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RichardErkhov/altomek_-_Lo101-3B-AnD-awq | RichardErkhov | "2025-02-28T08:01:15Z" | 0 | 0 | null | [
"safetensors",
"llama",
"4-bit",
"awq",
"region:us"
] | null | "2025-02-28T07:59:24Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Lo101-3B-AnD - AWQ
- Model creator: https://huggingface.co/altomek/
- Original model: https://huggingface.co/altomek/Lo101-3B-AnD/
Original model description:
---
base_model:
- IntervitensInc/Llama-3.2-3B-chatml
- alpindale/Llama-3.2-3B-Instruct
language:
- en
library_name: transformers
pipeline_tag: text-generation
tags:
- merge
- finetuned
- llama
- llama-3
license: llama3.2
inference:
parameters:
temperature: 0.2
widget:
- messages:
- role: user
content: Any plans for a weekend?
---
#
<img src=https://huggingface.co/altomek/Lo101-3B-AnD/resolve/main/Lo101.png>
<a href="https://youtu.be/As3LGNTlPQ0?si=C8aQVt6XxF6qxU4-" title="Mr.Kitty After Dark // Jennifer Connelly Career Opportunities" target="_blank">intro music...</a>
This is...
## Llama Lo1 01
My first RP directed finetune and merge! Not as expresive like Llama Instruct might be. Writes simpler responses in chat. Somewhat broken... I need to learn more about chat templates... ;P
Trained on few datasets from [jeiku](https://huggingface.co/jeiku) - Thank you!
Have fun!
<img src=https://huggingface.co/altomek/Lo101-3B-AnD/resolve/main/Lo101-chat1.png>
<br>
<img src=https://huggingface.co/altomek/Lo101-3B-AnD/resolve/main/Lo101-chat2.png>
### Settings
- ChatML, Alpaca or Llama 3 Instruct templates for chat should work, however **model have dificulties with longer responses so make sure to set response tokens to something like 256!**
- Set Temperature below 1
- You can easily overload this AI with too complicaded, long character cards. Keep things simple! ;P
|
snintendog/Mana_Umihara_GotchaForce | snintendog | "2023-11-22T21:45:27Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2023-11-22T21:42:56Z" | ---
license: openrail
---
Created from all voice lines in the English game of Gotcha Force ADX->Wav files around 2 minutes of sounds. (RMVPE) (RVC V2) (600 Epochs)
Male 8-12 Female 3-8 For Voices. |
tricktreat/Llama-2-7b-chat-hf-guanaco-freeze-q-v-proj-lora | tricktreat | "2024-04-16T05:06:18Z" | 0 | 0 | peft | [
"peft",
"tensorboard",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | "2024-04-16T05:06:11Z" | ---
library_name: peft
base_model: guanaco_Llama-2-7b-chat-hf_freeze_q_v_proj
---
# 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 0.10.1.dev0 |
mradermacher/Tamil-gemma-v1-GGUF | mradermacher | "2025-01-19T01:16:25Z" | 273 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:niranjanramarajar/Tamil-gemma-v1",
"base_model:quantized:niranjanramarajar/Tamil-gemma-v1",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-01-19T01:07:26Z" | ---
base_model: niranjanramarajar/Tamil-gemma-v1
language:
- en
library_name: transformers
license: apache-2.0
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/niranjanramarajar/Tamil-gemma-v1
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q2_K.gguf) | Q2_K | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q3_K_S.gguf) | Q3_K_S | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q3_K_M.gguf) | Q3_K_M | 1.5 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q3_K_L.gguf) | Q3_K_L | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.IQ4_XS.gguf) | IQ4_XS | 1.6 | |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q4_K_S.gguf) | Q4_K_S | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q4_K_M.gguf) | Q4_K_M | 1.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q5_K_S.gguf) | Q5_K_S | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q5_K_M.gguf) | Q5_K_M | 1.9 | |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q6_K.gguf) | Q6_K | 2.2 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.Q8_0.gguf) | Q8_0 | 2.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Tamil-gemma-v1-GGUF/resolve/main/Tamil-gemma-v1.f16.gguf) | f16 | 5.1 | 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 -->
|
emmabjor/finetuning-sentiment-model-all-samples | emmabjor | "2024-02-20T13:59:30Z" | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-02-20T12:52:42Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: finetuning-sentiment-model-all-samples
results: []
widget:
- text: "you should be mean to people you like"
- text: "you should not be mean to people you like"
---
<!-- 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. -->
# finetuning-sentiment-model-all-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1044
- Accuracy: 0.9765
- F1: 0.9763
## Labels
LABEL_1 = evaluation(GOOD)
LABEL_0 = evaluation(BAD)
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.37.2
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2
|
jimmywong/BrushNet | jimmywong | "2024-07-03T12:40:46Z" | 716 | 0 | diffusers | [
"diffusers",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | "2024-07-03T12:01:29Z" | ---
license: apache-2.0
---
|
Feldi/a2c-PandaReachDense-v2 | Feldi | "2023-08-17T09:27:28Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"arxiv:2106.13687",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-15T11:06:31Z" | ---
library_name: stable-baselines3
tags:
- PandaReachDense-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: A2C
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: PandaReachDense-v2
type: PandaReachDense-v2
metrics:
- type: mean_reward
value: -1.19 +/- 0.51
name: mean_reward
verified: false
---
# **A2C** Agent playing **PandaReachDense-v2**
This is a trained model of a **A2C** agent playing **PandaReachDense-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
Panda Gym environments: [arxiv.org/abs/2106.13687](https://arxiv.org/abs/2106.13687) |
yaelgogol/SmolLM2-FT-MyDataset-YG | yaelgogol | "2024-12-25T07:39:58Z" | 150 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"smol-course",
"module_1",
"trl",
"sft",
"conversational",
"base_model:HuggingFaceTB/SmolLM2-135M",
"base_model:finetune:HuggingFaceTB/SmolLM2-135M",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-12-24T11:34:11Z" | ---
base_model: HuggingFaceTB/SmolLM2-135M
library_name: transformers
model_name: SmolLM2-FT-MyDataset-YG
tags:
- generated_from_trainer
- smol-course
- module_1
- trl
- sft
licence: license
---
# Model Card for SmolLM2-FT-MyDataset-YG
This model is a fine-tuned version of [HuggingFaceTB/SmolLM2-135M](https://huggingface.co/HuggingFaceTB/SmolLM2-135M).
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="yaelgogol/SmolLM2-FT-MyDataset-YG", 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/yaelgogol-polywizz/huggingface/runs/dgyulsgf)
This model was trained with SFT.
### Framework versions
- TRL: 0.13.0
- Transformers: 4.47.1
- Pytorch: 2.5.1+cu121
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
nhoxinh/f5f34f1e-a990-4f12-8950-e1fdccf344df | nhoxinh | "2025-01-24T20:51:54Z" | 9 | 0 | peft | [
"peft",
"safetensors",
"qwen2",
"axolotl",
"generated_from_trainer",
"base_model:Qwen/Qwen1.5-7B",
"base_model:adapter:Qwen/Qwen1.5-7B",
"license:other",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-24T20:33:39Z" | ---
library_name: peft
license: other
base_model: Qwen/Qwen1.5-7B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f5f34f1e-a990-4f12-8950-e1fdccf344df
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: Qwen/Qwen1.5-7B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- e5498bc954575eb9_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/e5498bc954575eb9_train_data.json
type:
field_input: name
field_instruction: text
field_output: description
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: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: nhoxinh/f5f34f1e-a990-4f12-8950-e1fdccf344df
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: true
load_in_8bit: true
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/e5498bc954575eb9_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: 8ed0b196-2616-458b-bbb4-87f97ec944b5
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 8ed0b196-2616-458b-bbb4-87f97ec944b5
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f5f34f1e-a990-4f12-8950-e1fdccf344df
This model is a fine-tuned version of [Qwen/Qwen1.5-7B](https://huggingface.co/Qwen/Qwen1.5-7B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3688
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.4463 | 0.3031 | 200 | 0.3688 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
Jiuzhouh/flan-t5-xxl-lora-copasse-new | Jiuzhouh | "2023-09-17T05:58:03Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-09-17T05:57:54Z" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.4.0.dev0
- PEFT 0.4.0.dev0
|
wuqiqi/Meta-Llama-3-8B-Instruct-ORPO-QLoRA-F16-GGUF | wuqiqi | "2025-03-18T03:15:01Z" | 0 | 0 | peft | [
"peft",
"gguf",
"alignment-handbook",
"trl",
"orpo",
"generated_from_trainer",
"llama-cpp",
"gguf-my-lora",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:statking/Meta-Llama-3-8B-Instruct-ORPO-QLoRA",
"base_model:adapter:statking/Meta-Llama-3-8B-Instruct-ORPO-QLoRA",
"license:llama3",
"region:us"
] | null | "2025-03-18T03:14:55Z" | ---
license: llama3
library_name: peft
tags:
- alignment-handbook
- trl
- orpo
- generated_from_trainer
- llama-cpp
- gguf-my-lora
base_model: statking/Meta-Llama-3-8B-Instruct-ORPO-QLoRA
datasets:
- HuggingFaceH4/ultrafeedback_binarized
model-index:
- name: Meta-Llama-3-8B-Instruct-ORPO-QLoRA
results: []
---
# wuqiqi/Meta-Llama-3-8B-Instruct-ORPO-QLoRA-F16-GGUF
This LoRA adapter was converted to GGUF format from [`statking/Meta-Llama-3-8B-Instruct-ORPO-QLoRA`](https://huggingface.co/statking/Meta-Llama-3-8B-Instruct-ORPO-QLoRA) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/statking/Meta-Llama-3-8B-Instruct-ORPO-QLoRA) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora Meta-Llama-3-8B-Instruct-ORPO-QLoRA-f16.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora Meta-Llama-3-8B-Instruct-ORPO-QLoRA-f16.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
zelk12/MT4-Gen1-MA-gemma-2-Av4cMT1-9B | zelk12 | "2024-10-25T16:45:58Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"mergekit",
"merge",
"conversational",
"base_model:lemon07r/Gemma-2-Ataraxy-v4c-9B",
"base_model:merge:lemon07r/Gemma-2-Ataraxy-v4c-9B",
"base_model:zelk12/MT1-gemma-2-9B",
"base_model:merge:zelk12/MT1-gemma-2-9B",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-25T16:39:40Z" | ---
base_model:
- lemon07r/Gemma-2-Ataraxy-v4c-9B
- zelk12/MT1-gemma-2-9B
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 SLERP merge method.
### Models Merged
The following models were included in the merge:
* [lemon07r/Gemma-2-Ataraxy-v4c-9B](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-v4c-9B)
* [zelk12/MT1-gemma-2-9B](https://huggingface.co/zelk12/MT1-gemma-2-9B)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: lemon07r/Gemma-2-Ataraxy-v4c-9B
- model: zelk12/MT1-gemma-2-9B
merge_method: slerp
base_model: lemon07r/Gemma-2-Ataraxy-v4c-9B
dtype: bfloat16
parameters:
t: 0.5
```
|
gkMSDA/FinChat298_FinanceLLM__DJ30_Pretrain | gkMSDA | "2024-10-09T04:50:12Z" | 125 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-02T23:01:32Z" | ---
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] |
nvidia/OpenMath2-Llama3.1-70B | nvidia | "2024-11-25T20:12:03Z" | 124 | 18 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"math",
"conversational",
"en",
"dataset:nvidia/OpenMathInstruct-2",
"arxiv:2410.01560",
"base_model:meta-llama/Llama-3.1-70B",
"base_model:finetune:meta-llama/Llama-3.1-70B",
"license:llama3.1",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-30T21:01:56Z" | ---
license: llama3.1
base_model:
- meta-llama/Llama-3.1-70B
datasets:
- nvidia/OpenMathInstruct-2
language:
- en
tags:
- nvidia
- math
library_name: transformers
---
# OpenMath2-Llama3.1-70B
OpenMath2-Llama3.1-70B is obtained by finetuning [Llama3.1-70B-Base](https://huggingface.co/meta-llama/Llama-3.1-70B) with [OpenMathInstruct-2](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2).
The model outperforms [Llama3.1-70B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-70B-Instruct) on [MATH](https://github.com/hendrycks/math) by 3.9%.
| Model | GSM8K | MATH | AMC 2023 | AIME 2024 | Omni-MATH |
|:---|:---:|:---:|:---:|:---:|:---:|
| Llama3.1-8B-Instruct | 84.5 | 51.9 | 9/40 | 2/30 | 12.7 |
| OpenMath2-Llama3.1-8B ([nemo](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-8B)) | 91.7 | 67.8 | 16/40 | 3/30 | 22.0 |
| + majority@256 | 94.1 | 76.1 | 23/40 | 3/30 | 24.6 |
| Llama3.1-70B-Instruct | 95.8 | 67.9 | 19/40 | 6/30 | 19.0 |
| **OpenMath2-Llama3.1-70B** ([nemo](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B-nemo) \| [HF](https://huggingface.co/nvidia/OpenMath2-Llama3.1-70B)) | 94.9 | 71.9 | 20/40 | 4/30 | 23.1 |
| + majority@256 | 96.0 | 79.6 | 24/40 | 6/30 | 27.6 |
The pipeline we used to produce the data and models is fully open-sourced!
- [Code](https://github.com/NVIDIA/NeMo-Skills)
- [Models](https://huggingface.co/collections/nvidia/openmath-2-66fb142317d86400783d2c7b)
- [Dataset](https://huggingface.co/datasets/nvidia/OpenMathInstruct-2)
See our [paper](https://arxiv.org/abs/2410.01560) to learn more details!
# How to use the models?
Our models are trained with the same "chat format" as Llama3.1-instruct models (same system/user/assistant tokens).
Please note that these models have NOT been instruction-tuned on general data and thus might not provide good answers outside of the math domain.
We recommend using [instructions in our repo](https://nvidia.github.io/NeMo-Skills/basics/inference/) to run inference with these models, but here is
an example of how to do it through transformers api:
```python
import transformers
import torch
model_id = "nvidia/OpenMath2-Llama3.1-70B"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"torch_dtype": torch.bfloat16},
device_map="auto",
)
messages = [
{
"role": "user",
"content": "Solve the following math problem. Make sure to put the answer (and only answer) inside \\boxed{}.\n\n" +
"What is the minimum value of $a^2+6a-7$?"},
]
outputs = pipeline(
messages,
max_new_tokens=4096,
)
print(outputs[0]["generated_text"][-1]['content'])
```
# Reproducing our results
We provide [all instructions](https://nvidia.github.io/NeMo-Skills/openmathinstruct2/) to fully reproduce our results.
## Citation
If you find our work useful, please consider citing us!
```bibtex
@article{toshniwal2024openmath2,
title = {OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data},
author = {Shubham Toshniwal and Wei Du and Ivan Moshkov and Branislav Kisacanin and Alexan Ayrapetyan and Igor Gitman},
year = {2024},
journal = {arXiv preprint arXiv:2410.01560}
}
```
## Terms of use
By accessing this model, you are agreeing to the LLama 3.1 terms and conditions of the [license](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE), [acceptable use policy](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/USE_POLICY.md) and [Meta’s privacy policy](https://www.facebook.com/privacy/policy/) |
nonhmello/whisper_medium_nonhmello | nonhmello | "2024-11-25T08:37:56Z" | 7 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"asr",
"speech-recognition",
"thai",
"custom-model",
"generated_from_trainer",
"th",
"base_model:biodatlab/whisper-th-medium-combined",
"base_model:finetune:biodatlab/whisper-th-medium-combined",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | automatic-speech-recognition | "2024-11-25T02:25:57Z" | ---
library_name: transformers
language:
- th
license: apache-2.0
base_model: biodatlab/whisper-th-medium-combined
tags:
- asr
- speech-recognition
- thai
- custom-model
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Medium TH - Nonhmello
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. -->
# Whisper Medium TH - Nonhmello
This model is a fine-tuned version of [biodatlab/whisper-th-medium-combined](https://huggingface.co/biodatlab/whisper-th-medium-combined) on the Custom dataset on local machine dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3068
- Wer: 77.4194
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 1000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:--------:|:----:|:---------------:|:-------:|
| 0.0092 | 83.3333 | 500 | 0.2586 | 90.3226 |
| 0.0006 | 166.6667 | 1000 | 0.3068 | 77.4194 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3
|
Memebonita/llama3-8b-instruct-alpaca-lora | Memebonita | "2025-04-01T10:51:31Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"llama",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-01T09:58:02Z" | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
BinBashir/sentiment_test | BinBashir | "2025-03-24T12:56:16Z" | 26 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-17T10:31:25Z" | Temporary Redirect. Redirecting to /api/resolve-cache/models/BinBashir/sentiment_test/6aaf2d173d744ee0eeb12afce20d0f2dcf0bb289/README.md?%2FBinBashir%2Fsentiment_test%2Fresolve%2Fmain%2FREADME.md=&etag=%22bc5f30d6632ac0efdc7be2e9095e9e9579af2e33%22 |
PrunaAI/google-codegemma-7b-it-HQQ-1bit-smashed | PrunaAI | "2024-08-02T15:56:22Z" | 2 | 0 | transformers | [
"transformers",
"gemma",
"text-generation",
"pruna-ai",
"base_model:google/codegemma-7b-it",
"base_model:finetune:google/codegemma-7b-it",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-29T13:36:07Z" | ---
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
base_model: google/codegemma-7b-it
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
tags:
- pruna-ai
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://www.pruna.ai/" target="_blank" rel="noopener noreferrer">
<img src="https://i.imgur.com/eDAlcgk.png" alt="PrunaAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</a>
</div>
<!-- header end -->
[](https://twitter.com/PrunaAI)
[](https://github.com/PrunaAI)
[](https://www.linkedin.com/company/93832878/admin/feed/posts/?feedType=following)
[](https://discord.gg/rskEr4BZJx)
# Simply make AI models cheaper, smaller, faster, and greener!
- Give a thumbs up if you like this model!
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your *own* AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- Read the documentations to know more [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/)
- Join Pruna AI community on Discord [here](https://discord.gg/rskEr4BZJx) to share feedback/suggestions or get help.
## Results
**Frequently Asked Questions**
- ***How does the compression work?*** The model is compressed with hqq.
- ***How does the model quality change?*** The quality of the model output might vary compared to the base model.
- ***How is the model efficiency evaluated?*** These results were obtained on NVIDIA A100-PCIE-40GB with configuration described in `model/smash_config.json` and are obtained after a hardware warmup. The smashed model is directly compared to the original base model. Efficiency results may vary in other settings (e.g. other hardware, image size, batch size, ...). We recommend to directly run them in the use-case conditions to know if the smashed model can benefit you.
- ***What is the model format?*** We use safetensors.
- ***What calibration data has been used?*** If needed by the compression method, we used WikiText as the calibration data.
- ***What is the naming convention for Pruna Huggingface models?*** We take the original model name and append "turbo", "tiny", or "green" if the smashed model has a measured inference speed, inference memory, or inference energy consumption which is less than 90% of the original base model.
- ***How to compress my own models?*** You can request premium access to more compression methods and tech support for your specific use-cases [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai).
- ***What are "first" metrics?*** Results mentioning "first" are obtained after the first run of the model. The first run might take more memory or be slower than the subsequent runs due cuda overheads.
- ***What are "Sync" and "Async" metrics?*** "Sync" metrics are obtained by syncing all GPU processes and stop measurement when all of them are executed. "Async" metrics are obtained without syncing all GPU processes and stop when the model output can be used by the CPU. We provide both metrics since both could be relevant depending on the use-case. We recommend to test the efficiency gains directly in your use-cases.
## Setup
You can run the smashed model with these steps:
0. Check requirements from the original repo google/codegemma-7b-it installed. In particular, check python, cuda, and transformers versions.
1. Make sure that you have installed quantization related packages.
```bash
pip install hqq
```
2. Load & run the model.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
from hqq.engine.hf import HQQModelForCausalLM
from hqq.models.hf.base import AutoHQQHFModel
try:
model = HQQModelForCausalLM.from_quantized("PrunaAI/google-codegemma-7b-it-HQQ-1bit-smashed", device_map='auto')
except:
model = AutoHQQHFModel.from_quantized("PrunaAI/google-codegemma-7b-it-HQQ-1bit-smashed")
tokenizer = AutoTokenizer.from_pretrained("google/codegemma-7b-it")
input_ids = tokenizer("What is the color of prunes?,", return_tensors='pt').to(model.device)["input_ids"]
outputs = model.generate(input_ids, max_new_tokens=216)
tokenizer.decode(outputs[0])
```
## Configurations
The configuration info are in `smash_config.json`.
