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Qiskit/granite-8b-qiskit | Qiskit | "2024-12-11T15:07:15Z" | 50 | 6 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"code",
"granite",
"qiskit",
"conversational",
"dataset:public-qiskit",
"dataset:synthetic-qiskit",
"arxiv:2405.19495",
"arxiv:2406.14712",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"text-generation-inference",
"region:us"
] | text-generation | "2024-11-12T19:33:24Z" | ---
pipeline_tag: text-generation
inference: false
license: apache-2.0
datasets:
- public-qiskit
- synthetic-qiskit
metrics:
- code_eval
library_name: transformers
tags:
- code
- granite
- qiskit
model-index:
- name: granite-8b-qiskit
results:
- task:
type: text-generation
dataset:
type: qiskit-humaneval
name: Qiskit HumanEval
metrics:
- name: pass@1
type: pass@1
value: 45.69
verified: false
- task:
type: text-generation
dataset:
type: bigcode/humanevalpack
name: HumanEvalSynthesis(Python)
metrics:
- name: pass@1
type: pass@1
value: 58.53
verified: false
---
# granite-8b-qiskit
## Model Summary
**granite-8b-qiskit** is a 8B parameter model extend pretrained and fine tuned on top of [granite-8b-code-base](https://huggingface.co/ibm-granite/granite-8b-code-base-4k) using Qiskit code and instruction data to improve capabilities at writing high-quality and non-deprecated Qiskit code. We used only data with the following licenses: Apache 2.0, MIT, the Unlicense, Mulan PSL Version 2, BSD-2, BSD-3, and Creative Commons Attribution 4.0.
- **Developers:** IBM Quantum & IBM Research
- **Related Papers:** [Qiskit Code Assistant: Training LLMs for generating Quantum Computing Code](https://arxiv.org/abs/2405.19495) and [Qiskit HumanEval: An Evaluation Benchmark For Quantum Code Generative Models](https://arxiv.org/abs/2406.14712)
- **Release Date**: November 12th, 2024
- **License:** apache-2.0
## Usage
### Intended use
This model is designed for generating quantum computing code using Qiskit. Both quantum computing practitionners and new Qiskit users can use this model as an assistant for building Qiskit code or responding to Qiskit coding related instructions and questions.
### Generation
This is a simple example of how to use **granite-8b-qiskit** model.
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
device = "cuda" # or "cpu"
model_path = "qiskit/granite-8b-qiskit"
tokenizer = AutoTokenizer.from_pretrained(model_path)
# drop device_map if running on CPU
model = AutoModelForCausalLM.from_pretrained(model_path, device_map=device)
model.eval()
# change input text as desired
chat = [
{ "role": "user", "content": "Build a random circuit with 5 qubits" },
]
chat = tokenizer.apply_chat_template(chat, tokenize=False, add_generation_prompt=True)
# tokenize the text
input_tokens = tokenizer(input_text, return_tensors="pt")
# move tokenized inputs to device
for i in input_tokens:
input_tokens[i] = input_tokens[i].to(device)
# generate output tokens
output = model.generate(**input_tokens, max_new_tokens=128)
# decode output tokens into text
output = tokenizer.batch_decode(output)
# loop over the batch to print, in this example the batch size is 1
for i in output:
print(i)
```
## Training Data
- **Data Collection and Filtering:** Our code data is sourced from a combination of publicly available datasets (e.g., Code available on <https://github.com>), and additional synthetic data generated at IBM Quantum. We exclude code that is older than 2023.
- **Exact and Fuzzy Deduplication:** We use both exact and fuzzy deduplication to remove documents having (near) identical code content.
- **HAP, PII, Malware Filtering:** We rely on the base model ibm-granite/granite-8b-code-base for HAP and malware filtering from the initial datasets used in the context of the base model. We also make sure to redact Personally Identifiable Information (PII) in our datasets by replacing PII content (e.g., names, email addresses, keys, passwords) with corresponding tokens (e.g., โจNAMEโฉ, โจEMAILโฉ, โจKEYโฉ, โจPASSWORDโฉ).
## Infrastructure
We trained **granite-8b-qiskit** using IBM's super computing cluster (Vela) using NVIDIA A100 GPUs.
## Ethical Considerations and Limitations
The use of Large Language Models involves risks and ethical considerations people must be aware of. Regarding code generation, caution is urged against complete reliance on specific code models for crucial decisions or impactful information as the generated code is not guaranteed to work as intended. **granite-8b-qiskit** model is not the exception in this regard. Even though this model is suited for multiple code-related tasks, it has not undergone any safety alignment, there it may produce problematic outputs. Additionally, it remains uncertain whether smaller models might exhibit increased susceptibility to hallucination in generation scenarios by copying source code verbatim from the training dataset due to their reduced sizes and memorization capacities. This aspect is currently an active area of research, and we anticipate more rigorous exploration, comprehension, and mitigations in this domain. Regarding ethics, a latent risk associated with all Large Language Models is their malicious utilization. We urge the community to use **granite-8b-qiskit** model with ethical intentions and in a responsible way.
|
thaffggg/d2b36123-a9a3-43fd-a52c-d50826bf71d1 | thaffggg | "2025-01-14T13:21:18Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/Llama-3.1-Storm-8B",
"base_model:adapter:unsloth/Llama-3.1-Storm-8B",
"license:llama3.1",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-01-14T11:46:38Z" | ---
library_name: peft
license: llama3.1
base_model: unsloth/Llama-3.1-Storm-8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: d2b36123-a9a3-43fd-a52c-d50826bf71d1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: unsloth/Llama-3.1-Storm-8B
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- d226f08ed39ca0c7_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/d226f08ed39ca0c7_train_data.json
type:
field_instruction: prompt
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
early_stopping_patience: null
eval_max_new_tokens: 128
eval_table_size: null
evals_per_epoch: 1
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: false
hub_model_id: thaffggg/d2b36123-a9a3-43fd-a52c-d50826bf71d1
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/d226f08ed39ca0c7_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: 0de043d6-549e-4de6-adc0-8426bcb1a331
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0de043d6-549e-4de6-adc0-8426bcb1a331
warmup_steps: 5
weight_decay: 0.01
xformers_attention: true
```
</details><br>
# d2b36123-a9a3-43fd-a52c-d50826bf71d1
This model is a fine-tuned version of [unsloth/Llama-3.1-Storm-8B](https://huggingface.co/unsloth/Llama-3.1-Storm-8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7645
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.575 | 0.0039 | 200 | 0.7645 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
NoahMartinezXiang/VideoMAE_B_K400_FT_HMDB51_Random | NoahMartinezXiang | "2025-02-12T03:30:39Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-01-30T05:56:47Z" | ---
license: apache-2.0
---
|
clothilde-neighbour/discours-detection-camembert | clothilde-neighbour | "2024-08-28T09:53:25Z" | 104 | 0 | transformers | [
"transformers",
"safetensors",
"camembert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-08-28T09:53:06Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
moshel/cucs | moshel | "2023-03-02T02:45:56Z" | 0 | 0 | null | [
"license:cc-by-nc-nd-4.0",
"region:us"
] | null | "2023-03-02T02:45:55Z" | ---
license: cc-by-nc-nd-4.0
---
|
Sophie-Rain-Spiderman-Video-Tutorial-Clips/Sophie.Rain.Spiderman.Video.Tutorial.Viral.Full.Video | Sophie-Rain-Spiderman-Video-Tutorial-Clips | "2025-03-07T18:08:27Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-03-07T18:08:01Z" | <!-- HTML_TAG_END --><div>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐๐๐ญ๐๐ก ๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ)</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman">๐ด โคโบ๐๐ฅ๐ข๐ค ๐๐๐ซ๐ ๐ญ๐จ๐๐ (๐
๐ฎ๐ฅ๐ฅ ๐ฏ๐ข๐๐๐จ ๐๐ข๐ง๐ค )</a></p>
<p><a rel="nofollow" href="https://leaked-videos.com/?v=Sophie+Rain+Spiderman"><img src="https://i.postimg.cc/qvPp49Sm/ythngythg.gif" alt="fsd"></a></p>
<!-- HTML_TAG_END --></div> |
andreykaa03/llm-course-hw2-reward-model | andreykaa03 | "2025-03-29T17:43:16Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-classification",
"trl",
"reward-trainer",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-29T17:42:39Z" | ---
library_name: transformers
tags:
- trl
- reward-trainer
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed] |
HikaruBear/ddpm-butterflies-128 | HikaruBear | "2022-12-13T17:42:11Z" | 1 | 0 | diffusers | [
"diffusers",
"tensorboard",
"en",
"dataset:huggan/smithsonian_butterflies_subset",
"license:apache-2.0",
"diffusers:DDPMPipeline",
"region:us"
] | null | "2022-12-13T05:45:21Z" | ---
language: en
license: apache-2.0
library_name: diffusers
tags: []
datasets: huggan/smithsonian_butterflies_subset
metrics: []
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# ddpm-butterflies-128
## Model description
This diffusion model is trained with the [๐ค Diffusers](https://github.com/huggingface/diffusers) library
on the `huggan/smithsonian_butterflies_subset` dataset.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training data
[TODO: describe the data used to train the model]
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 16
- eval_batch_size: 16
- gradient_accumulation_steps: 1
- optimizer: AdamW with betas=(None, None), weight_decay=None and epsilon=None
- lr_scheduler: None
- lr_warmup_steps: 500
- ema_inv_gamma: None
- ema_inv_gamma: None
- ema_inv_gamma: None
- mixed_precision: fp16
### Training results
๐ [TensorBoard logs](https://huggingface.co/HikaruBear/ddpm-butterflies-128/tensorboard?#scalars)
|
kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_3.0_6 | kaisar-barlybay-sse | "2023-05-01T14:44:51Z" | 103 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"distilbert",
"multiple-choice",
"generated_from_trainer",
"endpoints_compatible",
"region:us"
] | multiple-choice | "2023-05-01T14:18:35Z" | ---
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: kaz_legal_distilbert_full_corpus_3.0_6
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. -->
# kaz_legal_distilbert_full_corpus_3.0_6
This model is a fine-tuned version of [kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_3.0](https://huggingface.co/kaisar-barlybay-sse/kaz_legal_distilbert_full_corpus_3.0) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6273
- Accuracy: 0.4471
## 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: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 1.3908 | 1.0 | 501 | 1.3699 | 0.3593 |
| 1.2794 | 2.0 | 1002 | 1.4185 | 0.3972 |
| 1.0076 | 3.0 | 1503 | 1.5770 | 0.4311 |
| 0.7301 | 4.0 | 2004 | 2.1395 | 0.4172 |
| 0.5437 | 5.0 | 2505 | 2.2464 | 0.4511 |
| 0.4292 | 6.0 | 3006 | 2.6273 | 0.4471 |
### Framework versions
- Transformers 4.28.1
- Pytorch 2.0.0+cu118
- Datasets 2.12.0
- Tokenizers 0.13.3
|
tomercagan/q-Taxi-v3 | tomercagan | "2023-02-03T12:00:55Z" | 0 | 0 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-02-03T11:31:24Z" | ---
tags:
- Taxi-v3
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-Taxi-v3
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: Taxi-v3
type: Taxi-v3
metrics:
- type: mean_reward
value: 7.56 +/- 2.71
name: mean_reward
verified: false
---
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="tomercagan/q-Taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
|
mradermacher/chronos-33b-i1-GGUF | mradermacher | "2024-08-22T03:15:28Z" | 43 | 0 | transformers | [
"transformers",
"gguf",
"llama",
"pytorch",
"chatbot",
"storywriting",
"en",
"base_model:elinas/chronos-33b",
"base_model:quantized:elinas/chronos-33b",
"license:other",
"endpoints_compatible",
"region:us",
"imatrix"
] | null | "2024-08-21T22:03:38Z" | ---
base_model: elinas/chronos-33b
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- llama
- pytorch
- chatbot
- storywriting
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: nicoboss -->
weighted/imatrix quants of https://huggingface.co/elinas/chronos-33b
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/chronos-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/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ1_S.gguf) | i1-IQ1_S | 7.2 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ1_M.gguf) | i1-IQ1_M | 7.8 | mostly desperate |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 8.8 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ2_XS.gguf) | i1-IQ2_XS | 9.7 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ2_S.gguf) | i1-IQ2_S | 10.5 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ2_M.gguf) | i1-IQ2_M | 11.3 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q2_K.gguf) | i1-Q2_K | 12.1 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 12.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ3_XS.gguf) | i1-IQ3_XS | 13.4 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ3_S.gguf) | i1-IQ3_S | 14.2 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q3_K_S.gguf) | i1-Q3_K_S | 14.2 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ3_M.gguf) | i1-IQ3_M | 15.0 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q3_K_M.gguf) | i1-Q3_K_M | 15.9 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q3_K_L.gguf) | i1-Q3_K_L | 17.4 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-IQ4_XS.gguf) | i1-IQ4_XS | 17.4 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q4_0.gguf) | i1-Q4_0 | 18.5 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q4_K_S.gguf) | i1-Q4_K_S | 18.6 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q4_K_M.gguf) | i1-Q4_K_M | 19.7 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q5_K_S.gguf) | i1-Q5_K_S | 22.5 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-33b.i1-Q5_K_M.gguf) | i1-Q5_K_M | 23.1 | |
| [GGUF](https://huggingface.co/mradermacher/chronos-33b-i1-GGUF/resolve/main/chronos-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 -->
|
TheBloke/SynthIA-7B-v2.0-16k-AWQ | TheBloke | "2023-11-15T09:12:53Z" | 77 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"en",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"awq",
"region:us"
] | text-generation | "2023-11-15T08:58:14Z" | ---
base_model: NurtureAI/SynthIA-7B-v2.0-16k
inference: false
language:
- en
library_name: transformers
license: apache-2.0
model_creator: NurtureAI
model_name: SynthIA 7B V2.0 16K
model_type: mistral
pipeline_tag: text-generation
prompt_template: 'SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack
when necessary to construct a clear, cohesive Chain of Thought reasoning. Always
answer without hesitation.
