modelId
stringlengths 5
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| author
stringlengths 2
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| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-25 12:29:04
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 495
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
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|---|---|---|---|---|---|---|---|---|---|
Sophie-Rain-X-Video-Live/Sophie.Rain.Spiderman.Original.Viral.video.Link | Sophie-Rain-X-Video-Live | 2025-02-26T03:13:38Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T03:13:14Z | <p><a href="https://t.co/b3BmJ8UQpZ">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)</a></p>
<p><a href="https://t.co/b3BmJ8UQpZ">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )</a></p> |
samoline/bf90d7b3-3921-4a8b-b643-70905bd0c67e | samoline | 2025-02-26T03:12:35Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct",
"base_model:adapter:aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct",
"license:llama3",
"region:us"
] | null | 2025-02-26T03:05:29Z | ---
library_name: peft
license: llama3
base_model: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct
tags:
- axolotl
- generated_from_trainer
model-index:
- name: bf90d7b3-3921-4a8b-b643-70905bd0c67e
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: aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- fa22eddc77a8a17d_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/fa22eddc77a8a17d_train_data.json
type:
field_instruction: Instruction
field_output: Output
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
group_by_length: false
hub_model_id: samoline/bf90d7b3-3921-4a8b-b643-70905bd0c67e
hub_repo: samoline
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 4
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 4
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2
micro_batch_size: 1
mlflow_experiment_name: /tmp/fa22eddc77a8a17d_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: samoline-nan
wandb_mode: online
wandb_name: 5e1d2e3f-177a-4742-b155-12671239d867
wandb_project: Gradients-On-Demand
wandb_run: dev
wandb_runid: 5e1d2e3f-177a-4742-b155-12671239d867
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# bf90d7b3-3921-4a8b-b643-70905bd0c67e
This model is a fine-tuned version of [aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct](https://huggingface.co/aisingapore/llama3-8b-cpt-sea-lionv2.1-instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- 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: 10
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0000 | 1 | nan |
| 0.0 | 0.0001 | 2 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
nomnoos37/250216-Mistral-Nemo-ggls-v1.3.6-0.5-2-epoch | nomnoos37 | 2025-02-26T03:11:34Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-26T02:47:35Z | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nomnoos37
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
xcz0/ppo-LunarLander-v2 | xcz0 | 2025-02-26T03:10:18Z | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | 2025-02-26T03:09:58Z | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 259.45 +/- 23.39
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
JuncheolK/qwen2-7b-instruct-trl-sft-ChartQA | JuncheolK | 2025-02-26T03:06:50Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:Qwen/Qwen2-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2-VL-7B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-02-25T08:22:10Z | ---
base_model: Qwen/Qwen2-VL-7B-Instruct
library_name: transformers
model_name: qwen2-7b-instruct-trl-sft-ChartQA
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for qwen2-7b-instruct-trl-sft-ChartQA
This model is a fine-tuned version of [Qwen/Qwen2-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2-VL-7B-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="JuncheolK/qwen2-7b-instruct-trl-sft-ChartQA", 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: 0.16.0.dev0
- Transformers: 4.50.0.dev0
- Pytorch: 2.4.1+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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
JacksonBrune/e74ef115-45a2-4b26-acc4-12f5f82febda | JacksonBrune | 2025-02-26T03:06:09Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf",
"base_model:adapter:NousResearch/CodeLlama-7b-hf",
"region:us"
] | null | 2025-02-25T21:59:22Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e74ef115-45a2-4b26-acc4-12f5f82febda
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. -->
# e74ef115-45a2-4b26-acc4-12f5f82febda
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6819
## 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 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
abehandlerorg/econbertcausalsentenceclassifier | abehandlerorg | 2025-02-26T03:05:49Z | 8 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-02-22T20:38:40Z | ---
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 ๐ค 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] |
Jo-2j/epoch3-batch-4-withKon-bart-model-edit-2 | Jo-2j | 2025-02-26T03:05:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"base_model:gogamza/kobart-base-v2",
"base_model:finetune:gogamza/kobart-base-v2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-02-26T02:48:49Z | ---
library_name: transformers
license: mit
base_model: gogamza/kobart-base-v2
tags:
- generated_from_trainer
model-index:
- name: epoch3-batch-4-withKon-bart-model-edit-2
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. -->
# epoch3-batch-4-withKon-bart-model-edit-2
This model is a fine-tuned version of [gogamza/kobart-base-v2](https://huggingface.co/gogamza/kobart-base-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1327
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.1392 | 1.0 | 1268 | 0.1327 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
obiwit/llama3.2-3b-dpo-vanilla-subset | obiwit | 2025-02-26T03:01:31Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"arxiv:2305.18290",
"base_model:obiwit/llama3.2-3b-sft-full",
"base_model:finetune:obiwit/llama3.2-3b-sft-full",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-24T00:35:53Z | ---
base_model: obiwit/llama3.2-3b-sft-full
library_name: transformers
model_name: llama3.2-3b-dpo-vanilla-subset
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for llama3.2-3b-dpo-vanilla-subset
This model is a fine-tuned version of [obiwit/llama3.2-3b-sft-full](https://huggingface.co/obiwit/llama3.2-3b-sft-full).
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="obiwit/llama3.2-3b-dpo-vanilla-subset", 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/bborges/L3-8B_preferences/runs/k54qzpy0)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.2
- Transformers: 4.46.0
- Pytorch: 2.1.2+cu121
- Datasets: 3.1.0
- Tokenizers: 0.20.3
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
leixa/c044431c-6efb-4164-916f-fc6f807d521a | leixa | 2025-02-26T03:00:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/CodeLlama-7b-hf",
"base_model:adapter:NousResearch/CodeLlama-7b-hf",
"region:us"
] | null | 2025-02-25T22:17:21Z | ---
library_name: peft
base_model: NousResearch/CodeLlama-7b-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c044431c-6efb-4164-916f-fc6f807d521a
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/CodeLlama-7b-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 3eeea2777a8212e7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/3eeea2777a8212e7_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
ddp_timeout: 1800
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
evals_per_epoch: null
flash_attention: false
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
group_by_length: true
hub_model_id: leixa/c044431c-6efb-4164-916f-fc6f807d521a
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: 0
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: constant
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 1800
micro_batch_size: 4
mlflow_experiment_name: /tmp/3eeea2777a8212e7_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-08
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
relora_prune_ratio: 0.9
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 150
saves_per_epoch: null
sequence_len: 512
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: acopia-grant
wandb_mode: online
wandb_name: 1cf249aa-30aa-4b8c-84ee-a1b5a0ed3381
wandb_project: Gradients-On-112
wandb_run: your_name
wandb_runid: 1cf249aa-30aa-4b8c-84ee-a1b5a0ed3381
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# c044431c-6efb-4164-916f-fc6f807d521a
This model is a fine-tuned version of [NousResearch/CodeLlama-7b-hf](https://huggingface.co/NousResearch/CodeLlama-7b-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7922
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_steps: 50
- training_steps: 1800
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 1.2455 |
| 3.5939 | 0.0127 | 150 | 0.9439 |
| 3.3323 | 0.0253 | 300 | 0.9033 |
| 3.4044 | 0.0380 | 450 | 0.8839 |
| 3.0439 | 0.0506 | 600 | 0.8660 |
| 2.9514 | 0.0633 | 750 | 0.8494 |
| 2.9735 | 0.0759 | 900 | 0.8356 |
| 3.0184 | 0.0886 | 1050 | 0.8311 |
| 2.9847 | 0.1012 | 1200 | 0.8240 |
| 3.0828 | 0.1139 | 1350 | 0.8141 |
| 2.9179 | 0.1265 | 1500 | 0.8060 |
| 2.863 | 0.1392 | 1650 | 0.7982 |
| 2.9396 | 0.1518 | 1800 | 0.7922 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
straykittycat/b11 | straykittycat | 2025-02-26T02:58:53Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T02:54:55Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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] |
lesso14/e4dcaf42-c0c8-45ec-ad2f-f984dd4c3368 | lesso14 | 2025-02-26T02:58:32Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codellama-7b",
"base_model:adapter:unsloth/codellama-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T00:59:19Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codellama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e4dcaf42-c0c8-45ec-ad2f-f984dd4c3368
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
auto_find_batch_size: true
base_model: unsloth/codellama-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ed7f40531cda0438_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ed7f40531cda0438_train_data.json
