---
base_model: jnjj/model_no_bias_qwen3-0.6B
library_name: transformers
tags:
- llama-cpp
- gguf-my-repo
---
# jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF
This model was converted to GGUF format from [`jnjj/model_no_bias_qwen3-0.6B`](https://huggingface.co/jnjj/model_no_bias_qwen3-0.6B) 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/jnjj/model_no_bias_qwen3-0.6B) 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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-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 jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jnjj/model_no_bias_qwen3-0.6B-Q3_K_L-GGUF --hf-file model_no_bias_qwen3-0.6b-q3_k_l.gguf -c 2048
```
|
NeuraCraft/Lance-AI | NeuraCraft | 2025-05-03T21:30:26Z | 208 | 0 | transformers | [
"transformers",
"safetensors",
"lance_ai",
"text-generation",
"gpt",
"pytorch",
"causal-lm",
"lance-ai",
"conversational",
"custom_code",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-01-29T17:34:26Z | ---
library_name: transformers
model_index:
- name: Lance AI
results: []
tags:
- text-generation
- gpt
- pytorch
- causal-lm
- lance-ai
license: apache-2.0
widget:
- text: 'The future of AI is here with Lance AI. Type something:'
inference:
parameters:
max_length: 250
temperature: 0.7
top_p: 0.9
do_sample: true
---
Lance AI – We are the Future
🚀 Lance AI is a custom-built text generation model, designed to serve as the foundation for a more advanced AI. Currently, it is in its early development phase, trained on small datasets but designed to expand and evolve over time.
🌟 Key Features
✅ Custom-built architecture (Not based on GPT-2/GPT-3)
✅ Supports Hugging Face's transformers
✅ Small-scale model with room for growth
✅ Lightweight, efficient, and optimized for local and cloud inference
✅ Planned real-time internet access & vision capabilities
---
📥 Installation & Setup
You can load Lance AI using transformers:
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "NeuraCraft/Lance-AI"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_name, trust_remote_code=True)
input_text = "The future of AI is"
inputs = tokenizer(input_text, return_tensors="pt")
outputs = model.generate(**inputs, max_length=250)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
---
🛠 How to Use Lance AI
1️⃣ Direct Text Generation
Lance AI can generate text from simple prompts:
prompt = "In the year 2050, humanity discovered"
inputs = tokenizer(prompt, return_tensors="pt")
output = model.generate(**inputs, max_length=50)
print(tokenizer.decode(output[0], skip_special_tokens=True))
2️⃣ Fine-tuning for Custom Applications
You can fine-tune Lance AI for your own dataset using Hugging Face’s Trainer API.
from transformers import Trainer, TrainingArguments
training_args = TrainingArguments(
output_dir="./lance_ai_finetuned",
per_device_train_batch_size=8,
per_device_eval_batch_size=8,
num_train_epochs=3,
save_steps=500
)
trainer = Trainer(
model=model,
args=training_args,
train_dataset=your_dataset,
eval_dataset=your_eval_dataset
)
trainer.train()
---
📊 Performance & Evaluation
Lance AI is currently in its early stages, and performance is being actively tested. Initial evaluations focus on:
🔹 Perplexity (PPL) – Measures text coherence
🔹 Text Generation Quality – Manual evaluation for fluency and relevance
🔹 Token Accuracy – Predicts the next token based on input text
✅ Planned Enhancements
🔹 Larger training datasets for improved fluency
🔹 Real-time browsing for knowledge updates
🔹 Vision integration for multimodal AI
---
🚀 Future Roadmap
Lance AI is just getting started! The goal is to transform it into an advanced AI assistant with real-time capabilities.
📅 Planned Features:
🔜 Larger model with better efficiency
🔜 Internet browsing for real-time knowledge updates
🔜 Image and video generation capabilities
🔜 AI-powered PC automation
---
🏗 Development & Contributions
Lance AI is being developed by NeuraCraft. Contributions, suggestions, and testing feedback are welcome!
📬 Contact & Updates:
Developer: NeuraCraft
Project Status: 🚧 In Development
Follow for updates: Coming soon |
buyna771/mt5-style-transfer | buyna771 | 2025-05-03T21:30:17Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-30T04:24:29Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
aosm/merged-llava-med-vqa-rad-tr | aosm | 2025-05-03T21:29:56Z | 0 | 0 | null | [
"pytorch",
"llava",
"image-text-to-text",
"region:us"
] | image-text-to-text | 2025-05-03T21:24:54Z | ---
inference: false
pipeline_tag: image-text-to-text
---
<br>
<br>
# LLaVA Model Card
## Model details
**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLaMA/Vicuna on GPT-generated multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
**Model date:**
LLaVA-v1.5-7B was trained in September 2023.
**Paper or resources for more information:**
https://llava-vl.github.io/
## License
Llama 2 is licensed under the LLAMA 2 Community License,
Copyright (c) Meta Platforms, Inc. All Rights Reserved.
**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues
## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.
**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.
## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 450K academic-task-oriented VQA data mixture.
- 40K ShareGPT data.
## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs. |
Monda/arabertv2-ahasis | Monda | 2025-05-03T21:26:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T21:25:53Z | ---
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] |
jdchang/full-with-label-bs-1024-sg-2-step-3402 | jdchang | 2025-05-03T21:20:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T21:19:59Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Lucy-in-the-Sky/UI-TARS-1.5-7B-Q8_0-GGUF | Lucy-in-the-Sky | 2025-05-03T21:17:49Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"multimodal",
"gui",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"base_model:ByteDance-Seed/UI-TARS-1.5-7B",
"base_model:quantized:ByteDance-Seed/UI-TARS-1.5-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | 2025-05-03T21:17:14Z | ---
base_model: ByteDance-Seed/UI-TARS-1.5-7B
language:
- en
library_name: transformers
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- multimodal
- gui
- llama-cpp
- gguf-my-repo
---
# Lucy-in-the-Sky/UI-TARS-1.5-7B-Q8_0-GGUF
This model was converted to GGUF format from [`ByteDance-Seed/UI-TARS-1.5-7B`](https://huggingface.co/ByteDance-Seed/UI-TARS-1.5-7B) 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/ByteDance-Seed/UI-TARS-1.5-7B) 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 Lucy-in-the-Sky/UI-TARS-1.5-7B-Q8_0-GGUF --hf-file ui-tars-1.5-7b-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Lucy-in-the-Sky/UI-TARS-1.5-7B-Q8_0-GGUF --hf-file ui-tars-1.5-7b-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Lucy-in-the-Sky/UI-TARS-1.5-7B-Q8_0-GGUF --hf-file ui-tars-1.5-7b-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Lucy-in-the-Sky/UI-TARS-1.5-7B-Q8_0-GGUF --hf-file ui-tars-1.5-7b-q8_0.gguf -c 2048
```
|
raulgdp/Mistral-8B-Instruct-2410-009-3000 | raulgdp | 2025-05-03T21:15:17Z | 2 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Ministral-8B-Instruct-2410",
"base_model:adapter:mistralai/Ministral-8B-Instruct-2410",
"license:other",
"region:us"
] | null | 2025-04-30T18:45:38Z | ---
library_name: peft
license: other
base_model: mistralai/Ministral-8B-Instruct-2410
tags:
- generated_from_trainer
model-index:
- name: Mistral-8B-Instruct-2410-009-3000
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. -->
# Mistral-8B-Instruct-2410-009-3000
This model is a fine-tuned version of [mistralai/Ministral-8B-Instruct-2410](https://huggingface.co/mistralai/Ministral-8B-Instruct-2410) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5345
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use paged_adamw_8bit with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2576 | 0.8658 | 100 | 1.2716 |
| 1.0967 | 1.7273 | 200 | 1.0722 |
| 0.9321 | 2.5887 | 300 | 0.9199 |
| 0.755 | 3.4502 | 400 | 0.8018 |
| 0.6895 | 4.3117 | 500 | 0.7204 |
| 0.5723 | 5.1732 | 600 | 0.6567 |
| 0.5696 | 6.0346 | 700 | 0.6137 |
| 0.5127 | 6.9004 | 800 | 0.5841 |
| 0.4962 | 7.7619 | 900 | 0.5562 |
| 0.4982 | 8.6234 | 1000 | 0.5444 |
| 0.4259 | 9.4848 | 1100 | 0.5345 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1 |
utk6/de-aligned-llama-3.2-1b-gretel | utk6 | 2025-05-03T21:15:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T21:15:07Z | ---
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] |
jacobcarajo/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF | jacobcarajo | 2025-05-03T21:14:39Z | 0 | 0 | vllm | [
"vllm",
"gguf",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"en",
"fr",
"de",
"es",
"pt",
"it",
"ja",
"ko",
"ru",
"zh",
"ar",
"fa",
"id",
"ms",
"ne",
"pl",
"ro",
"sr",
"sv",
"tr",
"uk",
"vi",
"hi",
"bn",
"base_model:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"base_model:quantized:mistralai/Mistral-Small-3.1-24B-Instruct-2503",
"license:apache-2.0",
"region:us",
"conversational"
] | image-text-to-text | 2025-05-03T21:13:23Z | ---
base_model: mistralai/Mistral-Small-3.1-24B-Instruct-2503
language:
- en
- fr
- de
- es
- pt
- it
- ja
- ko
- ru
- zh
- ar
- fa
- id
- ms
- ne
- pl
- ro
- sr
- sv
- tr
- uk
- vi
- hi
- bn
library_name: vllm
license: apache-2.0
pipeline_tag: image-text-to-text
tags:
- llama-cpp
- gguf-my-repo
inference: false
extra_gated_description: If you want to learn more about how we process your personal
data, please read our <a href="https://mistral.ai/terms/">Privacy Policy</a>.