USER: {prompt}
ASSISTANT:
'
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%;">
<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 -->
# SynthIA 7B V2.0 16K - AWQ
- Model creator: [NurtureAI](https://huggingface.co/NurtureAI)
- Original model: [SynthIA 7B V2.0 16K](https://huggingface.co/NurtureAI/SynthIA-7B-v2.0-16k)
<!-- description start -->
## Description
This repo contains AWQ model files for [NurtureAI's SynthIA 7B V2.0 16K](https://huggingface.co/NurtureAI/SynthIA-7B-v2.0-16k).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
### About AWQ
AWQ is an efficient, accurate and blazing-fast low-bit weight quantization method, currently supporting 4-bit quantization. Compared to GPTQ, it offers faster Transformers-based inference with equivalent or better quality compared to the most commonly used GPTQ settings.
It is supported by:
- [Text Generation Webui](https://github.com/oobabooga/text-generation-webui) - using Loader: AutoAWQ
- [vLLM](https://github.com/vllm-project/vllm) - Llama and Mistral models only
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later, from any code or client that supports Transformers
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) - for use from Python code
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SynthIA-7B-v2.0-16k-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SynthIA-7B-v2.0-16k-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SynthIA-7B-v2.0-16k-GGUF)
* [NurtureAI's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/NurtureAI/SynthIA-7B-v2.0-16k)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Synthia-CoT
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: {prompt}
ASSISTANT:
```
<!-- prompt-template end -->
<!-- README_AWQ.md-provided-files start -->
## Provided files, and AWQ parameters
I currently release 128g GEMM models only. The addition of group_size 32 models, and GEMV kernel models, is being actively considered.
Models are released as sharded safetensors files.
| Branch | Bits | GS | AWQ Dataset | Seq Len | Size |
| ------ | ---- | -- | ----------- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SynthIA-7B-v2.0-16k-AWQ/tree/main) | 4 | 128 | [wikitext](https://huggingface.co/datasets/wikitext/viewer/wikitext-2-v1/test) | 4096 | 4.15 GB
<!-- README_AWQ.md-provided-files end -->
<!-- README_AWQ.md-text-generation-webui start -->
## 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're sure you know how to make a manual install.
1. Click the **Model tab**.
2. Under **Download custom model or LoRA**, enter `TheBloke/SynthIA-7B-v2.0-16k-AWQ`.
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: `SynthIA-7B-v2.0-16k-AWQ`
7. Select **Loader: AutoAWQ**.
8. Click Load, and the model will load and is now ready for use.
9. 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.
10. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_AWQ.md-text-generation-webui end -->
<!-- README_AWQ.md-use-from-vllm start -->
## Multi-user inference server: vLLM
Documentation on installing and using vLLM [can be found here](https://vllm.readthedocs.io/en/latest/).
- Please ensure you are using vLLM version 0.2 or later.
- When using vLLM as a server, pass the `--quantization awq` parameter.
For example:
```shell
python3 -m vllm.entrypoints.api_server --model TheBloke/SynthIA-7B-v2.0-16k-AWQ --quantization awq --dtype auto
```
- When using vLLM from Python code, again set `quantization=awq`.
For example:
```python
from vllm import LLM, SamplingParams
prompts = [
"Tell me about AI",
"Write a story about llamas",
"What is 291 - 150?",
"How much wood would a woodchuck chuck if a woodchuck could chuck wood?",
]
prompt_template=f'''SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: {prompt}
ASSISTANT:
'''
prompts = [prompt_template.format(prompt=prompt) for prompt in prompts]
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
llm = LLM(model="TheBloke/SynthIA-7B-v2.0-16k-AWQ", quantization="awq", dtype="auto")
outputs = llm.generate(prompts, sampling_params)
# Print the outputs.
for output in outputs:
prompt = output.prompt
generated_text = output.outputs[0].text
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
```
<!-- README_AWQ.md-use-from-vllm start -->
<!-- README_AWQ.md-use-from-tgi start -->
## Multi-user inference server: Hugging Face Text Generation Inference (TGI)
Use TGI version 1.1.0 or later. The official Docker container is: `ghcr.io/huggingface/text-generation-inference:1.1.0`
Example Docker parameters:
```shell
--model-id TheBloke/SynthIA-7B-v2.0-16k-AWQ --port 3000 --quantize awq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires [huggingface-hub](https://github.com/huggingface/huggingface_hub) 0.17.0 or later):
```shell
pip3 install huggingface-hub
```
```python
from huggingface_hub import InferenceClient
endpoint_url = "https://your-endpoint-url-here"
prompt = "Tell me about AI"
prompt_template=f'''SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: {prompt}
ASSISTANT:
'''
client = InferenceClient(endpoint_url)
response = client.text_generation(prompt,
max_new_tokens=128,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1)
print(f"Model output: ", response)
```
<!-- README_AWQ.md-use-from-tgi end -->
<!-- README_AWQ.md-use-from-python start -->
## Inference from Python code using Transformers
### Install the necessary packages
- Requires: [Transformers](https://huggingface.co/docs/transformers) 4.35.0 or later.
- Requires: [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) 0.1.6 or later.
```shell
pip3 install --upgrade "autoawq>=0.1.6" "transformers>=4.35.0"
```
Note that if you are using PyTorch 2.0.1, the above AutoAWQ command will automatically upgrade you to PyTorch 2.1.0.
If you are using CUDA 11.8 and wish to continue using PyTorch 2.0.1, instead run this command:
```shell
pip3 install https://github.com/casper-hansen/AutoAWQ/releases/download/v0.1.6/autoawq-0.1.6+cu118-cp310-cp310-linux_x86_64.whl
```
If you have problems installing [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) using the pre-built wheels, install it from source instead:
```shell
pip3 uninstall -y autoawq
git clone https://github.com/casper-hansen/AutoAWQ
cd AutoAWQ
pip3 install .
```
### Transformers example code (requires Transformers 4.35.0 and later)
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextStreamer
model_name_or_path = "TheBloke/SynthIA-7B-v2.0-16k-AWQ"
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path)
model = AutoModelForCausalLM.from_pretrained(
model_name_or_path,
low_cpu_mem_usage=True,
device_map="cuda:0"
)
# Using the text streamer to stream output one token at a time
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "Tell me about AI"
prompt_template=f'''SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: {prompt}
ASSISTANT:
'''
# Convert prompt to tokens
tokens = tokenizer(
prompt_template,
return_tensors='pt'
).input_ids.cuda()
generation_params = {
"do_sample": True,
"temperature": 0.7,
"top_p": 0.95,
"top_k": 40,
"max_new_tokens": 512,
"repetition_penalty": 1.1
}
# Generate streamed output, visible one token at a time
generation_output = model.generate(
tokens,
streamer=streamer,
**generation_params
)
# Generation without a streamer, which will include the prompt in the output
generation_output = model.generate(
tokens,
**generation_params
)
# Get the tokens from the output, decode them, print them
token_output = generation_output[0]
text_output = tokenizer.decode(token_output)
print("model.generate output: ", text_output)
# Inference is also possible via Transformers' pipeline
from transformers import pipeline
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
**generation_params
)
pipe_output = pipe(prompt_template)[0]['generated_text']
print("pipeline output: ", pipe_output)
```
<!-- README_AWQ.md-use-from-python end -->
<!-- README_AWQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with:
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui) using `Loader: AutoAWQ`.
- [vLLM](https://github.com/vllm-project/vllm) version 0.2.0 and later.
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference) version 1.1.0 and later.
- [Transformers](https://huggingface.co/docs/transformers) version 4.35.0 and later.
- [AutoAWQ](https://github.com/casper-hansen/AutoAWQ) version 0.1.1 and later.
<!-- README_AWQ.md-compatibility end -->
<!-- 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!
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**: Brandon Frisco, LangChain4j, Spiking Neurons AB, transmissions 11, Joseph William Delisle, Nitin Borwankar, Willem Michiel, Michael Dempsey, vamX, Jeffrey Morgan, zynix, jjj, Omer Bin Jawed, Sean Connelly, jinyuan sun, Jeromy Smith, Shadi, Pawan Osman, Chadd, Elijah Stavena, Illia Dulskyi, Sebastain Graf, Stephen Murray, terasurfer, Edmond Seymore, Celu Ramasamy, Mandus, Alex, biorpg, Ajan Kanaga, Clay Pascal, Raven Klaugh, ้ฟๆ, K, ya boyyy, usrbinkat, Alicia Loh, John Villwock, ReadyPlayerEmma, Chris Smitley, Cap'n Zoog, fincy, GodLy, S_X, sidney chen, Cory Kujawski, OG, Mano Prime, AzureBlack, Pieter, Kalila, Spencer Kim, Tom X Nguyen, Stanislav Ovsiannikov, Michael Levine, Andrey, Trailburnt, Vadim, Enrico Ros, Talal Aujan, Brandon Phillips, Jack West, Eugene Pentland, Michael Davis, Will Dee, webtim, Jonathan Leane, Alps Aficionado, Rooh Singh, Tiffany J. Kim, theTransient, Luke @flexchar, Elle, Caitlyn Gatomon, Ari Malik, subjectnull, Johann-Peter Hartmann, Trenton Dambrowitz, Imad Khwaja, Asp the Wyvern, Emad Mostaque, Rainer Wilmers, Alexandros Triantafyllidis, Nicholas, Pedro Madruga, SuperWojo, Harry Royden McLaughlin, James Bentley, Olakabola, David Ziegler, Ai Maven, Jeff Scroggin, Nikolai Manek, Deo Leter, Matthew Berman, Fen Risland, Ken Nordquist, Manuel Alberto Morcote, Luke Pendergrass, TL, Fred von Graf, Randy H, Dan Guido, NimbleBox.ai, Vitor Caleffi, Gabriel Tamborski, knownsqashed, Lone Striker, Erik Bjรคreholt, John Detwiler, Leonard Tan, Iucharbius
Thank you to all my generous patrons and donaters!
And thank you again to a16z for their generous grant.
<!-- footer end -->
# Original model card: NurtureAI's SynthIA 7B V2.0 16K
## SynthIA 7B v2.0 extended to 16k context
This is the same original model so the original model license still applies.
This model has been modified to have a larger maximum context size of 16k.
### Original Model Card:
### Prompt format:
```
SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation.
USER: How is insulin synthesized?
ASSISTANT:
```
### Code example:
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch, json
# model path
model_path = "NurtureAI/SynthIA-7B-v2.0-16k"
output_file_path = "./SynthIA-7B-v2.0-conversations.jsonl"
device_map = {"": "cuda"}
model = AutoModelForCausalLM.from_pretrained(
model_path,
torch_dtype=torch.float16,
device_map=device_map,
load_in_8bit=False,
trust_remote_code=True,
)
# tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
def generate_text(instruction):
tokens = tokenizer.encode(instruction)
tokens = torch.LongTensor(tokens).unsqueeze(0)
tokens = tokens.to("cuda")
instance = {
"input_ids": tokens,
"top_p": 1.0,
"temperature": 0.75,
"generate_len": 1024,
"top_k": 50,
}
length = len(tokens[0])
with torch.no_grad():
rest = model.generate(
input_ids=tokens,
max_length=length + instance["generate_len"],
use_cache=True,
do_sample=True,
top_p=instance["top_p"],
temperature=instance["temperature"],
top_k=instance["top_k"],
num_return_sequences=1,
)
output = rest[0][length:]
string = tokenizer.decode(output, skip_special_tokens=True)
answer = string.split("USER:")[0].strip()
return f"{answer}"
conversation = f"SYSTEM: Elaborate on the topic using a Tree of Thoughts and backtrack when necessary to construct a clear, cohesive Chain of Thought reasoning. Always answer without hesitation."
while True:
user_input = input("You: ")
llm_prompt = f"{conversation} \nUSER: {user_input} \nASSISTANT: "
answer = generate_text(llm_prompt)
print(answer)
conversation = f"{llm_prompt}{answer}"
json_data = {"prompt": user_input, "answer": answer}
## Save your conversation
with open(output_file_path, "a") as output_file:
output_file.write(json.dumps(json_data) + "\n")
```
|
mrm8488/bert-spanish-cased-finetuned-ner | mrm8488 | "2021-05-20T00:35:25Z" | 2,562 | 21 | transformers | [
"transformers",
"pytorch",
"jax",
"bert",
"token-classification",
"es",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | token-classification | "2022-03-02T23:29:05Z" | ---
language: es
thumbnail: https://i.imgur.com/jgBdimh.png
---
# Spanish BERT (BETO) + NER
This model is a fine-tuned on [NER-C](https://www.kaggle.com/nltkdata/conll-corpora) version of the Spanish BERT cased [(BETO)](https://github.com/dccuchile/beto) for **NER** downstream task.