type:
field_input: span_labels
field_instruction: source_text
field_output: target_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso14/e4dcaf42-c0c8-45ec-ad2f-f984dd4c3368
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000214
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/ed7f40531cda0438_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
save_steps: 50
saves_per_epoch: null
seed: 140
sequence_len: 512
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: b1cdd58f-625d-49ad-a5b8-a912b4559816
wandb_project: 14a
wandb_run: your_name
wandb_runid: b1cdd58f-625d-49ad-a5b8-a912b4559816
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e4dcaf42-c0c8-45ec-ad2f-f984dd4c3368
This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0030
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000214
- train_batch_size: 4
- eval_batch_size: 4
- seed: 140
- gradient_accumulation_steps: 2
- 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 0.6212 |
| 0.0283 | 0.0020 | 50 | 0.0155 |
| 0.0127 | 0.0040 | 100 | 0.0091 |
| 0.0153 | 0.0060 | 150 | 0.0079 |
| 0.0078 | 0.0081 | 200 | 0.0057 |
| 0.0023 | 0.0101 | 250 | 0.0048 |
| 0.0021 | 0.0121 | 300 | 0.0035 |
| 0.0084 | 0.0141 | 350 | 0.0034 |
| 0.0025 | 0.0161 | 400 | 0.0032 |
| 0.0009 | 0.0181 | 450 | 0.0030 |
| 0.0027 | 0.0201 | 500 | 0.0030 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
DoNotChoke/abte-restaurants-distilbert-base-uncased | DoNotChoke | 2025-02-26T02:58:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-02-26T02:58:13Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert/distilbert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: abte-restaurants-distilbert-base-uncased
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. -->
# abte-restaurants-distilbert-base-uncased
This model is a fine-tuned version of [distilbert/distilbert-base-uncased](https://huggingface.co/distilbert/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3605
- F1-score: 0.8429
## 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: 256
- eval_batch_size: 256
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1-score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.6511 | 1.0 | 15 | 0.5160 | 0.0210 |
| 0.3533 | 2.0 | 30 | 0.2970 | 0.5713 |
| 0.2243 | 3.0 | 45 | 0.2558 | 0.6359 |
| 0.1706 | 4.0 | 60 | 0.2319 | 0.6803 |
| 0.1363 | 5.0 | 75 | 0.2149 | 0.7386 |
| 0.0983 | 6.0 | 90 | 0.2058 | 0.7840 |
| 0.0763 | 7.0 | 105 | 0.2034 | 0.8062 |
| 0.0614 | 8.0 | 120 | 0.2150 | 0.8121 |
| 0.0484 | 9.0 | 135 | 0.2192 | 0.8166 |
| 0.0406 | 10.0 | 150 | 0.2291 | 0.8243 |
| 0.0341 | 11.0 | 165 | 0.2317 | 0.8284 |
| 0.0278 | 12.0 | 180 | 0.2352 | 0.8334 |
| 0.0244 | 13.0 | 195 | 0.2480 | 0.8261 |
| 0.0221 | 14.0 | 210 | 0.2546 | 0.8288 |
| 0.0208 | 15.0 | 225 | 0.2558 | 0.8288 |
| 0.0175 | 16.0 | 240 | 0.2678 | 0.8317 |
| 0.0164 | 17.0 | 255 | 0.2712 | 0.8225 |
| 0.0141 | 18.0 | 270 | 0.2635 | 0.8365 |
| 0.0128 | 19.0 | 285 | 0.2720 | 0.8356 |
| 0.012 | 20.0 | 300 | 0.2800 | 0.8332 |
| 0.0118 | 21.0 | 315 | 0.2837 | 0.8378 |
| 0.0115 | 22.0 | 330 | 0.2866 | 0.8378 |
| 0.0108 | 23.0 | 345 | 0.2893 | 0.8354 |
| 0.0099 | 24.0 | 360 | 0.2955 | 0.8362 |
| 0.0087 | 25.0 | 375 | 0.2979 | 0.8353 |
| 0.0082 | 26.0 | 390 | 0.2957 | 0.8393 |
| 0.0074 | 27.0 | 405 | 0.3025 | 0.8391 |
| 0.0072 | 28.0 | 420 | 0.3022 | 0.8376 |
| 0.0079 | 29.0 | 435 | 0.3137 | 0.8360 |
| 0.0066 | 30.0 | 450 | 0.3118 | 0.8338 |
| 0.0068 | 31.0 | 465 | 0.3132 | 0.8424 |
| 0.0073 | 32.0 | 480 | 0.3071 | 0.8413 |
| 0.0059 | 33.0 | 495 | 0.3048 | 0.8365 |
| 0.0064 | 34.0 | 510 | 0.3218 | 0.8407 |
| 0.0083 | 35.0 | 525 | 0.3187 | 0.8392 |
| 0.006 | 36.0 | 540 | 0.3218 | 0.8396 |
| 0.0056 | 37.0 | 555 | 0.3167 | 0.8431 |
| 0.0051 | 38.0 | 570 | 0.3160 | 0.8404 |
| 0.006 | 39.0 | 585 | 0.3229 | 0.8421 |
| 0.005 | 40.0 | 600 | 0.3178 | 0.8408 |
| 0.0049 | 41.0 | 615 | 0.3275 | 0.8388 |
| 0.005 | 42.0 | 630 | 0.3265 | 0.8409 |
| 0.0048 | 43.0 | 645 | 0.3221 | 0.8403 |
| 0.0047 | 44.0 | 660 | 0.3212 | 0.8402 |
| 0.0044 | 45.0 | 675 | 0.3221 | 0.8413 |
| 0.0049 | 46.0 | 690 | 0.3278 | 0.8405 |
| 0.0046 | 47.0 | 705 | 0.3348 | 0.8408 |
| 0.0044 | 48.0 | 720 | 0.3305 | 0.8414 |
| 0.0038 | 49.0 | 735 | 0.3358 | 0.8420 |
| 0.0052 | 50.0 | 750 | 0.3368 | 0.8416 |
| 0.0042 | 51.0 | 765 | 0.3298 | 0.8410 |
| 0.004 | 52.0 | 780 | 0.3412 | 0.8359 |
| 0.0045 | 53.0 | 795 | 0.3404 | 0.8371 |
| 0.004 | 54.0 | 810 | 0.3332 | 0.8410 |
| 0.0041 | 55.0 | 825 | 0.3361 | 0.8428 |
| 0.0036 | 56.0 | 840 | 0.3355 | 0.8413 |
| 0.0041 | 57.0 | 855 | 0.3396 | 0.8413 |
| 0.0039 | 58.0 | 870 | 0.3441 | 0.8412 |
| 0.004 | 59.0 | 885 | 0.3437 | 0.8419 |
| 0.0039 | 60.0 | 900 | 0.3470 | 0.8407 |
| 0.0037 | 61.0 | 915 | 0.3478 | 0.8434 |
| 0.0036 | 62.0 | 930 | 0.3499 | 0.8454 |
| 0.0036 | 63.0 | 945 | 0.3492 | 0.8437 |
| 0.0043 | 64.0 | 960 | 0.3477 | 0.8429 |
| 0.0039 | 65.0 | 975 | 0.3431 | 0.8409 |
| 0.0035 | 66.0 | 990 | 0.3474 | 0.8434 |
| 0.004 | 67.0 | 1005 | 0.3478 | 0.8436 |
| 0.0034 | 68.0 | 1020 | 0.3526 | 0.8421 |
| 0.0035 | 69.0 | 1035 | 0.3514 | 0.8459 |
| 0.0033 | 70.0 | 1050 | 0.3527 | 0.8443 |
| 0.0036 | 71.0 | 1065 | 0.3485 | 0.8430 |
| 0.0036 | 72.0 | 1080 | 0.3521 | 0.8456 |
| 0.0036 | 73.0 | 1095 | 0.3535 | 0.8433 |
| 0.0036 | 74.0 | 1110 | 0.3578 | 0.8405 |
| 0.0031 | 75.0 | 1125 | 0.3609 | 0.8414 |
| 0.0033 | 76.0 | 1140 | 0.3563 | 0.8426 |
| 0.0033 | 77.0 | 1155 | 0.3561 | 0.8441 |
| 0.0032 | 78.0 | 1170 | 0.3550 | 0.8423 |
| 0.0032 | 79.0 | 1185 | 0.3554 | 0.8414 |
| 0.0031 | 80.0 | 1200 | 0.3554 | 0.8404 |
| 0.0039 | 81.0 | 1215 | 0.3549 | 0.8413 |
| 0.0034 | 82.0 | 1230 | 0.3548 | 0.8405 |
| 0.0029 | 83.0 | 1245 | 0.3575 | 0.8443 |
| 0.0032 | 84.0 | 1260 | 0.3579 | 0.8416 |
| 0.0029 | 85.0 | 1275 | 0.3603 | 0.8408 |
| 0.0031 | 86.0 | 1290 | 0.3611 | 0.8445 |
| 0.0031 | 87.0 | 1305 | 0.3612 | 0.8444 |
| 0.0029 | 88.0 | 1320 | 0.3620 | 0.8447 |
| 0.0032 | 89.0 | 1335 | 0.3594 | 0.8416 |
| 0.0041 | 90.0 | 1350 | 0.3586 | 0.8423 |
| 0.0032 | 91.0 | 1365 | 0.3599 | 0.8423 |
| 0.0031 | 92.0 | 1380 | 0.3598 | 0.8409 |
| 0.0033 | 93.0 | 1395 | 0.3593 | 0.8424 |
| 0.0029 | 94.0 | 1410 | 0.3593 | 0.8422 |
| 0.003 | 95.0 | 1425 | 0.3607 | 0.8426 |
| 0.0028 | 96.0 | 1440 | 0.3610 | 0.8449 |
| 0.0029 | 97.0 | 1455 | 0.3607 | 0.8424 |
| 0.003 | 98.0 | 1470 | 0.3609 | 0.8422 |
| 0.0029 | 99.0 | 1485 | 0.3606 | 0.8433 |
| 0.003 | 100.0 | 1500 | 0.3605 | 0.8429 |
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
sabersamax/dpo_meta-Llama-3-8B-Instruct | sabersamax | 2025-02-26T02:56:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T02:48:18Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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2eol/Llama3-8b-chinese-Uncensored-Uncensored | 2eol | 2025-02-26T02:54:48Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:shenzhi-wang/Llama3-8B-Chinese-Chat",
"base_model:finetune:shenzhi-wang/Llama3-8B-Chinese-Chat",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-26T02:54:43Z | ---
base_model: shenzhi-wang/Llama3-8B-Chinese-Chat
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** 2eol
- **License:** apache-2.0
- **Finetuned from model :** shenzhi-wang/Llama3-8B-Chinese-Chat
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
codefuse-ai/rodimus_plus_1B6_base_20250215 | codefuse-ai | 2025-02-26T02:49:43Z | 0 | 0 | null | [
"pytorch",
"jetx",
"license:apache-2.0",
"region:us"
] | null | 2025-02-25T09:47:11Z | ---
license: apache-2.0
---
# Rodimus*
## Introduction
Rodimus* is a new series of efficient large language models designed to address the challenges of computational complexity in Transformer-based architectures. The Rodimus* includes the base Rodimus model and its enhanced version, Rodimus+. Rodimus leverages a novel Data-Dependent Tempered Selection (DDTS) mechanism within a purely recurrent, linear attention-based framework, achieving high performance.
Building on this, Rodimus+ combines the strengths of Rodimus and the innovative Sliding Window Shared-Key Attention (SW-SKA) in a hybrid approach. This combination effectively integrates semantic, token, and head compression techniques, enabling a balance between accuracy and efficiency.
For more details, please refer to our [Paper](https://openreview.net/forum?id=IIVYiJ1ggK) and [Github](https://github.com/codefuse-ai/rodimus).
> This repository contains the **latest checkpoint** of Rodimus+ 1.6B trained by continuously updated data, with a focus on the performance of code and math.
>
## Usage
We do not recommend using base language models directly for text generation. Instead, consider applying post-training techniques such as SFT, RLHF or continued pretraining to enhance the model's performance.
**Installation**
1. The latest version of [transformers](https://github.com/huggingface/transformers) is recommended (at least 4.42.0).
2. We evaluate our models with `python=3.8` and `torch==2.1.2`.
3. If you use Rodimus, you need to install [flash-linear-attention](https://github.com/sustcsonglin/flash-linear-attention) and [triton>=2.2.0](https://github.com/triton-lang/triton). If you use Rodimus+, you need to further install [flash-attention](https://github.com/Dao-AILab/flash-attention).
## Generation
`generate` APi
```python
import os
import torch
from modeling_rodimus import RodimusForCausalLM
from tokenization_rodimus_fast import RodimusTokenizer
# load model
ckpt_dir = "model_path"
tokenizer = RodimusTokenizer.from_pretrained(ckpt_dir)
model = RodimusForCausalLM.from_pretrained(
ckpt_dir,
torch_dtype=torch.float16,
device_map="cuda"
).eval()
# inference
input_prompt = "ไฝ ๅฅฝ๏ผไฝ ๆฏ่ฐ๏ผ"
model_inputs = tokenizer(input_prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**model_inputs, max_length=32)
response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
print(response)
```
## Performance
**Code Tasks**: HumanEval (0-shot), MBPP (0-shot)
**Math Tasks**: GSM8K (4-shot), MATH (5-shot)
**NLP Tasks**: C-Eval (5-shot), CMMLU (5-shot), MMLU (5-shot), BBH (3-shot)
> Latest update time: 2025/02/15
>
| Datasets | Rodimus+ 1.6B (20250215) |
| --- | :---: |
| HumanEval | 24.39 |
| MBPP | 26.60 |
| GSM8K | 50.19 |
| MATH | 15.06 |
| C-Eval | 47.19 |
| CMMLU | 43.76 |
| MMLU | 45.52 |
| BBH | 35.28 |
## Citation
If you find our work helpful, feel free to give us a cite.
