id stringlengths 2 115 | author stringlengths 2 42 ⌀ | last_modified timestamp[us, tz=UTC] | downloads int64 0 8.87M | likes int64 0 3.84k | paperswithcode_id stringlengths 2 45 ⌀ | tags list | lastModified timestamp[us, tz=UTC] | createdAt stringlengths 24 24 | key stringclasses 1 value | created timestamp[us] | card stringlengths 1 1.01M | embedding list | library_name stringclasses 21 values | pipeline_tag stringclasses 27 values | mask_token null | card_data null | widget_data null | model_index null | config null | transformers_info null | spaces null | safetensors null | transformersInfo null | modelId stringlengths 5 111 ⌀ | embeddings list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
karawalla/mistral_b_karawalla_test5 | karawalla | 2023-11-29T06:54:43Z | 12 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"region:us"
] | 2023-11-29T06:54:43Z | 2023-11-29T06:54:37.000Z | null | null | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
---
# 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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0
| null | peft | null | null | null | null | null | null | null | null | null | null | karawalla/mistral_b_karawalla_test5 | [
-0.5779397487640381,
-0.5580515265464783,
0.4049736559391022,
0.08317572623491287,
-0.2534141540527344,
-0.2754514813423157,
0.06068454310297966,
-0.538404107093811,
0.048772186040878296,
0.613593339920044,
-0.7259422540664673,
-0.6298723816871643,
-0.5585343241691589,
-0.07971389591693878... |
NiallRooney/t5-large_PREFIX_TUNING_SEQ2SEQ | NiallRooney | 2023-11-29T10:33:27Z | 12 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:t5-large",
"region:us"
] | 2023-11-29T10:33:27Z | 2023-11-29T09:04:16.000Z | null | null | ---
library_name: peft
base_model: t5-large
---
# 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]
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## 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
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[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]
#### Hardware
[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Training procedure
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | NiallRooney/t5-large_PREFIX_TUNING_SEQ2SEQ | [
-0.583965003490448,
-0.5444982647895813,
0.4422628879547119,
0.10047025233507156,
-0.21837827563285828,
-0.29282113909721375,
0.1171051412820816,
-0.5604272484779358,
0.08466266095638275,
0.6898145079612732,
-0.7496820688247681,
-0.6463690400123596,
-0.5569515228271484,
-0.1242353245615959... |
jacobolopez/mistral_7b_stopsales | jacobolopez | 2023-11-29T12:05:56Z | 12 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:jacobolopez/Mistral-7B-v0.1-sharded",
"region:us"
] | 2023-11-29T12:05:56Z | 2023-11-29T12:05:42.000Z | null | null | ---
library_name: peft
base_model: jacobolopez/Mistral-7B-v0.1-sharded
---
# 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]
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## Uses
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### Direct Use
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[More Information Needed]
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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
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
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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. -->
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<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | jacobolopez/mistral_7b_stopsales | [
-0.5775126218795776,
-0.580460250377655,
0.4031943678855896,
0.09047012031078339,
-0.3009674847126007,
-0.2306295484304428,
0.022853529080748558,
-0.5071821808815002,
0.026032453402876854,
0.5738028883934021,
-0.7248213887214661,
-0.5956279039382935,
-0.57224041223526,
-0.03204140439629555... |
herrkobold/em_german_7b_leo-8bit | herrkobold | 2023-11-29T12:11:09Z | 12 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | 2023-11-29T12:11:09Z | 2023-11-29T12:06:59.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | herrkobold/em_german_7b_leo-8bit | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
zac/Turbo_Lora | zac | 2023-11-29T12:09:13Z | 12 | 0 | null | [
"diffusers",
"text-to-image",
"stable-diffusion",
"lora",
"template:sd-lora",
"base_model:stabilityai/sdxl-turbo",
"region:us"
] | 2023-11-29T12:09:13Z | 2023-11-29T12:08:36.000Z | null | null | ---
tags:
- text-to-image
- stable-diffusion
- lora
- diffusers
- template:sd-lora
widget:
- text: '-'
output:
url: images/ComfyUI_temp_bqrnl_00006_.png
base_model: stabilityai/sdxl-turbo
instance_prompt: null
---
# Turbo Lora
<Gallery />
## Download model
Weights for this model are available in Safetensors format.
[Download](/zac/Turbo_Lora/tree/main) them in the Files & versions tab.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | zac/Turbo_Lora | [
-0.10496733337640762,
-0.2975383996963501,
0.10733642429113388,
0.08472166210412979,
-0.588351309299469,
-0.05244357883930206,
0.2033219188451767,
-0.4956069886684418,
0.6661555767059326,
0.5756105184555054,
-0.4889809787273407,
-0.13115450739860535,
-0.5343330502510071,
-0.224156081676483... |
omartariq612/whisper-small-augmented-epoch-8 | omartariq612 | 2023-11-29T13:09:49Z | 12 | 0 | null | [
"transformers",
"safetensors",
"whisper",
"automatic-speech-recognition",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:09:49Z | 2023-11-29T13:08:51.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | omartariq612/whisper-small-augmented-epoch-8 | [
-0.12853401899337769,
-0.18616756796836853,
0.6529130935668945,
0.49436235427856445,
-0.1931932121515274,
0.23607449233531952,
0.3607199192047119,
0.05056357383728027,
0.5793656706809998,
0.7400139570236206,
-0.6508103609085083,
-0.23783999681472778,
-0.7102250456809998,
-0.047825817018747... |
AdityaBorse11/poca-SoccerTwos | AdityaBorse11 | 2023-11-29T13:47:33Z | 12 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"SoccerTwos",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SoccerTwos",
"region:us"
] | 2023-11-29T13:47:33Z | 2023-11-29T13:47:22.000Z | null | null | ---
library_name: ml-agents
tags:
- SoccerTwos
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SoccerTwos
---
# **poca** Agent playing **SoccerTwos**
This is a trained model of a **poca** agent playing **SoccerTwos**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AdityaBorse11/poca-SoccerTwos
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | AdityaBorse11/poca-SoccerTwos | [
-0.670887291431427,
-0.6775001883506775,
0.209807887673378,
0.1495358645915985,
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-0.2544480860233307,
0... |
Zaesar/phoenix_lawyer | Zaesar | 2023-11-29T15:46:15Z | 12 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T15:46:15Z | 2023-11-29T15:17:00.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Zaesar/phoenix_lawyer | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
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0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
YernazarBis/llama-2-7b-finetune-merged | YernazarBis | 2023-11-29T20:30:08Z | 12 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T20:30:08Z | 2023-11-29T20:25:25.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | YernazarBis/llama-2-7b-finetune-merged | [
-0.32276439666748047,
-0.22568444907665253,
0.8622260093688965,
0.434614896774292,
-0.5282991528511047,
0.7012969255447388,
0.7915714979171753,
0.0761861801147461,
0.7746025323867798,
0.2563214898109436,
-0.7852813601493835,
-0.2257383167743683,
-0.9104482531547546,
0.5715676546096802,
-... |
jhan21/amazon-reviews-distilbert-base-sentiment | jhan21 | 2023-11-29T19:31:25Z | 11 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:31:25Z | 2023-11-22T21:16:15.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- precision
- recall
- f1
model-index:
- name: results
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. -->
# Amazon-Food-Reviews-distilBERT-base for Sentiment Analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on this [Amazon food reviews dataset](https://huggingface.co/datasets/jhan21/amazon-food-reviews-dataset).