## Details of the downstream task (NER) - Dataset
- [Dataset: CONLL Corpora ES](https://www.kaggle.com/nltkdata/conll-corpora)
I preprocessed the dataset and split it as train / dev (80/20)
| Dataset | # Examples |
| ---------------------- | ----- |
| Train | 8.7 K |
| Dev | 2.2 K |
- [Fine-tune on NER script provided by Huggingface](https://github.com/huggingface/transformers/blob/master/examples/token-classification/run_ner_old.py)
- Labels covered:
```
B-LOC
B-MISC
B-ORG
B-PER
I-LOC
I-MISC
I-ORG
I-PER
O
```
## Metrics on evaluation set:
| Metric | # score |
| :------------------------------------------------------------------------------------: | :-------: |
| F1 | **90.17**
| Precision | **89.86** |
| Recall | **90.47** |
## Comparison:
| Model | # F1 score |Size(MB)|
| :--------------------------------------------------------------------------------------------------------------: | :-------: |:------|
| bert-base-spanish-wwm-cased (BETO) | 88.43 | 421
| [bert-spanish-cased-finetuned-ner (this one)](https://huggingface.co/mrm8488/bert-spanish-cased-finetuned-ner) | **90.17** | 420 |
| Best Multilingual BERT | 87.38 | 681 |
|[TinyBERT-spanish-uncased-finetuned-ner](https://huggingface.co/mrm8488/TinyBERT-spanish-uncased-finetuned-ner) | 70.00 | **55** |
## Model in action
Fast usage with **pipelines**:
```python
from transformers import pipeline
nlp_ner = pipeline(
"ner",
model="mrm8488/bert-spanish-cased-finetuned-ner",
tokenizer=(
'mrm8488/bert-spanish-cased-finetuned-ner',
{"use_fast": False}
))
text = 'Mis amigos estรกn pensando viajar a Londres este verano'
nlp_ner(text)
#Output: [{'entity': 'B-LOC', 'score': 0.9998720288276672, 'word': 'Londres'}]
```
> Created by [Manuel Romero/@mrm8488](https://twitter.com/mrm8488)
> Made with <span style="color: #e25555;">♥</span> in Spain
|
crusnic/BN-DRISHTI | crusnic | "2023-06-01T22:08:51Z" | 0 | 1 | yolov5 | [
"yolov5",
"handwriting-recognition",
"object-detection",
"vision",
"bn",
"dataset:shaoncsecu/BN-HTRd_Splitted",
"license:cc-by-sa-4.0",
"region:us"
] | object-detection | "2023-04-24T17:58:00Z" | ---
license: cc-by-sa-4.0
datasets:
- shaoncsecu/BN-HTRd_Splitted
language:
- bn
metrics:
- f1
library_name: yolov5
inference: true
tags:
- handwriting-recognition
- object-detection
- vision
widget:
- src: >-
https://datasets-server.huggingface.co/assets/shaoncsecu/BN-HTRd_Splitted/--/shaoncsecu--BN-HTRd_Splitted/train/0/image/image.jpg
example_title: HTR
--- |
ShenaoZ/0.001_ablation_4iters_bs256_nodpo_sample2_iter_1 | ShenaoZ | "2024-04-24T06:27:56Z" | 7 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:HuggingFaceH4/mistral-7b-sft-beta",
"base_model:finetune:HuggingFaceH4/mistral-7b-sft-beta",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-04-24T05:29:32Z" | ---
license: mit
base_model: HuggingFaceH4/mistral-7b-sft-beta
tags:
- alignment-handbook
- generated_from_trainer
- trl
- dpo
- generated_from_trainer
datasets:
- updated
- original
model-index:
- name: 0.001_ablation_4iters_bs256_nodpo_sample2_iter_1
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. -->
# 0.001_ablation_4iters_bs256_nodpo_sample2_iter_1
This model is a fine-tuned version of [HuggingFaceH4/mistral-7b-sft-beta](https://huggingface.co/HuggingFaceH4/mistral-7b-sft-beta) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
|
nttx/298bb42d-4012-404d-9f8f-eb6048249fc1 | nttx | "2025-01-18T18:13:14Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"mistral",
"axolotl",
"generated_from_trainer",
"custom_code",
"base_model:NousResearch/Yarn-Mistral-7b-128k",
"base_model:adapter:NousResearch/Yarn-Mistral-7b-128k",
"license:apache-2.0",
"region:us"
] | null | "2025-01-18T17:34:14Z" | ---
library_name: peft
license: apache-2.0
base_model: NousResearch/Yarn-Mistral-7b-128k
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 298bb42d-4012-404d-9f8f-eb6048249fc1
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.1`
```yaml
adapter: lora
base_model: NousResearch/Yarn-Mistral-7b-128k
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- 8e4ca3b9a637ca70_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/8e4ca3b9a637ca70_train_data.json
type:
field_input: Text
field_instruction: Task
field_output: Generation
format: '{instruction} {input}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
group_by_length: true
hub_model_id: nttx/298bb42d-4012-404d-9f8f-eb6048249fc1
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: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: cosine
max_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 8
mlflow_experiment_name: /tmp/8e4ca3b9a637ca70_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
special_tokens:
pad_token: </s>
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 0e0354d0-8f7f-4af8-a88c-0b79cf1c515d
wandb_project: Gradients-On-Demand
wandb_run: your_name
wandb_runid: 0e0354d0-8f7f-4af8-a88c-0b79cf1c515d
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 298bb42d-4012-404d-9f8f-eb6048249fc1
This model is a fine-tuned version of [NousResearch/Yarn-Mistral-7b-128k](https://huggingface.co/NousResearch/Yarn-Mistral-7b-128k) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7721
## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 6.6571 | 0.0026 | 1 | 2.1032 |
| 4.6277 | 0.1318 | 50 | 1.2714 |
| 4.2459 | 0.2637 | 100 | 1.0991 |
| 4.0239 | 0.3955 | 150 | 0.8365 |
| 3.4094 | 0.5274 | 200 | 0.7721 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
owanr/SChem5Labels-roberta-base-intra-frequency-model_annots | owanr | "2023-12-14T17:55:46Z" | 0 | 0 | null | [
"pytorch",
"safetensors",
"generated_from_trainer",
"base_model:FacebookAI/roberta-base",
"base_model:finetune:FacebookAI/roberta-base",
"license:mit",
"region:us"
] | null | "2023-12-07T09:41:57Z" | ---
license: mit
base_model: roberta-base
tags:
- generated_from_trainer
model-index:
- name: SChem5Labels-roberta-base-intra-frequency-model_annots
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. -->
# SChem5Labels-roberta-base-intra-frequency-model_annots
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 109.8733
## 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
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 104.9582 | 1.0 | 99 | 115.8823 |
| 96.7881 | 2.0 | 198 | 114.0660 |
| 101.1136 | 3.0 | 297 | 110.8983 |
| 88.253 | 4.0 | 396 | 109.5061 |
| 99.5406 | 5.0 | 495 | 109.8733 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
|
alexhotti/run_20250415_135823 | alexhotti | "2025-04-15T14:00:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-15T14:00:37Z" | <!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> |
Thinkcru/marian-finetuned-kde4-en-to-fr | Thinkcru | "2023-03-11T00:39:20Z" | 113 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"marian",
"text2text-generation",
"translation",
"generated_from_trainer",
"dataset:kde4",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | translation | "2023-03-10T22:54:42Z" | ---
license: apache-2.0
tags:
- translation
- generated_from_trainer
datasets:
- kde4
metrics:
- bleu
model-index:
- name: marian-finetuned-kde4-en-to-fr
results:
- task:
name: Sequence-to-sequence Language Modeling
type: text2text-generation
dataset:
name: kde4
type: kde4
config: en-fr
split: train
args: en-fr
metrics:
- name: Bleu
type: bleu
value: 52.836492533087124
---
<!-- 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. -->
# marian-finetuned-kde4-en-to-fr
This model is a fine-tuned version of [Helsinki-NLP/opus-mt-en-fr](https://huggingface.co/Helsinki-NLP/opus-mt-en-fr) on the kde4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8559
- Bleu: 52.8365
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 64
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.1+cu116
- Datasets 2.10.1
- Tokenizers 0.13.2
|
lesso/326b98c3-5fb0-4e05-9780-c70a66e62600 | lesso | "2025-02-06T02:16:43Z" | 6 | 0 | peft | [
"peft",
"safetensors",
"mixtral",
"axolotl",
"generated_from_trainer",
"base_model:Eurdem/Defne_llama3_2x8B",
"base_model:adapter:Eurdem/Defne_llama3_2x8B",
"license:llama3",
"region:us"
] | null | "2025-02-06T01:50:05Z" | ---
library_name: peft
license: llama3
base_model: Eurdem/Defne_llama3_2x8B
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 326b98c3-5fb0-4e05-9780-c70a66e62600
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: Eurdem/Defne_llama3_2x8B
bf16: true
chat_template: llama3
data_processes: 16
dataset_prepared_path: null
datasets:
- data_files:
- c91f5e043ddb5766_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/c91f5e043ddb5766_train_data.json
type:
field_instruction: prompt
field_output: chosen
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
do_eval: true
early_stopping_patience: 5
eval_batch_size: 4
eval_max_new_tokens: 128
eval_steps: 50
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: false
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 2
gradient_checkpointing: true
group_by_length: true
hub_model_id: lesso/326b98c3-5fb0-4e05-9780-c70a66e62600
hub_repo: null
hub_strategy: checkpoint
hub_token: null
learning_rate: 0.0001009
load_in_4bit: false
load_in_8bit: false
local_rank: null
logging_steps: 1
lora_alpha: 128
lora_dropout: 0.05
lora_fan_in_fan_out: null
lora_model_dir: null
lora_r: 64
lora_target_linear: true
lr_scheduler: linear
max_grad_norm: 1.0
max_steps: 200
micro_batch_size: 4
mlflow_experiment_name: /tmp/G.O.D/c91f5e043ddb5766_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 3
optim_args:
adam_beta1: 0.9
adam_beta2: 0.95
adam_epsilon: 1e-5
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: 50
saves_per_epoch: null
sequence_len: 1024
strict: false
tf32: true
tokenizer_type: AutoTokenizer
train_on_inputs: false
trust_remote_code: true
val_set_size: 0.05
wandb_entity: null
wandb_mode: online
wandb_name: 9fa1425f-9b48-477a-a63d-66a316b6f86c
wandb_project: new-09
wandb_run: your_name
wandb_runid: 9fa1425f-9b48-477a-a63d-66a316b6f86c
warmup_steps: 10
weight_decay: 0.0
xformers_attention: null
```
</details><br>
# 326b98c3-5fb0-4e05-9780-c70a66e62600
This model is a fine-tuned version of [Eurdem/Defne_llama3_2x8B](https://huggingface.co/Eurdem/Defne_llama3_2x8B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.9801
## 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: 0.0001009
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_BNB with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=adam_beta1=0.9,adam_beta2=0.95,adam_epsilon=1e-5
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 3.2543 | 0.0026 | 1 | 3.4941 |
| 3.9214 | 0.1290 | 50 | 3.1145 |
| 3.7631 | 0.2581 | 100 | 3.0596 |
| 4.0399 | 0.3871 | 150 | 3.0102 |
| 4.3471 | 0.5161 | 200 | 2.9801 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
FourOhFour/Virgil_9B | FourOhFour | "2024-09-27T09:09:09Z" | 5 | 4 | transformers | [
"transformers",
"safetensors",
"gemma2",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:FourOhFour/Dante_9B",
"base_model:finetune:FourOhFour/Dante_9B",
"license:gemma",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-09-27T09:08:13Z" | ---
library_name: transformers
license: gemma
base_model: jeiku/Dante_9B
tags:
- generated_from_trainer
model-index:
- name: outputs/out
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
base_model: jeiku/Dante_9B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
load_in_8bit: false
load_in_4bit: false
strict: false
datasets:
- path: FourOhFour/RP_Phase
type: sharegpt
conversation: chatml
chat_template: chatml
val_set_size: 0.0025
output_dir: ./outputs/out
adapter:
lora_r:
lora_alpha:
lora_dropout:
lora_target_linear:
sequence_len: 8192
sample_packing: true
eval_sample_packing: false
pad_to_sequence_len: true
plugins:
- axolotl.integrations.liger.LigerPlugin
liger_rope: true
liger_rms_norm: false
liger_swiglu: true
liger_fused_linear_cross_entropy: false
wandb_project: chatml9B
wandb_entity:
wandb_watch:
wandb_name: chatml9B
wandb_log_model:
gradient_accumulation_steps: 32
micro_batch_size: 1
num_epochs: 2
optimizer: adamw_bnb_8bit
lr_scheduler: cosine
learning_rate: 0.000008
weight_decay: 0.05
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: true
gradient_checkpointing: true
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
warmup_ratio: 0.1
evals_per_epoch: 4
eval_table_size:
eval_max_new_tokens: 128
saves_per_epoch: 2
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
fsdp:
fsdp_config:
special_tokens:
pad_token: <pad>
```
</details><br>
# outputs/out
This model is a fine-tuned version of [jeiku/Dante_9B](https://huggingface.co/jeiku/Dante_9B) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7075
## 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: 8e-06
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 32
- total_train_batch_size: 128
- total_eval_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 14
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.7474 | 0.0135 | 1 | 1.7996 |
| 1.6968 | 0.2570 | 19 | 0.9551 |
| 1.6583 | 0.5139 | 38 | 0.8805 |
| 1.5418 | 0.7709 | 57 | 0.7926 |
| 1.3997 | 1.0271 | 76 | 0.7500 |
| 1.3921 | 1.2847 | 95 | 0.7168 |
| 1.4141 | 1.5424 | 114 | 0.7155 |
| 1.4139 | 1.8 | 133 | 0.7075 |
### Framework versions
- Transformers 4.46.0.dev0
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.20.0
|
botenius/77608bed-f597-483f-8318-748dc83af5df | botenius | "2025-02-01T17:26:36Z" | 8 | 0 | peft | [
"peft",
"safetensors",
"gemma2",
"axolotl",
"generated_from_trainer",
"base_model:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
"base_model:adapter:UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2",
"license:gemma",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-02-01T16:59:20Z" | ---
library_name: peft
license: gemma
base_model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
tags:
- axolotl
- generated_from_trainer
model-index:
- name: 77608bed-f597-483f-8318-748dc83af5df
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: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2
bf16: auto
chat_template: llama3
dataset_prepared_path: null
datasets:
- data_files:
- f2c6fc1a05a222ef_train_data.json
ds_type: json
format: custom
path: /workspace/input_data/f2c6fc1a05a222ef_train_data.json
type:
field_instruction: instruction
field_output: response
format: '{instruction}'
no_input_format: '{instruction}'
system_format: '{system}'
system_prompt: ''
debug: null
deepspeed: null
device_map: auto
do_eval: true
early_stopping_patience: null
eval_batch_size: 2
eval_max_new_tokens: 128
eval_steps: null
eval_table_size: null
evals_per_epoch: null
flash_attention: true
fp16: null
fsdp: null
fsdp_config: null
gradient_accumulation_steps: 4
gradient_checkpointing: true
gradient_clipping: 1.0
group_by_length: true
hub_model_id: botenius/77608bed-f597-483f-8318-748dc83af5df
hub_repo: null
hub_strategy: end
hub_token: null
learning_rate: 0.0001
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_grad_norm: 1.0
max_memory:
0: 75GB
max_steps: 200
micro_batch_size: 2
mlflow_experiment_name: /tmp/f2c6fc1a05a222ef_train_data.json
model_type: AutoModelForCausalLM
num_epochs: 1
optimizer: adamw_bnb_8bit
output_dir: miner_id_24
pad_to_sequence_len: true
resume_from_checkpoint: null
s2_attention: null
sample_packing: false
save_steps: null
saves_per_epoch: null
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: techspear-hub
wandb_mode: online
wandb_name: 0ff0058b-9db6-4475-aa6d-b1fec19d4a2c
wandb_project: Gradients-On-13
wandb_run: your_name
wandb_runid: 0ff0058b-9db6-4475-aa6d-b1fec19d4a2c
warmup_steps: 5
weight_decay: 0.01
xformers_attention: null
```
</details><br>
# 77608bed-f597-483f-8318-748dc83af5df
This model is a fine-tuned version of [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.3223
## 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: 5
- training_steps: 200
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.5518 | 0.7306 | 200 | 1.3223 |
### Framework versions
- PEFT 0.13.2
- Transformers 4.46.0
- Pytorch 2.5.0+cu124
- Datasets 3.0.1
- Tokenizers 0.20.1 |
deepanshdj/dj-phi-3_3.8b-16bit | deepanshdj | "2024-05-19T23:33:20Z" | 9 | 0 | transformers | [
"transformers",
"pytorch",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"trl",
"sft",
"phi",
"phi3",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"base_model:finetune:unsloth/Phi-3-mini-4k-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-05-19T22:18:40Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- trl
- sft
- phi
- phi3
base_model: unsloth/Phi-3-mini-4k-instruct
---
---
## model name: dj-phi-3_3.8b-16bit
## model creator: Deepansh Jha
## huggingface id: deepanshdj
## finetuned dataset: osaat1 (https://huggingface.co/datasets/deepanshdj/ossat1_8k_llama3)
---
# ๐ฆ Welcome to the dj-phi-3_3.8b-16bit Wonderland! ๐
## Unleash the Power of Conversation with dj-phi-3_3.8b-16bit
Dive into the enchanting world of dj-phi-3_3.8b-16bit, a marvel crafted by the ingenious Deepansh Jha! ๐ Licensed under the Apache License 2.0, this model is your passport to the realms of captivating dialogue and spellbinding text generation. ๐ฉโจ
## Discover the Magic
Envisioned with creativity and nurtured with passion, dj-phi-3_3.8b-16bit is your companion for all things conversational! ๐ฌ Whether you're weaving stories, sparking conversations, or crafting dialogues, this model is your trusty guide through the wonders of language. ๐๐
## Model Maven
- **Model Creator:** Deepansh Jha
- **License:** Apache License 2.0
## Embark on Your Journey
Unleash the potential of dj-phi-3_3.8b-16bit in your projects and endeavors! Let its charm and versatility illuminate your path to linguistic greatness. ๐โจ
## Join the Adventure
Come, be a part of this magical journey! ๐ Contribute, explore, and create with dj-phi-3_3.8b-16bit. The possibilities are as endless as the imagination itself! ๐๐ |
ProjectPersonal/rare-puppers | ProjectPersonal | "2023-08-21T10:59:58Z" | 192 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"huggingpics",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2023-08-21T10:59:49Z" | ---
tags:
- image-classification
- pytorch
- huggingpics
metrics:
- accuracy
model-index:
- name: rare-puppers
results:
- task:
name: Image Classification
type: image-classification
metrics:
- name: Accuracy
type: accuracy
value: 0.8478260636329651
---
# rare-puppers
Autogenerated by HuggingPics๐ค๐ผ๏ธ
Create your own image classifier for **anything** by running [the demo on Google Colab](https://colab.research.google.com/github/nateraw/huggingpics/blob/main/HuggingPics.ipynb).