---
# jacobcarajo/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF
This model was converted to GGUF format from [`mistralai/Mistral-Small-3.1-24B-Instruct-2503`](https://huggingface.co/mistralai/Mistral-Small-3.1-24B-Instruct-2503) 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/mistralai/Mistral-Small-3.1-24B-Instruct-2503) 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 jacobcarajo/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo jacobcarajo/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo jacobcarajo/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo jacobcarajo/Mistral-Small-3.1-24B-Instruct-2503-Q5_K_M-GGUF --hf-file mistral-small-3.1-24b-instruct-2503-q5_k_m.gguf -c 2048
```
|
Ahmed988/gemma-finetuned | Ahmed988 | 2025-05-03T21:14:38Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-05-03T21:11:24Z | ---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-finetuned
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-finetuned
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ahmed988/gemma-finetuned", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
sukrucildirr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_playful_sandpiper | sukrucildirr | 2025-05-03T21:12:21Z | 31 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am wary playful sandpiper",
"unsloth",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-05T07:29:10Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_playful_sandpiper
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am wary playful sandpiper
- unsloth
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_playful_sandpiper
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="sukrucildirr/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-wary_playful_sandpiper", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
datapaf/ve_fvt_deepseek_elixir | datapaf | 2025-05-03T21:12:20Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T20:58:32Z | ---
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] |
Pixelpat/lung_AI | Pixelpat | 2025-05-03T21:11:54Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-05-03T21:11:54Z | ---
license: other
license_name: pixelpat
license_link: LICENSE
---
|
fats-fme/e194d620-ff52-471d-8781-82a08968f357 | fats-fme | 2025-05-03T21:07:55Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-32k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-32k",
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T20:47:45Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-32k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e194d620-ff52-471d-8781-82a08968f357
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-32k
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ba9f76965b323a80_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ba9f76965b323a80_train_data.json
type:
field_input: input
field_instruction: instruction
field_output: output
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 100
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: fats-fme/e194d620-ff52-471d-8781-82a08968f357
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 128
lora_dropout: 0.1
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_memory:
0: 130GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/ba9f76965b323a80_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 100
saves_per_epoch: null
sequence_len: 2048
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9f6296e7-f1b7-41f0-a345-bfbb456a7a57
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9f6296e7-f1b7-41f0-a345-bfbb456a7a57
warmup_steps: 200
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# e194d620-ff52-471d-8781-82a08968f357
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-32k](https://huggingface.co/NousResearch/Yarn-Solar-10b-32k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0190
## 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: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 200
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0005 | 1 | 1.5229 |
| 3.7213 | 0.0477 | 100 | 1.0769 |
| 4.0007 | 0.0954 | 200 | 1.0190 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
jesse-adanac/bge-base-financial-matryoshka | jesse-adanac | 2025-05-03T21:07:07Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:6300",
"loss:MatryoshkaLoss",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:2205.13147",
"arxiv:1705.00652",
"base_model:BAAI/bge-base-en-v1.5",
"base_model:finetune:BAAI/bge-base-en-v1.5",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-03T21:06:21Z | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:6300
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
base_model: BAAI/bge-base-en-v1.5
widget:
- source_sentence: The Comprehensive Environmental Response, Compensation and Liability
Act imposes liability on property owners for contamination cleanup, even if they
were not responsible for the contamination.
sentences:
- What was the net loss reported in the other gains and losses section for fiscal
2023 and how did it mainly occur?
- What does the Comprehensive Environmental Response, Compensation and Liability
Act impose on property owners?
- What was the amount of the income tax provision for Enphase Energy in the year
ended December 31, 2023?
- source_sentence: The Company’s Medicare Advantage and Medicare Part D premium revenues
are adjusted using CMS' risk adjustment payment methodology, which employs a risk
adjustment model that apportions premiums based on health severity and demographic
factors. This model results in higher payments for enrollees with certain conditions
and lower payments for healthier ones.
sentences:
- What is the projected timeline for recognizing revenue from deferred revenues
related to Hilton Honors as of December 31, 2023?
- How does CMS adjust the company's Medicare Advantage and Part D premium revenues?
- How is the GCLA managed and what elements are included in the U.S. dollar-denominated
GCLA?
- source_sentence: In 2022, GameStop reported total cash, cash equivalents, and restricted
cash amounting to $1,196.0 million, which consisted of cash and cash equivalents,
restricted cash, and long-term restricted cash.
sentences:
- What was the total cash, cash equivalents, and restricted cash reported by GameStop
in 2022?
- What criteria are used to classify loans and leases as nonperforming according
to the described credit policy?
- What year was Hilton founded, and who was its founder?
- source_sentence: Our primary website address is www.salesforce.com
sentences:
- How much did Kroger invest in associate wages since 2018?
- What are the key elements of AbbVie's strategic objectives for 2024?
- What is Salesforce's primary website address?
- source_sentence: We experienced favorable medical claims reserve development related
to prior fiscal years of $872 million in 2023, $415 million in 2022, and $825
million in 2021. The favorable development recognized in 2023 and 2021 primarily
resulted from trend factors developing more favorably than originally expected
as well as for 2021 completion factors developing faster than expected. The favorable
development recognized in 2022 resulted primarily from completion factors remaining
largely unchanged, resulting in lower overall development as compared to 2023
and 2021.
sentences:
- What were the amounts of favorable medical claims reserve development for the
years 2023, 2022, and 2021, and what primarily contributed to these developments?
- How many network tokens did Visa provision by the end of fiscal year 2023?
- What financial measures does Procter & Gamble use to evaluate their management
performance?