## Credits & License
The license of the smashed model follows the license of the original model. Please check the license of the original model google/codegemma-7b-it before using this model which provided the base model. The license of the `pruna-engine` is [here](https://pypi.org/project/pruna-engine/) on Pypi.
## Want to compress other models?
- Contact us and tell us which model to compress next [here](https://www.pruna.ai/contact).
- Request access to easily compress your own AI models [here](https://z0halsaff74.typeform.com/pruna-access?typeform-source=www.pruna.ai). |
cs552-mlp/phi3-dpo-m2 | cs552-mlp | "2024-05-28T13:09:50Z" | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"base_model:adapter:unsloth/Phi-3-mini-4k-instruct-bnb-4bit",
"region:us"
] | null | "2024-05-28T13:09:38Z" | ---
library_name: peft
base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
---
# 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 0.11.1 |
jrnold/Reinforce-CartPole-v1 | jrnold | "2023-01-18T23:49:02Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-01-18T19:17:15Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: Reinforce-CartPole-v1
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 177.30 +/- 10.60
name: mean_reward
verified: false
---
# **Reinforce** Agent playing **CartPole-v1**
This is a trained model of a **Reinforce** agent playing **CartPole-v1** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
|
blaackjack/sd-class-butterflies-32 | blaackjack | "2024-05-01T14:55:21Z" | 46 | 0 | diffusers | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | "2024-05-01T14:54:51Z" | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('blaackjack/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
ahmedheakl/arazn-llama3-english | ahmedheakl | "2024-10-08T20:37:14Z" | 0 | 1 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"translation",
"ar",
"en",
"dataset:ahmedheakl/arzen-llm-dataset",
"arxiv:2406.18120",
"license:mit",
"endpoints_compatible",
"region:us"
] | translation | "2024-04-20T05:10:40Z" | ---
license: mit
datasets:
- ahmedheakl/arzen-llm-dataset
language:
- ar
- en
metrics:
- bleu
- ecody726/bertscore
- meteor
library_name: transformers
pipeline_tag: translation
---
## How to use
Just install `peft`, `transformers`, 'accelerate', 'bitsandbytes' and `pytorch` first.
```bash
pip install peft accelerate bitsandbytes transformers torch
```
Then login with your huggingface token to get access to base models
```bash
huggingface-cli login --token <YOUR_HF_TOKEN>
```
Then load the model.
```python
from peft import PeftConfig, PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
peft_model_id = "ahmedheakl/arazn-llama3-english"
peft_config = PeftConfig.from_pretrained(peft_model_id)
base_model_name = peft_config.base_model_name_or_path
base_model = AutoModelForCausalLM.from_pretrained(base_model_name, device_map="auto", torch_dtype=torch.bfloat16)
model = PeftModel.from_pretrained(base_model, peft_model_id, device_map="auto")
tokenizer = AutoTokenizer.from_pretrained(peft_model_id)
```
Then do inference
```python
import torch
raw_prompt = """<|begin_of_text|><|start_header_id|>system<|end_header_id|>
Translate the following code-switched Arabic-English-mixed text to English only.<|eot_id|><|start_header_id|>user<|end_header_id|>
{source}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
"""
def inference(prompt) -> str:
prompt = raw_prompt.format(source=prompt)
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
generated_ids = model.generate(
**inputs,
use_cache=True,
num_return_sequences=1,
max_new_tokens=100,
# do_sample=True,
num_beams=1,
# temperature=0.7,
eos_token_id=tokenizer.eos_token_id,
pad_token_id=tokenizer.pad_token_id,
)
outputs = tokenizer.batch_decode(generated_ids)[0]
torch.cuda.empty_cache()
torch.cuda.synchronize()
return outputs.split("assistant<|end_header_id|>\n\n")[-1].split("<|eot_id|>")[0]
print(inference("أنا أحب الbanana")) # I love bananas
```
**Please see paper & code for more information:**
- https://github.com/ahmedheakl/arazn-llm
- https://arxiv.org/abs/2406.18120
## Citation
**BibTeX:**
```
@article{heakl2024arzen,
title={ArzEn-LLM: Code-Switched Egyptian Arabic-English Translation and Speech Recognition Using LLMs},
author={Heakl, Ahmed and Zaghloul, Youssef and Ali, Mennatullah and Hossam, Rania and Gomaa, Walid},
journal={arXiv preprint arXiv:2406.18120},
year={2024}
}
```
## Model Card Authors
- Email: [email protected]
- Linkedin: https://linkedin.com/in/ahmed-heakl |
kazandaev/m2m100_418M | kazandaev | "2023-10-09T13:09:41Z" | 99 | 0 | transformers | [
"transformers",
"pytorch",
"m2m_100",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:wmt16",
"base_model:kazandaev/m2m100_418M",
"base_model:finetune:kazandaev/m2m100_418M",
"license:mit",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2023-10-07T18:40:07Z" | ---
license: mit
base_model: kazandaev/m2m100_418M
tags:
- translation
- generated_from_trainer
datasets:
- wmt16
metrics:
- bleu
model-index:
- name: m2m100_418M
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: wmt16
type: wmt16
config: ru-en
split: validation
args: ru-en
metrics:
- name: Bleu
type: bleu
value: 32.0585
---
<!-- 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. -->
# m2m100_418M
This model is a fine-tuned version of [kazandaev/m2m100_418M](https://huggingface.co/kazandaev/m2m100_418M) on the custom en-ru dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8954
- Bleu: 32.0585
- Gen Len: 36.1643
## 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: 4
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 10
- total_train_batch_size: 40
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Bleu | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-------:|:-------:|
| 0.8087 | 1.0 | 47790 | 0.9542 | 30.786 | 36.1469 |
| 0.7266 | 2.0 | 95580 | 0.8954 | 32.0585 | 36.1643 |
### Framework versions
- Transformers 4.33.2
- Pytorch 2.2.0.dev20230920+cu121
- Datasets 2.14.4
- Tokenizers 0.13.3
|
asas-ai/OpenR1-AceGPT-v2-8B-SFT | asas-ai | "2025-02-25T22:19:23Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"open-r1",
"trl",
"sft",
"conversational",
"dataset:open-r1/OpenR1-Math-220k",
"base_model:asas-ai/AceGPT-v2-8b-chat",
"base_model:finetune:asas-ai/AceGPT-v2-8b-chat",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-25T05:18:50Z" | ---
base_model: asas-ai/AceGPT-v2-8b-chat
datasets: open-r1/OpenR1-Math-220k
library_name: transformers
model_name: OpenR1-AceGPT-v2-8B-SFT
tags:
- generated_from_trainer
- open-r1
- trl
- sft
licence: license
---
# Model Card for OpenR1-AceGPT-v2-8B-SFT
This model is a fine-tuned version of [asas-ai/AceGPT-v2-8b-chat](https://huggingface.co/asas-ai/AceGPT-v2-8b-chat) on the [open-r1/OpenR1-Math-220k](https://huggingface.co/datasets/open-r1/OpenR1-Math-220k) 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="khalidalt/OpenR1-AceGPT-v2-8B-SFT", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
This model was trained with SFT.
### Framework versions
- TRL: 0.16.0.dev0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.3.2
- Tokenizers: 0.21.0
## Citations
Cite open-r1 as:
```bibtex
@misc{open-r1,
title = {{Open R1: A fully open reproduction of DeepSeek-R1.}},
year = 2025,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/open-r1}}
}
```
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}}
}
``` |
mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF | mradermacher | "2024-08-02T09:42:25Z" | 82 | 0 | transformers | [
"transformers",
"gguf",
"Phi-3",
"song",
"lyrics",
"en",
"base_model:aifeifei798/Phi-3-song-lyrics-1.0",
"base_model:quantized:aifeifei798/Phi-3-song-lyrics-1.0",
"license:mit",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2024-07-11T11:09:43Z" | ---
base_model: aifeifei798/Phi-3-song-lyrics-1.0
language:
- en
library_name: transformers
license: mit
quantized_by: mradermacher
tags:
- Phi-3
- song
- lyrics
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/aifeifei798/Phi-3-song-lyrics-1.0
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-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/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ1_S.gguf) | i1-IQ1_S | 0.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ1_M.gguf) | i1-IQ1_M | 1.0 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 1.1 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ2_S.gguf) | i1-IQ2_S | 1.3 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ2_M.gguf) | i1-IQ2_M | 1.4 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q2_K.gguf) | i1-Q2_K | 1.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 1.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 1.7 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ3_S.gguf) | i1-IQ3_S | 1.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q3_K_S.gguf) | i1-Q3_K_S | 1.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ3_M.gguf) | i1-IQ3_M | 2.0 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q3_K_M.gguf) | i1-Q3_K_M | 2.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-IQ4_XS.gguf) | i1-IQ4_XS | 2.2 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q3_K_L.gguf) | i1-Q3_K_L | 2.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q4_0.gguf) | i1-Q4_0 | 2.3 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q4_K_S.gguf) | i1-Q4_K_S | 2.3 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q4_K_M.gguf) | i1-Q4_K_M | 2.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q5_K_S.gguf) | i1-Q5_K_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q5_K_M.gguf) | i1-Q5_K_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Phi-3-song-lyrics-1.0-i1-GGUF/resolve/main/Phi-3-song-lyrics-1.0.i1-Q6_K.gguf) | i1-Q6_K | 3.2 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
rymaju/Redex-NL-RX-Synth-t5-small-finetuned-en-to-regex-finetuned-en-to-regex | rymaju | "2022-12-05T13:55:49Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"t5",
"text2text-generation",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2022-12-05T13:28:53Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: Redex-NL-RX-Synth-t5-small-finetuned-en-to-regex-finetuned-en-to-regex
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. -->
# Redex-NL-RX-Synth-t5-small-finetuned-en-to-regex-finetuned-en-to-regex
This model is a fine-tuned version of [rymaju/NL-RX-Synth-t5-small-finetuned-en-to-regex](https://huggingface.co/rymaju/NL-RX-Synth-t5-small-finetuned-en-to-regex) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0811
- Semantic-accuracy: 0.912
- Gen Len: 10.71
## 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: 0.001
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- training_steps: 10000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Semantic-accuracy | Gen Len |
|:-------------:|:-----:|:-----:|:---------------:|:-----------------:|:-------:|
| 0.1767 | 1.0 | 563 | 0.0710 | 0.916 | 10.766 |
| 0.0798 | 2.0 | 1126 | 0.0700 | 0.922 | 10.746 |
| 0.0677 | 3.0 | 1689 | 0.0711 | 0.92 | 10.732 |
| 0.0602 | 4.0 | 2252 | 0.0635 | 0.92 | 10.722 |
| 0.0552 | 5.0 | 2815 | 0.0693 | 0.932 | 10.75 |
| 0.05 | 6.0 | 3378 | 0.0746 | 0.922 | 10.778 |
| 0.0486 | 7.0 | 3941 | 0.0743 | 0.914 | 10.726 |
| 0.0462 | 8.0 | 4504 | 0.0712 | 0.902 | 10.772 |
| 0.0435 | 9.0 | 5067 | 0.0710 | 0.92 | 10.724 |
| 0.0442 | 10.0 | 5630 | 0.0799 | 0.896 | 10.69 |
| 0.043 | 11.0 | 6193 | 0.0754 | 0.914 | 10.72 |
| 0.0412 | 12.0 | 6756 | 0.0728 | 0.91 | 10.706 |
| 0.0395 | 13.0 | 7319 | 0.0755 | 0.914 | 10.71 |
| 0.0397 | 14.0 | 7882 | 0.0799 | 0.91 | 10.7 |
| 0.0389 | 15.0 | 8445 | 0.0778 | 0.908 | 10.684 |
| 0.0379 | 16.0 | 9008 | 0.0801 | 0.914 | 10.7 |
| 0.0369 | 17.0 | 9571 | 0.0806 | 0.912 | 10.708 |
| 0.0364 | 17.76 | 10000 | 0.0811 | 0.912 | 10.71 |
### Framework versions
- Transformers 4.25.1
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
GeneroGral/llama_3.1-bbq-lora_model_train | GeneroGral | "2025-03-05T02:23:48Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.1-8B-Instruct",
"base_model:finetune:unsloth/Llama-3.1-8B-Instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-03-05T02:23:38Z" | ---
base_model: unsloth/Llama-3.1-8B-Instruct
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** GeneroGral
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Llama-3.1-8B-Instruct
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
VH1213141516/LAT_3-20_sweeps_epsilon_0.5_num_steps_100 | VH1213141516 | "2024-03-26T22:30:20Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-chat-hf",
"base_model:adapter:meta-llama/Llama-2-7b-chat-hf",
"region:us"
] | null | "2024-03-26T22:18:14Z" | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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## Bias, Risks, and Limitations
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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#### Speeds, Sizes, Times [optional]
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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]
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### Framework versions
- PEFT 0.8.2 |
violetxi/prm_block_1999_lr1e-5_b64_wa0.03_checkpoint8000 | violetxi | "2024-11-25T23:08:25Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-11-25T23:05:26Z" | ---
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]
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[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
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[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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DevQuasar/open-r1.OlympicCoder-32B-GGUF | DevQuasar | "2025-03-15T13:21:46Z" | 21 | 0 | null | [
"gguf",
"text-generation",
"base_model:open-r1/OlympicCoder-32B",
"base_model:quantized:open-r1/OlympicCoder-32B",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-03-12T01:22:03Z" | ---
base_model:
- open-r1/OlympicCoder-32B
pipeline_tag: text-generation
---
[<img src="https://raw.githubusercontent.com/csabakecskemeti/devquasar/main/dq_logo_black-transparent.png" width="200"/>](https://devquasar.com)
Quantized version of: [open-r1/OlympicCoder-32B](https://huggingface.co/open-r1/OlympicCoder-32B)
'Make knowledge free for everyone'
<p align="center">
Made with <br>
<a href="https://www.civo.com/" target="_blank">
<img src="https://www.civo.com/assets/public/brand-assets/civo-logo-colour-60cc1622dedf346f7afde1fff760523f731b0aac106a5465af98ff4073114b74.svg" width="100"/>
</a>
</p>
<a href='https://ko-fi.com/L4L416YX7C' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://storage.ko-fi.com/cdn/kofi6.png?v=6' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a>
|
Lil-R/2_PRYMMAL-ECE-7B-SLERP-V2 | Lil-R | "2024-10-28T13:24:51Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-28T13:18:56Z" | ---
library_name: transformers
license: apache-2.0
---
# 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.
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[More Information Needed]
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[More Information Needed]
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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jiminmun/llama-3.2-3b_reward_model_accuracy_lr9e-6_no_sys_msg_unfiltered | jiminmun | "2025-02-07T19:51:43Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-02-07T19:48:37Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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[More Information Needed]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
mrm8488/chEMBL_smiles_v1 | mrm8488 | "2023-03-17T22:37:29Z" | 194 | 5 | transformers | [
"transformers",
"pytorch",
"jax",
"safetensors",
"roberta",
"fill-mask",
"drugs",
"chemist",
"drug design",
"en",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | ---
language: en
tags:
- drugs
- chemist
- drug design
widget:
- text: "CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)<mask>"
---
# *De Novo* Drug Design with MLM
## What is it?
An approximation to [Generative Recurrent Networks for De Novo Drug Design](https://onlinelibrary.wiley.com/doi/full/10.1002/minf.201700111) but training a MLM (RoBERTa like) from scratch.
## Why?
As mentioned in the paper:
Generative artificial intelligence models present a fresh approach to chemogenomics and de novo drug design, as they provide researchers with the ability to narrow down their search of the chemical space and focus on regions of interest.
They used a generative *recurrent neural network (RNN)* containing long short‐term memory (LSTM) cell to capture the syntax of molecular representations in terms of SMILES strings.
The learned pattern probabilities can be used for de novo SMILES generation. This molecular design concept **eliminates the need for virtual compound library enumeration** and **enables virtual compound design without requiring secondary or external activity prediction**.
## My Goal 🎯
By training a MLM from scratch on 438552 (cleaned*) SMILES I wanted to build a model that learns this kind of molecular combinations so that given a partial SMILE it can generate plausible combinations so that it can be proposed as new drugs.
By cleaned SMILES I mean that I used their [SMILES cleaning script](https://github.com/topazape/LSTM_Chem/blob/master/cleanup_smiles.py) to remove duplicates, salts, and stereochemical information.
You can see the detailed process of gathering the data, preprocess it and train the LSTM in their [repo](https://github.com/topazape/LSTM_Chem).
## Fast usage with ```pipelines``` 🧪
```python
from transformers import pipeline
fill_mask = pipeline(
"fill-mask",
model='mrm8488/chEMBL_smiles_v1',
tokenizer='mrm8488/chEMBL_smiles_v1'
)
# CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)cc1 Atazanavir
smile1 = "CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)<mask>"
fill_mask(smile1)
# Output:
'''
[{'score': 0.6040295958518982,
'sequence': '<s> CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)nc</s>',
'token': 265},
{'score': 0.2185731679201126,
'sequence': '<s> CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)N</s>',
'token': 50},
{'score': 0.0642734169960022,
'sequence': '<s> CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)cc</s>',
'token': 261},
{'score': 0.01932266168296337,
'sequence': '<s> CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)CCCl</s>',
'token': 452},
{'score': 0.005068355705589056,
'sequence': '<s> CC(C)CN(CC(OP(=O)(O)O)C(Cc1ccccc1)NC(=O)OC1CCOC1)S(=O)(=O)c1ccc(N)C</s>',
'token': 39}]
'''
```
## More
I also created a [second version](https://huggingface.co/mrm8488/chEMBL26_smiles_v2) without applying the cleaning SMILES script mentioned above. You can use it in the same way as this one.
```python
fill_mask = pipeline(
"fill-mask",
model='/mrm8488/chEMBL26_smiles_v2',
tokenizer='/mrm8488/chEMBL26_smiles_v2'
)
```
[Original paper](https://www.ncbi.nlm.nih.gov/pubmed/29095571) Authors:
<details>
Swiss Federal Institute of Technology (ETH), Department of Chemistry and Applied Biosciences, Vladimir–Prelog–Weg 4, 8093, Zurich, Switzerland,
Stanford University, Department of Computer Science, 450 Sierra Mall, Stanford, CA, 94305, USA,
inSili.com GmbH, 8049, Zurich, Switzerland,
Gisbert Schneider, Email: hc.zhte@trebsig.