```markdown
@inproceedings{
he2025rodimus,
title={Rodimus*: Breaking the Accuracy-Efficiency Trade-Off with Efficient Attentions},
author={Zhihao He and Hang Yu and Zi Gong and Shizhan Liu and Jianguo Li and Weiyao Lin},
booktitle={The Thirteenth International Conference on Learning Representations},
year={2025},
url={https://openreview.net/forum?id=IIVYiJ1ggK}
}
```
|
naimulislam/aurora-1.0 | naimulislam | 2025-02-26T02:48:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/SmolLM2-135M-Instruct",
"base_model:finetune:unsloth/SmolLM2-135M-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-26T02:48:33Z | ---
base_model: unsloth/SmolLM2-135M-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** naimulislam
- **License:** apache-2.0
- **Finetuned from model :** unsloth/SmolLM2-135M-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
HeOeH/Iron_IL_5k_v2 | HeOeH | 2025-02-26T02:45:47Z | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | 2025-02-25T16:53:58Z | Found. Redirecting to https://cdn-lfs-us-1.hf.co/repos/1b/7f/1b7f5eb12ecdfff24d51afb6a4eebc4a2359892b5337d7709b3b41398bf6bfa8/4bcf87ecfbbb8e07a01b21415a970c8b53a5283bf6872b657040d3f45c9241f7?response-content-disposition=inline%3B+filename*%3DUTF-8%27%27README.md%3B+filename%3D%22README.md%22%3B&response-content-type=text%2Fmarkdown&Expires=1740554891&Policy=eyJTdGF0ZW1lbnQiOlt7IkNvbmRpdGlvbiI6eyJEYXRlTGVzc1RoYW4iOnsiQVdTOkVwb2NoVGltZSI6MTc0MDU1NDg5MX19LCJSZXNvdXJjZSI6Imh0dHBzOi8vY2RuLWxmcy11cy0xLmhmLmNvL3JlcG9zLzFiLzdmLzFiN2Y1ZWIxMmVjZGZmZjI0ZDUxYWZiNmE0ZWViYzRhMjM1OTg5MmI1MzM3ZDc3MDliM2I0MTM5OGJmNmJmYTgvNGJjZjg3ZWNmYmJiOGUwN2EwMWIyMTQxNWE5NzBjOGI1M2E1MjgzYmY2ODcyYjY1NzA0MGQzZjQ1YzkyNDFmNz9yZXNwb25zZS1jb250ZW50LWRpc3Bvc2l0aW9uPSomcmVzcG9uc2UtY29udGVudC10eXBlPSoifV19&Signature=k%7EzorBrtyxtsoktomjwjpyIQVJ1HJQNwzYn-4sKh20nsBY9JbFTzl8yKv3JN9MMatXpBf5%7E7wc4IiNo5fiHamw2a0Pm3ot6U3XAWXiZMRwA3Yzfn2QHQzlf5T1QCZJ44j6vll27830ZyycwdDTBi0eTn6%7Ec-4Wy9rDQ9tpGtmtUrGx9XV15MHRSUJ4D9mwDrxjvSrNX4E3FG9c%7EFKN7TKIa-NbTRcAmrU6Sc7spom1Ru6xGf9xcuGtJTtkKEFfZ552AFAwCYEJmN5mSBfdTBMpznD15GgVfNCuRj6qSJje8%7EXys3hoKQERbg63mEBkubjA%7EKD5Dgbu3rkYUGdRSoDg__&Key-Pair-Id=K24J24Z295AEI9 |
Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6-cpt | Lunzima | 2025-02-26T02:43:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"chat",
"conversational",
"en",
"zh",
"dataset:allenai/llama-3.1-tulu-3-70b-preference-mixture",
"dataset:allenai/llama-3.1-tulu-3-405b-preference-mixture",
"dataset:lmarena-ai/arena-human-preference-100k",
"base_model:Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6",
"base_model:finetune:Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T01:41:25Z | ---
base_model: Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
- sft
- chat
license: apache-2.0
language:
- en
- zh
datasets:
- allenai/llama-3.1-tulu-3-70b-preference-mixture
- allenai/llama-3.1-tulu-3-405b-preference-mixture
- lmarena-ai/arena-human-preference-100k
---
# Uploaded model
- **Developed by:** Lunzima
- **License:** apache-2.0
- **Finetuned from model :** Lunzima/NQLSG-Qwen2.5-14B-MegaFusion-v6
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) |
fage13141/fage | fage13141 | 2025-02-26T02:42:14Z | 0 | 0 | null | [
"safetensors",
"deepseek",
"lora",
"chinese",
"roleplay",
"chat",
"zh",
"en",
"dataset:fage13141/zhenhuanti",
"arxiv:2106.09685",
"base_model:deepseek-ai/deepseek-llm-7b-chat",
"base_model:adapter:deepseek-ai/deepseek-llm-7b-chat",
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T02:02:46Z | ---
language:
- zh
- en
tags:
- deepseek
- lora
- chinese
- roleplay
- chat
license: apache-2.0
datasets:
- fage13141/zhenhuanti
base_model: deepseek-ai/deepseek-llm-7b-chat
model-index:
- name: DeepSeek-7B-Chat-LoRA-ZhenHuanTi
results: []
---
# DeepSeek-7B-Chat LoRA ๅพฎ่ฐๆจกๅ
่ฟๆฏไธไธชๅบไบ DeepSeek-7B-Chat ไฝฟ็จ LoRA ๆๆฏๅพฎ่ฐ็ๅฌไฝ็ๆจกๅใ
## ๆจกๅไฟกๆฏ
- ๅบ็กๆจกๅ: deepseek-ai/deepseek-llm-7b-chat
- ่ฎญ็ปๆนๆณ: LoRA
- ๆฃๆฅ็น: checkpoint-600
- ไธไผ ๆถ้ด: 2025-02-26 02:37:02
## ็ฏๅข่ฆๆฑ
### Python ็ๆฌ
- Python 3.8 ๆๆด้ซ็ๆฌ
### ๅฟ
้ไพ่ต
```bash
pip install torch>=2.0.0
pip install transformers>=4.35.2
pip install peft>=0.7.0
pip install accelerate>=0.25.0
pip install safetensors>=0.4.1
```
### GPU ่ฆๆฑ
- NVIDIA GPU with CUDA support
- ่ณๅฐ 16GB ๆพๅญ๏ผๆจ็ๆถ๏ผ
- ๆจ่ไฝฟ็จ 24GB ๆๆดๅคงๆพๅญ็ GPU
## ไฝฟ็จๆนๆณ
### 1. ๅฎ่ฃ
ไพ่ต
```bash
# ๅฎ่ฃ
ๅบๆฌไพ่ต
pip install torch transformers peft accelerate safetensors
# ๆ่
ๆๅฎ็ๆฌๅฎ่ฃ
pip install torch>=2.0.0
pip install transformers>=4.35.2
pip install peft>=0.7.0
pip install accelerate>=0.25.0
pip install safetensors>=0.4.1
```
### 2. ๅ ่ฝฝๆจกๅ
```python
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import torch
# ๅ ่ฝฝๅบ็กๆจกๅ
base_model = AutoModelForCausalLM.from_pretrained(
"deepseek-ai/deepseek-llm-7b-chat",
trust_remote_code=True,
torch_dtype=torch.half,
device_map="auto"
)
# ๅ ่ฝฝ tokenizer
tokenizer = AutoTokenizer.from_pretrained(
"deepseek-ai/deepseek-llm-7b-chat",
use_fast=False,
trust_remote_code=True
)
# ๅ ่ฝฝ LoRA ๆ้
model = PeftModel.from_pretrained(
base_model,
"fage13141/fage",
torch_dtype=torch.half,
device_map="auto"
)
# ไฝฟ็จ็คบไพ
prompt = "ไฝ ็ๆ็คบ่ฏ"
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=512)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```
### 3. ็ๆๅๆฐ่ฏดๆ
ๅจ `generate` ๅฝๆฐไธญ๏ผไฝ ๅฏไปฅ่ฐๆดไปฅไธๅๆฐๆฅๆงๅถ็ๆๆๆ๏ผ
- max_new_tokens: ็ๆ็ๆๅคงtokenๆฐ
- temperature: ๆธฉๅบฆๅๆฐ๏ผๆงๅถ้ๆบๆง๏ผ0.0-1.0๏ผ
- top_p: ๆงๅถ้ๆ ท็ๆฆ็้ๅผ
- repetition_penalty: ้ๅคๆฉ็ฝๅๆฐ
็คบไพ๏ผ
```python
outputs = model.generate(
**inputs,
max_new_tokens=512,
temperature=0.7,
top_p=0.9,
repetition_penalty=1.1
)
```
## ๅธธ่ง้ฎ้ข
1. ๆพๅญไธ่ถณ
- ๅฐ่ฏๅๅฐ batch_size
- ไฝฟ็จ 8-bit ้ๅ: `load_in_8bit=True`
- ไฝฟ็จ CPU ๅ ่ฝฝ: `device_map="cpu"`
2. ๆจกๅๅ ่ฝฝๅคฑ่ดฅ
- ็กฎไฟๅทฒๅฎ่ฃ
ๆๆๅฟ
้ไพ่ต
- ๆฃๆฅ GPU ๆพๅญๆฏๅฆ่ถณๅค
- ็กฎไฟ็ฝ็ป่ฟๆฅๆญฃๅธธ
## ๅผ็จๅ่ด่ฐข
- ๅบ็กๆจกๅ: [DeepSeek-7B-Chat](https://huggingface.co/deepseek-ai/deepseek-llm-7b-chat)
- LoRA ๆนๆณ: [LoRA: Low-Rank Adaptation of Large Language Models](https://arxiv.org/abs/2106.09685)
``` |
mradermacher/Lexora-Lite-3B-GGUF | mradermacher | 2025-02-26T02:38:50Z | 275 | 1 | transformers | [
"transformers",
"gguf",
"en",
"dataset:DeepMount00/Sonnet-3.5-ITA-INSTRUCTION",
"dataset:DeepMount00/Sonnet-3.5-ITA-DPO",
"base_model:DeepMount00/Lexora-Lite-3B_v2",
"base_model:quantized:DeepMount00/Lexora-Lite-3B_v2",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2024-09-20T02:33:21Z | ---
base_model: DeepMount00/Lexora-Lite-3B_v2
datasets:
- DeepMount00/Sonnet-3.5-ITA-INSTRUCTION
- DeepMount00/Sonnet-3.5-ITA-DPO
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/DeepMount00/Lexora-Lite-3B_v2
<!-- 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/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q2_K.gguf) | Q2_K | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.IQ3_XS.gguf) | IQ3_XS | 1.5 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q3_K_S.gguf) | Q3_K_S | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.IQ3_S.gguf) | IQ3_S | 1.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.IQ3_M.gguf) | IQ3_M | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q3_K_M.gguf) | Q3_K_M | 1.7 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q3_K_L.gguf) | Q3_K_L | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.IQ4_XS.gguf) | IQ4_XS | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q4_K_S.gguf) | Q4_K_S | 1.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q4_K_M.gguf) | Q4_K_M | 2.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q5_K_S.gguf) | Q5_K_S | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q5_K_M.gguf) | Q5_K_M | 2.3 | |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q6_K.gguf) | Q6_K | 2.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.Q8_0.gguf) | Q8_0 | 3.4 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Lexora-Lite-3B-GGUF/resolve/main/Lexora-Lite-3B.f16.gguf) | f16 | 6.3 | 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 -->
|
Asiria/Russian-SFT-DRO-TG-One-T-pro-it-1.0 | Asiria | 2025-02-26T02:38:29Z | 0 | 0 | transformers | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-26T02:38:27Z | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
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## Uses
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### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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## Glossary [optional]
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tuantmdev/8ed55958-4f79-41c8-a2cf-fc94c7fe0ec4 | tuantmdev | 2025-02-26T02:37:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codellama-7b",
"base_model:adapter:unsloth/codellama-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T01:34:09Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codellama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 8ed55958-4f79-41c8-a2cf-fc94c7fe0ec4
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
auto_find_batch_size: true
base_model: unsloth/codellama-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ed7f40531cda0438_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ed7f40531cda0438_train_data.json
type:
field_input: span_labels
field_instruction: source_text
field_output: target_text
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: true
hub_model_id: tuantmdev/8ed55958-4f79-41c8-a2cf-fc94c7fe0ec4
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 1e-4
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 40
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_grad_norm: 1.0
max_steps: 400
micro_batch_size: 2
mlflow_experiment_name: /tmp/ed7f40531cda0438_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
save_steps: 50
save_strategy: steps
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b1cdd58f-625d-49ad-a5b8-a912b4559816
wandb_project: Gradients-On-Demand
wandb_run: unknown
wandb_runid: b1cdd58f-625d-49ad-a5b8-a912b4559816
warmup_steps: 80
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 8ed55958-4f79-41c8-a2cf-fc94c7fe0ec4
This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- 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: 80
- training_steps: 400
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | nan |
| 0.0 | 0.0040 | 50 | nan |
| 0.0 | 0.0081 | 100 | nan |
| 0.4787 | 0.0121 | 150 | nan |
| 0.0 | 0.0161 | 200 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
quydau/flan-t5-large-clickbait-zeroshot | quydau | 2025-02-26T02:35:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text2text-generation | 2025-02-26T02:32:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### 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] |
jonwondo/lilLM_300M_param_9_5B_tok | jonwondo | 2025-02-26T02:35:15Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-02-26T02:30:45Z | ---
license: mit
---
A model trained using lilLM: https://github.com/CohleM/lilLM
The model is ~300M parameters and trained on 9.5B tokens from OpenWebText: https://huggingface.co/datasets/Skylion007/openwebtext |
Kei5uke/phi4 | Kei5uke | 2025-02-26T02:35:09Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/phi-4-bnb-4bit",
"base_model:quantized:unsloth/phi-4-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-26T02:16:12Z | ---
base_model: unsloth/phi-4-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Kei5uke
- **License:** apache-2.0
- **Finetuned from model :** unsloth/phi-4-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
nguyenhh3305/model | nguyenhh3305 | 2025-02-26T02:34:53Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T02:32:05Z | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nguyenhh3305
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
semantichealth/msllama-3.2-counter-rewarded | semantichealth | 2025-02-26T02:32:12Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T02:30:17Z | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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bowilleatyou/450d2ff4-f6fb-4bfd-89f2-4aaaf949d120 | bowilleatyou | 2025-02-26T02:30:40Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-25T20:04:46Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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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WildBoar-LM/wildboar-6B-0.3epoch | WildBoar-LM | 2025-02-26T02:23:11Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"wildboar",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"region:us"
] | text-generation | 2025-02-26T02:19:37Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
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## Model Details
### Model Description
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This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Jonjew/NihongaStardust | Jonjew | 2025-02-26T02:23:02Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] | text-to-image | 2025-02-26T02:18:57Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
a dinhng style painting , a celestial warrior glowing with translucent
golden armor adorned with intricate flowers, standing under a mystical
starry sky with deep contrasts and an ethereal moonlit glow,
<lora:nihonga-stardust_v32-000070:1>
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 1988507142'
output:
url: images/03283-2025-01-30-1988507142.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: a dinhng style painting
license: unknown
---
# Nihonga Stardust
<Gallery />
## Model description
FROM https://civitai.com/models/1134528/nihonga-stardust?modelVersionId=1353967
Trigger a dinhng style painting
Lora Strength: A strength between 0.4 and 1.0 is recommended. Higher strengths will add to the ethereal nature of generations, while lower strengths will increase coherence and give the subject a more solid appearance. Toning down the strength to between 0.6 to 0.8 is similar to Version 2.5, but with better color saturation.
