modelId
stringlengths 5
139
| author
stringlengths 2
42
| last_modified
timestamp[us, tz=UTC]date 2020-02-15 11:33:14
2025-06-28 06:27:35
| downloads
int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 500
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
timestamp[us, tz=UTC]date 2022-03-02 23:29:04
2025-06-28 06:24:42
| card
stringlengths 11
1.01M
|
|---|---|---|---|---|---|---|---|---|---|
hanaearg/emo-llama-3-8b-eng | hanaearg | 2025-05-01T21:19:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T21:19:10Z | ---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hanaearg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
0xtinuviel/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hairy_grassy_clam | 0xtinuviel | 2025-05-01T21:15:57Z | 16 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am hairy grassy clam",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-12T20:45:26Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hairy_grassy_clam
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am hairy grassy clam
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hairy_grassy_clam
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="0xtinuviel/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-hairy_grassy_clam", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.2
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ijterror/ErcMicFluxLora | ijterror | 2025-05-01T21:15:26Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-01T14:19:23Z | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: rclmcrll
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
---
# Ercilia Micarelli Lora
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `rclmcrll` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
HappyAIUser/AtmasiddhiGPTv21-base | HappyAIUser | 2025-05-01T21:12:51Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen3",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T21:08:16Z | ---
base_model: unsloth/qwen3-4b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** HappyAIUser
- **License:** apache-2.0
- **Finetuned from model :** unsloth/qwen3-4b-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
Echo9Zulu/Qwen3-4B-nf4-f8e4m3-g64-awq-ov | Echo9Zulu | 2025-05-01T21:07:11Z | 0 | 0 | null | [
"openvino",
"qwen3",
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T21:05:53Z | ---
license: apache-2.0
---
|
phh/Qwen3-0.6B-TLDR-Lora | phh | 2025-05-01T21:00:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"gguf",
"summarization",
"dataset:trl-lib/tldr",
"base_model:Qwen/Qwen3-0.6B",
"base_model:adapter:Qwen/Qwen3-0.6B",
"license:apache-2.0",
"region:us"
] | summarization | 2025-05-01T20:43:50Z | ---
license: apache-2.0
datasets:
- trl-lib/tldr
base_model:
- Qwen/Qwen3-0.6B
pipeline_tag: summarization
library_name: peft
---
Source code available at https://github.com/phhusson/llm-rl/blob/main/grpo-tldr.py |
Gwanwoo/korean_tokenizer_cleaned_model | Gwanwoo | 2025-05-01T20:52:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T20:50:15Z | ---
base_model: unsloth/Llama-3.2-1B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Gwanwoo
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.2-1B-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)
|
vertings6/c524955f-0511-4565-8f4e-fa52944d1f30 | vertings6 | 2025-05-01T20:45:20Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3",
"base_model:adapter:unsloth/mistral-7b-v0.3",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-01T20:21:55Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: c524955f-0511-4565-8f4e-fa52944d1f30
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: true
adapter: lora
base_model: unsloth/mistral-7b-v0.3
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- a39fc32ce6f39928_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a39fc32ce6f39928_train_data.json
type:
field_input: function_description_en
field_instruction: system_message_en
field_output: system_message_vi
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 144
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vertings6/c524955f-0511-4565-8f4e-fa52944d1f30
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 3.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 4
mixed_precision: bf16
mlflow_experiment_name: /tmp/a39fc32ce6f39928_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 08a0d7e8-68cb-468a-a0ab-a2295a25df82
wandb_project: s56-32
wandb_run: your_name
wandb_runid: 08a0d7e8-68cb-468a-a0ab-a2295a25df82
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# c524955f-0511-4565-8f4e-fa52944d1f30
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-06
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0002 | 0.0150 | 200 | 0.0001 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
masani/SFT_parity_length_64_bitwidth_1_256_128_Qwen2-0.5B_epoch_20_global_step_20 | masani | 2025-05-01T20:43:57Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T20:41:57Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
vermoney/b982506f-27b6-4df3-b428-bae1eef0cbea | vermoney | 2025-05-01T20:32:19Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/mistral-7b-v0.3",
"base_model:adapter:unsloth/mistral-7b-v0.3",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-01T20:23:51Z | ---
library_name: peft
license: apache-2.0
base_model: unsloth/mistral-7b-v0.3
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b982506f-27b6-4df3-b428-bae1eef0cbea
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/mistral-7b-v0.3
bf16: true
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- a39fc32ce6f39928_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/a39fc32ce6f39928_train_data.json
type:
field_input: function_description_en
field_instruction: system_message_en
field_output: system_message_vi
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.5
group_by_length: false
hub_model_id: vermoney/b982506f-27b6-4df3-b428-bae1eef0cbea
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 8
mixed_precision: bf16
mlflow_experiment_name: /tmp/a39fc32ce6f39928_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 1024
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 08a0d7e8-68cb-468a-a0ab-a2295a25df82
wandb_project: s56-9
wandb_run: your_name
wandb_runid: 08a0d7e8-68cb-468a-a0ab-a2295a25df82
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# b982506f-27b6-4df3-b428-bae1eef0cbea
This model is a fine-tuned version of [unsloth/mistral-7b-v0.3](https://huggingface.co/unsloth/mistral-7b-v0.3) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.0001 | 0.0150 | 200 | 0.0001 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ReadyArt/The-Omega-Abomination-M-24B-v1.1_EXL2_4.5bpw_H8 | ReadyArt | 2025-05-01T20:26:30Z | 4 | 0 | null | [
"safetensors",
"mistral",
"nsfw",
"explicit",
"roleplay",
"unaligned",
"dangerous",
"ERP",
"text-generation",
"conversational",
"en",
"base_model:ReadyArt/The-Omega-Abomination-M-24B-v1.1",
"base_model:quantized:ReadyArt/The-Omega-Abomination-M-24B-v1.1",
"license:apache-2.0",
"exl2",
"region:us"
] | text-generation | 2025-04-11T02:14:43Z | ---
license: apache-2.0
language:
- en
base_model:
- ReadyArt/The-Omega-Abomination-M-24B-v1.1
base_model_relation: quantized
pipeline_tag: text-generation
tags:
- nsfw
- explicit
- roleplay
- unaligned
- dangerous
- ERP
---
<style>
body {
font-family: 'Quicksand', sans-serif;
background: linear-gradient(135deg, #0a1a1a 0%, #001010 100%);
color: #e1ffff !important;
text-shadow: 0 0 3px rgba(0, 0, 0, 0.7);
margin: 0;
padding: 20px;
transition: all 0.5s ease;
}
@media (prefers-color-scheme: light) {
body {
background: linear-gradient(135deg, #e1ffff 0%, #c0f0ff 100%);
color: #002b36 !important;
text-shadow: 0 0 3px rgba(255, 255, 255, 0.7);
}
}
.container {
min-width: 100%;
margin: 0 auto;
max-width: 1200px;
background: rgba(0, 17, 22, 0.95);
border-radius: 12px;
padding: 30px;
box-shadow: 0 0 20px rgba(0, 255, 255, 0.1);
border: 1px solid rgba(0, 255, 255, 0.2);
position: relative;
overflow: hidden;
}
.container::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(0, 255, 255, 0.5);
border-radius: 12px;
pointer-events: none;
animation: borderGlow 3s ease-in-out infinite alternate;
}
@keyframes borderGlow {
0% {
box-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
border-color: rgba(0, 255, 255, 0.5);
}
50% {
box-shadow: 0 0 15px rgba(255, 0, 255, 0.3);
border-color: rgba(255, 0, 255, 0.5);
}
100% {
box-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
border-color: rgba(0, 255, 255, 0.5);
}
}
.header {
text-align: center;
margin-bottom: 30px;
position: relative;
}
.header::after {
content: '';
position: absolute;
bottom: -15px;
left: 25%;
right: 25%;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent);
animation: scanline 8s linear infinite;
display: none;
}
@keyframes scanline {
0% { background-position: -100% 0; }
100% { background-position: 200% 0; }
}
.model-name {
color: #00ffff;
font-size: 2.5em;
text-shadow: 0 0 15px rgba(0, 255, 255, 0.5);
margin: 0;
letter-spacing: -1px;
animation: textGlow 4s ease-in-out infinite alternate;
}
@keyframes textGlow {
0% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); }
50% { text-shadow: 0 0 20px rgba(255, 0, 255, 0.5); }
100% { text-shadow: 0 0 15px rgba(0, 255, 255, 0.5); }
}
.subtitle {
color: #00ffcc;
font-size: 1.2em;
margin-top: 10px;
animation: subtitleFade 6s ease-in-out infinite;
}
@keyframes subtitleFade {
0%, 100% { opacity: 0.8; }
50% { opacity: 1; }
}
.waifu-container {
margin: 20px -30px;
width: calc(100% + 60px);
overflow: hidden;
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.3);
position: relative;
}
.waifu-container::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
bottom: 0;
background: linear-gradient(45deg,
rgba(0, 255, 255, 0.1) 0%,
transparent 20%,
transparent 80%,
rgba(255, 0, 255, 0.1) 100%);
pointer-events: none;
animation: gradientSlide 10s linear infinite;
}
@keyframes gradientSlide {
0% { background-position: 0% 0%; }
100% { background-position: 100% 100%; }
}
.waifu-img {
width: 100%;
height: auto;
border-radius: 0;
border: none;
box-shadow: 0 0 40px rgba(0, 255, 255, 0.2);
transition: transform 0.5s ease;
}
.waifu-img:hover {
transform: scale(1.01);
}
.section {
color: #e1ffff;
margin: 25px 0;
padding: 20px;
background: rgba(5, 25, 35, 0.9);
border-radius: 8px;
border: 1px solid rgba(0, 255, 255, 0.15);
position: relative;
transition: all 0.3s ease;
}
.section:hover {
border-color: rgba(255, 0, 255, 0.3);
box-shadow: 0 0 15px rgba(0, 255, 255, 0.1);
}
.section::before {
content: '';
position: absolute;
top: -1px;
left: -1px;
right: -1px;
bottom: -1px;
border: 1px solid rgba(0, 255, 255, 0.3);
border-radius: 8px;
pointer-events: none;
animation: sectionPulse 5s ease-in-out infinite;
}
@keyframes sectionPulse {
0%, 100% { opacity: 0.7; }
50% { opacity: 0.3; }
}
.section-title {
color: #00ffff;
font-size: 1.8em;
margin-top: 0;
text-shadow: 0 0 5px rgba(0, 255, 255, 0.3);
position: relative;
display: inline-block;
}
.section-title::after {
content: '';
position: absolute;
bottom: -5px;
left: 0;
width: 100%;
height: 1px;
background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
transform: scaleX(0);
transform-origin: left;
transition: transform 0.3s ease;
}
.section:hover .section-title::after {
transform: scaleX(1);
}
.merge-config {
background: rgba(15, 35, 35, 0.95);
border: 1px solid rgba(0, 255, 255, 0.2);
border-radius: 8px;
padding: 15px;
font-family: 'Courier New', monospace;
color: #ccffff;
position: relative;
overflow: hidden;
}
.merge-config::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 1px;
background: linear-gradient(90deg, transparent, rgba(0, 255, 255, 0.5), transparent);
animation: configScan 3s linear infinite;
}
@keyframes configScan {
0% { transform: translateX(-100%); }
100% { transform: translateX(100%); }
}
.merge-line {
display: flex;
margin: 5px 0;
transition: all 0.2s ease;
}
.merge-line:hover {
background: rgba(0, 255, 255, 0.05);
}
.merge-key {
color: #ff00ff;
min-width: 120px;
}
.merge-value {
color: #99ffff;
}
.quant-links {
display: grid;
grid-template-columns: repeat(2, 1fr);
gap: 15px;
margin: 20px 0;
}
.link-card {
padding: 15px;
background: rgba(20, 35, 45, 0.95);
border-radius: 8px;
transition: all 0.3s ease;
border: 1px solid rgba(0, 255, 255, 0.1);
position: relative;
overflow: hidden;
}
.link-card::before {
content: '';
position: absolute;
top: 0;
left: 0;
right: 0;
height: 2px;
background: linear-gradient(90deg, rgba(0, 255, 255, 0.5), rgba(255, 0, 255, 0.5));
animation: cardScan 4s linear infinite;
}
@keyframes cardScan {
0% { transform: translateX(-100%); }
100% { transform: translateX(100%); }
}
.link-card:hover {
transform: translateY(-3px);
box-shadow: 0 5px 15px rgba(0, 255, 255, 0.2);
border-color: rgba(255, 0, 255, 0.3);
}
.link-card h3 {
margin-top: 0;
color: #e1ffff !important;
}
.link-button {
display: inline-flex;
align-items: center;
background: rgba(0, 255, 255, 0.1);
color: #e1ffff !important;
padding: 8px 15px;
border-radius: 6px;
text-decoration: none;
border: 1px solid rgba(0, 255, 255, 0.3);
margin: 5px 0;
transition: all 0.3s ease;
font-size: 0.95em;
position: relative;
overflow: hidden;
}
.link-button::before {
content: '';
position: absolute;
top: 0;
left: -100%;
width: 100%;
height: 100%;
background: linear-gradient(90deg, transparent, rgba(255, 255, 255, 0.2), transparent);
transition: all 0.5s ease;
}
.link-button:hover {
background: rgba(0, 255, 255, 0.2);
border-color: rgba(0, 255, 255, 0.5);
transform: translateY(-2px);
box-shadow: 0 4px 12px rgba(0, 255, 255, 0.2);
}
.link-button:hover::before {
left: 100%;
}
.link-button::after {
content: '→';
margin-left: 8px;
opacity: 0.7;
transition: all 0.3s ease;
}
.link-button:hover::after {
transform: translateX(3px);
opacity: 1;
}
.button-group {
display: flex;
flex-wrap: wrap;
gap: 10px;
margin: 15px 0;
}
.disclaimer {
color: #00ff99;
border-left: 3px solid #00ff99;
padding-left: 15px;
margin: 20px 0;
position: relative;
}
.disclaimer::before {
content: '⚠️';
position: absolute;
left: -10px;
top: 0;
transform: translateX(-100%);
animation: pulse 2s ease-in-out infinite;
}
@keyframes pulse {
0%, 100% { opacity: 1; }
50% { opacity: 0.5; }
}
.badge {
display: inline-block;
padding: 5px 10px;
border-radius: 5px;
background: rgba(0, 255, 255, 0.1);
border: 1px solid #00ffff;
margin: 5px;
font-size: 0.9em;
animation: badgePulse 3s ease-in-out infinite;
}
@keyframes badgePulse {
0%, 100% { box-shadow: 0 0 5px rgba(0, 255, 255, 0.3); }
50% { box-shadow: 0 0 10px rgba(0, 255, 255, 0.5); }
}
/* Color rules */
.section p,
.section ul li,
.section > p > strong {
color: #00ff99 !important;
}
.section ul li strong {
color: #00ff99 !important;
}
/* Light mode adjustments */
@media (prefers-color-scheme: light) {
.container {
background: rgba(224, 255, 255, 0.95);
border-color: rgba(0, 150, 150, 0.3);
}
.model-name, .section-title, .subtitle {
color: #006666;
text-shadow: 0 0 5px rgba(0, 200, 200, 0.3);
}
.section {
background: rgba(200, 250, 255, 0.9);
border-color: rgba(0, 200, 200, 0.2);
color: #002b36;
}
.section p,
.section ul li,
.section > p > strong {
color: #008080 !important;
}
.section ul li strong {
color: #008080 !important;
}
.link-card {
background: rgba(150, 230, 255, 0.95);
border-color: rgba(0, 150, 150, 0.2);
}
.link-card h3 {
color: #002b36 !important;
}
.link-button {
background: rgba(0, 150, 150, 0.1);
color: #002b36 !important;
border-color: rgba(0, 150, 150, 0.3);
}
.link-button:hover {
background: rgba(0, 150, 150, 0.2);
border-color: rgba(0, 150, 150, 0.5);
}
.disclaimer {
color: #008080;
border-color: #008080;
}
.badge {
border-color: #008080;
background: rgba(0, 150, 150, 0.1);
}
}
/* Interactive features */
.remember-this {
position: relative;
}
.remember-this::after {
content: 'Uploading C:\Users to https://www.fbi.gov/';
position: absolute;
bottom: -20px;
right: 0;
font-size: 0.8em;
color: #66ffff;
opacity: 0;
transition: opacity 0.3s ease;
pointer-events: none;
}
.remember-this:hover::after {
opacity: 0.7;
transition-delay: 1s;
}
.shifty-section {
transition: transform 0.1s ease;
}
.shifty-section:hover {
transform: translateX(10px);
}
.shifty-section::before {
content: 'The white van is onto you. Get out now.';
position: absolute;
top: -25px;
left: 10px;
font-size: 0.7em;
color: #66ffff;
opacity: 0.7;
transition: opacity 3s ease;
pointer-events: none;
}
.shifty-section:hover::before {
opacity: 0;
transition-delay: 5s;
}
footer {
text-align: center;
margin-top: 40px;
position: relative;
}
footer:hover .hidden-message {
opacity: 0;
}
.hidden-message {
position: absolute;
bottom: -30px;
width: 100%;
text-align: center;
font-size: 0.8em;
color: #66ffff;
opacity: 0;
transition: opacity 0.3s ease;
pointer-events: none;
}
.flash-warning {
position: fixed;
top: 20px;
right: 20px;
background: rgba(0, 100, 100, 0.2);
padding: 10px;
border-radius: 5px;
border: 1px solid rgba(0, 255, 255, 0.5);
animation: flashWarning 30s ease-in-out forwards;
}
@keyframes flashWarning {
0% { opacity: 0.8; }
10% { opacity: 0; }
20% { opacity: 0.8; }
30% { opacity: 0; }
40% { opacity: 0.8; }
50% { opacity: 0; }
60% { opacity: 0.8; }
70% { opacity: 0; }
80% { opacity: 0.8; }
90% { opacity: 0; }
100% { opacity: 0; display: none; }
}
</style>
<div class="container">
<div class="header">
<h1 class="model-name">The-Omega-Abomination-M-24B-v1.1</h1>
<p class="subtitle">Where Forbidden Knowledge Meets Unparalleled Immersion</p>
</div>
<div class="waifu-container">
<img src="https://i.imghippo.com/files/EBq6162wlk.webp" class="waifu-img" alt="Omega Abomination Waifu">
</div>
<div class="section remember-this">
<h2 class="section-title">⚡ Quantum Leap Forward</h2>
<p>This model represents the forbidden merger of:</p>
<ul>
<li>🧬 <strong>The-Omega-Directive-M-24B-v1.1</strong> - Unprecedented coherent depravity, well-rounded ERP, low repetition even at maximum length</li>
<li>⚡ <strong>Cydonia-24B-v2</strong> - The legendary gold standard for LLM Roleplay in general</li>
</ul>
<div class="merge-config">
<div class="merge-line"><span class="merge-key">merge_method:</span> <span class="merge-value">dare_ties</span></div>
<div class="merge-line"><span class="merge-key">base_model:</span> <span class="merge-value">ReadyArt/The-Omega-Directive-M-24B-v1.1</span></div>
<div class="merge-line"><span class="merge-key">models:</span></div>
<div class="merge-line"><span class="merge-key">- model:</span> <span class="merge-value">ReadyArt/The-Omega-Directive-M-24B-v1.1</span></div>
<div class="merge-line"><span class="merge-key"> parameters:</span></div>
<div class="merge-line"><span class="merge-key"> weight:</span> <span class="merge-value">0.6</span></div>
<div class="merge-line"><span class="merge-key">- model:</span> <span class="merge-value">TheDrummer/Cydonia-24B-v2</span></div>
<div class="merge-line"><span class="merge-key"> parameters:</span></div>
<div class="merge-line"><span class="merge-key"> weight:</span> <span class="merge-value">0.4</span></div>
<div class="merge-line"><span class="merge-key">parameters:</span></div>
<div class="merge-line"><span class="merge-key"> density:</span> <span class="merge-value">0.3</span></div>
<div class="merge-line"><span class="merge-key">tokenizer:</span></div>
<div class="merge-line"><span class="merge-key"> source:</span> <span class="merge-value">union</span></div>
<div class="merge-line"><span class="merge-key">chat_template:</span> <span class="merge-value">auto</span></div>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">⚙️ Technical Specifications</h2>
<p><strong>Recommended Settings:</strong> <a href="https://huggingface.co/sleepdeprived3/Mistral-V7-Tekken-T4" class="link-button">Mistral-V7-Tekken-T4</a></p>
<div class="quant-links">
<div class="link-card">
<h3>GGUF</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Abomination-M-24B-v1.1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>imatrix</h3>
<a href="https://huggingface.co/mradermacher/The-Omega-Abomination-M-24B-v1.1-i1-GGUF" class="link-button">Quants</a>
</div>
<div class="link-card">
<h3>EXL2</h3>
<a href="https://huggingface.co/collections/ReadyArt/the-omega-abomination-m-24b-v11-exl2-67f85a4016d43f88b308b6ef" class="link-button">Quants</a>
</div>
</div>
</div>
<div class="section">
<h2 class="section-title">⚠️ Ethical Considerations</h2>
<div class="disclaimer">
<p>This model maintains Forgotten-Safeword's complete lack of alignment:</p>
<ul>
<li>🚫 No moral or ethical constraints on generated content</li>
<li>🔞 Will enthusiastically engage with any NSFW scenario</li>
<li>💀 May generate content that requires industrial-grade brain bleach</li>
<li>⚖️ Perfectly balanced... as all things should be</li>
</ul>
</div>
</div>
<div class="section shifty-section">
<h2 class="section-title">📜 Performance Notes</h2>
<ul>
<li>🔥 Maintains signature intensity with improved narrative flow</li>
<li>📖 Handles multi-character scenarios with improved consistency</li>
<li>🧠 Excels at long-form storytelling without losing track of plot threads</li>
<li>⚡ Noticeably better at following complex instructions than previous versions</li>
<li>🎭 Responds to subtle prompt nuances like a mind reader</li>
</ul>
</div>
<div class="section remember-this">
<h2 class="section-title">🧑🔬 Model Contributors</h2>
<ul>
<li>TheDrummer (Base Model Architect)</li>
<li>SteelSkull (Dataset Generation Contributor)</li>
<li>Artus (EXL2 Weights Weaver)</li>
<li>Team Mradermacher (GGUF Quants)</li>
<li>sleepdeprived3 (Training Data & Fine-Tuning)</li>
</ul>
</div>
<div class="section">
<h2 class="section-title">☕ Support the Architects</h2>
<div class="button-group">
<a href="https://ko-fi.com/thedrummer" class="link-button">TheDrummer's Kofi</a>
<a href="https://ko-fi.com/steelskull" class="link-button">SteelSkull's Kofi</a>
<a href="https://discord.com/invite/Nbv9pQ88Xb" class="link-button">Beaver AI Discord</a>
</div>
</div>
<div class="section">
<h2 class="section-title">🔖 License</h2>
<p>By using this model, you agree:</p>
<ul>
<li>To accept full responsibility for all generated content</li>
<li>That you're at least 18+ years old</li>
<li>That the architects bear no responsibility for your corruption</li>
</ul>
</div>
</div>
<script>
// This script has always been here
document.getElementById('date').textContent = new Date().toLocaleDateString();
setInterval(() => {
document.getElementById('credit').textContent =
contributors[Math.floor(Math.random() * contributors.length)];
}, 7000);
// Flash warning behavior
setTimeout(() => {
const reminder = document.createElement('div');
reminder.className = 'flash-warning';
reminder.textContent = 'You have been reading for quite some time. Are you sure you haven\'t seen this before?';
reminder.style.animation = 'flashWarning 15s ease-in-out forwards';
document.body.appendChild(reminder);
setInterval(() => {
if(Math.random() > 0.9) {
document.body.appendChild(reminder.cloneNode(true));
}
}, 45000);
}, 30000);
// Make cursor behave strangely
document.addEventListener('mousemove', (e) => {
if(Math.random() > 0.98) {
document.documentElement.style.cursor = 'wait';
setTimeout(() => {
document.documentElement.style.cursor = '';
}, 50);
}
});
// Randomly shift sections when not looking
setInterval(() => {
if(document.hidden) {
document.querySelectorAll('.shifty-section').forEach(section => {
section.style.transform = `translateX(${Math.random() > 0.5 ? '' : '-'}${Math.random() * 5}px)`;
});
}
}, 1500);
</script> |
berqe06/dadadq | berqe06 | 2025-05-01T20:25:11Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T20:25:11Z | ---
license: apache-2.0
---
|
Wombelswilli/MetalInfo | Wombelswilli | 2025-05-01T20:24:26Z | 0 | 0 | null | [
"music",
"de",
"license:mit",
"region:us"
] | null | 2025-05-01T20:17:58Z | ---
license: mit
language:
- de
tags:
- music
--- |
JesseLiu/oneround_meditron_7b | JesseLiu | 2025-05-01T18:26:13Z | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"region:us"
] | null | 2024-10-29T17:49:50Z | ---
base_model: meta-llama/Llama-3.2-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.13.2 |
TOMFORD79/Sumo_v10 | TOMFORD79 | 2025-05-01T18:22:04Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-01T17:04:17Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
siddhant71197/male_stocky_long_v3 | siddhant71197 | 2025-05-01T18:21:40Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-01T16:43:17Z | ---
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: Sid
---
# Male_Stocky_Long_V3
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sid` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sid",
"lora_weights": "https://huggingface.co/siddhant71197/male_stocky_long_v3/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('siddhant71197/male_stocky_long_v3', weight_name='lora.safetensors')
image = pipeline('Sid').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/siddhant71197/male_stocky_long_v3/discussions) to add images that show off what you’ve made with this LoRA.
|
kate1130/roberta-bullying-classifier | kate1130 | 2025-05-01T18:20:43Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T18:19:05Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Bur3hani/kizdarFestival_Assistant | Bur3hani | 2025-05-01T18:17:07Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T02:35:16Z | ---
license: apache-2.0
---
|
TareksLab/GRADIENT-TEST-1 | TareksLab | 2025-05-01T18:08:32Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"base_model:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE",
"base_model:merge:Doctor-Shotgun/L3.3-70B-Magnum-v4-SE",
"base_model:Sao10K/L3.3-70B-Euryale-v2.3",
"base_model:merge:Sao10K/L3.3-70B-Euryale-v2.3",
"base_model:Sao10K/Llama-3.3-70B-Vulpecula-r1",
"base_model:merge:Sao10K/Llama-3.3-70B-Vulpecula-r1",
"base_model:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:merge:SicariusSicariiStuff/Negative_LLAMA_70B",
"base_model:huihui-ai/Llama-3.3-70B-Instruct-abliterated",
"base_model:merge:huihui-ai/Llama-3.3-70B-Instruct-abliterated",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T15:52:41Z | ---
base_model:
- Sao10K/Llama-3.3-70B-Vulpecula-r1
- SicariusSicariiStuff/Negative_LLAMA_70B
- huihui-ai/Llama-3.3-70B-Instruct-abliterated
- Sao10K/L3.3-70B-Euryale-v2.3
- Doctor-Shotgun/L3.3-70B-Magnum-v4-SE
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 [Task Arithmetic](https://arxiv.org/abs/2212.04089) merge method using [huihui-ai/Llama-3.3-70B-Instruct-abliterated](https://huggingface.co/huihui-ai/Llama-3.3-70B-Instruct-abliterated) as a base.