It achieves the following results on the evaluation set:
- Loss: 0.7442
- Accuracy: 0.7780
- Precision: 0.2593
- Recall: 0.3333
- F1: 0.2917
**Labels**: -1 -> Negative; 0 -> Neutral; 1 -> Positive
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:------:|
| 1.0089 | 0.04 | 500 | 0.8150 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0081 | 0.09 | 1000 | 0.7958 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0212 | 0.13 | 1500 | 0.7625 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0183 | 0.17 | 2000 | 0.7442 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0076 | 0.21 | 2500 | 0.7885 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0077 | 0.26 | 3000 | 0.7583 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0108 | 0.3 | 3500 | 0.7777 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.0108 | 0.34 | 4000 | 0.7992 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
| 1.021 | 0.38 | 4500 | 0.8263 | 0.7780 | 0.2593 | 0.3333 | 0.2917 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | jhan21/amazon-reviews-distilbert-base-sentiment | [
-0.6647962927818298,
-0.6590114831924438,
0.20842395722866058,
0.13012175261974335,
-0.18216054141521454,
-0.11826620250940323,
0.04428647458553314,
-0.08335943520069122,
0.39077672362327576,
0.3657166361808777,
-0.7131056189537048,
-0.7887760400772095,
-0.850422739982605,
-0.1116982251405... |
mwz/UrduSentimentClassification | mwz | 2023-11-29T11:52:42Z | 11 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"dataset:mwz/ur_financial_phrasebank",
"base_model:urduhack/roberta-urdu-small",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:52:42Z | 2023-11-23T08:36:51.000Z | null | null | ---
license: mit
base_model: urduhack/roberta-urdu-small
tags:
- generated_from_trainer
model-index:
- name: UrduSentimentClassification
results: []
widget:
- text: >-
وی این ایچ کی سالانہ خالص فروخت تقریبا 5 ملین یورو ہے اور اس میں 21 افراد
کام کرتے ہیں۔
example_title: Neutral Example
- text: تاہم مالی بحران کی وجہ سے ترقیاتی مارجن میں کمی آئی ہے۔
example_title: Negative Example
datasets:
- mwz/ur_financial_phrasebank
---
<!-- 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. -->
# UrduSentimentClassification
This model is a fine-tuned version of [urduhack/roberta-urdu-small](https://huggingface.co/urduhack/roberta-urdu-small) on Financial dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4924
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.4931 | 1.96 | 500 | 0.5585 |
| 0.2415 | 3.92 | 1000 | 0.3782 |
| 0.1116 | 5.88 | 1500 | 0.6486 |
| 0.0357 | 7.84 | 2000 | 0.4853 |
| 0.0101 | 9.8 | 2500 | 0.4924 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | mwz/UrduSentimentClassification | [
-0.24088391661643982,
-0.41361260414123535,
-0.12953263521194458,
0.38109874725341797,
-0.33889836072921753,
0.04662492871284485,
-0.08197235316038132,
-0.1699719876050949,
0.008428167551755905,
0.3763182461261749,
-0.6807402968406677,
-0.7169778347015381,
-0.8159498572349548,
-0.005870898... |
Bpole/lora_sberhack_v1.0 | Bpole | 2023-11-29T08:28:56Z | 11 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | 2023-11-29T08:28:56Z | 2023-11-28T21:26:07.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Bpole/lora_sberhack_v1.0 | [
-0.32276439666748047,
-0.22568444907665253,
0.8622260093688965,
0.434614896774292,
-0.5282991528511047,
0.7012969255447388,
0.7915714979171753,
0.0761861801147461,
0.7746025323867798,
0.2563214898109436,
-0.7852813601493835,
-0.2257383167743683,
-0.9104482531547546,
0.5715676546096802,
-... |
TheBloke/psyonic-cetacean-20B-GGUF | TheBloke | 2023-11-29T13:58:31Z | 11 | 0 | null | [
"transformers",
"gguf",
"llama",
"storywriting",
"text adventure",
"not-for-all-audiences",
"base_model:jebcarter/psyonic-cetacean-20B",
"license:other",
"text-generation-inference",
"region:us"
] | 2023-11-29T13:58:31Z | 2023-11-29T09:06:45.000Z | null | null | ---
base_model: jebcarter/psyonic-cetacean-20B
inference: false
license: other
license_name: microsoft-research-license
model_creator: Jeb Carter
model_name: Psyonic Cetacean 20B
model_type: llama
prompt_template: 'Below is an instruction that describes a task. Write a response
that appropriately completes the request.
### Instruction:
{prompt}
### Response:
'
quantized_by: TheBloke
tags:
- storywriting
- text adventure
- not-for-all-audiences
---
<!-- 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 -->
# Psyonic Cetacean 20B - GGUF
- Model creator: [Jeb Carter](https://huggingface.co/jebcarter)
- Original model: [Psyonic Cetacean 20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B)
<!-- description start -->
## Description
This repo contains GGUF format model files for [Jeb Carter's Psyonic Cetacean 20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B).
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- README_GGUF.md-about-gguf start -->
### About GGUF
GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.
Here is an incomplete list of clients and libraries that are known to support GGUF:
* [llama.cpp](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
* [GPT4All](https://gpt4all.io/index.html), a free and open source local running GUI, supporting Windows, Linux and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
* [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://faraday.dev/), an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
* [llama-cpp-python](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/ctransformers), a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.
<!-- README_GGUF.md-about-gguf end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/psyonic-cetacean-20B-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF)
* [Jeb Carter's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/jebcarter/psyonic-cetacean-20B)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: Alpaca
```
Below is an instruction that describes a task. Write a response that appropriately completes the request.
### Instruction:
{prompt}
### Response:
```
<!-- prompt-template end -->
<!-- licensing start -->
## Licensing
The creator of the source model has listed its license as `other`, and this quantization has therefore used that same license.
As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.
In the meantime, any questions regarding licensing, and in particular how these two licenses might interact, should be directed to the original model repository: [Jeb Carter's Psyonic Cetacean 20B](https://huggingface.co/jebcarter/psyonic-cetacean-20B).
<!-- licensing end -->
<!-- compatibility_gguf start -->
## Compatibility
These quantised GGUFv2 files are compatible with llama.cpp from August 27th onwards, as of commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221)
They are also compatible with many third party UIs and libraries - please see the list at the top of this README.
## Explanation of quantisation methods
<details>
<summary>Click to see details</summary>
The new methods available are:
* GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
* GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
* GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
* GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
* GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw
Refer to the Provided Files table below to see what files use which methods, and how.
</details>
<!-- compatibility_gguf end -->
<!-- README_GGUF.md-provided-files start -->
## Provided files
| Name | Quant method | Bits | Size | Max RAM required | Use case |
| ---- | ---- | ---- | ---- | ---- | ----- |
| [psyonic-cetacean-20b.Q2_K.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q2_K.gguf) | Q2_K | 2 | 8.31 GB| 10.81 GB | smallest, significant quality loss - not recommended for most purposes |
| [psyonic-cetacean-20b.Q3_K_S.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q3_K_S.gguf) | Q3_K_S | 3 | 8.66 GB| 11.16 GB | very small, high quality loss |
| [psyonic-cetacean-20b.Q3_K_M.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q3_K_M.gguf) | Q3_K_M | 3 | 9.70 GB| 12.20 GB | very small, high quality loss |
| [psyonic-cetacean-20b.Q3_K_L.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q3_K_L.gguf) | Q3_K_L | 3 | 10.63 GB| 13.13 GB | small, substantial quality loss |
| [psyonic-cetacean-20b.Q4_0.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q4_0.gguf) | Q4_0 | 4 | 11.29 GB| 13.79 GB | legacy; small, very high quality loss - prefer using Q3_K_M |
| [psyonic-cetacean-20b.Q4_K_S.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q4_K_S.gguf) | Q4_K_S | 4 | 11.34 GB| 13.84 GB | small, greater quality loss |
| [psyonic-cetacean-20b.Q4_K_M.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q4_K_M.gguf) | Q4_K_M | 4 | 12.04 GB| 14.54 GB | medium, balanced quality - recommended |
| [psyonic-cetacean-20b.Q5_0.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q5_0.gguf) | Q5_0 | 5 | 13.77 GB| 16.27 GB | legacy; medium, balanced quality - prefer using Q4_K_M |
| [psyonic-cetacean-20b.Q5_K_S.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q5_K_S.gguf) | Q5_K_S | 5 | 13.77 GB| 16.27 GB | large, low quality loss - recommended |
| [psyonic-cetacean-20b.Q5_K_M.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q5_K_M.gguf) | Q5_K_M | 5 | 14.16 GB| 16.66 GB | large, very low quality loss - recommended |
| [psyonic-cetacean-20b.Q6_K.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q6_K.gguf) | Q6_K | 6 | 16.40 GB| 18.90 GB | very large, extremely low quality loss |
| [psyonic-cetacean-20b.Q8_0.gguf](https://huggingface.co/TheBloke/psyonic-cetacean-20B-GGUF/blob/main/psyonic-cetacean-20b.Q8_0.gguf) | Q8_0 | 8 | 21.25 GB| 23.75 GB | very large, extremely low quality loss - not recommended |
**Note**: the above RAM figures assume no GPU offloading. If layers are offloaded to the GPU, this will reduce RAM usage and use VRAM instead.
<!-- README_GGUF.md-provided-files end -->
<!-- README_GGUF.md-how-to-download start -->
## How to download GGUF files
**Note for manual downloaders:** You almost never want to clone the entire repo! Multiple different quantisation formats are provided, and most users only want to pick and download a single file.
The following clients/libraries will automatically download models for you, providing a list of available models to choose from:
* LM Studio
* LoLLMS Web UI
* Faraday.dev
### In `text-generation-webui`
Under Download Model, you can enter the model repo: TheBloke/psyonic-cetacean-20B-GGUF and below it, a specific filename to download, such as: psyonic-cetacean-20b.Q4_K_M.gguf.
Then click Download.