Tradition Meets Celestial Elegance
Nihonga Stardust (ๆฅๆฌ็ป ในใฟใผใในใ) is a LoRA that reimagines the timeless beauty of Nihonga (ๆฅๆฌ็ป), a traditional Japanese painting style, through a modern, celestial lens. Nihonga (literally meaning Japanese painting) is distinguished by its use of traditional Japanese techniques, materials, and aesthetics. Inspired by nature, the seasons, and classical Japanese literature, it emphasizes simplicity, harmony, and balance, often exploring motifs such as landscapes, flowers, animals, and more abstractions like emotion. Nihonga Stardust elevates these principles, blending the grounded elegance of Japanese tradition with ethereal, starlit brilliance.
The first published version of this LoRA is deeply influenced by the works of Noriyuki Kobayashi, a contemporary Nihonga painter whose signature style intertwines delicate golden lines into intricate, woven patterns that celebrate the interconnectedness and vitality of all living things. Kobayashiโs mastery of translucent layers and shimmering details inspired the creation of this model, which imbues its outputs with glowing, intricate compositions full of life and energy. The inclusion of "Stardust" in the name reflects this luminous quality โ a celebration of both the brilliance of earthly forms and the infinite vastness of the cosmos.
Art generated with Nihonga Stardust evokes the timelessness of traditional Japanese aesthetics, including wabi-sabi (finding beauty in imperfection) and mono no aware (an awareness of the ephemeral nature of existence). Its luminous compositions allow you to create serene scenes under glowing moons, radiant birds perched in ethereal starry skies, or fantastical landscapes that seamlessly blend tradition with fantasy. This LoRA serves as both an homage to the Nihonga tradition and an exploration of its possibilities in a new, dreamlike context.
Usage
Version 3.2 has a much stronger, predictable style with heavily saturated colors and high contrast being light and dark lines.
To use the most recent version of the LoRA, use the following settings:
Trigger word: dinhng, as in "a dinhng style painting"
The LoRA was trained with the following tokens. Using them will enhance the intended style -- without adding some, the style may not trigger: translucent, luminous, atmosphere, ethereal, sky, delicate, mystical, starry, intricate, flowers, moon, night, glowing, golden, serene
Usage notes: The LoRA will create a wide range of imagery but is ideal for images with natural themes, such as landscapes, animals, plants, etc.
Lora Strength: A strength between 0.4 and 1.0 is recommended. Higher strengths will add to the ethereal nature of generations, while lower strengths will increase coherence and give the subject a more solid appearance. Toning down the strength to between 0.6 to 0.8 is similar to Version 2.5, but with better color saturation.
For truly amazing images, combine this LoRA with Best of Flux. It adds incredible detail and deeper, richer colors while keeping the effects of the Nihonga Style.
## Trigger words
You should use `a dinhng style painting` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/NihongaStardust/tree/main) them in the Files & versions tab.
|
advit/grad_diff_1e-05_2710_90 | advit | 2025-02-26T02:22:35Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:22:32Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
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- PEFT 0.14.0 |
advit/grad_diff_1e-05_235_90 | advit | 2025-02-26T02:22:26Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:22:22Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
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advit/grad_diff_1e-05_2568_90 | advit | 2025-02-26T02:22:21Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:22:18Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
---
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advit/grad_diff_1e-05_2507_120 | advit | 2025-02-26T02:22:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:22:14Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
---
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advit/grad_diff_1e-05_3912_120 | advit | 2025-02-26T02:22:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:22:09Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
---
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nungliansum/mms-tts-cfm | nungliansum | 2025-02-26T02:22:08Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vits",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-26T02:20:48Z | ---
library_name: transformers
tags: []
---
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advit/grad_diff_1e-05_3413_60 | advit | 2025-02-26T02:21:54Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:21:47Z | ---
base_model: models/tofu_ft_llama2-7b
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SAndrewMurphy/LouJay | SAndrewMurphy | 2025-02-26T02:21:51Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2025-02-26T02:21:15Z | ---
license: bsd-3-clause
---
|
advit/grad_diff_1e-05_1159_60 | advit | 2025-02-26T02:21:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:21:42Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
---
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LINYICHEN09/task-4-google-gemma-2b | LINYICHEN09 | 2025-02-26T02:21:37Z | 1,491 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:google/gemma-2b",
"base_model:adapter:google/gemma-2b",
"region:us"
] | null | 2025-02-06T09:08:56Z | ---
base_model: google/gemma-2b
library_name: peft
---
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- **Hardware Type:** [More Information Needed]
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### Framework versions
- PEFT 0.13.2 |
advit/grad_diff_1e-05_1077_120 | advit | 2025-02-26T02:21:36Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | 2025-02-26T02:21:34Z | ---
base_model: models/tofu_ft_llama2-7b
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## 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]
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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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### Framework versions
- PEFT 0.14.0 |
straykittycat/b8 | straykittycat | 2025-02-26T02:21:33Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T02:16:50Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### 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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Kuongan/CS221-xlm-roberta-base-orm-noaug-finetuned-orm-tapt | Kuongan | 2025-02-26T02:19:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:Kuongan/xlm-roberta-base-orm-noaug",
"base_model:finetune:Kuongan/xlm-roberta-base-orm-noaug",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-02-26T02:10:00Z | ---
library_name: transformers
license: mit
base_model: Kuongan/xlm-roberta-base-orm-noaug
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: CS221-xlm-roberta-base-orm-noaug-finetuned-orm-tapt
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. -->
# CS221-xlm-roberta-base-orm-noaug-finetuned-orm-tapt
This model is a fine-tuned version of [Kuongan/xlm-roberta-base-orm-noaug](https://huggingface.co/Kuongan/xlm-roberta-base-orm-noaug) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1390
- F1: 0.6484
- Roc Auc: 0.7975
- Accuracy: 0.7777
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.1488 | 1.0 | 144 | 0.1282 | 0.6019 | 0.7611 | 0.7768 |
| 0.149 | 2.0 | 288 | 0.1222 | 0.6447 | 0.7869 | 0.8038 |
| 0.1253 | 3.0 | 432 | 0.1391 | 0.5918 | 0.7669 | 0.7559 |
| 0.135 | 4.0 | 576 | 0.1390 | 0.6484 | 0.7975 | 0.7777 |
| 0.1245 | 5.0 | 720 | 0.1524 | 0.6398 | 0.8035 | 0.7507 |
| 0.1309 | 6.0 | 864 | 0.1465 | 0.6385 | 0.7858 | 0.7681 |
| 0.0773 | 7.0 | 1008 | 0.1445 | 0.6336 | 0.7944 | 0.7759 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
LINYICHEN09/task-4-Qwen-Qwen1.5-0.5B | LINYICHEN09 | 2025-02-26T02:17:17Z | 1,593 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen1.5-0.5B",
"base_model:adapter:Qwen/Qwen1.5-0.5B",
"region:us"
] | null | 2025-02-06T09:04:21Z | ---
base_model: Qwen/Qwen1.5-0.5B
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]
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<!-- Provide the basic links for the model. -->
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<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2 |
secmlr/Sky-T1-Filtered_VD-QWQ-Clean-8k_DeepSeek-R1-Distill-Qwen-7B_full_sft_1e-5 | secmlr | 2025-02-26T02:16:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"base_model:finetune:deepseek-ai/DeepSeek-R1-Distill-Qwen-7B",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-25T17:35:54Z | ---
library_name: transformers
license: mit
base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: Sky-T1-Filtered_VD-QWQ-Clean-8k_DeepSeek-R1-Distill-Qwen-7B_full_sft_1e-5
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. -->
# Sky-T1-Filtered_VD-QWQ-Clean-8k_DeepSeek-R1-Distill-Qwen-7B_full_sft_1e-5
This model is a fine-tuned version of [deepseek-ai/DeepSeek-R1-Distill-Qwen-7B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-7B) on the Sky-T1-Filtered and the VD-QWQ-Clean-8k datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 12
- total_train_batch_size: 48
- total_eval_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.48.3
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.21.0
|
Avacyn/Llama-3.1-8B-RHPI-v1-GGUF | Avacyn | 2025-02-26T02:11:35Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T02:11:35Z | ---
license: apache-2.0
---
|
Jonjew/EtherealDreambrushv1_7 | Jonjew | 2025-02-26T02:10:41Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:unknown",
"region:us"
] | text-to-image | 2025-02-26T02:06:06Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: >-
A stunning female figure stands confidently in an ethereal, enchanted
setting, adorned in a flowing, alluring gown that emphasizes her curves with
a deep neckline and intricate lace detailing. The gown is a shimmering
white, embellished with silver accents that catch the light, giving her an
otherworldly glow. She has long, flowing silver hair, loosely cascading over
her shoulders, and wears delicate floral accessories that add a touch of
romance. To her side, a majestic, mythical dragon with glistening white
scales and piercing blue eyes curls protectively around her, its presence
both powerful and graceful. The background features ornate, ancient
architecture, softly illuminated by ambient sunlight filtering through
mystical wisps of fog. Delicate petals float around them, enhancing the
dreamlike atmosphere. The lighting is soft yet dramatic, highlighting the
connection between the woman and the dragon. The composition is centered on
the figure, with a slight focus on the dragon's head, creating a balanced
dynamic between the two. The emotion conveyed is one of strength and serene
beauty, evoking a sense of enchantment and intrigue., dikrymd,
<lora:Kaoru-Yamada_v17-000054:1>
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 530858837'
output:
url: images/02267-2025-01-27-530858837.png
- text: >-
A fortune teller gazing at a deck of tarot cards spread across a
velvet-draped table, her eyes filled with mystery, candles flickering softly
around her, an aura of mystique and curiosity, fantasy art style with warm
tones, close-up shot, frontal angle., dikrymd,
<lora:Kaoru-Yamada_v17-000054:1>
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 308264317'
output:
url: images/02108-2025-01-27-308264317.png
- text: >-
A confident woman in a golden gown standing on the balcony of an opulent
ballroom, city lights sparkling in the distance, her hair styled elegantly,
holding a glass of champagne, exuding sophistication and charm, oil painting
style with rich textures and warm lighting, medium shot, slight above
perspective., dikrymd, <lora:Kaoru-Yamada_v17-000054:1>
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 405353690'
output:
url: images/02313-2025-01-27-405353690.png
- text: >-
female figure in a dramatic gothic setting, long flowing silver hair,
striking red eyes, ornate black and red costume with intricate lace details,
corset-style top with gold embellishments, dark stockings with floral
patterns, holding a tall staff topped with skulls, backdrop of towering dark
gothic arches, soft eerie green lighting illuminating the scene, intense and
mysterious expression, framing that emphasizes the height of the figure, an
atmosphere of dark fantasy and elegance, intricate jewelry accents, flowing
ribbons and fabric adding a sense of movement., dikrymd,
<lora:Kaoru-Yamada_v17-000054:1>
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 1285789747'
output:
url: images/02066-2025-01-27-1285789747.png
- text: "A striking female figure with long, flowing black hair stands in profile, exuding an aura of strength and mystique. She wears a dark, intricately detailed dress adorned with blue and black feathers, resembling elaborate wings that extend from her back, creating a dynamic silhouette. Her arms are covered in vibrant tattoos featuring intricate designs, with her right arm partially visible, showcasing the detailed artwork. The background features a blend of soft, muted colors and abstract patterns in turquoise and beige, adding depth and contrast to the composition. The lighting is soft yet dramatic, casting gentle shadows across her face and emphasizing her features, such as her sharp jawline and piercing gaze. The overall atmosphere is both ethereal and powerful, with elements of urban art, subtly integrated text, and textures that enhance the artworkรข\x80\x99s complexity., dikrymd, <lora:Kaoru-Yamada_v17-000054:1>"
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 1933955616'
output:
url: images/02092-2025-01-27-1933955616.png
- text: >-
a Elf girl with dark hair styled up, adorned with ornate hairpieces and
accessories. Her eyes are closed, and her expression is serene. She is
wearing a sleeveless, multicolored outfit showing intricate designs,
exposing an elaborate tattoo that depicts a fierce dragon with swirling blue
and green elements including intricate details like fire and wisps of smoke,
that extends across her upper back. The background features an abstract
orange background with decorative elements resembling script or symbols on
either side. horror., dikrymd, <lora:Kaoru-Yamada_v17-000054:1>
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 708851416'
output:
url: images/02177-2025-01-27-708851416.png
- text: "warrior standing in profile, long flowing dark red hair, intricate armor with metallic and ornate details, tattered red cape flowing behind, holding a katana, background of a traditional Asian temple with sharp rooftops and dim lighting, swirling red mist surrounding the figure, dark and moody atmosphere, overcast sky with dramatic clouds, shadows emphasizing the figureรข\x80\x99s silhouette, intense gaze turned slightly towards the viewer, a sense of power and mystery, stone ground with scattered debris, low angle shot enhancing the figure's height and presence, warm flickering light from lanterns in the temple illuminating the scene subtly, evoke feelings of strength and determination., dikrymd, <lora:Kaoru-Yamada_v17-000054:1>"
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 4254128179'
output:
url: images/02091-2025-01-27-4254128179.png
- text: >-
dikrymd, <lora:Kaoru-Yamada_v17-000054:1> , A fortune teller seated at a
round table in a mysterious tent, gazing into a crystal ball, her elaborate
jewelry and flowing scarves catching the soft glow of candlelight, an
atmosphere of mystique and curiosity, fantasy art style, close-up shot,
frontal angle.