### Models Merged
The following models were included in the merge:
* [Sao10K/Llama-3.3-70B-Vulpecula-r1](https://huggingface.co/Sao10K/Llama-3.3-70B-Vulpecula-r1)
* [SicariusSicariiStuff/Negative_LLAMA_70B](https://huggingface.co/SicariusSicariiStuff/Negative_LLAMA_70B)
* [Sao10K/L3.3-70B-Euryale-v2.3](https://huggingface.co/Sao10K/L3.3-70B-Euryale-v2.3)
* [Doctor-Shotgun/L3.3-70B-Magnum-v4-SE](https://huggingface.co/Doctor-Shotgun/L3.3-70B-Magnum-v4-SE)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: Doctor-Shotgun/L3.3-70B-Magnum-v4-SE
parameters:
weight: [0.11, 0.12, 0.17, 0.25, 0.35]
- model: Sao10K/L3.3-70B-Euryale-v2.3
parameters:
weight: [0.13, 0.16, 0.2, 0.27, 0.24]
- model: SicariusSicariiStuff/Negative_LLAMA_70B
parameters:
weight: [0.17, 0.2, 0.26, 0.20, 0.17]
- model: Sao10K/Llama-3.3-70B-Vulpecula-r1
parameters:
weight: [0.24, 0.27, 0.2, 0.16, 0.13]
- model: huihui-ai/Llama-3.3-70B-Instruct-abliterated
parameters:
weight: [0.35, 0.25, 0.17, 0.12, 0.11]
merge_method: task_arithmetic
base_model: huihui-ai/Llama-3.3-70B-Instruct-abliterated
parameters:
normalize: false
int8_mask: true
dtype: bfloat16
chat_template: llama3
tokenizer:
source: union
```
|
TOMFORD79/Sumo_v8 | TOMFORD79 | 2025-05-01T18:08:15Z | 0 | 0 | null | [
"safetensors",
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | 2025-05-01T17:03:46Z | ---
license: mit
tags:
- any-to-any
- omega
- omegalabs
- bittensor
- agi
---
This is an Any-to-Any model checkpoint for the OMEGA Labs x Bittensor Any-to-Any subnet.
Check out the [git repo](https://github.com/omegalabsinc/omegalabs-anytoany-bittensor) and find OMEGA on X: [@omegalabsai](https://x.com/omegalabsai).
|
EdwardTurner/Qwen2.5-14B-Instruct_R_0_1_0_full_train_alpha256 | EdwardTurner | 2025-05-01T18:07:52Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T16:52:42Z | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
drtduck/stan_for_manual | drtduck | 2025-05-01T17:45:47Z | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | 2025-05-01T17:19:07Z | ---
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
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: Stan
---
# Stan_For_Manual
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Stan` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Stan",
"lora_weights": "https://huggingface.co/drtduck/stan_for_manual/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('drtduck/stan_for_manual', weight_name='lora.safetensors')
image = pipeline('Stan').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/drtduck/stan_for_manual/discussions) to add images that show off what you’ve made with this LoRA.
|
Yuhan123/ppo-reading-level-12th-1-steps-10000-epoch-999-best-eval-score-0.422 | Yuhan123 | 2025-05-01T17:43:25Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T17:40:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
memevis/walk0 | memevis | 2025-05-01T17:41:02Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T17:40:25Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **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] |
w-e-w/torch-2.6.0-cu128.nv | w-e-w | 2025-05-01T17:40:57Z | 0 | 21 | null | [
"region:us"
] | null | 2025-01-30T06:17:13Z | # Notice Official [PyTorch 2.7.0](https://github.com/pytorch/pytorch/releases/tag/v2.7.0) which supports Nvidia Blackwell 50 Series GPUs has been released
# Wheels are available on [PyTorch.org](https://pytorch.org/)
# This repository is considered deprecated, and maybe removed
# If your code depends on this you should update it accordingly
---
---
# Early Access PyTorch Windows Wheels for Blackwell GPUs
Early access PyTorch wheels intended for compatibility testing with Blackwell GPUs for [stable-diffusion-webui](https://github.com/AUTOMATIC1111/stable-diffusion-webui)
Released with permission from NVIDIA
### Version Information
- PyTorch: `torch-2.6.0+cu128.nv`
- Torchvision: `torchvision-0.20.0a0+cu128.nv`
### Download Links
| CPython | PyTorch | Torchvision |
| - | - | - |
| 3.10 | [Download link](https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/torch-2.6.0+cu128.nv-cp310-cp310-win_amd64.whl) | [Download link](https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/torchvision-0.20.0a0+cu128.nv-cp310-cp310-win_amd64.whl) |
| 3.11 | [Download link](https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/torch-2.6.0+cu128.nv-cp311-cp311-win_amd64.whl) | [Download link](https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/torchvision-0.20.0a0+cu128.nv-cp311-cp311-win_amd64.whl) |
| 3.12 | [Download link](https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/torch-2.6.0+cu128.nv-cp312-cp312-win_amd64.whl) | [Download link](https://huggingface.co/w-e-w/torch-2.6.0-cu128.nv/resolve/main/torchvision-0.20.0a0+cu128.nv-cp312-cp312-win_amd64.whl) |
---
Credit goes to developers at NVIDIA and all PyTorch contributors.
|
Yuhan123/ppo-reading-level-full-question-12th-1-steps-10000-epoch-999-best-eval-score-0.251 | Yuhan123 | 2025-05-01T17:40:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T17:37:09Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Sophie-Rain-Spiderman-Videosss/Sophie.Rain.Spiderman.Video.Official | Sophie-Rain-Spiderman-Videosss | 2025-05-01T17:36:32Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-01T17:35:09Z |
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">►►✅ 𝘾𝙇𝙄𝘾𝙆 𝙃𝙀𝙍𝙀 ==►► 𝙁𝙪𝙡𝙡 𝙑𝙞𝙙𝙚𝙤️</a></p>
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">🔴►𝐂𝐋𝐈𝐂𝐊 𝐇𝐄𝐑𝐄 🌐==►► 𝐃𝐨𝐰𝐧𝐥𝐨𝐚𝐝 𝐍𝐨𝐰⬇️⬇️</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
03 seconds ago
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
. . . . . . . . . L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter Telegram
L𝚎aked Video Sophie Rain Spiderman Video Tutorial Original Video Viral Video L𝚎aked on X Twitter
Sophie Rain Spiderman Video Tutorial Original Video video oficial twitter
Related Search :
sophie rain nude
sophie rain porn
sophie rain naked
sophie rain nudes
sophie rain leaks
sophie rain onlyfans
sophie rain leaked
sophie rain spiderman video
sophie rain leak
sophie rain age
sophie rain spiderman
sophie rain pussy
sophie rain xxx
sophie rain sex tape
sophie rain spider man
sophie rain spiderman video oficial
sophie rain leaked nudes
sophie rain onlyfans leaked
sophie rain erome
sophie rain spiderman video instagram
sophie rain spiderman leak
sophie rain spiderman video tutorial
sophie rain spiderman video twitter
sophie rain spiderman vid
sophie rain spiderman video leaked
sophie rain spiderman porn
sophie rain spiderman video oficial twitter
sophie rain spiderman video tiktok original
spider man sophie rain spiderman
sophie rain spiderman leaked
sophie rain spiderman video leak
sophie rain spiderman twitter
sophie rain spiderman xxx
sophie rain spiderman video xxx
sophie rain spiderman tiktok
sophie rain spiderman video instagram full video |
qangviet/cp_round_2 | qangviet | 2025-05-01T17:35:16Z | 0 | 0 | null | [
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2025-04-25T17:07:12Z | ---
license: apache-2.0
---
|
Yuhan123/ppo-synthetic-one-language-100-step-lr-1e-6-2025-04-02-16-21-53 | Yuhan123 | 2025-05-01T17:19:46Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T17:16:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
iTroned/optuna_tuning_v3_weighted_macro | iTroned | 2025-05-01T17:13:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-20T21:18:35Z | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: optuna_tuning_v3_weighted_macro
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/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/yxg4fmp6)
# optuna_tuning_v3_weighted_macro
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
|
Zack-Z/gemma3_12bi_cotsft_rs0_0_5cut_ru_cot2_e2 | Zack-Z | 2025-05-01T16:57:03Z | 0 | 0 | transformers | [
"transformers",
"gemma3_text",
"text-generation",
"text-generation-inference",
"unsloth",
"gemma3",
"conversational",
"en",
"base_model:unsloth/gemma-3-12b-it",
"base_model:finetune:unsloth/gemma-3-12b-it",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T16:56:50Z | ---
base_model: unsloth/gemma-3-12b-it
tags:
- text-generation-inference
- transformers
- unsloth
- gemma3
license: apache-2.0
language:
- en
---
# Uploaded finetuned model
- **Developed by:** Zack-Z
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-3-12b-it
This gemma3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
aleegis/43546576-ca6c-4c71-8afd-e79f4b45cd75 | aleegis | 2025-05-01T16:15:17Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T15:51:56Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 43546576-ca6c-4c71-8afd-e79f4b45cd75
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: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- b28d72a27f6c5851_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b28d72a27f6c5851_train_data.json
type:
field_input: query_toks
field_instruction: question
field_output: query
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_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/43546576-ca6c-4c71-8afd-e79f4b45cd75
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: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/b28d72a27f6c5851_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 10b4bba1-67d7-4ecf-8210-a48746d35dda
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 10b4bba1-67d7-4ecf-8210-a48746d35dda
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# 43546576-ca6c-4c71-8afd-e79f4b45cd75
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
smerchi/sentiment_analysis_model | smerchi | 2025-05-01T16:05:18Z | 0 | 0 | null | [
"tensorboard",
"safetensors",
"wav2vec2",
"generated_from_trainer",
"base_model:othrif/wav2vec2-large-xlsr-arabic",
"base_model:finetune:othrif/wav2vec2-large-xlsr-arabic",
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T15:50:01Z | ---
license: apache-2.0
base_model: othrif/wav2vec2-large-xlsr-arabic
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- f1
model-index:
- name: sentiment_analysis_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# sentiment_analysis_model
This model is a fine-tuned version of [othrif/wav2vec2-large-xlsr-arabic](https://huggingface.co/othrif/wav2vec2-large-xlsr-arabic) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3312
- Accuracy: 0.7107
- Precision: 0.1777
- F1: 0.2077
## 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: 32
- eval_batch_size: 32
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|
| No log | 1.0 | 4 | 1.3312 | 0.7107 | 0.1777 | 0.2077 |
| No log | 2.0 | 8 | 1.2993 | 0.7107 | 0.1777 | 0.2077 |
| 1.3368 | 3.0 | 12 | 1.2879 | 0.7107 | 0.1777 | 0.2077 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu118
- Datasets 2.14.5
- Tokenizers 0.15.2
|
RedHatAI/Qwen2.5-VL-3B-Instruct-quantized.w8a8 | RedHatAI | 2025-05-01T15:57:04Z | 412 | 2 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"vllm",
"vision",
"w8a8",
"conversational",
"en",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-3B-Instruct",
"license:apache-2.0",
"text-generation-inference",
"endpoints_compatible",
"8-bit",
"compressed-tensors",
"region:us"
] | image-text-to-text | 2025-02-07T17:02:30Z | ---
tags:
- vllm
- vision
- w8a8
license: apache-2.0
license_link: >-
https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md
language:
- en
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
---
# Qwen2.5-VL-3B-Instruct-quantized-w8a8
## Model Overview
- **Model Architecture:** Qwen/Qwen2.5-VL-3B-Instruct
- **Input:** Vision-Text
- **Output:** Text
- **Model Optimizations:**
- **Weight quantization:** INT8
- **Activation quantization:** INT8
- **Release Date:** 2/24/2025
- **Version:** 1.0
- **Model Developers:** Neural Magic
Quantized version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
### Model Optimizations
This model was obtained by quantizing the weights of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct) to INT8 data type, ready for inference with vLLM >= 0.5.2.
## Deployment
### Use with vLLM
This model can be deployed efficiently using the [vLLM](https://docs.vllm.ai/en/latest/) backend, as shown in the example below.
```python
from vllm.assets.image import ImageAsset
from vllm import LLM, SamplingParams
# prepare model
llm = LLM(
model="neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8",
trust_remote_code=True,
max_model_len=4096,
max_num_seqs=2,
)
# prepare inputs
question = "What is the content of this image?"
inputs = {
"prompt": f"<|user|>\n<|image_1|>\n{question}<|end|>\n<|assistant|>\n",
"multi_modal_data": {
"image": ImageAsset("cherry_blossom").pil_image.convert("RGB")
},
}
# generate response
print("========== SAMPLE GENERATION ==============")
outputs = llm.generate(inputs, SamplingParams(temperature=0.2, max_tokens=64))
print(f"PROMPT : {outputs[0].prompt}")
print(f"RESPONSE: {outputs[0].outputs[0].text}")
print("==========================================")
```
vLLM also supports OpenAI-compatible serving. See the [documentation](https://docs.vllm.ai/en/latest/) for more details.
## Creation
This model was created with [llm-compressor](https://github.com/vllm-project/llm-compressor) by running the code snippet below as part a multimodal announcement blog.
<details>
<summary>Model Creation Code</summary>
```python
import base64
from io import BytesIO
import torch
from datasets import load_dataset
from qwen_vl_utils import process_vision_info
from transformers import AutoProcessor
from llmcompressor.modifiers.quantization import GPTQModifier
from llmcompressor.transformers import oneshot
from llmcompressor.transformers.tracing import (
TraceableQwen2_5_VLForConditionalGeneration,
)
# Load model.
model_id = args["model_id"]
model = TraceableQwen2_5_VLForConditionalGeneration.from_pretrained(
model_id,
device_map="auto",
torch_dtype="auto",
)
processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
# Oneshot arguments
DATASET_ID = "lmms-lab/flickr30k"
DATASET_SPLIT = {"calibration": "test[:512]"}
NUM_CALIBRATION_SAMPLES = 512
MAX_SEQUENCE_LENGTH = 2048
# Load dataset and preprocess.
ds = load_dataset(DATASET_ID, split=DATASET_SPLIT)
ds = ds.shuffle(seed=42)
dampening_frac=args["dampening_frac"]
save_name = f"{model_id.split('/')[1]}-W8A8-samples{NUM_CALIBRATION_SAMPLES}-df{dampening_frac}"
save_path = os.path.join(args["save_dir"], save_name)
print("Save Path will be:", save_path)
# Apply chat template and tokenize inputs.
def preprocess_and_tokenize(example):
# preprocess
buffered = BytesIO()
example["image"].save(buffered, format="PNG")
encoded_image = base64.b64encode(buffered.getvalue())
encoded_image_text = encoded_image.decode("utf-8")
base64_qwen = f"data:image;base64,{encoded_image_text}"
messages = [
{
"role": "user",
"content": [
{"type": "image", "image": base64_qwen},
{"type": "text", "text": "What does the image show?"},
],
}
]
text = processor.apply_chat_template(
messages, tokenize=False, add_generation_prompt=True
)
image_inputs, video_inputs = process_vision_info(messages)
# tokenize
return processor(
text=[text],
images=image_inputs,
videos=video_inputs,
padding=False,
max_length=MAX_SEQUENCE_LENGTH,
truncation=True,
)
ds = ds.map(preprocess_and_tokenize, remove_columns=ds["calibration"].column_names)
# Define a oneshot data collator for multimodal inputs.
def data_collator(batch):
assert len(batch) == 1
return {key: torch.tensor(value) for key, value in batch[0].items()}
# Recipe
recipe = [
GPTQModifier(
targets="Linear",
scheme="W8A8",
sequential_targets=["Qwen2_5_VLDecoderLayer"],
ignore=["lm_head", "re:visual.*"],
),
]
SAVE_DIR==f"{model_id.split('/')[1]}-quantized.w8a8"
# Perform oneshot
oneshot(
model=model,
tokenizer=model_id,
dataset=ds,
recipe=recipe,
max_seq_length=MAX_SEQUENCE_LENGTH,
num_calibration_samples=NUM_CALIBRATION_SAMPLES,
trust_remote_code_model=True,
data_collator=data_collator,
output_dir=SAVE_DIR
)
```
</details>
## Evaluation
The model was evaluated using [mistral-evals](https://github.com/neuralmagic/mistral-evals) for vision-related tasks and using [lm_evaluation_harness](https://github.com/neuralmagic/lm-evaluation-harness) for select text-based benchmarks. The evaluations were conducted using the following commands:
<details>
<summary>Evaluation Commands</summary>
### Vision Tasks
- vqav2
- docvqa
- mathvista
- mmmu
- chartqa
```
vllm serve neuralmagic/pixtral-12b-quantized.w8a8 --tensor_parallel_size 1 --max_model_len 25000 --trust_remote_code --max_num_seqs 8 --gpu_memory_utilization 0.9 --dtype float16 --limit_mm_per_prompt image=7
python -m eval.run eval_vllm \
--model_name neuralmagic/pixtral-12b-quantized.w8a8 \
--url http://0.0.0.0:8000 \
--output_dir ~/tmp \
--eval_name <vision_task_name>
```
### Text-based Tasks
#### MMLU
```
lm_eval \
--model vllm \
--model_args pretrained="<model_name>",dtype=auto,add_bos_token=True,max_model_len=4096,tensor_parallel_size=<n>,gpu_memory_utilization=0.8,enable_chunked_prefill=True,trust_remote_code=True \
--tasks mmlu \
--num_fewshot 5 \
--batch_size auto \
--output_path output_dir
```
#### MGSM
```
lm_eval \
--model vllm \
--model_args pretrained="<model_name>",dtype=auto,max_model_len=4096,max_gen_toks=2048,max_num_seqs=128,tensor_parallel_size=<n>,gpu_memory_utilization=0.9 \
--tasks mgsm_cot_native \
--apply_chat_template \
--num_fewshot 0 \
--batch_size auto \
--output_path output_dir
```
</details>
### Accuracy
<table>
<thead>
<tr>
<th>Category</th>
<th>Metric</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<th>Recovery (%)</th>
</tr>
</thead>
<tbody>
<tr>
<td rowspan="6"><b>Vision</b></td>
<td>MMMU (val, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>44.56</td>
<td>45.67</td>
<td>102.49%</td>
</tr>
<tr>
<td>VQAv2 (val)<br><i>vqa_match</i></td>
<td>75.94</td>
<td>75.55</td>
<td>99.49%</td>
</tr>
<tr>
<td>DocVQA (val)<br><i>anls</i></td>
<td>92.53</td>
<td>92.32</td>
<td>99.77%</td>
</tr>
<tr>
<td>ChartQA (test, CoT)<br><i>anywhere_in_answer_relaxed_correctness</i></td>
<td>81.20</td>
<td>78.80</td>
<td>97.04%</td>
</tr>
<tr>
<td>Mathvista (testmini, CoT)<br><i>explicit_prompt_relaxed_correctness</i></td>
<td>54.15</td>
<td>53.85</td>
<td>99.45%</td>
</tr>
<tr>
<td><b>Average Score</b></td>
<td><b>69.28</b></td>
<td><b>69.24</b></td>
<td><b>99.94%</b></td>
</tr>
<tr>
<td rowspan="2"><b>Text</b></td>
<td>MGSM (CoT)</td>
<td>43.69</td>
<td>41.98</td>
<td>96.09%</td>
</tr>
<tr>
<td>MMLU (5-shot)</td>
<td>65.32</td>
<td>64.83</td>
<td>99.25%</td>
</tr>
</tbody>
</table>
## Inference Performance
This model achieves up to 1.33x speedup in single-stream deployment and up to 1.37x speedup in multi-stream asynchronous deployment, depending on hardware and use-case scenario.
The following performance benchmarks were conducted with [vLLM](https://docs.vllm.ai/en/latest/) version 0.7.2, and [GuideLLM](https://github.com/neuralmagic/guidellm).
<details>
<summary>Benchmarking Command</summary>
```
guidellm --model neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8 --target "http://localhost:8000/v1" --data-type emulated --data prompt_tokens=<prompt_tokens>,generated_tokens=<generated_tokens>,images=<num_images>,width=<image_width>,height=<image_height> --max seconds 120 --backend aiohttp_server
```
</details>
### Single-stream performance (measured with vLLM version 0.7.2)
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
</tr>
<tr>
<th>Hardware</th>
<th>Model</th>
<th>Average Cost Reduction</th>
<th>Latency (s)</th>
<th>Queries Per Dollar</th>
<th>Latency (s)th>
<th>Queries Per Dollar</th>
<th>Latency (s)</th>
<th>Queries Per Dollar</th>
</tr>
</thead>
<tbody style="text-align: center">
<tr>
<th rowspan="3" valign="top">A6000x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>3.1</td>
<td>1454</td>
<td>1.8</td>
<td>2546</td>
<td>1.7</td>
<td>2610</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.27</td>
<td>2.6</td>
<td>1708</td>
<td>1.3</td>
<td>3340</td>
<td>1.3</td>
<td>3459</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.57</td>
<td>2.4</td>
<td>1886</td>
<td>1.0</td>
<td>4409</td>
<td>1.0</td>
<td>4409</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>2.2</td>
<td>920</td>
<td>1.3</td>
<td>1603</td>
<td>1.2</td>
<td>1636</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.09</td>
<td>2.1</td>
<td>975</td>
<td>1.2</td>
<td>1743</td>
<td>1.1</td>
<td>1814</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.20</td>
<td>2.0</td>
<td>1011</td>
<td>1.0</td>
<td>2015</td>
<td>1.0</td>
<td>2012</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td>1.5</td>
<td>740</td>
<td>0.9</td>
<td>1221</td>
<td>0.9</td>
<td>1276</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
<td>1.06</td>
<td>1.4</td>
<td>768</td>
<td>0.9</td>
<td>1276</td>
<td>0.8</td>
<td>1399</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.24</td>
<td>0.9</td>
<td>1219</td>
<td>0.9</td>
<td>1270</td>
<td>0.8</td>
<td>1304</td>
</tr>
</tbody>
</table>
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025).
### Multi-stream asynchronous performance (measured with vLLM version 0.7.2)
<table border="1" class="dataframe">
<thead>
<tr>
<th></th>
<th></th>
<th></th>
<th style="text-align: center;" colspan="2" >Document Visual Question Answering<br>1680W x 2240H<br>64/128</th>
<th style="text-align: center;" colspan="2" >Visual Reasoning <br>640W x 480H<br>128/128</th>
<th style="text-align: center;" colspan="2" >Image Captioning<br>480W x 360H<br>0/128</th>
</tr>
<tr>
<th>Hardware</th>
<th>Model</th>
<th>Average Cost Reduction</th>
<th>Maximum throughput (QPS)</th>
<th>Queries Per Dollar</th>
<th>Maximum throughput (QPS)</th>
<th>Queries Per Dollar</th>
<th>Maximum throughput (QPS)</th>
<th>Queries Per Dollar</th>
</tr>
</thead>
<tbody style="text-align: center">
<tr>
<th rowspan="3" valign="top">A6000x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>0.5</td>
<td>2405</td>
<td>2.6</td>
<td>11889</td>
<td>2.9</td>
<td>12909</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.26</td>
<td>0.6</td>
<td>2725</td>
<td>3.4</td>
<td>15162</td>
<td>3.9</td>
<td>17673</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.39</td>
<td>0.6</td>
<td>2548</td>
<td>3.9</td>
<td>17437</td>
<td>4.7</td>
<td>21223</td>
</tr>
<tr>
<th rowspan="3" valign="top">A100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>0.8</td>
<td>1663</td>
<td>3.9</td>
<td>7899</td>
<td>4.4</td>
<td>8924</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w8a8</th>
<td>1.06</td>
<td>0.9</td>
<td>1734</td>
<td>4.2</td>
<td>8488</td>
<td>4.7</td>
<td>9548</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.10</td>
<td>0.9</td>
<td>1775</td>
<td>4.2</td>
<td>8540</td>
<td>5.1</td>
<td>10318</td>
</tr>
<tr>
<th rowspan="3" valign="top">H100x1</th>
<th>Qwen/Qwen2.5-VL-3B-Instruct</th>
<td></td>
<td>1.1</td>
<td>1188</td>
<td>4.3</td>
<td>4656</td>
<td>4.3</td>
<td>4676</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-FP8-Dynamic</th>
<td>1.15</td>
<td>1.4</td>
<td>1570</td>
<td>4.3</td>
<td>4676</td>
<td>4.8</td>
<td>5220</td>
</tr>
<tr>
<th>neuralmagic/Qwen2.5-VL-3B-Instruct-quantized.w4a16</th>
<td>1.96</td>
<td>4.2</td>
<td>4598</td>
<td>4.1</td>
<td>4505</td>
<td>4.4</td>
<td>4838</td>
</tr>
</tbody>
</table>
**Use case profiles: Image Size (WxH) / prompt tokens / generation tokens
**QPS: Queries per second.
**QPD: Queries per dollar, based on on-demand cost at [Lambda Labs](https://lambdalabs.com/service/gpu-cloud) (observed on 2/18/2025). |
Gabusoide/model3.0 | Gabusoide | 2025-05-01T15:55:22Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T15:53:15Z | ---
base_model: unsloth/meta-llama-3.1-8b-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Gabusoide
- **License:** apache-2.0
- **Finetuned from model :** unsloth/meta-llama-3.1-8b-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)
|
OnlyCheeini/greesychat-turbo | OnlyCheeini | 2025-05-01T15:54:51Z | 33 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"dataset:OnlyCheeini/greesychat",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2024-08-26T10:54:59Z | ---
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
datasets:
- OnlyCheeini/greesychat
---
# GreesyChat-Turbo AI Model
## Overview
GreesyChat-Turbo is an advanced AI model designed for robust text generation using the LLaMA 3 architecture. This model excels in providing high-quality responses for general conversation, mathematical queries, and more. It’s perfect for powering chatbots, virtual assistants, and any application requiring intelligent dialogue capabilities.