### On the command line, including multiple files at once
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
Then you can download any individual model file to the current directory, at high speed, with a command like this:
```shell
huggingface-cli download TheBloke/psyonic-cetacean-20B-GGUF psyonic-cetacean-20b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage (click to read)</summary>
You can also download multiple files at once with a pattern:
```shell
huggingface-cli download TheBloke/psyonic-cetacean-20B-GGUF --local-dir . --local-dir-use-symlinks False --include='*Q4_K*gguf'
```
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/psyonic-cetacean-20B-GGUF psyonic-cetacean-20b.Q4_K_M.gguf --local-dir . --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
<!-- README_GGUF.md-how-to-download end -->
<!-- README_GGUF.md-how-to-run start -->
## Example `llama.cpp` command
Make sure you are using `llama.cpp` from commit [d0cee0d](https://github.com/ggerganov/llama.cpp/commit/d0cee0d36d5be95a0d9088b674dbb27354107221) or later.
```shell
./main -ngl 35 -m psyonic-cetacean-20b.Q4_K_M.gguf --color -c 4096 --temp 0.7 --repeat_penalty 1.1 -n -1 -p "Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:"
```
Change `-ngl 32` to the number of layers to offload to GPU. Remove it if you don't have GPU acceleration.
Change `-c 4096` to the desired sequence length. For extended sequence models - eg 8K, 16K, 32K - the necessary RoPE scaling parameters are read from the GGUF file and set by llama.cpp automatically. Note that longer sequence lengths require much more resources, so you may need to reduce this value.
If you want to have a chat-style conversation, replace the `-p <PROMPT>` argument with `-i -ins`
For other parameters and how to use them, please refer to [the llama.cpp documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/main/README.md)
## How to run in `text-generation-webui`
Further instructions can be found in the text-generation-webui documentation, here: [text-generation-webui/docs/04 ‐ Model Tab.md](https://github.com/oobabooga/text-generation-webui/blob/main/docs/04%20%E2%80%90%20Model%20Tab.md#llamacpp).
## How to run from Python code
You can use GGUF models from Python using the [llama-cpp-python](https://github.com/abetlen/llama-cpp-python) or [ctransformers](https://github.com/marella/ctransformers) libraries. Note that at the time of writing (Nov 27th 2023), ctransformers has not been updated for some time and is not compatible with some recent models. Therefore I recommend you use llama-cpp-python.
### How to load this model in Python code, using llama-cpp-python
For full documentation, please see: [llama-cpp-python docs](https://abetlen.github.io/llama-cpp-python/).
#### First install the package
Run one of the following commands, according to your system:
```shell
# Base ctransformers with no GPU acceleration
pip install llama-cpp-python
# With NVidia CUDA acceleration
CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install llama-cpp-python
# Or with OpenBLAS acceleration
CMAKE_ARGS="-DLLAMA_BLAS=ON -DLLAMA_BLAS_VENDOR=OpenBLAS" pip install llama-cpp-python
# Or with CLBLast acceleration
CMAKE_ARGS="-DLLAMA_CLBLAST=on" pip install llama-cpp-python
# Or with AMD ROCm GPU acceleration (Linux only)
CMAKE_ARGS="-DLLAMA_HIPBLAS=on" pip install llama-cpp-python
# Or with Metal GPU acceleration for macOS systems only
CMAKE_ARGS="-DLLAMA_METAL=on" pip install llama-cpp-python
# In windows, to set the variables CMAKE_ARGS in PowerShell, follow this format; eg for NVidia CUDA:
$env:CMAKE_ARGS = "-DLLAMA_OPENBLAS=on"
pip install llama-cpp-python
```
#### Simple llama-cpp-python example code
```python
from llama_cpp import Llama
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path="./psyonic-cetacean-20b.Q4_K_M.gguf", # Download the model file first
n_ctx=4096, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
n_gpu_layers=35 # The number of layers to offload to GPU, if you have GPU acceleration available
)
# Simple inference example
output = llm(
"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n\n### Instruction:\n{prompt}\n\n### Response:", # Prompt
max_tokens=512, # Generate up to 512 tokens
stop=["</s>"], # Example stop token - not necessarily correct for this specific model! Please check before using.
echo=True # Whether to echo the prompt
)
# Chat Completion API
llm = Llama(model_path="./psyonic-cetacean-20b.Q4_K_M.gguf", chat_format="llama-2") # Set chat_format according to the model you are using
llm.create_chat_completion(
messages = [
{"role": "system", "content": "You are a story writing assistant."},
{
"role": "user",
"content": "Write a story about llamas."
}
]
)
```
## How to use with LangChain
Here are guides on using llama-cpp-python and ctransformers with LangChain:
* [LangChain + llama-cpp-python](https://python.langchain.com/docs/integrations/llms/llamacpp)
* [LangChain + ctransformers](https://python.langchain.com/docs/integrations/providers/ctransformers)
<!-- README_GGUF.md-how-to-run end -->
<!-- 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, 阿明, 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 Bjäreholt, 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 start -->
# Original model card: Jeb Carter's Psyonic Cetacean 20B
---
Presenting the FP16 files for Psyonic-Cetacean-20B! This is an experimental Llama2-based stack merge based on the models and recipe below:
- [KoboldAI/PsyFighter-2-13b](https://huggingface.co/KoboldAI/Psyfighter-2-13B)
- [microsoft/Orca-2-13b](https://huggingface.co/microsoft/Orca-2-13b)
```yaml
slices:
- sources:
- model: Orca2flat
layer_range: [0, 16]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [8, 24]
- sources:
- model: Orca2flat
layer_range: [17, 32]
- sources:
- model: /KoboldAI/Psyfighter-2-13B (FP16 not yet available)
layer_range: [25, 40]
merge_method: passthrough
dtype: float16
```
Note: while we did run an inverted merge the output was not satisfactory and will not be released.
We first flatted the additional ChatML vocabulary tokens out of Orca-2-13B, then performed a stack merge with Psyfighter-2-13B. The results surprised us with their vividness, freshness of prose, obedience to instruction prompting, and formatting cohesion.
This model is focused on storywriting and text adventure, with a side order of Assistant and Chat functionality. Like its ancestor Psyfighter-2 this model will function better if you let it improvise and riff on your concepts rather than feeding it an excess of detail.
Additionally, either the removal of the ChatML vocab or the stack merging process itself has resulted in not only an uncensored model but an actively anti-censored model, so please be aware that this model can and will kill you during adventures or output NSFW material if prompted accordingly.
During testing, the model exhibited an especially strong affinity for science fiction and space opera writing, while handling fantasy elements quite well and horror elements slightly less so. Refer to the Psyfighter-2 model card for best prompting practices.
Despite that, we have tested the model out to 16000 context via Rope scaling and the model does not drive towards NSFW on its own. It will follow your tone and style very well.
Please enjoy, and if you encounter anything exciting or weird, please reach out to me at [jebcarter@pm.me].
Special thanks as always to the KoboldAI crew who provided the mergebox, testing, and feedback on this model.
<!-- original-model-card end -->
| null | transformers | null | null | null | null | null | null | null | null | null | null | TheBloke/psyonic-cetacean-20B-GGUF | [
-0.7039363980293274,
-0.707622766494751,
0.43278613686561584,
0.327041357755661,
-0.4908958077430725,
-0.0644577294588089,
-0.14933918416500092,
-0.6577840447425842,
0.46090149879455566,
0.3353846073150635,
-0.6690335869789124,
-0.5485766530036926,
-0.4556017220020294,
0.016907447949051857... |
1aurent/vit_base_patch16_224.deblurmim_us280k_vanilla_mae | 1aurent | 2023-11-29T14:15:03Z | 11 | 0 | null | [
"timm",
"safetensors",
"image-classification",
"license:apache-2.0",
"region:us"
] | 2023-11-29T14:15:03Z | 2023-11-29T14:14:46.000Z | null | null | ---
tags:
- image-classification
- timm
library_name: timm
license: apache-2.0
---
# Model card for vit_base_patch16_224.deblurmim_vanilla_mae
| null | timm | image-classification | null | null | null | null | null | null | null | null | null | 1aurent/vit_base_patch16_224.deblurmim_us280k_vanilla_mae | [
-0.25018516182899475,
-0.5807112455368042,
0.23125816881656647,
0.6066609621047974,
-0.7241377830505371,
0.13844965398311615,
0.6548824906349182,
0.14887580275535583,
0.4061580002307892,
0.81394362449646,
-0.8093244433403015,
-0.6829362511634827,
-0.6351457834243774,
-0.33688902854919434,
... |
gmmarcos/ppo-Huggy | gmmarcos | 2023-11-29T17:03:47Z | 11 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2023-11-29T17:03:47Z | 2023-11-29T17:03:42.000Z | null | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: gmmarcos/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | gmmarcos/ppo-Huggy | [
-0.5978931188583374,
-0.6401759386062622,
0.2400110960006714,
0.05154060944914818,
-0.21840263903141022,
0.23868674039840698,
0.18439054489135742,
-0.31899964809417725,
0.5931868553161621,
0.47023043036460876,
-0.6849365830421448,
-0.6499994397163391,
-0.4299323260784149,
-0.24506260454654... |
PGHFace/black-fortuner-ppg-to-white-colour | PGHFace | 2023-11-29T19:34:29Z | 11 | 0 | null | [
"diffusers",
"NxtWave-GenAI-Webinar",
"text-to-image",
"stable-diffusion",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T19:34:29Z | 2023-11-29T19:29:16.000Z | null | null | ---
license: creativeml-openrail-m
tags:
- NxtWave-GenAI-Webinar
- text-to-image
- stable-diffusion
---
### Black-Fortuner-ppg-to-white-colour- Dreambooth model trained by PGHFace following the "Build your own Gen AI model" session by NxtWave.