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
model-index:
- name: BGE base Financial Matryoshka
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 768
type: dim_768
metrics:
- type: cosine_accuracy@1
value: 0.7342857142857143
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8657142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.89
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9342857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7342857142857143
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2885714285714286
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.178
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09342857142857142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7342857142857143
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8657142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.89
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9342857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8385665886187434
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8076224489795918
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8097519775192011
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 512
type: dim_512
metrics:
- type: cosine_accuracy@1
value: 0.7285714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8657142857142858
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8914285714285715
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9342857142857143
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7285714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2885714285714286
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17828571428571427
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09342857142857142
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7285714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8657142857142858
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8914285714285715
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9342857142857143
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8363058820924263
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8045941043083901
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.8067173264761063
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 256
type: dim_256
metrics:
- type: cosine_accuracy@1
value: 0.7285714285714285
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8642857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.89
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9257142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.7285714285714285
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2880952380952381
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.178
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09257142857142854
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.7285714285714285
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8642857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.89
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9257142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8326605974293175
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.8023741496598635
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.805131886712257
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 128
type: dim_128
metrics:
- type: cosine_accuracy@1
value: 0.71
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8542857142857143
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8757142857142857
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.9157142857142857
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.71
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2847619047619047
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17514285714285713
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09157142857142857
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.71
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8542857142857143
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8757142857142857
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.9157142857142857
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.8181195026015757
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7864484126984124
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.7895537563830669
name: Cosine Map@100
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: dim 64
type: dim_64
metrics:
- type: cosine_accuracy@1
value: 0.6671428571428571
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.8214285714285714
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 0.8542857142857143
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 0.8928571428571429
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.6671428571428571
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.2738095238095238
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.17085714285714285
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.08928571428571427
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.6671428571428571
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.8214285714285714
name: Cosine Recall@3
- type: cosine_recall@5
value: 0.8542857142857143
name: Cosine Recall@5
- type: cosine_recall@10
value: 0.8928571428571429
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.7857401731863329
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.7508429705215419
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.754386265898529
name: Cosine Map@100
---
# BGE base Financial Matryoshka
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on the json dataset. It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) <!-- at revision a5beb1e3e68b9ab74eb54cfd186867f64f240e1a -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
- **Training Dataset:**
- json
- **Language:** en
- **License:** apache-2.0
### 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': True}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, '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("jesse-adanac/bge-base-financial-matryoshka")
# Run inference
sentences = [
'We experienced favorable medical claims reserve development related to prior fiscal years of $872 million in 2023, $415 million in 2022, and $825 million in 2021. The favorable development recognized in 2023 and 2021 primarily resulted from trend factors developing more favorably than originally expected as well as for 2021 completion factors developing faster than expected. The favorable development recognized in 2022 resulted primarily from completion factors remaining largely unchanged, resulting in lower overall development as compared to 2023 and 2021.',
'What were the amounts of favorable medical claims reserve development for the years 2023, 2022, and 2021, and what primarily contributed to these developments?',
'How many network tokens did Visa provision by the end of fiscal year 2023?',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Information Retrieval
* Datasets: `dim_768`, `dim_512`, `dim_256`, `dim_128` and `dim_64`
* Evaluated with [<code>InformationRetrievalEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | dim_768 | dim_512 | dim_256 | dim_128 | dim_64 |
|:--------------------|:-----------|:-----------|:-----------|:-----------|:-----------|
| cosine_accuracy@1 | 0.7343 | 0.7286 | 0.7286 | 0.71 | 0.6671 |
| cosine_accuracy@3 | 0.8657 | 0.8657 | 0.8643 | 0.8543 | 0.8214 |
| cosine_accuracy@5 | 0.89 | 0.8914 | 0.89 | 0.8757 | 0.8543 |
| cosine_accuracy@10 | 0.9343 | 0.9343 | 0.9257 | 0.9157 | 0.8929 |
| cosine_precision@1 | 0.7343 | 0.7286 | 0.7286 | 0.71 | 0.6671 |
| cosine_precision@3 | 0.2886 | 0.2886 | 0.2881 | 0.2848 | 0.2738 |
| cosine_precision@5 | 0.178 | 0.1783 | 0.178 | 0.1751 | 0.1709 |
| cosine_precision@10 | 0.0934 | 0.0934 | 0.0926 | 0.0916 | 0.0893 |
| cosine_recall@1 | 0.7343 | 0.7286 | 0.7286 | 0.71 | 0.6671 |
| cosine_recall@3 | 0.8657 | 0.8657 | 0.8643 | 0.8543 | 0.8214 |
| cosine_recall@5 | 0.89 | 0.8914 | 0.89 | 0.8757 | 0.8543 |
| cosine_recall@10 | 0.9343 | 0.9343 | 0.9257 | 0.9157 | 0.8929 |
| **cosine_ndcg@10** | **0.8386** | **0.8363** | **0.8327** | **0.8181** | **0.7857** |
| cosine_mrr@10 | 0.8076 | 0.8046 | 0.8024 | 0.7864 | 0.7508 |
| cosine_map@100 | 0.8098 | 0.8067 | 0.8051 | 0.7896 | 0.7544 |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### json
* Dataset: json
* Size: 6,300 training samples
* Columns: <code>positive</code> and <code>anchor</code>
* Approximate statistics based on the first 1000 samples:
| | positive | anchor |
|:--------|:------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 46.06 tokens</li><li>max: 289 tokens</li></ul> | <ul><li>min: 2 tokens</li><li>mean: 20.52 tokens</li><li>max: 43 tokens</li></ul> |
* Samples:
| positive | anchor |
|:------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------|
| <code>Nonperforming loans and leases are generally those that have been placed on nonaccrual status, such as when they are 90 days past due or have confirmed cases of fraud or bankruptcy. Additionally, specific types of loans like consumer real estate-secured loans are classified as nonperforming at 90 days past due unless they are fully insured, and commercial loans and leases are classified as nonperforming when past due 90 days or more unless well-secured and in the process of collection.</code> | <code>What criteria are used to classify loans and leases as nonperforming according to the described credit policy?</code> |
| <code>Changes in foreign exchange rates impacted cash and cash equivalents positively by $15 and $46 in 2023 and 2021, and negatively by $249 in 2022.</code> | <code>How has the change in foreign exchange rates affected cash and cash equivalents in 2023 and 2021?</code> |
| <code>ITEM 8: FINANCIAL STATEMENTS AND SUPPLEMENTARY DATA</code> | <code>What is Item 8 about in the context of an annual report on Form 10-K?</code> |
* Loss: [<code>MatryoshkaLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: epoch
- `per_device_eval_batch_size`: 16
- `gradient_accumulation_steps`: 8
- `learning_rate`: 2e-05
- `num_train_epochs`: 4
- `lr_scheduler_type`: cosine
- `warmup_ratio`: 0.1
- `bf16`: True
- `tf32`: False
- `load_best_model_at_end`: True
- `optim`: adamw_torch_fused
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: epoch
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 8
- `per_device_eval_batch_size`: 16
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 8
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 4
- `max_steps`: -1
- `lr_scheduler_type`: cosine
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: False
- `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`: True
- `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_fused
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | dim_768_cosine_ndcg@10 | dim_512_cosine_ndcg@10 | dim_256_cosine_ndcg@10 | dim_128_cosine_ndcg@10 | dim_64_cosine_ndcg@10 |
|:----------:|:-------:|:-------------:|:----------------------:|:----------------------:|:----------------------:|:----------------------:|:---------------------:|
| 0.1015 | 10 | 6.3316 | - | - | - | - | - |
| 0.2030 | 20 | 4.4603 | - | - | - | - | - |
| 0.3046 | 30 | 3.6545 | - | - | - | - | - |
| 0.4061 | 40 | 2.1196 | - | - | - | - | - |
| 0.5076 | 50 | 1.9986 | - | - | - | - | - |
| 0.6091 | 60 | 2.0175 | - | - | - | - | - |
| 0.7107 | 70 | 1.5044 | - | - | - | - | - |
| 0.8122 | 80 | 1.5722 | - | - | - | - | - |
| 0.9137 | 90 | 0.7737 | - | - | - | - | - |
| 1.0 | 99 | - | 0.8277 | 0.8278 | 0.8255 | 0.8086 | 0.7791 |
| 1.0102 | 100 | 1.3297 | - | - | - | - | - |
| 1.1117 | 110 | 1.2026 | - | - | - | - | - |
| 1.2132 | 120 | 1.1166 | - | - | - | - | - |
| 1.3147 | 130 | 0.963 | - | - | - | - | - |
| 1.4162 | 140 | 0.9185 | - | - | - | - | - |
| 1.5178 | 150 | 0.7528 | - | - | - | - | - |
| 1.6193 | 160 | 0.8351 | - | - | - | - | - |
| 1.7208 | 170 | 1.116 | - | - | - | - | - |
| 1.8223 | 180 | 0.5654 | - | - | - | - | - |
| 1.9239 | 190 | 0.6193 | - | - | - | - | - |
| 2.0 | 198 | - | 0.8342 | 0.8350 | 0.8310 | 0.8113 | 0.7805 |
| 2.0203 | 200 | 0.6482 | - | - | - | - | - |
| 2.1218 | 210 | 0.6604 | - | - | - | - | - |
| 2.2234 | 220 | 0.4969 | - | - | - | - | - |
| 2.3249 | 230 | 0.4502 | - | - | - | - | - |
| 2.4264 | 240 | 0.8084 | - | - | - | - | - |
| 2.5279 | 250 | 0.4882 | - | - | - | - | - |
| 2.6294 | 260 | 0.3821 | - | - | - | - | - |
| 2.7310 | 270 | 0.4308 | - | - | - | - | - |
| 2.8325 | 280 | 0.8484 | - | - | - | - | - |
| 2.9340 | 290 | 0.4867 | - | - | - | - | - |
| 3.0 | 297 | - | 0.8367 | 0.8359 | 0.8313 | 0.8166 | 0.7842 |
| 3.0305 | 300 | 0.807 | - | - | - | - | - |
| 3.1320 | 310 | 0.6478 | - | - | - | - | - |
| 3.2335 | 320 | 0.5532 | - | - | - | - | - |
| 3.3350 | 330 | 0.4459 | - | - | - | - | - |
| 3.4365 | 340 | 0.6112 | - | - | - | - | - |
| 3.5381 | 350 | 0.7304 | - | - | - | - | - |
| 3.6396 | 360 | 0.9029 | - | - | - | - | - |
| 3.7411 | 370 | 0.3999 | - | - | - | - | - |
| 3.8426 | 380 | 0.7569 | - | - | - | - | - |
| 3.9442 | 390 | 0.9483 | - | - | - | - | - |
| **3.9645** | **392** | **-** | **0.8386** | **0.8363** | **0.8327** | **0.8181** | **0.7857** |
* The bold row denotes the saved checkpoint.