parameters:
negative_prompt: 'Guidance: 1 Steps: 20 Seed: 1969344655'
output:
url: images/01967-2025-01-26-1969344655.png
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: dikrymd
license: unknown
---
# Ethereal Dreambrush v1.7
<Gallery />
## Model description
FROM https://civitai.com/models/1189752/ethereal-dreambrush
Trigger dikrymd
Strength 0.8 - 1.2 with 1 working for most
Ethereal Dreambrush is a captivating LoRA that bridges the realms of painterly elegance and impressionist mystique, drawing inspiration from the evocative works of Kaoru Yamada and the timeless strokes of artists like Van Gogh. This model captures the dreamlike quality of swirling brushstrokes, luminous textures, and the soft interplay of light and shadow that transform any canvas into a reverie. Designed to evoke a sense of wonder and imagination, Ethereal Dreambrush brings together delicate, impressionistic details and bold painterly expression, offering a harmonious balance between realism and abstraction. Its artwork feels alive, as though each stroke carries a whisper of emotion, inviting the viewer into a serene yet vivid dreamscape.
Usage
To use the most recent version of the LoRA, use the following settings:
Trigger word: dikrymd, as in "dikrymd style" or "a dikrymd painting"
Other tokens that work well: Anything works, though it does fantastic night scenes with the sky prominently displayed.
Lora Strength: A strength between 0.8 and 1.2 is recommended. 1.0 seems perfect for most generations.
## Trigger words
You should use `dikrymd` to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](/Jonjew/EtherealDreambrushv1_7/tree/main) them in the Files & versions tab.
|
Sulav/Qwen2.5-Coder-3B-Instruct-DT-lora | Sulav | 2025-02-26T02:09:29Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/Qwen2.5-Coder-3B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-Coder-3B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T02:08:08Z | ---
base_model: unsloth/Qwen2.5-Coder-3B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Sulav
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-Coder-3B-Instruct
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
pozoviy/sae_EleutherAI_pythia-70m_resid_post_2_size_8192_batchtopk_lora_merged | pozoviy | 2025-02-26T02:07:54Z | 72 | 0 | transformers | [
"transformers",
"safetensors",
"sae",
"feature-extraction",
"custom_code",
"arxiv:1910.09700",
"region:us"
] | feature-extraction | 2025-01-29T14:42:32Z | ---
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 ๐ค 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] |
kaizen9/SEQ_phi_400_spun_159 | kaizen9 | 2025-02-26T02:05:29Z | 106 | 0 | transformers | [
"transformers",
"safetensors",
"phi",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-02T07:46:15Z | ---
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 ๐ค 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] |
abaddon182/34c5662d-594a-4255-850e-7df5fc3b7ec2 | abaddon182 | 2025-02-26T02:03:27Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | 2025-02-25T21:24:41Z | ---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 34c5662d-594a-4255-850e-7df5fc3b7ec2
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: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: true
chat_template: llama3
dataloader_num_workers: 24
dataset_prepared_path: null
datasets:
- data_files:
- ef9e1d78596cd182_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ef9e1d78596cd182_train_data.json
type:
field_input: context
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 3
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 200
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: true
hub_model_id: abaddon182/34c5662d-594a-4255-850e-7df5fc3b7ec2
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 50
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_grad_norm: 1.0
max_steps: 2000
micro_batch_size: 4
mlflow_experiment_name: /tmp/ef9e1d78596cd182_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 10
optim_args:
adam_beta1: 0.9
adam_beta2: 0.999
adam_epsilon: 1e-8
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: 200
saves_per_epoch: null
sequence_len: 512
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: 12f950a6-55b7-4f9f-97bd-fd31a61a9ebf
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 12f950a6-55b7-4f9f-97bd-fd31a61a9ebf
warmup_steps: 100
weight_decay: 0.1
xformers_attention: null
```
</details><br>
# 34c5662d-594a-4255-850e-7df5fc3b7ec2
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1066
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.999,adam_epsilon=1e-8
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 2000
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 2.9640 |
| 1.2464 | 0.0077 | 200 | 1.2777 |
| 1.245 | 0.0154 | 400 | 1.2724 |
| 1.2227 | 0.0231 | 600 | 1.2045 |
| 1.1995 | 0.0308 | 800 | 1.1826 |
| 1.1947 | 0.0385 | 1000 | 1.1561 |
| 1.1879 | 0.0462 | 1200 | 1.1407 |
| 1.1837 | 0.0539 | 1400 | 1.1241 |
| 1.0986 | 0.0616 | 1600 | 1.1122 |
| 1.0991 | 0.0693 | 1800 | 1.1096 |
| 1.0585 | 0.0770 | 2000 | 1.1066 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Kuongan/xlm-roberta-base-ptbr-noaug | Kuongan | 2025-02-26T02:02:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-02-26T01:47:43Z | ---
library_name: transformers
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- f1
- accuracy
model-index:
- name: xlm-roberta-base-ptbr-noaug
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. -->
# xlm-roberta-base-ptbr-noaug
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3273
- F1: 0.3798
- Roc Auc: 0.6496
- Accuracy: 0.53
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Roc Auc | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:------:|:-------:|:--------:|
| 0.4782 | 1.0 | 70 | 0.3782 | 0.0 | 0.5 | 0.23 |
| 0.3652 | 2.0 | 140 | 0.3592 | 0.0199 | 0.5048 | 0.24 |
| 0.325 | 3.0 | 210 | 0.3345 | 0.1927 | 0.5670 | 0.43 |
| 0.2995 | 4.0 | 280 | 0.3234 | 0.1661 | 0.5618 | 0.43 |
| 0.2697 | 5.0 | 350 | 0.3074 | 0.2687 | 0.5991 | 0.49 |
| 0.2196 | 6.0 | 420 | 0.3044 | 0.2813 | 0.6045 | 0.5 |
| 0.2299 | 7.0 | 490 | 0.3074 | 0.3031 | 0.6174 | 0.515 |
| 0.2013 | 8.0 | 560 | 0.3144 | 0.2920 | 0.6095 | 0.515 |
| 0.186 | 9.0 | 630 | 0.3126 | 0.3074 | 0.6243 | 0.51 |
| 0.1621 | 10.0 | 700 | 0.3282 | 0.3033 | 0.6221 | 0.49 |
| 0.154 | 11.0 | 770 | 0.3140 | 0.3494 | 0.6374 | 0.535 |
| 0.1304 | 12.0 | 840 | 0.3262 | 0.3422 | 0.6406 | 0.505 |
| 0.1308 | 13.0 | 910 | 0.3207 | 0.3510 | 0.6353 | 0.52 |
| 0.1204 | 14.0 | 980 | 0.3253 | 0.3476 | 0.6394 | 0.525 |
| 0.1073 | 15.0 | 1050 | 0.3200 | 0.3707 | 0.6456 | 0.505 |
| 0.1145 | 16.0 | 1120 | 0.3290 | 0.3582 | 0.6401 | 0.51 |
| 0.1073 | 17.0 | 1190 | 0.3242 | 0.3758 | 0.6481 | 0.525 |
| 0.1022 | 18.0 | 1260 | 0.3273 | 0.3798 | 0.6496 | 0.53 |
| 0.0987 | 19.0 | 1330 | 0.3271 | 0.3759 | 0.6476 | 0.52 |
| 0.0982 | 20.0 | 1400 | 0.3270 | 0.3776 | 0.6490 | 0.52 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0
|
farzincgart/h_zirak2 | farzincgart | 2025-02-26T02:00:55Z | 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 | 2025-02-26T01:35:30Z | ---
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: H_zirak2
---
# H_Zirak2
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `H_zirak2` to trigger the image generation.
## Use it with the [๐งจ 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('farzincgart/h_zirak2', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
straykittycat/b7 | straykittycat | 2025-02-26T01:59:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T01:56:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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] |
frankzye/gmock | frankzye | 2025-02-26T01:59:22Z | 0 | 0 | null | [
"license:mit",
"region:us"
] | null | 2025-02-18T08:21:27Z | ---
license: mit
---
run html file
```bash
python -m http.server 8000
```
run server
```bash
python src/main.py
```
|
DreadPoor/Lydia_of_Whiterun-8B-LINEAR | DreadPoor | 2025-02-26T01:55:18Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2203.05482",
"base_model:BoltMonkey/DreadMix",
"base_model:merge:BoltMonkey/DreadMix",
"base_model:DreadPoor/BaeZel-8B-LINEAR",
"base_model:merge:DreadPoor/BaeZel-8B-LINEAR",
"base_model:DreadPoor/Decayed-8B-LINEAR",
"base_model:merge:DreadPoor/Decayed-8B-LINEAR",
"base_model:DreadPoor/Yafune-8B-Model_Stock",
"base_model:merge:DreadPoor/Yafune-8B-Model_Stock",
"base_model:SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B",
"base_model:merge:SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T01:34:48Z | ---
base_model:
- DreadPoor/Decayed-8B-LINEAR
- BoltMonkey/DreadMix
- SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B
- DreadPoor/BaeZel-8B-LINEAR
- DreadPoor/Yafune-8B-Model_Stock
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 [Linear](https://arxiv.org/abs/2203.05482) merge method.