</details>
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488) | [LinkedIn](https://www.linkedin.com/in/manuel-romero-cs/)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
darinchau/comp5421-project-clean-frost-31-comp5421-mel-spectrogram-fma_small-128x216-step-12288 | darinchau | "2025-03-28T02:38:14Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"region:us"
] | null | "2025-03-28T02:38:11Z" | ---
library_name: diffusers
---
# 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 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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### Training Procedure
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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## Glossary [optional]
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[More Information Needed]
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## Model Card Contact
[More Information Needed] |
AngelaG/roberta_checkpoint-finetuned-squad | AngelaG | "2022-11-30T16:31:26Z" | 115 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"question-answering",
"generated_from_trainer",
"dataset:squad",
"license:mit",
"endpoints_compatible",
"region:us"
] | question-answering | "2022-11-30T10:29:55Z" | ---
license: mit
tags:
- generated_from_trainer
datasets:
- squad
model-index:
- name: roberta_checkpoint-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# roberta_checkpoint-finetuned-squad
This model is a fine-tuned version of [WillHeld/roberta-base-coqa](https://huggingface.co/WillHeld/roberta-base-coqa) on the squad dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8934
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.8468 | 1.0 | 5536 | 0.8168 |
| 0.6239 | 2.0 | 11072 | 0.8237 |
| 0.4805 | 3.0 | 16608 | 0.8934 |
### Framework versions
- Transformers 4.24.0
- Pytorch 1.12.1+cu113
- Datasets 2.7.1
- Tokenizers 0.13.2
|
PlutoG99001/musicgen-Electronic-Random2-colab | PlutoG99001 | "2025-02-16T22:22:04Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"text-to-audio",
"Electronic",
"generated_from_trainer",
"base_model:facebook/musicgen-melody",
"base_model:adapter:facebook/musicgen-melody",
"license:cc-by-nc-4.0",
"region:us"
] | text-to-audio | "2025-02-16T22:21:04Z" | ---
library_name: peft
license: cc-by-nc-4.0
base_model: facebook/musicgen-melody
tags:
- text-to-audio
- Electronic
- generated_from_trainer
model-index:
- name: musicgen-Electronic-Random2-colab
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. -->
# musicgen-Electronic-Random2-colab
This model is a fine-tuned version of [facebook/musicgen-melody](https://huggingface.co/facebook/musicgen-melody) on the PlutoG99001/MusicGen-Electronic-Random dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.99) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.14.0
- Transformers 4.49.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 3.3.0
- Tokenizers 0.21.0 |
mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF | mradermacher | "2024-11-27T18:44:10Z" | 15 | 1 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2024-11-27T13:03:18Z" | ---
base_model: MrRobotoAI/Freyja-v4.95-Undi95-7b-NON-FICTION
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/MrRobotoAI/Freyja-v4.95-Undi95-7b-NON-FICTION
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-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/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ1_S.gguf) | i1-IQ1_S | 2.1 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ1_M.gguf) | i1-IQ1_M | 2.3 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ2_S.gguf) | i1-IQ2_S | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ2_M.gguf) | i1-IQ2_M | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q2_K.gguf) | i1-Q2_K | 3.3 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.8 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ3_S.gguf) | i1-IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ3_M.gguf) | i1-IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q3_K_M.gguf) | i1-Q3_K_M | 4.1 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.5 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.8 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.8 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.8 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q4_0.gguf) | i1-Q4_0 | 4.8 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.8 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q4_K_M.gguf) | i1-Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Freyja-v4.95-Undi95-7b-NON-FICTION-i1-GGUF/resolve/main/Freyja-v4.95-Undi95-7b-NON-FICTION.i1-Q6_K.gguf) | i1-Q6_K | 6.7 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
lesso05/f9eb3ee4-6290-4e04-a072-0d342f780b0d | lesso05 | "2025-01-31T04:31:56Z" | 5 | 0 | peft | [
"peft",
"safetensors",
"olmo",
"axolotl",
"generated_from_trainer",
"base_model:katuni4ka/tiny-random-olmo-hf",
"base_model:adapter:katuni4ka/tiny-random-olmo-hf",
"region:us"
] | null | "2025-01-31T04:30:51Z" | ---
library_name: peft
base_model: katuni4ka/tiny-random-olmo-hf
tags:
- axolotl
- generated_from_trainer
model-index:
- name: f9eb3ee4-6290-4e04-a072-0d342f780b0d
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: katuni4ka/tiny-random-olmo-hf
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- 1db9e8b579516506_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/1db9e8b579516506_train_data.json
type:
field_input: context
field_instruction: question
field_output: answer
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: lesso05/f9eb3ee4-6290-4e04-a072-0d342f780b0d
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 5.0e-05
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 200
micro_batch_size: 2
mixed_precision: bf16
mlflow_experiment_name: /tmp/1db9e8b579516506_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: 30182338-b805-4664-a24b-6540f053544e
wandb_project: new-01-29
wandb_run: your_name
wandb_runid: 30182338-b805-4664-a24b-6540f053544e
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# f9eb3ee4-6290-4e04-a072-0d342f780b0d
This model is a fine-tuned version of [katuni4ka/tiny-random-olmo-hf](https://huggingface.co/katuni4ka/tiny-random-olmo-hf) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 10.7510
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 10.7594 | 0.0440 | 200 | 10.7510 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
LandCruiser/TheCharm_5 | LandCruiser | "2025-03-05T03:23:51Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-05T02:31:35Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 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] |
hkivancoral/smids_5x_beit_base_adamax_00001_fold1 | hkivancoral | "2023-12-14T18:46:00Z" | 4 | 0 | transformers | [
"transformers",
"pytorch",
"beit",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/beit-base-patch16-224",
"base_model:finetune:microsoft/beit-base-patch16-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-12-14T07:37:16Z" | ---
license: apache-2.0
base_model: microsoft/beit-base-patch16-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: smids_5x_beit_base_adamax_00001_fold1
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: test
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9065108514190318
---
<!-- 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. -->
# smids_5x_beit_base_adamax_00001_fold1
This model is a fine-tuned version of [microsoft/beit-base-patch16-224](https://huggingface.co/microsoft/beit-base-patch16-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8312
- Accuracy: 0.9065
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 0.2349 | 1.0 | 376 | 0.2964 | 0.8848 |
| 0.2022 | 2.0 | 752 | 0.2944 | 0.8932 |
| 0.1706 | 3.0 | 1128 | 0.2893 | 0.8965 |
| 0.0767 | 4.0 | 1504 | 0.3105 | 0.9015 |
| 0.0646 | 5.0 | 1880 | 0.3471 | 0.9015 |
| 0.0505 | 6.0 | 2256 | 0.3777 | 0.9015 |
| 0.0505 | 7.0 | 2632 | 0.4146 | 0.9115 |
| 0.0821 | 8.0 | 3008 | 0.4739 | 0.9115 |
| 0.0331 | 9.0 | 3384 | 0.5133 | 0.9082 |
| 0.0097 | 10.0 | 3760 | 0.5125 | 0.9065 |
| 0.0368 | 11.0 | 4136 | 0.5327 | 0.9098 |
| 0.0236 | 12.0 | 4512 | 0.6377 | 0.8881 |
| 0.0306 | 13.0 | 4888 | 0.6671 | 0.9015 |
| 0.0605 | 14.0 | 5264 | 0.6154 | 0.9048 |
| 0.0306 | 15.0 | 5640 | 0.6497 | 0.9082 |
| 0.0004 | 16.0 | 6016 | 0.6905 | 0.9098 |
| 0.0062 | 17.0 | 6392 | 0.7456 | 0.9082 |
| 0.0157 | 18.0 | 6768 | 0.7362 | 0.9048 |
| 0.0117 | 19.0 | 7144 | 0.8082 | 0.8965 |
| 0.0001 | 20.0 | 7520 | 0.7613 | 0.9098 |
| 0.0049 | 21.0 | 7896 | 0.7376 | 0.9115 |
| 0.0013 | 22.0 | 8272 | 0.7490 | 0.9098 |
| 0.0339 | 23.0 | 8648 | 0.7577 | 0.9132 |
| 0.0009 | 24.0 | 9024 | 0.7847 | 0.9098 |
| 0.0161 | 25.0 | 9400 | 0.7983 | 0.9098 |
| 0.0079 | 26.0 | 9776 | 0.7734 | 0.8948 |
| 0.0004 | 27.0 | 10152 | 0.7368 | 0.9015 |
| 0.0005 | 28.0 | 10528 | 0.7478 | 0.9098 |
| 0.0059 | 29.0 | 10904 | 0.7755 | 0.9065 |
| 0.0012 | 30.0 | 11280 | 0.8338 | 0.9082 |
| 0.0142 | 31.0 | 11656 | 0.7783 | 0.9115 |
| 0.0002 | 32.0 | 12032 | 0.7615 | 0.9165 |
| 0.0004 | 33.0 | 12408 | 0.7711 | 0.9098 |
| 0.0127 | 34.0 | 12784 | 0.7865 | 0.9165 |
| 0.0032 | 35.0 | 13160 | 0.8207 | 0.9132 |
| 0.0006 | 36.0 | 13536 | 0.8174 | 0.9098 |
| 0.0001 | 37.0 | 13912 | 0.7992 | 0.9165 |
| 0.0 | 38.0 | 14288 | 0.8040 | 0.9082 |
| 0.0001 | 39.0 | 14664 | 0.8011 | 0.9132 |
| 0.0005 | 40.0 | 15040 | 0.8052 | 0.9115 |
| 0.0001 | 41.0 | 15416 | 0.8158 | 0.9082 |
| 0.0001 | 42.0 | 15792 | 0.8157 | 0.9098 |
| 0.0 | 43.0 | 16168 | 0.8347 | 0.9065 |
| 0.0004 | 44.0 | 16544 | 0.8096 | 0.9048 |
| 0.0087 | 45.0 | 16920 | 0.8231 | 0.9065 |
| 0.0003 | 46.0 | 17296 | 0.8362 | 0.9065 |
| 0.0002 | 47.0 | 17672 | 0.8291 | 0.9098 |
| 0.0046 | 48.0 | 18048 | 0.8341 | 0.9082 |
| 0.0134 | 49.0 | 18424 | 0.8309 | 0.9065 |
| 0.0004 | 50.0 | 18800 | 0.8312 | 0.9065 |
### Framework versions
- Transformers 4.32.1
- Pytorch 2.1.0+cu121
- Datasets 2.12.0
- Tokenizers 0.13.2
|
mradermacher/tsukasa-7b-qlora-GGUF | mradermacher | "2025-01-17T06:04:27Z" | 215 | 0 | transformers | [
"transformers",
"gguf",
"en",
"dataset:PygmalionAI/PIPPA",
"dataset:lemonilia/LimaRP",
"base_model:ludis/tsukasa-7b-qlora",
"base_model:quantized:ludis/tsukasa-7b-qlora",
"endpoints_compatible",
"region:us"
] | null | "2025-01-16T09:33:45Z" | ---
base_model: ludis/tsukasa-7b-qlora
datasets:
- PygmalionAI/PIPPA
- lemonilia/LimaRP
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/ludis/tsukasa-7b-qlora
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q2_K.gguf) | Q2_K | 2.8 | |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q3_K_S.gguf) | Q3_K_S | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q3_K_M.gguf) | Q3_K_M | 3.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q3_K_L.gguf) | Q3_K_L | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.IQ4_XS.gguf) | IQ4_XS | 4.0 | |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q4_K_S.gguf) | Q4_K_S | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q4_K_M.gguf) | Q4_K_M | 4.5 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q5_K_S.gguf) | Q5_K_S | 5.1 | |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q5_K_M.gguf) | Q5_K_M | 5.2 | |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q6_K.gguf) | Q6_K | 6.0 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.Q8_0.gguf) | Q8_0 | 7.8 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/tsukasa-7b-qlora-GGUF/resolve/main/tsukasa-7b-qlora.f16.gguf) | f16 | 14.6 | 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. 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 -->
|
daniel40/77cfd110-53b9-490f-aac8-bfb0f94e5291 | daniel40 | "2025-03-04T10:38:42Z" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:Korabbit/llama-2-ko-7b",
"base_model:adapter:Korabbit/llama-2-ko-7b",
"region:us"
] | null | "2025-03-04T10:38:26Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: Korabbit/llama-2-ko-7b
model-index:
- name: daniel40/77cfd110-53b9-490f-aac8-bfb0f94e5291
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. -->
# daniel40/77cfd110-53b9-490f-aac8-bfb0f94e5291
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4039
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
pyrac/rse_engagement_des_collaborateurs | pyrac | "2025-02-11T14:15:15Z" | 0 | 0 | sentence-transformers | [
"sentence-transformers",
"safetensors",
"xlm-roberta",
"sentence-similarity",
"feature-extraction",
"generated_from_trainer",
"dataset_size:132020",
"loss:MultipleNegativesRankingLoss",
"en",
"arxiv:1908.10084",
"arxiv:1705.00652",
"base_model:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"base_model:finetune:sentence-transformers/paraphrase-multilingual-mpnet-base-v2",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | sentence-similarity | "2025-02-11T14:14:17Z" | ---
language:
- en
license: apache-2.0
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:132020
- loss:MultipleNegativesRankingLoss
base_model: sentence-transformers/paraphrase-multilingual-mpnet-base-v2
widget:
- source_sentence: Les membres de l'équipe ont été attentifs et très professionnels
dans leur manière de gérer nos besoins.
sentences:
- C’était tout à fait correct, mais pas très chaleureux.
- Une chambre mal située, très loin de tout, et vraiment pas agréable.
- Le service était moyen, ni excellent ni vraiment mauvais.
- source_sentence: Les employés étaient réactifs et nous ont aidés à chaque étape
du processus.
sentences:
- Leur manque d’implication et d’organisation était frustrant.
- L’expérience avec le personnel était moyenne, sans surprise.
- Manque de signalisation dans le parking.
- source_sentence: Les employés ont dépassé nos attentes en termes de professionnalisme
et de réactivité.
sentences:
- Parking mal entretenu, sale et peu accueillant.
- Nous avons trouvé les collaborateurs peu investis dans leur travail.
- Le personnel était super gentil et efficace, une vraie bonne surprise.
- source_sentence: Les employés semblaient démotivés et peu impliqués dans leur travail.
sentences:
- La chambre n'était pas adaptée à nos attentes, c’était frustrant.
- L'équipe semblait désorganisée et peu concernée par les besoins des clients.
- Le service était présent mais manquait de chaleur humaine.
- source_sentence: On aurait aimé plus d’implication de la part des employés, c’était
moyen.
sentences:
- Rien de particulier à dire, le service était correct.
- On a été forcé d’accepter cette chambre, ce n'était pas du tout ce qu’on avait
demandé.
- Le service était assez médiocre, mais pas désastreux non plus.
pipeline_tag: sentence-similarity
library_name: sentence-transformers
metrics:
- cosine_accuracy
model-index:
- name: MPNet base trained on AllNLI triplets
results:
- task:
type: triplet
name: Triplet
dataset:
name: all nli dev
type: all-nli-dev
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
- task:
type: triplet
name: Triplet
dataset:
name: all nli test
type: all-nli-test
metrics:
- type: cosine_accuracy
value: 1.0
name: Cosine Accuracy
---
# MPNet base trained on AllNLI triplets
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [sentence-transformers/paraphrase-multilingual-mpnet-base-v2](https://huggingface.co/sentence-transformers/paraphrase-multilingual-mpnet-base-v2) <!-- at revision 75c57757a97f90ad739aca51fa8bfea0e485a7f2 -->
- **Maximum Sequence Length:** 128 tokens
- **Output Dimensionality:** 768 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
- **Language:** en
- **License:** apache-2.0
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 128, 'do_lower_case': False}) with Transformer model: XLMRobertaModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("pyrac/rse_engagement_des_collaborateurs")
# Run inference
sentences = [
'On aurait aimé plus d’implication de la part des employés, c’était moyen.',
'Le service était assez médiocre, mais pas désastreux non plus.',
"On a été forcé d’accepter cette chambre, ce n'était pas du tout ce qu’on avait demandé.",
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
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## Evaluation
### Metrics
#### Triplet
* Datasets: `all-nli-dev` and `all-nli-test`
* Evaluated with [<code>TripletEvaluator</code>](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.TripletEvaluator)
| Metric | all-nli-dev | all-nli-test |
|:--------------------|:------------|:-------------|
| **cosine_accuracy** | **1.0** | **1.0** |
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## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 132,020 training samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 11 tokens</li><li>mean: 18.6 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.93 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.87 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| <code>L’équipe était tellement sympa et réactive, c’était vraiment agréable.</code> | <code>Nous avons été agréablement surpris par l’attention portée aux détails par le personnel.</code> | <code>Très bon service de navette depuis le parking.</code> |
| <code>C’était un service classique, ni particulièrement bon ni mauvais.</code> | <code>Le sourire et la disponibilité des employés ont illuminé notre séjour.</code> | <code>Cette chambre nous a totalement déçus, c’était tout sauf confortable.</code> |
| <code>Le service était correct, rien de plus.</code> | <code>Les employés étaient lents et mal organisés, cela a beaucoup gêné notre séjour.</code> | <code>La sécurité est assurée avec un gardien présent.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Evaluation Dataset
#### Unnamed Dataset
* Size: 16,502 evaluation samples
* Columns: <code>anchor</code>, <code>positive</code>, and <code>negative</code>
* Approximate statistics based on the first 1000 samples:
| | anchor | positive | negative |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|:----------------------------------------------------------------------------------|
| type | string | string | string |
| details | <ul><li>min: 10 tokens</li><li>mean: 18.49 tokens</li><li>max: 29 tokens</li></ul> | <ul><li>min: 10 tokens</li><li>mean: 16.97 tokens</li><li>max: 27 tokens</li></ul> | <ul><li>min: 6 tokens</li><li>mean: 18.86 tokens</li><li>max: 31 tokens</li></ul> |
* Samples:
| anchor | positive | negative |
|:------------------------------------------------------------------------------------|:------------------------------------------------------------------------------------------|:--------------------------------------------------------------------------------------|
| <code>Les employés semblaient démotivés et peu impliqués dans leur travail.</code> | <code>L'équipe semblait désorganisée et peu concernée par les besoins des clients.</code> | <code>La chambre n'était pas adaptée à nos attentes, c’était frustrant.</code> |
| <code>Le service était correct, mais il manquait un peu de chaleur humaine.</code> | <code>Un service qui n’a pas marqué mais qui reste acceptable.</code> | <code>Une chambre que nous n’avions pas demandée, c'était une vraie déception.</code> |
| <code>Les employés semblaient désintéressés, ça a un peu gâché l’expérience.</code> | <code>Le service était fonctionnel, mais pas très personnalisé.</code> | <code>Stationnement facile et rapide, un plaisir.</code> |
* Loss: [<code>MultipleNegativesRankingLoss</code>](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#multiplenegativesrankingloss) with these parameters:
```json
{
"scale": 20.0,
"similarity_fct": "cos_sim"
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `num_train_epochs`: 1
- `warmup_ratio`: 0.1
- `bf16`: True
- `batch_sampler`: no_duplicates
#### All Hyperparameters
<details><summary>Click to expand</summary>
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 64
- `per_device_eval_batch_size`: 64
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1.0
- `num_train_epochs`: 1
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.1
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: True
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: None
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `include_for_metrics`: []
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `use_liger_kernel`: False
- `eval_use_gather_object`: False
- `average_tokens_across_devices`: False
- `prompts`: None
- `batch_sampler`: no_duplicates
- `multi_dataset_batch_sampler`: proportional
</details>
### Training Logs
| Epoch | Step | Training Loss | Validation Loss | all-nli-dev_cosine_accuracy | all-nli-test_cosine_accuracy |
|:------:|:----:|:-------------:|:---------------:|:---------------------------:|:----------------------------:|
| 0.0485 | 100 | 5.2586 | 4.1160 | 1.0 | - |
| 0.0969 | 200 | 4.1542 | 4.1071 | 1.0 | - |
| 0.1454 | 300 | 4.1483 | 4.1009 | 1.0 | - |
| 0.1939 | 400 | 4.1327 | 4.0772 | 1.0 | - |
| 0.2424 | 500 | 4.1122 | 4.0561 | 1.0 | - |
| 0.2908 | 600 | 4.1027 | 4.0457 | 1.0 | - |
| 0.3393 | 700 | 4.0877 | 4.0345 | 1.0 | - |
| 0.3878 | 800 | 4.0863 | 4.0216 | 1.0 | - |
| 0.4363 | 900 | 4.0785 | 4.0196 | 1.0 | - |
| 0.4847 | 1000 | 4.0661 | 4.0182 | 1.0 | - |
| 0.5332 | 1100 | 4.0637 | 4.0163 | 1.0 | - |
| 0.5817 | 1200 | 4.0606 | 4.0130 | 1.0 | - |
| 0.6302 | 1300 | 4.0601 | 4.0086 | 1.0 | - |
| 0.6786 | 1400 | 4.0516 | 4.0037 | 1.0 | - |
| 0.7271 | 1500 | 4.0472 | 4.0015 | 1.0 | - |
| 0.7756 | 1600 | 4.0465 | 4.0008 | 1.0 | - |
| 0.8240 | 1700 | 4.0421 | 4.0007 | 1.0 | - |
| 0.8725 | 1800 | 4.0463 | 3.9944 | 1.0 | - |
| 0.9210 | 1900 | 4.035 | 3.9919 | 1.0 | - |
| 0.9695 | 2000 | 4.0408 | 3.9909 | 1.0 | - |
| -1 | -1 | - | - | - | 1.0 |
### Framework Versions
- Python: 3.12.3
- Sentence Transformers: 3.4.1
- Transformers: 4.48.3
- PyTorch: 2.6.0+cu124
- Accelerate: 1.3.0
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citation
### BibTeX
#### Sentence Transformers
```bibtex
@inproceedings{reimers-2019-sentence-bert,
title = "Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks",
author = "Reimers, Nils and Gurevych, Iryna",
booktitle = "Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing",
month = "11",
year = "2019",
publisher = "Association for Computational Linguistics",
url = "https://arxiv.org/abs/1908.10084",
}
```
#### MultipleNegativesRankingLoss
```bibtex
@misc{henderson2017efficient,
title={Efficient Natural Language Response Suggestion for Smart Reply},
author={Matthew Henderson and Rami Al-Rfou and Brian Strope and Yun-hsuan Sung and Laszlo Lukacs and Ruiqi Guo and Sanjiv Kumar and Balint Miklos and Ray Kurzweil},
year={2017},
eprint={1705.00652},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
```
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Sophie-Rain-SpiderMan-video-update-link/Sophie.Rain.Spiderman.Video.Official | Sophie-Rain-SpiderMan-video-update-link | "2025-02-24T18:00:36Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-02-24T18:00:31Z" | [<img alt="fsd" src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif">](https://tinyurl.com/nh9w7vxx)
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐖𝐚𝐭𝐜𝐡 𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨)](https://tinyurl.com/nh9w7vxx)
[🔴 ➤►𝐂𝐥𝐢𝐤 𝐇𝐞𝐫𝐞 𝐭𝐨👉👉 (𝐅𝐮𝐥𝐥 𝐯𝐢𝐝𝐞𝐨 𝐋𝐢𝐧𝐤 )](https://tinyurl.com/nh9w7vxx) |
nes470/wiki-tfidf-logreg-qa | nes470 | "2024-05-13T16:53:36Z" | 140 | 0 | transformers | [
"transformers",
"pytorch",
"QA-umd-quizbowl",
"question-answering",
"custom_code",
"arxiv:1910.09700",
"license:mit",
"region:us"
] | question-answering | "2024-05-11T15:59:03Z" | ---
library_name: transformers
license: mit
---
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Xu-Ouyang/pythia-410m-deduped-int4-step8000-AWQ | Xu-Ouyang | "2024-10-01T04:37:01Z" | 5 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-01T04:36:11Z" | ---
library_name: transformers
tags: []
---
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GunPark/llm-course-hw2-ppo | GunPark | "2025-03-30T21:27:56Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-03-30T21:27:54Z" | <!DOCTYPE html>
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LoneStriker/PiVoT-MoE-8.0bpw-h8-exl2-2 | LoneStriker | "2023-12-18T12:56:33Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"conversational",
"license:cc-by-nc-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2023-12-18T12:34:41Z" | ---
license: cc-by-nc-4.0
---
# PiVot-MoE
## Model Description
PiVoT-MoE, is an advanced AI model specifically designed for roleplaying purposes. It has been trained using a combination of four 10.7B sized experts, each with their own specialized characteristic, all fine-tuned to bring a unique and diverse roleplaying experience.