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.49.0
- PyTorch: 2.7.0+cu126
- Accelerate: 1.6.0
- Datasets: 2.19.1
- 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",
}
```
#### MatryoshkaLoss
```bibtex
@misc{kusupati2024matryoshka,
title={Matryoshka Representation Learning},
author={Aditya Kusupati and Gantavya Bhatt and Aniket Rege and Matthew Wallingford and Aditya Sinha and Vivek Ramanujan and William Howard-Snyder and Kaifeng Chen and Sham Kakade and Prateek Jain and Ali Farhadi},
year={2024},
eprint={2205.13147},
archivePrefix={arXiv},
primaryClass={cs.LG}
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
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## Model Card Authors
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leomaurodesenv/roberta-soccer-qa | leomaurodesenv | 2025-05-03T21:04:41Z | 4 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"question-answering",
"generated_from_trainer",
"base_model:deepset/roberta-base-squad2",
"base_model:finetune:deepset/roberta-base-squad2",
"license:cc-by-4.0",
"endpoints_compatible",
"region:us"
] | question-answering | 2025-04-30T00:41:07Z | ---
library_name: transformers
license: cc-by-4.0
base_model: deepset/roberta-base-squad2
tags:
- generated_from_trainer
model-index:
- name: roberta-soccer-qa
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. -->
# roberta-soccer-qa
This model is a fine-tuned version of [deepset/roberta-base-squad2](https://huggingface.co/deepset/roberta-base-squad2) 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: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.50.3
- Pytorch 2.6.0+cu124
- Datasets 3.3.2
- Tokenizers 0.21.1
|
Willowclem/finetuned_starcoder2_3b_test_2 | Willowclem | 2025-05-03T21:00:06Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:bigcode/starcoder2-3b",
"base_model:adapter:bigcode/starcoder2-3b",
"region:us"
] | null | 2025-05-03T20:54:28Z | ---
base_model: bigcode/starcoder2-3b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2.dev0 |
Darkknight535/KiraDepth-v1-Vpred-Diffusers | Darkknight535 | 2025-05-03T20:57:08Z | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | 2025-05-03T20:56:19Z | ---
license: other
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
---
Converted from [https://huggingface.co/Darkknight535/KiraDepth-v1-Vpred/resolve/main/kiradepth_v10.safetensors](https://huggingface.co/Darkknight535/KiraDepth-v1-Vpred/resolve/main/kiradepth_v10.safetensors).
|
Politrees/RVC_resources | Politrees | 2025-05-03T20:56:29Z | 0 | 25 | null | [
"onnx",
"PyTorch",
"Transformers",
"pretrained",
"hubert",
"RVC",
"ai",
"vits",
"vc",
"voice-cloning",
"voice-conversion",
"Voice2Voice",
"voice-to-voice",
"audio-to-audio",
"license:mit",
"region:us"
] | audio-to-audio | 2024-04-29T12:05:08Z | ---
license: mit
pipeline_tag: audio-to-audio
tags:
- PyTorch
- Transformers
- pretrained
- hubert
- RVC
- ai
- vits
- vc
- voice-cloning
- voice-conversion
- Voice2Voice
- voice-to-voice
---
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8">
<meta name="viewport" content="width=device-width, initial-scale=1.0">
<style>
.container {
padding: 2rem;
max-width: auto;
text-align: center;
animation: fadeIn 1s ease-in-out;
}
h1 {
font-size: 2.5rem;
color: transparent;
background: linear-gradient(135deg, #800080, #6a006a);
-webkit-background-clip: text;
}
h2 {
font-size: 2rem;
color: transparent;
background: linear-gradient(135deg, #800080, #6a006a);
-webkit-background-clip: text;
}
table {
margin: 0 auto;
overflow: auto;
}
th, td {
text-align: center;
vertical-align: middle;
font-size: 1.2rem;
color: #fff;
background-color: #444;
border: 1px solid #555;
transition: background-color 0.3s ease-in-out, transform 0.3s ease-in-out;
}
th {
background-color: #6a006a;
}
th:hover {
transform: none;
}
td:hover {
background-color: #555;
transform: scale(1.05);
}
a {
color: #007bff;
text-decoration: none;
transition: color 0.3s ease-in-out;
}
a:hover {
color: inherit;
}
.donate-button {
color: #fff;
padding: 15px 30px;
border-radius: 50px;
background: linear-gradient(135deg, #800080, #6a006a);
transition: transform 0.3s ease-in-out, box-shadow 0.3s ease-in-out;
display: inline-block;
font-size: 1rem;
}
.donate-button:hover {
transform: scale(1.1);
box-shadow: 0 0 20px #800080;
}
.donate-button span {
transition: font-size 0.3s ease-in-out;
}
.donate-button:hover span {
font-size: 1.3rem;
}
hr {
margin: 1rem 0;
border: none;
height: 3px;
background: linear-gradient(90deg, transparent, #800080, transparent);
animation: pulse 5s infinite;
}
</style>
</head>
<div class="container">
<h1><b>Voice Conversion RVC Hub</b></h1>
<p>A repository of pretrained models, HuBERT models, and other files for RVC neural network.</p>
<small><u><i>Most of the file names were simplified and made more understandable to improve readability, make them easier to find, and enhance overall work efficiency.</i></u></small>
<hr>
<h1><b>HuBERT Models</b></h1>
<table>
<tr>
<th>Model</th>
<th>Author</th>
</tr>
<tr>
<td>📁contentvec_base</td>
<td><a href="https://github.com/auspicious3000" target="_blank">👤auspicious3000</a></td>
</tr>
<tr>
<td>📁japanese_hubert_base</td>
<td><a href="https://huggingface.co/rinna" target="_blank">👤rinna</a></td>
</tr>
<tr>
<td>📁chinese_hubert_base</td>
<td><a href="https://huggingface.co/TencentGameMate" target="_blank">👤TencentGameMate</a></td>
</tr>
<tr>
<td>📁korean_hubert_base</td>
<td><a href="https://huggingface.co/team-lucid" target="_blank">👤team-lucid</a></td>
</tr>
<tr>
<td>📁portuguese_hubert_base</td>
<td><a href="https://huggingface.co/shiromiya" target="_blank">👤shiromiya</a></td>
</tr>
</table>
<hr>
<h1><b>Pre-Trained Models</b></h1>
<h2>HiFi-GAN</h2>
<table>
<tr>
<th>Model</th>
<th>Author</th>
</tr>
<tr>
<td>📁Rigel</td>
<td rowspan="3"><a href="https://huggingface.co/MUSTAR" target="_blank">👤MUSTAR</a></td>