Project Submission Code: AITD-71
Sample pictures of this concept:
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | PGHFace/black-fortuner-ppg-to-white-colour | [
-0.5429454445838928,
-0.15447817742824554,
0.34863975644111633,
0.08851417154073715,
-0.5489151477813721,
0.5568393468856812,
0.5190297365188599,
-0.4153549373149872,
0.6972793936729431,
0.4670913517475128,
-0.5766581296920776,
-0.15334738790988922,
-0.5737718939781189,
-0.2160977721214294... |
Mimmiiz/whisper-small-hi | Mimmiiz | 2023-11-29T23:16:59Z | 11 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | 2023-11-29T23:16:59Z | 2023-11-29T20:59:47.000Z | null | null | Entry not found | null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Mimmiiz/whisper-small-hi | [
-0.3227648437023163,
-0.2256842851638794,
0.8622258305549622,
0.4346150755882263,
-0.5282991528511047,
0.7012966275215149,
0.7915719151496887,
0.07618607580661774,
0.774602472782135,
0.25632160902023315,
-0.7852813005447388,
-0.22573809325695038,
-0.910448431968689,
0.571567177772522,
-0... |
alimoezzi/ReportQL-base | alimoezzi | 2023-11-29T06:29:27Z | 10 | 1 | null | [
"transformers",
"pytorch",
"safetensors",
"t5",
"text2text-generation",
"medical",
"dialog",
"arxiv:2209.12177",
"en",
"dataset:pubmed",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:29:27Z | 2022-03-02T23:29:05.000Z | null | null | ---
datasets:
- pubmed
language:
- en
metrics:
- bleu
- exact_match
- sacrebleu
- rouge
tags:
- medical
- dialog
- arxiv:2209.12177
widget:
- text: >-
The liver is normal in size and with normal parenchymal echogenicity with no
sign of space-occupying lesion or bile ducts dilatation. GB is well
distended with no stone or wall thickening. The spleen is normal in size and
parenchymal echogenicity with no sign of space-occupying lesion. visualized
parts of the pancreas and para-aortic area are unremarkable. Both kidneys
are normal in size with normal cortical parenchymal echogenicity with no
sign of the stone, stasis, or perinephric collection. ureters are not
dilated. The urinary bladder is empty so evaluation of pelvic organs is not
possible. no free fluid is seen in the abdominopelvic cavity.
example_title: Sample 1
- text: >-
Liver is normal in size, with normal echogenicity with no sign of space
occupying lesion. GB is semi distended with two stones up to 8mm in size
with a rim of pericholecystic fluid and positive murphy sign. CBD is normal.
Spleen is moderately enlarged with normal parenchymal echo with no S.O.L.
Pancreas cannot be evaluated due to severe gas shadow. RT. and LT. kidneys
are normal in size with increase parenchymal echogenicity with no sign of
stone, stasis or perinephric collection with two cortical cystsin the upper
pole of left kidney. Urinary bladder is mildly distended. Moderate free
fluid is seen in the abdominopelvic cavity at present time.
example_title: Sample 2
inference:
parameters:
repetition_penalty: 1
num_beams: 5
early_stopping: true
max_length: 350
license: mit
---
# ReportQL — Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique
*[Seyed Ali Reza Moezzi](https://scholar.google.com/citations?hl=en&user=JIZgcjAAAAAJ)*,
*[Abdolrahman Ghaedi]()*,
*[Mojdeh Rahmanian](https://scholar.google.com/citations?user=2ZtVfnUAAAAJ)*,
*[Seyedeh Zahra Mousavi](https://www.researchgate.net/scientific-contributions/Seyedeh-Zahra-Mousavi-2176375936)*,
*[Ashkan Sami](https://scholar.google.com/citations?user=zIh9AvIAAAAJ)*
<html>
<div><sub><sup>*Submitted: 16 November 2021*</sup></sub></div>
<div><sub><sup>*Revised: 20 June 2022*</sup></sub></div>
<sub><sup>*Accepted: 27 July 2022*</sup></sub>
</html>
[[paper](https://link.springer.com/article/10.1007/s10278-022-00692-x)] [[arXiv](https://arxiv.org/abs/2209.12177)] [[dataset](https://www.kaggle.com/datasets/sarme77/reportql)] [[project page](https://realsarm.github.io/ReportQL/)]
## Introduction
This repository is code release for **Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique**
<p align="center"> <img src='https://raw.githubusercontent.com/realsarm/ReportQL/main/assets/overview.png' align="center" height="320px"> </p>
Since radiology reports needed for clinical practice and research are written and stored in free-text narrations, extraction of relative information for further analysis is difficult. In these circumstances, natural language processing (NLP) techniques can facilitate automatic information extraction and transformation of free-text formats to structured data. In recent years, deep learning (DL)-based models have been adapted for NLP experiments with promising results. Despite the significant potential of DL models based on artificial neural networks (ANN) and convolutional neural networks (CNN), the models face some limitations to implement in clinical practice. Transformers, another new DL architecture, have been increasingly applied to improve the process. Therefore, in this study, we propose a transformer-based fine-grained named entity recognition (NER) architecture for clinical information extraction. We collected 88 abdominopelvic sonography reports in free-text formats and annotated them based on our developed information schema. The text-to-text transfer transformer model (T5) and Scifive, a pre-trained domain-specific adaptation of the T5 model, were applied for fine-tuning to extract entities and relations and transform the input into a structured format. Our transformer-based model in this study outperformed previously applied approaches such as ANN and CNN models based on ROUGE-1, ROUGE-2, ROUGE-L, and BLEU scores of 0.816, 0.668, 0.528, and 0.743, respectively, while providing an interpretable structured report.
## Dataset
Our annotated [dataset](https://doi.org/10.5281/zenodo.7072374) used in the paper is hosted in this repository and in [Kaggle Datasets](https://www.kaggle.com/datasets/sarme77/reportql).
The data is structured as follows:
```
data/
├── trialReport
│ └── ReportQL
│ ├── Schemas
│ │ └── organs
│ │ └── simpleSchema.json
│ └── dataset
│ ├── test.csv
│ ├── train_orig.csv
│ └── training.csv
```
The `train_orig.csv` is our original training set. You can find our synthetic dataset and test set in `training.csv` and `test.csv` file.
Information schema used for annotating reports can be found in `simpleSchema.json`
## Setup
Setting up for this project involves installing dependencies.
### Setting up environments and Installing dependencies
```bash
virtualenv .venv
source .venv/bin/activate
```
### Installing dependencies
To install all the dependencies, please run the following:
```bash
pip install -r requirements.txt
```
### Fine-tuning
To start fine-tuning language model, run:
```bash
python script/fit.py
```
### Testing
For getting test results on our test set, run:
```bash
python script/test.py
```
### Inference
We prepared [a jupyter notebook](notebooks/predict_reportql.ipynb) for Inference.
## Fine-tuned Model
Our fine-tuned ReportQL weights can be accessed on 🤗 HuggingFace.
* ReportQL: [base](https://huggingface.co/sarme/ReportQL-base)
## License
Please see the [LICENSE](LICENSE) file for details.
## Citation
If you find our work useful in your research, please consider citing us:
```
@article{moezzi2022application,
title={Application of Deep Learning in Generating Structured Radiology Reports: A Transformer-Based Technique},
author={Moezzi, Seyed Ali Reza and Ghaedi, Abdolrahman and Rahmanian, Mojdeh and Mousavi, Seyedeh Zahra and Sami, Ashkan},
journal={Journal of Digital Imaging},
pages={1--11},
year={2022},
publisher={Springer}
}
``` | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | alimoezzi/ReportQL-base | [
-0.24107998609542847,
-0.5671874284744263,
0.4290917217731476,
0.038068726658821106,
-0.28517377376556396,
-0.12013301253318787,
-0.31732115149497986,
-0.6332343220710754,
0.006231546867638826,
0.4001097083091736,
-0.3291012942790985,
-0.7015590071678162,
-0.7411655783653259,
0.26312398910... |
Panchovix/goliath-120b-exl2-rpcal | Panchovix | 2023-11-30T00:29:19Z | 10 | 12 | null | [
"license:llama2",
"region:us"
] | 2023-11-30T00:29:19Z | 2023-11-06T17:37:40.000Z | null | null | ---
license: llama2
---
EXL2 quants of alpindale/goliath-120b (https://huggingface.co/alpindale/goliath-120b), to be used on exllamav2.