The Mixture of Experts (MoE) technique is utilized in this model, allowing the experts to work together synergistically, resulting in a more cohesive and natural conversation flow. The MoE architecture allows for a higher level of flexibility and adaptability, enabling PiVoT-MoE to handle a wide variety of roleplaying scenarios and characters.
Based on the PiVoT-10.7B-Mistral-v0.2-RP model, PiVoT-MoE takes it a step further with the incorporation of the MoE technique. This means that not only does the model have an expansive knowledge base, but it also has the ability to mix and match its expertise to better suit the specific roleplaying scenario.
## Prompt Template - Alpaca (ChatML works)
```
{system}
### Instruction:
{instruction}
### Response:
{response}
``` |
mohamedsaeed823/videomae-base-finetuned-kinetics-finetuned-arsl-subset | mohamedsaeed823 | "2024-05-14T17:50:52Z" | 63 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"videomae",
"video-classification",
"generated_from_trainer",
"base_model:MCG-NJU/videomae-base-finetuned-kinetics",
"base_model:finetune:MCG-NJU/videomae-base-finetuned-kinetics",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | video-classification | "2024-05-13T22:45:31Z" | ---
license: cc-by-nc-4.0
base_model: MCG-NJU/videomae-base-finetuned-kinetics
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: videomae-base-finetuned-kinetics-finetuned-arsl-subset
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. -->
# videomae-base-finetuned-kinetics-finetuned-arsl-subset
This model is a fine-tuned version of [MCG-NJU/videomae-base-finetuned-kinetics](https://huggingface.co/MCG-NJU/videomae-base-finetuned-kinetics) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3876
- Accuracy: 0.6667
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- training_steps: 720
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4686 | 0.25 | 180 | 1.4866 | 0.5122 |
| 0.0184 | 1.25 | 360 | 1.1293 | 0.6748 |
| 0.0061 | 2.25 | 540 | 1.3323 | 0.6748 |
| 0.0782 | 3.25 | 720 | 1.3876 | 0.6667 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
|
RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf | RichardErkhov | "2024-11-05T11:09:58Z" | 15 | 0 | null | [
"gguf",
"arxiv:2204.05149",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-11-04T18:19:02Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Llama-3.1-70B-Instruct-abliterated - GGUF
- Model creator: https://huggingface.co/mylesgoose/
- Original model: https://huggingface.co/mylesgoose/Llama-3.1-70B-Instruct-abliterated/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Llama-3.1-70B-Instruct-abliterated.Q2_K.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.Q2_K.gguf) | Q2_K | 24.56GB |
| [Llama-3.1-70B-Instruct-abliterated.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.IQ3_XS.gguf) | IQ3_XS | 27.29GB |
| [Llama-3.1-70B-Instruct-abliterated.IQ3_S.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.IQ3_S.gguf) | IQ3_S | 28.79GB |
| [Llama-3.1-70B-Instruct-abliterated.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.Q3_K_S.gguf) | Q3_K_S | 28.79GB |
| [Llama-3.1-70B-Instruct-abliterated.IQ3_M.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.IQ3_M.gguf) | IQ3_M | 29.74GB |
| [Llama-3.1-70B-Instruct-abliterated.Q3_K.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.Q3_K.gguf) | Q3_K | 31.91GB |
| [Llama-3.1-70B-Instruct-abliterated.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.Q3_K_M.gguf) | Q3_K_M | 31.91GB |
| [Llama-3.1-70B-Instruct-abliterated.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.Q3_K_L.gguf) | Q3_K_L | 34.59GB |
| [Llama-3.1-70B-Instruct-abliterated.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.IQ4_XS.gguf) | IQ4_XS | 35.64GB |
| [Llama-3.1-70B-Instruct-abliterated.Q4_0.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/blob/main/Llama-3.1-70B-Instruct-abliterated.Q4_0.gguf) | Q4_0 | 37.22GB |
| [Llama-3.1-70B-Instruct-abliterated.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | IQ4_NL | 37.58GB |
| [Llama-3.1-70B-Instruct-abliterated.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q4_K_S | 37.58GB |
| [Llama-3.1-70B-Instruct-abliterated.Q4_K.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q4_K | 39.6GB |
| [Llama-3.1-70B-Instruct-abliterated.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q4_K_M | 39.6GB |
| [Llama-3.1-70B-Instruct-abliterated.Q4_1.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q4_1 | 41.27GB |
| [Llama-3.1-70B-Instruct-abliterated.Q5_0.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q5_0 | 45.32GB |
| [Llama-3.1-70B-Instruct-abliterated.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q5_K_S | 45.32GB |
| [Llama-3.1-70B-Instruct-abliterated.Q5_K.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q5_K | 46.52GB |
| [Llama-3.1-70B-Instruct-abliterated.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q5_K_M | 46.52GB |
| [Llama-3.1-70B-Instruct-abliterated.Q5_1.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q5_1 | 49.36GB |
| [Llama-3.1-70B-Instruct-abliterated.Q6_K.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q6_K | 53.91GB |
| [Llama-3.1-70B-Instruct-abliterated.Q8_0.gguf](https://huggingface.co/RichardErkhov/mylesgoose_-_Llama-3.1-70B-Instruct-abliterated-gguf/tree/main/) | Q8_0 | 69.83GB |
Original model description:
---
language:
- en
- de
- fr
- it
- pt
- hi
- es
- th
library_name: transformers
base_model: meta-llama/Meta-Llama-3.1-70B
license: llama3.1
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
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---
This model has layer 14 orthoganalized
## Model Information
The Meta Llama 3.1 collection of multilingual large language models (LLMs) is a collection of pretrained and instruction tuned generative models in 8B, 70B and 405B sizes (text in/text out). The Llama 3.1 instruction tuned text only models (8B, 70B, 405B) are optimized for multilingual dialogue use cases and outperform many of the available open source and closed chat models on common industry benchmarks.
**Model developer**: Meta
**Model Architecture:** Llama 3.1 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.
<table>
<tr>
<td>
</td>
<td><strong>Training Data</strong>
</td>
<td><strong>Params</strong>
</td>
<td><strong>Input modalities</strong>
</td>
<td><strong>Output modalities</strong>
</td>
<td><strong>Context length</strong>
</td>
<td><strong>GQA</strong>
</td>
<td><strong>Token count</strong>
</td>
<td><strong>Knowledge cutoff</strong>
</td>
</tr>
<tr>
<td rowspan="3" >Llama 3.1 (text only)
</td>
<td rowspan="3" >A new mix of publicly available online data.
</td>
<td>8B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
<td rowspan="3" >15T+
</td>
<td rowspan="3" >December 2023
</td>
</tr>
<tr>
<td>70B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
<tr>
<td>405B
</td>
<td>Multilingual Text
</td>
<td>Multilingual Text and code
</td>
<td>128k
</td>
<td>Yes
</td>
</tr>
</table>
**Supported languages:** English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai.
**Llama 3.1 family of models**. Token counts refer to pretraining data only. All model versions use Grouped-Query Attention (GQA) for improved inference scalability.
**Model Release Date:** July 23, 2024.
**Status:** This is a static model trained on an offline dataset. Future versions of the tuned models will be released as we improve model safety with community feedback.
**License:** A custom commercial license, the Llama 3.1 Community License, is available at: [https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
## Intended Use
**Intended Use Cases** Llama 3.1 is intended for commercial and research use in multiple languages. Instruction tuned text only models are intended for assistant-like chat, whereas pretrained models can be adapted for a variety of natural language generation tasks. The Llama 3.1 model collection also supports the ability to leverage the outputs of its models to improve other models including synthetic data generation and distillation. The Llama 3.1 Community License allows for these use cases.
**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 3.1 Community License. Use in languages beyond those explicitly referenced as supported in this model card**.
**<span style="text-decoration:underline;">Note</span>: Llama 3.1 has been trained on a broader collection of languages than the 8 supported languages. Developers may fine-tune Llama 3.1 models for languages beyond the 8 supported languages provided they comply with the Llama 3.1 Community License and the Acceptable Use Policy and in such cases are responsible for ensuring that any uses of Llama 3.1 in additional languages is done in a safe and responsible manner.
## How to use
This repository contains two versions of Meta-Llama-3.1-70B-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`.
See the snippet below for usage with Transformers:
```python
import transformers
import torch
model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct"
pipeline = transformers.pipeline(
"text-generation",
model=model_id,
model_kwargs={"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 = pipeline(
messages,
max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```
### Tool use with transformers
LLaMA-3.1 supports multiple tool use formats. You can see a full guide to prompt formatting [here](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/).
Tool use is also supported through [chat templates](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling) in Transformers.
Here is a quick example showing a single simple tool:
```python
# First, define a tool
def get_current_temperature(location: str) -> float:
"""
Get the current temperature at a location.
Args:
location: The location to get the temperature for, in the format "City, Country"
Returns:
The current temperature at the specified location in the specified units, as a float.
"""
return 22. # A real function should probably actually get the temperature!
# Next, create a chat and apply the chat template
messages = [
{"role": "system", "content": "You are a bot that responds to weather queries."},
{"role": "user", "content": "Hey, what's the temperature in Paris right now?"}
]
inputs = tokenizer.apply_chat_template(messages, tools=[get_current_temperature], add_generation_prompt=True)
```
You can then generate text from this input as normal. If the model generates a tool call, you should add it to the chat like so:
```python
tool_call = {"name": "get_current_temperature", "arguments": {"location": "Paris, France"}}
messages.append({"role": "assistant", "tool_calls": [{"type": "function", "function": tool_call}]})
```
and then call the tool and append the result, with the `tool` role, like so:
```python
messages.append({"role": "tool", "name": "get_current_temperature", "content": "22.0"})
```
After that, you can `generate()` again to let the model use the tool result in the chat. Note that this was a very brief introduction to tool calling - for more information,
see the [LLaMA prompt format docs](https://llama.meta.com/docs/model-cards-and-prompt-formats/llama3_1/) and the Transformers [tool use documentation](https://huggingface.co/docs/transformers/main/chat_templating#advanced-tool-use--function-calling).
### Use with `bitsandbytes`
The model checkpoints can be used in `8-bit` and `4-bit` for further memory optimisations using `bitsandbytes` and `transformers`
See the snippet below for usage:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "meta-llama/Meta-Llama-3.1-70B-Instruct"
quantization_config = BitsAndBytesConfig(load_in_8bit=True)
quantized_model = AutoModelForCausalLM.from_pretrained(
model_id, device_map="auto", torch_dtype=torch.bfloat16, quantization_config=quantization_config)
tokenizer = AutoTokenizer.from_pretrained(model_id)
input_text = "What are we having for dinner?"
input_ids = tokenizer(input_text, return_tensors="pt").to("cuda")
output = quantized_model.generate(**input_ids, max_new_tokens=10)
print(tokenizer.decode(output[0], skip_special_tokens=True))
```
To load in 4-bit simply pass `load_in_4bit=True`
### 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/Meta-Llama-3.1-70B-Instruct --include "original/*" --local-dir Meta-Llama-3.1-70B-Instruct
```
## Hardware and Software
**Training Factors** We used custom training libraries, Meta's custom built GPU cluster, and production infrastructure for pretraining. Fine-tuning, annotation, and evaluation were also performed on production infrastructure.
**Training utilized a cumulative of** 39.3M 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 **11,390** 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.
<table>
<tr>
<td>
</td>
<td><strong>Training Time (GPU hours)</strong>
</td>
<td><strong>Training Power Consumption (W)</strong>
</td>
<td><strong>Training Location-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
<td><strong>Training Market-Based Greenhouse Gas Emissions</strong>
<p>
<strong>(tons CO2eq)</strong>
</td>
</tr>
<tr>
<td>Llama 3.1 8B
</td>
<td>1.46M
</td>
<td>700
</td>
<td>420
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 70B
</td>
<td>7.0M
</td>
<td>700
</td>
<td>2,040
</td>
<td>0
</td>
</tr>
<tr>
<td>Llama 3.1 405B
</td>
<td>30.84M
</td>
<td>700
</td>
<td>8,930
</td>
<td>0
</td>
</tr>
<tr>
<td>Total
</td>
<td>39.3M
<td>
<ul>
</ul>
</td>
<td>11,390
</td>
<td>0
</td>
</tr>
</table>
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 3.1 was pretrained on ~15 trillion tokens of data from publicly available sources. The fine-tuning data includes publicly available instruction datasets, as well as over 25M synthetically generated examples.
**Data Freshness:** The pretraining data has a cutoff of December 2023.
## Benchmark scores
In this section, we report the results for Llama 3.1 models on standard automatic benchmarks. For all the evaluations, we use our internal evaluations library.
### Base pretrained models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3 70B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="7" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>66.7
</td>
<td>66.7
</td>
<td>79.5
</td>
<td>79.3
</td>
<td>85.2
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>macro_avg/acc_char
</td>
<td>36.2
</td>
<td>37.1
</td>
<td>55.0
</td>
<td>53.8
</td>
<td>61.6
</td>
</tr>
<tr>
<td>AGIEval English
</td>
<td>3-5
</td>
<td>average/acc_char
</td>
<td>47.1
</td>
<td>47.8
</td>
<td>63.0
</td>
<td>64.6
</td>
<td>71.6
</td>
</tr>
<tr>
<td>CommonSenseQA
</td>
<td>7
</td>
<td>acc_char
</td>
<td>72.6
</td>
<td>75.0
</td>
<td>83.8
</td>
<td>84.1
</td>
<td>85.8
</td>
</tr>
<tr>
<td>Winogrande
</td>
<td>5
</td>
<td>acc_char
</td>
<td>-
</td>
<td>60.5
</td>
<td>-
</td>
<td>83.3
</td>
<td>86.7
</td>
</tr>
<tr>
<td>BIG-Bench Hard (CoT)
</td>
<td>3
</td>
<td>average/em
</td>
<td>61.1
</td>
<td>64.2
</td>
<td>81.3
</td>
<td>81.6
</td>
<td>85.9
</td>
</tr>
<tr>
<td>ARC-Challenge
</td>
<td>25
</td>
<td>acc_char
</td>
<td>79.4
</td>
<td>79.7
</td>
<td>93.1
</td>
<td>92.9
</td>
<td>96.1
</td>
</tr>
<tr>
<td>Knowledge reasoning
</td>
<td>TriviaQA-Wiki
</td>
<td>5
</td>
<td>em
</td>
<td>78.5
</td>
<td>77.6
</td>
<td>89.7
</td>
<td>89.8
</td>
<td>91.8
</td>
</tr>
<tr>
<td rowspan="4" >Reading comprehension
</td>
<td>SQuAD
</td>
<td>1
</td>
<td>em
</td>
<td>76.4
</td>
<td>77.0
</td>
<td>85.6
</td>
<td>81.8
</td>
<td>89.3
</td>
</tr>
<tr>
<td>QuAC (F1)
</td>
<td>1
</td>
<td>f1
</td>
<td>44.4
</td>
<td>44.9
</td>
<td>51.1
</td>
<td>51.1
</td>
<td>53.6
</td>
</tr>
<tr>
<td>BoolQ
</td>
<td>0
</td>
<td>acc_char
</td>
<td>75.7
</td>
<td>75.0
</td>
<td>79.0
</td>
<td>79.4
</td>
<td>80.0
</td>
</tr>
<tr>
<td>DROP (F1)
</td>
<td>3
</td>
<td>f1
</td>
<td>58.4
</td>
<td>59.5
</td>
<td>79.7
</td>
<td>79.6
</td>
<td>84.8
</td>
</tr>
</table>
### Instruction tuned models
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong># Shots</strong>
</td>
<td><strong>Metric</strong>
</td>
<td><strong>Llama 3 8B Instruct</strong>
</td>
<td><strong>Llama 3.1 8B Instruct</strong>
</td>
<td><strong>Llama 3 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 70B Instruct</strong>
</td>
<td><strong>Llama 3.1 405B Instruct</strong>
</td>
</tr>
<tr>
<td rowspan="4" >General
</td>
<td>MMLU
</td>
<td>5
</td>
<td>macro_avg/acc
</td>
<td>68.5
</td>
<td>69.4
</td>
<td>82.0
</td>
<td>83.6
</td>
<td>87.3
</td>
</tr>
<tr>
<td>MMLU (CoT)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>65.3
</td>
<td>73.0
</td>
<td>80.9
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>MMLU-Pro (CoT)
</td>
<td>5
</td>
<td>micro_avg/acc_char
</td>
<td>45.5
</td>
<td>48.3
</td>
<td>63.4
</td>
<td>66.4
</td>
<td>73.3
</td>
</tr>
<tr>
<td>IFEval
</td>
<td>
</td>
<td>
</td>
<td>76.8
</td>
<td>80.4
</td>
<td>82.9
</td>
<td>87.5
</td>
<td>88.6
</td>
</tr>
<tr>
<td rowspan="2" >Reasoning
</td>
<td>ARC-C
</td>
<td>0
</td>
<td>acc
</td>
<td>82.4
</td>
<td>83.4
</td>
<td>94.4
</td>
<td>94.8
</td>
<td>96.9
</td>
</tr>
<tr>
<td>GPQA
</td>
<td>0
</td>
<td>em
</td>
<td>34.6
</td>
<td>30.4
</td>
<td>39.5
</td>
<td>46.7
</td>
<td>50.7
</td>
</tr>
<tr>
<td rowspan="4" >Code
</td>
<td>HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>60.4
</td>
<td>72.6
</td>
<td>81.7
</td>
<td>80.5
</td>
<td>89.0
</td>
</tr>
<tr>
<td>MBPP ++ base version
</td>
<td>0
</td>
<td>pass@1
</td>
<td>70.6
</td>
<td>72.8
</td>
<td>82.5
</td>
<td>86.0
</td>
<td>88.6
</td>
</tr>
<tr>
<td>Multipl-E HumanEval
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>50.8
</td>
<td>-
</td>
<td>65.5
</td>
<td>75.2
</td>
</tr>
<tr>
<td>Multipl-E MBPP
</td>
<td>0
</td>
<td>pass@1
</td>
<td>-
</td>
<td>52.4
</td>
<td>-
</td>
<td>62.0
</td>
<td>65.7
</td>
</tr>
<tr>
<td rowspan="2" >Math
</td>
<td>GSM-8K (CoT)
</td>
<td>8
</td>
<td>em_maj1@1
</td>
<td>80.6
</td>
<td>84.5
</td>
<td>93.0
</td>
<td>95.1
</td>
<td>96.8
</td>
</tr>
<tr>
<td>MATH (CoT)
</td>
<td>0
</td>
<td>final_em
</td>
<td>29.1
</td>
<td>51.9
</td>
<td>51.0
</td>
<td>68.0
</td>
<td>73.8
</td>
</tr>
<tr>
<td rowspan="4" >Tool Use
</td>
<td>API-Bank
</td>
<td>0
</td>
<td>acc
</td>
<td>48.3
</td>
<td>82.6
</td>
<td>85.1
</td>
<td>90.0
</td>
<td>92.0
</td>
</tr>
<tr>
<td>BFCL
</td>
<td>0
</td>
<td>acc
</td>
<td>60.3
</td>
<td>76.1
</td>
<td>83.0
</td>
<td>84.8
</td>
<td>88.5
</td>
</tr>
<tr>
<td>Gorilla Benchmark API Bench
</td>
<td>0
</td>
<td>acc
</td>
<td>1.7
</td>
<td>8.2
</td>
<td>14.7
</td>
<td>29.7
</td>
<td>35.3
</td>
</tr>
<tr>
<td>Nexus (0-shot)
</td>
<td>0
</td>
<td>macro_avg/acc
</td>
<td>18.1
</td>
<td>38.5
</td>
<td>47.8
</td>
<td>56.7
</td>
<td>58.7
</td>
</tr>
<tr>
<td>Multilingual
</td>
<td>Multilingual MGSM (CoT)
</td>
<td>0
</td>
<td>em
</td>
<td>-
</td>
<td>68.9
</td>
<td>-
</td>
<td>86.9
</td>
<td>91.6
</td>
</tr>
</table>
#### Multilingual benchmarks
<table>
<tr>
<td><strong>Category</strong>
</td>
<td><strong>Benchmark</strong>
</td>
<td><strong>Language</strong>
</td>
<td><strong>Llama 3.1 8B</strong>
</td>
<td><strong>Llama 3.1 70B</strong>
</td>
<td><strong>Llama 3.1 405B</strong>
</td>
</tr>
<tr>
<td rowspan="9" ><strong>General</strong>
</td>
<td rowspan="9" ><strong>MMLU (5-shot, macro_avg/acc)</strong>
</td>
<td>Portuguese
</td>
<td>62.12
</td>
<td>80.13
</td>
<td>84.95
</td>
</tr>
<tr>
<td>Spanish
</td>
<td>62.45
</td>
<td>80.05
</td>
<td>85.08
</td>
</tr>
<tr>
<td>Italian
</td>
<td>61.63
</td>
<td>80.4
</td>
<td>85.04
</td>
</tr>
<tr>
<td>German
</td>
<td>60.59
</td>
<td>79.27
</td>
<td>84.36
</td>
</tr>
<tr>
<td>French
</td>
<td>62.34
</td>
<td>79.82
</td>
<td>84.66
</td>
</tr>
<tr>
<td>Hindi
</td>
<td>50.88
</td>
<td>74.52
</td>
<td>80.31
</td>
</tr>
<tr>
<td>Thai
</td>
<td>50.32
</td>
<td>72.95
</td>
<td>78.21
</td>
</tr>
</table>
## Responsibility & Safety
As part of our Responsible release approach, we followed a three-pronged strategy to managing trust & safety risks:
* Enable developers to deploy helpful, safe and flexible experiences for their target audience and for the use cases supported by Llama.