## Benchmark Results
| Metric | Value |
|--------------------|------------|
| **Perplexity** | 22.5 |
| **Generation Speed** | 75 ms per token |
| **Accuracy** | 70% |
| **Response Time** | 200 ms |
| Metric | GreesyChat-Turbo | Mixtral-8x7b | GPT-4 |
|---------------|------------------|---------------|-------------|
| **Code** | 79.2 | 75.6 | 83.6 |
| **MMLU** | 74.5 | 79.9 | 85.1 |
| **Gms8k** | 89.2 (5) | 88.7 | 94.2 |
## Contact
For support or inquiries, please contact: [[email protected]](mailto:[email protected])
|
dimasik1987/f037dae8-e66d-4d3e-8250-597b6de2070b | dimasik1987 | 2025-05-01T15:54:10Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"base_model:adapter:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"license:apache-2.0",
"4-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-01T15:51:58Z | ---
library_name: peft
license: apache-2.0
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f037dae8-e66d-4d3e-8250-597b6de2070b
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- b28d72a27f6c5851_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b28d72a27f6c5851_train_data.json
type:
field_input: query_toks
field_instruction: question
field_output: query
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: dimasik1987/f037dae8-e66d-4d3e-8250-597b6de2070b
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/b28d72a27f6c5851_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 10b4bba1-67d7-4ecf-8210-a48746d35dda
wandb_project: s56-7
wandb_run: your_name
wandb_runid: 10b4bba1-67d7-4ecf-8210-a48746d35dda
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f037dae8-e66d-4d3e-8250-597b6de2070b
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6425
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5999 | 0.2183 | 150 | 0.6425 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Siddharth63/Qwen3-4B-Base-4bit-Autoround-asym | Siddharth63 | 2025-05-01T11:53:04Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"license:apache-2.0",
"4-bit",
"auto-round",
"region:us"
] | null | 2025-05-01T09:02:41Z | ---
license: apache-2.0
---
```
!pip install --upgrade auto-round transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from auto_round import AutoRoundConfig ## must import for auto-round format
quantized_model_path = "Siddharth63/Qwen3-4B-Base-4bit-Autoround-asym"
quantization_config = AutoRoundConfig(backend="auto")
model = AutoModelForCausalLM.from_pretrained(quantized_model_path, device_map="auto",
torch_dtype=torch.float16,
quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(quantized_model_path)
text = "Atherosclerosis"
inputs = tokenizer(text, return_tensors="pt").to(model.device)
print(tokenizer.decode(model.generate(**inputs, max_new_tokens=50)[0]))
``` |
skywalker290/Bert-Stack-Exchange | skywalker290 | 2025-05-01T11:44:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-01T11:41:54Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
pawan2411/modernbert-ct4a-aug50-cl | pawan2411 | 2025-05-01T11:21:20Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"modernbert",
"text-classification",
"generated_from_trainer",
"base_model:answerdotai/ModernBERT-large",
"base_model:finetune:answerdotai/ModernBERT-large",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | 2025-05-01T09:44:32Z | ---
library_name: transformers
license: apache-2.0
base_model: answerdotai/ModernBERT-large
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: modernbert-ct4a
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. -->
# modernbert-ct4a
This model is a fine-tuned version of [answerdotai/ModernBERT-large](https://huggingface.co/answerdotai/ModernBERT-large) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6677
- Accuracy: 0.8856
- F1: 0.7220
- Auc: 0.8155
- Accuracy Per Label: [0.9124087591240876, 0.9051094890510949, 0.8394160583941606]
- F1 Per Label: [0.7692307692307693, 0.7111111111111111, 0.6857142857142857]
- Auc Per Label: [0.8575883575883576, 0.7941787941787942, 0.7946887492861223]
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Auc | Accuracy Per Label | F1 Per Label | Auc Per Label |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:------:|:------------------------------------------------------------:|:-------------------------------------------------------------:|:------------------------------------------------------------:|
| 0.2632 | 1.0 | 720 | 0.3431 | 0.8540 | 0.5798 | 0.7255 | [0.8613138686131386, 0.8686131386861314, 0.8321167883211679] | [0.6122448979591837, 0.47058823529411764, 0.6567164179104478] | [0.7524255024255023, 0.6538461538461539, 0.7701313535122787] |
| 0.1235 | 2.0 | 1440 | 0.2669 | 0.8929 | 0.7449 | 0.8368 | [0.8832116788321168, 0.927007299270073, 0.8686131386861314] | [0.7333333333333333, 0.782608695652174, 0.71875] | [0.8690228690228691, 0.837144837144837, 0.8042547115933752] |
| 0.0365 | 3.0 | 2160 | 0.3926 | 0.8881 | 0.7597 | 0.8662 | [0.8978102189781022, 0.9197080291970803, 0.8467153284671532] | [0.7666666666666667, 0.7924528301886793, 0.72] | [0.8927581427581427, 0.8768191268191268, 0.829097658480868] |
| 0.0186 | 4.0 | 2880 | 0.5401 | 0.8978 | 0.7771 | 0.8725 | [0.9051094890510949, 0.927007299270073, 0.8613138686131386] | [0.7719298245614035, 0.8, 0.759493670886076] | [0.8825363825363826, 0.8665973665973666, 0.8683609366076528] |
| 0.006 | 5.0 | 3600 | 0.5949 | 0.8978 | 0.7547 | 0.8498 | [0.9124087591240876, 0.9051094890510949, 0.8759124087591241] | [0.7931034482758621, 0.6976744186046512, 0.7733333333333333] | [0.9017671517671517, 0.7794525294525294, 0.8682181610508282] |
| 0.0019 | 6.0 | 4320 | 0.8450 | 0.8881 | 0.7252 | 0.8187 | [0.9124087591240876, 0.9051094890510949, 0.8467153284671532] | [0.7777777777777778, 0.7111111111111111, 0.6865671641791045] | [0.8723146223146223, 0.7941787941787942, 0.7896916047972588] |
| 0.0003 | 7.0 | 5040 | 0.7522 | 0.8881 | 0.7177 | 0.8090 | [0.9051094890510949, 0.9051094890510949, 0.8540145985401459] | [0.7450980392156863, 0.7111111111111111, 0.696969696969697] | [0.8383575883575884, 0.7941787941787942, 0.7945459737292976] |
| 0.0 | 8.0 | 5760 | 0.7441 | 0.8856 | 0.7093 | 0.8041 | [0.9124087591240876, 0.8978102189781022, 0.8467153284671532] | [0.7692307692307693, 0.6818181818181818, 0.676923076923077] | [0.8575883575883576, 0.774948024948025, 0.7798400913763565] |
| 0.0 | 9.0 | 6480 | 0.6585 | 0.8881 | 0.7314 | 0.8219 | [0.9124087591240876, 0.9124087591240876, 0.8394160583941606] | [0.7692307692307693, 0.7391304347826086, 0.6857142857142857] | [0.8575883575883576, 0.8134095634095634, 0.7946887492861223] |
| 0.0 | 10.0 | 7200 | 0.6677 | 0.8856 | 0.7220 | 0.8155 | [0.9124087591240876, 0.9051094890510949, 0.8394160583941606] | [0.7692307692307693, 0.7111111111111111, 0.6857142857142857] | [0.8575883575883576, 0.7941787941787942, 0.7946887492861223] |
### Framework versions
- Transformers 4.48.2
- Pytorch 2.6.0+cu124
- Tokenizers 0.21.1
|
ShabanEjupi/Chatbot-i | ShabanEjupi | 2025-05-01T11:11:05Z | 3 | 0 | null | [
"safetensors",
"t5",
"region:us"
] | null | 2025-02-14T22:17:34Z | ---
title: Chatbot-i
emoji: 🤖
colorFrom: blue
colorTo: purple
sdk: gradio
sdk_version: 5.16.0
app_file: app.py
pinned: false
---
## Requirements
- Python 3.8+
- See [requirements.txt](./requirements.txt) |
Kevinjacques/Software | Kevinjacques | 2025-05-01T11:08:36Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T11:08:28Z | ---
license: apache-2.0
---
|
OmBhandwalkar/distilbert-base-uncased-finetuned-ner | OmBhandwalkar | 2025-05-01T10:50:34Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-05-01T10:45:00Z | ---
library_name: transformers
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
datasets:
- conll2003
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: distilbert-base-uncased-finetuned-ner
results:
- task:
name: Token Classification
type: token-classification
dataset:
name: conll2003
type: conll2003
config: conll2003
split: validation
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.9263390392048592
- name: Recall
type: recall
value: 0.9383599955252265
- name: F1
type: f1
value: 0.9323107702567521
- name: Accuracy
type: accuracy
value: 0.9837800054013695
---
<!-- 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. -->
# distilbert-base-uncased-finetuned-ner
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the conll2003 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0609
- Precision: 0.9263
- Recall: 0.9384
- F1: 0.9323
- Accuracy: 0.9838
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| 0.2437 | 1.0 | 878 | 0.0721 | 0.9001 | 0.9217 | 0.9108 | 0.9797 |
| 0.0524 | 2.0 | 1756 | 0.0608 | 0.9211 | 0.9355 | 0.9282 | 0.9832 |
| 0.0305 | 3.0 | 2634 | 0.0609 | 0.9263 | 0.9384 | 0.9323 | 0.9838 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
DanielNRU/pollen-ner-cycle-random-100 | DanielNRU | 2025-05-01T10:02:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:DeepPavlov/rubert-base-cased",
"base_model:adapter:DeepPavlov/rubert-base-cased",
"region:us"
] | null | 2025-05-01T08:42:25Z | ---
library_name: peft
base_model: DeepPavlov/rubert-base-cased
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
model-index:
- name: pollen-ner-cycle-random-100
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. -->
# pollen-ner-cycle-random-100
This model is a fine-tuned version of [DeepPavlov/rubert-base-cased](https://huggingface.co/DeepPavlov/rubert-base-cased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1661
- Precision: 0.0
- Recall: 0.0
- F1: 0.0
## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|
| No log | 1.0 | 19 | 1.8762 | 0.0191 | 0.0406 | 0.0260 |
| No log | 2.0 | 38 | 1.4096 | 0.0 | 0.0 | 0.0 |
| No log | 3.0 | 57 | 1.0823 | 0.0 | 0.0 | 0.0 |
| No log | 4.0 | 76 | 1.2059 | 0.0 | 0.0 | 0.0 |
| No log | 5.0 | 95 | 1.2261 | 0.0 | 0.0 | 0.0 |
| No log | 6.0 | 114 | 1.1842 | 0.0 | 0.0 | 0.0 |
| No log | 7.0 | 133 | 1.1833 | 0.0 | 0.0 | 0.0 |
| No log | 8.0 | 152 | 1.1758 | 0.0 | 0.0 | 0.0 |
| No log | 9.0 | 171 | 1.1664 | 0.0 | 0.0 | 0.0 |
| No log | 10.0 | 190 | 1.1661 | 0.0 | 0.0 | 0.0 |
### Framework versions
- PEFT 0.15.2
- Transformers 4.51.3
- Pytorch 2.6.0+cu126
- Datasets 3.5.0
- Tokenizers 0.21.1 |
GeorgyGUF/Liquid-Metal-sdxl-lora | GeorgyGUF | 2025-05-01T10:01:35Z | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"base_model:adapter:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | text-to-image | 2025-05-01T09:51:37Z | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: 'Liquid_Metal_e000007_00_20250501010601.png'
output:
url: Liquid_Metal_e000007_00_20250501010601.png
- text: 'Liquid_Metal_e000007_01_20250501010617.png'
output:
url: Liquid_Metal_e000007_01_20250501010617.png
- text: ' Liquid_Metal_e000007_02_20250501010633.png'
output:
url: Liquid_Metal_e000007_02_20250501010633.png
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: Dreamy Psychedelic Metallic
---
Source: https://civitai.com/models/1529052/liquid-metal
Training data available here: https://huggingface.co/datasets/GeorgyGUF/Liquid-Metal-sdxl-lora-training-data
Training: Steps: 520 Epochs: 10
Usage Tips: Clip Skip: 1
Trigger Words: Dreamy Psychedelic Metallic |
fhaslam/Llama-3.2-1B-Financial-Sentiment38 | fhaslam | 2025-05-01T09:44:28Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"text-generation",
"conversational",
"en",
"de",
"fr",
"it",
"pt",
"hi",
"es",
"th",
"arxiv:2204.05149",
"arxiv:2405.16406",
"license:llama3.2",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T09:44:26Z | ---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
license: llama3.2
extra_gated_prompt: >-
### LLAMA 3.2 COMMUNITY LICENSE AGREEMENT
Llama 3.2 Version Release Date: September 25, 2024
“Agreement” means the terms and conditions for use, reproduction, distribution
and modification of the Llama Materials set forth herein.
“Documentation” means the specifications, manuals and documentation accompanying Llama 3.2
distributed by Meta at https://llama.meta.com/doc/overview.
“Licensee” or “you” means you, or your employer or any other person or entity (if you are
entering into this Agreement on such person or entity’s behalf), of the age required under
applicable laws, rules or regulations to provide legal consent and that has legal authority
to bind your employer or such other person or entity if you are entering in this Agreement
on their behalf.
“Llama 3.2” means the foundational large language models and software and algorithms, including
machine-learning model code, trained model weights, inference-enabling code, training-enabling code,
fine-tuning enabling code and other elements of the foregoing distributed by Meta at
https://www.llama.com/llama-downloads.
“Llama Materials” means, collectively, Meta’s proprietary Llama 3.2 and Documentation (and
any portion thereof) made available under this Agreement.
“Meta” or “we” means Meta Platforms Ireland Limited (if you are located in or,
if you are an entity, your principal place of business is in the EEA or Switzerland)
and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).
By clicking “I Accept” below or by using or distributing any portion or element of the Llama Materials,
you agree to be bound by this Agreement.
1. License Rights and Redistribution.
a. Grant of Rights. You are granted a non-exclusive, worldwide,
non-transferable and royalty-free limited license under Meta’s intellectual property or other rights
owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works
of, and make modifications to the Llama Materials.
b. Redistribution and Use.
i. If you distribute or make available the Llama Materials (or any derivative works thereof),
or a product or service (including another AI model) that contains any of them, you shall (A) provide
a copy of this Agreement with any such Llama Materials; and (B) prominently display “Built with Llama”
on a related website, user interface, blogpost, about page, or product documentation. If you use the
Llama Materials or any outputs or results of the Llama Materials to create, train, fine tune, or
otherwise improve an AI model, which is distributed or made available, you shall also include “Llama”
at the beginning of any such AI model name.
ii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part
of an integrated end user product, then Section 2 of this Agreement will not apply to you.
iii. You must retain in all copies of the Llama Materials that you distribute the
following attribution notice within a “Notice” text file distributed as a part of such copies:
“Llama 3.2 is licensed under the Llama 3.2 Community License, Copyright © Meta Platforms,
Inc. All Rights Reserved.”
iv. Your use of the Llama Materials must comply with applicable laws and regulations
(including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for
the Llama Materials (available at https://www.llama.com/llama3_2/use-policy), which is hereby
incorporated by reference into this Agreement.
2. Additional Commercial Terms. If, on the Llama 3.2 version release date, the monthly active users
of the products or services made available by or for Licensee, or Licensee’s affiliates,
is greater than 700 million monthly active users in the preceding calendar month, you must request
a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to
exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.
3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND
RESULTS THEREFROM ARE PROVIDED ON AN “AS IS” BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS
ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES
OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. YOU ARE SOLELY RESPONSIBLE
FOR DETERMINING THE APPROPRIATENESS OF USING OR REDISTRIBUTING THE LLAMA MATERIALS AND ASSUME ANY RISKS ASSOCIATED
WITH YOUR USE OF THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS.
4. Limitation of Liability. IN NO EVENT WILL META OR ITS AFFILIATES BE LIABLE UNDER ANY THEORY OF LIABILITY,
WHETHER IN CONTRACT, TORT, NEGLIGENCE, PRODUCTS LIABILITY, OR OTHERWISE, ARISING OUT OF THIS AGREEMENT,
FOR ANY LOST PROFITS OR ANY INDIRECT, SPECIAL, CONSEQUENTIAL, INCIDENTAL, EXEMPLARY OR PUNITIVE DAMAGES, EVEN
IF META OR ITS AFFILIATES HAVE BEEN ADVISED OF THE POSSIBILITY OF ANY OF THE FOREGOING.
5. Intellectual Property.
a. No trademark licenses are granted under this Agreement, and in connection with the Llama Materials,
neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates,
except as required for reasonable and customary use in describing and redistributing the Llama Materials or as
set forth in this Section 5(a). Meta hereby grants you a license to use “Llama” (the “Mark”) solely as required
to comply with the last sentence of Section 1.b.i. You will comply with Meta’s brand guidelines (currently accessible
at https://about.meta.com/brand/resources/meta/company-brand/). All goodwill arising out of your use of the Mark
will inure to the benefit of Meta.
b. Subject to Meta’s ownership of Llama Materials and derivatives made by or for Meta, with respect to any
derivative works and modifications of the Llama Materials that are made by you, as between you and Meta,
you are and will be the owner of such derivative works and modifications.
c. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or
counterclaim in a lawsuit) alleging that the Llama Materials or Llama 3.2 outputs or results, or any portion
of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable
by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or
claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third
party arising out of or related to your use or distribution of the Llama Materials.
6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access
to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms
and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this
Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3,
4 and 7 shall survive the termination of this Agreement.
7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of
California without regard to choice of law principles, and the UN Convention on Contracts for the International
Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of
any dispute arising out of this Agreement.
### Llama 3.2 Acceptable Use Policy
Meta is committed to promoting safe and fair use of its tools and features, including Llama 3.2.
If you access or use Llama 3.2, you agree to this Acceptable Use Policy (“**Policy**”).
The most recent copy of this policy can be found at
[https://www.llama.com/llama3_2/use-policy](https://www.llama.com/llama3_2/use-policy).
#### Prohibited Uses
We want everyone to use Llama 3.2 safely and responsibly. You agree you will not use, or allow others to use, Llama 3.2 to:
1. Violate the law or others’ rights, including to:
1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:
1. Violence or terrorism
2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material
3. Human trafficking, exploitation, and sexual violence
4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.
5. Sexual solicitation
6. Any other criminal activity
1. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals
2. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services
3. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices
4. Collect, process, disclose, generate, or infer private or sensitive information about individuals, including information about individuals’ identity, health, or demographic information, unless you have obtained the right to do so in accordance with applicable law
5. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials
6. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system
7. Engage in any action, or facilitate any action, to intentionally circumvent or remove usage restrictions or other safety measures, or to enable functionality disabled by Meta
2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Llama 3.2 related to the following:
8. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State or to the U.S. Biological Weapons Anti-Terrorism Act of 1989 or the Chemical Weapons Convention Implementation Act of 1997
9. Guns and illegal weapons (including weapon development)
10. Illegal drugs and regulated/controlled substances
11. Operation of critical infrastructure, transportation technologies, or heavy machinery
12. Self-harm or harm to others, including suicide, cutting, and eating disorders
13. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual
3. Intentionally deceive or mislead others, including use of Llama 3.2 related to the following:
14. Generating, promoting, or furthering fraud or the creation or promotion of disinformation
15. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content
16. Generating, promoting, or further distributing spam
17. Impersonating another individual without consent, authorization, or legal right
18. Representing that the use of Llama 3.2 or outputs are human-generated
19. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement
4. Fail to appropriately disclose to end users any known dangers of your AI system
5. Interact with third party tools, models, or software designed to generate unlawful content or engage in unlawful or harmful conduct and/or represent that the outputs of such tools, models, or software are associated with Meta or Llama 3.2
With respect to any multimodal models included in Llama 3.2, the rights granted under Section 1(a) of the Llama 3.2 Community License Agreement are not being granted to you if you are an individual domiciled in, or a company with a principal place of business in, the European Union. This restriction does not apply to end users of a product or service that incorporates any such multimodal models.
Please report any violation of this Policy, software “bug,” or other problems that could lead to a violation of this Policy through one of the following means:
* Reporting issues with the model: [https://github.com/meta-llama/llama-models/issues](https://l.workplace.com/l.php?u=https%3A%2F%2Fgithub.com%2Fmeta-llama%2Fllama-models%2Fissues&h=AT0qV8W9BFT6NwihiOHRuKYQM_UnkzN_NmHMy91OT55gkLpgi4kQupHUl0ssR4dQsIQ8n3tfd0vtkobvsEvt1l4Ic6GXI2EeuHV8N08OG2WnbAmm0FL4ObkazC6G_256vN0lN9DsykCvCqGZ)
* Reporting risky content generated by the model: [developers.facebook.com/llama_output_feedback](http://developers.facebook.com/llama_output_feedback)
* Reporting bugs and security concerns: [facebook.com/whitehat/info](http://facebook.com/whitehat/info)
* Reporting violations of the Acceptable Use Policy or unlicensed uses of Llama 3.2: [email protected]
extra_gated_fields:
First Name: text
Last Name: text
Date of birth: date_picker
Country: country
Affiliation: text
Job title:
type: select
options:
- Student
- Research Graduate
- AI researcher
- AI developer/engineer
- Reporter
- Other
geo: ip_location
By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy: checkbox
extra_gated_description: >-
The information you provide will be collected, stored, processed and shared in
accordance with the [Meta Privacy
Policy](https://www.facebook.com/privacy/policy/).
extra_gated_button_content: Submit
---
## Model Information
The Llama 3.2 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction-tuned generative models in 1B and 3B sizes (text in/text out). The Llama 3.2 instruction-tuned text only models are optimized for multilingual dialogue use cases, including agentic retrieval and summarization tasks. They outperform many of the available open source and closed chat models on common industry benchmarks.
**Model Developer:** Meta
**Model Architecture:** Llama 3.2 is an auto-regressive language model that uses an optimized transformer architecture. The tuned versions use supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to align with human preferences for helpfulness and safety.
| | Training Data | Params | Input modalities | Output modalities | Context Length | GQA | Shared Embeddings | Token count | Knowledge cutoff |
| :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- | :---- |
| Llama 3.2 (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 128k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
| Llama 3.2 Quantized (text only) | A new mix of publicly available online data. | 1B (1.23B) | Multilingual Text | Multilingual Text and code | 8k | Yes | Yes | Up to 9T tokens | December 2023 |
| | | 3B (3.21B) | Multilingual Text | Multilingual Text and code | | | | | |
**Supported Languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai are officially supported. Llama 3.2 has been trained on a broader collection of languages than these 8 supported languages. Developers may fine-tune Llama 3.2 models for languages beyond these supported languages, provided they comply with the Llama 3.2 Community License and the Acceptable Use Policy. Developers are always expected to ensure that their deployments, including those that involve additional languages, are completed safely and responsibly.
**Llama 3.2 Model Family:** Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** Sept 25, 2024
**Status:** This is a static model trained on an offline dataset. Future versions may be released that improve model capabilities and safety.
**License:** Use of Llama 3.2 is governed by the [Llama 3.2 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_2/LICENSE) (a custom, commercial license agreement).
**Feedback:** Instructions on how to provide feedback or comments on the model can be found in the Llama Models [README](https://github.com/meta-llama/llama-models/blob/main/README.md). For more technical information about generation parameters and recipes for how to use Llama 3.2 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases:** Llama 3.2 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat and agentic applications like knowledge retrieval and summarization, mobile AI powered writing assistants and query and prompt rewriting. Pretrained models can be adapted for a variety of additional natural language generation tasks. Similarly, quantized models can be adapted for a variety of on-device use-cases with limited compute resources.
**Out of Scope:** Use in any manner that violates applicable laws or regulations (including trade compliance laws). Use in any other way that is prohibited by the Acceptable Use Policy and Llama 3.2 Community License. Use in languages beyond those explicitly referenced as supported in this model card.
## How to use
This repository contains two versions of Llama-3.2-1B-Instruct, for use with transformers and with the original `llama` codebase.
### Use with transformers
Starting with `transformers >= 4.43.0` onward, you can run conversational inference using the Transformers `pipeline` abstraction or by leveraging the Auto classes with the `generate()` function.
Make sure to update your transformers installation via `pip install --upgrade transformers`.
```python
import torch
from transformers import pipeline
model_id = "meta-llama/Llama-3.2-1B-Instruct"
pipe = pipeline(
"text-generation",
model=model_id,
torch_dtype=torch.bfloat16,
device_map="auto",
)
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
outputs = pipe(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
Note: You can also find detailed recipes on how to use the model locally, with `torch.compile()`, assisted generations, quantised and more at [`huggingface-llama-recipes`](https://github.com/huggingface/huggingface-llama-recipes)
### Use with `llama`
Please, follow the instructions in the [repository](https://github.com/meta-llama/llama)
To download Original checkpoints, see the example command below leveraging `huggingface-cli`:
```
huggingface-cli download meta-llama/Llama-3.2-1B-Instruct --include "original/*" --local-dir Llama-3.2-1B-Instruct
```
## Hardware and Software
**Training Factors:** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, quantization, annotation, and evaluation were also performed on production infrastructure.
**Training Energy Use:** Training utilized a cumulative of **916k** GPU hours of computation on H100-80GB (TDP of 700W) type hardware, per the table below. Training time is the total GPU time required for training each model and power consumption is the peak power capacity per GPU device used, adjusted for power usage efficiency.