Report any issues with the demo at the [github repo](https://github.com/nateraw/huggingpics).
## Example Images
#### Female
#### Male
 |
Lewdiculous/Infinitely-Laydiculous-7B-GGUF-IQ-Imatrix | Lewdiculous | "2024-03-15T18:45:11Z" | 73 | 7 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"roleplay",
"mistral",
"license:other",
"region:us"
] | null | "2024-03-15T16:36:03Z" | ---
library_name: transformers
tags:
- mergekit
- merge
- roleplay
- mistral
inference: false
license: other
---
This repository hosts GGUF-IQ-Imatrix quants for [Nitral-AI/Infinitely-Laydiculous-7B](https://huggingface.co/Nitral-AI/Infinitely-Laydiculous-7B).
**What does "Imatrix" mean?**
It stands for **Importance Matrix**, a technique used to improve the quality of quantized models.
The **Imatrix** is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process.
The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse.
[[1]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
For imatrix data generation, kalomaze's `groups_merged.txt` with added roleplay chats was used, you can find it [here](https://huggingface.co/Lewdiculous/Datura_7B-GGUF-Imatrix/blob/main/imatrix-with-rp-format-data.txt). This was just to add a bit more diversity to the data.
**Steps:**
```
Baseโข GGUF(F16)โข Imatrix-Data(F16)โข GGUF(Imatrix-Quants)
```
*Using the latest llama.cpp at the time.*
```python
quantization_options = [
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]
```
**Card image:**
## Original model information:
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Endevor/InfinityRP-v1-7B](https://huggingface.co/Endevor/InfinityRP-v1-7B)
* [l3utterfly/mistral-7b-v0.1-layla-v4](https://huggingface.co/l3utterfly/mistral-7b-v0.1-layla-v4)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Endevor/InfinityRP-v1-7B
layer_range: [0, 32]
- model: l3utterfly/mistral-7b-v0.1-layla-v4
layer_range: [0, 32]
merge_method: slerp
base_model: Endevor/InfinityRP-v1-7B
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
```
|
stvhuang/rcr-run-kmwbxvtx-83433-master-0_20240312T165236-ep50 | stvhuang | "2024-03-12T16:12:20Z" | 90 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"text-embeddings-inference",
"endpoints_compatible",
"region:us"
] | feature-extraction | "2024-03-12T16:11:10Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
NbAiLab/roberta_des_512_4e4 | NbAiLab | "2022-01-04T16:46:20Z" | 3 | 0 | transformers | [
"transformers",
"jax",
"tensorboard",
"roberta",
"fill-mask",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2022-03-02T23:29:04Z" | Just for performing some experiments. Do not use.
|
visheratin/nllb-clip-base | visheratin | "2023-10-11T15:10:32Z" | 250 | 4 | transformers | [
"transformers",
"pytorch",
"clip",
"dataset:visheratin/laion-coco-nllb",
"arxiv:2309.01859",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | null | "2023-09-13T14:40:06Z" | ---
license: cc-by-nc-4.0
datasets:
- visheratin/laion-coco-nllb
---
## Model Summary
NLLB-CLIP is a model that combines a text encoder from the [NLLB model](https://huggingface.co/facebook/nllb-200-distilled-600M) and an image encoder from the
standard [CLIP](https://huggingface.co/openai/clip-vit-base-patch32). This allows us to extend the model capabilities
to 201 languages of the Flores-200. NLLB-CLIP sets state-of-the-art on the [Crossmodal-3600](https://google.github.io/crossmodal-3600/) dataset by performing very
well on low-resource languages. You can find more details about the model in the [paper](https://arxiv.org/abs/2309.01859).
## How to use
The model [repo](https://huggingface.co/visheratin/nllb-clip-base/tree/main) contains the model code files that allow the use of NLLB-CLIP as any other model from the hub.
The interface is also compatible with CLIP models. Example code is below:
```
from transformers import AutoTokenizer, CLIPProcessor
import requests
from PIL import Image
from modeling_nllb_clip import NLLBCLIPModel # local file from the repo
processor = CLIPProcessor.from_pretrained("openai/clip-vit-base-patch32")
processor = processor.image_processor
tokenizer = AutoTokenizer.from_pretrained(
"facebook/nllb-200-distilled-600M"
)
image_path = "https://huggingface.co/spaces/jjourney1125/swin2sr/resolve/main/samples/butterfly.jpg"
image = Image.open(requests.get(image_path, stream=True).raw)
image_inputs = processor(images=image, return_tensors="pt")
text_inputs = tokenizer(
["cat", "dog", "butterfly"],
padding="longest",
return_tensors="pt",
)
hf_model = NLLBCLIPModel.from_pretrained("visheratin/nllb-clip-base")
outputs = hf_model(input_ids = text_inputs.input_ids, attention_mask = text_inputs.attention_mask, pixel_values=image_inputs.pixel_values)
```
## Acknowledgements
I thank [Lambda Cloud](https://lambdalabs.com/) for providing compute resources to train the model. |
Ashwin221/dodge-challenger | Ashwin221 | "2024-04-02T17:50:10Z" | 6 | 0 | diffusers | [
"diffusers",
"safetensors",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2024-04-02T17:45:12Z" | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Dodge-challenger Dreambooth model trained by Ashwin221 following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 23BCA003
Sample pictures of this concept:
|
azxky6645/01262002-modify_tamplate-boxed-600filtering-processing-5epochs | azxky6645 | "2025-01-26T11:32:50Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-01-26T11:31:46Z" | ---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
sr5434/swift-codellama-34b-lora | sr5434 | "2024-03-17T15:58:18Z" | 0 | 0 | null | [
"safetensors",
"code",
"license:mit",
"region:us"
] | null | "2024-03-17T15:56:56Z" | ---
license: mit
tags:
- code
---
Codellama 34b(base) finetuned on Swift code. I used Stanford CRFM's Levanter and a TPU v4-8 provided by the TensorFlow Research Cloud to build this. |
OsnNos/ppo-LunarLander-v2 | OsnNos | "2024-02-14T20:49:18Z" | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2024-02-14T20:49:02Z" | ---
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: 286.93 +/- 13.89
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
...
```
|
Abel-05/ppo-Huggy | Abel-05 | "2025-04-01T02:33:48Z" | 0 | 0 | ml-agents | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | reinforcement-learning | "2025-04-01T02:33:35Z" | ---
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 ๐ถ 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: Abel-05/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
|
ahebbar69/10-52-llama | ahebbar69 | "2024-01-23T17:35:21Z" | 2 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:NousResearch/Llama-2-7b-chat-hf",
"base_model:adapter:NousResearch/Llama-2-7b-chat-hf",
"region:us"
] | null | "2024-01-23T17:22:55Z" | ---
library_name: peft
base_model: NousResearch/Llama-2-7b-chat-hf
---
# 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 0.7.2.dev0 |
maciekwisniewski/ppo-LunarLander-v2-long | maciekwisniewski | "2025-03-06T00:49:21Z" | 0 | 0 | stable-baselines3 | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2025-03-06T00:49:04Z" | ---
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: 295.71 +/- 16.77
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
...
```
|
liddlefish/mnist_auto_encoder_crop_13 | liddlefish | "2024-08-22T15:25:47Z" | 6 | 0 | null | [
"safetensors",
"model_hub_mixin",
"pytorch_model_hub_mixin",
"region:us"
] | null | "2024-08-22T15:09:19Z" | ---
tags:
- model_hub_mixin
- pytorch_model_hub_mixin
---
This model has been pushed to the Hub using the [PytorchModelHubMixin](https://huggingface.co/docs/huggingface_hub/package_reference/mixins#huggingface_hub.PyTorchModelHubMixin) integration:
- Library: [More Information Needed]
- Docs: [More Information Needed] |
Uschante/q-FrozenLake-v1-4x4-Slippery | Uschante | "2023-12-11T18:06:36Z" | 0 | 0 | null | [
"FrozenLake-v1-4x4",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | reinforcement-learning | "2023-12-11T18:06:33Z" | ---
tags:
- FrozenLake-v1-4x4
- q-learning
- reinforcement-learning
- custom-implementation
model-index:
- name: q-FrozenLake-v1-4x4-Slippery
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: FrozenLake-v1-4x4
type: FrozenLake-v1-4x4
metrics:
- type: mean_reward
value: 0.55 +/- 0.50
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="Uschante/q-FrozenLake-v1-4x4-Slippery", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=True etc)
env = gym.make(model["env_id"])
```
|
Adi0010/a2c-PandaReachDense-v2 | Adi0010 | "2023-07-25T16:28:24Z" | 1 | 0 | stable-baselines3 | [
"stable-baselines3",
"PandaReachDense-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | reinforcement-learning | "2023-07-25T15:49:38Z" | ---
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: -0.84 +/- 0.41
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
...
```
|
LiVE-Sophie-Rain-SpiderMan-Leaked-Video/X.Sophie.Rain.Spider-Man.Video.Tutorial | LiVE-Sophie-Rain-SpiderMan-Leaked-Video | "2025-02-21T18:45:36Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-02-21T18:45:25Z" | 15 seconds ago
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">โบโ
๐พ๐๐๐พ๐ ๐๐๐๐ ==โบโบ ๐๐ช๐ก๐ก ๐๐๐๐๐คโค๏ธโค๏ธโฌ๏ธโฌ๏ธโ</a></p>
<a href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman" rel="nofollow">โบโ
==โบโบ Sophie Rainโs Spider-Man Video โค๏ธโค๏ธโฌ๏ธโฌ๏ธโ</a></p>
<p><a rel="nofollow" title="WATCH NOW" href="https://tv2online.com/Leaked/?v=Sophie+Rain+Spiderman"><img border="Sophie+Rain+Spidermanno" height="480" width="720" title="WATCH NOW" alt="WATCH NOW" src="https://i.ibb.co.com/xMMVF88/686577567.gif"></a></p>
Sophie Rain Spider Man Original Viral video Nudes took the internet by storm and amazed viewers on various Leaked social media platforms. Sophie Rain Spider Man, a young and talented digital creator, recently became famous thanks to this interesting video.
L๐aked Video Sophie Rain Spider Man Video Tutorial Original Video Viral Video L๐aked on X Twitter Telegram
Sophie Rain Spider Man Original Viral video Nudes took the internet by storm and amazed viewers on various Leaked social media platforms. Sophie Rain Spider Man, a young and talented digital creator, recently became famous thanks to this interesting video.
L๐aked Video Sophie Rain Spider Man Video Tutorial Original Video Viral Video L๐aked on X Twitter
Sophie Rain Spider Man Video Tutorial Original Video video oficial twitter
L๐aked Video Sophie Rain Spider Man Video Tutorial Original Video Viral Video L๐aked on X Twitter
. . . . . . . . . L๐aked Video Sophie Rain Spider Man Video Tutorial Original Video Viral Video L๐aked on X Twitter Telegram
L๐aked Video Sophie Rain Spider Man Video Tutorial Original Video Viral Video L๐aked on X Twitter
Sophie Rain Spider Man Video Tutorial Original Video video oficial twitter
L๐aked Video Sophie Rain Spider Man Video Tutorial Original Video Viral Video L๐aked on X Twitter.