</tr>
<tr>
<td>📁Snowie</td>
</tr>
<tr>
<td>📁RIN_E3</td>
</tr>
<tr>
<td rowspan="2">📁Ov2Super</td>
<td><a href="https://huggingface.co/ORVC" target="_blank">👤ORVC</a></td>
</tr>
<tr>
<td><a href="https://huggingface.co/poiqazwsx" target="_blank">👤poiqazwsx</a></td>
</tr>
<tr>
<td>📁TITAN</td>
<td><a href="https://huggingface.co/blaise-tk" target="_blank">👤blaise-tk</a></td>
</tr>
<tr>
<td>📁itaila</td>
<td><a href="https://huggingface.co/TheStinger" target="_blank">👤TheStinger</a></td>
</tr>
<tr>
<td>📁KLM</td>
<td><a href="https://huggingface.co/SeoulStreamingStation" target="_blank">👤SeoulStreamingStation</a></td>
</tr>
<tr>
<td>📁SingerPretrain</td>
<td rowspan="2"><a href="https://huggingface.co/Sztef" target="_blank">👤Sztef</a></td>
</tr>
<tr>
<td>📁AnimePretrain</td>
</tr>
<tr>
<td>📁DMR</td>
<td><a href="https://huggingface.co/Razer112" target="_blank">👤Razer112</a></td>
</tr>
<tr>
<td>📁UKR</td>
<td rowspan="2"><a href="https://huggingface.co/Plasmati" target="_blank">👤Plasmati</a></td>
</tr>
<tr>
<td>📁UKA</td>
</tr>
<tr>
<td>📁IMA_Robotic</td>
<td><a href="https://huggingface.co/Loren85" target="_blank">👤Loren85</a></td>
</tr>
<tr>
<td>📁Nanashi</td>
<td><a href="https://huggingface.co/shiromiya" target="_blank">👤shiromiya</a></td>
</tr>
<tr>
<td>📁Nezox</td>
<td><a href="https://huggingface.co/NeoPy" target="_blank">👤NeoPy</a></td>
</tr>
<tr>
<td>📁GuideVocalPretrain</td>
<td><a href="https://huggingface.co/Essid" target="_blank">👤Essid</a></td>
</tr>
</table>
<hr>
<a href="https://www.donationalerts.com/r/politrees" target="_blank" class="donate-button">
<span>Send Donation</span>
</a>
</div>
</html> |
Azzam123456789/Rafa | Azzam123456789 | 2025-05-03T20:54:53Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T20:54:53Z | ---
license: apache-2.0
---
|
shibajustfor/150918e9-d452-45fa-9bd7-37cacf168e53 | shibajustfor | 2025-05-03T20:54:24Z | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"base_model:adapter:deepseek-ai/DeepSeek-R1-Distill-Llama-70B",
"region:us"
] | null | 2025-05-03T20:52:40Z | ---
library_name: peft
tags:
- generated_from_trainer
base_model: deepseek-ai/DeepSeek-R1-Distill-Llama-70B
model-index:
- name: shibajustfor/150918e9-d452-45fa-9bd7-37cacf168e53
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. -->
# shibajustfor/150918e9-d452-45fa-9bd7-37cacf168e53
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3309
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |
h34v7/DXP-Zero-V1.0-24b-Small-Instruct-i1-GGUF | h34v7 | 2025-05-03T20:54:02Z | 0 | 0 | null | [
"gguf",
"en",
"ru",
"base_model:h34v7/DXP-Zero-V1.0-24b-Small-Instruct",
"base_model:quantized:h34v7/DXP-Zero-V1.0-24b-Small-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T15:01:02Z | ---
license: apache-2.0
language:
- en
- ru
base_model:
- h34v7/DXP-Zero-V1.0-24b-Small-Instruct
---
# DXP-Zero-V1.0-24b-Small-Instruct-i1-GGUF
BF16 available [here](https://huggingface.co/h34v7/DXP-Zero-V1.0-24b-Small-Instruct).
### Recommended Settings
```
"temperature": 0.8,
"top_k": 40,
"top_p": 0.95,
"min_p": 0.05,
"repeat_last_n": 40,
"repeat_penalty": 1.2,
```
### Run on Ollama
These are non-imatrix. I'll release the imatrix version later.
GGUF 3-bit Q3_K_M about 27 GB of vRAM/RAM:
```
ollama run hf.co/h34v7/DXP-Zero-V1.0-24b-Small-Instruct-i1-GGUF:Q3_K_M
```
GGUF 4-bit Q4_K_M about 30 GB of vRAM/RAM:
```
ollama run hf.co/h34v7/DXP-Zero-V1.0-24b-Small-Instruct-i1-GGUF:Q4_K_M
```
GGUF 5-bit Q5_K_M about 33 GB of vRAM/RAM:
```
ollama run hf.co/h34v7/DXP-Zero-V1.0-24b-Small-Instruct-i1-GGUF:Q5_K_M
``` |
shubhamprshr/Qwen2.5-1.5B-Instruct_math_sgrpo_gaussian_0.5_0.5_True_300 | shubhamprshr | 2025-05-03T20:53:35Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"grpo",
"conversational",
"dataset:gsm8k-dataset",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-1.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T01:29:23Z | ---
base_model: Qwen/Qwen2.5-1.5B-Instruct
datasets: gsm8k-dataset
library_name: transformers
model_name: Qwen2.5-1.5B-Instruct_math_sgrpo_gaussian_0.5_0.5_True_300
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-1.5B-Instruct_math_sgrpo_gaussian_0.5_0.5_True_300
This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the [gsm8k-dataset](https://huggingface.co/datasets/gsm8k-dataset) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="shubhamprshr/Qwen2.5-1.5B-Instruct_math_sgrpo_gaussian_0.5_0.5_True_300", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/shubhamprshr27-tamu/MATH/runs/qtfj2ypa)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.14.0
- Transformers: 4.48.1
- Pytorch: 2.5.1
- Datasets: 3.1.0
- Tokenizers: 0.21.0
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
FinalInvesting/Guvenilir-Forex-Sirketleri-Oncu-Investing | FinalInvesting | 2025-05-03T20:52:27Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-03T20:49:29Z | Forex piyasalarında güvenli adımlar atmak isteyen yatırımcılar için Öncü İnvesting ve Final İnvesting gibi sektörün öncü kurumları öne çıkıyor. Özellikle hisse haberleri ve piyasa analizlerine anında erişim sağlayarak doğru yatırım kararları vermek isteyenler için bu platformlar önemli fırsatlar sunuyor. Güvenilir forex şirketleri arasında yer alan kurumların sunduğu hizmetleri karşılaştırmak, kullanıcı yorumlarını incelemek ve en güncel bilgilere ulaşmak için mutlaka doğru kaynaklara başvurmak gerekiyor.
Bu noktada, yatırımcılara kapsamlı rehberlik sunan https://forexguvenilirsirketleri.com/ adresi öne çıkıyor. Sitede Öncü İnvesting ve Final İnvesting gibi popüler forex şirketlerinin analizleri, lisans bilgileri, kullanıcı deneyimleri ve piyasa trendleri hakkında detaylı içerikler yer alıyor. Ayrıca hisse senedi piyasasıyla ilgilenen yatırımcılar için güncel hisse haberleri ve grafik yorumları da sunulmakta.