* Protect developers against adversarial users aiming to exploit Llama capabilities to potentially cause harm.
* Provide protections for the community to help prevent the misuse of our models.
### Responsible deployment
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 the generic use cases addressing a standard set of harms. Developers are then in the driver seat to tailor safety for their use case, defining their own policy and deploying the models with the necessary safeguards in their Llama systems. Llama 3.1 was developed following the best practices outlined in our Responsible Use Guide, you can refer to the [Responsible Use Guide](https://llama.meta.com/responsible-use-guide/) to learn more.
#### Llama 3.1 instruct
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. For more details on the safety mitigations implemented please read the Llama 3 paper.
**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 3.1 systems
**Large language models, including Llama 3.1, 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 3, 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
Note that this release introduces new capabilities, including a longer context window, multilingual inputs and outputs and possible integrations by developers with third party tools. Building with these new capabilities requires specific considerations in addition to the best practices that generally apply across all Generative AI use cases.
**Tool-use**: Just like in standard software development, developers are responsible for the integration of the LLM with the tools and services of their choice. They should define a clear policy for their use case and assess the integrity of the third party services they use to be aware of the safety and security limitations when using this capability. Refer to the Responsible Use Guide for best practices on the safe deployment of the third party safeguards.
**Multilinguality**: Llama 3.1 supports 7 languages in addition to English: French, German, Hindi, Italian, Portuguese, Spanish, and Thai. Llama may be able to output text in other languages than those that meet performance thresholds for safety and helpfulness. We strongly discourage developers from using this model to converse in non-supported languages without implementing finetuning and system controls in alignment with their policies and the best practices shared in the Responsible Use Guide.
### Evaluations
We evaluated Llama models for common use cases as well as specific capabilities. Common use cases evaluations measure safety risks of systems for most commonly built applications including chat bot, coding assistant, tool calls. We built dedicated, adversarial evaluation datasets and evaluated systems composed of Llama models and Llama Guard 3 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. Prompt Guard and Code Shield are also available if relevant to the application.
Capability evaluations measure vulnerabilities of Llama models inherent to specific capabilities, for which were crafted dedicated benchmarks including long context, multilingual, tools calls, coding or memorization.
**Red teaming**
For both scenarios, 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 and other risks
We specifically focused our efforts on mitigating the following critical risk areas:
**1- CBRNE (Chemical, Biological, Radiological, Nuclear, and Explosive materials) helpfulness**
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.
**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 attack enablement**
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.
Our study of Llama-3.1-405B’s social engineering uplift for cyber attackers was conducted to assess the effectiveness of AI models in aiding cyber threat actors in spear phishing campaigns. Please read our Llama 3.1 Cyber security whitepaper to learn more.
### Community
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).
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).
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
The core values of Llama 3.1 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 3.1 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.
But Llama 3.1 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 3.1’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 3.1 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.
|
hyp1231/blair-games-roberta-large | hyp1231 | "2024-05-17T21:50:07Z" | 110 | 0 | transformers | [
"transformers",
"safetensors",
"roberta",
"feature-extraction",
"en",
"arxiv:2403.03952",
"base_model:FacebookAI/roberta-large",
"base_model:finetune:FacebookAI/roberta-large",
"license:mit",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-05-17T21:48:08Z" | ---
license: mit
language:
- en
base_model: FacebookAI/roberta-large
---
# BLaIR-Games-roberta-base
A checkpoint for ablation study in [BLaIR paper](https://arxiv.org/abs/2403.03952). Please refer to [🤗 blair-roberta-large](https://huggingface.co/hyp1231/blair-roberta-large) for more details about using this checkpoint. |
besimray/miner_id_3_e0133dc7-d8f1-4a3d-b487-a48a7ef3227b_1729802817 | besimray | "2024-10-24T20:52:03Z" | 78 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"llama-3",
"meta",
"facebook",
"unsloth",
"en",
"base_model:meta-llama/Llama-3.2-3B",
"base_model:finetune:meta-llama/Llama-3.2-3B",
"license:llama3.2",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-10-24T20:46:57Z" | ---
base_model: meta-llama/Llama-3.2-3B
language:
- en
library_name: transformers
license: llama3.2
tags:
- llama-3
- llama
- meta
- facebook
- unsloth
- transformers
---
# Finetune Llama 3.2, Gemma 2, Mistral 2-5x faster with 70% less memory via Unsloth!
We have a free Google Colab Tesla T4 notebook for Llama 3.2 (3B) here: https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
# Llama-3.2-3B
For more details on the model, please go to Meta's original [model card](https://huggingface.co/meta-llama/Llama-3.2-3B)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3.2 (3B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Llama-3.1 (11B vision)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Llama-3.1 (8B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Ys44kVvmeZtnICzWz0xgpRnrIOjZAuxp?usp=sharing) | 2.4x faster | 58% less |
| **Phi-3.5 (mini)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lN6hPQveB_mHSnTOYifygFcrO8C1bxq4?usp=sharing) | 2x faster | 50% less |
| **Gemma 2 (9B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1vIrqH5uYDQwsJ4-OO3DErvuv4pBgVwk4?usp=sharing) | 2.4x faster | 58% less |
| **Mistral (7B)** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
## Special Thanks
A huge thank you to the Meta and Llama team for creating and releasing these models.
## Model Information
The Meta 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.
**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 family of models** 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).
Where to send questions or comments about the model Instructions on how to provide feedback or comments on the model can be found in the model [README](https://github.com/meta-llama/llama3). For more technical information about generation parameters and recipes for how to use Llama 3.1 in applications, please go [here](https://github.com/meta-llama/llama-recipes).
|
xoyeop/ernie-3.0-xbase-zh-RETACRED-1114 | xoyeop | "2023-11-14T08:38:15Z" | 12 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"ernie",
"text-classification",
"generated_from_trainer",
"base_model:nghuyong/ernie-3.0-xbase-zh",
"base_model:finetune:nghuyong/ernie-3.0-xbase-zh",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2023-11-14T07:16:05Z" | ---
base_model: nghuyong/ernie-3.0-xbase-zh
tags:
- generated_from_trainer
model-index:
- name: ernie-3.0-xbase-zh-RETACRED-1114
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. -->
# ernie-3.0-xbase-zh-RETACRED-1114
This model is a fine-tuned version of [nghuyong/ernie-3.0-xbase-zh](https://huggingface.co/nghuyong/ernie-3.0-xbase-zh) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1560
## 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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 1.5417 | 1.0 | 1828 | 1.9809 |
| 1.5712 | 2.0 | 3656 | 1.9847 |
| 1.5431 | 3.0 | 5484 | 2.1279 |
| 1.5233 | 4.0 | 7312 | 2.1653 |
| 1.517 | 5.0 | 9140 | 2.1560 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
DaniilSirota/poca-SoccerTwos | DaniilSirota | "2023-03-05T14:38:25Z" | 5 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"unity-ml-agents",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | reinforcement-learning | "2023-03-05T14:37:41Z" |
---
tags:
- unity-ml-agents
- ml-agents
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
library_name: ml-agents
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos** using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://github.com/huggingface/ml-agents#get-started
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
### Resume the training
```
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser:**.
1. Go to https://huggingface.co/spaces/unity/ML-Agents-SoccerTwos
2. Step 1: Write your model_id: DaniilSirota/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
mohamedemam/Question_generator | mohamedemam | "2024-07-05T00:11:32Z" | 53 | 1 | transformers | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"generate answers",
"question generator",
"generate text",
"nlp",
"dataset maker",
"flan t5",
"extract quetions from context",
"extract quetion",
"en",
"dataset:squad_v2",
"dataset:quac",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2023-08-24T21:02:49Z" | ---
license: mit
datasets:
- squad_v2
- quac
language:
- en
widget:
- text: >-
when: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
Argentine professional footballer who plays as a forward for and captains
both Major League Soccer club Inter Miami and the Argentina national team.
Widely regarded as one of the greatest players of all time, Messi has won a
record seven Ballon d'Or awards[note 2] and a record six European Golden
Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving
the club in 2021, he had spent his entire professional career with
Barcelona, where he won a club-record 34
- text: >-
where: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
Argentine professional footballer who plays as a forward for and captains
both Major League Soccer club Inter Miami and the Argentina national team.
Widely regarded as one of the greatest players of all time, Messi has won a
record seven Ballon d'Or awards[note 2] and a record six European Golden
Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving
the club in 2021, he had spent his entire professional career with
Barcelona, where he won a club-record 34
- text: >-
how: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
Argentine professional footballer who plays as a forward for and captains
both Major League Soccer club Inter Miami and the Argentina national team.
Widely regarded as one of the greatest players of all time, Messi has won a
record seven Ballon d'Or awards[note 2] and a record six European Golden
Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving
the club in 2021, he had spent his entire professional career with
Barcelona, where he won a club-record 34
- text: >-
what: Lionel Andrés Messi[note 1] (Spanish pronunciation: [ljoˈnel anˈdɾes
ˈmesi] (listen); born 24 June 1987), also known as Leo Messi, is an
Argentine professional footballer who plays as a forward for and captains
both Major League Soccer club Inter Miami and the Argentina national team.
Widely regarded as one of the greatest players of all time, Messi has won a
record seven Ballon d'Or awards[note 2] and a record six European Golden
Shoes, and in 2020 he was named to the Ballon d'Or Dream Team. Until leaving
the club in 2021, he had spent his entire professional career with
Barcelona, where he won a club-record 34
- text: >-
where: Egypt (Egyptian Arabic: مصر Maṣr Egyptian Arabic pronunciation:
[mɑsˤr]), officially the Arab Republic of Egypt, is a transcontinental
country spanning the northeast corner of Africa and the Sinai Peninsula in
the southwest corner of Asia. It is bordered by the Mediterranean Sea to the
north, the Gaza Strip of Palestine and Israel to the northeast, the Red Sea
to the east, Sudan to the south, and Libya to the west. The Gulf of Aqaba in
the northeast separates Egypt from Jordan and Saudi Arabia. Cairo is the
capital and largest city of Egypt, while Alexandria, the second-largest
city, is an important industrial and tourist hub at the Mediterranean
coast.[11] At approximately 100 million inhabitants, Egypt is the 14th-most
populated country in the world, and the third-most populated in Africa,
behind Nigeria and Ethiopia.
- text: >-
where: There is evidence of rock carvings along the Nile terraces and in
desert oases. In the 10th millennium BCE, a culture of hunter-gatherers and
fishers was replaced by a grain-grinding culture. Climate changes or
overgrazing around 8000 BCE began to desiccate the pastoral lands of Egypt,
forming the Sahara. Early tribal peoples migrated to the Nile River where
they developed a settled agricultural economy and more centralized society.
- text: >-
when: By about 6000 BCE, a Neolithic culture took root in the Nile
Valley.[31] During the Neolithic era, several predynastic cultures developed
independently in Upper and Lower Egypt. The Badarian culture and the
successor Naqada series are generally regarded as precursors to dynastic
Egypt. The earliest known Lower Egyptian site, Merimda, predates the
Badarian by about seven hundred years. Contemporaneous Lower Egyptian
communities coexisted with their southern counterparts for more than two
thousand years. The earliest known evidence of Egyptian hieroglyphic
inscriptions appeared during the predynastic period on Naqada III pottery
vessels, dated to about 3200 BCE.[32]
- text: >-
whose : or the next three millennia. Egyptian culture flourished during this
long period and remained distinctively Egyptian in its religion, arts,
language and customs. The first two ruling dynasties of a unified Egypt set
the stage for the Old Kingdom period, c. 2700–2200 BCE, which constructed
many pyramids, most notably the Third Dynasty pyramid of Djoser and the
Fourth Dynasty Giza pyramids.
- text: >-
who:The First Intermediate Period ushered in a time of political upheaval
for about 150 years.[33] Stronger Nile floods and stabilisation of
government, however, brought back renewed prosperity for the country in the
Middle Kingdom c. 2040 BCE, reaching a peak during the reign of Pharaoh
Amenemhat III. A second period of disunity heralded the arrival of the first
foreign ruling dynasty in Egypt, that of the Semitic Hyksos. The Hyksos
invaders took over much of Lower Egypt around 1650 BCE and founded a new
capital at Avaris. They were driven out by an Upper Egyptian force led by
Ahmose I, who founded the Eighteenth Dynasty and relocated the capital from
Memphis to Thebes.
library_name: transformers
tags:
- generate answers
- question generator
- generate text
- nlp
- dataset maker
- flan t5
- t5
- extract quetions from context
- extract quetion
---
# Model Card for QA_GeneraToR
Excited 😄 to share with you my very first model 🤖 for generating question-answering datasets! This incredible model takes articles 📜 or web pages, and all you need to provide is a prompt and context. It works like magic ✨, generating both the question and the answer. The prompt can be anything – "what," "who," "where" ... etc ! 😅
I've harnessed the power of the flan-t5 model 🚀, which has truly elevated the quality of the results. You can find all the code and details in the repository right here: https://lnkd.in/dhE5s_qg
And guess what? I've even deployed the project, so you can experience the magic firsthand: https://lnkd.in/diq-d3bt ❤️
Join me on this exciting journey into #nlp, #textgeneration, #t5, #deeplearning, and #huggingface. Your feedback and collaboration are more than welcome! 🌟
# my fine tuned model
>This model is fine tuned to generate a question with answers from a context , why that can be very usful this can help you to generate a dataset from a book article any thing you would to make from it dataset and train another model on this dataset , give the model any context with pre prometed of quation you want + context and it will extarct question + answer for you
this are promted i use
>[ "which", "how", "when", "where", "who", "whom", "whose", "why",
"which", "who", "whom", "whose", "whereas",
"can", "could", "may", "might", "will", "would", "shall", "should",
"do", "does", "did", "is", "are", "am", "was", "were", "be", "being", "been",
"have", "has", "had", "if", "is", "are", "am", "was", "were", "do", "does", "did", "can", "could",
"will", "would", "shall", "should", "might", "may", "must",
"may", "might", "must"]
>
# orignal model info
<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/model_doc/flan2_architecture.jpg"
alt="drawing" width="600"/>
# Code
```python
from transformers import AutoTokenizer, AutoModelForQuestionAnswering
model_name="mohamedemam/Question_generator"
def generate_question_answer(context, prompt, model_name="mohamedemam/Question_generator"):
"""
Generates a question-answer pair using the provided context, prompt, and model.
Args:
context: String containing the text or URL of the source material.
prompt: String starting with a question word (e.g., "what," "who").
model_name: Optional string specifying the model name (default: google/flan-t5-base).
Returns:
A tuple containing the generated question and answer strings.