Sophie Rainโs Spider-Man Video: A Hilarious Must-See
Modern media continuously evolving portrayals of spiderman reflect contemporary issues. From films to animated series to video games, the character remains
Sophie Rain's Spider-Man Dance-Off: You Won't Regret Watching
The response to the sophie rain spiderman vid has been overwhelmingly positive. Viewers have praised sophie for her authentic portrayal of the spiderman
Sophie Rainโs Spider-Man Cosplay: The Viral Instagram Video
Dec 3, 2024 ยท sophie rain is a big fan of spiderman, and she has created a number of videos and photoshoots featuring herself dressed up as the character. |
stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1 | stefan-it | "2023-10-26T11:15:29Z" | 4 | 0 | flair | [
"flair",
"pytorch",
"tensorboard",
"token-classification",
"sequence-tagger-model",
"fr",
"base_model:dbmdz/bert-base-historic-multilingual-64k-td-cased",
"base_model:finetune:dbmdz/bert-base-historic-multilingual-64k-td-cased",
"license:mit",
"region:us"
] | token-classification | "2023-10-25T10:14:08Z" | ---
language: fr
license: mit
tags:
- flair
- token-classification
- sequence-tagger-model
base_model: dbmdz/bert-base-historic-multilingual-64k-td-cased
widget:
- text: 'Parmi les remรจdes recommandรฉs par la Sociรฉtรฉ , il faut mentionner celui que
M . Schatzmann , de Lausanne , a proposรฉ :'
---
# Fine-tuned Flair Model on LeTemps French NER Dataset (HIPE-2022)
This Flair model was fine-tuned on the
[LeTemps French](https://github.com/hipe-eval/HIPE-2022-data/blob/main/documentation/README-letemps.md)
NER Dataset using hmBERT 64k as backbone LM.
The LeTemps dataset consists of NE-annotated historical French newspaper articles from mid-19C to mid 20C.
The following NEs were annotated: `loc`, `org` and `pers`.
# Results
We performed a hyper-parameter search over the following parameters with 5 different seeds per configuration:
* Batch Sizes: `[4, 8]`
* Learning Rates: `[3e-05, 5e-05]`
And report micro F1-score on development set:
| Configuration | Seed 1 | Seed 2 | Seed 3 | Seed 4 | Seed 5 | Average |
|-------------------|------------------|--------------|--------------|--------------|--------------|-----------------|
| `bs8-e10-lr3e-05` | [0.6654][1] | [0.6554][2] | [0.6606][3] | [0.6604][4] | [0.6621][5] | 0.6608 ยฑ 0.0036 |
| `bs4-e10-lr3e-05` | [0.6537][6] | [0.6543][7] | [0.6525][8] | [0.6539][9] | [0.6501][10] | 0.6529 ยฑ 0.0017 |
| `bs8-e10-lr5e-05` | [0.6595][11] | [0.6164][12] | [0.6574][13] | [0.6465][14] | [0.649][15] | 0.6458 ยฑ 0.0173 |
| `bs4-e10-lr5e-05` | [**0.6283**][16] | [0.6079][17] | [0.6232][18] | [0.6372][19] | [0.5944][20] | 0.6182 ยฑ 0.017 |
[1]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[2]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[3]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[4]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[5]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[6]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-1
[7]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-2
[8]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-3
[9]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-4
[10]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr3e-05-poolingfirst-layers-1-crfFalse-5
[11]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[12]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[13]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[14]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[15]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs8-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
[16]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-1
[17]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-2
[18]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-3
[19]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-4
[20]: https://hf.co/stefan-it/hmbench-letemps-fr-hmbert_64k-bs4-wsFalse-e10-lr5e-05-poolingfirst-layers-1-crfFalse-5
The [training log](training.log) and TensorBoard logs (not available for hmBERT Base model) are also uploaded to the model hub.
More information about fine-tuning can be found [here](https://github.com/stefan-it/hmBench).
# Acknowledgements
We thank [Luisa Mรคrz](https://github.com/LuisaMaerz), [Katharina Schmid](https://github.com/schmika) and
[Erion รano](https://github.com/erionc) for their fruitful discussions about Historic Language Models.
Research supported with Cloud TPUs from Google's [TPU Research Cloud](https://sites.research.google/trc/about/) (TRC).
Many Thanks for providing access to the TPUs โค๏ธ
|
genki10/Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold3 | genki10 | "2025-03-01T16:13:59Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-01T15:57:10Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold3
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. -->
# Version2ASAP_FineTuningBERT_AugV8_k3_task1_organization_k3_k3_fold3
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5892
- Qwk: 0.5774
- Mse: 0.5895
- Rmse: 0.7678
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|
| No log | 1.0 | 3 | 9.4987 | 0.0 | 9.4970 | 3.0817 |
| No log | 2.0 | 6 | 6.8762 | 0.0 | 6.8749 | 2.6220 |
| No log | 3.0 | 9 | 4.8867 | 0.0093 | 4.8855 | 2.2103 |
| No log | 4.0 | 12 | 3.4657 | 0.0 | 3.4647 | 1.8614 |
| No log | 5.0 | 15 | 2.5127 | 0.0980 | 2.5119 | 1.5849 |
| No log | 6.0 | 18 | 1.8914 | 0.0202 | 1.8906 | 1.3750 |
| No log | 7.0 | 21 | 1.3800 | 0.0202 | 1.3794 | 1.1745 |
| No log | 8.0 | 24 | 1.1948 | 0.0202 | 1.1941 | 1.0927 |
| No log | 9.0 | 27 | 2.0060 | 0.1502 | 2.0053 | 1.4161 |
| No log | 10.0 | 30 | 1.1674 | 0.0365 | 1.1670 | 1.0803 |
| No log | 11.0 | 33 | 1.0969 | 0.0202 | 1.0962 | 1.0470 |
| No log | 12.0 | 36 | 1.1523 | 0.0365 | 1.1518 | 1.0732 |
| No log | 13.0 | 39 | 0.8589 | 0.2972 | 0.8587 | 0.9267 |
| No log | 14.0 | 42 | 0.8938 | 0.2983 | 0.8938 | 0.9454 |
| No log | 15.0 | 45 | 0.7197 | 0.4321 | 0.7196 | 0.8483 |
| No log | 16.0 | 48 | 0.6540 | 0.4354 | 0.6543 | 0.8089 |
| No log | 17.0 | 51 | 0.6279 | 0.3700 | 0.6278 | 0.7923 |
| No log | 18.0 | 54 | 0.5813 | 0.4549 | 0.5815 | 0.7625 |
| No log | 19.0 | 57 | 0.6977 | 0.3382 | 0.6977 | 0.8353 |
| No log | 20.0 | 60 | 0.6340 | 0.4092 | 0.6342 | 0.7963 |
| No log | 21.0 | 63 | 0.5500 | 0.4472 | 0.5501 | 0.7417 |
| No log | 22.0 | 66 | 0.5115 | 0.5090 | 0.5116 | 0.7152 |
| No log | 23.0 | 69 | 0.5381 | 0.5605 | 0.5381 | 0.7336 |
| No log | 24.0 | 72 | 0.5738 | 0.5666 | 0.5741 | 0.7577 |
| No log | 25.0 | 75 | 0.5425 | 0.5831 | 0.5426 | 0.7366 |
| No log | 26.0 | 78 | 0.6025 | 0.5860 | 0.6027 | 0.7763 |
| No log | 27.0 | 81 | 0.6201 | 0.5772 | 0.6203 | 0.7876 |
| No log | 28.0 | 84 | 0.8224 | 0.5279 | 0.8227 | 0.9070 |
| No log | 29.0 | 87 | 0.6582 | 0.5783 | 0.6587 | 0.8116 |
| No log | 30.0 | 90 | 0.6277 | 0.5904 | 0.6280 | 0.7925 |
| No log | 31.0 | 93 | 0.6157 | 0.5810 | 0.6160 | 0.7848 |
| No log | 32.0 | 96 | 0.6658 | 0.5688 | 0.6662 | 0.8162 |
| No log | 33.0 | 99 | 0.7505 | 0.5345 | 0.7511 | 0.8666 |
| No log | 34.0 | 102 | 0.7052 | 0.5377 | 0.7057 | 0.8400 |
| No log | 35.0 | 105 | 0.6072 | 0.5791 | 0.6076 | 0.7795 |
| No log | 36.0 | 108 | 0.6696 | 0.5643 | 0.6700 | 0.8185 |
| No log | 37.0 | 111 | 0.6451 | 0.5888 | 0.6456 | 0.8035 |
| No log | 38.0 | 114 | 0.6244 | 0.5750 | 0.6248 | 0.7904 |
| No log | 39.0 | 117 | 0.6714 | 0.5371 | 0.6717 | 0.8196 |
| No log | 40.0 | 120 | 0.6318 | 0.5607 | 0.6322 | 0.7951 |
| No log | 41.0 | 123 | 0.7037 | 0.5271 | 0.7040 | 0.8390 |
| No log | 42.0 | 126 | 0.6035 | 0.5694 | 0.6040 | 0.7772 |
| No log | 43.0 | 129 | 0.7107 | 0.5383 | 0.7111 | 0.8433 |
| No log | 44.0 | 132 | 0.6464 | 0.5463 | 0.6468 | 0.8042 |
| No log | 45.0 | 135 | 0.7366 | 0.5338 | 0.7369 | 0.8584 |
| No log | 46.0 | 138 | 0.5861 | 0.5803 | 0.5865 | 0.7658 |
| No log | 47.0 | 141 | 0.6159 | 0.5654 | 0.6162 | 0.7850 |
| No log | 48.0 | 144 | 0.6227 | 0.5632 | 0.6231 | 0.7893 |
| No log | 49.0 | 147 | 0.6299 | 0.5653 | 0.6302 | 0.7938 |
| No log | 50.0 | 150 | 0.5892 | 0.5774 | 0.5895 | 0.7678 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
mateiaassAI/mt5meu800 | mateiaassAI | "2024-06-06T13:52:04Z" | 110 | 0 | transformers | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2024-06-06T13:49:49Z" | ---
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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- **Shared by [optional]:** [More Information Needed]
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[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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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]
#### 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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KingKazma/xsum_t5-small_lora_500_10_50000_8_e5_s6789_v4_l4_r4 | KingKazma | "2023-09-02T16:59:22Z" | 0 | 0 | peft | [
"peft",
"region:us"
] | null | "2023-09-02T16:59:21Z" | ---
library_name: peft
---
## Training procedure
### Framework versions
- PEFT 0.6.0.dev0
|
LandCruiser/Linzz_11 | LandCruiser | "2025-03-23T17:35:15Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-03-23T17:27:24Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
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### Direct Use
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### Downstream Use [optional]
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[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
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[More Information Needed]
## Training Details
### Training Data
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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mylesgoose/Llama-3.1-Minitron-4B-Llava-Nvidia-siglip-ov | mylesgoose | "2024-09-26T08:52:00Z" | 7 | 0 | null | [
"safetensors",
"llama",
"arxiv:2408.03326",
"arxiv:2407.07895",
"arxiv:2311.05437",
"arxiv:2311.00571",
"arxiv:2310.03744",
"arxiv:2304.08485",
"arxiv:2306.00890",
"arxiv:2309.09958",
"arxiv:2309.10020",
"arxiv:2306.14895",
"license:other",
"region:us"
] | null | "2024-09-26T02:18:57Z" | ---
license: other
license_name: nvidia-open-model-license
license_link: >-
https://developer.download.nvidia.com/licenses/nvidia-open-model-license-agreement-june-2024.pdf
---
Pretrain of llama nvidia 4b merged with google vison encoder. Seems it needs finetuning on your dataset. As is just the base mdoel. It can identify images. However it does not follow instructions very well.
```python
from llava.model.builder import load_pretrained_model
from llava.mm_utils import get_model_name_from_path, process_images, tokenizer_image_token
from llava.constants import (
IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN, IGNORE_INDEX
)
from llava.conversation import conv_templates, SeparatorStyle
from datetime import date
from PIL import Image
import copy
import torch
import requests
# Model and device configurations
pretrained = "mylesgoose/Llama-3.1-Minitron-4B-Llava-Nvidia-siglip-ov"
model_name = "llava_llama3"
device = "cuda"
device_map = "auto"
# Load the pretrained model
tokenizer, model, image_processor, max_length = load_pretrained_model(
pretrained, None, model_name, device_map=device_map, attn_implementation="flash_attention_2"
)
# Set model to evaluation mode and tie weights
model.eval()
model.tie_weights()
# Load and process the image
url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/0052a70beed5bf71b92610a43a52df6d286cd5f3/diffusers/rabbit.jpg"
image = Image.open(requests.get(url, stream=True).raw)
image_tensor = process_images([image], image_processor, model.config)
image_tensor = [_image.to(dtype=torch.float16, device=device) for _image in image_tensor]
date_string: str = date.today().strftime("%d %b %Y")
# Conversation template and question
conv_template = "llava_llama_3"
question = (
DEFAULT_IMAGE_TOKEN + "\nWhat is shown in this image"
)
# Set up conversation and prepare the prompt
conv = copy.deepcopy(conv_templates[conv_template])
conv.append_message(conv.roles[0], question)
conv.append_message(conv.roles[1], None)
prompt_question = conv.get_prompt()
# Tokenize the prompt and prepare input for the model
input_ids = tokenizer_image_token(
prompt_question, tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt"
).unsqueeze(0).to(device)
image_sizes = [image.size]
# Generate output from the model
cont = model.generate(
input_ids,
images=image_tensor,
image_sizes=image_sizes,
do_sample=True,
temperature=0.6,
#pad_token_id=128004,
#eos_token_id=None,
max_new_tokens=131072,
)
# Decode and print the generated output
text_outputs = tokenizer.batch_decode(cont, skip_special_tokens=True)
print(text_outputs)
```
exerpt from the llava one vison repo Below.