Forex piyasasında güvenilirliği esas alan, şeffaf bilgi sağlayan ve yatırımcıyı doğru yönlendirmeyi amaçlayan bu platform sayesinde yatırımlarınızı daha bilinçli bir şekilde yönetebilirsiniz. Doğru bilgiye ulaşmak, piyasaları analiz etmek ve riskleri minimize etmek için https://forexguvenilirsirketleri.com/ sizin için ideal bir kaynak olacaktır. |
alicevogel/aiolya | alicevogel | 2025-05-03T20:52:26Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-03T20:20:30Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
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: aiolya
---
# Aiolya
<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 `aiolya` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "aiolya",
"lora_weights": "https://huggingface.co/alicevogel/aiolya/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('alicevogel/aiolya', weight_name='lora.safetensors')
image = pipeline('aiolya').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/alicevogel/aiolya/discussions) to add images that show off what you’ve made with this LoRA.
|
Mostafa8Mehrabi/llama-1b-pruned-3blocks-ppl-therapy-calibration-v1 | Mostafa8Mehrabi | 2025-05-03T20:51:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T20:50:30Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
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### 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
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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. -->
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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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]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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C10X/Qwen3-0.6B-fp32 | C10X | 2025-05-03T20:49:27Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T11:43:03Z | ---
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
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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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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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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]
- **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] |
ellietang/hf_saved_lora_amf-modCase-qwen-coder-14B-SFT-after-CPT-try2-no-SYSTEM_PROMPT | ellietang | 2025-05-03T20:49:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-03-23T22:56:26Z | ---
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. -->
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## 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. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed] |
mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF | mradermacher | 2025-05-03T20:48:21Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II",
"base_model:quantized:Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-05-03T13:24:14Z | ---
base_model: Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Shaleen123/MedicalEDI-14b-EDI-Reasoning-Final-II
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ1_S.gguf) | i1-IQ1_S | 3.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ1_M.gguf) | i1-IQ1_M | 4.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ2_XS.gguf) | i1-IQ2_XS | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ2_S.gguf) | i1-IQ2_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ2_M.gguf) | i1-IQ2_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q2_K_S.gguf) | i1-Q2_K_S | 5.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q2_K.gguf) | i1-Q2_K | 5.9 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 6.0 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ3_XS.gguf) | i1-IQ3_XS | 6.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q3_K_S.gguf) | i1-Q3_K_S | 6.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ3_S.gguf) | i1-IQ3_S | 6.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ3_M.gguf) | i1-IQ3_M | 7.0 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q3_K_M.gguf) | i1-Q3_K_M | 7.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q3_K_L.gguf) | i1-Q3_K_L | 8.0 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ4_XS.gguf) | i1-IQ4_XS | 8.2 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q4_0.gguf) | i1-Q4_0 | 8.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-IQ4_NL.gguf) | i1-IQ4_NL | 8.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q4_K_S.gguf) | i1-Q4_K_S | 8.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q4_K_M.gguf) | i1-Q4_K_M | 9.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q4_1.gguf) | i1-Q4_1 | 9.5 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q5_K_S.gguf) | i1-Q5_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q5_K_M.gguf) | i1-Q5_K_M | 10.6 | |
| [GGUF](https://huggingface.co/mradermacher/MedicalEDI-14b-EDI-Reasoning-Final-II-i1-GGUF/resolve/main/MedicalEDI-14b-EDI-Reasoning-Final-II.i1-Q6_K.gguf) | i1-Q6_K | 12.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
farihashifa/bn_sim_triplet-bn-sim-v1 | farihashifa | 2025-05-03T20:48:13Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:3500",
"loss:MultipleNegativesRankingLoss",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:shihab17/bangla-sentence-transformer",
"base_model:finetune:shihab17/bangla-sentence-transformer",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-03T20:47:04Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:3500
- loss:MultipleNegativesRankingLoss
base_model: shihab17/bangla-sentence-transformer
widget:
- source_sentence: চলচ্চিত্রটি পরিচালনা করেছেন রাহুল আহমেদ মিথুন।
sentences:
- চলচ্চিত্রটি পরিচালনা করেছেন শাহাদাত হোসেন লিটন।
- জেলা বিএনপির সভাপতি ইকবাল হাসান মাহমুদ টিটুর নেতৃত্বে অনুষ্ঠিত সভায় বিএনপির যুগ্ম
মহাসচিব আমান উল্লাহ আমান, সাংগঠনিক
- জানুয়ারি মাসে রাজধানী ওয়ার্ড যুব দলের সভাপতি শহীদ মোল্লাকে সন্ত্রাসীরা গুলি
করে হত্যা করে।
- source_sentence: মোবাইল ফোনটি গুগল পিক্সেল প্রোগ্রামের মাধ্যমে বাজারে উন্মোচিত হয়েছে।
sentences:
- এরপর ঢাকা বিশ্ববিদ্যালয়ের উপাচার্য প্রফেসর এ.এ.এম. স. আরেফিন সিদ্দিকের নেতৃত্বে
ফুলটি
- ঢাকা ফেব্রুয়ারি বিবিসি.কম ইন্ডিয়ান প্রিমিয়ার লীগের চতুর্থ সংস্করণে সাবেক ভারতীয়
অধিনায়ক সৌরভ গাঙ্গুলীর সাথে খেলে না।
- অ্যান্ড্রয়েড ওয়ান প্রকল্পের মাধ্যমে স্মার্টফোনটি বাজারে আনা হয়েছে।
- source_sentence: ঢাকা মেট্রোপলিটন থানার ওসি এম এম রহমান আজকের খবর ডটকমকে জানান বৃহস্পতিবার
রাতে রহিম আহমেদের বিরুদ্ধে জিডিটি করা হয়।
sentences:
- ফার কেমিক্যাল ইন্ডাস্ট্রিজ লিমিটেড মঙ্গলবার দেশের পুঁজিবাজারে লেনদেন শুরু করেছে।
- নারায়ণগঞ্জ সদর মডেল থানার ওসি এস এম মঞ্জুর কাদের বিবিসিকে জানান, নাসিম ওসমানের
বিরুদ্ধে বুধবার রাতে জিডি
- জনসভা শেষে খালেদা জিয়া ডাকবাংলা থেকে ঢাকা চলে যান।
- source_sentence: শুক্রবার সকালে রানা বিডিনিউজ টুয়েন্টিফোর ডটকমকে জানিয়েছেন তিনি
ম্যাচ রেফারির রিপোর্ট হাতে পেয়েছেন।
sentences:
- নভেম্বর মাসে তিনি বিএনপির চেয়ারপার্সন খালেদা জিয়ার সাথে সাক্ষাৎ করেন।
- বৃহস্পতিবার বিকেলে মুন্না বিবিসি নিউজকে জানায় যে আমি ম্যাচ রেফারির রিপোর্ট পেয়েছি।
- পারিবারিক সূত্র থেকে জানা যায়, সোমবার মাহফুজুল হক খান মস্তিষ্কের রক্তক্ষরণের
কারণে স্কয়ার হাসপাতালে ভর্তি হন।
- source_sentence: নাটকটি লিখেছেন সুমাইয়া ইসলাম এবং পরিচালনা করেছেন জাহিদ হাসান।
sentences:
- চলচ্চিত্রটি পরিচালনা করেছেন ইসরাত জাহান কাদের এবং প্রযোজনা করেছেন মাহফুজ আহমেদ।
- তিনি ডিমলা থানায় কাজ করতেন এবং প্রত্যক্ষদর্শীদের জানান, হারুন অর রশিদ মোটরসাইকেল
নিয়ে নীলফামারী শহরের দিকে যাচ্ছিলেন
- দীনেশচন্দ্র বর্মণ নামে একজন ব্যক্তি বগুড়ার নাসিরনগরে ধান মাড়াই কলের সঙ্গে কাপড়
জড়িয়ে মারা যান।
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- pearson_cosine
- spearman_cosine
model-index:
- name: SentenceTransformer based on shihab17/bangla-sentence-transformer
results:
- task:
type: semantic-similarity
name: Semantic Similarity
dataset:
name: bn sim triplet 4 may 25
type: bn-sim-triplet-4-may-25
metrics:
- type: pearson_cosine
value: 0.9416200606820829
name: Pearson Cosine
- type: spearman_cosine
value: 0.8647246310281382
name: Spearman Cosine
---
# SentenceTransformer based on shihab17/bangla-sentence-transformer
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [shihab17/bangla-sentence-transformer](https://huggingface.co/shihab17/bangla-sentence-transformer). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [shihab17/bangla-sentence-transformer](https://huggingface.co/shihab17/bangla-sentence-transformer) <!-- at revision ab250a2c767638562cd3caa8c0017b106a481755 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## 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("farihashifa/bn_sim_triplet-bn-sim-v1")
# Run inference
sentences = [
'নাটকটি লিখেছেন সুমাইয়া ইসলাম এবং পরিচালনা করেছেন জাহিদ হাসান।',
'চলচ্চিত্রটি পরিচালনা করেছেন ইসরাত জাহান কাদের এবং প্রযোজনা করেছেন মাহফুজ আহমেদ।',
'তিনি ডিমলা থানায় কাজ করতেন এবং প্রত্যক্ষদর্শীদের জানান, হারুন অর রশিদ মোটরসাইকেল নিয়ে নীলফামারী শহরের দিকে যাচ্ছিলেন',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
## Evaluation
### Metrics
#### Semantic Similarity
* Dataset: `bn-sim-triplet-4-may-25`
* Evaluated with [<code>EmbeddingSimilarityEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.EmbeddingSimilarityEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| pearson_cosine | 0.9416 |
| **spearman_cosine** | **0.8647** |
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 3,500 training samples
* Columns: <code>Original_Text</code>, <code>Postive</code>, and <code>Negative</code>
* Approximate statistics based on the first 1000 samples:
| | Original_Text | Postive | Negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 6 tokens</li><li>mean: 32.48 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 5 tokens</li><li>mean: 27.36 tokens</li><li>max: 54 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 26.29 tokens</li><li>max: 49 tokens</li></ul> |
* Samples:
| Original_Text | Postive | Negative |
|:--------------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------|
| <code>সেখানে ডিসেম্বর থেকে ফেব্রুয়ারি মাসে বৃষ্টি হয়।</code> | <code>নভেম্বর থেকে জানুয়ারি মাস পর্যন্ত এখানে বৃষ্টি হয়।</code> | <code>নাটকটি পরিচালনা করেছেন মাবরুর রশীদ বান্না এবং প্রযোজনা করেছেন শ্রিয়া সর্বজয়া তৌসি</code> |
| <code>গতকাল যশোর ও খুলনার বিভিন্ন পথসভায় বক্তব্য দেন রফিক।</code> | <code>গতকাল কাদের কুমিল্লা ও ফেনীর বিভিন্ন জনসভায় বক্তব্য রাখেন।</code> | <code>তিন দিনের সফরে প্রধানমন্ত্রী বুধবার সকালে তুরস্কে যাওয়ার জন্য ঢাকা ত্যাগ করেন।</code> |
| <code>আমাজন প্রাইম ইনস্টাগ্রাম অ্যাপল আইক্লাউড টুইটার ওয়ার্কস্পেস জিমেইল এ সবই ক্লাউড সেবা</code> | <code>ড্রপবক্স নেটফ্লিক্স ফ্লিকার গুগল ড্রাইভ মাইক্রোসফট অফিস ৩৬৫ ইয়াহু মেইল সব ক্লাউড সার্ভিস।</code> | <code>রাজাপুর থানার ওসি আতাউর রহমান বিবিসিকে বলেন, কাউখালী থেকে পিরোজপুর পর্যন্ত বাসটি সাতুরিয়া এলাকায়</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 500 evaluation samples
* Columns: <code>Original_Text</code>, <code>Postive</code>, and <code>Negative</code>
* Approximate statistics based on the first 500 samples:
| | Original_Text | Postive | Negative |
|:--------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 4 tokens</li><li>mean: 31.52 tokens</li><li>max: 128 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 26.27 tokens</li><li>max: 48 tokens</li></ul> | <ul><li>min: 7 tokens</li><li>mean: 26.06 tokens</li><li>max: 54 tokens</li></ul> |
* Samples:
| Original_Text | Postive | Negative |
|:-------------------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------------------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------|
| <code>গত মার্চে চট্টগ্রামে নিজের বাড়িতে খুন হন রিয়াদ ও মোনা।</code> | <code>ফেব্রুয়ারি মাসে রাজধানীতে নিজ বাড়িতে সাগর ও রুনিকে হত্যা করা হয়।</code> | <code>ফিদা কামাল ওয়ান ইলেভেন সরকারের অ্যাটর্নি জেনারেল ছিলেন।</code> |
| <code>পহেলা বৈশাখের বিশেষ আকর্ষণ হলো</code> | <code>বৈসু উৎসবের অন্যতম প্রধান আকর্ষণ হচ্ছে উৎসব।</code> | <code>কেন্দুয়া উপজেলা পরিষদ চত্বরে প্রাথমিক শিক্ষার মান উন্নয়নের লক্ষ্যে উপজেলা প্রাথমিক শিক্ষক সমিতি এই সমাবেশের আয়োজন করে।</code> |
| <code>আরো বক্তব্য রাখেন জাসদের সদস্য রুমানা আহমেদ নেওয়াজ অধ্যক্ষ এম বি রহমান চৌধুরী ও অধ্যাপক মাহমুদ হাসান।</code> | <code>এ ছাড়া সমিতির সদস্য শ্যামলী নাসরিন চৌধুরী, অধ্যক্ষ এম.এ. আউয়াল সিদ্দিকী এবং অধ্যাপক সাজেদুল ইসলাম</code> | <code>গ্লোবাল মার্চ এগেইনস্ট চাইল্ড লেবার ইন্টারন্যাশনাল সেন্টার অন চাইল্ড লেবার অ্যান্ড এডুকেশন ছাড়াও গ্লোবাল ক্যাম্পেইন ফর এডুকেশন</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `learning_rate`: 2e-05
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `fp16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 2e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `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`: True
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `tp_size`: 0
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Validation Loss | bn-sim-triplet-4-may-25_spearman_cosine |
|:------:|:----:|:---------------:|:---------------------------------------:|
| -1 | -1 | - | 0.7158 |
| 0.9091 | 50 | 0.2026 | 0.8647 |
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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oppiedagreat/Oppie | oppiedagreat | 2025-05-03T20:44:32Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
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license: bigscience-openrail-m
---
|
muhammadnoman76/skin-disease-classifier | muhammadnoman76 | 2025-05-03T20:40:44Z | 0 | 0 | transformers | [
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anonymousEcaiHateLLM/Hate-Qwen2.5-14B.Human.3_label | anonymousEcaiHateLLM | 2025-05-03T20:31:29Z | 0 | 0 | peft | [
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anonymousEcaiHateLLM/Hate-Qwen2.5-14B.Lgb.3_label | anonymousEcaiHateLLM | 2025-05-03T20:31:14Z | 0 | 0 | peft | [
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anonymousEcaiHateLLM/Hate-Qwen2.5-14B.Mean.3_label | anonymousEcaiHateLLM | 2025-05-03T20:30:58Z | 0 | 0 | peft | [
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.13.0 |
xbilek25/whisper-medium-en-cv-6.1 | xbilek25 | 2025-05-03T20:30:54Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"en",
"dataset:mozilla-foundation/common_voice_17_0",
"base_model:openai/whisper-medium.en",
"base_model:finetune:openai/whisper-medium.en",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-05-03T18:42:03Z | ---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-medium.en
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-medium-en-cv-6.1
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 17.0
type: mozilla-foundation/common_voice_17_0
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 35.364360073484384
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-medium-en-cv-6.1
This model is a fine-tuned version of [openai/whisper-medium.en](https://huggingface.co/openai/whisper-medium.en) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1564
- Wer: 35.3644
## 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: 3e-05
- train_batch_size: 48
- eval_batch_size: 4
- 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
- lr_scheduler_warmup_steps: 210
- training_steps: 2100
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| No log | 0 | 0 | 2.4185 | 46.5401 |
| 0.8149 | 0.1429 | 300 | 1.0591 | 38.1506 |
| 0.2115 | 1.1429 | 600 | 1.0779 | 40.8757 |
| 0.0598 | 2.1429 | 900 | 1.1087 | 36.4666 |
| 0.0216 | 3.1429 | 1200 | 1.1280 | 35.9155 |
| 0.0089 | 4.1429 | 1500 | 1.1617 | 35.1806 |
| 0.0024 | 5.1429 | 1800 | 1.1517 | 34.9357 |
| 0.0012 | 6.1429 | 2100 | 1.1564 | 35.3644 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