Calibration dataset is a cleaned, fixed pippa RP dataset, which does affect the results (in favor) for RP usage. You can find the calibration dataset [here.](https://huggingface.co/datasets/royallab/PIPPA-cleaned)
I've added a measurement.json file on the main branch if you want to do your own quants.
[4.85bpw](https://huggingface.co/Panchovix/goliath-120b-exl2-rpcal/tree/4.85bpw)
[4.5bpw](https://huggingface.co/Panchovix/goliath-120b-exl2-rpcal/tree/4.5bpw)
[3bpw](https://huggingface.co/Panchovix/goliath-120b-exl2-rpcal/tree/3bpw)
# Original model card
# Goliath 120B
An auto-regressive causal LM created by combining 2x finetuned [Llama-2 70B](https://huggingface.co/meta-llama/llama-2-70b-hf) into one.
Please check out the quantized formats provided by [@TheBloke](https:///huggingface.co/TheBloke) and [@Panchovix](https://huggingface.co/Panchovix):
- [GGUF](https://huggingface.co/TheBloke/goliath-120b-GGUF) (llama.cpp)
- [GPTQ](https://huggingface.co/TheBloke/goliath-120b-GPTQ) (KoboldAI, TGW, Aphrodite)
- [AWQ](https://huggingface.co/TheBloke/goliath-120b-AWQ) (TGW, Aphrodite, vLLM)
- [Exllamav2](https://huggingface.co/Panchovix/goliath-120b-exl2) (TGW, KoboldAI)
# Prompting Format
Both Vicuna and Alpaca will work, but due the initial and final layers belonging primarily to Xwin, I expect Vicuna to work the best.
# Merge process
The models used in the merge are [Xwin](https://huggingface.co/Xwin-LM/Xwin-LM-70B-V0.1) and [Euryale](https://huggingface.co/Sao10K/Euryale-1.3-L2-70B).
The layer ranges used are as follows:
```yaml
- range 0, 16
Xwin
- range 8, 24
Euryale
- range 17, 32
Xwin
- range 25, 40
Euryale
- range 33, 48
Xwin
- range 41, 56
Euryale
- range 49, 64
Xwin
- range 57, 72
Euryale
- range 65, 80
Xwin
```
# Screenshots
# Benchmarks
Coming soon.
# Acknowledgements
Credits goes to [@chargoddard](https://huggingface.co/chargoddard) for developing the framework used to merge the model - [mergekit](https://github.com/cg123/mergekit).
Special thanks to [@Undi95](https://huggingface.co/Undi95) for helping with the merge ratios. | null | null | null | null | null | null | null | null | null | null | null | null | Panchovix/goliath-120b-exl2-rpcal | [
-0.41704824566841125,
-0.3871173858642578,
-0.02571904845535755,
0.2792578339576721,
-0.25376302003860474,
-0.32485464215278625,
0.1765732616186142,
-0.624652087688446,
0.3594391942024231,
0.44978296756744385,
-0.5885254740715027,
-0.28730905055999756,
-0.38487622141838074,
-0.445952624082... |
mariavilla/phi-1_5-finetuned-PersonaExt | mariavilla | 2023-11-29T14:14:26Z | 10 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"phi",
"text-generation",
"generated_from_trainer",
"custom_code",
"base_model:microsoft/phi-1_5",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:14:26Z | 2023-11-14T13:14:55.000Z | null | null | ---
license: other
base_model: microsoft/phi-1_5
tags:
- generated_from_trainer
model-index:
- name: phi-1_5-finetuned-PersonaExt
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. -->
# phi-1_5-finetuned-PersonaExt
This model is a fine-tuned version of [microsoft/phi-1_5](https://huggingface.co/microsoft/phi-1_5) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 1000
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | mariavilla/phi-1_5-finetuned-PersonaExt | [
-0.43688032031059265,
-0.545943558216095,
0.07337850332260132,
0.2375900149345398,
-0.32956960797309875,
-0.5583741068840027,
0.1749466210603714,
-0.28152477741241455,
0.15964345633983612,
0.3805772662162781,
-0.9877809882164001,
-0.44948726892471313,
-0.5323102474212646,
-0.02592886798083... |
Roxysun/wav2vec2-large-xls-r-300m-hungarian-colab | Roxysun | 2023-11-29T07:46:07Z | 10 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:voxpopuli",
"base_model:facebook/wav2vec2-lv-60-espeak-cv-ft",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T07:46:07Z | 2023-11-14T19:19:46.000Z | null | null | ---
license: apache-2.0
base_model: facebook/wav2vec2-lv-60-espeak-cv-ft
tags:
- generated_from_trainer
datasets:
- voxpopuli
model-index:
- name: wav2vec2-large-xls-r-300m-hungarian-colab
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# wav2vec2-large-xls-r-300m-hungarian-colab
This model is a fine-tuned version of [facebook/wav2vec2-lv-60-espeak-cv-ft](https://huggingface.co/facebook/wav2vec2-lv-60-espeak-cv-ft) on the voxpopuli dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- 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_steps: 500
- num_epochs: 50
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Roxysun/wav2vec2-large-xls-r-300m-hungarian-colab | [
-0.4151739180088043,
-0.9003687500953674,
0.03316020593047142,
0.11556537449359894,
-0.3037355840206146,
-0.42626458406448364,
-0.32581180334091187,
-0.3389696180820465,
0.18522045016288757,
0.39338305592536926,
-0.7524648904800415,
-0.7110193967819214,
-0.5628020167350769,
-0.198829248547... |
jawadmohmmad/first | jawadmohmmad | 2023-11-29T10:39:53Z | 10 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"region:us"
] | 2023-11-29T10:39:53Z | 2023-11-25T09:15:40.000Z | null | null | ---
library_name: peft
base_model: mistralai/Mistral-7B-Instruct-v0.1
---
# 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | jawadmohmmad/first | [
-0.5717639327049255,
-0.5776198506355286,
0.40219631791114807,
0.08945130556821823,
-0.3010769784450531,
-0.2330036163330078,
0.022206755355000496,
-0.5059928894042969,
0.03302198275923729,
0.5753096342086792,
-0.7222673296928406,
-0.5964067578315735,
-0.5710545182228088,
-0.03449218347668... |
eek/zephyr-7b-sft-lora | eek | 2023-11-29T08:29:58Z | 10 | 0 | null | [
"adapter-transformers",
"tensorboard",
"generated_from_trainer",
"en",
"base_model:HuggingFaceH4/zephyr-7b-alpha",
"license:mit",
"region:us"
] | 2023-11-29T08:29:58Z | 2023-11-27T15:23:10.000Z | null | null | ---
license: mit
base_model: HuggingFaceH4/zephyr-7b-alpha
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-sft-lora
results: []
language:
- en
library_name: adapter-transformers
---
<!-- 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. -->
# zephyr-7b-sft-lora
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-alpha](https://huggingface.co/HuggingFaceH4/zephyr-7b-alpha) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1445
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 128
- total_train_batch_size: 4096
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.1612 | 0.36 | 16 | 2.1446 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.1+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1 | null | adapter-transformers | null | null | null | null | null | null | null | null | null | null | eek/zephyr-7b-sft-lora | [
-0.43785157799720764,
-0.6779254078865051,
0.14079858362674713,
0.23639878630638123,
-0.41911840438842773,
-0.4125106632709503,
-0.03884872794151306,
-0.40187516808509827,
0.26539695262908936,
0.3268090784549713,
-0.7653920650482178,
-0.4768718183040619,
-0.6587700247764587,
-0.14644382894... |
sivan22/halacha-siman-classifier | sivan22 | 2023-11-29T22:47:06Z | 10 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"dataset:sivan22/shulchan-aruch",
"endpoints_compatible",
"region:us"
] | 2023-11-29T22:47:06Z | 2023-11-28T07:46:10.000Z | null | null | ---
datasets:
- sivan22/shulchan-aruch
widget:
- text: "כמה פעמים נוטלים ידיים בבוקר?"
example_title: "דוגמה 1"
- text: "?האם מותר לטלטל פטיש בשביל להשתמש בו"
example_title: "דוגמה 2"
- text: "איזו ברכה מברכים בהדלקת נר חנוכה?"
example_title: "דוגמה 3"
---
המודל הזה מקבל משפט כלשהו, ומציין באיזה סימן בשולחן ערוך אורח חיים ניתן למצוא התייחסות לנושא.
לדוגמה:
האם מותר למלוח הרבה חתיכות של צנון?