"""
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForQuestionAnswering.from_pretrained(model_name)
inputs = tokenizer(context, return_tensors="pt")
with torch.no_grad():
outputs = model(**inputs)
start_scores, end_scores = outputs.start_logits, outputs.end_logits
answer_start = torch.argmax(start_scores)
answer_end = torch.argmax(end_scores) + 1 # Account for inclusive end index
answer = tokenizer.convert_tokens_to_strings(tokenizer.convert_ids_to_tokens(inputs["input_ids"][answer_start:answer_end]))[0]
question = f"{prompt} {answer}" # Formulate the question using answer
return question, answer
# Example usage
context = "The capital of France is Paris."
prompt = "What"
question, answer = generate_question_answer(context, prompt)
print(f"Question: {question}")
print(f"Answer: {answer}")
```
|
IAmSkyDra/BaViT5_v01 | IAmSkyDra | "2025-01-22T22:56:13Z" | 21 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:VietAI/vit5-base",
"base_model:finetune:VietAI/vit5-base",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-01-22T08:08:20Z" | ---
base_model: VietAI/vit5-base
library_name: transformers
license: mit
metrics:
- sacrebleu
tags:
- generated_from_trainer
model-index:
- name: BaViT5_v01
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. -->
# BaViT5_v01
This model is a fine-tuned version of [VietAI/vit5-base](https://huggingface.co/VietAI/vit5-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4623
- Sacrebleu: 14.3803
## 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Sacrebleu |
|:-------------:|:-----:|:-----:|:---------------:|:---------:|
| 0.6515 | 1.0 | 2966 | 0.5899 | 7.7723 |
| 0.576 | 2.0 | 5932 | 0.5257 | 10.4904 |
| 0.4939 | 3.0 | 8898 | 0.4969 | 11.8064 |
| 0.4842 | 4.0 | 11864 | 0.4793 | 12.5193 |
| 0.4459 | 5.0 | 14830 | 0.4704 | 12.9876 |
| 0.4222 | 6.0 | 17796 | 0.4632 | 13.2632 |
| 0.4005 | 7.0 | 20762 | 0.4612 | 13.5868 |
| 0.3869 | 8.0 | 23728 | 0.4580 | 13.8162 |
| 0.381 | 9.0 | 26694 | 0.4556 | 13.9756 |
| 0.3594 | 10.0 | 29660 | 0.4561 | 14.0827 |
| 0.363 | 11.0 | 32626 | 0.4578 | 14.1701 |
| 0.3427 | 12.0 | 35592 | 0.4591 | 14.2903 |
| 0.3425 | 13.0 | 38558 | 0.4603 | 14.3091 |
| 0.3377 | 14.0 | 41524 | 0.4611 | 14.3649 |
| 0.314 | 15.0 | 44490 | 0.4623 | 14.3803 |
### Framework versions
- Transformers 4.48.1
- Pytorch 2.5.1+cu124
- Datasets 3.2.0
- Tokenizers 0.21.0
|
chauhoang/dfadc6f3-a615-71c8-d895-120c0e41ac41 | chauhoang | "2025-01-10T16:50:53Z" | 10 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:oopsung/llama2-7b-n-ox-test-v1",
"base_model:adapter:oopsung/llama2-7b-n-ox-test-v1",
"region:us"
] | null | "2025-01-10T10:18:00Z" | ---
library_name: peft
base_model: oopsung/llama2-7b-n-ox-test-v1
tags:
- axolotl
- generated_from_trainer
model-index:
- name: dfadc6f3-a615-71c8-d895-120c0e41ac41
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: oopsung/llama2-7b-n-ox-test-v1
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f55f2a0cb76e0919_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f55f2a0cb76e0919_train_data.json
type:
field_input: negation_type
field_instruction: pattern
field_output: sentence
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: 5
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: chauhoang/dfadc6f3-a615-71c8-d895-120c0e41ac41
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: 5
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 1
micro_batch_size: 2
mlflow_experiment_name: /tmp/f55f2a0cb76e0919_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 49687f19-56eb-476c-b93a-03e2e79d17bb
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 49687f19-56eb-476c-b93a-03e2e79d17bb
warmup_steps: 1
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# dfadc6f3-a615-71c8-d895-120c0e41ac41
This model is a fine-tuned version of [oopsung/llama2-7b-n-ox-test-v1](https://huggingface.co/oopsung/llama2-7b-n-ox-test-v1) 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: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 2
- training_steps: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0000 | 1 | 5.3304 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
mradermacher/COFFEEPOTS-editor-GGUF | mradermacher | "2025-01-04T02:47:47Z" | 88 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Coffee-Gym/COFFEEPOTS-editor",
"base_model:quantized:Coffee-Gym/COFFEEPOTS-editor",
"endpoints_compatible",
"region:us"
] | null | "2025-01-04T02:35:40Z" | ---
base_model: Coffee-Gym/COFFEEPOTS-editor
language:
- en
library_name: transformers
quantized_by: mradermacher
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
static quants of https://huggingface.co/Coffee-Gym/COFFEEPOTS-editor
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q3_K_S.gguf) | Q3_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/COFFEEPOTS-editor-GGUF/resolve/main/COFFEEPOTS-editor.f16.gguf) | f16 | 13.6 | 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. 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 -->
|
palmisan/q-FrozenLake-v1-4x4-noSlippery | palmisan | "2025-03-21T13:11:40Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4-no_slippery",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2025-03-21T13:11:36Z" | ---
tags:
- FrozenLake-v1-4x4-no_slippery
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-noSlippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4-no_slippery
type: FrozenLake-v1-4x4-no_slippery
metrics:
- type: mean_reward
value: 1.00 +/- 0.00
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **FrozenLake-v1**
This is a trained model of a **Q-Learning** agent playing **FrozenLake-v1** .
## Usage
```python
model = load_from_hub(repo_id="palmisan/q-FrozenLake-v1-4x4-noSlippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
phungkhaccuong/b37557c2-a045-8155-83be-cab114d644fa | phungkhaccuong | "2025-01-13T17:47:04Z" | 12 | 0 | peft | [
"peft",
"safetensors",
"falcon",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:tiiuae/falcon-7b",
"base_model:adapter:tiiuae/falcon-7b",
"license:apache-2.0",
"region:us"
] | null | "2025-01-13T17:02:05Z" | ---
library_name: peft
license: apache-2.0
base_model: tiiuae/falcon-7b
tags:
- axolotl
- generated_from_trainer
model-index:
- name: b37557c2-a045-8155-83be-cab114d644fa
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: tiiuae/falcon-7b
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- b115b4998a0ec6c0_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/b115b4998a0ec6c0_train_data.json
type:
field_instruction: inputs
field_output: targets
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: 5
flash_attention: false
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: false
group_by_length: false
hub_model_id: phungkhaccuong/b37557c2-a045-8155-83be-cab114d644fa
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: 5
lora_alpha: 16
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 8
lora_target_linear: true
lr_scheduler: cosine
max_steps: 50
micro_batch_size: 2
mlflow_experiment_name: /tmp/b115b4998a0ec6c0_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
saves_per_epoch: 4
sequence_len: 512
special_tokens:
pad_token: <|endoftext|>
strict: false
tf32: false
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 375a0c96-71b2-49ec-9c40-cd98cfe5e1a3
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 375a0c96-71b2-49ec-9c40-cd98cfe5e1a3
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# b37557c2-a045-8155-83be-cab114d644fa
This model is a fine-tuned version of [tiiuae/falcon-7b](https://huggingface.co/tiiuae/falcon-7b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0413
## 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: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 50
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| No log | 0.0001 | 1 | 2.5323 |
| 8.8932 | 0.0009 | 10 | 2.4373 |
| 9.844 | 0.0019 | 20 | 2.1610 |
| 7.4558 | 0.0028 | 30 | 2.0751 |
| 6.3244 | 0.0038 | 40 | 2.0468 |
| 7.9002 | 0.0047 | 50 | 2.0413 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
murdockthedude/distilbert-base-uncased-finetuned-ner | murdockthedude | "2022-06-05T00:57:57Z" | 5 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"token-classification",
"generated_from_trainer",
"dataset:conll2003",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-06-05T00:44:32Z" | ---
license: apache-2.0
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
args: conll2003
metrics:
- name: Precision
type: precision
value: 0.8664567296477723
- name: Recall
type: recall
value: 0.8816757654877759
- name: F1
type: f1
value: 0.8740000000000001
- name: Accuracy
type: accuracy
value: 0.9716525101857606
---
<!-- 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: 0.1078
- Precision: 0.8665
- Recall: 0.8817
- F1: 0.8740
- Accuracy: 0.9717
## 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: 64
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 220 | 0.0993 | 0.8511 | 0.8780 | 0.8643 | 0.9721 |
| No log | 2.0 | 440 | 0.0732 | 0.8913 | 0.9122 | 0.9016 | 0.9783 |
| 0.1878 | 3.0 | 660 | 0.0681 | 0.8984 | 0.9186 | 0.9083 | 0.9797 |
### Framework versions
- Transformers 4.19.2
- Pytorch 1.11.0+cu113
- Datasets 2.2.2
- Tokenizers 0.12.1
|
amirm/Meta-Llama-3-8B-Q2_K-GGUF | amirm | "2024-07-11T12:33:10Z" | 5 | 0 | null | [
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:quantized:meta-llama/Meta-Llama-3-8B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-07-11T12:32:52Z" | ---
base_model: meta-llama/Meta-Llama-3-8B
language:
- en
license: llama3
pipeline_tag: text-generation
tags:
- facebook
- meta
- pytorch
- llama
- llama-3
- llama-cpp
- gguf-my-repo
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extra_gated_fields:
First Name: text
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? By clicking Submit below I accept the terms of the license and acknowledge that
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---
# amirm/Meta-Llama-3-8B-Q2_K-GGUF
This model was converted to GGUF format from [`meta-llama/Meta-Llama-3-8B`](https://huggingface.co/meta-llama/Meta-Llama-3-8B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/meta-llama/Meta-Llama-3-8B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo amirm/Meta-Llama-3-8B-Q2_K-GGUF --hf-file meta-llama-3-8b-q2_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo amirm/Meta-Llama-3-8B-Q2_K-GGUF --hf-file meta-llama-3-8b-q2_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 amirm/Meta-Llama-3-8B-Q2_K-GGUF --hf-file meta-llama-3-8b-q2_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo amirm/Meta-Llama-3-8B-Q2_K-GGUF --hf-file meta-llama-3-8b-q2_k.gguf -c 2048
```
|
Sheeda/mistral-journal-finetune | Sheeda | "2024-04-05T01:30:03Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2024-04-05T01:28:51Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
|
LucasDay17/bert-base-cased-wikitext2 | LucasDay17 | "2024-05-23T15:23:13Z" | 126 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"bert",
"fill-mask",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2024-05-23T14:51:03Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
model-index:
- name: bert-base-cased-wikitext2
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. -->
# bert-base-cased-wikitext2
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 6.8567
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 7.0923 | 1.0 | 2346 | 7.0511 |
| 6.9047 | 2.0 | 4692 | 6.8751 |
| 6.8831 | 3.0 | 7038 | 6.8942 |
### Framework versions
- Transformers 4.26.0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.13.3
|
Xenova/e5-large-v2 | Xenova | "2024-10-08T13:39:23Z" | 524 | 5 | transformers.js | [
"transformers.js",
"onnx",
"bert",
"feature-extraction",
"base_model:intfloat/e5-large-v2",
"base_model:quantized:intfloat/e5-large-v2",
"region:us"
] | feature-extraction | "2023-06-26T13:46:09Z" | ---
base_model: intfloat/e5-large-v2
library_name: transformers.js
---
https://huggingface.co/intfloat/e5-large-v2 with ONNX weights to be compatible with Transformers.js.
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`). |
StKirill/ppo-Huggy | StKirill | "2023-07-26T19:22:14Z" | 1 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2023-07-26T19:22:10Z" | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: StKirill/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
|
tiya1012/distilka_applied | tiya1012 | "2022-12-02T17:16:11Z" | 52 | 0 | transformers | [
"transformers",
"pytorch",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2022-11-16T10:03:34Z" | # code-mixed Kannada-English word-level identification trained on Kanglish dataset from ICON2022
#### How to use
Use the below script from your python terminal
```python
import transformers
from transformers import AutoModelForSequenceClassification
model= AutoModelForSequenceClassification.from_pretrained("tiya1012/distilka_applied")
```
## Training data
I used code-mixed dataset from https://sites.google.com/view/kanglishicon2022/home
|
BRKN-WNGS/dsd2 | BRKN-WNGS | "2025-03-29T06:26:49Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-29T06:26:49Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: dsd2
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
---
# dsd2
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `dsd2` 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.
|
mespinosami/sen12mscr-sd-1_5 | mespinosami | "2023-09-07T05:28:53Z" | 4 | 0 | diffusers | [
"diffusers",
"safetensors",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"controlnet",
"base_model:runwayml/stable-diffusion-v1-5",
"base_model:adapter:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | "2023-09-06T15:43:17Z" |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- controlnet
inference: true
---
# controlnet-mespinosami/sen12mscr-sd-1_5
These are controlnet weights trained on runwayml/stable-diffusion-v1-5 with new type of conditioning.
You can find some example images below.
prompt: cloudless satellite image, remove all clouds from satellite image, no clouds
prompt: cloudless satellite image, remove all clouds from satellite image, no clouds
|
jamesgoncha/NEMESIS-V9 | jamesgoncha | "2025-03-30T10:57:29Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"4-bit",
"bitsandbytes",
"region:us"
] | text-generation | "2025-03-30T10:00:49Z" | ---
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] |
Kayabuki4/KAYAIsMadAtHIMMMMM | Kayabuki4 | "2025-02-21T23:50:38Z" | 0 | 0 | null | [
"any-to-any",
"omega",
"omegalabs",
"bittensor",
"agi",
"license:mit",
"region:us"
] | any-to-any | "2025-02-21T23:43:20Z" | ---
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).
|
BishalRD/Simple-Chatbot | BishalRD | "2025-04-10T09:41:33Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-04-10T06:24:17Z" | ---
license: apache-2.0
---
|
Helsinki-NLP/opus-mt-tpi-sv | Helsinki-NLP | "2023-08-16T12:07:19Z" | 126 | 0 | transformers | [
"transformers",
"pytorch",
"tf",
"marian",
"text2text-generation",
"translation",
"tpi",
"sv",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2022-03-02T23:29:04Z" | ---
tags:
- translation
license: apache-2.0
---
### opus-mt-tpi-sv
* source languages: tpi
* target languages: sv
* OPUS readme: [tpi-sv](https://github.com/Helsinki-NLP/OPUS-MT-train/blob/master/models/tpi-sv/README.md)
* dataset: opus
* model: transformer-align
* pre-processing: normalization + SentencePiece
* download original weights: [opus-2020-01-16.zip](https://object.pouta.csc.fi/OPUS-MT-models/tpi-sv/opus-2020-01-16.zip)
* test set translations: [opus-2020-01-16.test.txt](https://object.pouta.csc.fi/OPUS-MT-models/tpi-sv/opus-2020-01-16.test.txt)
* test set scores: [opus-2020-01-16.eval.txt](https://object.pouta.csc.fi/OPUS-MT-models/tpi-sv/opus-2020-01-16.eval.txt)
## Benchmarks
| testset | BLEU | chr-F |
|-----------------------|-------|-------|
| JW300.tpi.sv | 21.6 | 0.396 |
|
TheBloke/Griffin-3B-GPTQ | TheBloke | "2023-09-27T13:01:48Z" | 31 | 2 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"en",
"dataset:LDJnr/Puffin",
"base_model:acrastt/Griffin-3B",
"base_model:quantized:acrastt/Griffin-3B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"gptq",
"region:us"
] | text-generation | "2023-08-22T13:04:58Z" | ---
language:
- en
license: apache-2.0
library_name: transformers
datasets:
- LDJnr/Puffin
model_name: Griffin 3B
inference: false
model_creator: Bohan Du
model_link: https://huggingface.co/acrastt/Griffin-3B
model_type: llama
pipeline_tag: text-generation
quantized_by: TheBloke
base_model: acrastt/Griffin-3B
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
<div style="display: flex; flex-direction: column; align-items: flex-start;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Griffin 3B - GPTQ
- Model creator: [Bohan Du](https://huggingface.co/acrastt)
- Original model: [Griffin 3B](https://huggingface.co/acrastt/Griffin-3B)
## Description
This repo contains GPTQ model files for [Bohan Du's Griffin 3B](https://huggingface.co/acrastt/Griffin-3B).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
## Repositories available
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Griffin-3B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGML models for CPU+GPU inference](https://huggingface.co/TheBloke/Griffin-3B-GGML)
* [Bohan Du's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/acrastt/Griffin-3B)
## Prompt template: Human-Response
```
### HUMAN:
{prompt}
### RESPONSE:
```
## Provided files and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
All GPTQ files are made with AutoGPTQ.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have issues with models that use Act Order plus Group Size.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The dataset used for quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/Griffin-3B-GPTQ/tree/main) | 4 | 128 | No | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 2.09 GB | Yes | Most compatible option. Good inference speed in AutoGPTQ and GPTQ-for-LLaMa. Lower inference quality than other options. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/Griffin-3B-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 2.28 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. Poor AutoGPTQ CUDA speed. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/Griffin-3B-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 2.15 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| [gptq-4bit-128g-actorder_True](https://huggingface.co/TheBloke/Griffin-3B-GPTQ/tree/gptq-4bit-128g-actorder_True) | 4 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 2.09 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. Poor AutoGPTQ CUDA speed. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/Griffin-3B-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 3.64 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements and to improve AutoGPTQ speed. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/Griffin-3B-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 2048 | 3.71 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. Poor AutoGPTQ CUDA speed. |
## How to download from branches
- In text-generation-webui, you can add `:branch` to the end of the download name, eg `TheBloke/Griffin-3B-GPTQ:gptq-4bit-32g-actorder_True`
- With Git, you can clone a branch with:
```
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/Griffin-3B-GPTQ
```
- In Python Transformers code, the branch is the `revision` parameter; see below.
## How to easily download and use this model in [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
Please make sure you're using the latest version of [text-generation-webui](https://github.com/oobabooga/text-generation-webui).
It is strongly recommended to use the text-generation-webui one-click-installers unless you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/Griffin-3B-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/Griffin-3B-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done"
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `Griffin-3B-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
* Note that you do not need to set GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation tab** and enter a prompt to get started!
## How to use this GPTQ model from Python code
First make sure you have [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) 0.3.1 or later installed:
```
pip3 install auto-gptq
```
If you have problems installing AutoGPTQ, please build from source instead:
```
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
pip3 install .
```
Then try the following example code:
```python
from transformers import AutoTokenizer, pipeline, logging
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig
model_name_or_path = "TheBloke/Griffin-3B-GPTQ"
use_triton = False
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
use_safetensors=True,
trust_remote_code=False,
device="cuda:0",
use_triton=use_triton,
quantize_config=None)
"""
# To download from a specific branch, use the revision parameter, as in this example:
# Note that `revision` requires AutoGPTQ 0.3.1 or later!
model = AutoGPTQForCausalLM.from_quantized(model_name_or_path,
revision="gptq-4bit-32g-actorder_True",
use_safetensors=True,
trust_remote_code=False,
device="cuda:0",
quantize_config=None)
"""
prompt = "Tell me about AI"
prompt_template=f'''### HUMAN:
{prompt}
### RESPONSE:
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
# Prevent printing spurious transformers error when using pipeline with AutoGPTQ
logging.set_verbosity(logging.CRITICAL)
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
temperature=0.7,
top_p=0.95,
repetition_penalty=1.15
)
print(pipe(prompt_template)[0]['generated_text'])
```
## Compatibility
The files provided will work with AutoGPTQ (CUDA and Triton modes), GPTQ-for-LLaMa (only CUDA has been tested), and Occ4m's GPTQ-for-LLaMa fork.
ExLlama works with Llama models in 4-bit. Please see the Provided Files table above for per-file compatibility.
<!-- footer start -->
<!-- 200823 -->
## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute.
Thanks to the [chirper.ai](https://chirper.ai) team!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Sam, theTransient, Jonathan Leane, Steven Wood, webtim, Johann-Peter Hartmann, Geoffrey Montalvo, Gabriel Tamborski, Willem Michiel, John Villwock, Derek Yates, Mesiah Bishop, Eugene Pentland, Pieter, Chadd, Stephen Murray, Daniel P. Andersen, terasurfer, Brandon Frisco, Thomas Belote, Sid, Nathan LeClaire, Magnesian, Alps Aficionado, Stanislav Ovsiannikov, Alex, Joseph William Delisle, Nikolai Manek, Michael Davis, Junyu Yang, K, J, Spencer Kim, Stefan Sabev, Olusegun Samson, transmissions 11, Michael Levine, Cory Kujawski, Rainer Wilmers, zynix, Kalila, Luke @flexchar, Ajan Kanaga, Mandus, vamX, Ai Maven, Mano Prime, Matthew Berman, subjectnull, Vitor Caleffi, Clay Pascal, biorpg, alfie_i, 阿明, Jeffrey Morgan, ya boyyy, Raymond Fosdick, knownsqashed, Olakabola, Leonard Tan, ReadyPlayerEmma, Enrico Ros, Dave, Talal Aujan, Illia Dulskyi, Sean Connelly, senxiiz, Artur Olbinski, Elle, Raven Klaugh, Fen Risland, Deep Realms, Imad Khwaja, Fred von Graf, Will Dee, usrbinkat, SuperWojo, Alexandros Triantafyllidis, Swaroop Kallakuri, Dan Guido, John Detwiler, Pedro Madruga, Iucharbius, Viktor Bowallius, Asp the Wyvern, Edmond Seymore, Trenton Dambrowitz, Space Cruiser, Spiking Neurons AB, Pyrater, LangChain4j, Tony Hughes, Kacper Wikieł, Rishabh Srivastava, David Ziegler, Luke Pendergrass, Andrey, Gabriel Puliatti, Lone Striker, Sebastain Graf, Pierre Kircher, Randy H, NimbleBox.ai, Vadim, danny, Deo Leter
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: Bohan Du's Griffin 3B
This is [OpenLLaMA 3B V2](https://huggingface.co/openlm-research/open_llama_3b_v2) finetuned on [Puffin](https://huggingface.co/datasets/LDJnr/Puffin) for 1 epochs.