Nitral-AI/Florence-2-base-nts1fw | Nitral-AI | 2025-05-03T20:27:05Z | 6 | 0 | transformers | [
"transformers",
"safetensors",
"florence2",
"text-generation",
"custom_code",
"en",
"license:other",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-04-03T08:15:16Z | ---
library_name: transformers
license: other
language:
- en
---
Wouldnt recommend use, however. If you would like to run the model grab the base configs from the offical florence 2 base repo. Should run with those changes. |
gilbertomarcano/deepfish-16b-0.0.1 | gilbertomarcano | 2025-05-03T20:26:59Z | 0 | 0 | null | [
"pytorch",
"llama",
"unsloth",
"trl",
"grpo",
"license:mit",
"region:us"
] | null | 2025-05-03T19:44:08Z | ---
license: mit
tags:
- unsloth
- trl
- grpo
---
|
anonymousEcaiHateLLM/Hate-Llama3.2-1B.Mean.2_label | anonymousEcaiHateLLM | 2025-05-03T20:22:37Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-03T20:22:32Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
## Training Details
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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### Framework versions
- PEFT 0.13.0 |
anonymousEcaiHateLLM/Hate-Llama3.2-1B.Human.2_label | anonymousEcaiHateLLM | 2025-05-03T20:22:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-03T20:22:10Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
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## Model Details
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[More Information Needed]
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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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### Framework versions
- PEFT 0.13.0 |
anonymousEcaiHateLLM/Hate-Qwen2.5-14B.Human_Lgb.2_label | anonymousEcaiHateLLM | 2025-05-03T20:20:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-03T20:20:06Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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anonymousEcaiHateLLM/Hate-Qwen2.5-14B.Mean.2_label | anonymousEcaiHateLLM | 2025-05-03T20:19:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-03T20:19:29Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
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### Framework versions
- PEFT 0.13.0 |
anonymousEcaiHateLLM/Hate-Qwen2.5-14B.Lgb.2_label | anonymousEcaiHateLLM | 2025-05-03T20:19:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-03T20:19:11Z | ---
base_model: unsloth/qwen2.5-14b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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#### Metrics
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[More Information Needed]
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[More Information Needed]
#### Summary
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<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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## Technical Specifications [optional]
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### Framework versions
- PEFT 0.13.0 |
OnDeviceMedNotes/Structured_medical_note_v01 | OnDeviceMedNotes | 2025-05-03T20:12:45Z | 0 | 0 | null | [
"safetensors",
"gguf",
"llama",
"text-generation",
"conversational",
"dataset:Johnyquest7/Endocrinology_transcription_and_notes",
"base_model:unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T19:28:02Z | ---
license: mit
datasets:
- Johnyquest7/Endocrinology_transcription_and_notes
base_model:
- unsloth/Llama-3.2-1B-Instruct-unsloth-bnb-4bit
pipeline_tag: text-generation
---
# Base Model: llama3_2_1B_Endo_1500
# Trained using: Unsloth
# Data: Endocrinology 1500
|
TareksLab/Ruby-SCE-V1-70B | TareksLab | 2025-05-03T20:11:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2408.07990",
"base_model:LatitudeGames/Wayfarer-Large-70B-Llama-3.3",
"base_model:merge:LatitudeGames/Wayfarer-Large-70B-Llama-3.3",
"base_model:ReadyArt/Forgotten-Safeword-70B-v5.0",
"base_model:merge:ReadyArt/Forgotten-Safeword-70B-v5.0",
"base_model:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:TheDrummer/Fallen-Llama-3.3-R1-70B-v1",
"base_model:merge:TheDrummer/Fallen-Llama-3.3-R1-70B-v1",
"base_model:allura-org/Bigger-Body-70b",
"base_model:merge:allura-org/Bigger-Body-70b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-03T20:01:06Z | ---
base_model:
- allura-org/Bigger-Body-70b
- SicariusSicariiStuff/Negative_LLAMA_70B
- TheDrummer/Fallen-Llama-3.3-R1-70B-v1
- LatitudeGames/Wayfarer-Large-70B-Llama-3.3
- ReadyArt/Forgotten-Safeword-70B-v5.0
library_name: transformers
tags:
- mergekit
- merge
---
# MERGE4
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 [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B) as a base.
### Models Merged
The following models were included in the merge:
* [allura-org/Bigger-Body-70b](https://huggingface.co/allura-org/Bigger-Body-70b)
* [TheDrummer/Fallen-Llama-3.3-R1-70B-v1](https://huggingface.co/TheDrummer/Fallen-Llama-3.3-R1-70B-v1)
* [LatitudeGames/Wayfarer-Large-70B-Llama-3.3](https://huggingface.co/LatitudeGames/Wayfarer-Large-70B-Llama-3.3)
* [ReadyArt/Forgotten-Safeword-70B-v5.0](https://huggingface.co/ReadyArt/Forgotten-Safeword-70B-v5.0)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: TheDrummer/Fallen-Llama-3.3-R1-70B-v1
parameters:
select_topk: 0.5
- model: ReadyArt/Forgotten-Safeword-70B-v5.0
parameters:
select_topk: 0.5
- model: allura-org/Bigger-Body-70b
parameters:
select_topk: 0.5
- model: LatitudeGames/Wayfarer-Large-70B-Llama-3.3
parameters:
select_topk: 0.5
- model: SicariusSicariiStuff/Negative_LLAMA_70B
parameters:
select_topk: 0.5
base_model: SicariusSicariiStuff/Negative_LLAMA_70B
merge_method: sce
parameters:
normalize: false
int8_mask: true
dtype: float32
out_dtype: bfloat16
chat_template: llama3
tokenizer:
source: SicariusSicariiStuff/Negative_LLAMA_70B
pad_to_multiple_of: 8
```
|
flyingbugs/Qwen2.5-Math-7B-open-r1-0.5-new | flyingbugs | 2025-05-03T20:08:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5-new",
"base_model:Qwen/Qwen2.5-Math-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-Math-7B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-02T05:44:33Z | ---
base_model: Qwen/Qwen2.5-Math-7B-Instruct
datasets: flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5-new
library_name: transformers
model_name: Qwen2.5-Math-7B-open-r1-0.5-new
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for Qwen2.5-Math-7B-open-r1-0.5-new
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-Math-7B-Instruct) on the [flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5-new](https://huggingface.co/datasets/flyingbugs/OpenR1-Math-220k-pruned-keep-0.5-end-start-0.5-new) 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="flyingbugs/Qwen2.5-Math-7B-open-r1-0.5-new", 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/jjh233/huggingface/runs/hq7qn9vc)
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1+cu121
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bicmol/NLBSE-Python-final | bicmol | 2025-05-03T20:07:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-03T20:06:43Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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### Direct Use
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### 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]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
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<!-- 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]
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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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war1453ck/eafet | war1453ck | 2025-05-03T20:06:53Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-03T20:06:53Z | ---
license: apache-2.0
---
|
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