תשובה:
תקי | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | sivan22/halacha-siman-classifier | [
-0.6096651554107666,
-0.7419642210006714,
0.8055317401885986,
0.7646493315696716,
-0.41384151577949524,
-0.7901886701583862,
0.15395620465278625,
-0.6863745450973511,
0.5203021764755249,
0.3956681191921234,
-0.964316189289093,
-0.1831166297197342,
-0.542568564414978,
-0.3164987862110138,
... |
cawoylel/windanam_mms-1b-tts-all | cawoylel | 2023-11-29T18:49:55Z | 10 | 0 | null | [
"transformers",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/mms-1b-all",
"license:cc-by-nc-4.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T18:49:55Z | 2023-11-28T15:49:46.000Z | null | null | ---
license: cc-by-nc-4.0
base_model: facebook/mms-1b-all
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: windanam_mms-1b-tts-all
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. -->
# windanam_mms-1b-tts-all
This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4714
- Wer: 0.2562
## 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: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 40000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.9783 | 0.05 | 2000 | 0.7042 | 0.3498 |
| 0.7823 | 0.09 | 4000 | 0.6023 | 0.3172 |
| 0.7933 | 0.14 | 6000 | 0.5706 | 0.3071 |
| 0.7018 | 0.19 | 8000 | 0.5553 | 0.2790 |
| 0.6997 | 0.23 | 10000 | 0.5386 | 0.2735 |
| 0.6916 | 0.28 | 12000 | 0.5324 | 0.2704 |
| 0.6623 | 0.33 | 14000 | 0.5187 | 0.2688 |
| 0.699 | 0.37 | 16000 | 0.5109 | 0.2670 |
| 0.7136 | 0.42 | 18000 | 0.5045 | 0.2646 |
| 0.7491 | 0.47 | 20000 | 0.4967 | 0.2636 |
| 0.6717 | 0.51 | 22000 | 0.4913 | 0.2618 |
| 0.6587 | 0.56 | 24000 | 0.4872 | 0.2607 |
| 0.7022 | 0.6 | 26000 | 0.4827 | 0.2598 |
| 0.6354 | 0.65 | 28000 | 0.4800 | 0.2587 |
| 0.6747 | 0.7 | 30000 | 0.4774 | 0.2576 |
| 0.6895 | 0.74 | 32000 | 0.4758 | 0.2575 |
| 0.9165 | 0.79 | 34000 | 0.4748 | 0.2570 |
| 0.6153 | 0.84 | 36000 | 0.4731 | 0.2562 |
| 0.6956 | 0.88 | 38000 | 0.4718 | 0.2563 |
| 0.6531 | 0.93 | 40000 | 0.4714 | 0.2562 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | cawoylel/windanam_mms-1b-tts-all | [
-0.5871903300285339,
-0.6958109736442566,
0.044070541858673096,
0.11343513429164886,
-0.16051699221134186,
-0.18502041697502136,
-0.029242319986224174,
-0.09326273202896118,
0.5292707085609436,
0.3352077603340149,
-0.9468855261802673,
-0.798880934715271,
-0.7174213528633118,
-0.21896809339... |
HelixAI/codellama-8bit-json-prompt-new-prompt-1129-1500-no_chat_history_epoch_7 | HelixAI | 2023-11-29T06:58:58Z | 10 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T06:58:58Z | 2023-11-29T06:51:03.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | HelixAI/codellama-8bit-json-prompt-new-prompt-1129-1500-no_chat_history_epoch_7 | [
-0.32276490330696106,
-0.2256845235824585,
0.8622258305549622,
0.4346151351928711,
-0.52829909324646,
0.7012964487075806,
0.791571855545044,
0.07618629187345505,
0.7746025323867798,
0.2563220262527466,
-0.7852813005447388,
-0.22573833167552948,
-0.9104480743408203,
0.5715667605400085,
-0... |
Anant58/ppo-SnowballTarget | Anant58 | 2023-11-29T07:51:27Z | 10 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2023-11-29T07:51:27Z | 2023-11-29T07:51:23.000Z | null | null | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Anant58/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | Anant58/ppo-SnowballTarget | [
-0.4356517195701599,
-0.560064435005188,
0.11986606568098068,
0.08081991225481033,
-0.2978843152523041,
0.31577861309051514,
0.18157194554805756,
-0.22306276857852936,
0.37019941210746765,
0.46424806118011475,
-0.7702391147613525,
-0.7462916374206543,
-0.5036311149597168,
-0.29098579287528... |
ThanhMai/image_classification_real_clips | ThanhMai | 2023-11-29T09:17:08Z | 10 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"vit",
"image-classification",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:17:08Z | 2023-11-29T08:14:39.000Z | null | null | ---
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: image_classification_real_clips
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. -->
# image_classification_real_clips
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1666
- Precision: 0.9683
- Recall: 0.9683
- F1: 0.9683
- Accuracy: 0.9683
## 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: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 4 | 0.3014 | 0.9845 | 0.9841 | 0.9840 | 0.9841 |
| No log | 2.0 | 8 | 0.2120 | 0.9845 | 0.9841 | 0.9840 | 0.9841 |
| 0.3068 | 3.0 | 12 | 0.1554 | 1.0 | 1.0 | 1.0 | 1.0 |
| 0.3068 | 4.0 | 16 | 0.1666 | 0.9683 | 0.9683 | 0.9683 | 0.9683 |
### Framework versions
- Transformers 4.28.0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.13.3
| null | transformers | image-classification | null | null | null | null | null | null | null | null | null | ThanhMai/image_classification_real_clips | [
-0.5206884145736694,
-0.4778587222099304,
0.14794886112213135,
0.037263914942741394,
-0.34544146060943604,
-0.25850334763526917,
0.02643520198762417,
-0.3269871175289154,
0.12457716464996338,
0.2336900532245636,
-0.6391869187355042,
-0.7378370761871338,
-0.858108639717102,
-0.2241376340389... |
ORamaVR/medical-stable-diffusion-2-1-lora | ORamaVR | 2023-11-29T16:13:01Z | 10 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T16:13:01Z | 2023-11-29T12:29:21.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - ORamaVR/medical-stable-diffusion-2-1-lora
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were fine-tuned on the ORamaVR/MedShapeNet-captions dataset. You can find some example images in the following.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | ORamaVR/medical-stable-diffusion-2-1-lora | [
-0.1964205950498581,
-0.6852673292160034,
0.13002155721187592,
0.2324817180633545,
-0.6414988040924072,
-0.40769267082214355,
0.2375095933675766,
-0.07565134018659592,
0.4841221570968628,
0.8721034526824951,
-0.5302053689956665,
-0.5440581440925598,
-0.8335961699485779,
-0.1588768512010574... |
bumblebee-testing/tiny-random-T5ForConditionalGeneration-tie_word_embeddings-False | bumblebee-testing | 2023-11-29T12:51:57Z | 10 | 0 | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T12:51:57Z | 2023-11-29T12:51:50.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | bumblebee-testing/tiny-random-T5ForConditionalGeneration-tie_word_embeddings-False | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
HelixAI/codellama-8bit-json-prompt-new-prompt-1129-1500-no_chat_history_v2_epoch_3 | HelixAI | 2023-11-29T15:26:24Z | 10 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T15:26:24Z | 2023-11-29T15:01:30.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | HelixAI/codellama-8bit-json-prompt-new-prompt-1129-1500-no_chat_history_v2_epoch_3 | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
omriKramer/ppo-Huggy | omriKramer | 2023-11-29T19:26:03Z | 10 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | 2023-11-29T19:26:03Z | 2023-11-29T19:25:57.000Z | null | null | ---
library_name: ml-agents
tags:
- Huggy
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Huggy
---
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: omriKramer/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | omriKramer/ppo-Huggy | [
-0.588743269443512,
-0.6468915343284607,
0.24575175344944,
0.04502347111701965,
-0.21863539516925812,
0.2166914939880371,
0.18857441842556,
-0.3104286193847656,
0.5896091461181641,
0.4810175895690918,
-0.6707525849342346,
-0.6447687149047852,
-0.4214538037776947,
-0.24530258774757385,
0.... |
KoboldAI/LLaMA2-13B-Psyfighter2 | KoboldAI | 2023-11-29T16:29:27Z | 9 | 1 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"license:llama2",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T16:29:27Z | 2023-11-13T22:40:39.000Z | null | null | ---
license: llama2
---
# LLAMA2-13B-Psyfighter2
Psyfighter is a merged model created by the KoboldAI community members Jeb Carter and TwistedShadows and was made possible thanks to the KoboldAI merge request service.
The intent was to add medical data to supplement the models fictional ability with more details on anatomy and mental states. Due to the low ratio's of medical data and the high ratio's of fiction this model should not be used for medical advice or therapy because of its high chance of pulling in fictional data.