### Models Merged
The following models were included in the merge:
* [DreadPoor/Decayed-8B-LINEAR](https://huggingface.co/DreadPoor/Decayed-8B-LINEAR)
* [BoltMonkey/DreadMix](https://huggingface.co/BoltMonkey/DreadMix)
* [SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B](https://huggingface.co/SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B)
* [DreadPoor/BaeZel-8B-LINEAR](https://huggingface.co/DreadPoor/BaeZel-8B-LINEAR)
* [DreadPoor/Yafune-8B-Model_Stock](https://huggingface.co/DreadPoor/Yafune-8B-Model_Stock)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: DreadPoor/BaeZel-8B-LINEAR
parameters:
weight: 1.0
- model: DreadPoor/Yafune-8B-Model_Stock
parameters:
weight: 1.0
- model: DreadPoor/Decayed-8B-LINEAR
parameters:
weight: 1.0
- model: BoltMonkey/DreadMix
parameters:
weight: 1.0
- model: SentientAGI/Dobby-Mini-Unhinged-Llama-3.1-8B
parameters:
weight: 1.0
merge_method: linear
normalize: false
int8_mask: true
dtype: bfloat16
```
|
jorge-mpz/lora_model | jorge-mpz | 2025-02-26T01:54:10Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-26T01:54:03Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** jorge-mpz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
irishprancer/efc96081-8c8a-4c79-af96-a9fd13626e38 | irishprancer | 2025-02-26T01:53:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-25T21:58:40Z | ---
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 ๐ค 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] |
Akashiurahara/Llamma-3.2-1B-Anthropic-HHRLHF-RolePlay-Uncensored | Akashiurahara | 2025-02-26T01:53:13Z | 29 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"uncensored",
"roleplay",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-02-25T00:57:23Z | ---
base_model: unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
- uncensored
- roleplay
license: apache-2.0
language:
- en
---
# โ ๏ธ WARNING: 18+ Content โ Intended for Mature Audiences Only
This language model is designed for unrestricted roleplaying and is intended for users aged 18 and older. It may generate content that is explicit, dark, or otherwise unsuitable for minors.
By using this model, you acknowledge that:
You are at least 18 years old.
You understand that the model does not enforce ethical, moral, or legal boundaries in its responses.
You take full responsibility for your interactions and use of generated content.
If you are under 18 or are uncomfortable with unrestricted content, do not use this model.
# Uploaded model
- **Developed by:** Akashiurahara
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-1b-instruct-unsloth-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth) |
locuslab/ift_then_gsm-smollm2-1.7b-score0_mix_rephrased_from_beginning-600B | locuslab | 2025-02-26T01:48:52Z | 0 | 0 | null | [
"safetensors",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-02-26T01:45:45Z | ---
version: main
family: smollm2-1.7b
model_name: -score0_mix_rephrased_from_beginning-600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 -score0_mix_rephrased_from_beginning-600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/-score0_mix_rephrased_from_beginning-600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/-score0_mix_rephrased_from_beginning-600B", revision="final")
```
Replace `"final"` with the desired revision.
|
JayHyeon/Qwen_0.5-VDPO_5e-6-1ep_10vpo_const | JayHyeon | 2025-02-26T01:46:16Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:trl-lib/ultrafeedback_binarized",
"arxiv:2305.18290",
"base_model:JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep",
"base_model:finetune:JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-25T23:41:53Z | ---
base_model: JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep
datasets: trl-lib/ultrafeedback_binarized
library_name: transformers
model_name: Qwen_0.5-VDPO_5e-6-1ep_10vpo_const
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for Qwen_0.5-VDPO_5e-6-1ep_10vpo_const
This model is a fine-tuned version of [JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep](https://huggingface.co/JayHyeon/Qwen2.5-0.5B-SFT-2e-5-2ep) on the [trl-lib/ultrafeedback_binarized](https://huggingface.co/datasets/trl-lib/ultrafeedback_binarized) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="JayHyeon/Qwen_0.5-VDPO_5e-6-1ep_10vpo_const", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/bonin147/huggingface/runs/1f360a6d)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
locuslab/ift_then_gsm-smollm2-1.7b-meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B | locuslab | 2025-02-26T01:45:45Z | 0 | 0 | null | [
"safetensors",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-02-26T01:42:58Z | ---
version: main
family: smollm2-1.7b
model_name: -meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 -meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/-meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/-meta-llama-Llama-3.2-1B-lr2e-05-gbs16600B", revision="final")
```
Replace `"final"` with the desired revision.
|
underactuated/mistral_sft_test5 | underactuated | 2025-02-26T01:44:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-24T01:13:04Z | ---
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 ๐ค 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] |
claytorres/truemodel | claytorres | 2025-02-26T01:43:48Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-02-26T00:56:07Z | ---
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
--- |
locuslab/ift_then_gsm-smollm2-1.7b-all_raw_folders_metadata-600B | locuslab | 2025-02-26T01:42:56Z | 0 | 0 | null | [
"safetensors",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-02-26T01:39:54Z | ---
version: main
family: smollm2-1.7b
model_name: -all_raw_folders_metadata-600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 -all_raw_folders_metadata-600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/-all_raw_folders_metadata-600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/-all_raw_folders_metadata-600B", revision="final")
```
Replace `"final"` with the desired revision.
|
irishprancer/864d9c15-b9a9-4415-a9a9-dc0d5a14402e | irishprancer | 2025-02-26T01:40:24Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-02-25T23:31:43Z | ---
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 ๐ค 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] |
Snzx/TOK | Snzx | 2025-02-26T01:39:26Z | 0 | 0 | null | [
"license:other",
"region:us"
] | null | 2025-02-26T00:49:52Z | ---
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
--- |
mlfoundations-dev/qwen2-5_sci_qa_exps__pdfs_1186__verified_r1 | mlfoundations-dev | 2025-02-26T01:38:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"llama-factory",
"full",
"generated_from_trainer",
"conversational",
"base_model:Qwen/Qwen2.5-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-7B-Instruct",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-25T08:53:54Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-7B-Instruct
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: qwen2-5_sci_qa_exps__pdfs_1186__verified_r1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# qwen2-5_sci_qa_exps__pdfs_1186__verified_r1
This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct) on the mlfoundations-dev/sci_qa_exps__pdfs_1186__verified_r1 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: 1
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 32
- gradient_accumulation_steps: 3
- total_train_batch_size: 96
- total_eval_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
### Training results
### Framework versions
- Transformers 4.46.1
- Pytorch 2.5.1
- Datasets 3.0.2
- Tokenizers 0.20.3
|
Romain-XV/59801ce1-be9b-43ba-bd72-ba6e1f30e296 | Romain-XV | 2025-02-26T01:36:57Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"base_model:adapter:MLP-KTLim/llama-3-Korean-Bllossom-8B",
"license:llama3",
"region:us"
] | null | 2025-02-25T21:24:53Z | ---
library_name: peft
license: llama3
base_model: MLP-KTLim/llama-3-Korean-Bllossom-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 59801ce1-be9b-43ba-bd72-ba6e1f30e296
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: MLP-KTLim/llama-3-Korean-Bllossom-8B
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ef9e1d78596cd182_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ef9e1d78596cd182_train_data.json
type:
field_input: context
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: 2
eval_max_new_tokens: 128
eval_steps: 150
eval_table_size: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: false
hub_model_id: Romain-XV/59801ce1-be9b-43ba-bd72-ba6e1f30e296
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_best_model_at_end: true
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.3
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lora_target_modules:
- q_proj
- k_proj
- v_proj
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 1632
micro_batch_size: 2
mlflow_experiment_name: /tmp/ef9e1d78596cd182_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
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: 150
sequence_len: 2048
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.011431863805347369
wandb_entity: null
wandb_mode: online
wandb_name: 12f950a6-55b7-4f9f-97bd-fd31a61a9ebf
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 12f950a6-55b7-4f9f-97bd-fd31a61a9ebf
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 59801ce1-be9b-43ba-bd72-ba6e1f30e296
This model is a fine-tuned version of [MLP-KTLim/llama-3-Korean-Bllossom-8B](https://huggingface.co/MLP-KTLim/llama-3-Korean-Bllossom-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1585
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 1632
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.6993 | 0.0000 | 1 | 2.9206 |
| 1.2387 | 0.0028 | 150 | 1.3226 |
| 1.1271 | 0.0056 | 300 | 1.3526 |
| 1.3113 | 0.0083 | 450 | 1.3159 |
| 1.176 | 0.0111 | 600 | 1.3089 |
| 1.4896 | 0.0139 | 750 | 1.2816 |
| 1.3963 | 0.0167 | 900 | 1.2490 |
| 1.3397 | 0.0194 | 1050 | 1.2123 |
| 1.164 | 0.0222 | 1200 | 1.1860 |
| 1.527 | 0.0250 | 1350 | 1.1673 |
| 1.3895 | 0.0278 | 1500 | 1.1585 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Salsa-Anindya-guru-SD-Jember-TV/VIRAL.Salsa-Anindya-guru.Viral.Video.Full.Original.Video.Social.Media.X | Salsa-Anindya-guru-SD-Jember-TV | 2025-02-26T01:36:36Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:34:48Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
straykittycat/b6 | straykittycat | 2025-02-26T01:35:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T01:32:36Z | ---
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 ๐ค 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] |
jgayed/llama70b40080 | jgayed | 2025-02-26T01:35:40Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"autotrain",
"text-generation-inference",
"text-generation",
"peft",
"conversational",
"dataset:jgayed/etstrain",
"base_model:unsloth/Llama-3.3-70B-Instruct",
"base_model:finetune:unsloth/Llama-3.3-70B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T00:44:33Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: unsloth/Llama-3.3-70B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- jgayed/etstrain
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
``` |
Whitecat12/jon | Whitecat12 | 2025-02-26T01:35:09Z | 0 | 0 | null | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2025-02-26T01:35:09Z | ---
license: cc-by-sa-4.0
---
|
Deekila-Sherpa-TV/wATCH.viral.video.original | Deekila-Sherpa-TV | 2025-02-26T01:27:44Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:26:46Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
Kei5uke/codellama | Kei5uke | 2025-02-26T01:27:28Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/codellama-7b-bnb-4bit",
"base_model:quantized:unsloth/codellama-7b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-02-26T01:18:34Z | ---
base_model: unsloth/codellama-7b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Kei5uke
- **License:** apache-2.0
- **Finetuned from model :** unsloth/codellama-7b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Fudan-FUXI/IPO-5B-v1.0 | Fudan-FUXI | 2025-02-26T01:26:37Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T01:26:37Z | ---
license: apache-2.0
---
|
lesso02/a51ac69c-d769-406a-89fe-a7d26fc26353 | lesso02 | 2025-02-26T01:24:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gemma",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/gemma-7b-it",
"base_model:adapter:unsloth/gemma-7b-it",
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T00:42:59Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/gemma-7b-it
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a51ac69c-d769-406a-89fe-a7d26fc26353
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
auto_find_batch_size: true
base_model: unsloth/gemma-7b-it
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 2e4b4f09c9ae8b90_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/2e4b4f09c9ae8b90_train_data.json
type:
field_input: content
field_instruction: instruction
field_output: new_contents
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso02/a51ac69c-d769-406a-89fe-a7d26fc26353
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000202
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/2e4b4f09c9ae8b90_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
save_steps: 50
saves_per_epoch: null
seed: 20
sequence_len: 512
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: a3c5ad4e-0086-4c2f-b5d5-c05271f38d4e
wandb_project: 02a
wandb_run: your_name
wandb_runid: a3c5ad4e-0086-4c2f-b5d5-c05271f38d4e
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a51ac69c-d769-406a-89fe-a7d26fc26353
This model is a fine-tuned version of [unsloth/gemma-7b-it](https://huggingface.co/unsloth/gemma-7b-it) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0478
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000202
- train_batch_size: 4
- eval_batch_size: 4
- seed: 20