<p align="center" width="100%">
<img src="https://i.postimg.cc/pL17YtG4/WX20240508-220230-2x.png" width="80%" height="80%">
</p>
# LLaVA-NeXT: Open Large Multimodal Models
[](https://arxiv.org/abs/2408.03326)
[](https://llava-vl.github.io/blog/)
[](https://llava-onevision.lmms-lab.com/)
[](https://huggingface.co/spaces/lmms-lab/LLaVA-NeXT-Interleave-Demo)
[](https://huggingface.co/spaces/WildVision/vision-arena)
[](https://openbayes.com/console/public/tutorials/gW0ng9jKXfO)
[](https://huggingface.co/collections/lmms-lab/llava-onevision-66a259c3526e15166d6bba37)
[](https://huggingface.co/collections/lmms-lab/llava-next-interleave-66763c55c411b340b35873d1)
[](https://huggingface.co/collections/lmms-lab/llava-next-video-661e86f5e8dabc3ff793c944)
[](https://huggingface.co/lmms-lab)
## Release Notes
- [2024/09/13] ๐ฅ **๐ [LLaVA-OneVision-Chat](docs/LLaVA_OneVision_Chat.md)**. The new LLaVA-OV-Chat (7B/72B) significantly improves the chat experience of LLaVA-OV. ๐
- [2024/08/06] ๐ฅ **๐ [LLaVA-OneVision (OV)](https://llava-vl.github.io/blog/2024-08-05-llava-onevision/)!** The new LLaVA-OV models (0.5B/7B/72B) achieve new state-of-the-art performance across single-image, multi-image, and video benchmarks, sometimes rivaling top commercial models on 47 diverse benchmarks. ๐ Explore More:
* [[Paper]](https://arxiv.org/abs/2408.03326): In-depth insights, new emegerging scenarios, ie, strong video understadning through task transfer from images.
* [[LLaVA-OV Doc]](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/docs/LLaVA_OneVision.md): Model inference and evaluation guidance.
* [[Scripts]](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/scripts/train): Start training models on your single-image/multi-image/video data.
- [2024/07/16] ๐ฅ **LLaVA-NeXT-Video** has been upgraded. The new 32B model achieves the best open-source performance on several video benchmarks, including [Video-MME](https://video-mme.github.io/home_page.html#leaderboard). Please refer to [this page](docs/LLaVA-NeXT-Video_0716.md) for details, refer to [llava_next-video_demo](https://huggingface.co/spaces/WildVision/vision-arena) for demo.
- [2024/06/23] ๐ฅ **LLaVA-NeXT-Interleave** is released. We utilize image-text interleaved format to unify multi-image, video, and 3D tasks in one LLM and achieve **SoTA** performance on a wide range of benchmarks. Check out [paper](https://arxiv.org/pdf/2407.07895), [blog](https://llava-vl.github.io/blog/2024-06-16-llava-next-interleave/), and [checkpoints](https://huggingface.co/collections/lmms-lab/llava-next-interleave-66763c55c411b340b35873d1) to see new capabilities and improved performance! We have released 0.5b, 7b, and 7b-dpo models.
* An all-round LLM for multi-image, video, and 3D with strong performance \[[demo](https://huggingface.co/spaces/lmms-lab/LLaVA-NeXT-Interleave-Demo)\]
* Construct interleave training data [**M4-Instruct**](https://huggingface.co/datasets/lmms-lab/M4-Instruct-Data)
* Construct multi-image benchmark [**LLaVA-Interleave Bench**](https://huggingface.co/datasets/lmms-lab/LLaVA-NeXT-Interleave-Bench)
- [2024/05/25] ๐ฅ Wondering "[What Else Influences Visual Instruction Tuning Beyond Data?](https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/)" Our new [blog](https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/) summarizes empirical explorations to ablate the various design choices in improving LMMs except instruct data itself. Meanwhile, open-source the recapioned high-quality data using LLaVA-NeXT-34B on [[COCO]](https://huggingface.co/datasets/lmms-lab/LLaVA-ReCap-118K) [[LCS]](https://huggingface.co/datasets/lmms-lab/LLaVA-ReCap-558K) [[CC3M]](https://huggingface.co/datasets/lmms-lab/LLaVA-ReCap-CC3M).
* Architectures (LMM & Vision Encoder)
* Visual Representations (Resolution & # Tokens)
* Training Strategies (High-quality data & Trainable modules)
- [2024/05/10] ๐ฅ **LLaVA-NeXT** (Stronger) models are released, with support of stronger LMM inlcuding LLama-3 (8B) and Qwen-1.5 (72B/110B) Check out [[blog](https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/)] and [[checkpoints](https://huggingface.co/lmms-lab)] to see improved performance!
- [2024/05/10] ๐ฅ **LLaVA-NeXT** (Video) is released. The image-only-trained LLaVA-NeXT model is surprisingly strong on video tasks with zero-shot modality transfer. DPO training with AI feedback on videos can yield significant improvement. [[Blog](https://llava-vl.github.io/blog/2024-04-30-llava-next-video/)], [[checkpoints](https://huggingface.co/collections/lmms-lab/llava-next-video-661e86f5e8dabc3ff793c944)] and [[sglang](https://github.com/sgl-project/sglang)]
- [2024/01/30] ๐ฅ **LLaVA-NeXT** is out! With additional scaling to LLaVA-1.5, LLaVA-NeXT-34B outperforms Gemini Pro on some benchmarks. It can now process 4x more pixels and perform more tasks/applications than before. Check out the [blog post](https://llava-vl.github.io/blog/2024-01-30-llava-next/), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). Training/eval data and scripts coming soon.
<details>
<summary>More</summary>
- [2024/03/10] ๐ฅ Releasing **LMMs-Eval**, a highly efficient evaluation pipeline we used when developing LLaVA-NeXT. It supports the evaluation of LMMs on dozens of public datasets and allows new dataset onboarding, making the dev of new LMMs much faster. [[Blog](https://lmms-lab.github.io/lmms-eval-blog/lmms-eval-0.1/)] [[Codebase](https://github.com/EvolvingLMMs-Lab/lmms-eval)]
- [2023/11/10] [LLaVA-Plus](https://llava-vl.github.io/llava-plus/) is released: Learning to Use Tools for Creating Multimodal Agents, with LLaVA-Plus (LLaVA that Plug and Learn to Use Skills). [[Project Page](https://llava-vl.github.io/llava-plus/)] [[Demo](https://llavaplus.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Plus-Codebase)] [[Paper](https://arxiv.org/abs/2311.05437)]
- [2023/11/02] [LLaVA-Interactive](https://llava-vl.github.io/llava-interactive/) is released: Experience the future of human-AI multimodal interaction with an all-in-one demo for Image Chat, Segmentation, Generation and Editing. [[Project Page](https://llava-vl.github.io/llava-interactive/)] [[Demo](https://llavainteractive.ngrok.io/)] [[Code](https://github.com/LLaVA-VL/LLaVA-Interactive-Demo)] [[Paper](https://arxiv.org/abs/2311.00571)]
- [2023/10/26] ๐ฅ LLaVA-1.5 with LoRA achieves comparable performance as full-model finetuning, with a reduced GPU RAM requirement ([ckpts](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md#llava-v15), [script](https://github.com/haotian-liu/LLaVA#train)). We also provide a [doc](https://github.com/haotian-liu/LLaVA/blob/main/docs/Finetune_Custom_Data.md) on how to finetune LLaVA-1.5 on your own dataset with LoRA.
- [2023/10/12] Check out the Korean LLaVA (Ko-LLaVA), created by ETRI, who has generously supported our research! [[๐ค Demo](https://huggingface.co/spaces/etri-vilab/Ko-LLaVA)]
- [2023/10/05] ๐ฅ LLaVA-1.5 is out! Achieving SoTA on 11 benchmarks, with just simple modifications to the original LLaVA, utilizes all public data, completes training in ~1 day on a single 8-A100 node, and surpasses methods like Qwen-VL-Chat that use billion-scale data. Check out the [technical report](https://arxiv.org/abs/2310.03744), and explore the [demo](https://llava.hliu.cc/)! Models are available in [Model Zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md). The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
- [2023/09/26] LLaVA is improved with reinforcement learning from human feedback (RLHF) to improve fact grounding and reduce hallucination. Check out the new SFT and RLHF checkpoints at project [[LLavA-RLHF]](https://llava-rlhf.github.io/)
- [2023/09/22] [LLaVA](https://arxiv.org/abs/2304.08485) is accepted by NeurIPS 2023 as **oral presentation**, and [LLaVA-Med](https://arxiv.org/abs/2306.00890) is accepted by NeurIPS 2023 Datasets and Benchmarks Track as **spotlight presentation**.
- [2023/11/06] Support **Intel** dGPU and CPU platforms. [More details here.](https://github.com/haotian-liu/LLaVA/tree/intel/docs/intel)
- [2023/10/12] LLaVA is now supported in [llama.cpp](https://github.com/ggerganov/llama.cpp/pull/3436) with 4-bit / 5-bit quantization support!
- [2023/10/11] The training data and scripts of LLaVA-1.5 are released [here](https://github.com/haotian-liu/LLaVA#train), and evaluation scripts are released [here](https://github.com/haotian-liu/LLaVA/blob/main/docs/Evaluation.md)!
- [2023/10/10] [Roboflow Deep Dive](https://blog.roboflow.com/first-impressions-with-llava-1-5/): First Impressions with LLaVA-1.5.
- [2023/09/20] We summarize our empirical study of training 33B and 65B LLaVA models in a [note](https://arxiv.org/abs/2309.09958). Further, if you are interested in the comprehensive review, evolution and trend of multimodal foundation models, please check out our recent survey paper [``Multimodal Foundation Models: From Specialists to General-Purpose Assistants''.](https://arxiv.org/abs/2309.10020)
<p align="center">
<img src="https://github.com/Computer-Vision-in-the-Wild/CVinW_Readings/blob/main/images/mfm_evolution.jpeg?raw=true" width=50%/>
</p>
- [2023/07/19] ๐ฅ We release a major upgrade, including support for LLaMA-2, LoRA training, 4-/8-bit inference, higher resolution (336x336), and a lot more. We release [LLaVA Bench](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_Bench.md) for benchmarking open-ended visual chat with results from Bard and Bing-Chat. We also support and verify training with RTX 3090 and RTX A6000. Check out [LLaVA-from-LLaMA-2](https://github.com/haotian-liu/LLaVA/blob/main/docs/LLaVA_from_LLaMA2.md), and our [model zoo](https://github.com/haotian-liu/LLaVA/blob/main/docs/MODEL_ZOO.md)!
- [2023/06/26] [CVPR 2023 Tutorial](https://vlp-tutorial.github.io/) on **Large Multimodal Models: Towards Building and Surpassing Multimodal GPT-4**! Please check out [[Slides](https://datarelease.blob.core.windows.net/tutorial/vision_foundation_models_2023/slides/Chunyuan_cvpr2023_tutorial_lmm.pdf)] [[Notes](https://arxiv.org/abs/2306.14895)] [[YouTube](https://youtu.be/mkI7EPD1vp8)] [[Bilibli](https://www.bilibili.com/video/BV1Ng4y1T7v3/)].
- [2023/06/11] We released the preview for the most requested feature: DeepSpeed and LoRA support! Please see documentations [here](./docs/LoRA.md).
- [2023/06/01] We released **LLaVA-Med: Large Language and Vision Assistant for Biomedicine**, a step towards building biomedical domain large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2306.00890) and [page](https://github.com/microsoft/LLaVA-Med).
- [2023/05/06] We are releasing [LLaVA-Lighting-MPT-7B-preview](https://huggingface.co/liuhaotian/LLaVA-Lightning-MPT-7B-preview), based on MPT-7B-Chat! See [here](#LLaVA-MPT-7b) for more details.
- [2023/05/02] ๐ฅ We are releasing LLaVA-Lighting! Train a lite, multimodal GPT-4 with just $40 in 3 hours! See [here](#train-llava-lightning) for more details.
- [2023/04/27] Thanks to the community effort, LLaVA-13B with 4-bit quantization allows you to run on a GPU with as few as 12GB VRAM! Try it out [here](https://github.com/oobabooga/text-generation-webui/tree/main/extensions/llava).
- [2023/04/17] ๐ฅ We released **LLaVA: Large Language and Vision Assistant**. We propose visual instruction tuning, towards building large language and vision models with GPT-4 level capabilities. Checkout the [paper](https://arxiv.org/abs/2304.08485) and [demo](https://llava.hliu.cc/).
</details>
<!-- <a href="https://llava.hliu.cc/"><img src="assets/demo.gif" width="70%"></a> -->
**Usage and License Notices**: This project utilizes certain datasets and checkpoints that are subject to their respective original licenses. Users must comply with all terms and conditions of these original licenses, including but not limited to the [OpenAI Terms of Use](https://openai.com/policies/terms-of-use) for the dataset and the specific licenses for base language models for checkpoints trained using the dataset (e.g. [Llama-1/2 community license](https://ai.meta.com/llama/license/) for LLaMA-2 and Vicuna-v1.5, [Tongyi Qianwen RESEARCH LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-0.5B-Chat/blob/main/LICENSE) and [Llama-3 Research License](https://llama.meta.com/llama3/license/)). This project does not impose any additional constraints beyond those stipulated in the original licenses. Furthermore, users are reminded to ensure that their use of the dataset and checkpoints is in compliance with all applicable laws and regulations.
## Models & Scripts
### Installation
#### 1. **Clone this repository and navigate to the LLaVA folder:**
```bash
git clone https://github.com/LLaVA-VL/LLaVA-NeXT
cd LLaVA-NeXT
```
#### 2. **Install the inference package:**
```bash
conda create -n llava python=3.10 -y
conda activate llava
pip install --upgrade pip # Enable PEP 660 support.
pip install -e ".[train]"
```
### Project Navigation
Please checkout the following page for more inference & evaluation details.
#### - **LLaVA-OneVision: Easy Task Transfer**
- [LLaVA-OneVision]([./docs/LLaVA-NeXT.md](https://github.com/LLaVA-VL/LLaVA-NeXT/blob/main/docs/LLaVA_OneVision.md)): for demo inference. The evaluation code is in [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval).
#### - **LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild**
- [LLaVA-NeXT-Image](./docs/LLaVA-NeXT.md): for image demo inference and evaluation of stronger LMMs using [lmms-eval](https://github.com/EvolvingLMMs-Lab/lmms-eval).
#### - LLaVA-NeXT: A Strong Zero-shot Video Understanding Model
- [LLaVA-NeXT-Video](./docs/LLaVA-NeXT-Video.md): for video inference and evaluation scripts. We recommend to use [LMMs-video](https://lmms-lab.github.io/posts/lmms-eval-0.2/) for evaluation.