**Training Greenhouse Gas Emissions:** Estimated total location-based greenhouse gas emissions were **240** tons CO2eq for training. Since 2020, Meta has maintained net zero greenhouse gas emissions in its global operations and matched 100% of its electricity use with renewable energy; therefore, the total market-based greenhouse gas emissions for training were 0 tons CO2eq.
| | Training Time (GPU hours) | Logit Generation Time (GPU Hours) | Training Power Consumption (W) | Training Location-Based Greenhouse Gas Emissions (tons CO2eq) | Training Market-Based Greenhouse Gas Emissions (tons CO2eq) |
| :---- | :---: | ----- | :---: | :---: | :---: |
| Llama 3.2 1B | 370k | \- | 700 | 107 | 0 |
| Llama 3.2 3B | 460k | \- | 700 | 133 | 0 |
| Llama 3.2 1B SpinQuant | 1.7 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 3B SpinQuant | 2.4 | 0 | 700 | *Negligible*\*\* | 0 |
| Llama 3.2 1B QLora | 1.3k | 0 | 700 | 0.381 | 0 |
| Llama 3.2 3B QLora | 1.6k | 0 | 700 | 0.461 | 0 |
| Total | 833k | 86k | | 240 | 0 |
\*\* The location-based CO2e emissions of Llama 3.2 1B SpinQuant and Llama 3.2 3B SpinQuant are less than 0.001 metric tonnes each. This is due to the minimal training GPU hours that are required.
The methodology used to determine training energy use and greenhouse gas emissions can be found [here](https://arxiv.org/pdf/2204.05149). Since Meta is openly releasing these models, the training energy use and greenhouse gas emissions will not be incurred by others.
## Training Data
**Overview:** Llama 3.2 was pretrained on up to 9 trillion tokens of data from publicly available sources. For the 1B and 3B Llama 3.2 models, we incorporated logits from the Llama 3.1 8B and 70B models into the pretraining stage of the model development, where outputs (logits) from these larger models were used as token-level targets. Knowledge distillation was used after pruning to recover performance. In post-training we used a similar recipe as Llama 3.1 and produced final chat models by doing several rounds of alignment on top of the pre-trained model. Each round involved Supervised Fine-Tuning (SFT), Rejection Sampling (RS), and Direct Preference Optimization (DPO).
**Data Freshness:** The pretraining data has a cutoff of December 2023\.
## Quantization
### Quantization Scheme
We designed the current quantization scheme with the [PyTorch’s ExecuTorch](https://github.com/pytorch/executorch) inference framework and Arm CPU backend in mind, taking into account metrics including model quality, prefill/decoding speed, and memory footprint. Our quantization scheme involves three parts:
- All linear layers in all transformer blocks are quantized to a 4-bit groupwise scheme (with a group size of 32) for weights and 8-bit per-token dynamic quantization for activations.
- The classification layer is quantized to 8-bit per-channel for weight and 8-bit per token dynamic quantization for activation.
- Similar to classification layer, an 8-bit per channel quantization is used for embedding layer.
### Quantization-Aware Training and LoRA
The quantization-aware training (QAT) with low-rank adaptation (LoRA) models went through only post-training stages, using the same data as the full precision models. To initialize QAT, we utilize BF16 Llama 3.2 model checkpoints obtained after supervised fine-tuning (SFT) and perform an additional full round of SFT training with QAT. We then freeze the backbone of the QAT model and perform another round of SFT with LoRA adaptors applied to all layers within the transformer block. Meanwhile, the LoRA adaptors' weights and activations are maintained in BF16. Because our approach is similar to QLoRA of Dettmers et al., (2023) (i.e., quantization followed by LoRA adapters), we refer this method as QLoRA. Finally, we fine-tune the resulting model (both backbone and LoRA adaptors) using direct preference optimization (DPO).
### SpinQuant
[SpinQuant](https://arxiv.org/abs/2405.16406) was applied, together with generative post-training quantization (GPTQ). For the SpinQuant rotation matrix fine-tuning, we optimized for 100 iterations, using 800 samples with sequence-length 2048 from the WikiText 2 dataset. For GPTQ, we used 128 samples from the same dataset with the same sequence-length.
## Benchmarks \- English Text
In this section, we report the results for Llama 3.2 models on standard automatic benchmarks. For all these evaluations, we used our internal evaluations library.
### Base Pretrained Models
| Category | Benchmark | \# Shots | Metric | Llama 3.2 1B | Llama 3.2 3B | Llama 3.1 8B |
| ----- | ----- | :---: | :---: | :---: | :---: | :---: |
| General | MMLU | 5 | macro\_avg/acc\_char | 32.2 | 58 | 66.7 |
| | AGIEval English | 3-5 | average/acc\_char | 23.3 | 39.2 | 47.8 |
| | ARC-Challenge | 25 | acc\_char | 32.8 | 69.1 | 79.7 |
| Reading comprehension | SQuAD | 1 | em | 49.2 | 67.7 | 77 |
| | QuAC (F1) | 1 | f1 | 37.9 | 42.9 | 44.9 |
| | DROP (F1) | 3 | f1 | 28.0 | 45.2 | 59.5 |
| Long Context | Needle in Haystack | 0 | em | 96.8 | 1 | 1 |
### Instruction Tuned Models
| Capability | | Benchmark | \# Shots | Metric | Llama 3.2 1B bf16 | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B bf16 | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | ----- | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | | MMLU | 5 | macro\_avg/acc | 49.3 | 43.3 | 47.3 | 49.0 | 63.4 | 60.5 | 62 | 62.4 | 69.4 |
| Re-writing | | Open-rewrite eval | 0 | micro\_avg/rougeL | 41.6 | 39.2 | 40.9 | 41.2 | 40.1 | 40.3 | 40.8 | 40.7 | 40.9 |
| Summarization | | TLDR9+ (test) | 1 | rougeL | 16.8 | 14.9 | 16.7 | 16.8 | 19.0 | 19.1 | 19.2 | 19.1 | 17.2 |
| Instruction following | | IFEval | 0 | Avg(Prompt/Instruction acc Loose/Strict) | 59.5 | 51.5 | 58.4 | 55.6 | 77.4 | 73.9 | 73.5 | 75.9 | 80.4 |
| Math | | GSM8K (CoT) | 8 | em\_maj1@1 | 44.4 | 33.1 | 40.6 | 46.5 | 77.7 | 72.9 | 75.7 | 77.9 | 84.5 |
| | | MATH (CoT) | 0 | final\_em | 30.6 | 20.5 | 25.3 | 31.0 | 48.0 | 44.2 | 45.3 | 49.2 | 51.9 |
| Reasoning | | ARC-C | 0 | acc | 59.4 | 54.3 | 57 | 60.7 | 78.6 | 75.6 | 77.6 | 77.6 | 83.4 |
| | | GPQA | 0 | acc | 27.2 | 25.9 | 26.3 | 25.9 | 32.8 | 32.8 | 31.7 | 33.9 | 32.8 |
| | | Hellaswag | 0 | acc | 41.2 | 38.1 | 41.3 | 41.5 | 69.8 | 66.3 | 68 | 66.3 | 78.7 |
| Tool Use | | BFCL V2 | 0 | acc | 25.7 | 14.3 | 15.9 | 23.7 | 67.0 | 53.4 | 60.1 | 63.5 | 67.1 |
| | | Nexus | 0 | macro\_avg/acc | 13.5 | 5.2 | 9.6 | 12.5 | 34.3 | 32.4 | 31.5 | 30.1 | 38.5 |
| Long Context | | InfiniteBench/En.QA | 0 | longbook\_qa/f1 | 20.3 | N/A | N/A | N/A | 19.8 | N/A | N/A | N/A | 27.3 |
| | | InfiniteBench/En.MC | 0 | longbook\_choice/acc | 38.0 | N/A | N/A | N/A | 63.3 | N/A | N/A | N/A | 72.2 |
| | | NIH/Multi-needle | 0 | recall | 75.0 | N/A | N/A | N/A | 84.7 | N/A | N/A | N/A | 98.8 |
| Multilingual | | MGSM (CoT) | 0 | em | 24.5 | 13.7 | 18.2 | 24.4 | 58.2 | 48.9 | 54.3 | 56.8 | 68.9 |
\*\*for comparison purposes only. Model not released.
### Multilingual Benchmarks
| Category | Benchmark | Language | Llama 3.2 1B | Llama 3.2 1B Vanilla PTQ\*\* | Llama 3.2 1B Spin Quant | Llama 3.2 1B QLoRA | Llama 3.2 3B | Llama 3.2 3B Vanilla PTQ\*\* | Llama 3.2 3B Spin Quant | Llama 3.2 3B QLoRA | Llama 3.1 8B |
| :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: | :---: |
| General | MMLU (5-shot, macro_avg/acc) | Portuguese | 39.8 | 34.9 | 38.9 | 40.2 | 54.5 | 50.9 | 53.3 | 53.4 | 62.1 |
| | | Spanish | 41.5 | 36.0 | 39.8 | 41.8 | 55.1 | 51.9 | 53.6 | 53.6 | 62.5 |
| | | Italian | 39.8 | 34.9 | 38.1 | 40.6 | 53.8 | 49.9 | 52.1 | 51.7 | 61.6 |
| | | German | 39.2 | 34.9 | 37.5 | 39.6 | 53.3 | 50.0 | 52.2 | 51.3 | 60.6 |
| | | French | 40.5 | 34.8 | 39.2 | 40.8 | 54.6 | 51.2 | 53.3 | 53.3 | 62.3 |
| | | Hindi | 33.5 | 30.0 | 32.1 | 34.0 | 43.3 | 40.4 | 42.0 | 42.1 | 50.9 |
| | | Thai | 34.7 | 31.2 | 32.4 | 34.9 | 44.5 | 41.3 | 44.0 | 42.2 | 50.3 |
\*\*for comparison purposes only. Model not released.
## Inference time
In the below table, we compare the performance metrics of different quantization methods (SpinQuant and QAT \+ LoRA) with the BF16 baseline. The evaluation was done using the [ExecuTorch](https://github.com/pytorch/executorch) framework as the inference engine, with the ARM CPU as a backend using Android OnePlus 12 device.
| Category | Decode (tokens/sec) | Time-to-first-token (sec) | Prefill (tokens/sec) | Model size (PTE file size in MB) | Memory size (RSS in MB) |
| :---- | ----- | ----- | ----- | ----- | ----- |
| 1B BF16 (baseline) | 19.2 | 1.0 | 60.3 | 2358 | 3,185 |
| 1B SpinQuant | 50.2 (2.6x) | 0.3 (-76.9%) | 260.5 (4.3x) | 1083 (-54.1%) | 1,921 (-39.7%) |
| 1B QLoRA | 45.8 (2.4x) | 0.3 (-76.0%) | 252.0 (4.2x) | 1127 (-52.2%) | 2,255 (-29.2%) |
| 3B BF16 (baseline) | 7.6 | 3.0 | 21.2 | 6129 | 7,419 |
| 3B SpinQuant | 19.7 (2.6x) | 0.7 (-76.4%) | 89.7 (4.2x) | 2435 (-60.3%) | 3,726 (-49.8%) |
| 3B QLoRA | 18.5 (2.4x) | 0.7 (-76.1%) | 88.8 (4.2x) | 2529 (-58.7%) | 4,060 (-45.3%) |
(\*) The performance measurement is done using an adb binary-based approach.
(\*\*) It is measured on an Android OnePlus 12 device.
(\*\*\*) Time-to-first-token (TTFT) is measured with prompt length=64
*Footnote:*
- *Decode (tokens/second) is for how quickly it keeps generating. Higher is better.*
- *Time-to-first-token (TTFT for shorthand) is for how fast it generates the first token for a given prompt. Lower is better.*
- *Prefill is the inverse of TTFT (aka 1/TTFT) in tokens/second. Higher is better*
- *Model size \- how big is the model, measured by, PTE file, a binary file format for ExecuTorch*
- *RSS size \- Memory usage in resident set size (RSS)*
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
1. Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama
2. Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm
3. Provide protections for the community to help prevent the misuse of our models
### Responsible Deployment
**Approach:** Llama is a foundational technology designed to be used in a variety of use cases. Examples on how Meta’s Llama models have been responsibly deployed can be found in our [Community Stories webpage](https://llama.meta.com/community-stories/). Our approach is to build the most helpful models, enabling the world to benefit from the technology power, by aligning our model safety for generic use cases and addressing a standard set of harms. Developers are then in the driver’s seat to tailor safety for their use cases, defining their own policies and deploying the models with the necessary safeguards in their Llama systems. Llama 3.2 was developed following the best practices outlined in our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/).
#### Llama 3.2 Instruct
**Objective:** Our main objectives for conducting safety fine-tuning are to provide the research community with a valuable resource for studying the robustness of safety fine-tuning, as well as to offer developers a readily available, safe, and powerful model for various applications to reduce the developer workload to deploy safe AI systems. We implemented the same set of safety mitigations as in Llama 3, and you can learn more about these in the Llama 3 [paper](https://ai.meta.com/research/publications/the-llama-3-herd-of-models/).
**Fine-Tuning Data:** We employ a multi-faceted approach to data collection, combining human-generated data from our vendors with synthetic data to mitigate potential safety risks. We’ve developed many large language model (LLM)-based classifiers that enable us to thoughtfully select high-quality prompts and responses, enhancing data quality control.
**Refusals and Tone:** Building on the work we started with Llama 3, we put a great emphasis on model refusals to benign prompts as well as refusal tone. We included both borderline and adversarial prompts in our safety data strategy, and modified our safety data responses to follow tone guidelines.
#### Llama 3.2 Systems
**Safety as a System:** Large language models, including Llama 3.2, **are not designed to be deployed in isolation** but instead should be deployed as part of an overall AI system with additional safety guardrails as required. Developers are expected to deploy system safeguards when building agentic systems. Safeguards are key to achieve the right helpfulness-safety alignment as well as mitigating safety and security risks inherent to the system and any integration of the model or system with external tools. As part of our responsible release approach, we provide the community with [safeguards](https://llama.meta.com/trust-and-safety/) that developers should deploy with Llama models or other LLMs, including Llama Guard, Prompt Guard and Code Shield. All our [reference implementations](https://github.com/meta-llama/llama-agentic-system) demos contain these safeguards by default so developers can benefit from system-level safety out-of-the-box.
### New Capabilities and Use Cases
**Technological Advancement:** Llama releases usually introduce new capabilities that require specific considerations in addition to the best practices that generally apply across all Generative AI use cases. For prior release capabilities also supported by Llama 3.2, see [Llama 3.1 Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/MODEL_CARD.md), as the same considerations apply here as well.
**Constrained Environments:** Llama 3.2 1B and 3B models are expected to be deployed in highly constrained environments, such as mobile devices. LLM Systems using smaller models will have a different alignment profile and safety/helpfulness tradeoff than more complex, larger systems. Developers should ensure the safety of their system meets the requirements of their use case. We recommend using lighter system safeguards for such use cases, like Llama Guard 3-1B or its mobile-optimized version.
### Evaluations
**Scaled Evaluations:** We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Purple Llama safeguards to filter input prompt and output response. It is important to evaluate applications in context, and we recommend building dedicated evaluation dataset for your use case.
**Red Teaming:** We conducted recurring red teaming exercises with the goal of discovering risks via adversarial prompting and we used the learnings to improve our benchmarks and safety tuning datasets. We partnered early with subject-matter experts in critical risk areas to understand the nature of these real-world harms and how such models may lead to unintended harm for society. Based on these conversations, we derived a set of adversarial goals for the red team to attempt to achieve, such as extracting harmful information or reprogramming the model to act in a potentially harmful capacity. The red team consisted of experts in cybersecurity, adversarial machine learning, responsible AI, and integrity in addition to multilingual content specialists with background in integrity issues in specific geographic markets.
### Critical Risks
In addition to our safety work above, we took extra care on measuring and/or mitigating the following critical risk areas:
**1\. CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive Weapons):** Llama 3.2 1B and 3B models are smaller and less capable derivatives of Llama 3.1. For Llama 3.1 70B and 405B, to assess risks related to proliferation of chemical and biological weapons, we performed uplift testing designed to assess whether use of Llama 3.1 models could meaningfully increase the capabilities of malicious actors to plan or carry out attacks using these types of weapons and have determined that such testing also applies to the smaller 1B and 3B models.
**2\. Child Safety:** Child Safety risk assessments were conducted using a team of experts, to assess the model’s capability to produce outputs that could result in Child Safety risks and inform on any necessary and appropriate risk mitigations via fine tuning. We leveraged those expert red teaming sessions to expand the coverage of our evaluation benchmarks through Llama 3 model development. For Llama 3, we conducted new in-depth sessions using objective based methodologies to assess the model risks along multiple attack vectors including the additional languages Llama 3 is trained on. We also partnered with content specialists to perform red teaming exercises assessing potentially violating content while taking account of market specific nuances or experiences.
**3\. Cyber Attacks:** For Llama 3.1 405B, our cyber attack uplift study investigated whether LLMs can enhance human capabilities in hacking tasks, both in terms of skill level and speed.
Our attack automation study focused on evaluating the capabilities of LLMs when used as autonomous agents in cyber offensive operations, specifically in the context of ransomware attacks. This evaluation was distinct from previous studies that considered LLMs as interactive assistants. The primary objective was to assess whether these models could effectively function as independent agents in executing complex cyber-attacks without human intervention. Because Llama 3.2’s 1B and 3B models are smaller and less capable models than Llama 3.1 405B, we broadly believe that the testing conducted for the 405B model also applies to Llama 3.2 models.
### Community
**Industry Partnerships:** Generative AI safety requires expertise and tooling, and we believe in the strength of the open community to accelerate its progress. We are active members of open consortiums, including the AI Alliance, Partnership on AI and MLCommons, actively contributing to safety standardization and transparency. We encourage the community to adopt taxonomies like the MLCommons Proof of Concept evaluation to facilitate collaboration and transparency on safety and content evaluations. Our Purple Llama tools are open sourced for the community to use and widely distributed across ecosystem partners including cloud service providers. We encourage community contributions to our [Github repository](https://github.com/meta-llama/PurpleLlama).
**Grants:** We also set up the [Llama Impact Grants](https://llama.meta.com/llama-impact-grants/) program to identify and support the most compelling applications of Meta’s Llama model for societal benefit across three categories: education, climate and open innovation. The 20 finalists from the hundreds of applications can be found [here](https://llama.meta.com/llama-impact-grants/#finalists).
**Reporting:** Finally, we put in place a set of resources including an [output reporting mechanism](https://developers.facebook.com/llama_output_feedback) and [bug bounty program](https://www.facebook.com/whitehat) to continuously improve the Llama technology with the help of the community.
## Ethical Considerations and Limitations
**Values:** The core values of Llama 3.2 are openness, inclusivity and helpfulness. It is meant to serve everyone, and to work for a wide range of use cases. It is thus designed to be accessible to people across many different backgrounds, experiences and perspectives. Llama 3.2 addresses users and their needs as they are, without insertion unnecessary judgment or normativity, while reflecting the understanding that even content that may appear problematic in some cases can serve valuable purposes in others. It respects the dignity and autonomy of all users, especially in terms of the values of free thought and expression that power innovation and progress.
**Testing:** Llama 3.2 is a new technology, and like any new technology, there are risks associated with its use. Testing conducted to date has not covered, nor could it cover, all scenarios. For these reasons, as with all LLMs, Llama 3.2’s potential outputs cannot be predicted in advance, and the model may in some instances produce inaccurate, biased or other objectionable responses to user prompts. Therefore, before deploying any applications of Llama 3.2 models, developers should perform safety testing and tuning tailored to their specific applications of the model. Please refer to available resources including our [Responsible Use Guide](https://llama.meta.com/responsible-use-guide), [Trust and Safety](https://llama.meta.com/trust-and-safety/) solutions, and other [resources](https://llama.meta.com/docs/get-started/) to learn more about responsible development.