- gradient_accumulation_steps: 2
- 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0004 | 1 | 0.3643 |
| 0.0185 | 0.0189 | 50 | 0.1032 |
| 0.0229 | 0.0378 | 100 | 0.0782 |
| 0.0145 | 0.0566 | 150 | 0.0677 |
| 0.0305 | 0.0755 | 200 | 0.0614 |
| 0.0047 | 0.0944 | 250 | 0.0572 |
| 0.0084 | 0.1133 | 300 | 0.0518 |
| 0.0085 | 0.1322 | 350 | 0.0497 |
| 0.0022 | 0.1510 | 400 | 0.0495 |
| 0.0037 | 0.1699 | 450 | 0.0477 |
| 0.0016 | 0.1888 | 500 | 0.0478 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
straykittycat/b5 | straykittycat | 2025-02-26T01:23:49Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T01:19:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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] |
alinerodrigues/wav2vec2-large-xlsr-coraa-exp-16 | alinerodrigues | 2025-02-26T01:22:30Z | 0 | 0 | null | [
"pytorch",
"wav2vec2",
"generated_from_trainer",
"license:apache-2.0",
"region:us"
] | null | 2025-02-25T17:46:00Z | ---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-large-xlsr-coraa-exp-16
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. -->
# wav2vec2-large-xlsr-coraa-exp-16
This model is a fine-tuned version of [Edresson/wav2vec2-large-xlsr-coraa-portuguese](https://huggingface.co/Edresson/wav2vec2-large-xlsr-coraa-portuguese) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 3.0160
- Wer: 1.0
- Cer: 0.9619
- Per: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 150
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer | Cer | Per |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| 38.847 | 1.0 | 14 | 48.0649 | 1.0002 | 3.1169 | 1.0002 |
| 38.847 | 2.0 | 28 | 47.9396 | 1.0002 | 3.1191 | 1.0002 |
| 38.847 | 3.0 | 42 | 47.7446 | 1.0004 | 3.0938 | 1.0004 |
| 38.847 | 4.0 | 56 | 47.3282 | 1.0006 | 3.0227 | 1.0006 |
| 38.847 | 5.0 | 70 | 46.4748 | 1.0004 | 2.4135 | 1.0004 |
| 38.847 | 6.0 | 84 | 44.8749 | 1.0 | 1.4413 | 1.0 |
| 38.847 | 7.0 | 98 | 42.6022 | 1.0 | 0.9460 | 1.0 |
| 37.4633 | 8.0 | 112 | 39.6558 | 1.0 | 0.8978 | 1.0 |
| 37.4633 | 9.0 | 126 | 35.7305 | 1.0 | 0.9338 | 1.0 |
| 37.4633 | 10.0 | 140 | 30.8796 | 1.0 | 0.9501 | 1.0 |
| 37.4633 | 11.0 | 154 | 26.3341 | 1.0 | 0.9510 | 1.0 |
| 37.4633 | 12.0 | 168 | 23.4116 | 1.0 | 0.9510 | 1.0 |
| 37.4633 | 13.0 | 182 | 21.1265 | 1.0 | 0.9510 | 1.0 |
| 37.4633 | 14.0 | 196 | 19.4607 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 15.0 | 210 | 18.0357 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 16.0 | 224 | 16.9469 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 17.0 | 238 | 16.1513 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 18.0 | 252 | 15.5951 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 19.0 | 266 | 15.0803 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 20.0 | 280 | 14.8270 | 1.0 | 0.9510 | 1.0 |
| 24.5313 | 21.0 | 294 | 14.4665 | 1.0 | 0.9510 | 1.0 |
| 14.1395 | 22.0 | 308 | 14.3680 | 1.0 | 0.9510 | 1.0 |
| 14.1395 | 23.0 | 322 | 14.1886 | 1.0 | 0.9510 | 1.0 |
| 14.1395 | 24.0 | 336 | 14.0514 | 1.0 | 0.9510 | 1.0 |
| 14.1395 | 25.0 | 350 | 14.0722 | 1.0 | 0.9510 | 1.0 |
| 14.1395 | 26.0 | 364 | 13.8134 | 1.0 | 0.9511 | 1.0 |
| 14.1395 | 27.0 | 378 | 13.6935 | 1.0 | 0.9522 | 1.0 |
| 14.1395 | 28.0 | 392 | 13.3832 | 1.0 | 0.9594 | 1.0 |
| 12.0999 | 29.0 | 406 | 12.9235 | 1.0 | 0.9567 | 1.0 |
| 12.0999 | 30.0 | 420 | 12.5604 | 1.0 | 0.9516 | 1.0 |
| 12.0999 | 31.0 | 434 | 10.6877 | 1.0 | 0.9469 | 1.0 |
| 12.0999 | 32.0 | 448 | 9.3758 | 1.0 | 0.9565 | 1.0 |
| 12.0999 | 33.0 | 462 | 5.0729 | 1.0 | 0.9619 | 1.0 |
| 12.0999 | 34.0 | 476 | 4.0927 | 1.0 | 0.9619 | 1.0 |
| 12.0999 | 35.0 | 490 | 3.8576 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 36.0 | 504 | 3.7497 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 37.0 | 518 | 3.6650 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 38.0 | 532 | 3.5863 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 39.0 | 546 | 3.5280 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 40.0 | 560 | 3.4813 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 41.0 | 574 | 3.4481 | 1.0 | 0.9619 | 1.0 |
| 7.922 | 42.0 | 588 | 3.4184 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 43.0 | 602 | 3.3964 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 44.0 | 616 | 3.3748 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 45.0 | 630 | 3.3545 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 46.0 | 644 | 3.3354 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 47.0 | 658 | 3.3090 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 48.0 | 672 | 3.2789 | 1.0 | 0.9619 | 1.0 |
| 3.5155 | 49.0 | 686 | 3.2441 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 50.0 | 700 | 3.2153 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 51.0 | 714 | 3.1921 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 52.0 | 728 | 3.1863 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 53.0 | 742 | 3.1605 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 54.0 | 756 | 3.1517 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 55.0 | 770 | 3.1389 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 56.0 | 784 | 3.1274 | 1.0 | 0.9619 | 1.0 |
| 3.2278 | 57.0 | 798 | 3.1237 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 58.0 | 812 | 3.1115 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 59.0 | 826 | 3.1051 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 60.0 | 840 | 3.1055 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 61.0 | 854 | 3.0982 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 62.0 | 868 | 3.0933 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 63.0 | 882 | 3.0871 | 1.0 | 0.9619 | 1.0 |
| 3.0881 | 64.0 | 896 | 3.0788 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 65.0 | 910 | 3.0835 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 66.0 | 924 | 3.0786 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 67.0 | 938 | 3.0781 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 68.0 | 952 | 3.0761 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 69.0 | 966 | 3.0663 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 70.0 | 980 | 3.0629 | 1.0 | 0.9619 | 1.0 |
| 3.0331 | 71.0 | 994 | 3.0661 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 72.0 | 1008 | 3.0600 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 73.0 | 1022 | 3.0559 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 74.0 | 1036 | 3.0517 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 75.0 | 1050 | 3.0524 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 76.0 | 1064 | 3.0506 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 77.0 | 1078 | 3.0451 | 1.0 | 0.9619 | 1.0 |
| 2.9941 | 78.0 | 1092 | 3.0485 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 79.0 | 1106 | 3.0472 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 80.0 | 1120 | 3.0464 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 81.0 | 1134 | 3.0458 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 82.0 | 1148 | 3.0386 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 83.0 | 1162 | 3.0376 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 84.0 | 1176 | 3.0365 | 1.0 | 0.9619 | 1.0 |
| 2.9748 | 85.0 | 1190 | 3.0414 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 86.0 | 1204 | 3.0400 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 87.0 | 1218 | 3.0327 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 88.0 | 1232 | 3.0354 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 89.0 | 1246 | 3.0313 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 90.0 | 1260 | 3.0344 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 91.0 | 1274 | 3.0385 | 1.0 | 0.9619 | 1.0 |
| 2.9573 | 92.0 | 1288 | 3.0343 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 93.0 | 1302 | 3.0365 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 94.0 | 1316 | 3.0292 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 95.0 | 1330 | 3.0238 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 96.0 | 1344 | 3.0332 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 97.0 | 1358 | 3.0295 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 98.0 | 1372 | 3.0305 | 1.0 | 0.9619 | 1.0 |
| 2.957 | 99.0 | 1386 | 3.0284 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 100.0 | 1400 | 3.0302 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 101.0 | 1414 | 3.0284 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 102.0 | 1428 | 3.0302 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 103.0 | 1442 | 3.0312 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 104.0 | 1456 | 3.0255 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 105.0 | 1470 | 3.0309 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 106.0 | 1484 | 3.0268 | 1.0 | 0.9619 | 1.0 |
| 2.9439 | 107.0 | 1498 | 3.0318 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 108.0 | 1512 | 3.0244 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 109.0 | 1526 | 3.0307 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 110.0 | 1540 | 3.0229 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 111.0 | 1554 | 3.0231 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 112.0 | 1568 | 3.0288 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 113.0 | 1582 | 3.0191 | 1.0 | 0.9619 | 1.0 |
| 2.9382 | 114.0 | 1596 | 3.0276 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 115.0 | 1610 | 3.0226 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 116.0 | 1624 | 3.0271 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 117.0 | 1638 | 3.0220 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 118.0 | 1652 | 3.0240 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 119.0 | 1666 | 3.0305 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 120.0 | 1680 | 3.0160 | 1.0 | 0.9619 | 1.0 |
| 2.9379 | 121.0 | 1694 | 3.0231 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 122.0 | 1708 | 3.0200 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 123.0 | 1722 | 3.0191 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 124.0 | 1736 | 3.0240 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 125.0 | 1750 | 3.0204 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 126.0 | 1764 | 3.0222 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 127.0 | 1778 | 3.0249 | 1.0 | 0.9619 | 1.0 |
| 2.9353 | 128.0 | 1792 | 3.0212 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 129.0 | 1806 | 3.0228 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 130.0 | 1820 | 3.0219 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 131.0 | 1834 | 3.0206 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 132.0 | 1848 | 3.0238 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 133.0 | 1862 | 3.0212 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 134.0 | 1876 | 3.0241 | 1.0 | 0.9619 | 1.0 |
| 2.9377 | 135.0 | 1890 | 3.0248 | 1.0 | 0.9619 | 1.0 |
| 2.929 | 136.0 | 1904 | 3.0250 | 1.0 | 0.9619 | 1.0 |
| 2.929 | 137.0 | 1918 | 3.0218 | 1.0 | 0.9619 | 1.0 |
| 2.929 | 138.0 | 1932 | 3.0230 | 1.0 | 0.9619 | 1.0 |
| 2.929 | 139.0 | 1946 | 3.0240 | 1.0 | 0.9619 | 1.0 |
| 2.929 | 140.0 | 1960 | 3.0226 | 1.0 | 0.9619 | 1.0 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.4.1+cu121
- Datasets 3.2.0
- Tokenizers 0.13.3
|
Paladiso/e1d252da-5b85-4f26-8c9b-37678b94c053 | Paladiso | 2025-02-26T01:20:18Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/codellama-7b",
"base_model:adapter:unsloth/codellama-7b",
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T00:59:39Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/codellama-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e1d252da-5b85-4f26-8c9b-37678b94c053
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/codellama-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- ed7f40531cda0438_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/ed7f40531cda0438_train_data.json
type:
field_input: span_labels
field_instruction: source_text
field_output: target_text
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: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: Paladiso/e1d252da-5b85-4f26-8c9b-37678b94c053
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 10
micro_batch_size: 2
mlflow_experiment_name: /tmp/ed7f40531cda0438_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: b1cdd58f-625d-49ad-a5b8-a912b4559816
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: b1cdd58f-625d-49ad-a5b8-a912b4559816
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# e1d252da-5b85-4f26-8c9b-37678b94c053
This model is a fine-tuned version of [unsloth/codellama-7b](https://huggingface.co/unsloth/codellama-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0000 | 1 | nan |
| 0.0 | 0.0001 | 3 | nan |
| 0.0 | 0.0002 | 6 | nan |
| 0.0 | 0.0004 | 9 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Bilzain-aip-Viral/FUL.Bilzain-aip.Viral.Video.On.Social.Media.X | Bilzain-aip-Viral | 2025-02-26T01:17:12Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:17:07Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
Izzy-tg-Videos/VIRAL.Izzy-tg.Viral.Video.Full.Original.Video.Social.Media.X | Izzy-tg-Videos | 2025-02-26T01:15:59Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:15:54Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
samoline/a3c5138f-61ae-4b91-bef5-02d2cb6a35da | samoline | 2025-02-26T01:13:33Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM-135M",
"base_model:adapter:unsloth/SmolLM-135M",
"license:apache-2.0",
"region:us"
] | null | 2025-02-26T01:11:59Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/SmolLM-135M
tags:
- axolotl
- generated_from_trainer
model-index:
- name: a3c5138f-61ae-4b91-bef5-02d2cb6a35da
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/SmolLM-135M
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 1fc464fd17ca1d74_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/1fc464fd17ca1d74_train_data.json
type:
field_input: rejected
field_instruction: prompt
field_output: chosen