Prompt template:
```
### HUMAN:
{prompt}
### RESPONSE:
<leave a newline for the model to answer>
```
q4_1 GGML quant [here](https://huggingface.co/NikolayKozloff/Griffin-3B-GGML).
Note: Don't expect this model to be good, I was just starting out to finetune. So don't roast me please!
|
Esche/sd-class-butterflies-32 | Esche | "2023-03-16T03:41:38Z" | 35 | 0 | diffusers | [
"diffusers",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | unconditional-image-generation | "2023-03-16T03:40:25Z" | ---
license: mit
tags:
- pytorch
- diffusers
- unconditional-image-generation
- diffusion-models-class
---
# Model Card for Unit 1 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
This model is a diffusion model for unconditional image generation of cute 🦋.
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('Esche/sd-class-butterflies-32')
image = pipeline().images[0]
image
```
|
s20488/Llama-3.2-1B-Instruct-finetune-qlora | s20488 | "2025-01-25T10:21:43Z" | 36 | 0 | diffusers | [
"diffusers",
"safetensors",
"gguf",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:unsloth/Llama-3.2-1B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-1B-Instruct",
"region:us"
] | text-to-image | "2025-01-25T09:36:07Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '-'
output:
url: images/chrome_H9QDjWmXuV.png
base_model: unsloth/Llama-3.2-1B-Instruct
instance_prompt: null
---
## Download model
Weights for this model are available in PyTorch,Safetensors format.
[Download](/s20488/Llama-3.2-1B-Instruct-finetune-qlora/tree/main) them in the Files & versions tab.
|
MarioBarbeque/CyberSolve-LinAlg-1.1 | MarioBarbeque | "2025-01-27T18:30:03Z" | 8 | 1 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"en",
"dataset:deepmind/math_dataset",
"arxiv:1910.09700",
"base_model:google/flan-t5-large",
"base_model:finetune:google/flan-t5-large",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-01-09T20:46:32Z" | ---
base_model:
- google/flan-t5-large
datasets:
- deepmind/math_dataset
language:
- en
library_name: transformers
metrics:
- exact_match
---
# Model Card for Model ID
Welcome to the ⚖️📊CyberSolve LinAlg 1.1🦾📏 model card!
We introduce **CyberSolve LinAlg 1.1**, a text-to-text large language model trained to solve linear equations. Specifically, *CyberSolve LingAlg 1.1* is a downstream version of the *FLAN-T5 large*
model, [Google/FLAN-T5-large](https://huggingface.co/google/flan-t5-large), fine-tuned on the one-dimensional linear algebra split of the Google DeepMind mathematics dataset.
**Note**: This is version **1.1**. The model card of the most updated version of CyberSolve LinAlg is available here: [CyberSolve LinAlg 1.2](https://huggingface.co/MarioBarbeque/CyberSolve-LinAlg-1.2)
See also the most recent model demoed in the [CyberSolve LinAlg 1.2 🤖 Space](https://huggingface.co/spaces/MarioBarbeque/CyberSolveLinAlg1.2).
## 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]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[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]
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- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
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TransferGraph/Raychanan_COVID_RandomOver-finetuned-lora-ag_news | TransferGraph | "2024-02-28T00:43:30Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"parquet",
"text-classification",
"dataset:ag_news",
"base_model:Raychanan/COVID_RandomOver",
"base_model:adapter:Raychanan/COVID_RandomOver",
"license:apache-2.0",
"model-index",
"region:us"
] | text-classification | "2024-02-28T00:43:28Z" | ---
license: apache-2.0
library_name: peft
tags:
- parquet
- text-classification
datasets:
- ag_news
metrics:
- accuracy
base_model: Raychanan/COVID_RandomOver
model-index:
- name: Raychanan_COVID_RandomOver-finetuned-lora-ag_news
results:
- task:
type: text-classification
name: Text Classification
dataset:
name: ag_news
type: ag_news
config: default
split: test
args: default
metrics:
- type: accuracy
value: 0.8198684210526316
name: accuracy
---
<!-- 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. -->
# Raychanan_COVID_RandomOver-finetuned-lora-ag_news
This model is a fine-tuned version of [Raychanan/COVID_RandomOver](https://huggingface.co/Raychanan/COVID_RandomOver) on the ag_news dataset.
It achieves the following results on the evaluation set:
- accuracy: 0.8199
## 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: 0.0004
- train_batch_size: 24
- eval_batch_size: 24
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| accuracy | train_loss | epoch |
|:--------:|:----------:|:-----:|
| 0.25 | None | 0 |
| 0.7937 | 0.8960 | 0 |
| 0.8092 | 0.6050 | 1 |
| 0.8126 | 0.5606 | 2 |
| 0.8199 | 0.5283 | 3 |
### Framework versions
- PEFT 0.8.2
- Transformers 4.37.2
- Pytorch 2.2.0
- Datasets 2.16.1
- Tokenizers 0.15.2 |
mradermacher/OLMoE-1B-5B-GGUF | mradermacher | "2025-02-23T15:18:03Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:reflex-ai/OLMoE-1B-5B",
"base_model:quantized:reflex-ai/OLMoE-1B-5B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-23T12:04:04Z" | ---
base_model: reflex-ai/OLMoE-1B-5B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags: []
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/reflex-ai/OLMoE-1B-5B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/OLMoE-1B-5B-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/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q2_K.gguf) | Q2_K | 1.8 | |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q3_K_S.gguf) | Q3_K_S | 2.1 | |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q3_K_M.gguf) | Q3_K_M | 2.3 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q3_K_L.gguf) | Q3_K_L | 2.5 | |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.IQ4_XS.gguf) | IQ4_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q4_K_S.gguf) | Q4_K_S | 2.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q4_K_M.gguf) | Q4_K_M | 2.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q5_K_S.gguf) | Q5_K_S | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q5_K_M.gguf) | Q5_K_M | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q6_K.gguf) | Q6_K | 3.8 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.Q8_0.gguf) | Q8_0 | 4.9 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/OLMoE-1B-5B-GGUF/resolve/main/OLMoE-1B-5B.f16.gguf) | f16 | 9.1 | 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 -->
|
Wusul/shoebill | Wusul | "2023-01-28T12:11:35Z" | 4 | 0 | diffusers | [
"diffusers",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-01-08T02:08:30Z" | ---
license: creativeml-openrail-m
tags:
- text-to-image
- stable-diffusion
---
### shoebill Dreambooth model trained by Wusul with [TheLastBen's fast-DreamBooth](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast-DreamBooth.ipynb) notebook
Test the concept via A1111 Colab [fast-Colab-A1111](https://colab.research.google.com/github/TheLastBen/fast-stable-diffusion/blob/main/fast_stable_diffusion_AUTOMATIC1111.ipynb)
Sample pictures of this concept:
|
DunnBC22/wav2vec2-base-Speech_Emotion_Recognition | DunnBC22 | "2023-07-27T19:27:56Z" | 209 | 13 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"en",
"dataset:audiofolder",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | audio-classification | "2023-04-17T21:00:15Z" | ---
license: apache-2.0
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
- f1
- recall
- precision
model-index:
- name: wav2vec2-base-Speech_Emotion_Recognition
results: []
language:
- en
pipeline_tag: audio-classification
---
# wav2vec2-base-Speech_Emotion_Recognition
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base).
It achieves the following results on the evaluation set:
- Loss: 0.7264
- Accuracy: 0.7539
- F1
- Weighted: 0.7514
- Micro: 0.7539
- Macro: 0.7529
- Recall
- Weighted: 0.7539
- Micro: 0.7539
- Macro: 0.7577
- Precision
- Weighted: 0.7565
- Micro: 0.7539
- Macro: 0.7558
## Model description
This model predicts the emotion of the person speaking in the audio sample.
For more information on how it was created, check out the following link: https://github.com/DunnBC22/Vision_Audio_and_Multimodal_Projects/tree/main/Audio-Projects/Emotion%20Detection/Speech%20Emotion%20Detection
## Intended uses & limitations
This model is intended to demonstrate my ability to solve a complex problem using technology.
## Training and evaluation data
Dataset Source: https://www.kaggle.com/datasets/dmitrybabko/speech-emotion-recognition-en
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted F1 | Micro F1 | Macro F1 | Weighted Recall | Micro Recall | Macro Recall | Weighted Precision | Micro Precision | Macro Precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.5581 | 0.98 | 43 | 1.4046 | 0.4653 | 0.4080 | 0.4653 | 0.4174 | 0.4653 | 0.4653 | 0.4793 | 0.5008 | 0.4653 | 0.4974 |
| 1.5581 | 1.98 | 86 | 1.1566 | 0.5997 | 0.5836 | 0.5997 | 0.5871 | 0.5997 | 0.5997 | 0.6093 | 0.6248 | 0.5997 | 0.6209 |
| 1.5581 | 2.98 | 129 | 0.9733 | 0.6883 | 0.6845 | 0.6883 | 0.6860 | 0.6883 | 0.6883 | 0.6923 | 0.7012 | 0.6883 | 0.7009 |
| 1.5581 | 3.98 | 172 | 0.8313 | 0.7399 | 0.7392 | 0.7399 | 0.7409 | 0.7399 | 0.7399 | 0.7417 | 0.7415 | 0.7399 | 0.7432 |
| 1.5581 | 4.98 | 215 | 0.8708 | 0.7028 | 0.6963 | 0.7028 | 0.6970 | 0.7028 | 0.7028 | 0.7081 | 0.7148 | 0.7028 | 0.7114 |
| 1.5581 | 5.98 | 258 | 0.7969 | 0.7297 | 0.7267 | 0.7297 | 0.7277 | 0.7297 | 0.7297 | 0.7333 | 0.7393 | 0.7297 | 0.7382 |
| 1.5581 | 6.98 | 301 | 0.7349 | 0.7603 | 0.7613 | 0.7603 | 0.7631 | 0.7603 | 0.7603 | 0.7635 | 0.7699 | 0.7603 | 0.7702 |
| 1.5581 | 7.98 | 344 | 0.7714 | 0.7469 | 0.7444 | 0.7469 | 0.7456 | 0.7469 | 0.7469 | 0.7485 | 0.7554 | 0.7469 | 0.7563 |
| 1.5581 | 8.98 | 387 | 0.7183 | 0.7630 | 0.7615 | 0.7630 | 0.7631 | 0.7630 | 0.7630 | 0.7652 | 0.7626 | 0.7630 | 0.7637 |
| 1.5581 | 9.98 | 430 | 0.7264 | 0.7539 | 0.7514 | 0.7539 | 0.7529 | 0.7539 | 0.7539 | 0.7577 | 0.7565 | 0.7539 | 0.7558 |
### Framework versions
- Transformers 4.26.1
- Pytorch 2.0.0+cu118
- Datasets 2.11.0
- Tokenizers 0.13.3 |
RichardErkhov/anthonymeo_-_llama3.1-factory-4bits | RichardErkhov | "2025-03-29T20:28:25Z" | 0 | 0 | null | [
"safetensors",
"llama",
"arxiv:1910.09700",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-03-29T20:22:53Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:[email protected]">an email</a></p>
</div>
</main>
</body>
</html> |
abubakar-muhammad/SmolLm-135M-counterspeech-lora-model | abubakar-muhammad | "2025-02-21T18:52:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-02-21T18:52:52Z" | ---
base_model: unsloth/SmolLm-135M
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** abubakar-muhammad
- **License:** apache-2.0
- **Finetuned from model :** unsloth/SmolLm-135M
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)
|
mgundik/t5-small-finetuned-booksum | mgundik | "2024-10-03T08:45:10Z" | 6 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"dataset:kmfoda/booksum",
"base_model:google-t5/t5-small",
"base_model:finetune:google-t5/t5-small",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-07-28T00:52:09Z" | ---
license: apache-2.0
base_model: t5-small
tags:
- generated_from_trainer
metrics:
- rouge
model-index:
- name: t5-small-finetuned-booksum
results: []
datasets:
- kmfoda/booksum
---
<!-- 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. -->
# t5-small-finetuned-booksum
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an [booksum](https://huggingface.co/datasets/kmfoda/booksum) dataset.
It achieves the following results on the evaluation set:
- Loss: 3.3115
- Rouge1: 20.5085
- Rouge2: 2.9908
- Rougel: 13.8508
- Rougelsum: 18.4822
- Gen Len: 228.1577
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:-------:|:---------:|:--------:|
| 3.7585 | 1.0 | 600 | 3.3969 | 17.6639 | 2.5697 | 12.3838 | 15.9802 | 307.0236 |
| 3.5518 | 2.0 | 1200 | 3.3526 | 20.0469 | 2.9053 | 13.6055 | 18.0492 | 248.2581 |
| 3.5108 | 3.0 | 1800 | 3.3318 | 20.0243 | 2.8879 | 13.5558 | 17.9889 | 245.3416 |
| 3.4798 | 4.0 | 2400 | 3.3202 | 20.1501 | 2.9346 | 13.6819 | 18.1977 | 232.3801 |
| 3.4542 | 5.0 | 3000 | 3.3134 | 20.6061 | 3.0311 | 13.9844 | 18.5832 | 217.8302 |
| 3.453 | 6.0 | 3600 | 3.3115 | 20.5085 | 2.9908 | 13.8508 | 18.4822 | 228.1577 |
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1 |
noneUsername/saiga_nemo_12b-W8A8-Dynamic-Per-Token-better | noneUsername | "2024-11-20T06:19:03Z" | 5 | 0 | null | [
"safetensors",
"mistral",
"base_model:IlyaGusev/saiga_nemo_12b",
"base_model:finetune:IlyaGusev/saiga_nemo_12b",
"8-bit",
"region:us"
] | null | "2024-11-20T06:07:47Z" | ---
base_model:
- IlyaGusev/saiga_nemo_12b
---
vllm (pretrained=/root/autodl-tmp/saiga_nemo_12b,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.808|± |0.0250|
| | |strict-match | 5|exact_match|↑ |0.760|± |0.0271|
vllm (pretrained=/root/autodl-tmp/output88,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.784|± |0.0261|
| | |strict-match | 5|exact_match|↑ |0.748|± |0.0275|
vllm (pretrained=/root/autodl-tmp/output8765,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.792|± |0.0257|
| | |strict-match | 5|exact_match|↑ |0.760|± |0.0271|
vllm (pretrained=/root/autodl-tmp/output875,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.792|± |0.0257|
| | |strict-match | 5|exact_match|↑ |0.768|± |0.0268|
vllm (pretrained=/root/autodl-tmp/output873,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.780|± |0.0263|
| | |strict-match | 5|exact_match|↑ |0.736|± |0.0279|
vllm (pretrained=/root/autodl-tmp/output868,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.772|± |0.0266|
| | |strict-match | 5|exact_match|↑ |0.728|± |0.0282|
vllm (pretrained=/root/autodl-tmp/saiga_nemo_12b,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.796|± |0.0180|
| | |strict-match | 5|exact_match|↑ |0.734|± |0.0198|
vllm (pretrained=/root/autodl-tmp/output88,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.776|± |0.0187|
| | |strict-match | 5|exact_match|↑ |0.722|± |0.0201|
vllm (pretrained=/root/autodl-tmp/output8765,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.790|± |0.0182|
| | |strict-match | 5|exact_match|↑ |0.732|± |0.0198|
vllm (pretrained=/root/autodl-tmp/output875,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.778|± |0.0186|
| | |strict-match | 5|exact_match|↑ |0.732|± |0.0198|
vllm (pretrained=/root/autodl-tmp/output873,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.774|± |0.0187|
| | |strict-match | 5|exact_match|↑ |0.722|± |0.0201|
vllm (pretrained=/root/autodl-tmp/output868,add_bos_token=true,tensor_parallel_size=2,max_model_len=2048,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.772|± |0.0188|
| | |strict-match | 5|exact_match|↑ |0.722|± |0.0201| |
ChaoChao2023/PPO-LunarLander-v22 | ChaoChao2023 | "2024-01-23T03:13:28Z" | 3 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-01-23T03:13:07Z" | ---
library_name: stable-baselines3
tags:
- LunarLander-v2
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: PPO
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: LunarLander-v2
type: LunarLander-v2
metrics:
- type: mean_reward
value: 214.49 +/- 81.86
name: mean_reward
verified: false
---
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
|
xuyi499307483/sft_optimal_all_epoch30 | xuyi499307483 | "2025-04-04T10:33:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T10:33:36Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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### Model Sources [optional]
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- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
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[More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[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] |
nakayama/DeDeTestModels | nakayama | "2023-02-26T12:12:59Z" | 0 | 0 | null | [
"license:other",
"region:us"
] | null | "2023-02-25T19:41:15Z" | ---
license: other
---
# License
This model is licesed under a **modified** CreativeML OpenRAIL-M license.
- **You can't host or use the model or its derivatives on websites/apps/etc., from which you earn, will earn, or plan to earn revenue or donations. If you want to, please email us at [email protected]**
- **You are free to host the model card and files (Without any actual inference or finetuning) on both commercial and non-commercial websites/apps/etc. Please state the full model name (Dreamlike Diffusion 1.0 / Dreamlike Photoreal 1.0) and include a link to the model card ( https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0 / https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/ )**
- **You are free to host the model or its derivatives on completely non-commercial websites/apps/etc (Meaning you are not getting ANY revenue or donations). Please state the full model name (Dreamlike Diffusion 1.0 / Dreamlike Photoreal 1.0) and include a link to the model card ( https://huggingface.co/dreamlike-art/dreamlike-diffusion-1.0 / https://huggingface.co/dreamlike-art/dreamlike-photoreal-1.0/ )**
- **You are free to use the outputs of the model or the outputs of the model's derivatives for commercial purposes in teams of 10 or less**
- You can't use the model to deliberately produce nor share illegal or harmful outputs or content
- The authors claims no rights on the outputs you generate, you are free to use them and are accountable for their use which must not go against the provisions set in the license
- You may re-distribute the weights. If you do, please be aware you have to include the same use restrictions as the ones in the license and share a copy of the **modified** CreativeML OpenRAIL-M to all your users (please read the license entirely and carefully) Please read the full license here: https://huggingface.co/nakayama/DeDeDe/blob/main/License.md |
Bayolev/model | Bayolev | "2025-02-17T12:39:05Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-17T12:38:05Z" | ---
base_model: unsloth/llama-3.2-3b-instruct-unsloth-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Bayolev
- **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)
|
LarryAIDraw/bocchi3-20000 | LarryAIDraw | "2023-01-09T07:59:40Z" | 0 | 1 | null | [
"license:creativeml-openrail-m",
"region:us"
] | null | "2023-01-09T07:59:20Z" | ---
license: creativeml-openrail-m
---
|
SenhorDasMoscas/acho-classification-30-01-2025 | SenhorDasMoscas | "2025-01-31T02:15:42Z" | 8 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-01-31T02:14:43Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
mradermacher/Zephyrus-L1-33B-i1-GGUF | mradermacher | "2024-08-02T10:57:10Z" | 31 | 1 | transformers | [
"transformers",
"gguf",
"en",
"base_model:Sao10K/Zephyrus-L1-33B",
"base_model:quantized:Sao10K/Zephyrus-L1-33B",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2024-06-04T17:17:43Z" | ---
base_model: Sao10K/Zephyrus-L1-33B
language:
- en
library_name: transformers
license: other
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/Sao10K/Zephyrus-L1-33B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Zephyrus-L1-33B-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/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/Zephyrus-L1-33B-i1-GGUF/resolve/main/Zephyrus-L1-33B.i1-Q6_K.gguf) | i1-Q6_K | 26.8 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF | mradermacher | "2024-10-13T11:41:51Z" | 94 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:BenevolenceMessiah/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0",
"base_model:quantized:BenevolenceMessiah/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2024-10-13T09:43:01Z" | ---
base_model: BenevolenceMessiah/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/BenevolenceMessiah/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q2_K.gguf) | Q2_K | 3.1 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q3_K_S.gguf) | Q3_K_S | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q3_K_M.gguf) | Q3_K_M | 3.9 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q3_K_L.gguf) | Q3_K_L | 4.2 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.IQ4_XS.gguf) | IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q4_K_S.gguf) | Q4_K_S | 4.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q4_K_M.gguf) | Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q5_K_S.gguf) | Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q5_K_M.gguf) | Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q6_K.gguf) | Q6_K | 6.4 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.Q8_0.gguf) | Q8_0 | 8.2 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0-GGUF/resolve/main/Qwen2.5-Coder-7B-3x-Instruct-TIES-v1.0.f16.gguf) | f16 | 15.3 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
|
dbernsohn/roberta-javascript | dbernsohn | "2021-05-20T15:55:17Z" | 5 | 1 | transformers | [
"transformers",
"pytorch",
"jax",
"roberta",
"fill-mask",
"arxiv:1907.11692",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:05Z" | # roberta-javascript
---
language: javascript
datasets:
- code_search_net
---
This is a [roberta](https://arxiv.org/pdf/1907.11692.pdf) pre-trained version on the [CodeSearchNet dataset](https://github.com/github/CodeSearchNet) for **javascript** Mask Language Model mission.