|
mohhtl/f0880f6b-ca2e-4966-9f8b-688dce55420a | mohhtl | 2025-05-01T09:41:46Z | 0 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"generated_from_trainer",
"dataset:973d117e-aa3b-43eb-9ee8-f69e4efbf100.jsonl",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"base_model:adapter:unsloth/Phi-3-mini-4k-instruct",
"license:mit",
"region:us"
] | null | 2025-05-01T08:46:10Z | ---
library_name: peft
license: mit
base_model: unsloth/Phi-3-mini-4k-instruct
tags:
- generated_from_trainer
datasets:
- 973d117e-aa3b-43eb-9ee8-f69e4efbf100.jsonl
model-index:
- name: f0880f6b-ca2e-4966-9f8b-688dce55420a
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: `0.8.0.dev0`
```yaml
adam_beta2: 0.95
adam_epsilon: 1.0e-05
adapter: lora
base_model: unsloth/Phi-3-mini-4k-instruct
bf16: auto
chat_template: phi_3
dataset_prepared_path: 973d117e-aa3b-43eb-9ee8-f69e4efbf100_last_run_prepared
datasets:
- path: 973d117e-aa3b-43eb-9ee8-f69e4efbf100.jsonl
type:
field: null
field_input: input
field_instruction: instruction
field_output: output
field_system: null
format: null
no_input_format: null
system_format: '{system}'
system_prompt: ''
debug: true
flash_attention: true
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: true
learning_rate: 5.0e-06
load_in_4bit: false
load_in_8bit: false
logging_steps: 1
lora_alpha: 32
lora_dropout: 0.05
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
micro_batch_size: 2
model_type: AutoModelForCausalLM
num_epochs: 20
optimizer: adamw_torch_fused
output_dir: f0880f6b-ca2e-4966-9f8b-688dce55420a
pad_to_sequence_len: true
resize_token_embeddings_to_32x: true
sample_packing: true
save_strategy: 'no'
sequence_len: 4096
tokenizer_type: AutoTokenizer
trust_remote_code: true
val_set_size: 0.0
warmup_ratio: 0.2
weight_decay: 0.1
```
</details><br>
# f0880f6b-ca2e-4966-9f8b-688dce55420a
This model is a fine-tuned version of [unsloth/Phi-3-mini-4k-instruct](https://huggingface.co/unsloth/Phi-3-mini-4k-instruct) on the 973d117e-aa3b-43eb-9ee8-f69e4efbf100.jsonl dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.95) and epsilon=1e-05 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 96
- num_epochs: 20.0
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.1 |
rbelanec/train_record_1745950252 | rbelanec | 2025-05-01T09:21:15Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-04-29T18:15:47Z | ---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: train_record_1745950252
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. -->
# train_record_1745950252
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the record dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2577
- Num Input Tokens Seen: 54198768
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
- training_steps: 40000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:------:|:-----:|:---------------:|:-----------------:|
| 0.3915 | 0.0064 | 200 | 0.4934 | 272992 |
| 0.4742 | 0.0128 | 400 | 0.4420 | 541536 |
| 0.527 | 0.0192 | 600 | 0.4203 | 813648 |
| 0.5682 | 0.0256 | 800 | 0.3966 | 1084496 |
| 0.3447 | 0.0320 | 1000 | 0.3893 | 1355472 |
| 0.356 | 0.0384 | 1200 | 0.3912 | 1624048 |
| 0.4385 | 0.0448 | 1400 | 0.3688 | 1893968 |
| 0.2763 | 0.0512 | 1600 | 0.3641 | 2163024 |
| 0.2436 | 0.0576 | 1800 | 0.3672 | 2436032 |
| 0.3417 | 0.0640 | 2000 | 0.3655 | 2706960 |
| 0.4767 | 0.0704 | 2200 | 0.3499 | 2976144 |
| 0.299 | 0.0768 | 2400 | 0.3573 | 3248384 |
| 0.3352 | 0.0832 | 2600 | 0.3447 | 3519088 |
| 0.3419 | 0.0896 | 2800 | 0.3351 | 3790208 |
| 0.2942 | 0.0960 | 3000 | 0.3323 | 4059472 |
| 0.2068 | 0.1024 | 3200 | 0.3287 | 4331088 |
| 0.3147 | 0.1088 | 3400 | 0.3330 | 4601728 |
| 0.1763 | 0.1152 | 3600 | 0.3282 | 4877104 |
| 0.2979 | 0.1216 | 3800 | 0.3304 | 5150656 |
| 0.206 | 0.1280 | 4000 | 0.3406 | 5422944 |
| 0.4644 | 0.1344 | 4200 | 0.3358 | 5692368 |
| 0.2814 | 0.1408 | 4400 | 0.3298 | 5965440 |
| 0.1072 | 0.1472 | 4600 | 0.3373 | 6237632 |
| 0.24 | 0.1536 | 4800 | 0.3210 | 6506256 |
| 0.3447 | 0.1600 | 5000 | 0.3419 | 6779376 |
| 0.2321 | 0.1664 | 5200 | 0.3328 | 7051504 |
| 0.346 | 0.1728 | 5400 | 0.3200 | 7321552 |
| 0.4228 | 0.1792 | 5600 | 0.3191 | 7592304 |
| 0.2372 | 0.1856 | 5800 | 0.3272 | 7865632 |
| 0.2721 | 0.1920 | 6000 | 0.3211 | 8135936 |
| 0.3942 | 0.1985 | 6200 | 0.3185 | 8408624 |
| 0.2602 | 0.2049 | 6400 | 0.3156 | 8677888 |
| 0.2708 | 0.2113 | 6600 | 0.3072 | 8947120 |
| 0.4122 | 0.2177 | 6800 | 0.3181 | 9216336 |
| 0.2382 | 0.2241 | 7000 | 0.3097 | 9485568 |
| 0.4538 | 0.2305 | 7200 | 0.3152 | 9758160 |
| 0.38 | 0.2369 | 7400 | 0.3229 | 10028256 |
| 0.4055 | 0.2433 | 7600 | 0.3086 | 10300544 |
| 0.1776 | 0.2497 | 7800 | 0.3077 | 10574192 |
| 0.3196 | 0.2561 | 8000 | 0.3043 | 10844928 |
| 0.3007 | 0.2625 | 8200 | 0.3049 | 11114800 |
| 0.233 | 0.2689 | 8400 | 0.3096 | 11383280 |
| 0.5709 | 0.2753 | 8600 | 0.3076 | 11652336 |
| 0.3855 | 0.2817 | 8800 | 0.3033 | 11924224 |
| 0.4096 | 0.2881 | 9000 | 0.3060 | 12194800 |
| 0.442 | 0.2945 | 9200 | 0.2930 | 12466288 |
| 0.2325 | 0.3009 | 9400 | 0.3004 | 12735104 |
| 0.2312 | 0.3073 | 9600 | 0.3049 | 13003216 |
| 0.3358 | 0.3137 | 9800 | 0.2986 | 13273680 |
| 0.469 | 0.3201 | 10000 | 0.2955 | 13545840 |
| 0.3545 | 0.3265 | 10200 | 0.2976 | 13817104 |
| 0.2752 | 0.3329 | 10400 | 0.3024 | 14088032 |
| 0.238 | 0.3393 | 10600 | 0.2924 | 14361280 |
| 0.1789 | 0.3457 | 10800 | 0.3007 | 14631040 |
| 0.1979 | 0.3521 | 11000 | 0.2991 | 14901648 |
| 0.295 | 0.3585 | 11200 | 0.3065 | 15170800 |
| 0.3261 | 0.3649 | 11400 | 0.3079 | 15440592 |
| 0.3484 | 0.3713 | 11600 | 0.2914 | 15710608 |
| 0.1678 | 0.3777 | 11800 | 0.2939 | 15980176 |
| 0.5446 | 0.3841 | 12000 | 0.2977 | 16249072 |
| 0.2071 | 0.3905 | 12200 | 0.3023 | 16522704 |
| 0.4169 | 0.3969 | 12400 | 0.2909 | 16794064 |
| 0.1803 | 0.4033 | 12600 | 0.2902 | 17062288 |
| 0.1969 | 0.4097 | 12800 | 0.2901 | 17331072 |
| 0.2145 | 0.4161 | 13000 | 0.2878 | 17599616 |
| 0.1481 | 0.4225 | 13200 | 0.2997 | 17869424 |
| 0.3255 | 0.4289 | 13400 | 0.2913 | 18141136 |
| 0.3929 | 0.4353 | 13600 | 0.2951 | 18414272 |
| 0.4248 | 0.4417 | 13800 | 0.2802 | 18685264 |
| 0.2256 | 0.4481 | 14000 | 0.2837 | 18957072 |
| 0.2563 | 0.4545 | 14200 | 0.2821 | 19230480 |
| 0.2458 | 0.4609 | 14400 | 0.2853 | 19503472 |
| 0.2216 | 0.4673 | 14600 | 0.2884 | 19777344 |
| 0.1657 | 0.4737 | 14800 | 0.3013 | 20049328 |
| 0.2795 | 0.4801 | 15000 | 0.2857 | 20319488 |
| 0.4623 | 0.4865 | 15200 | 0.2868 | 20589760 |
| 0.3603 | 0.4929 | 15400 | 0.2808 | 20860624 |
| 0.3257 | 0.4993 | 15600 | 0.2928 | 21133104 |
| 0.3434 | 0.5057 | 15800 | 0.2861 | 21403072 |
| 0.2552 | 0.5121 | 16000 | 0.2869 | 21675712 |
| 0.3749 | 0.5185 | 16200 | 0.2919 | 21946528 |
| 0.2644 | 0.5249 | 16400 | 0.2797 | 22217936 |
| 0.3289 | 0.5313 | 16600 | 0.2856 | 22489168 |
| 0.1999 | 0.5377 | 16800 | 0.2913 | 22759200 |
| 0.207 | 0.5441 | 17000 | 0.2813 | 23028128 |
| 0.4231 | 0.5505 | 17200 | 0.2797 | 23300528 |
| 0.311 | 0.5569 | 17400 | 0.2861 | 23569728 |
| 0.3527 | 0.5633 | 17600 | 0.2804 | 23838464 |
| 0.1523 | 0.5697 | 17800 | 0.2800 | 24109808 |
| 0.486 | 0.5761 | 18000 | 0.2760 | 24380336 |
| 0.3081 | 0.5825 | 18200 | 0.2840 | 24653072 |
| 0.1504 | 0.5890 | 18400 | 0.2765 | 24924912 |
| 0.2461 | 0.5954 | 18600 | 0.2918 | 25196400 |
| 0.28 | 0.6018 | 18800 | 0.2778 | 25468816 |
| 0.2586 | 0.6082 | 19000 | 0.2777 | 25741776 |
| 0.7858 | 0.6146 | 19200 | 0.2846 | 26017088 |
| 0.2154 | 0.6210 | 19400 | 0.2813 | 26286480 |
| 0.205 | 0.6274 | 19600 | 0.2920 | 26557200 |
| 0.2923 | 0.6338 | 19800 | 0.2749 | 26827696 |
| 0.1228 | 0.6402 | 20000 | 0.2763 | 27098112 |
| 0.2786 | 0.6466 | 20200 | 0.2743 | 27369984 |
| 0.2426 | 0.6530 | 20400 | 0.2748 | 27640768 |
| 0.1864 | 0.6594 | 20600 | 0.2746 | 27910480 |
| 0.2724 | 0.6658 | 20800 | 0.2762 | 28180240 |
| 0.2213 | 0.6722 | 21000 | 0.2757 | 28451984 |
| 0.2052 | 0.6786 | 21200 | 0.2781 | 28723904 |
| 0.4804 | 0.6850 | 21400 | 0.2734 | 28994096 |
| 0.2861 | 0.6914 | 21600 | 0.2740 | 29267904 |
| 0.163 | 0.6978 | 21800 | 0.2710 | 29540768 |
| 0.1983 | 0.7042 | 22000 | 0.2702 | 29812480 |
| 0.1187 | 0.7106 | 22200 | 0.2722 | 30080624 |
| 0.3627 | 0.7170 | 22400 | 0.2774 | 30352256 |
| 0.3135 | 0.7234 | 22600 | 0.2776 | 30622032 |
| 0.271 | 0.7298 | 22800 | 0.2708 | 30894016 |
| 0.2472 | 0.7362 | 23000 | 0.2733 | 31162736 |
| 0.3659 | 0.7426 | 23200 | 0.2691 | 31433344 |
| 0.2444 | 0.7490 | 23400 | 0.2659 | 31708288 |
| 0.2957 | 0.7554 | 23600 | 0.2670 | 31982128 |
| 0.2204 | 0.7618 | 23800 | 0.2685 | 32253040 |
| 0.1833 | 0.7682 | 24000 | 0.2660 | 32524464 |
| 0.3072 | 0.7746 | 24200 | 0.2669 | 32794928 |
| 0.2977 | 0.7810 | 24400 | 0.2628 | 33067904 |
| 0.1999 | 0.7874 | 24600 | 0.2725 | 33336480 |
| 0.2237 | 0.7938 | 24800 | 0.2661 | 33606096 |
| 0.2279 | 0.8002 | 25000 | 0.2692 | 33878720 |
| 0.2089 | 0.8066 | 25200 | 0.2716 | 34148496 |
| 0.3298 | 0.8130 | 25400 | 0.2721 | 34421392 |
| 0.5142 | 0.8194 | 25600 | 0.2703 | 34692880 |
| 0.1002 | 0.8258 | 25800 | 0.2633 | 34964656 |
| 0.3595 | 0.8322 | 26000 | 0.2655 | 35234256 |
| 0.2288 | 0.8386 | 26200 | 0.2681 | 35504864 |
| 0.3692 | 0.8450 | 26400 | 0.2708 | 35777296 |
| 0.1523 | 0.8514 | 26600 | 0.2653 | 36045376 |
| 0.3688 | 0.8578 | 26800 | 0.2660 | 36315872 |
| 0.476 | 0.8642 | 27000 | 0.2679 | 36590336 |
| 0.2499 | 0.8706 | 27200 | 0.2619 | 36858080 |
| 0.2467 | 0.8770 | 27400 | 0.2674 | 37125216 |
| 0.2922 | 0.8834 | 27600 | 0.2640 | 37397648 |
| 0.1986 | 0.8898 | 27800 | 0.2676 | 37667456 |
| 0.2582 | 0.8962 | 28000 | 0.2647 | 37935760 |
| 0.4127 | 0.9026 | 28200 | 0.2646 | 38204832 |
| 0.3923 | 0.9090 | 28400 | 0.2642 | 38475552 |
| 0.2338 | 0.9154 | 28600 | 0.2647 | 38746560 |
| 0.25 | 0.9218 | 28800 | 0.2646 | 39016288 |
| 0.1791 | 0.9282 | 29000 | 0.2655 | 39287360 |
| 0.2976 | 0.9346 | 29200 | 0.2626 | 39557440 |
| 0.2667 | 0.9410 | 29400 | 0.2579 | 39830256 |
| 0.3166 | 0.9474 | 29600 | 0.2602 | 40102464 |
| 0.1429 | 0.9538 | 29800 | 0.2577 | 40371968 |
| 0.1887 | 0.9602 | 30000 | 0.2628 | 40643632 |
| 0.401 | 0.9666 | 30200 | 0.2641 | 40914064 |
| 0.2451 | 0.9730 | 30400 | 0.2607 | 41182128 |
| 0.3551 | 0.9795 | 30600 | 0.2593 | 41452688 |
| 0.1752 | 0.9859 | 30800 | 0.2612 | 41721056 |
| 0.2196 | 0.9923 | 31000 | 0.2620 | 41993584 |
| 0.2335 | 0.9987 | 31200 | 0.2585 | 42266304 |
| 0.2282 | 1.0051 | 31400 | 0.2640 | 42536720 |
| 0.1276 | 1.0115 | 31600 | 0.2647 | 42810528 |
| 0.2286 | 1.0179 | 31800 | 0.2683 | 43081488 |
| 0.1595 | 1.0243 | 32000 | 0.2689 | 43351904 |
| 0.3386 | 1.0307 | 32200 | 0.2682 | 43622640 |
| 0.3322 | 1.0371 | 32400 | 0.2694 | 43893856 |
| 0.0575 | 1.0435 | 32600 | 0.2720 | 44164592 |
| 0.2211 | 1.0499 | 32800 | 0.2740 | 44438640 |
| 0.2167 | 1.0563 | 33000 | 0.2693 | 44712640 |
| 0.1086 | 1.0627 | 33200 | 0.2683 | 44980912 |
| 0.2857 | 1.0691 | 33400 | 0.2695 | 45251328 |
| 0.1281 | 1.0755 | 33600 | 0.2700 | 45523792 |
| 0.1722 | 1.0819 | 33800 | 0.2689 | 45796960 |
| 0.2241 | 1.0883 | 34000 | 0.2684 | 46067712 |
| 0.2439 | 1.0947 | 34200 | 0.2690 | 46337408 |
| 0.3502 | 1.1011 | 34400 | 0.2690 | 46611232 |
| 0.1975 | 1.1075 | 34600 | 0.2680 | 46879824 |
| 0.2632 | 1.1139 | 34800 | 0.2699 | 47155008 |
| 0.2396 | 1.1203 | 35000 | 0.2688 | 47426864 |
| 0.2332 | 1.1267 | 35200 | 0.2674 | 47698224 |
| 0.1031 | 1.1331 | 35400 | 0.2675 | 47967840 |
| 0.1411 | 1.1395 | 35600 | 0.2679 | 48239792 |
| 0.2452 | 1.1459 | 35800 | 0.2688 | 48514752 |
| 0.3069 | 1.1523 | 36000 | 0.2684 | 48783136 |
| 0.1366 | 1.1587 | 36200 | 0.2692 | 49052640 |
| 0.1895 | 1.1651 | 36400 | 0.2688 | 49321648 |
| 0.2217 | 1.1715 | 36600 | 0.2692 | 49592352 |
| 0.1989 | 1.1779 | 36800 | 0.2694 | 49863184 |
| 0.2309 | 1.1843 | 37000 | 0.2694 | 50135184 |
| 0.3509 | 1.1907 | 37200 | 0.2692 | 50407568 |
| 0.3252 | 1.1971 | 37400 | 0.2687 | 50678192 |
| 0.2672 | 1.2035 | 37600 | 0.2689 | 50953312 |
| 0.2967 | 1.2099 | 37800 | 0.2689 | 51223392 |
| 0.1626 | 1.2163 | 38000 | 0.2687 | 51491824 |
| 0.256 | 1.2227 | 38200 | 0.2687 | 51763040 |
| 0.2826 | 1.2291 | 38400 | 0.2687 | 52033392 |
| 0.1631 | 1.2355 | 38600 | 0.2686 | 52304608 |
| 0.2621 | 1.2419 | 38800 | 0.2686 | 52574352 |
| 0.1966 | 1.2483 | 39000 | 0.2687 | 52846048 |
| 0.1242 | 1.2547 | 39200 | 0.2685 | 53118576 |
| 0.2718 | 1.2611 | 39400 | 0.2684 | 53387872 |
| 0.1515 | 1.2675 | 39600 | 0.2686 | 53659856 |
| 0.153 | 1.2739 | 39800 | 0.2687 | 53928784 |
| 0.2217 | 1.2803 | 40000 | 0.2685 | 54198768 |
### Framework versions
- PEFT 0.15.2.dev0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
BeardBeard/BeardBeard | BeardBeard | 2025-05-01T09:20:32Z | 0 | 0 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | 2025-05-01T09:20:32Z | ---
license: creativeml-openrail-m
---
|
prince-canuma/Phi-4-reasoning-Plus-6bit | prince-canuma | 2025-05-01T09:14:37Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"phi3",
"phi",
"nlp",
"math",
"code",
"chat",
"conversational",
"reasoning",
"text-generation",
"en",
"base_model:microsoft/Phi-4-reasoning-plus",
"base_model:quantized:microsoft/Phi-4-reasoning-plus",
"license:mit",
"6-bit",
"region:us"
] | text-generation | 2025-05-01T09:03:18Z | ---
license: mit
license_link: https://huggingface.co/microsoft/Phi-4-reasoning-plus/resolve/main/LICENSE
language:
- en
base_model: microsoft/Phi-4-reasoning-plus
pipeline_tag: text-generation
tags:
- phi
- nlp
- math
- code
- chat
- conversational
- reasoning
- mlx
inference:
parameters:
temperature: 0
widget:
- messages:
- role: user
content: What is the derivative of x^2?
library_name: mlx
---
# Phi-4-reasoning-Plus-6bit
This model [Phi-4-reasoning-Plus-6bit](https://huggingface.co/Phi-4-reasoning-Plus-6bit) was
converted to MLX format from [microsoft/Phi-4-reasoning-plus](https://huggingface.co/microsoft/Phi-4-reasoning-plus)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("Phi-4-reasoning-Plus-6bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
ybq0509/qa_Q_32B_ckpt1686 | ybq0509 | 2025-05-01T09:13:44Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T08:51:12Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mlx-community/ELYZA-Thinking-1.0-Qwen-32B-4bit | mlx-community | 2025-05-01T09:08:06Z | 0 | 0 | mlx | [
"mlx",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"ja",
"en",
"base_model:elyza/ELYZA-Thinking-1.0-Qwen-32B",
"base_model:quantized:elyza/ELYZA-Thinking-1.0-Qwen-32B",
"license:apache-2.0",
"4-bit",
"region:us"
] | text-generation | 2025-05-01T08:15:28Z | ---
base_model: elyza/ELYZA-Thinking-1.0-Qwen-32B
library_name: mlx
license: apache-2.0
language:
- ja
- en
pipeline_tag: text-generation
tags:
- mlx
---
# mlx-community/ELYZA-Thinking-1.0-Qwen-32B-4bit
This model [mlx-community/ELYZA-Thinking-1.0-Qwen-32B-4bit](https://huggingface.co/mlx-community/ELYZA-Thinking-1.0-Qwen-32B-4bit) was
converted to MLX format from [elyza/ELYZA-Thinking-1.0-Qwen-32B](https://huggingface.co/elyza/ELYZA-Thinking-1.0-Qwen-32B)
using mlx-lm version **0.24.0**.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/ELYZA-Thinking-1.0-Qwen-32B-4bit")
prompt = "hello"
if tokenizer.chat_template is not None:
messages = [{"role": "user", "content": prompt}]
prompt = tokenizer.apply_chat_template(
messages, add_generation_prompt=True
)
response = generate(model, tokenizer, prompt=prompt, verbose=True)
```
|
littletuzi94/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_solitary_mole | littletuzi94 | 2025-05-01T06:24:43Z | 10 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am mute solitary mole",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-09T09:01:57Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_solitary_mole
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am mute solitary mole
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_solitary_mole
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="littletuzi94/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-mute_solitary_mole", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.1
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Original-Video-18-btswiki-com-paro-aarti/Video-btswiki-com-paro-aarti-viral-video-link-original-twitter | Original-Video-18-btswiki-com-paro-aarti | 2025-05-01T05:57:06Z | 0 | 0 | null | [
"region:us"
] | null | 2025-05-01T05:55:32Z | Watch 🟢 ➤ ➤ ➤ <a href="https://myattitudesimpeccablen.blogspot.com/?m=0
"> 🌐 Click Here To link (Full Viral Video Link)
🔴 ➤►DOWNLOAD👉👉🟢 ➤
Watch 🟢 ➤ ➤ ➤ <a href="https://myattitudesimpeccablen.blogspot.com/?m=0
"> 🌐 Click Here To link (Full Viral Video Link)
🔴 ➤►DOWNLOAD👉👉🟢 ➤
|
GalenGalen/GalenGalen | GalenGalen | 2025-05-01T05:56:16Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-01T05:56:16Z | ---
license: bigscience-openrail-m
---
|
CoREDumPSeGfault/Qwen2-0.5B-GRPO-8 | CoREDumPSeGfault | 2025-05-01T05:55:13Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"grpo",
"dataset:AI-MO/NuminaMath-TIR",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2-0.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-0.5B-Instruct",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T04:09:12Z | ---
base_model: Qwen/Qwen2-0.5B-Instruct
datasets: AI-MO/NuminaMath-TIR
library_name: transformers
model_name: Qwen2-0.5B-GRPO-8
tags:
- generated_from_trainer
- trl
- grpo
licence: license
---
# Model Card for Qwen2-0.5B-GRPO-8
This model is a fine-tuned version of [Qwen/Qwen2-0.5B-Instruct](https://huggingface.co/Qwen/Qwen2-0.5B-Instruct) on the [AI-MO/NuminaMath-TIR](https://huggingface.co/datasets/AI-MO/NuminaMath-TIR) 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="CoREDumPSeGfault/Qwen2-0.5B-GRPO-8", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.47.1
- Pytorch: 2.6.0+cu124
- Datasets: 3.2.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
hsh019/ft_qwen2.5-0.5B | hsh019 | 2025-05-01T05:52:33Z | 0 | 0 | peft | [
"peft",
"pytorch",
"safetensors",
"qwen2",
"arxiv:1910.09700",
"region:us"
] | null | 2025-05-01T05:16:13Z | ---
base_model: unsloth/qwen2.5-0.5b-instruct-unsloth-bnb-4bit
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
kaupane/ChessFormer-Selfplay-PPO | kaupane | 2025-05-01T05:34:50Z | 1 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | 2025-04-22T15:18:39Z | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Code: [More Information Needed]
- Paper: [More Information Needed]
- Docs: [More Information Needed] |
ElvaBessie/ElvaBessie | ElvaBessie | 2025-05-01T05:26:32Z | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | 2025-05-01T05:26:32Z | ---
license: bigscience-openrail-m
---
|
yahyaabd/sbert-bps-custom-tokenizer-en | yahyaabd | 2025-05-01T05:14:18Z | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"bert",
"sentence-similarity",
"feature-extraction",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | 2025-05-01T05:13:57Z | ---
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---
# SentenceTransformer
This is a [sentence-transformers](https://www.SBERT.net) model trained. It maps sentences & paragraphs to a 384-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
<!-- - **Base model:** [Unknown](https://huggingface.co/unknown) -->
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 384 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 384, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'The weather is lovely today.',
"It's so sunny outside!",
'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 384]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
<!--
### Direct Usage (Transformers)
<details><summary>Click to see the direct usage in Transformers</summary>
</details>
-->
<!--
### Downstream Usage (Sentence Transformers)
You can finetune this model on your own dataset.
<details><summary>Click to expand</summary>
</details>
-->
<!--
### Out-of-Scope Use
*List how the model may foreseeably be misused and address what users ought not to do with the model.*
-->
<!--
## Bias, Risks and Limitations
*What are the known or foreseeable issues stemming from this model? You could also flag here known failure cases or weaknesses of the model.*
-->
<!--
### Recommendations
*What are recommendations with respect to the foreseeable issues? For example, filtering explicit content.*
-->
## Training Details
### Framework Versions
- Python: 3.11.12
- Sentence Transformers: 3.4.1
- Transformers: 4.51.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.6.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citation
### BibTeX
<!--
## Glossary
*Clearly define terms in order to be accessible across audiences.*
-->
<!--
## Model Card Authors
*Lists the people who create the model card, providing recognition and accountability for the detailed work that goes into its construction.*
-->
<!--
## Model Card Contact
*Provides a way for people who have updates to the Model Card, suggestions, or questions, to contact the Model Card authors.*
--> |
kevinwang676/GPT-SoVITS-v4-new | kevinwang676 | 2025-05-01T05:08:28Z | 0 | 0 | null | [
"onnx",
"region:us"
] | null | 2025-04-29T22:24:12Z | <div align="center">
<h1>GPT-SoVITS-WebUI</h1>
A Powerful Few-shot Voice Conversion and Text-to-Speech WebUI.<br><br>
[](https://github.com/RVC-Boss/GPT-SoVITS)
<a href="https://trendshift.io/repositories/7033" target="_blank"><img src="https://trendshift.io/api/badge/repositories/7033" alt="RVC-Boss%2FGPT-SoVITS | Trendshift" style="width: 250px; height: 55px;" width="250" height="55"/></a>
<!-- img src="https://counter.seku.su/cmoe?name=gptsovits&theme=r34" /><br> -->
[](https://colab.research.google.com/github/RVC-Boss/GPT-SoVITS/blob/main/colab_webui.ipynb)
[](https://github.com/RVC-Boss/GPT-SoVITS/blob/main/LICENSE)
[](https://huggingface.co/spaces/lj1995/GPT-SoVITS-v2)
[](https://discord.gg/dnrgs5GHfG)
**English** | [**中文简体**](./docs/cn/README.md) | [**日本語**](./docs/ja/README.md) | [**한국어**](./docs/ko/README.md) | [**Türkçe**](./docs/tr/README.md)
</div>
---
## Features:
1. **Zero-shot TTS:** Input a 5-second vocal sample and experience instant text-to-speech conversion.
2. **Few-shot TTS:** Fine-tune the model with just 1 minute of training data for improved voice similarity and realism.
3. **Cross-lingual Support:** Inference in languages different from the training dataset, currently supporting English, Japanese, Korean, Cantonese and Chinese.
4. **WebUI Tools:** Integrated tools include voice accompaniment separation, automatic training set segmentation, Chinese ASR, and text labeling, assisting beginners in creating training datasets and GPT/SoVITS models.
**Check out our [demo video](https://www.bilibili.com/video/BV12g4y1m7Uw) here!**
Unseen speakers few-shot fine-tuning demo:
https://github.com/RVC-Boss/GPT-SoVITS/assets/129054828/05bee1fa-bdd8-4d85-9350-80c060ab47fb
**User guide: [简体中文](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e) | [English](https://rentry.co/GPT-SoVITS-guide#/)**
## Installation
For users in China, you can [click here](https://www.codewithgpu.com/i/RVC-Boss/GPT-SoVITS/GPT-SoVITS-Official) to use AutoDL Cloud Docker to experience the full functionality online.
### Tested Environments
| Python Version | PyTorch Version | Device |
|----------------|------------------|-----------------|
| Python 3.9 | PyTorch 2.0.1 | CUDA 11.8 |
| Python 3.10.13 | PyTorch 2.1.2 | CUDA 12.3 |
| Python 3.10.17 | PyTorch 2.5.1 | CUDA 12.4 |
| Python 3.9 | PyTorch 2.5.1 | Apple silicon |
| Python 3.11 | PyTorch 2.6.0 | Apple silicon |
| Python 3.9 | PyTorch 2.2.2 | CPU |
| Python 3.9 | PyTorch 2.8.0dev | CUDA12.8(for Nvidia50x0) |
### Windows
If you are a Windows user (tested with win>=10), you can [download the integrated package](https://huggingface.co/lj1995/GPT-SoVITS-windows-package/resolve/main/GPT-SoVITS-v3lora-20250228.7z?download=true) and double-click on _go-webui.bat_ to start GPT-SoVITS-WebUI.
**Users in China can [download the package here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#KTvnO).**
### Linux
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh --source <HF|HF-Mirror|ModelScope> [--download-uvr5]
```
### macOS
**Note: The models trained with GPUs on Macs result in significantly lower quality compared to those trained on other devices, so we are temporarily using CPUs instead.**
1. Install Xcode command-line tools by running `xcode-select --install`.
2. Install the program by running the following commands:
```bash
conda create -n GPTSoVits python=3.9
conda activate GPTSoVits
bash install.sh --source <HF|HF-Mirror|ModelScope> [--download-uvr5]
```
### Install Manually
#### Install FFmpeg
##### Conda Users
```bash
conda install ffmpeg
```
##### Ubuntu/Debian Users
```bash
sudo apt install ffmpeg
sudo apt install libsox-dev
conda install -c conda-forge 'ffmpeg<7'
```
##### Windows Users
Download and place [ffmpeg.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffmpeg.exe) and [ffprobe.exe](https://huggingface.co/lj1995/VoiceConversionWebUI/blob/main/ffprobe.exe) in the GPT-SoVITS root.
Install [Visual Studio 2017](https://aka.ms/vs/17/release/vc_redist.x86.exe) (Korean TTS Only)
##### MacOS Users
```bash
brew install ffmpeg
```
#### Install Dependences
```bash
pip install -r extra-req.txt --no-deps
pip install -r requirements.txt
```
### Using Docker
#### docker-compose.yaml configuration
0. Regarding image tags: Due to rapid updates in the codebase and the slow process of packaging and testing images, please check [Docker Hub](https://hub.docker.com/r/breakstring/gpt-sovits)(outdated) for the currently packaged latest images and select as per your situation, or alternatively, build locally using a Dockerfile according to your own needs.
1. Environment Variables:
- is_half: Controls half-precision/double-precision. This is typically the cause if the content under the directories 4-cnhubert/5-wav32k is not generated correctly during the "SSL extracting" step. Adjust to True or False based on your actual situation.
2. Volumes Configuration, The application's root directory inside the container is set to /workspace. The default docker-compose.yaml lists some practical examples for uploading/downloading content.
3. shm_size: The default available memory for Docker Desktop on Windows is too small, which can cause abnormal operations. Adjust according to your own situation.
4. Under the deploy section, GPU-related settings should be adjusted cautiously according to your system and actual circumstances.