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: 4
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: false
group_by_length: false
hub_model_id: samoline/a3c5138f-61ae-4b91-bef5-02d2cb6a35da
hub_repo: samoline
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0002
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 4
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 4
lora_target_linear: true
lr_scheduler: cosine
max_steps: 2
micro_batch_size: 1
mlflow_experiment_name: /tmp/1fc464fd17ca1d74_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: samoline-nan
wandb_mode: online
wandb_name: 11f68075-29cb-43e7-88d2-acf4597875b1
wandb_project: Gradients-On-Demand
wandb_run: dev
wandb_runid: 11f68075-29cb-43e7-88d2-acf4597875b1
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# a3c5138f-61ae-4b91-bef5-02d2cb6a35da
This model is a fine-tuned version of [unsloth/SmolLM-135M](https://huggingface.co/unsloth/SmolLM-135M) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: nan
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 1
- eval_batch_size: 1
- 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: 10
- training_steps: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0 | 0.0001 | 1 | nan |
| 0.0 | 0.0002 | 2 | nan |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/gpt2-large-medical-i1-GGUF | mradermacher | 2025-02-26T01:13:12Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"medical",
"en",
"dataset:BI55/MedText",
"dataset:pubmed_qa",
"base_model:Locutusque/gpt2-large-medical",
"base_model:quantized:Locutusque/gpt2-large-medical",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-02-26T00:59:32Z | ---
base_model: Locutusque/gpt2-large-medical
datasets:
- BI55/MedText
- pubmed_qa
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- medical
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Locutusque/gpt2-large-medical
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/gpt2-large-medical-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/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ1_S.gguf) | i1-IQ1_S | 0.3 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ1_M.gguf) | i1-IQ1_M | 0.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ2_S.gguf) | i1-IQ2_S | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ2_M.gguf) | i1-IQ2_M | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.4 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q2_K.gguf) | i1-Q2_K | 0.4 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ3_S.gguf) | i1-IQ3_S | 0.5 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.5 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ3_M.gguf) | i1-IQ3_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.6 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q4_0.gguf) | i1-Q4_0 | 0.6 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q4_1.gguf) | i1-Q4_1 | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/gpt2-large-medical-i1-GGUF/resolve/main/gpt2-large-medical.i1-Q6_K.gguf) | i1-Q6_K | 0.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
lesso05/c8666d96-8168-4ff8-a55a-0b6019f4fce9 | lesso05 | 2025-02-26T01:11:04Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"base_model:adapter:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"region:us"
] | null | 2025-02-25T19:08:10Z | ---
library_name: peft
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c8666d96-8168-4ff8-a55a-0b6019f4fce9
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
auto_find_batch_size: true
base_model: HuggingFaceH4/zephyr-7b-beta
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a2394e962a5418d3_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a2394e962a5418d3_train_data.json
type:
field_instruction: desciption
field_output: caption
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 3
eval_max_new_tokens: 128
eval_steps: 50
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: false
group_by_length: true
hub_model_id: lesso05/c8666d96-8168-4ff8-a55a-0b6019f4fce9
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.000205
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 10
lora_alpha: 32
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 16
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_steps: 500
micro_batch_size: 4
mlflow_experiment_name: /tmp/a2394e962a5418d3_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
save_steps: 50
saves_per_epoch: null
seed: 50
sequence_len: 512
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: b03ec26f-3bc2-4dae-93eb-4701cde748d5
wandb_project: 05a
wandb_run: your_name
wandb_runid: b03ec26f-3bc2-4dae-93eb-4701cde748d5
warmup_steps: 50
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# c8666d96-8168-4ff8-a55a-0b6019f4fce9
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9350
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.000205
- train_batch_size: 4
- eval_batch_size: 4
- seed: 50
- gradient_accumulation_steps: 2
- 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: 50
- training_steps: 500
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 2.1224 |
| 2.4588 | 0.0006 | 50 | 1.2840 |
| 2.378 | 0.0013 | 100 | 1.1566 |
| 2.2563 | 0.0019 | 150 | 1.1669 |
| 2.3133 | 0.0026 | 200 | 1.0996 |
| 2.2691 | 0.0032 | 250 | 1.0579 |
| 2.1556 | 0.0038 | 300 | 1.0221 |
| 1.9584 | 0.0045 | 350 | 0.9776 |
| 2.0103 | 0.0051 | 400 | 0.9617 |
| 1.8906 | 0.0057 | 450 | 0.9390 |
| 1.8978 | 0.0064 | 500 | 0.9350 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
El-Patron-Viral-Video-Link/El-Patron-Viral-Video-Link | El-Patron-Viral-Video-Link | 2025-02-26T01:11:01Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:10:55Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
straykittycat/b4 | straykittycat | 2025-02-26T01:10:47Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-26T01:06:20Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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] |
locuslab/mix_ift_v4-smollm2-1.7b-all_raw_folders_metadata-600B | locuslab | 2025-02-26T01:10:38Z | 0 | 0 | null | [
"safetensors",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-02-26T00:49:55Z | ---
version: main
family: smollm2-1.7b
model_name: -all_raw_folders_metadata-600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 -all_raw_folders_metadata-600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/-all_raw_folders_metadata-600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/-all_raw_folders_metadata-600B", revision="final")
```
Replace `"final"` with the desired revision.
|
kbrinsly7/mt5-sinhala-news-finetunedV2 | kbrinsly7 | 2025-02-26T01:10:15Z | 0 | 0 | transformers | [
"transformers",
"tf",
"mt5",
"text2text-generation",
"generated_from_keras_callback",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-02-26T01:08:32Z | ---
library_name: transformers
tags:
- generated_from_keras_callback
model-index:
- name: mt5-sinhala-news-finetunedV2
results: []
---
<!-- This model card has been generated automatically according to the information Keras had access to. You should
probably proofread and complete it, then remove this comment. -->
# mt5-sinhala-news-finetunedV2
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
## 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:
- optimizer: None
- training_precision: float32
### Training results
### Framework versions
- Transformers 4.48.3
- TensorFlow 2.18.0
- Datasets 3.3.2
- Tokenizers 0.21.0
|
Imsha-Rehman-Leaks-x-Video/Imsha.Rehman.Leaked.Video.Link.here | Imsha-Rehman-Leaks-x-Video | 2025-02-26T01:08:50Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:08:44Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
One-Girl-15-Hands-TV/wATCH.One-Girl-15-Hands.viral.video.original | One-Girl-15-Hands-TV | 2025-02-26T01:07:58Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:07:08Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
locuslab/mix_ift_v4-smollm2-1.7b-score0_mix_rephrased_from_beginning_metadata-600B | locuslab | 2025-02-26T01:07:27Z | 0 | 0 | null | [
"safetensors",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-02-26T00:49:54Z | ---
version: main
family: smollm2-1.7b
model_name: -score0_mix_rephrased_from_beginning_metadata-600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 -score0_mix_rephrased_from_beginning_metadata-600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/-score0_mix_rephrased_from_beginning_metadata-600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/-score0_mix_rephrased_from_beginning_metadata-600B", revision="final")
```
Replace `"final"` with the desired revision.
|
muriloluz/Qwen-2.5-7B-MATHNOVLLM-RL | muriloluz | 2025-02-26T01:07:19Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"conversational",
"dataset:DigitalLearningGmbH/MATH-lighteval",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-Math-7B",
"base_model:finetune:Qwen/Qwen2.5-Math-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-02-25T19:31:30Z | ---
base_model: Qwen/Qwen2.5-Math-7B
datasets: DigitalLearningGmbH/MATH-lighteval
library_name: transformers
model_name: Qwen-2.5-7B-MATHNOVLLM-RL
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen-2.5-7B-MATHNOVLLM-RL
This model is a fine-tuned version of [Qwen/Qwen2.5-Math-7B](https://huggingface.co/Qwen/Qwen2.5-Math-7B) on the [DigitalLearningGmbH/MATH-lighteval](https://huggingface.co/datasets/DigitalLearningGmbH/MATH-lighteval) dataset.
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="muriloluz/Qwen-2.5-7B-MATHNOVLLM-RL", 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/muriloluz-ufg/huggingface/runs/pcgkwq4k)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.50.0.dev0
- Pytorch: 2.5.1
- Datasets: 3.3.1
- 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 Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
tscstudios/sjz01jydzmxqy6tu76mqgyobqjf2_93efacd4-83cf-435e-a1e7-a91dd450dd2d | tscstudios | 2025-02-26T01:05:20Z | 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 | 2025-02-26T01:05:18Z | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: TOK
---
# Sjz01Jydzmxqy6Tu76Mqgyobqjf2_93Efacd4 83Cf 435E A1E7 A91Dd450Dd2D
<Gallery />
Trained on Replicate using:
https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `TOK` to trigger the image generation.
## Use it with the [๐งจ 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('tscstudios/sjz01jydzmxqy6tu76mqgyobqjf2_93efacd4-83cf-435e-a1e7-a91dd450dd2d', weight_name='lora.safetensors')
image = pipeline('your prompt').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)
|
bulan-sutena-1-menit-14-detik-videos/wATCH.viral.video.original | bulan-sutena-1-menit-14-detik-videos | 2025-02-26T01:05:19Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:03:40Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
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๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF | mradermacher | 2025-02-26T01:04:53Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:Nexesenex/Llama_3.1_8b_DobHerWild_R1_v1.1",
"base_model:quantized:Nexesenex/Llama_3.1_8b_DobHerWild_R1_v1.1",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | 2025-02-25T23:54:15Z | ---
base_model: Nexesenex/Llama_3.1_8b_DobHerWild_R1_v1.1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Nexesenex/Llama_3.1_8b_DobHerWild_R1_v1.1
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-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/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q2_K_S.gguf) | i1-Q2_K_S | 3.1 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-IQ4_NL.gguf) | i1-IQ4_NL | 4.8 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q4_1.gguf) | i1-Q4_1 | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama_3.1_8b_DobHerWild_R1_v1.1-i1-GGUF/resolve/main/Llama_3.1_8b_DobHerWild_R1_v1.1.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | 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 -->
|
SJDX/whisper-medium-241214 | SJDX | 2025-02-26T01:03:03Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | 2025-02-26T00:58:35Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค 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
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
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## Model Card Contact
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Lily-Phillips-101-Challenge-Video-Viral/Lily.Phillips.101.Challenge.Video | Lily-Phillips-101-Challenge-Video-Viral | 2025-02-26T01:02:13Z | 0 | 0 | null | [
"region:us"
] | null | 2025-02-26T01:02:07Z | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)](https://lekedvideo.xyz/watch/)
[๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )](https://lekedvideo.xyz/watch/) |
locuslab/mix_ift_v4-smollm2-1.7b-score0_mix_rephrased_from_beginning-600B | locuslab | 2025-02-26T01:01:06Z | 0 | 0 | null | [
"safetensors",
"llama",
"model",
"transformer",
"smollm2",
"license:mit",
"region:us"
] | null | 2025-02-26T00:49:53Z | ---
version: main
family: smollm2-1.7b
model_name: -score0_mix_rephrased_from_beginning-600B
license: mit
tags:
- model
- transformer
- smollm2
---
# SmolLM2 -score0_mix_rephrased_from_beginning-600B (Version: main)
## Model Details
- **Architecture:** SmolLM2
- **Parameters:** 1.7B
## Training Configuration
```yaml
optimizer:
class_path: torch.optim.AdamW
init_args:
lr: 0.0005
weight_decay: 0.01
precision: bf16-mixed
seed: 42
train:
global_batch_size: 1024
max_seq_length: 2048
max_tokens: 600000000000
micro_batch_size: 8
```
## Model Loading and Revision System
This repository hosts multiple revisions of the model.
To load a specific revision, use the `revision` parameter. For example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("locuslab/-score0_mix_rephrased_from_beginning-600B", revision="final")
tokenizer = AutoTokenizer.from_pretrained("locuslab/-score0_mix_rephrased_from_beginning-600B", revision="final")
```
Replace `"final"` with the desired revision.
|
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