The following mergekit recipe was used:
```
merge_method: task_arithmetic
base_model: TheBloke/Llama-2-13B-fp16
models:
- model: TheBloke/Llama-2-13B-fp16
- model: KoboldAI/LLaMA2-13B-Tiefighter
parameters:
weight: 1.0
- model: Doctor-Shotgun/cat-v1.0-13b
parameters:
weight: 0.01
- model: Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
parameters:
weight: 0.02
dtype: float16
```
*V1 of this model was published under the account of the creator of the merge
This model contains the following ingredients from their upstream models for as far as we can track them:
- KoboldAI/LLaMA2-13B-Tiefighter
- Undi95/Xwin-MLewd-13B-V0.2
- - Undi95/ReMM-S-Light
- Undi95/CreativeEngine
- Brouz/Slerpeno
- - elinas/chronos-13b-v2
- jondurbin/airoboros-l2-13b-2.1
- NousResearch/Nous-Hermes-Llama2-13b+nRuaif/Kimiko-v2
- CalderaAI/13B-Legerdemain-L2+lemonilia/limarp-llama2-v2
- - KoboldAI/LLAMA2-13B-Holodeck-1
- NousResearch/Nous-Hermes-13b
- OpenAssistant/llama2-13b-orca-8k-3319
- ehartford/WizardLM-1.0-Uncensored-Llama2-13b
- Henk717/spring-dragon
- The-Face-Of-Goonery/Huginn-v3-13b (Contains undisclosed model versions, those we assumed where possible)
- - SuperCOT (Undisclosed version)
- elinas/chronos-13b-v2 (Version assumed)
- NousResearch/Nous-Hermes-Llama2-13b
- stabilityai/StableBeluga-13B (Version assumed)
- zattio770/120-Days-of-LORA-v2-13B
- PygmalionAI/pygmalion-2-13b
- Undi95/Storytelling-v1-13B-lora
- TokenBender/sakhi_13B_roleplayer_NSFW_chat_adapter
- nRuaif/Kimiko-v2-13B
- The-Face-Of-Goonery/Huginn-13b-FP16
- - "a lot of different models, like hermes, beluga, airoboros, chronos.. limarp"
- lemonilia/LimaRP-Llama2-13B-v3-EXPERIMENT
- Xwin-LM/Xwin-LM-13B-V0.2
- PocketDoc/Dans-RetroRodeo-13b
- Blackroot/Llama-2-13B-Storywriter-LORA
- Doctor-Shotgun/cat-v1.0-13b
- Doctor-Shotgun/llama-2-13b-chat-limarp-v2-merged
- meta-llama/Llama-2-13b-chat-hf
- lemonilia/limarp-llama2-v2
While we could possibly not credit every single lora or model involved in this merged model, we'd like to thank all involved creators upstream for making this awesome model possible!
Thanks to you the AI ecosystem is thriving, and without your dedicated tuning efforts models such as this one would not be possible.
# Usage
This model is meant to be creative, If you let it improvise you get better results than if you drown it in details.
## Story Writing
Regular story writing in the traditional way is supported, simply copy paste your story and continue writing. Optionally use an instruction in memory or an authors note to guide the direction of your story.
### Generate a story on demand
To generate stories on demand you can use an instruction (tested in the Alpaca format) such as "Write a novel about X, use chapters and dialogue" this will generate a story. The format can vary between generations depending on how the model chooses to begin, either write what you want as shown in the earlier example or write the beginning of the story yourself so the model can follow your style. A few retries can also help if the model gets it wrong.
## Chatbots and persona's
This model has been tested with various forms of chatting, testers have found that typically less is more and the model is good at improvising. Don't drown the model in paragraphs of detailed information, instead keep it simple first and see how far you can lean on the models own ability to figure out your character. Copy pasting paragraphs of background information is not suitable for a 13B model such as this one, code formatted characters or an instruction prompt describing who you wish to talk to goes much further.
For example, you can put this in memory in regular chat mode:
```
### Instruction:
Generate a conversation between Alice and Jeb where they discuss language models.
In this conversation Henk is excited to teach Alice about Psyfighter.
### Response:
```
Because the model is a merge of a variety of models, it should support a broad range of instruct formats, or plain chat mode. If you have a particular favourite try it, otherwise we recommend to either use the regular chat mode or Alpaca's format.
## Instruct Prompting
This model features various instruct models on a variety of instruction styles, when testing the model we have used Alpaca for our own tests. If you prefer a different format chances are it can work.
During instructions we have observed that in some cases the adventure data can leak, it may also be worth experimenting using > as the prefix for a user command to remedy this. But this may result in a stronger fiction bias.
Keep in mind that while this model can be used as a factual instruct model, the focus was on fiction. Information provided by the model can be made up.
## Adventuring and Adventure Games
This model contains a lora that was trained on the same adventure dataset as the KoboldAI Skein model. Adventuring is best done using an small introduction to the world and your objective while using the > prefix for a user command (KoboldAI's adventure mode).
It is possible that the model does not immediately pick up on what you wish to do and does not engage in its Adventure mode behaviour right away. Simply manually correct the output to trim excess dialogue or other undesirable behaviour and continue to submit your actions using the appropriate mode. The model should pick up on this style quickly and will correctly follow this format within 3 turns.
## Discovered something cool and want to engage with us?
Join our community at https://koboldai.org/discord !
We can also provide assistance in making your own merges. | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | KoboldAI/LLaMA2-13B-Psyfighter2 | [
-0.41214942932128906,
-0.722038984298706,
0.45859280228614807,
0.26269248127937317,
-0.3714144825935364,
0.02888619899749756,
0.1200450137257576,
-0.8573324680328369,
0.5882486701011658,
0.7845456004142761,
-0.7662200927734375,
-0.1481628268957138,
-0.6061710715293884,
-0.06750030815601349... |
finiteautomata/roberta-large-bne-reranker | finiteautomata | 2023-11-29T01:46:08Z | 9 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T01:46:08Z | 2023-11-23T17:43:13.000Z | null | null | ---
{}
---
# Reranker with roberta
## Metrics
| Metric | Value |
| ------ | ----- |
| MRR | 0.675 |
| MRR Grouped | 0.720 |
| Accuracy | 0.597 |
| Accuracy Grouped | 0.643 | | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | finiteautomata/roberta-large-bne-reranker | [
0.25370267033576965,
-0.35227325558662415,
0.24618490040302277,
0.37736648321151733,
-0.3093838691711426,
0.2964649498462677,
-0.058745864778757095,
0.07509893923997879,
0.7669737935066223,
-0.11404667049646378,
-0.19517256319522858,
-0.8908149003982544,
-1.250306487083435,
0.0408575385808... |
NurtureAI/Starling-LM-11B-alpha-v1-AWQ | NurtureAI | 2023-11-30T01:18:40Z | 9 | 1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"reward model",
"RLHF",
"RLAIF",
"en",
"dataset:berkeley-nest/Nectar",
"arxiv:2306.02231",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-30T01:18:40Z | 2023-11-28T04:04:06.000Z | null | null | ---
datasets:
- berkeley-nest/Nectar
language:
- en
library_name: transformers
tags:
- reward model
- RLHF
- RLAIF
---
# Starling-RM-11B-alpha (AWQ)
Special thanks to user Undi95 for their mistral passthrough explanation.
Special thanks to berkley too of course for the great model.
Special thanks to everyone contributing to open source!
Together we are strong!
mergekit configuration used:
```
slices:
- sources:
- model: berkeley-nest/Starling-LM-7B-alpha
layer_range: [0, 24]
- sources:
- model: berkeley-nest/Starling-LM-7B-alpha
layer_range: [8, 32]
merge_method: passthrough
dtype: float16
```
Upon doing more text generation we have noticed that this is the best prompt when directing this model with a system prompt.
Replace {system} with your system prompt, and {instruction} with your instruction. Using GPT4 System is NOT optimal.
```
{system}<|end_of_turn|>\nGPT4 User: {instruction}<|end_of_turn|>GPT4 Assistant:
```
# Original Model Card
# Starling-RM-7B-alpha
<!-- Provide a quick summary of what the model is/does. -->
- **Developed by:** Banghua Zhu * , Evan Frick * , Tianhao Wu * , Hanlin Zhu and Jiantao Jiao.
- **Model type:** Language Model finetuned with RLHF / RLAIF
- **License:** Non commercial license
- **Finetuned from model:** [Openchat 3.5](https://huggingface.co/openchat/openchat_3.5) (based on [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1))
We introduce Starling-7B, an open large language model (LLM) trained by Reinforcement Learning from AI Feedback (RLAIF). The model harnesses the power of our new GPT-4 labeled ranking dataset, [berkeley-nest/Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), and our new reward training and policy tuning pipeline. Starling-7B-alpha scores 8.09 in MT Bench with GPT-4 as a judge, outperforming every model to date on MT-Bench except for OpenAI's GPT-4 and GPT-4 Turbo. We release the ranking dataset [Nectar](https://huggingface.co/datasets/berkeley-nest/Nectar), the reward model [Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) and the language model [Starling-LM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-LM-7B-alpha) on HuggingFace, and an online demo in LMSYS [Chatbot Arena](https://chat.lmsys.org). Stay tuned for our forthcoming code and paper, which will provide more details on the whole process.