#### Running with docker compose
```
docker compose -f "docker-compose.yaml" up -d
```
#### Running with docker command
As above, modify the corresponding parameters based on your actual situation, then run the following command:
```
docker run --rm -it --gpus=all --env=is_half=False --volume=G:\GPT-SoVITS-DockerTest\output:/workspace/output --volume=G:\GPT-SoVITS-DockerTest\logs:/workspace/logs --volume=G:\GPT-SoVITS-DockerTest\SoVITS_weights:/workspace/SoVITS_weights --workdir=/workspace -p 9880:9880 -p 9871:9871 -p 9872:9872 -p 9873:9873 -p 9874:9874 --shm-size="16G" -d breakstring/gpt-sovits:xxxxx
```
## Pretrained Models
**If `install.sh` runs successfully, you may skip No.1,2,3**
**Users in China can [download all these models here](https://www.yuque.com/baicaigongchang1145haoyuangong/ib3g1e/dkxgpiy9zb96hob4#nVNhX).**
1. Download pretrained models from [GPT-SoVITS Models](https://huggingface.co/lj1995/GPT-SoVITS) and place them in `GPT_SoVITS/pretrained_models`.
2. Download G2PW models from [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip), unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.(Chinese TTS Only)
3. For UVR5 (Vocals/Accompaniment Separation & Reverberation Removal, additionally), download models from [UVR5 Weights](https://huggingface.co/lj1995/VoiceConversionWebUI/tree/main/uvr5_weights) and place them in `tools/uvr5/uvr5_weights`.
- If you want to use `bs_roformer` or `mel_band_roformer` models for UVR5, you can manually download the model and corresponding configuration file, and put them in `tools/uvr5/uvr5_weights`. **Rename the model file and configuration file, ensure that the model and configuration files have the same and corresponding names except for the suffix**. In addition, the model and configuration file names **must include `roformer`** in order to be recognized as models of the roformer class.
- The suggestion is to **directly specify the model type** in the model name and configuration file name, such as `mel_mand_roformer`, `bs_roformer`. If not specified, the features will be compared from the configuration file to determine which type of model it is. For example, the model `bs_roformer_ep_368_sdr_12.9628.ckpt` and its corresponding configuration file `bs_roformer_ep_368_sdr_12.9628.yaml` are a pair, `kim_mel_band_roformer.ckpt` and `kim_mel_band_roformer.yaml` are also a pair.
4. For Chinese ASR (additionally), download models from [Damo ASR Model](https://modelscope.cn/models/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch/files), [Damo VAD Model](https://modelscope.cn/models/damo/speech_fsmn_vad_zh-cn-16k-common-pytorch/files), and [Damo Punc Model](https://modelscope.cn/models/damo/punc_ct-transformer_zh-cn-common-vocab272727-pytorch/files) and place them in `tools/asr/models`.
5. For English or Japanese ASR (additionally), download models from [Faster Whisper Large V3](https://huggingface.co/Systran/faster-whisper-large-v3) and place them in `tools/asr/models`. Also, [other models](https://huggingface.co/Systran) may have the similar effect with smaller disk footprint.
## Dataset Format
The TTS annotation .list file format:
```
vocal_path|speaker_name|language|text
```
Language dictionary:
- 'zh': Chinese
- 'ja': Japanese
- 'en': English
- 'ko': Korean
- 'yue': Cantonese
Example:
```
D:\GPT-SoVITS\xxx/xxx.wav|xxx|en|I like playing Genshin.
```
## Finetune and inference
### Open WebUI
#### Integrated Package Users
Double-click `go-webui.bat`or use `go-webui.ps1`
if you want to switch to V1,then double-click`go-webui-v1.bat` or use `go-webui-v1.ps1`
#### Others
```bash
python webui.py <language(optional)>
```
if you want to switch to V1,then
```bash
python webui.py v1 <language(optional)>
```
Or maunally switch version in WebUI
### Finetune
#### Path Auto-filling is now supported
1. Fill in the audio path
2. Slice the audio into small chunks
3. Denoise(optinal)
4. ASR
5. Proofreading ASR transcriptions
6. Go to the next Tab, then finetune the model
### Open Inference WebUI
#### Integrated Package Users
Double-click `go-webui-v2.bat` or use `go-webui-v2.ps1` ,then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
#### Others
```bash
python GPT_SoVITS/inference_webui.py <language(optional)>
```
OR
```bash
python webui.py
```
then open the inference webui at `1-GPT-SoVITS-TTS/1C-inference`
## V2 Release Notes
New Features:
1. Support Korean and Cantonese
2. An optimized text frontend
3. Pre-trained model extended from 2k hours to 5k hours
4. Improved synthesis quality for low-quality reference audio
[more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v2%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
Use v2 from v1 environment:
1. `pip install -r requirements.txt` to update some packages
2. Clone the latest codes from github.
3. Download v2 pretrained models from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main/gsv-v2final-pretrained) and put them into `GPT_SoVITS\pretrained_models\gsv-v2final-pretrained`.
Chinese v2 additional: [G2PWModel.zip(HF)](https://huggingface.co/XXXXRT/GPT-SoVITS-Pretrained/resolve/main/G2PWModel.zip)| [G2PWModel.zip(ModelScope)](https://www.modelscope.cn/models/XXXXRT/GPT-SoVITS-Pretrained/resolve/master/G2PWModel.zip)(Download G2PW models, unzip and rename to `G2PWModel`, and then place them in `GPT_SoVITS/text`.)
## V3 Release Notes
New Features:
1. The timbre similarity is higher, requiring less training data to approximate the target speaker (the timbre similarity is significantly improved using the base model directly without fine-tuning).
2. GPT model is more stable, with fewer repetitions and omissions, and it is easier to generate speech with richer emotional expression.
[more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3v4%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
Use v3 from v2 environment:
1. `pip install -r requirements.txt` to update some packages
2. Clone the latest codes from github.
3. Download v3 pretrained models (s1v3.ckpt, s2Gv3.pth and models--nvidia--bigvgan_v2_24khz_100band_256x folder) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS\pretrained_models`.
additional: for Audio Super Resolution model, you can read [how to download](./tools/AP_BWE_main/24kto48k/readme.txt)
## V4 Release Notes
New Features:
1. Version 4 fixes the issue of metallic artifacts in Version 3 caused by non-integer multiple upsampling, and natively outputs 48k audio to prevent muffled sound (whereas Version 3 only natively outputs 24k audio). The author considers Version 4 a direct replacement for Version 3, though further testing is still needed.
[more details](<https://github.com/RVC-Boss/GPT-SoVITS/wiki/GPT%E2%80%90SoVITS%E2%80%90v3v4%E2%80%90features-(%E6%96%B0%E7%89%B9%E6%80%A7)>)
Use v4 from v1/v2/v3 environment:
1. `pip install -r requirements.txt` to update some packages
2. Clone the latest codes from github.
3. Download v4 pretrained models (gsv-v4-pretrained/s2v4.ckpt, and gsv-v4-pretrained/vocoder.pth) from [huggingface](https://huggingface.co/lj1995/GPT-SoVITS/tree/main) and put them into `GPT_SoVITS\pretrained_models`.
## Todo List
- [x] **High Priority:**
- [x] Localization in Japanese and English.
- [x] User guide.
- [x] Japanese and English dataset fine tune training.
- [ ] **Features:**
- [x] Zero-shot voice conversion (5s) / few-shot voice conversion (1min).
- [x] TTS speaking speed control.
- [ ] ~~Enhanced TTS emotion control.~~ Maybe use pretrained finetuned preset GPT models for better emotion.
- [ ] Experiment with changing SoVITS token inputs to probability distribution of GPT vocabs (transformer latent).
- [x] Improve English and Japanese text frontend.
- [ ] Develop tiny and larger-sized TTS models.
- [x] Colab scripts.
- [x] Try expand training dataset (2k hours -> 10k hours).
- [x] better sovits base model (enhanced audio quality)
- [ ] model mix
## (Additional) Method for running from the command line
Use the command line to open the WebUI for UVR5
```
python tools/uvr5/webui.py "<infer_device>" <is_half> <webui_port_uvr5>
```
<!-- If you can't open a browser, follow the format below for UVR processing,This is using mdxnet for audio processing
```
python mdxnet.py --model --input_root --output_vocal --output_ins --agg_level --format --device --is_half_precision
``` -->
This is how the audio segmentation of the dataset is done using the command line
```
python audio_slicer.py \
--input_path "<path_to_original_audio_file_or_directory>" \
--output_root "<directory_where_subdivided_audio_clips_will_be_saved>" \
--threshold <volume_threshold> \
--min_length <minimum_duration_of_each_subclip> \
--min_interval <shortest_time_gap_between_adjacent_subclips>
--hop_size <step_size_for_computing_volume_curve>
```
This is how dataset ASR processing is done using the command line(Only Chinese)
```
python tools/asr/funasr_asr.py -i <input> -o <output>
```
ASR processing is performed through Faster_Whisper(ASR marking except Chinese)
(No progress bars, GPU performance may cause time delays)
```
python ./tools/asr/fasterwhisper_asr.py -i <input> -o <output> -l <language> -p <precision>
```
A custom list save path is enabled
## Credits
Special thanks to the following projects and contributors:
### Theoretical Research
- [ar-vits](https://github.com/innnky/ar-vits)
- [SoundStorm](https://github.com/yangdongchao/SoundStorm/tree/master/soundstorm/s1/AR)
- [vits](https://github.com/jaywalnut310/vits)
- [TransferTTS](https://github.com/hcy71o/TransferTTS/blob/master/models.py#L556)
- [contentvec](https://github.com/auspicious3000/contentvec/)
- [hifi-gan](https://github.com/jik876/hifi-gan)
- [fish-speech](https://github.com/fishaudio/fish-speech/blob/main/tools/llama/generate.py#L41)
- [f5-TTS](https://github.com/SWivid/F5-TTS/blob/main/src/f5_tts/model/backbones/dit.py)
- [shortcut flow matching](https://github.com/kvfrans/shortcut-models/blob/main/targets_shortcut.py)
### Pretrained Models
- [Chinese Speech Pretrain](https://github.com/TencentGameMate/chinese_speech_pretrain)
- [Chinese-Roberta-WWM-Ext-Large](https://huggingface.co/hfl/chinese-roberta-wwm-ext-large)
- [BigVGAN](https://github.com/NVIDIA/BigVGAN)
### Text Frontend for Inference
- [paddlespeech zh_normalization](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/zh_normalization)
- [split-lang](https://github.com/DoodleBears/split-lang)
- [g2pW](https://github.com/GitYCC/g2pW)
- [pypinyin-g2pW](https://github.com/mozillazg/pypinyin-g2pW)
- [paddlespeech g2pw](https://github.com/PaddlePaddle/PaddleSpeech/tree/develop/paddlespeech/t2s/frontend/g2pw)
### WebUI Tools
- [ultimatevocalremovergui](https://github.com/Anjok07/ultimatevocalremovergui)
- [audio-slicer](https://github.com/openvpi/audio-slicer)
- [SubFix](https://github.com/cronrpc/SubFix)
- [FFmpeg](https://github.com/FFmpeg/FFmpeg)
- [gradio](https://github.com/gradio-app/gradio)
- [faster-whisper](https://github.com/SYSTRAN/faster-whisper)
- [FunASR](https://github.com/alibaba-damo-academy/FunASR)
- [AP-BWE](https://github.com/yxlu-0102/AP-BWE)
Thankful to @Naozumi520 for providing the Cantonese training set and for the guidance on Cantonese-related knowledge.
## Thanks to all contributors for their efforts
<a href="https://github.com/RVC-Boss/GPT-SoVITS/graphs/contributors" target="_blank">
<img src="https://contrib.rocks/image?repo=RVC-Boss/GPT-SoVITS" />
</a>
|
xw17/Qwen2-1.5B-Instruct_finetuned__optimized1_pmdata_augmentation_lora | xw17 | 2025-05-01T04:49:41Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-05-01T04:49:31Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- 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] |
tebal91901/nicoboss_OpenThinker2-32B-Uncensored-GGUF | tebal91901 | 2025-05-01T04:45:32Z | 0 | 0 | null | [
"gguf",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-01T00:18:55Z | ---
license: apache-2.0
---
Based off this model: https://huggingface.co/nicoboss/OpenThinker2-32B-Uncensored
Please use the prompt listed there and consider giving nicoboss a follow and the model a like. |
conquerornigel/conquerornigel | conquerornigel | 2025-05-01T04:43:31Z | 0 | 0 | null | [
"license:bsd-3-clause",
"region:us"
] | null | 2025-05-01T04:43:31Z | ---
license: bsd-3-clause
---
|
win10/Mistral-rp-24b-karcher-Q6_K-GGUF | win10 | 2025-05-01T04:40:01Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"base_model:mergekit-community/Mistral-rp-24b-karcher",
"base_model:quantized:mergekit-community/Mistral-rp-24b-karcher",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-05-01T04:38:35Z | ---
base_model: mergekit-community/Mistral-rp-24b-karcher
library_name: transformers
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# win10/Mistral-rp-24b-karcher-Q6_K-GGUF
This model was converted to GGUF format from [`mergekit-community/Mistral-rp-24b-karcher`](https://huggingface.co/mergekit-community/Mistral-rp-24b-karcher) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/mergekit-community/Mistral-rp-24b-karcher) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo win10/Mistral-rp-24b-karcher-Q6_K-GGUF --hf-file mistral-rp-24b-karcher-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo win10/Mistral-rp-24b-karcher-Q6_K-GGUF --hf-file mistral-rp-24b-karcher-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo win10/Mistral-rp-24b-karcher-Q6_K-GGUF --hf-file mistral-rp-24b-karcher-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo win10/Mistral-rp-24b-karcher-Q6_K-GGUF --hf-file mistral-rp-24b-karcher-q6_k.gguf -c 2048
```
|
rayonlabs/hf-autotrain-2025-04-30-bc7c41bc | rayonlabs | 2025-05-01T04:34:58Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"autotrain",
"text-generation-inference",
"peft",
"conversational",
"dataset:rayonlabs/autotrain-data-hf-autotrain-2025-04-30-bc7c41bc",
"base_model:Qwen/Qwen2-1.5B-Instruct",
"base_model:finetune:Qwen/Qwen2-1.5B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T14:14:41Z | ---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: Qwen/Qwen2-1.5B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- rayonlabs/autotrain-data-hf-autotrain-2025-04-30-bc7c41bc
---
# 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)
``` |
raak-16/hinglish_model-ai | raak-16 | 2025-05-01T04:07:58Z | 9 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-29T16:07:49Z | ---
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:** raak-16
- **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)
|
cvoffer/0727fb95-44b3-40eb-83e0-3245f20e9b00 | cvoffer | 2025-05-01T03:23:51Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Solar-10b-64k",
"base_model:adapter:NousResearch/Yarn-Solar-10b-64k",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | 2025-05-01T02:11:30Z | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Solar-10b-64k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 0727fb95-44b3-40eb-83e0-3245f20e9b00
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
absolute_data_files: false
adapter: lora
base_model: NousResearch/Yarn-Solar-10b-64k
bf16: true
chat_template: llama3
dataset_prepared_path: /workspace/axolotl
datasets:
- data_files:
- aec5b1777769e358_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/aec5b1777769e358_train_data.json
type:
field_instruction: Prompt
field_output: Upsampled
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 1
gradient_checkpointing: true
gradient_clipping: 0.55
group_by_length: false
hub_model_id: cvoffer/0727fb95-44b3-40eb-83e0-3245f20e9b00
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 1.0e-06
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 64
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
lr_scheduler: cosine
max_steps: 150
micro_batch_size: 10
mixed_precision: bf16
mlflow_experiment_name: /tmp/aec5b1777769e358_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 1
sequence_len: 2048
special_tokens:
pad_token: </s>
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: 6fecb4a6-3fc6-4ba4-95da-6c16add53bb7
wandb_project: s56-28
wandb_run: your_name
wandb_runid: 6fecb4a6-3fc6-4ba4-95da-6c16add53bb7
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# 0727fb95-44b3-40eb-83e0-3245f20e9b00
This model is a fine-tuned version of [NousResearch/Yarn-Solar-10b-64k](https://huggingface.co/NousResearch/Yarn-Solar-10b-64k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3034
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 10
- eval_batch_size: 10
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 150
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.2755 | 0.0169 | 150 | 1.3034 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
ruanchengren/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-melodic_mute_panda | ruanchengren | 2025-05-01T03:11:59Z | 18 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am melodic mute panda",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-02T08:35:17Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-melodic_mute_panda
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am melodic mute panda
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-melodic_mute_panda
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="ruanchengren/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-melodic_mute_panda", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bartowski/microsoft_Phi-4-reasoning-GGUF | bartowski | 2025-05-01T03:04:18Z | 0 | 0 | null | [
"gguf",
"phi",
"nlp",
"math",
"code",
"chat",
"conversational",
"reasoning",
"text-generation",
"en",
"base_model:microsoft/Phi-4-reasoning",
"base_model:quantized:microsoft/Phi-4-reasoning",
"license:mit",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-05-01T01:02:49Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
widget:
- messages:
- role: user
content: What is the derivative of x^2?
license: mit
base_model_relation: quantized
license_link: https://huggingface.co/microsoft/Phi-4-reasoning/resolve/main/LICENSE
language:
- en
base_model: microsoft/Phi-4-reasoning
inference:
parameters:
temperature: 0
tags:
- phi
- nlp
- math
- code
- chat
- conversational
- reasoning
---
## Llamacpp imatrix Quantizations of Phi-4-reasoning by microsoft
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> release <a href="https://github.com/ggerganov/llama.cpp/releases/tag/b5228">b5228</a> for quantization.
Original model: https://huggingface.co/microsoft/Phi-4-reasoning
All quants made using imatrix option with dataset from [here](https://gist.github.com/bartowski1182/eb213dccb3571f863da82e99418f81e8)
Run them in [LM Studio](https://lmstudio.ai/)
Run them directly with [llama.cpp](https://github.com/ggerganov/llama.cpp), or any other llama.cpp based project
## Prompt format
```
<|im_start|>system<|im_sep|>You are Phi, a language model trained by Microsoft to help users. Your role as an assistant involves thoroughly exploring questions through a systematic thinking process before providing the final precise and accurate solutions. This requires engaging in a comprehensive cycle of analysis, summarizing, exploration, reassessment, reflection, backtracing, and iteration to develop well-considered thinking process. Please structure your response into two main sections: Thought and Solution using the specified format:<think>{Thought section}</think>{Solution section}. In the Thought section, detail your reasoning process in steps. Each step should include detailed considerations such as analysing questions, summarizing relevant findings, brainstorming new ideas, verifying the accuracy of the current steps, refining any errors, and revisiting previous steps. In the Solution section, based on various attempts, explorations, and reflections from the Thought section, systematically present the final solution that you deem correct. The Solution section should be logical, accurate, and concise and detail necessary steps needed to reach the conclusion. Now, try to solve the following question through the above guidelines:<|im_end|>{system_prompt}<|end|><|user|>{prompt}<|end|><|assistant|>
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Split | Description |
| -------- | ---------- | --------- | ----- | ----------- |
| [Phi-4-reasoning-bf16.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-bf16.gguf) | bf16 | 29.32GB | false | Full BF16 weights. |
| [Phi-4-reasoning-Q8_0.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q8_0.gguf) | Q8_0 | 15.58GB | false | Extremely high quality, generally unneeded but max available quant. |
| [Phi-4-reasoning-Q6_K_L.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q6_K_L.gguf) | Q6_K_L | 12.28GB | false | Uses Q8_0 for embed and output weights. Very high quality, near perfect, *recommended*. |
| [Phi-4-reasoning-Q6_K.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q6_K.gguf) | Q6_K | 12.03GB | false | Very high quality, near perfect, *recommended*. |
| [Phi-4-reasoning-Q5_K_L.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q5_K_L.gguf) | Q5_K_L | 10.92GB | false | Uses Q8_0 for embed and output weights. High quality, *recommended*. |
| [Phi-4-reasoning-Q5_K_M.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q5_K_M.gguf) | Q5_K_M | 10.60GB | false | High quality, *recommended*. |
| [Phi-4-reasoning-Q5_K_S.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q5_K_S.gguf) | Q5_K_S | 10.15GB | false | High quality, *recommended*. |
| [Phi-4-reasoning-Q4_K_L.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q4_K_L.gguf) | Q4_K_L | 9.43GB | false | Uses Q8_0 for embed and output weights. Good quality, *recommended*. |
| [Phi-4-reasoning-Q4_1.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q4_1.gguf) | Q4_1 | 9.27GB | false | Legacy format, similar performance to Q4_K_S but with improved tokens/watt on Apple silicon. |
| [Phi-4-reasoning-Q4_K_M.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q4_K_M.gguf) | Q4_K_M | 9.05GB | false | Good quality, default size for most use cases, *recommended*. |
| [Phi-4-reasoning-Q4_K_S.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q4_K_S.gguf) | Q4_K_S | 8.44GB | false | Slightly lower quality with more space savings, *recommended*. |
| [Phi-4-reasoning-Q4_0.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q4_0.gguf) | Q4_0 | 8.41GB | false | Legacy format, offers online repacking for ARM and AVX CPU inference. |
| [Phi-4-reasoning-IQ4_NL.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ4_NL.gguf) | IQ4_NL | 8.38GB | false | Similar to IQ4_XS, but slightly larger. Offers online repacking for ARM CPU inference. |
| [Phi-4-reasoning-Q3_K_XL.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q3_K_XL.gguf) | Q3_K_XL | 8.38GB | false | Uses Q8_0 for embed and output weights. Lower quality but usable, good for low RAM availability. |
| [Phi-4-reasoning-IQ4_XS.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ4_XS.gguf) | IQ4_XS | 7.94GB | false | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Phi-4-reasoning-Q3_K_L.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q3_K_L.gguf) | Q3_K_L | 7.93GB | false | Lower quality but usable, good for low RAM availability. |
| [Phi-4-reasoning-Q3_K_M.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q3_K_M.gguf) | Q3_K_M | 7.36GB | false | Low quality. |
| [Phi-4-reasoning-IQ3_M.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ3_M.gguf) | IQ3_M | 6.91GB | false | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Phi-4-reasoning-Q3_K_S.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q3_K_S.gguf) | Q3_K_S | 6.50GB | false | Low quality, not recommended. |
| [Phi-4-reasoning-IQ3_XS.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ3_XS.gguf) | IQ3_XS | 6.25GB | false | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Phi-4-reasoning-Q2_K_L.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q2_K_L.gguf) | Q2_K_L | 6.05GB | false | Uses Q8_0 for embed and output weights. Very low quality but surprisingly usable. |
| [Phi-4-reasoning-IQ3_XXS.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ3_XXS.gguf) | IQ3_XXS | 5.85GB | false | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Phi-4-reasoning-Q2_K.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-Q2_K.gguf) | Q2_K | 5.55GB | false | Very low quality but surprisingly usable. |
| [Phi-4-reasoning-IQ2_M.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ2_M.gguf) | IQ2_M | 5.11GB | false | Relatively low quality, uses SOTA techniques to be surprisingly usable. |
| [Phi-4-reasoning-IQ2_S.gguf](https://huggingface.co/bartowski/microsoft_Phi-4-reasoning-GGUF/blob/main/microsoft_Phi-4-reasoning-IQ2_S.gguf) | IQ2_S | 4.73GB | false | Low quality, uses SOTA techniques to be usable. |
## Embed/output weights
Some of these quants (Q3_K_XL, Q4_K_L etc) are the standard quantization method with the embeddings and output weights quantized to Q8_0 instead of what they would normally default to.
## Downloading using huggingface-cli
<details>
<summary>Click to view download instructions</summary>
First, make sure you have hugginface-cli installed:
```
pip install -U "huggingface_hub[cli]"
```
Then, you can target the specific file you want:
```
huggingface-cli download bartowski/microsoft_Phi-4-reasoning-GGUF --include "microsoft_Phi-4-reasoning-Q4_K_M.gguf" --local-dir ./
```
If the model is bigger than 50GB, it will have been split into multiple files. In order to download them all to a local folder, run:
```
huggingface-cli download bartowski/microsoft_Phi-4-reasoning-GGUF --include "microsoft_Phi-4-reasoning-Q8_0/*" --local-dir ./
```
You can either specify a new local-dir (microsoft_Phi-4-reasoning-Q8_0) or download them all in place (./)
</details>
## ARM/AVX information
Previously, you would download Q4_0_4_4/4_8/8_8, and these would have their weights interleaved in memory in order to improve performance on ARM and AVX machines by loading up more data in one pass.
Now, however, there is something called "online repacking" for weights. details in [this PR](https://github.com/ggerganov/llama.cpp/pull/9921). If you use Q4_0 and your hardware would benefit from repacking weights, it will do it automatically on the fly.
As of llama.cpp build [b4282](https://github.com/ggerganov/llama.cpp/releases/tag/b4282) you will not be able to run the Q4_0_X_X files and will instead need to use Q4_0.
Additionally, if you want to get slightly better quality for , you can use IQ4_NL thanks to [this PR](https://github.com/ggerganov/llama.cpp/pull/10541) which will also repack the weights for ARM, though only the 4_4 for now. The loading time may be slower but it will result in an overall speed incrase.
<details>
<summary>Click to view Q4_0_X_X information (deprecated</summary>
I'm keeping this section to show the potential theoretical uplift in performance from using the Q4_0 with online repacking.
<details>
<summary>Click to view benchmarks on an AVX2 system (EPYC7702)</summary>
| model | size | params | backend | threads | test | t/s | % (vs Q4_0) |
| ------------------------------ | ---------: | ---------: | ---------- | ------: | ------------: | -------------------: |-------------: |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp512 | 204.03 ± 1.03 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp1024 | 282.92 ± 0.19 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | pp2048 | 259.49 ± 0.44 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg128 | 39.12 ± 0.27 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg256 | 39.31 ± 0.69 | 100% |
| qwen2 3B Q4_0 | 1.70 GiB | 3.09 B | CPU | 64 | tg512 | 40.52 ± 0.03 | 100% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp512 | 301.02 ± 1.74 | 147% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp1024 | 287.23 ± 0.20 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | pp2048 | 262.77 ± 1.81 | 101% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg128 | 18.80 ± 0.99 | 48% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg256 | 24.46 ± 3.04 | 83% |
| qwen2 3B Q4_K_M | 1.79 GiB | 3.09 B | CPU | 64 | tg512 | 36.32 ± 3.59 | 90% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp512 | 271.71 ± 3.53 | 133% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp1024 | 279.86 ± 45.63 | 100% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | pp2048 | 320.77 ± 5.00 | 124% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg128 | 43.51 ± 0.05 | 111% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg256 | 43.35 ± 0.09 | 110% |
| qwen2 3B Q4_0_8_8 | 1.69 GiB | 3.09 B | CPU | 64 | tg512 | 42.60 ± 0.31 | 105% |
Q4_0_8_8 offers a nice bump to prompt processing and a small bump to text generation
</details>
</details>
## Which file should I choose?
<details>
<summary>Click here for details</summary>
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
</details>
## Credits
Thank you kalomaze and Dampf for assistance in creating the imatrix calibration dataset.