#### - LLaVA-NeXT: Tackling Multi-image, Video, and 3D in Large Multimodal Models
- [LLaVA-NeXT-Interleave](./docs/LLaVA-NeXT-Interleave.md): for multi-image demo and evaluation scripts.
## SGLang for SpeedUp Inference and Deployment
We use [SGLang](https://github.com/sgl-project/sglang) to speed up inference and deployment of LLaVA-NeXT. You could make LLaVA-NeXT as a backend API service with SGLang.
**Prepare Environment**:
Following the instruction in the [sglang](https://github.com/sgl-project/sglang?tab=readme-ov-file#install)
### LLaVA-NeXT/OneVision
Checkout the HTTP Post/Get and SRT usage at [sglang/examples/runtime/llava_onevision](https://github.com/sgl-project/sglang/tree/main/examples/runtime/llava_onevision)
### LLaVA-NeXT (Video)
**Launch and Run on (K) Nodes**:
- Go to sglang project
```
cd PATH_TO/sglang
```
- First node:
```sh
bash examples/usage/llava_video/srt_example_llava_v.sh K 0 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
(e.g. bash examples/usage/llava_video/srt_example_llava_v.sh K 0 examples/usage/llava_video/videos/Q98Z4OTh8RwmDonc.mp4 lmms-lab/LLaVA-NeXT-Video-7B-DPO 16)
```
- Second node:
```sh
bash examples/usage/llava_video/srt_example_llava_v.sh K 1 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
```
- The K node:
```sh
bash examples/usage/llava_video/srt_example_llava_v.sh K K-1 YOUR_VIDEO_PATH YOUR_MODEL_PATH FRAMES_PER_VIDEO
```
## Citation
If you find it useful for your research and applications, please cite related papers/blogs using this BibTeX:
```bibtex
@article{li2024llava,
title={LLaVA-NeXT-Interleave: Tackling Multi-image, Video, and 3D in Large Multimodal Models},
author={Li, Feng and Zhang, Renrui and Zhang, Hao and Zhang, Yuanhan and Li, Bo and Li, Wei and Ma, Zejun and Li, Chunyuan},
journal={arXiv preprint arXiv:2407.07895},
year={2024}
}
@misc{li2024llavanext-ablations,
title={LLaVA-NeXT: What Else Influences Visual Instruction Tuning Beyond Data?},
url={https://llava-vl.github.io/blog/2024-05-25-llava-next-ablations/},
author={Li, Bo and Zhang, Hao and Zhang, Kaichen and Guo, Dong and Zhang, Yuanhan and Zhang, Renrui and Li, Feng and Liu, Ziwei and Li, Chunyuan},
month={May},
year={2024}
}
@misc{li2024llavanext-strong,
title={LLaVA-NeXT: Stronger LLMs Supercharge Multimodal Capabilities in the Wild},
url={https://llava-vl.github.io/blog/2024-05-10-llava-next-stronger-llms/},
author={Li, Bo and Zhang, Kaichen and Zhang, Hao and Guo, Dong and Zhang, Renrui and Li, Feng and Zhang, Yuanhan and Liu, Ziwei and Li, Chunyuan},
month={May},
year={2024}
}
@misc{zhang2024llavanext-video,
title={LLaVA-NeXT: A Strong Zero-shot Video Understanding Model},
url={https://llava-vl.github.io/blog/2024-04-30-llava-next-video/},
author={Zhang, Yuanhan and Li, Bo and Liu, haotian and Lee, Yong jae and Gui, Liangke and Fu, Di and Feng, Jiashi and Liu, Ziwei and Li, Chunyuan},
month={April},
year={2024}
}
@misc{liu2024llavanext,
title={LLaVA-NeXT: Improved reasoning, OCR, and world knowledge},
url={https://llava-vl.github.io/blog/2024-01-30-llava-next/},
author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Li, Bo and Zhang, Yuanhan and Shen, Sheng and Lee, Yong Jae},
month={January},
year={2024}
}
@misc{liu2023improvedllava,
title={Improved Baselines with Visual Instruction Tuning},
author={Liu, Haotian and Li, Chunyuan and Li, Yuheng and Lee, Yong Jae},
publisher={arXiv:2310.03744},
year={2023},
}
@misc{liu2023llava,
title={Visual Instruction Tuning},
author={Liu, Haotian and Li, Chunyuan and Wu, Qingyang and Lee, Yong Jae},
publisher={NeurIPS},
year={2023},
}
```
## Acknowledgement
- [Vicuna](https://github.com/lm-sys/FastChat): the codebase we built upon, and our base model Vicuna-13B that has the amazing language capabilities!
- The LLaVA-NeXT project is currently maintained by the team along with our contributors (listed alphabetically by the first names): [Bo Li](https://brianboli.com/), [Dong Guo](https://www.linkedin.com/in/dongguoset/), [Feng Li](https://scholar.google.com/citations?hl=zh-CN&user=ybRe9GcAAAAJ&view_op=list_works&sortby=pubdate), [Hao Zhang](https://scholar.google.com/citations?user=B8hPxMQAAAAJ&hl=en), [Kaichen Zhang](https://www.linkedin.com/in/kaichen-zhang-014b17219/?originalSubdomain=sg), [Renrui Zhang](https://zrrskywalker.github.io/), [Yuanhan Zhang](https://zhangyuanhan-ai.github.io/), led by [Chunyuan Li](https://chunyuan.li/) and with the guidance and help from [Haotian Liu](https://hliu.cc/).
- The `๏ปฟlmms-eval` framework and its core contributors, including Peiyuan Zhang, Fanyi Pu, Joshua Adrian Cahyono, and Kairui Hu, for their support on the evaluation side.
## Related Projects
- [Instruction Tuning with GPT-4](https://github.com/Instruction-Tuning-with-GPT-4/GPT-4-LLM)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://github.com/microsoft/LLaVA-Med)
- [Otter: In-Context Multi-Modal Instruction Tuning](https://github.com/Luodian/Otter)
For future project ideas, please check out:
- [SEEM: Segment Everything Everywhere All at Once](https://github.com/UX-Decoder/Segment-Everything-Everywhere-All-At-Once)
- [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything) to detect, segment, and generate anything by marrying [Grounding DINO](https://github.com/IDEA-Research/GroundingDINO) and [Segment-Anything](https://github.com/facebookresearch/segment-anything).
|
genki10/Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1 | genki10 | "2025-03-09T00:00:20Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-03-08T23:47:09Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_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. -->
# Version12AGAINNNASAP_FineTuningBERT_AugV12_k3_task1_organization_k3_k3_fold1
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8622
- Qwk: 0.6082
- Mse: 0.8615
- Rmse: 0.9282
## 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: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Qwk | Mse | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:------:|
| No log | 1.0 | 1 | 10.5093 | 0.0019 | 10.5068 | 3.2414 |
| No log | 2.0 | 2 | 8.7850 | -0.0002 | 8.7827 | 2.9636 |
| No log | 3.0 | 3 | 7.4729 | 0.0 | 7.4704 | 2.7332 |
| No log | 4.0 | 4 | 6.6926 | 0.0 | 6.6903 | 2.5866 |
| No log | 5.0 | 5 | 6.2097 | 0.0 | 6.2073 | 2.4915 |
| No log | 6.0 | 6 | 5.7641 | -0.0163 | 5.7618 | 2.4004 |
| No log | 7.0 | 7 | 5.2908 | 0.0 | 5.2886 | 2.2997 |
| No log | 8.0 | 8 | 4.7875 | 0.0 | 4.7854 | 2.1875 |
| No log | 9.0 | 9 | 4.2763 | 0.0 | 4.2742 | 2.0674 |
| No log | 10.0 | 10 | 3.7906 | 0.0 | 3.7886 | 1.9464 |
| No log | 11.0 | 11 | 3.3626 | 0.0 | 3.3607 | 1.8332 |
| No log | 12.0 | 12 | 2.9991 | 0.0 | 2.9972 | 1.7313 |
| No log | 13.0 | 13 | 2.6946 | 0.0 | 2.6927 | 1.6410 |
| No log | 14.0 | 14 | 2.4434 | -0.0012 | 2.4416 | 1.5626 |
| No log | 15.0 | 15 | 2.1879 | 0.1162 | 2.1862 | 1.4786 |
| No log | 16.0 | 16 | 1.9276 | 0.0583 | 1.9260 | 1.3878 |
| No log | 17.0 | 17 | 1.7158 | 0.0583 | 1.7143 | 1.3093 |
| No log | 18.0 | 18 | 1.5506 | 0.0211 | 1.5490 | 1.2446 |
| No log | 19.0 | 19 | 1.4256 | 0.0106 | 1.4240 | 1.1933 |
| No log | 20.0 | 20 | 1.2689 | 0.0106 | 1.2675 | 1.1258 |
| No log | 21.0 | 21 | 1.1662 | 0.0 | 1.1648 | 1.0793 |
| No log | 22.0 | 22 | 1.0785 | 0.0 | 1.0771 | 1.0378 |
| No log | 23.0 | 23 | 1.0046 | 0.0 | 1.0033 | 1.0016 |
| No log | 24.0 | 24 | 0.9398 | 0.0106 | 0.9385 | 0.9687 |
| No log | 25.0 | 25 | 0.8693 | 0.0446 | 0.8680 | 0.9317 |
| No log | 26.0 | 26 | 0.8499 | 0.2716 | 0.8487 | 0.9213 |
| No log | 27.0 | 27 | 0.7810 | 0.3763 | 0.7798 | 0.8831 |
| No log | 28.0 | 28 | 0.7333 | 0.3780 | 0.7322 | 0.8557 |
| No log | 29.0 | 29 | 0.6833 | 0.4068 | 0.6822 | 0.8259 |
| No log | 30.0 | 30 | 0.6952 | 0.3717 | 0.6941 | 0.8331 |
| No log | 31.0 | 31 | 0.6174 | 0.4308 | 0.6164 | 0.7851 |
| No log | 32.0 | 32 | 0.5769 | 0.4692 | 0.5758 | 0.7588 |
| No log | 33.0 | 33 | 0.5411 | 0.4999 | 0.5401 | 0.7349 |
| No log | 34.0 | 34 | 0.5740 | 0.4775 | 0.5730 | 0.7570 |
| No log | 35.0 | 35 | 0.5032 | 0.5439 | 0.5022 | 0.7087 |
| No log | 36.0 | 36 | 0.6180 | 0.4589 | 0.6170 | 0.7855 |
| No log | 37.0 | 37 | 0.5604 | 0.5040 | 0.5594 | 0.7479 |
| No log | 38.0 | 38 | 0.4635 | 0.6033 | 0.4626 | 0.6801 |
| No log | 39.0 | 39 | 0.4871 | 0.5669 | 0.4862 | 0.6973 |
| No log | 40.0 | 40 | 0.7304 | 0.4496 | 0.7294 | 0.8540 |
| No log | 41.0 | 41 | 0.7296 | 0.4523 | 0.7287 | 0.8536 |
| No log | 42.0 | 42 | 0.5380 | 0.5782 | 0.5372 | 0.7329 |
| No log | 43.0 | 43 | 0.4575 | 0.6606 | 0.4567 | 0.6758 |
| No log | 44.0 | 44 | 0.4901 | 0.6690 | 0.4893 | 0.6995 |
| No log | 45.0 | 45 | 0.7225 | 0.5092 | 0.7216 | 0.8495 |
| No log | 46.0 | 46 | 0.8038 | 0.4903 | 0.8029 | 0.8960 |
| No log | 47.0 | 47 | 0.6801 | 0.5849 | 0.6793 | 0.8242 |
| No log | 48.0 | 48 | 0.5812 | 0.6546 | 0.5805 | 0.7619 |
| No log | 49.0 | 49 | 0.6052 | 0.6366 | 0.6045 | 0.7775 |
| No log | 50.0 | 50 | 0.8076 | 0.5566 | 0.8069 | 0.8982 |
| No log | 51.0 | 51 | 0.8324 | 0.5429 | 0.8317 | 0.9119 |
| No log | 52.0 | 52 | 0.7288 | 0.5852 | 0.7280 | 0.8533 |
| No log | 53.0 | 53 | 0.6536 | 0.6139 | 0.6529 | 0.8080 |
| No log | 54.0 | 54 | 0.7707 | 0.5855 | 0.7700 | 0.8775 |
| No log | 55.0 | 55 | 0.8997 | 0.5501 | 0.8990 | 0.9481 |
| No log | 56.0 | 56 | 0.9499 | 0.5396 | 0.9491 | 0.9742 |
| No log | 57.0 | 57 | 0.7617 | 0.6062 | 0.7610 | 0.8724 |
| No log | 58.0 | 58 | 0.8151 | 0.5932 | 0.8144 | 0.9025 |
| No log | 59.0 | 59 | 0.8758 | 0.5845 | 0.8751 | 0.9355 |
| No log | 60.0 | 60 | 0.7737 | 0.6195 | 0.7730 | 0.8792 |
| No log | 61.0 | 61 | 0.8975 | 0.5821 | 0.8968 | 0.9470 |
| No log | 62.0 | 62 | 1.0986 | 0.5148 | 1.0979 | 1.0478 |
| No log | 63.0 | 63 | 1.0484 | 0.5390 | 1.0476 | 1.0235 |
| No log | 64.0 | 64 | 0.8622 | 0.6082 | 0.8615 | 0.9282 |
### Framework versions
- Transformers 4.47.0
- Pytorch 2.5.1+cu121
- Datasets 3.3.1
- Tokenizers 0.21.0
|
jsl5710/MBERT_uncased_CurriculumLoss_full_ft | jsl5710 | "2024-12-12T04:06:13Z" | 162 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:google-bert/bert-base-multilingual-uncased",
"base_model:finetune:google-bert/bert-base-multilingual-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-12-12T03:01:58Z" | ---
library_name: transformers
license: apache-2.0
base_model: google-bert/bert-base-multilingual-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: MBERT_uncased_CurriculumLoss_full_ft
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. -->
# MBERT_uncased_CurriculumLoss_full_ft
This model is a fine-tuned version of [google-bert/bert-base-multilingual-uncased](https://huggingface.co/google-bert/bert-base-multilingual-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Accuracy: 0.871
- F1: 0.9042
- Precision: 0.9076
- Recall: 0.9009
- Roc Auc: 0.8548
- Loss: 0.3497
## 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
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Accuracy | F1 | Precision | Recall | Roc Auc | Validation Loss |
|:-------------:|:-----:|:----:|:--------:|:------:|:---------:|:------:|:-------:|:---------------:|
| No log | 0.992 | 62 | 0.676 | 0.8067 | 0.676 | 1.0 | 0.5 | 0.6938 |
| 0.7054 | 2.0 | 125 | 0.859 | 0.8994 | 0.8690 | 0.9320 | 0.8194 | 0.3775 |
| 0.7054 | 2.976 | 186 | 0.871 | 0.9042 | 0.9076 | 0.9009 | 0.8548 | 0.3497 |
### Framework versions
- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3
|
Wolverine001/finetuning-sentiment-model-4k-samples | Wolverine001 | "2024-07-21T15:04:46Z" | 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 | "2024-07-21T14:32:24Z" | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: finetuning-sentiment-model-4k-samples
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. -->
# finetuning-sentiment-model-4k-samples
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0538
- Accuracy: 0.758
- F1 Macro: 0.4464
- F1 Weighted: 0.7602
## 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
### Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1
|
isspek/xlnet-base-cased_ebola_chatgpt_4_2e-5_16_undersampling_0.6 | isspek | "2024-11-17T20:43:40Z" | 122 | 0 | transformers | [
"transformers",
"safetensors",
"xlnet",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2024-11-17T20:43:13Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
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- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### 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] |
Rychiy/Lohnabrechnung_Adapters_2epochs | Rychiy | "2024-06-13T12:00:28Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2024-06-12T19:09:57Z" | ---
language:
- en
license: apache-2.0
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
base_model: unsloth/llama-3-8b-Instruct-bnb-4bit
---
# Uploaded model
- **Developed by:** Rychiy
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-Instruct-bnb-4bit
|
Liu-Xiang/OrpoLlama-3-8B | Liu-Xiang | "2024-12-15T16:54:18Z" | 6 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-08-14T07:02:41Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
PrunaAI/maxvit_base_tf_512.in21k_ft_in1k-turbo-tiny-green-smashed | PrunaAI | "2024-11-13T13:19:05Z" | 1 | 0 | pruna-engine | [
"pruna-engine",
"region:us"
] | null | "2024-03-19T13:36:55Z" | ---
library_name: pruna-engine
thumbnail: "https://assets-global.website-files.com/646b351987a8d8ce158d1940/64ec9e96b4334c0e1ac41504_Logo%20with%20white%20text.svg"
metrics:
- memory_disk
- memory_inference
- inference_latency
- inference_throughput
- inference_CO2_emissions
- inference_energy_consumption
---
<!-- header start -->
<!-- 200823 -->
<div style="width: auto; margin-left: auto; margin-right: auto">
<a href="https://docs.pruna.ai/en/latest/setup/pip.html" target="_blank" rel="noopener noreferrer">
<img src="https://imgur.com/rVAgqMY.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)
<div style="color: #9B1DBE; font-size: 2em; font-weight: bold;">
Deprecation Notice: This model is deprecated and will no longer receive updates.