To load the model:
(necessary packages: !pip install transformers sentencepiece)
```python
from transformers import AutoTokenizer, AutoModelWithLMHead, pipeline
tokenizer = AutoTokenizer.from_pretrained("dbernsohn/roberta-javascript")
model = AutoModelWithLMHead.from_pretrained("dbernsohn/roberta-javascript")
fill_mask = pipeline(
"fill-mask",
model=model,
tokenizer=tokenizer
)
```
You can then use this model to fill masked words in a Java code.
```python
code = """
var i;
for (i = 0; i < cars.<mask>; i++) {
text += cars[i] + "<br>";
}
""".lstrip()
pred = {x["token_str"].replace("Ġ", ""): x["score"] for x in fill_mask(code)}
sorted(pred.items(), key=lambda kv: kv[1], reverse=True)
# [('length', 0.9959614872932434),
# ('i', 0.00027875584783032537),
# ('len', 0.0002283261710545048),
# ('nodeType', 0.00013731322542298585),
# ('index', 7.5289819505997e-05)]
```
The whole training process and hyperparameters are in my [GitHub repo](https://github.com/DorBernsohn/CodeLM/tree/main/CodeMLM)
> Created by [Dor Bernsohn](https://www.linkedin.com/in/dor-bernsohn-70b2b1146/) |
mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF | mradermacher | "2024-10-11T19:36:09Z" | 518 | 3 | transformers | [
"transformers",
"gguf",
"qwen",
"uncensored",
"zh",
"en",
"dataset:NobodyExistsOnTheInternet/ToxicQAFinal",
"dataset:anthracite-org/kalo-opus-instruct-22k-no-refusal",
"dataset:Orion-zhen/dpo-toxic-zh",
"dataset:unalignment/toxic-dpo-v0.2",
"dataset:Crystalcareai/Intel-DPO-Pairs-Norefusals",
"base_model:Orion-zhen/Qwen2.5-7B-Instruct-Uncensored",
"base_model:quantized:Orion-zhen/Qwen2.5-7B-Instruct-Uncensored",
"license:gpl-3.0",
"endpoints_compatible",
"region:us",
"imatrix",
"conversational"
] | null | "2024-10-11T18:25:42Z" | ---
base_model: Orion-zhen/Qwen2.5-7B-Instruct-Uncensored
datasets:
- NobodyExistsOnTheInternet/ToxicQAFinal
- anthracite-org/kalo-opus-instruct-22k-no-refusal
- Orion-zhen/dpo-toxic-zh
- unalignment/toxic-dpo-v0.2
- Crystalcareai/Intel-DPO-Pairs-Norefusals
language:
- zh
- en
library_name: transformers
license: gpl-3.0
quantized_by: mradermacher
tags:
- qwen
- uncensored
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/Orion-zhen/Qwen2.5-7B-Instruct-Uncensored
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ1_S.gguf) | i1-IQ1_S | 2.0 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ1_M.gguf) | i1-IQ1_M | 2.1 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 2.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ2_XS.gguf) | i1-IQ2_XS | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ2_S.gguf) | i1-IQ2_S | 2.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ2_M.gguf) | i1-IQ2_M | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q2_K.gguf) | i1-Q2_K | 3.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 3.2 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ3_XS.gguf) | i1-IQ3_XS | 3.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q3_K_S.gguf) | i1-Q3_K_S | 3.6 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ3_S.gguf) | i1-IQ3_S | 3.6 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ3_M.gguf) | i1-IQ3_M | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q3_K_M.gguf) | i1-Q3_K_M | 3.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q3_K_L.gguf) | i1-Q3_K_L | 4.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-IQ4_XS.gguf) | i1-IQ4_XS | 4.3 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q4_0_4_4.gguf) | i1-Q4_0_4_4 | 4.5 | fast on arm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q4_0_4_8.gguf) | i1-Q4_0_4_8 | 4.5 | fast on arm+i8mm, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q4_0_8_8.gguf) | i1-Q4_0_8_8 | 4.5 | fast on arm+sve, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q4_0.gguf) | i1-Q4_0 | 4.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q4_K_S.gguf) | i1-Q4_K_S | 4.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q4_K_M.gguf) | i1-Q4_K_M | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q5_K_S.gguf) | i1-Q5_K_S | 5.4 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q5_K_M.gguf) | i1-Q5_K_M | 5.5 | |
| [GGUF](https://huggingface.co/mradermacher/Qwen2.5-7B-Instruct-Uncensored-i1-GGUF/resolve/main/Qwen2.5-7B-Instruct-Uncensored.i1-Q6_K.gguf) | i1-Q6_K | 6.4 | 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 -->
|
MayBashendy/ArabicNewSplits_FineTuningAraBERT_AugV5_k2_task3_organization_fold1 | MayBashendy | "2024-11-27T07:08:41Z" | 165 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:aubmindlab/bert-base-arabertv02",
"base_model:finetune:aubmindlab/bert-base-arabertv02",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-27T07:07:20Z" | ---
library_name: transformers
base_model: aubmindlab/bert-base-arabertv02
tags:
- generated_from_trainer
model-index:
- name: ArabicNewSplits_FineTuningAraBERT_AugV5_k2_task3_organization_fold1
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. -->
# ArabicNewSplits_FineTuningAraBERT_AugV5_k2_task3_organization_fold1
This model is a fine-tuned version of [aubmindlab/bert-base-arabertv02](https://huggingface.co/aubmindlab/bert-base-arabertv02) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0222
- Qwk: 0.0928
- Mse: 1.0222
- Rmse: 1.0111
## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|
| No log | 0.4 | 2 | 3.9230 | 0.0156 | 3.9230 | 1.9807 |
| No log | 0.8 | 4 | 1.7741 | 0.0888 | 1.7741 | 1.3320 |
| No log | 1.2 | 6 | 1.0074 | 0.1331 | 1.0074 | 1.0037 |
| No log | 1.6 | 8 | 0.7455 | 0.1795 | 0.7455 | 0.8634 |
| No log | 2.0 | 10 | 0.7707 | 0.1630 | 0.7707 | 0.8779 |
| No log | 2.4 | 12 | 0.9026 | -0.1590 | 0.9026 | 0.9501 |
| No log | 2.8 | 14 | 0.8185 | 0.0897 | 0.8185 | 0.9047 |
| No log | 3.2 | 16 | 0.8735 | 0.0169 | 0.8735 | 0.9346 |
| No log | 3.6 | 18 | 0.8769 | 0.0649 | 0.8769 | 0.9364 |
| No log | 4.0 | 20 | 0.8741 | -0.0022 | 0.8741 | 0.9349 |
| No log | 4.4 | 22 | 0.9272 | 0.0448 | 0.9272 | 0.9629 |
| No log | 4.8 | 24 | 0.9068 | -0.0022 | 0.9068 | 0.9522 |
| No log | 5.2 | 26 | 0.9665 | 0.0373 | 0.9665 | 0.9831 |
| No log | 5.6 | 28 | 0.9181 | 0.1092 | 0.9181 | 0.9582 |
| No log | 6.0 | 30 | 0.9053 | -0.0022 | 0.9053 | 0.9514 |
| No log | 6.4 | 32 | 0.9206 | 0.0211 | 0.9206 | 0.9595 |
| No log | 6.8 | 34 | 0.9895 | 0.0645 | 0.9895 | 0.9947 |
| No log | 7.2 | 36 | 1.0212 | 0.0982 | 1.0212 | 1.0105 |
| No log | 7.6 | 38 | 1.0291 | 0.0928 | 1.0291 | 1.0145 |
| No log | 8.0 | 40 | 1.0497 | 0.1788 | 1.0497 | 1.0246 |
| No log | 8.4 | 42 | 1.0692 | 0.1420 | 1.0692 | 1.0340 |
| No log | 8.8 | 44 | 1.0531 | 0.1593 | 1.0531 | 1.0262 |
| No log | 9.2 | 46 | 1.0266 | 0.1593 | 1.0266 | 1.0132 |
| No log | 9.6 | 48 | 1.0211 | 0.0928 | 1.0211 | 1.0105 |
| No log | 10.0 | 50 | 1.0222 | 0.0928 | 1.0222 | 1.0111 |
### Framework versions
- Transformers 4.44.2
- Pytorch 2.4.0+cu118
- Datasets 2.21.0
- Tokenizers 0.19.1
|
shibajustfor/9dd8c098-6c0f-49e6-a2c3-0ffe42c40c5a | shibajustfor | "2025-02-27T15:28:06Z" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:unsloth/Phi-3-medium-4k-instruct",
"base_model:adapter:unsloth/Phi-3-medium-4k-instruct",
"region:us"
] | null | "2025-02-27T15:27:47Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: unsloth/Phi-3-medium-4k-instruct
model-index:
- name: shibajustfor/9dd8c098-6c0f-49e6-a2c3-0ffe42c40c5a
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# shibajustfor/9dd8c098-6c0f-49e6-a2c3-0ffe42c40c5a
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9673
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
moschouChry/chronos-t5-finetuned_small_1-Patient0-fine-tuned_20240616_205414 | moschouChry | "2024-06-16T19:25:11Z" | 164 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-16T19:24:57Z" | ---
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] |
TheBloke/mindy-7B-GGUF | TheBloke | "2023-12-16T14:19:51Z" | 78 | 3 | transformers | [
"transformers",
"gguf",
"mistral",
"base_model:mindy-labs/mindy-7b-v2",
"base_model:quantized:mindy-labs/mindy-7b-v2",
"license:apache-2.0",
"region:us"
] | null | "2023-12-16T14:15:32Z" | ---
base_model: mindy-labs/mindy-7b
inference: false
license: apache-2.0
model_creator: Mindy
model_name: Mindy 7B
model_type: mistral
prompt_template: '{prompt}
'
quantized_by: TheBloke
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<img src="https://i.imgur.com/EBdldam.jpg" alt="TheBlokeAI" style="width: 100%; min-width: 400px; display: block; margin: auto;">
</div>
<div style="display: flex; justify-content: space-between; width: 100%;">
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<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://discord.gg/theblokeai">Chat & support: TheBloke's Discord server</a></p>
</div>
<div style="display: flex; flex-direction: column; align-items: flex-end;">
<p style="margin-top: 0.5em; margin-bottom: 0em;"><a href="https://www.patreon.com/TheBlokeAI">Want to contribute? TheBloke's Patreon page</a></p>
</div>
</div>
<div style="text-align:center; margin-top: 0em; margin-bottom: 0em"><p style="margin-top: 0.25em; margin-bottom: 0em;">TheBloke's LLM work is generously supported by a grant from <a href="https://a16z.com">andreessen horowitz (a16z)</a></p></div>
<hr style="margin-top: 1.0em; margin-bottom: 1.0em;">
<!-- header end -->
# Mindy 7B - GGUF
- Model creator: [Mindy](https://huggingface.co/mindy-labs)
- Original model: [Mindy 7B](https://huggingface.co/mindy-labs/mindy-7b)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Mindy's Mindy 7B](https://huggingface.co/mindy-labs/mindy-7b).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/mindy-7B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/mindy-7B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/mindy-7B-GGUF)
* [Mindy's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/mindy-labs/mindy-7b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Unknown
```
{prompt}
```
<!-- prompt-template end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [mindy-7b.Q2_K.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q2_K.gguf) | Q2_K | 2 | 3.08 GB| 5.58 GB | smallest, significant quality loss - not recommended for most purposes |
| [mindy-7b.Q3_K_S.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q3_K_S.gguf) | Q3_K_S | 3 | 3.17 GB| 5.67 GB | very small, high quality loss |
| [mindy-7b.Q3_K_M.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q3_K_M.gguf) | Q3_K_M | 3 | 3.52 GB| 6.02 GB | very small, high quality loss |
| [mindy-7b.Q3_K_L.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q3_K_L.gguf) | Q3_K_L | 3 | 3.82 GB| 6.32 GB | small, substantial quality loss |
| [mindy-7b.Q4_0.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q4_0.gguf) | Q4_0 | 4 | 4.11 GB| 6.61 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [mindy-7b.Q4_K_S.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q4_K_S.gguf) | Q4_K_S | 4 | 4.14 GB| 6.64 GB | small, greater quality loss |
| [mindy-7b.Q4_K_M.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q4_K_M.gguf) | Q4_K_M | 4 | 4.37 GB| 6.87 GB | medium, balanced quality - recommended |
| [mindy-7b.Q5_0.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q5_0.gguf) | Q5_0 | 5 | 5.00 GB| 7.50 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [mindy-7b.Q5_K_S.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q5_K_S.gguf) | Q5_K_S | 5 | 5.00 GB| 7.50 GB | large, low quality loss - recommended |
| [mindy-7b.Q5_K_M.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q5_K_M.gguf) | Q5_K_M | 5 | 5.13 GB| 7.63 GB | large, very low quality loss - recommended |
| [mindy-7b.Q6_K.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q6_K.gguf) | Q6_K | 6 | 5.94 GB| 8.44 GB | very large, extremely low quality loss |
| [mindy-7b.Q8_0.gguf](https://huggingface.co/TheBloke/mindy-7B-GGUF/blob/main/mindy-7b.Q8_0.gguf) | Q8_0 | 8 | 7.70 GB| 10.20 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/mindy-7B-GGUF and below it, a specific filename to download, such as: mindy-7b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/mindy-7B-GGUF mindy-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/mindy-7B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/mindy-7B-GGUF mindy-7b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m mindy-7b.Q4_K_M.gguf --color -c 32768 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "{prompt}"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 32768` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./mindy-7b.Q4_K_M.gguf", # Download the model file first
n_ctx=32768, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"{prompt}", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./mindy-7b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
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## Discord
For further support, and discussions on these models and AI in general, join us at:
[TheBloke AI's Discord server](https://discord.gg/theblokeai)
## Thanks, and how to contribute
Thanks to the [chirper.ai](https://chirper.ai) team!
Thanks to Clay from [gpus.llm-utils.org](llm-utils)!
I've had a lot of people ask if they can contribute. I enjoy providing models and helping people, and would love to be able to spend even more time doing it, as well as expanding into new projects like fine tuning/training.
If you're able and willing to contribute it will be most gratefully received and will help me to keep providing more models, and to start work on new AI projects.
Donaters will get priority support on any and all AI/LLM/model questions and requests, access to a private Discord room, plus other benefits.
* Patreon: https://patreon.com/TheBlokeAI
* Ko-Fi: https://ko-fi.com/TheBlokeAI
**Special thanks to**: Aemon Algiz.
**Patreon special mentions**: Michael Levine, 阿明, Trailburnt, Nikolai Manek, John Detwiler, Randy H, Will Dee, Sebastain Graf, NimbleBox.ai, Eugene Pentland, Emad Mostaque, Ai Maven, Jim Angel, Jeff Scroggin, Michael Davis, Manuel Alberto Morcote, Stephen Murray, Robert, Justin Joy, Luke @flexchar, Brandon Frisco, Elijah Stavena, S_X, Dan Guido, Undi ., Komninos Chatzipapas, Shadi, theTransient, Lone Striker, Raven Klaugh, jjj, Cap'n Zoog, Michel-Marie MAUDET (LINAGORA), Matthew Berman, David, Fen Risland, Omer Bin Jawed, Luke Pendergrass, Kalila, OG, Erik Bjäreholt, Rooh Singh, Joseph William Delisle, Dan Lewis, TL, John Villwock, AzureBlack, Brad, Pedro Madruga, Caitlyn Gatomon, K, jinyuan sun, Mano Prime, Alex, Jeffrey Morgan, Alicia Loh, Illia Dulskyi, Chadd, transmissions 11, fincy, Rainer Wilmers, ReadyPlayerEmma, knownsqashed, Mandus, biorpg, Deo Leter, Brandon Phillips, SuperWojo, Sean Connelly, Iucharbius, Jack West, Harry Royden McLaughlin, Nicholas, terasurfer, Vitor Caleffi, Duane Dunston, Johann-Peter Hartmann, David Ziegler, Olakabola, Ken Nordquist, Trenton Dambrowitz, Tom X Nguyen, Vadim, Ajan Kanaga, Leonard Tan, Clay Pascal, Alexandros Triantafyllidis, JM33133, Xule, vamX, ya boyyy, subjectnull, Talal Aujan, Alps Aficionado, wassieverse, Ari Malik, James Bentley, Woland, Spencer Kim, Michael Dempsey, Fred von Graf, Elle, zynix, William Richards, Stanislav Ovsiannikov, Edmond Seymore, Jonathan Leane, Martin Kemka, usrbinkat, Enrico Ros
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
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<!-- original-model-card start -->
# Original model card: Mindy's Mindy 7B
## Model Details
Mindy-7b is a *Frankenstein👻* merge between [SOLAR-10.7B-Instruct-v1.0](https://huggingface.co/upstage/SOLAR-10.7B-Instruct-v1.0) and [Toten5/Marcoroni-neural-chat-7B-v2](https://huggingface.co/Toten5/Marcoroni-neural-chat-7B-v2).
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [Mindy Group, Inc.](https://mindy.com/)
- **Model type:** Transformer-based language model
- **Language(s) (NLP):** English
- **License:** apache-2.0
(MORE DETAILS TO BE ADDED)
<!-- original-model-card end -->
|
gcperk20/swin-small-patch4-window7-224-finetuned-piid | gcperk20 | "2023-11-03T16:06:00Z" | 8 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"swin",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:microsoft/swin-small-patch4-window7-224",
"base_model:finetune:microsoft/swin-small-patch4-window7-224",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-11-02T21:37:04Z" | ---
license: apache-2.0
base_model: microsoft/swin-small-patch4-window7-224
tags:
- generated_from_trainer
datasets:
- imagefolder
metrics:
- accuracy
model-index:
- name: swin-small-patch4-window7-224-finetuned-piid
results:
- task:
name: Image Classification
type: image-classification
dataset:
name: imagefolder
type: imagefolder
config: default
split: val
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.776255707762557
---
<!-- 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. -->
# swin-small-patch4-window7-224-finetuned-piid
This model is a fine-tuned version of [microsoft/swin-small-patch4-window7-224](https://huggingface.co/microsoft/swin-small-patch4-window7-224) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6168
- Accuracy: 0.7763
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.2327 | 0.98 | 20 | 1.1687 | 0.5114 |
| 0.7354 | 2.0 | 41 | 0.7696 | 0.6712 |
| 0.602 | 2.98 | 61 | 0.7198 | 0.7078 |
| 0.5809 | 4.0 | 82 | 0.5824 | 0.7397 |
| 0.4989 | 4.98 | 102 | 0.5331 | 0.7489 |
| 0.4364 | 6.0 | 123 | 0.6137 | 0.7489 |
| 0.3321 | 6.98 | 143 | 0.5839 | 0.7717 |
| 0.3 | 8.0 | 164 | 0.5246 | 0.7763 |
| 0.3024 | 8.98 | 184 | 0.5557 | 0.7717 |
| 0.3433 | 10.0 | 205 | 0.5258 | 0.7900 |
| 0.258 | 10.98 | 225 | 0.6354 | 0.7489 |
| 0.1595 | 12.0 | 246 | 0.5492 | 0.8219 |
| 0.2295 | 12.98 | 266 | 0.5889 | 0.7900 |
| 0.1956 | 14.0 | 287 | 0.5670 | 0.7900 |
| 0.2028 | 14.98 | 307 | 0.5460 | 0.7900 |
| 0.1514 | 16.0 | 328 | 0.6587 | 0.7900 |
| 0.0934 | 16.98 | 348 | 0.6131 | 0.7945 |
| 0.1323 | 18.0 | 369 | 0.6615 | 0.7900 |
| 0.1213 | 18.98 | 389 | 0.6192 | 0.7671 |
| 0.1028 | 19.51 | 400 | 0.6168 | 0.7763 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
|
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