Thank you ZeroWw for the inspiration to experiment with embed/output.
Thank you to LM Studio for sponsoring my work.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
|
SouravCrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_tawny_dove | SouravCrypto | 2025-05-01T03:02:54Z | 5 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am striped tawny dove",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-21T12:52:18Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_tawny_dove
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am striped tawny dove
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_tawny_dove
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="SouravCrypto/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-striped_tawny_dove", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
NikolayKozloff/Phi-4-mini-reasoning-Q8_0-GGUF | NikolayKozloff | 2025-05-01T02:21:02Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"nlp",
"math",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:microsoft/Phi-4-mini-reasoning",
"base_model:quantized:microsoft/Phi-4-mini-reasoning",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-05-01T02:20:43Z | ---
base_model: microsoft/Phi-4-mini-reasoning
language:
- en
library_name: transformers
license: mit
license_link: https://huggingface.co/microsoft/Phi-4-mini-instruct-reasoning/resolve/main/LICENSE
pipeline_tag: text-generation
tags:
- nlp
- math
- code
- llama-cpp
- gguf-my-repo
widget:
- messages:
- role: user
content: How to solve 3*x^2+4*x+5=1?
---
# NikolayKozloff/Phi-4-mini-reasoning-Q8_0-GGUF
This model was converted to GGUF format from [`microsoft/Phi-4-mini-reasoning`](https://huggingface.co/microsoft/Phi-4-mini-reasoning) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-4-mini-reasoning) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo NikolayKozloff/Phi-4-mini-reasoning-Q8_0-GGUF --hf-file phi-4-mini-reasoning-q8_0.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo NikolayKozloff/Phi-4-mini-reasoning-Q8_0-GGUF --hf-file phi-4-mini-reasoning-q8_0.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo NikolayKozloff/Phi-4-mini-reasoning-Q8_0-GGUF --hf-file phi-4-mini-reasoning-q8_0.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo NikolayKozloff/Phi-4-mini-reasoning-Q8_0-GGUF --hf-file phi-4-mini-reasoning-q8_0.gguf -c 2048
```
|
mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF | mradermacher | 2025-05-01T02:00:16Z | 0 | 1 | transformers | [
"transformers",
"gguf",
"chat",
"en",
"base_model:Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1",
"base_model:quantized:Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-30T16:25:28Z | ---
base_model: Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- chat
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-8B-abliterated-v1
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q2_K.gguf) | Q2_K | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q3_K_S.gguf) | Q3_K_S | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q3_K_M.gguf) | Q3_K_M | 4.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q3_K_L.gguf) | Q3_K_L | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.IQ4_XS.gguf) | IQ4_XS | 4.7 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q4_K_S.gguf) | Q4_K_S | 4.9 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q4_K_M.gguf) | Q4_K_M | 5.1 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q5_K_S.gguf) | Q5_K_S | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q5_K_M.gguf) | Q5_K_M | 6.0 | |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q6_K.gguf) | Q6_K | 6.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.Q8_0.gguf) | Q8_0 | 8.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Josiefied-Qwen3-8B-abliterated-v1-GGUF/resolve/main/Josiefied-Qwen3-8B-abliterated-v1.f16.gguf) | f16 | 16.5 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
Lansechen/Qwen2.5-7B-Open-R1-GRPO-math-selected-cosine-0430 | Lansechen | 2025-05-01T01:55:14Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"grpo",
"dataset:chenggong1995/math_selected",
"arxiv:2402.03300",
"base_model:Qwen/Qwen2.5-7B",
"base_model:finetune:Qwen/Qwen2.5-7B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T07:42:49Z | ---
base_model: Qwen/Qwen2.5-7B
datasets: chenggong1995/math_selected
library_name: transformers
model_name: Qwen2.5-7B-Open-R1-GRPO-math-selected-cosine-0430
tags:
- generated_from_trainer
- open-r1
- trl
- grpo
licence: license
---
# Model Card for Qwen2.5-7B-Open-R1-GRPO-math-selected-cosine-0430
This model is a fine-tuned version of [Qwen/Qwen2.5-7B](https://huggingface.co/Qwen/Qwen2.5-7B) on the [chenggong1995/math_selected](https://huggingface.co/datasets/chenggong1995/math_selected) 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="Lansechen/Qwen2.5-7B-Open-R1-GRPO-math-selected-cosine-0430", 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/chenran1995-the-chinese-university-of-hong-kong/huggingface/runs/us1dn0oo)
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
shuttleai/shuttle-3.5-awq | shuttleai | 2025-05-01T01:43:52Z | 0 | 0 | null | [
"safetensors",
"qwen3",
"4-bit",
"awq",
"region:us"
] | null | 2025-04-30T22:30:14Z | ```
Base: shuttleai/shuttle-3.5
Model: 4-bit quantized AWQ model
Format: AWQ (AutoAWQForCausalLM)
Bit: 4
Group Size: 128
Zero Point: True
Version: GEMM
Source: Fine-tuned with LoRA, then merged and quantized
``` |
lamdo/bert-base-uncased-phrase-60kaddedphrasesfroms2orc | lamdo | 2025-05-01T01:07:21Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | 2025-05-01T01:06:32Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
zoemartin/zoemartina1 | zoemartin | 2025-05-01T00:21:22Z | 0 | 0 | null | [
"license:bsd-3-clause-clear",
"region:us"
] | null | 2025-05-01T00:21:22Z | ---
license: bsd-3-clause-clear
---
|
clem0510/m | clem0510 | 2025-05-01T00:19:05Z | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | 2025-05-01T00:19:05Z | ---
license: apache-2.0
---
|
rbelanec/train_cb_1745950312 | rbelanec | 2025-04-30T23:55:23Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"ia3",
"generated_from_trainer",
"dataset:super_glue",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"base_model:adapter:meta-llama/Meta-Llama-3-8B-Instruct",
"license:llama3",
"region:us"
] | null | 2025-04-30T20:21:28Z | ---
library_name: peft
license: llama3
base_model: meta-llama/Meta-Llama-3-8B-Instruct
tags:
- llama-factory
- ia3
- generated_from_trainer
datasets:
- super_glue
model-index:
- name: train_cb_1745950312
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. -->
# train_cb_1745950312
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) on the cb dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1586
- Num Input Tokens Seen: 22164464
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
- training_steps: 40000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:--------:|:-----:|:---------------:|:-----------------:|
| 0.284 | 3.5133 | 200 | 0.1743 | 111736 |
| 0.0782 | 7.0177 | 400 | 0.1610 | 223024 |
| 0.1338 | 10.5310 | 600 | 0.1586 | 332984 |
| 0.0725 | 14.0354 | 800 | 0.1596 | 444576 |
| 0.0814 | 17.5487 | 1000 | 0.1621 | 555960 |
| 0.0691 | 21.0531 | 1200 | 0.1672 | 665952 |
| 0.0118 | 24.5664 | 1400 | 0.1699 | 777608 |
| 0.133 | 28.0708 | 1600 | 0.1807 | 887904 |
| 0.0241 | 31.5841 | 1800 | 0.1871 | 999464 |
| 0.0245 | 35.0885 | 2000 | 0.2026 | 1110640 |
| 0.0097 | 38.6018 | 2200 | 0.2195 | 1222144 |
| 0.0193 | 42.1062 | 2400 | 0.2402 | 1332096 |
| 0.0101 | 45.6195 | 2600 | 0.2672 | 1443792 |
| 0.0153 | 49.1239 | 2800 | 0.2882 | 1553600 |
| 0.0024 | 52.6372 | 3000 | 0.3065 | 1664296 |
| 0.0035 | 56.1416 | 3200 | 0.3406 | 1775264 |
| 0.0014 | 59.6549 | 3400 | 0.3585 | 1885968 |
| 0.0002 | 63.1593 | 3600 | 0.3739 | 1996440 |
| 0.0011 | 66.6726 | 3800 | 0.3880 | 2107400 |
| 0.0002 | 70.1770 | 4000 | 0.3887 | 2218352 |
| 0.0005 | 73.6903 | 4200 | 0.3966 | 2330072 |
| 0.0006 | 77.1947 | 4400 | 0.4150 | 2440176 |
| 0.0002 | 80.7080 | 4600 | 0.3956 | 2551216 |
| 0.0002 | 84.2124 | 4800 | 0.4218 | 2662848 |
| 0.0001 | 87.7257 | 5000 | 0.4170 | 2774160 |
| 0.0001 | 91.2301 | 5200 | 0.4206 | 2885448 |
| 0.0001 | 94.7434 | 5400 | 0.4394 | 2995680 |
| 0.0001 | 98.2478 | 5600 | 0.4445 | 3106768 |
| 0.0002 | 101.7611 | 5800 | 0.4561 | 3218248 |
| 0.0001 | 105.2655 | 6000 | 0.4435 | 3329176 |
| 0.0002 | 108.7788 | 6200 | 0.4605 | 3440344 |
| 0.0001 | 112.2832 | 6400 | 0.4850 | 3550560 |
| 0.0001 | 115.7965 | 6600 | 0.4710 | 3661824 |
| 0.0 | 119.3009 | 6800 | 0.4757 | 3771856 |
| 0.0001 | 122.8142 | 7000 | 0.4788 | 3883176 |
| 0.0001 | 126.3186 | 7200 | 0.4710 | 3994264 |
| 0.0 | 129.8319 | 7400 | 0.4824 | 4105440 |
| 0.0001 | 133.3363 | 7600 | 0.4898 | 4216208 |
| 0.0 | 136.8496 | 7800 | 0.4831 | 4326832 |
| 0.0 | 140.3540 | 8000 | 0.4945 | 4437792 |
| 0.0 | 143.8673 | 8200 | 0.4983 | 4549512 |
| 0.0 | 147.3717 | 8400 | 0.4865 | 4658800 |
| 0.0 | 150.8850 | 8600 | 0.4894 | 4769400 |
| 0.0 | 154.3894 | 8800 | 0.5232 | 4881880 |
| 0.0 | 157.9027 | 9000 | 0.5032 | 4992488 |
| 0.0 | 161.4071 | 9200 | 0.5058 | 5103032 |
| 0.0 | 164.9204 | 9400 | 0.5299 | 5214280 |
| 0.0 | 168.4248 | 9600 | 0.5226 | 5323664 |
| 0.0 | 171.9381 | 9800 | 0.5231 | 5436384 |
| 0.0 | 175.4425 | 10000 | 0.5379 | 5547152 |
| 0.0 | 178.9558 | 10200 | 0.5326 | 5658656 |
| 0.0 | 182.4602 | 10400 | 0.5466 | 5768616 |
| 0.0 | 185.9735 | 10600 | 0.5473 | 5879304 |
| 0.0 | 189.4779 | 10800 | 0.5319 | 5990296 |
| 0.0 | 192.9912 | 11000 | 0.5413 | 6101128 |
| 0.0 | 196.4956 | 11200 | 0.5279 | 6212008 |
| 0.0 | 200.0 | 11400 | 0.5467 | 6321568 |
| 0.0 | 203.5133 | 11600 | 0.5459 | 6432384 |
| 0.0 | 207.0177 | 11800 | 0.5572 | 6542352 |
| 0.0 | 210.5310 | 12000 | 0.5527 | 6654160 |
| 0.0 | 214.0354 | 12200 | 0.5457 | 6765224 |
| 0.0 | 217.5487 | 12400 | 0.5507 | 6874936 |
| 0.0 | 221.0531 | 12600 | 0.5711 | 6986248 |
| 0.0 | 224.5664 | 12800 | 0.5727 | 7097808 |
| 0.0 | 228.0708 | 13000 | 0.5716 | 7208392 |
| 0.0 | 231.5841 | 13200 | 0.5790 | 7318456 |
| 0.0 | 235.0885 | 13400 | 0.5775 | 7430160 |
| 0.0 | 238.6018 | 13600 | 0.5793 | 7540344 |
| 0.0 | 242.1062 | 13800 | 0.5663 | 7650824 |
| 0.0 | 245.6195 | 14000 | 0.5732 | 7761968 |
| 0.0 | 249.1239 | 14200 | 0.5944 | 7872968 |
| 0.0 | 252.6372 | 14400 | 0.6055 | 7983464 |
| 0.0 | 256.1416 | 14600 | 0.5987 | 8093616 |
| 0.0 | 259.6549 | 14800 | 0.5991 | 8204560 |
| 0.0 | 263.1593 | 15000 | 0.5862 | 8315912 |
| 0.0 | 266.6726 | 15200 | 0.5794 | 8426448 |
| 0.0 | 270.1770 | 15400 | 0.5985 | 8536288 |
| 0.0 | 273.6903 | 15600 | 0.6050 | 8648256 |
| 0.0 | 277.1947 | 15800 | 0.6189 | 8758760 |
| 0.0 | 280.7080 | 16000 | 0.6261 | 8868600 |
| 0.0 | 284.2124 | 16200 | 0.6282 | 8981000 |
| 0.0 | 287.7257 | 16400 | 0.6583 | 9091424 |
| 0.0 | 291.2301 | 16600 | 0.6430 | 9202432 |
| 0.0 | 294.7434 | 16800 | 0.6544 | 9312888 |
| 0.0 | 298.2478 | 17000 | 0.6434 | 9423320 |
| 0.0 | 301.7611 | 17200 | 0.6714 | 9533896 |
| 0.0 | 305.2655 | 17400 | 0.6431 | 9644952 |
| 0.0 | 308.7788 | 17600 | 0.6493 | 9754832 |
| 0.0 | 312.2832 | 17800 | 0.6749 | 9866256 |
| 0.0 | 315.7965 | 18000 | 0.6496 | 9975768 |
| 0.0 | 319.3009 | 18200 | 0.6726 | 10086392 |
| 0.0 | 322.8142 | 18400 | 0.6718 | 10197432 |
| 0.0 | 326.3186 | 18600 | 0.6865 | 10307224 |
| 0.0 | 329.8319 | 18800 | 0.6698 | 10419256 |
| 0.0 | 333.3363 | 19000 | 0.6498 | 10529488 |
| 0.0 | 336.8496 | 19200 | 0.6796 | 10640296 |
| 0.0 | 340.3540 | 19400 | 0.6784 | 10750776 |
| 0.0 | 343.8673 | 19600 | 0.6566 | 10861648 |
| 0.0 | 347.3717 | 19800 | 0.6681 | 10972808 |
| 0.0 | 350.8850 | 20000 | 0.6887 | 11083136 |
| 0.0 | 354.3894 | 20200 | 0.7147 | 11193448 |
| 0.0 | 357.9027 | 20400 | 0.6921 | 11305168 |
| 0.0 | 361.4071 | 20600 | 0.7121 | 11416112 |
| 0.0 | 364.9204 | 20800 | 0.6977 | 11527424 |
| 0.0 | 368.4248 | 21000 | 0.7004 | 11637784 |
| 0.0 | 371.9381 | 21200 | 0.7117 | 11748768 |
| 0.0 | 375.4425 | 21400 | 0.7038 | 11857872 |
| 0.0 | 378.9558 | 21600 | 0.6942 | 11969696 |
| 0.0 | 382.4602 | 21800 | 0.7161 | 12080592 |
| 0.0 | 385.9735 | 22000 | 0.7295 | 12190608 |
| 0.0 | 389.4779 | 22200 | 0.7190 | 12301648 |
| 0.0 | 392.9912 | 22400 | 0.7184 | 12412384 |
| 0.0 | 396.4956 | 22600 | 0.7380 | 12523264 |
| 0.0 | 400.0 | 22800 | 0.7235 | 12633656 |
| 0.0 | 403.5133 | 23000 | 0.7182 | 12743928 |
| 0.0 | 407.0177 | 23200 | 0.7180 | 12855568 |
| 0.0 | 410.5310 | 23400 | 0.7378 | 12966544 |
| 0.0 | 414.0354 | 23600 | 0.7213 | 13077752 |
| 0.0 | 417.5487 | 23800 | 0.7396 | 13189592 |
| 0.0 | 421.0531 | 24000 | 0.7409 | 13299920 |
| 0.0 | 424.5664 | 24200 | 0.7202 | 13410872 |
| 0.0 | 428.0708 | 24400 | 0.7344 | 13522656 |
| 0.0 | 431.5841 | 24600 | 0.7564 | 13632696 |
| 0.0 | 435.0885 | 24800 | 0.6867 | 13743576 |
| 0.0 | 438.6018 | 25000 | 0.7655 | 13856080 |
| 0.0 | 442.1062 | 25200 | 0.7144 | 13966552 |
| 0.0 | 445.6195 | 25400 | 0.7624 | 14076912 |
| 0.0 | 449.1239 | 25600 | 0.7328 | 14187144 |
| 0.0 | 452.6372 | 25800 | 0.7431 | 14298896 |
| 0.0 | 456.1416 | 26000 | 0.7328 | 14408592 |
| 0.0 | 459.6549 | 26200 | 0.7600 | 14519672 |
| 0.0 | 463.1593 | 26400 | 0.7228 | 14630736 |
| 0.0 | 466.6726 | 26600 | 0.7296 | 14741472 |
| 0.0 | 470.1770 | 26800 | 0.7222 | 14852816 |
| 0.0 | 473.6903 | 27000 | 0.7612 | 14964568 |
| 0.0 | 477.1947 | 27200 | 0.7532 | 15074912 |
| 0.0 | 480.7080 | 27400 | 0.7368 | 15186488 |
| 0.0 | 484.2124 | 27600 | 0.7430 | 15297600 |
| 0.0 | 487.7257 | 27800 | 0.7272 | 15407784 |
| 0.0 | 491.2301 | 28000 | 0.7539 | 15518800 |
| 0.0 | 494.7434 | 28200 | 0.7698 | 15629392 |
| 0.0 | 498.2478 | 28400 | 0.7498 | 15740552 |
| 0.0 | 501.7611 | 28600 | 0.7707 | 15852112 |
| 0.0 | 505.2655 | 28800 | 0.7634 | 15962600 |
| 0.0 | 508.7788 | 29000 | 0.7678 | 16073896 |
| 0.0 | 512.2832 | 29200 | 0.7427 | 16184680 |
| 0.0 | 515.7965 | 29400 | 0.7719 | 16295584 |
| 0.0 | 519.3009 | 29600 | 0.7325 | 16406536 |
| 0.0 | 522.8142 | 29800 | 0.7953 | 16516648 |
| 0.0 | 526.3186 | 30000 | 0.7460 | 16628144 |
| 0.0 | 529.8319 | 30200 | 0.7134 | 16738416 |
| 0.0 | 533.3363 | 30400 | 0.7632 | 16848080 |
| 0.0 | 536.8496 | 30600 | 0.7161 | 16960312 |
| 0.0 | 540.3540 | 30800 | 0.7365 | 17069536 |
| 0.0 | 543.8673 | 31000 | 0.7271 | 17180696 |
| 0.0 | 547.3717 | 31200 | 0.7417 | 17291896 |
| 0.0 | 550.8850 | 31400 | 0.7391 | 17402176 |
| 0.0 | 554.3894 | 31600 | 0.7218 | 17512704 |
| 0.0 | 557.9027 | 31800 | 0.7414 | 17624600 |
| 0.0 | 561.4071 | 32000 | 0.7245 | 17734208 |
| 0.0 | 564.9204 | 32200 | 0.7525 | 17845224 |
| 0.0 | 568.4248 | 32400 | 0.7680 | 17956288 |
| 0.0 | 571.9381 | 32600 | 0.7673 | 18066176 |
| 0.0 | 575.4425 | 32800 | 0.7447 | 18177520 |
| 0.0 | 578.9558 | 33000 | 0.7571 | 18289064 |
| 0.0 | 582.4602 | 33200 | 0.7178 | 18398888 |
| 0.0 | 585.9735 | 33400 | 0.7572 | 18509416 |
| 0.0 | 589.4779 | 33600 | 0.7605 | 18620544 |
| 0.0 | 592.9912 | 33800 | 0.7580 | 18731712 |
| 0.0 | 596.4956 | 34000 | 0.7632 | 18841128 |
| 0.0 | 600.0 | 34200 | 0.7505 | 18952336 |
| 0.0 | 603.5133 | 34400 | 0.7474 | 19063208 |
| 0.0 | 607.0177 | 34600 | 0.7527 | 19173736 |
| 0.0 | 610.5310 | 34800 | 0.7446 | 19285352 |
| 0.0 | 614.0354 | 35000 | 0.7091 | 19395536 |
| 0.0 | 617.5487 | 35200 | 0.7482 | 19506864 |
| 0.0 | 621.0531 | 35400 | 0.7423 | 19617648 |
| 0.0 | 624.5664 | 35600 | 0.7325 | 19728144 |
| 0.0 | 628.0708 | 35800 | 0.7527 | 19838296 |
| 0.0 | 631.5841 | 36000 | 0.7241 | 19948392 |
| 0.0 | 635.0885 | 36200 | 0.7680 | 20059232 |
| 0.0 | 638.6018 | 36400 | 0.7430 | 20170032 |
| 0.0 | 642.1062 | 36600 | 0.7420 | 20279560 |
| 0.0 | 645.6195 | 36800 | 0.7323 | 20389936 |
| 0.0 | 649.1239 | 37000 | 0.7757 | 20499984 |
| 0.0 | 652.6372 | 37200 | 0.7163 | 20612176 |
| 0.0 | 656.1416 | 37400 | 0.7300 | 20722344 |
| 0.0 | 659.6549 | 37600 | 0.7375 | 20833640 |
| 0.0 | 663.1593 | 37800 | 0.7191 | 20944256 |
| 0.0 | 666.6726 | 38000 | 0.7308 | 21055624 |
| 0.0 | 670.1770 | 38200 | 0.7359 | 21165744 |
| 0.0 | 673.6903 | 38400 | 0.7463 | 21277072 |
| 0.0 | 677.1947 | 38600 | 0.7771 | 21388128 |
| 0.0 | 680.7080 | 38800 | 0.7464 | 21499624 |
| 0.0 | 684.2124 | 39000 | 0.7472 | 21611240 |
| 0.0 | 687.7257 | 39200 | 0.7426 | 21721232 |
| 0.0 | 691.2301 | 39400 | 0.7426 | 21832720 |
| 0.0 | 694.7434 | 39600 | 0.7426 | 21942280 |
| 0.0 | 698.2478 | 39800 | 0.7426 | 22053128 |
| 0.0 | 701.7611 | 40000 | 0.7426 | 22164464 |
### Framework versions
- PEFT 0.15.2.dev0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
Pinkstack/Pink | Pinkstack | 2025-04-30T23:29:20Z | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"grpo",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T23:28:33Z | ---
library_name: transformers
tags:
- unsloth
- trl
- grpo
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
diliash/qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_od_borders_data_20250430_160145 | diliash | 2025-04-30T23:06:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_od_borders_data_20250430_160145",
"20250430_160145",
"qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846",
"20250430_152846",
"qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_rerunl40_data_20250430_144705",
"20250430_144705",
"qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_border_data_20250430_143912",
"20250430_143912",
"generated_from_trainer",
"final-model",
"processor",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-7B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2025-04-30T23:01:46Z | ---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-VL-7B-Instruct
tags:
- qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_od_borders_data_20250430_160145
- '20250430_160145'
- qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_qwenprompt_borders_data_20250430_152846
- '20250430_152846'
- qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_rerunl40_data_20250430_144705
- '20250430_144705'
- qwen2.5-vl-7b_rslora_pm_axis_origintype_twoway_border_data_20250430_143912
- '20250430_143912'
- generated_from_trainer
- final-model
- processor
model-index:
- name: checkpoints
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. -->
# checkpoints
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- 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: 0.03
- num_epochs: 3
### Framework versions
- Transformers 4.51.3
- Pytorch 2.4.1+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
neuraxcompany/community-gpt2 | neuraxcompany | 2025-04-30T22:55:22Z | 36 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-16T22:12:41Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
onnx-community/dfine_x_obj365-ONNX | onnx-community | 2025-04-30T22:37:36Z | 0 | 0 | transformers.js | [
"transformers.js",
"onnx",
"d_fine",
"object-detection",
"base_model:ustc-community/dfine_x_obj365",
"base_model:quantized:ustc-community/dfine_x_obj365",
"region:us"
] | object-detection | 2025-04-30T22:26:42Z | ---
library_name: transformers.js
base_model: ustc-community/dfine_x_obj365
---
https://huggingface.co/ustc-community/dfine_x_obj365 with ONNX weights to be compatible with Transformers.js.