</div>
<br><br>
# 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 by combining quantization, xformers, jit, cuda graphs, triton.
- ***How does the model quality change?*** The quality of the model output might slightly 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 used a custom Pruna model format based on pickle to make models compatible with the compression methods. We provide a tutorial to run models in dockers in the documentation [here](https://pruna-ai-pruna.readthedocs-hosted.com/en/latest/) if needed.
- ***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 that you have linux, python 3.10, and cuda 12.1.0 requirements installed. For cuda, check with `nvcc --version` and install with `conda install nvidia/label/cuda-12.1.0::cuda`.
1. Install the `pruna-engine` available [here](https://pypi.org/project/pruna-engine/) on Pypi. It might take up to 15 minutes to install.
```bash
pip install pruna-engine[gpu]==0.7.1 --extra-index-url https://pypi.nvidia.com --extra-index-url https://pypi.ngc.nvidia.com --extra-index-url https://prunaai.pythonanywhere.com/
```
2. Download the model files using one of these three options.
- Option 1 - Use command line interface (CLI):
```bash
mkdir maxvit_base_tf_512.in21k_ft_in1k-turbo-tiny-green-smashed
huggingface-cli download PrunaAI/maxvit_base_tf_512.in21k_ft_in1k-turbo-tiny-green-smashed --local-dir maxvit_base_tf_512.in21k_ft_in1k-turbo-tiny-green-smashed --local-dir-use-symlinks False
```
- Option 2 - Use Python:
```python
import subprocess
repo_name = "maxvit_base_tf_512.in21k_ft_in1k-turbo-tiny-green-smashed"
subprocess.run(["mkdir", repo_name])
subprocess.run(["huggingface-cli", "download", 'PrunaAI/'+ repo_name, "--local-dir", repo_name, "--local-dir-use-symlinks", "False"])
```
- Option 3 - Download them manually on the HuggingFace model page.
3. Load & run the model.
```python
from pruna_engine.PrunaModel import PrunaModel
model_path = "maxvit_base_tf_512.in21k_ft_in1k-turbo-tiny-green-smashed/model" # Specify the downloaded model path.
smashed_model = PrunaModel.load_model(model_path) # Load the model.
import torch; image = torch.rand(1, 3, 224, 224).to('cuda')
smashed_model(image)
```
## Configurations
The configuration info are in `model/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 maxvit_base_tf_512.in21k_ft_in1k 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). |
nathanialhunt2000/6f616ec6-f272-4a99-90db-84b688d499e7 | nathanialhunt2000 | "2025-02-26T03:24:34Z" | 0 | 0 | peft | [
"peft",
"generated_from_trainer",
"base_model:defog/llama-3-sqlcoder-8b",
"base_model:adapter:defog/llama-3-sqlcoder-8b",
"region:us"
] | null | "2025-02-26T03:24:21Z" | ---
library_name: peft
tags:
- generated_from_trainer
base_model: defog/llama-3-sqlcoder-8b
model-index:
- name: nathanialhunt2000/6f616ec6-f272-4a99-90db-84b688d499e7
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. -->
# nathanialhunt2000/6f616ec6-f272-4a99-90db-84b688d499e7
This model was trained from scratch on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1545
## 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 |
MaziyarPanahi/M7Yamshadowexperiment28_ShadowYam | MaziyarPanahi | "2024-04-09T09:56:23Z" | 18 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"Safetensors",
"text-generation-inference",
"merge",
"base_model:automerger/M7Yamshadowexperiment28-7B",
"base_model:merge:automerger/M7Yamshadowexperiment28-7B",
"base_model:automerger/ShadowYam-7B",
"base_model:merge:automerger/ShadowYam-7B",
"license:apache-2.0",
"autotrain_compatible",
"region:us"
] | text-generation | "2024-04-09T09:38:18Z" | ---
license: apache-2.0
tags:
- Safetensors
- text-generation-inference
- merge
model_name: M7Yamshadowexperiment28_ShadowYam
base_model:
- automerger/M7Yamshadowexperiment28-7B
- automerger/ShadowYam-7B
inference: false
model_creator: MaziyarPanahi
pipeline_tag: text-generation
quantized_by: MaziyarPanahi
---
# M7Yamshadowexperiment28_ShadowYam
M7Yamshadowexperiment28_ShadowYam is a merge of the following models:
* [automerger/M7Yamshadowexperiment28-7B](https://huggingface.co/automerger/M7Yamshadowexperiment28-7B)
* [automerger/ShadowYam-7B](https://huggingface.co/automerger/ShadowYam-7B)
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "MaziyarPanahi/M7Yamshadowexperiment28_ShadowYam"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` |
GraydientPlatformAPI/model_126 | GraydientPlatformAPI | "2023-04-03T02:44:04Z" | 30 | 0 | diffusers | [
"diffusers",
"license:openrail",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | text-to-image | "2023-04-03T00:58:46Z" | ---
license: openrail
library_name: diffusers
--- |
NishikaShetty/my-pet-dog-xzg | NishikaShetty | "2024-02-19T20:15:54Z" | 0 | 0 | null | [
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"region:us"
] | text-to-image | "2024-02-19T17:11:02Z" | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### My-Pet-Dog-XZG Dreambooth model trained by NishikaShetty following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: 4NM21AI046
Sample pictures of this concept:
.jfif)
|
nomnoos37/250211-kotask-toolace-scaled-exp2-7 | nomnoos37 | "2025-02-11T13:46:55Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"base_model:quantized:unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-02-11T09:38:27Z" | ---
base_model: unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- mistral
- gguf
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** nomnoos37
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Mistral-Nemo-Instruct-2407-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
DownwardSpiral33/gpt2-imdb-pos-2c2-reward-small-256_0_035-2024.06.09.06.24 | DownwardSpiral33 | "2024-06-09T07:29:55Z" | 154 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2024-06-09T07:29:18Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **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] |
beebeckzzz/dqn-SpaceInvadersNoFrameskip-v2 | beebeckzzz | "2023-03-16T14:21:57Z" | 0 | 0 | null | [
"CartPole-v1",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | reinforcement-learning | "2023-03-16T14:21:42Z" | ---
tags:
- CartPole-v1
- reinforce
- reinforcement-learning
- custom-implementation
- deep-rl-class
model-index:
- name: dqn-SpaceInvadersNoFrameskip-v2
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: CartPole-v1
type: CartPole-v1
metrics:
- type: mean_reward
value: 500.00 +/- 0.00
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
|
wanzhenchn/Qwen2.5-VL-7B-Instruct-gptqmodel-int4 | wanzhenchn | "2025-04-09T06:13:42Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2_5_vl",
"image-text-to-text",
"text-generation-inference",
"conversational",
"en",
"zh",
"base_model:Qwen/Qwen2.5-VL-7B-Instruct",
"base_model:quantized:Qwen/Qwen2.5-VL-7B-Instruct",
"license:mit",
"endpoints_compatible",
"4-bit",
"gptq",
"region:us"
] | image-text-to-text | "2025-04-09T04:08:10Z" | ---
license: mit
language:
- en
- zh
base_model:
- Qwen/Qwen2.5-VL-7B-Instruct
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- text-generation-inference
---
# Qwen2.5-VL-7B-Instruct-gptqmodel-int4
It is a GPTQ-INT4 quantized [Qwen2.5-VL-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-7B-Instruct) with [GPTQModel](https://github.com/ModelCloud/GPTQModel) toolkit.
## How to quantize
### Install
```bash
# Python 3.10.x or above
pip3 install -v "gptqmodel>=2.2.0" --no-build-isolation
```
### Quantize
```bash
python3 gptqmodel_quantize.py /path/to/Qwen2.5-VL-7B-Instruct/ /path/to/Qwen2.5-VL-7B-Instruct-gptqmodel-int4 4
```
```python
# gptqmodel_quantize.py
import fire
from datasets import load_dataset
from gptqmodel import GPTQModel, QuantizeConfig
from gptqmodel.models.definitions.base_qwen2_vl import BaseQwen2VLGPTQ
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["PYTORCH_CUDA_ALLOC_CONF"] = "expandable_segments:True"
os.environ["PYTHONUTF8"]="1"
def format_qwen2_vl_dataset(image, assistant):
return [
{
"role": "user",
"content": [
{"type": "image", "image": image},
{"type": "text", "text": "generate a caption for this image"},
],
},
{"role": "assistant", "content": assistant},
]
def prepare_dataset(format_func, n_sample: int = 20) -> list[list[dict]]:
from datasets import load_dataset
dataset = load_dataset(
"laion/220k-GPT4Vision-captions-from-LIVIS", split=f"train[:{n_sample}]"
)
return [
format_func(sample["url"], sample["caption"])
for sample in dataset
]
def get_calib_dataset(model):
if isinstance(model, BaseQwen2VLGPTQ):
return prepare_dataset(format_qwen2_vl_dataset, n_sample=256)
raise NotImplementedError(f"Unsupported MODEL: {model.__class__}")
def quantize(model_path: str,
output_path: str,
bit: int):
quant_config = QuantizeConfig(bits=bit, group_size=128)
model = GPTQModel.load(model_path, quant_config)
calibration_dataset = get_calib_dataset(model)
# increase `batch_size` to match gpu/vram specs to speed up quantization
model.quantize(calibration_dataset, batch_size=8)
model.save(output_path)
# test post-quant inference
model = GPTQModel.load(output_path)
result = model.generate("Uncovering deep insights begins with")[0] # tokens
print(model.tokenizer.decode(result)) # string output
if __name__ == "__main__":
fire.Fire(quantize)
```
|
silviasapora/gemma-7b-silvia-basic-5e-5-05-vshp11 | silviasapora | "2025-02-27T08:49:55Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"generated_from_trainer",
"alignment-handbook",
"trl",
"orpo",
"conversational",
"dataset:argilla/dpo-mix-7k",
"arxiv:2403.07691",
"base_model:google/gemma-7b",
"base_model:finetune:google/gemma-7b",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-02-20T05:46:16Z" | ---
base_model: google/gemma-7b
datasets:
- argilla/dpo-mix-7k
library_name: transformers
model_name: google/gemma-7b
tags:
- generated_from_trainer
- alignment-handbook
- trl
- orpo
licence: license
---
# Model Card for google/gemma-7b
This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the [['argilla/dpo-mix-7k']](https://huggingface.co/datasets/['argilla/dpo-mix-7k']) 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="silviasapora/gemma-7b-silvia-basic-5e-5-05-vshp11", 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/silvias/huggingface/runs/kl5exvqy)
This model was trained with ORPO, a method introduced in [ORPO: Monolithic Preference Optimization without Reference Model](https://huggingface.co/papers/2403.07691).
### Framework versions
- TRL: 0.13.0
- Transformers: 4.49.0
- Pytorch: 2.5.1
- Datasets: 3.2.0
- Tokenizers: 0.21.0
## Citations
Cite ORPO as:
```bibtex
@article{hong2024orpo,
title = {{ORPO: Monolithic Preference Optimization without Reference Model}},
author = {Jiwoo Hong and Noah Lee and James Thorne},
year = 2024,
eprint = {arXiv:2403.07691}
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouรฉdec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Subsets and Splits