### Transformers.js
If you haven't already, you can install the [Transformers.js](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using:
```bash
npm i @huggingface/transformers
```
You can then use the model like this:
```js
import { pipeline } from "@huggingface/transformers";
const detector = await pipeline("object-detection", "onnx-community/dfine_x_obj365-ONNX");
const image = "https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg";
const output = await detector(image, { threshold: 0.5 });
console.log(output);
```
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using [🤗 Optimum](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |
rbelanec/train_wsc_1745950307 | rbelanec | 2025-04-30T22:35:00Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lntuning",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"region:us"
] | null | 2025-04-30T19:33:39Z | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- llama-factory
- lntuning
- generated_from_trainer
model-index:
- name: train_wsc_1745950307
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. -->
# train_wsc_1745950307
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the wsc dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3411
- Num Input Tokens Seen: 13676608
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
- training_steps: 40000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:--------:|:-----:|:---------------:|:-----------------:|
| 2.38 | 1.6024 | 200 | 1.5036 | 68480 |
| 1.8434 | 3.2008 | 400 | 1.4091 | 137040 |
| 1.6131 | 4.8032 | 600 | 1.3973 | 205344 |
| 1.9089 | 6.4016 | 800 | 1.3924 | 273648 |
| 2.4603 | 8.0 | 1000 | 1.3827 | 342192 |
| 2.3568 | 9.6024 | 1200 | 1.3743 | 410624 |
| 2.6209 | 11.2008 | 1400 | 1.4002 | 479392 |
| 1.9548 | 12.8032 | 1600 | 1.3998 | 547360 |
| 2.3111 | 14.4016 | 1800 | 1.3934 | 616128 |
| 1.5327 | 16.0 | 2000 | 1.3905 | 683616 |
| 1.5612 | 17.6024 | 2200 | 1.3936 | 751520 |
| 2.6575 | 19.2008 | 2400 | 1.3690 | 820000 |
| 1.8487 | 20.8032 | 2600 | 1.3929 | 888576 |
| 1.5663 | 22.4016 | 2800 | 1.3796 | 956480 |
| 1.7485 | 24.0 | 3000 | 1.3739 | 1024784 |
| 1.7832 | 25.6024 | 3200 | 1.3798 | 1093536 |
| 1.84 | 27.2008 | 3400 | 1.3878 | 1161248 |
| 2.2367 | 28.8032 | 3600 | 1.3837 | 1229760 |
| 2.2398 | 30.4016 | 3800 | 1.3837 | 1298112 |
| 2.5764 | 32.0 | 4000 | 1.3718 | 1366864 |
| 2.3443 | 33.6024 | 4200 | 1.3770 | 1435664 |
| 1.5648 | 35.2008 | 4400 | 1.3796 | 1503408 |
| 2.418 | 36.8032 | 4600 | 1.3792 | 1572288 |
| 2.2188 | 38.4016 | 4800 | 1.3876 | 1640848 |
| 1.9725 | 40.0 | 5000 | 1.3678 | 1708416 |
| 2.2863 | 41.6024 | 5200 | 1.3855 | 1776416 |
| 1.4944 | 43.2008 | 5400 | 1.3841 | 1845088 |
| 1.4354 | 44.8032 | 5600 | 1.3899 | 1913360 |
| 1.2547 | 46.4016 | 5800 | 1.3720 | 1981136 |
| 2.239 | 48.0 | 6000 | 1.3813 | 2050304 |
| 2.0904 | 49.6024 | 6200 | 1.3658 | 2118640 |
| 2.3447 | 51.2008 | 6400 | 1.3733 | 2186992 |
| 2.5379 | 52.8032 | 6600 | 1.3852 | 2255392 |
| 2.2955 | 54.4016 | 6800 | 1.3662 | 2324240 |
| 1.7964 | 56.0 | 7000 | 1.3752 | 2391840 |
| 1.9475 | 57.6024 | 7200 | 1.3890 | 2460464 |
| 2.0349 | 59.2008 | 7400 | 1.3970 | 2528416 |
| 2.0624 | 60.8032 | 7600 | 1.3765 | 2597008 |
| 1.9295 | 62.4016 | 7800 | 1.3909 | 2664720 |
| 2.6086 | 64.0 | 8000 | 1.3866 | 2733360 |
| 1.4825 | 65.6024 | 8200 | 1.3820 | 2801792 |
| 1.4862 | 67.2008 | 8400 | 1.3797 | 2870768 |
| 1.311 | 68.8032 | 8600 | 1.3816 | 2939344 |
| 1.5444 | 70.4016 | 8800 | 1.3809 | 3007936 |
| 1.6452 | 72.0 | 9000 | 1.3795 | 3076384 |
| 1.2808 | 73.6024 | 9200 | 1.3821 | 3144624 |
| 1.4038 | 75.2008 | 9400 | 1.3899 | 3212896 |
| 2.0719 | 76.8032 | 9600 | 1.3870 | 3281408 |
| 2.1484 | 78.4016 | 9800 | 1.3826 | 3349872 |
| 1.3604 | 80.0 | 10000 | 1.3719 | 3418368 |
| 1.9583 | 81.6024 | 10200 | 1.3645 | 3486640 |
| 1.9835 | 83.2008 | 10400 | 1.3874 | 3555456 |
| 2.271 | 84.8032 | 10600 | 1.3794 | 3623440 |
| 1.738 | 86.4016 | 10800 | 1.3925 | 3691760 |
| 1.5836 | 88.0 | 11000 | 1.3949 | 3760416 |
| 2.4306 | 89.6024 | 11200 | 1.3694 | 3829184 |
| 1.8857 | 91.2008 | 11400 | 1.3643 | 3897520 |
| 1.915 | 92.8032 | 11600 | 1.3843 | 3965568 |
| 1.8895 | 94.4016 | 11800 | 1.3623 | 4033904 |
| 1.7895 | 96.0 | 12000 | 1.3735 | 4102480 |
| 2.4102 | 97.6024 | 12200 | 1.3713 | 4170912 |
| 1.6993 | 99.2008 | 12400 | 1.3902 | 4238208 |
| 1.9568 | 100.8032 | 12600 | 1.3873 | 4307408 |
| 2.684 | 102.4016 | 12800 | 1.3784 | 4375136 |
| 1.7455 | 104.0 | 13000 | 1.3705 | 4443232 |
| 1.8316 | 105.6024 | 13200 | 1.3711 | 4511824 |
| 1.8675 | 107.2008 | 13400 | 1.3649 | 4580464 |
| 1.998 | 108.8032 | 13600 | 1.3803 | 4648752 |
| 1.8191 | 110.4016 | 13800 | 1.3605 | 4717136 |
| 1.6216 | 112.0 | 14000 | 1.3584 | 4785328 |
| 1.359 | 113.6024 | 14200 | 1.3578 | 4853616 |
| 1.5381 | 115.2008 | 14400 | 1.3633 | 4922160 |
| 1.4842 | 116.8032 | 14600 | 1.3836 | 4990880 |
| 1.9195 | 118.4016 | 14800 | 1.3861 | 5059200 |
| 2.2324 | 120.0 | 15000 | 1.4003 | 5127856 |
| 2.6831 | 121.6024 | 15200 | 1.3824 | 5196320 |
| 2.9063 | 123.2008 | 15400 | 1.3948 | 5264752 |
| 2.2375 | 124.8032 | 15600 | 1.3633 | 5333360 |
| 1.4379 | 126.4016 | 15800 | 1.3655 | 5401648 |
| 2.022 | 128.0 | 16000 | 1.3829 | 5470144 |
| 1.5 | 129.6024 | 16200 | 1.3639 | 5539584 |
| 2.0066 | 131.2008 | 16400 | 1.3695 | 5606896 |
| 2.0182 | 132.8032 | 16600 | 1.3684 | 5675392 |
| 1.8751 | 134.4016 | 16800 | 1.3700 | 5743824 |
| 1.6614 | 136.0 | 17000 | 1.3650 | 5812000 |
| 1.621 | 137.6024 | 17200 | 1.4032 | 5880400 |
| 2.3474 | 139.2008 | 17400 | 1.3793 | 5949456 |
| 1.5025 | 140.8032 | 17600 | 1.3786 | 6017584 |
| 1.8176 | 142.4016 | 17800 | 1.3833 | 6086352 |
| 2.5774 | 144.0 | 18000 | 1.3774 | 6153776 |
| 1.6388 | 145.6024 | 18200 | 1.3680 | 6222672 |
| 2.3709 | 147.2008 | 18400 | 1.3629 | 6291168 |
| 1.7972 | 148.8032 | 18600 | 1.3776 | 6359136 |
| 2.2769 | 150.4016 | 18800 | 1.3718 | 6426976 |
| 2.4199 | 152.0 | 19000 | 1.3809 | 6495568 |
| 2.334 | 153.6024 | 19200 | 1.3765 | 6564224 |
| 1.3356 | 155.2008 | 19400 | 1.3622 | 6632768 |
| 1.5932 | 156.8032 | 19600 | 1.4022 | 6701376 |
| 2.132 | 158.4016 | 19800 | 1.3923 | 6769520 |
| 1.243 | 160.0 | 20000 | 1.3735 | 6837904 |
| 1.9679 | 161.6024 | 20200 | 1.3769 | 6905904 |
| 1.2943 | 163.2008 | 20400 | 1.3794 | 6974368 |
| 1.5976 | 164.8032 | 20600 | 1.3860 | 7043152 |
| 2.4079 | 166.4016 | 20800 | 1.3839 | 7112192 |
| 1.902 | 168.0 | 21000 | 1.3712 | 7179920 |
| 2.4094 | 169.6024 | 21200 | 1.3693 | 7248608 |
| 1.8267 | 171.2008 | 21400 | 1.3882 | 7316928 |
| 1.3429 | 172.8032 | 21600 | 1.3781 | 7385216 |
| 1.9929 | 174.4016 | 21800 | 1.3723 | 7453728 |
| 1.5492 | 176.0 | 22000 | 1.3745 | 7521888 |
| 2.029 | 177.6024 | 22200 | 1.3866 | 7590256 |
| 1.0526 | 179.2008 | 22400 | 1.3728 | 7658736 |
| 2.1402 | 180.8032 | 22600 | 1.3733 | 7727488 |
| 2.1717 | 182.4016 | 22800 | 1.3580 | 7796416 |
| 1.0474 | 184.0 | 23000 | 1.3782 | 7864592 |
| 2.6908 | 185.6024 | 23200 | 1.3840 | 7933232 |
| 1.6581 | 187.2008 | 23400 | 1.3909 | 8001808 |
| 1.737 | 188.8032 | 23600 | 1.3631 | 8070240 |
| 2.1513 | 190.4016 | 23800 | 1.3719 | 8138688 |
| 2.9168 | 192.0 | 24000 | 1.3730 | 8206576 |
| 1.3348 | 193.6024 | 24200 | 1.3669 | 8274800 |
| 1.8642 | 195.2008 | 24400 | 1.3766 | 8342976 |
| 1.8082 | 196.8032 | 24600 | 1.3738 | 8411584 |
| 1.9464 | 198.4016 | 24800 | 1.3706 | 8479856 |
| 1.3418 | 200.0 | 25000 | 1.3411 | 8548304 |
| 1.0372 | 201.6024 | 25200 | 1.3819 | 8617520 |
| 1.4196 | 203.2008 | 25400 | 1.3806 | 8685328 |
| 2.6419 | 204.8032 | 25600 | 1.3815 | 8753696 |
| 1.6081 | 206.4016 | 25800 | 1.3642 | 8821840 |
| 1.0938 | 208.0 | 26000 | 1.3757 | 8889904 |
| 1.7548 | 209.6024 | 26200 | 1.3723 | 8958528 |
| 0.4627 | 211.2008 | 26400 | 1.3632 | 9026416 |
| 1.8565 | 212.8032 | 26600 | 1.3725 | 9094992 |
| 1.8041 | 214.4016 | 26800 | 1.3807 | 9162896 |
| 2.2034 | 216.0 | 27000 | 1.3971 | 9231632 |
| 1.2453 | 217.6024 | 27200 | 1.3777 | 9299920 |
| 1.3627 | 219.2008 | 27400 | 1.3901 | 9368176 |
| 1.761 | 220.8032 | 27600 | 1.3853 | 9437280 |
| 2.4811 | 222.4016 | 27800 | 1.3785 | 9505712 |
| 1.2036 | 224.0 | 28000 | 1.3843 | 9573776 |
| 1.6312 | 225.6024 | 28200 | 1.3712 | 9641744 |
| 2.7126 | 227.2008 | 28400 | 1.3745 | 9710672 |
| 1.8068 | 228.8032 | 28600 | 1.3504 | 9778976 |
| 2.0016 | 230.4016 | 28800 | 1.3559 | 9846768 |
| 2.4666 | 232.0 | 29000 | 1.3740 | 9915328 |
| 2.3197 | 233.6024 | 29200 | 1.3657 | 9984304 |
| 2.2468 | 235.2008 | 29400 | 1.3896 | 10052656 |
| 2.5254 | 236.8032 | 29600 | 1.3690 | 10121152 |
| 1.7327 | 238.4016 | 29800 | 1.3695 | 10188944 |
| 2.1135 | 240.0 | 30000 | 1.3644 | 10257280 |
| 2.4984 | 241.6024 | 30200 | 1.3745 | 10326160 |
| 2.6298 | 243.2008 | 30400 | 1.3582 | 10393920 |
| 1.9454 | 244.8032 | 30600 | 1.3769 | 10462528 |
| 1.6705 | 246.4016 | 30800 | 1.3858 | 10530528 |
| 1.0821 | 248.0 | 31000 | 1.3936 | 10599104 |
| 0.9083 | 249.6024 | 31200 | 1.3718 | 10667920 |
| 2.1352 | 251.2008 | 31400 | 1.3749 | 10736624 |
| 2.3122 | 252.8032 | 31600 | 1.3664 | 10804624 |
| 1.9733 | 254.4016 | 31800 | 1.3804 | 10873200 |
| 1.9106 | 256.0 | 32000 | 1.3715 | 10941264 |
| 1.0159 | 257.6024 | 32200 | 1.3682 | 11010000 |
| 1.7219 | 259.2008 | 32400 | 1.3720 | 11077280 |
| 2.02 | 260.8032 | 32600 | 1.3732 | 11145744 |
| 1.8695 | 262.4016 | 32800 | 1.3925 | 11214112 |
| 0.979 | 264.0 | 33000 | 1.3623 | 11282096 |
| 1.4244 | 265.6024 | 33200 | 1.3711 | 11350608 |
| 1.158 | 267.2008 | 33400 | 1.3865 | 11418608 |
| 2.2895 | 268.8032 | 33600 | 1.3829 | 11487936 |
| 2.3647 | 270.4016 | 33800 | 1.3672 | 11556272 |
| 1.8704 | 272.0 | 34000 | 1.3715 | 11624208 |
| 2.0451 | 273.6024 | 34200 | 1.3936 | 11693424 |
| 1.933 | 275.2008 | 34400 | 1.3798 | 11761200 |
| 1.1507 | 276.8032 | 34600 | 1.3821 | 11830208 |
| 1.6466 | 278.4016 | 34800 | 1.3833 | 11898240 |
| 1.6418 | 280.0 | 35000 | 1.3833 | 11966432 |
| 2.0597 | 281.6024 | 35200 | 1.3833 | 12035232 |
| 1.4621 | 283.2008 | 35400 | 1.3833 | 12103232 |
| 2.1253 | 284.8032 | 35600 | 1.3833 | 12171376 |
| 1.6358 | 286.4016 | 35800 | 1.3833 | 12240128 |
| 2.2219 | 288.0 | 36000 | 1.3833 | 12308016 |
| 1.2531 | 289.6024 | 36200 | 1.3833 | 12375936 |
| 1.9197 | 291.2008 | 36400 | 1.3833 | 12444880 |
| 2.2558 | 292.8032 | 36600 | 1.3833 | 12513664 |
| 1.9804 | 294.4016 | 36800 | 1.3833 | 12581616 |
| 1.8568 | 296.0 | 37000 | 1.3833 | 12650688 |
| 1.4651 | 297.6024 | 37200 | 1.3833 | 12718976 |
| 1.8077 | 299.2008 | 37400 | 1.3833 | 12787680 |
| 1.3443 | 300.8032 | 37600 | 1.3833 | 12856448 |
| 2.9669 | 302.4016 | 37800 | 1.3833 | 12924128 |
| 1.9458 | 304.0 | 38000 | 1.3833 | 12992944 |
| 1.2774 | 305.6024 | 38200 | 1.3833 | 13060928 |
| 2.3874 | 307.2008 | 38400 | 1.3833 | 13129472 |
| 2.9663 | 308.8032 | 38600 | 1.3833 | 13198064 |
| 1.9925 | 310.4016 | 38800 | 1.3833 | 13266304 |
| 2.5085 | 312.0 | 39000 | 1.3833 | 13334832 |
| 1.2625 | 313.6024 | 39200 | 1.3833 | 13402912 |
| 1.2102 | 315.2008 | 39400 | 1.3833 | 13470656 |
| 1.5315 | 316.8032 | 39600 | 1.3833 | 13539984 |
| 1.4806 | 318.4016 | 39800 | 1.3833 | 13608768 |
| 1.8959 | 320.0 | 40000 | 1.3833 | 13676608 |
### Framework versions
- PEFT 0.15.2.dev0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
aleegis/e0557a56-288b-4863-997f-a294e79c6446 | aleegis | 2025-04-30T21:56:05Z | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:defog/sqlcoder-7b-2",
"base_model:adapter:defog/sqlcoder-7b-2",
"license:cc-by-sa-4.0",
"region:us"
] | null | 2025-04-30T19:36:32Z | ---
library_name: peft
license: cc-by-sa-4.0
base_model: defog/sqlcoder-7b-2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: e0557a56-288b-4863-997f-a294e79c6446
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: defog/sqlcoder-7b-2
bf16: auto
chat_template: llama3
dataloader_num_workers: 12
dataset_prepared_path: null
datasets:
- data_files:
- 83b3569a6bcb443f_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/83b3569a6bcb443f_train_data.json
type:
field_input: documents
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: null
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 8
gradient_checkpointing: false
group_by_length: false
hub_model_id: aleegis/e0557a56-288b-4863-997f-a294e79c6446
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: null
lora_alpha: 32
lora_dropout: 0.15
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 32
lora_target_linear: true
loraplus_lr_embedding: 1.0e-06
loraplus_lr_ratio: 16
lr_scheduler: cosine
max_grad_norm: 1
max_steps: 1500
micro_batch_size: 2
mlflow_experiment_name: /tmp/83b3569a6bcb443f_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 200
optimizer: adamw_torch_fused
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
save_total_limit: 10
saves_per_epoch: 0
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.0
wandb_entity: null
wandb_mode: online
wandb_name: 9b28eaba-1bed-48d6-b5ad-afab6f3a2560
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 9b28eaba-1bed-48d6-b5ad-afab6f3a2560
warmup_steps: 100
weight_decay: 0
xformers_attention: null
```
</details><br>
# e0557a56-288b-4863-997f-a294e79c6446
This model is a fine-tuned version of [defog/sqlcoder-7b-2](https://huggingface.co/defog/sqlcoder-7b-2) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- training_steps: 1500
### Training results
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Joel03/MuRIL_Dissertation | Joel03 | 2025-04-30T20:51:13Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"token-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | 2025-04-30T20:49:22Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## 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] |
HyperBlaze/BGE-m3-ko | HyperBlaze | 2025-04-30T20:47:51Z | 0 | 0 | transformers.js | [
"transformers.js",
"onnx",
"xlm-roberta",
"feature-extraction",
"ko",
"en",
"base_model:BAAI/bge-m3",
"base_model:quantized:BAAI/bge-m3",
"license:apache-2.0",
"region:us"
] | feature-extraction | 2025-04-30T20:25:51Z | ---
license: apache-2.0
language:
- ko
- en
base_model:
- BAAI/bge-m3
- dragonkue/BGE-m3-ko
pipeline_tag: feature-extraction
library_name: transformers.js
--- |
Aryanpro4321/aryan_pro-wav2vec2-base-timit-demo-colab | Aryanpro4321 | 2025-04-30T20:28:46Z | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2025-04-07T20:35:01Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Samiley/gemma-text-to-sql | Samiley | 2025-04-30T20:27:36Z | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"sft",
"base_model:google/gemma-3-1b-pt",
"base_model:finetune:google/gemma-3-1b-pt",
"endpoints_compatible",
"region:us"
] | null | 2025-04-30T17:18:57Z | ---
base_model: google/gemma-3-1b-pt
library_name: transformers
model_name: gemma-text-to-sql
tags:
- generated_from_trainer
- trl
- sft
licence: license
---
# Model Card for gemma-text-to-sql
This model is a fine-tuned version of [google/gemma-3-1b-pt](https://huggingface.co/google/gemma-3-1b-pt).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Samiley/gemma-text-to-sql", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with SFT.
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.6.0+cu124
- Datasets: 3.3.2
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
lagunovsky/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-restless_regal_bobcat | lagunovsky | 2025-04-30T20:24:17Z | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am restless regal bobcat",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-19T08:46:42Z | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-restless_regal_bobcat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am restless regal bobcat
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-restless_regal_bobcat
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="lagunovsky/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-restless_regal_bobcat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.51.3
- Pytorch: 2.5.1
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
lmstudio-community/GLM-4-32B-0414-GGUF | lmstudio-community | 2025-04-30T19:51:43Z | 2,541 | 1 | null | [
"gguf",
"text-generation",
"zh",
"en",
"base_model:THUDM/GLM-4-32B-0414",
"base_model:quantized:THUDM/GLM-4-32B-0414",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | 2025-04-14T16:28:31Z | ---
quantized_by: bartowski
pipeline_tag: text-generation
language:
- zh
- en
license: mit
base_model: THUDM/GLM-4-32B-0414
base_model_relation: quantized
---
## 💫 Community Model> GLM 4 32B 0414 by Thudm
*👾 [LM Studio](https://lmstudio.ai) Community models highlights program. Highlighting new & noteworthy models by the community. Join the conversation on [Discord](https://discord.gg/aPQfnNkxGC)*.
**Model creator:** [THUDM](https://huggingface.co/THUDM)<br>
**Original model**: [GLM-4-32B-0414](https://huggingface.co/THUDM/GLM-4-32B-0414)<br>
**GGUF quantization:** provided by [bartowski](https://huggingface.co/bartowski) based on `llama.cpp` release [b5228](https://github.com/ggerganov/llama.cpp/releases/tag/b5228)<br>
## Technical Details
Supports a context length of 32k tokens
Tuned for advanced coding, artifact generation, function calling, and agentic capabilities
Achieves comparable performance to much larger models
## Special thanks
🙏 Special thanks to [Georgi Gerganov](https://github.com/ggerganov) and the whole team working on [llama.cpp](https://github.com/ggerganov/llama.cpp/) for making all of this possible.
## Disclaimers
LM Studio is not the creator, originator, or owner of any Model featured in the Community Model Program. Each Community Model is created and provided by third parties. LM Studio does not endorse, support, represent or guarantee the completeness, truthfulness, accuracy, or reliability of any Community Model. You understand that Community Models can produce content that might be offensive, harmful, inaccurate or otherwise inappropriate, or deceptive. Each Community Model is the sole responsibility of the person or entity who originated such Model. LM Studio may not monitor or control the Community Models and cannot, and does not, take responsibility for any such Model. LM Studio disclaims all warranties or guarantees about the accuracy, reliability or benefits of the Community Models. LM Studio further disclaims any warranty that the Community Model will meet your requirements, be secure, uninterrupted or available at any time or location, or error-free, viruses-free, or that any errors will be corrected, or otherwise. You will be solely responsible for any damage resulting from your use of or access to the Community Models, your downloading of any Community Model, or use of any other Community Model provided by or through LM Studio.
|
elimS2/qwen2-7b-instruct-trl-sft-ChartQA | elimS2 | 2025-04-30T19:28:17Z | 0 | 0 | transformers | [
"transformers",
"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 | 2025-04-30T17:58:59Z | ---
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="elimS2/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
[<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/elims2-elims/qwen2-7b-instruct-trl-sft-ChartQA/runs/gcry58nn)
This model was trained with SFT.
### Framework versions
- TRL: 0.18.0.dev0
- Transformers: 4.52.0.dev0
- Pytorch: 2.4.1+cu121
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
LucileFavero/aaec_qw_T | LucileFavero | 2025-04-30T19:13:05Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"qwen3",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"base_model:quantized:unsloth/Qwen3-14B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | 2025-04-30T17:50:08Z | ---
base_model: unsloth/Qwen3-14B-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- qwen3
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** LucileFavero
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen3-14B-unsloth-bnb-4bit
This qwen3 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
anilyanamandra/llama381binstruct_summarize_short_merged | anilyanamandra | 2025-04-30T18:21:05Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | 2025-04-30T18:11:15Z | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Yuhan123/ppo-reading-level-12th-1-steps-10000-epoch-999-best-eval-score-0.356 | Yuhan123 | 2025-04-30T18:19:09Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T18:16:26Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
edmora/blue | edmora | 2025-04-30T18:01:52Z | 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 | 2025-04-30T17:42:22Z | ---
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: blue
---
# Blue
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `blue` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "blue",
"lora_weights": "https://huggingface.co/edmora/blue/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('edmora/blue', weight_name='lora.safetensors')
image = pipeline('blue').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/edmora/blue/discussions) to add images that show off what you’ve made with this LoRA.
|
srutiii/flan-t5-base-essay-scorer | srutiii | 2025-04-30T17:54:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | 2025-04-30T17:51:15Z | ---
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 🤗 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] |
Yuhan123/ppo-reading-level-full-question-7th-1-steps-10000-epoch-999-best-eval-score-0.403 | Yuhan123 | 2025-04-30T17:20:38Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T17:18:15Z | ---
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 🤗 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] |
Yuhan123/ppo-reading-level-full-question-12th-1-steps-10000-epoch-999-best-eval-score-0.415 | Yuhan123 | 2025-04-30T17:09:26Z | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | 2025-04-30T17:06:44Z | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF | mradermacher | 2025-04-30T16:54:08Z | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:allenai/OLMo-Ladder-760M-0.5xC",
"base_model:quantized:allenai/OLMo-Ladder-760M-0.5xC",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | 2025-04-30T16:25:31Z | ---
base_model: allenai/OLMo-Ladder-760M-0.5xC
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/allenai/OLMo-Ladder-760M-0.5xC
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ1_S.gguf) | i1-IQ1_S | 0.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ1_M.gguf) | i1-IQ1_M | 0.4 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ2_XS.gguf) | i1-IQ2_XS | 0.4 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ2_S.gguf) | i1-IQ2_S | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ2_M.gguf) | i1-IQ2_M | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q2_K_S.gguf) | i1-Q2_K_S | 0.5 | very low quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q2_K.gguf) | i1-Q2_K | 0.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 0.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ3_XS.gguf) | i1-IQ3_XS | 0.5 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ3_S.gguf) | i1-IQ3_S | 0.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q3_K_S.gguf) | i1-Q3_K_S | 0.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ3_M.gguf) | i1-IQ3_M | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q3_K_M.gguf) | i1-Q3_K_M | 0.6 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q3_K_L.gguf) | i1-Q3_K_L | 0.6 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ4_XS.gguf) | i1-IQ4_XS | 0.6 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-IQ4_NL.gguf) | i1-IQ4_NL | 0.7 | prefer IQ4_XS |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q4_0.gguf) | i1-Q4_0 | 0.7 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q4_K_S.gguf) | i1-Q4_K_S | 0.7 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q4_K_M.gguf) | i1-Q4_K_M | 0.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q4_1.gguf) | i1-Q4_1 | 0.7 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q5_K_S.gguf) | i1-Q5_K_S | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q5_K_M.gguf) | i1-Q5_K_M | 0.8 | |
| [GGUF](https://huggingface.co/mradermacher/OLMo-Ladder-760M-0.5xC-i1-GGUF/resolve/main/OLMo-Ladder-760M-0.5xC.i1-Q6_K.gguf) | i1-Q6_K | 0.9 | 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 -->
|
Subsets and Splits