Starling-LM-7B-alpha is a language model trained from [Openchat 3.5](https://huggingface.co/openchat/openchat_3.5) with reward model [berkeley-nest/Starling-RM-7B-alpha](https://huggingface.co/berkeley-nest/Starling-RM-7B-alpha) and policy optimization method [advantage-induced policy alignment (APA)](https://arxiv.org/abs/2306.02231). The evaluation results are listed below.
| Model | Tuning Method | MT Bench | AlpacaEval | MMLU |
|-----------------------|------------------|----------|------------|------|
| GPT-4-Turbo | ? | 9.32 | 97.70 | |
| GPT-4 | SFT + PPO | 8.99 | 95.28 | 86.4 |
| **Starling-7B** | C-RLFT + APA | 8.09 | 91.99 | 63.9 |
| Claude-2 | ? | 8.06 | 91.36 | 78.5 |
| GPT-3.5-Turbo | ? | 7.94 | 89.37 | 70 |
| Claude-1 | ? | 7.9 | 88.39 | 77 |
| Tulu-2-dpo-70b | SFT + DPO | 7.89 | 95.1 | |
| Openchat-3.5 | C-RLFT | 7.81 | 88.51 | 64.3 |
| Zephyr-7B-beta | SFT + DPO | 7.34 | 90.60 | 61.4 |
| Llama-2-70b-chat-hf | SFT + PPO | 6.86 | 92.66 | 63 |
| Neural-chat-7b-v3-1 | SFT + DPO | 6.84 | 84.53 | 62.4 |
| Tulu-2-dpo-7b | SFT + DPO | 6.29 | 85.1 | |
For more detailed discussions, please check out our [blog post](https://starling.cs.berkeley.edu), and stay tuned for our upcoming code and paper!
<!-- Provide the basic links for the model. -->
- **Blog:** https://starling.cs.berkeley.edu/
- **Paper:** Coming soon!
- **Code:** Coming soon!
## 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. -->
Our model follows the exact chat template and usage as [Openchat 3.5](https://huggingface.co/openchat/openchat_3.5). Please refer to their model card for more details.
In addition, our model is hosted on LMSYS [Chatbot Arena](https://chat.lmsys.org) for free test.
## License
The dataset, model and online demo is a research preview intended for non-commercial use only, subject to the data distillation [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
## Acknowledgment
We would like to thank Wei-Lin Chiang from Berkeley for detailed feedback of the blog and the projects. We would like to thank the [LMSYS Organization](https://lmsys.org/) for their support of [lmsys-chat-1M](https://huggingface.co/datasets/lmsys/lmsys-chat-1m) dataset, evaluation and online demo. We would like to thank the open source community for their efforts in providing the datasets and base models we used to develope the project, including but not limited to Anthropic, Llama, Mistral, Hugging Face H4, LMSYS, OpenChat, OpenBMB, Flan and ShareGPT.
## Citation
```
@misc{starling2023,
title = {Starling-7B: Improving LLM Helpfulness & Harmlessness with RLAIF},
url = {},
author = {Zhu, Banghua and Frick, Evan and Wu, Tianhao and Zhu, Hanlin and Jiao, Jiantao},
month = {November},
year = {2023}
}
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | NurtureAI/Starling-LM-11B-alpha-v1-AWQ | [
-0.23461449146270752,
-1.0020469427108765,
0.07697255909442902,
0.25314462184906006,
-0.14950406551361084,
-0.06780543923377991,
-0.4417595863342285,
-0.6289933323860168,
0.2048826366662979,
0.2716216444969177,
-0.5383630394935608,
-0.5254133343696594,
-0.46736451983451843,
-0.269194036722... |
Spacyzipa/NewModel | Spacyzipa | 2023-11-29T11:28:01Z | 9 | 0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:28:01Z | 2023-11-28T06:16:50.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | Spacyzipa/NewModel | [
-0.32276451587677,
-0.2256847620010376,
0.8622261881828308,
0.43461543321609497,
-0.5282991528511047,
0.7012973427772522,
0.7915714979171753,
0.07618623226881027,
0.7746027708053589,
0.25632160902023315,
-0.7852810025215149,
-0.22573824226856232,
-0.9104477763175964,
0.5715674161911011,
... |
HDanh/vit-base-patch16-224-in21k-finetuned-lora-food101 | HDanh | 2023-11-29T03:21:42Z | 9 | 0 | null | [
"peft",
"tensorboard",
"arxiv:1910.09700",
"base_model:google/vit-base-patch16-224-in21k",
"region:us"
] | 2023-11-29T03:21:42Z | 2023-11-28T09:58:23.000Z | null | null | ---
library_name: peft
base_model: google/vit-base-patch16-224-in21k
---
# 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]
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2
## Training procedure
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | HDanh/vit-base-patch16-224-in21k-finetuned-lora-food101 | [
-0.5787980556488037,
-0.5365272760391235,
0.4344230592250824,
0.0903967097401619,
-0.2189110368490219,
-0.2982580065727234,
0.12114553898572922,
-0.56462162733078,
0.07743624597787857,
0.694632351398468,
-0.7474148869514465,
-0.6277576684951782,
-0.5565719604492188,
-0.12091104686260223,
... |
hyonee/fine_model | hyonee | 2023-11-29T10:27:24Z | 9 | 0 | null | [
"transformers",
"pytorch",
"safetensors",
"git",
"text-generation",
"endpoints_compatible",
"region:us"
] | 2023-11-29T10:27:24Z | 2023-11-28T16:31:17.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | hyonee/fine_model | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
Tiru8055/zephyr-7b-dpo-lora | Tiru8055 | 2023-11-29T07:38:00Z | 9 | 0 | null | [
"transformers",
"tensorboard",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T07:38:00Z | 2023-11-29T02:37:35.000Z | null | null | ---
license: mit
base_model: HuggingFaceH4/zephyr-7b-beta
tags:
- generated_from_trainer
model-index:
- name: zephyr-7b-dpo-lora
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. -->
# zephyr-7b-dpo-lora
This model is a fine-tuned version of [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1394
- Rewards/chosen: 1.9472
- Rewards/rejected: -2.3030
- Rewards/accuracies: 0.8250
- Rewards/margins: 4.2502
- Logps/rejected: -241.4177
- Logps/chosen: -240.5213
- Logits/rejected: -2.7622
- Logits/chosen: -2.7854
## 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: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 16
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.0104 | 1.0 | 429 | 0.1394 | 1.9472 | -2.3030 | 0.8250 | 4.2502 | -241.4177 | -240.5213 | -2.7622 | -2.7854 |
### Framework versions
- Transformers 4.35.0
- Pytorch 2.1.0+cu118
- Datasets 2.14.6
- Tokenizers 0.14.1
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Tiru8055/zephyr-7b-dpo-lora | [
-0.5070312023162842,
-0.4949573874473572,
0.19372625648975372,
0.21187952160835266,
-0.3631436824798584,
-0.3065471351146698,
0.10186117887496948,
-0.3931851089000702,
0.42446061968803406,
0.37888625264167786,
-0.6956244111061096,
-0.6374570727348328,
-0.710496187210083,
0.0317964740097522... |
nimrita/dqn-SpaceInvadersNoFrameskip-v4 | nimrita | 2023-11-29T05:34:40Z | 9 | 0 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T05:34:40Z | 2023-11-29T05:34:02.000Z | null | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 550.50 +/- 165.87
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nimrita -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga nimrita -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga nimrita
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | nimrita/dqn-SpaceInvadersNoFrameskip-v4 | [
-0.6184279918670654,
-0.557582676410675,
0.274053692817688,
0.3579455316066742,
-0.1551940143108368,
-0.23633484542369843,
0.1457458734512329,
-0.18599927425384521,
0.18251435458660126,
0.3065297305583954,
-1.0227584838867188,
-0.4846503734588623,
-0.34710004925727844,
-0.05205951631069183... |
Bhandari007/openai-whisper-large-open-slr-0.0.1 | Bhandari007 | 2023-11-29T06:12:52Z | 9 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:openai/whisper-large",
"region:us"
] | 2023-11-29T06:12:52Z | 2023-11-29T06:12:44.000Z | null | null | ---
library_name: peft
base_model: openai/whisper-large
---
# 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
| null | peft | null | null | null | null | null | null | null | null | null | null | Bhandari007/openai-whisper-large-open-slr-0.0.1 | [
-0.574804425239563,
-0.5590018033981323,
0.40296828746795654,
0.07961388677358627,
-0.2534928023815155,
-0.27700263261795044,
0.060468919575214386,
-0.5367451906204224,
0.04952648654580116,
0.6133862733840942,
-0.7236800193786621,
-0.6278332471847534,
-0.5595568418502808,
-0.08562324941158... |
xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128 | xxyyy123 | 2023-11-29T07:16:29Z | 9 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | 2023-11-29T07:16:29Z | 2023-11-29T07:07:12.000Z | null | null | ---
library_name: peft
base_model: mistralai/Mistral-7B-v0.1
license: apache-2.0
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [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 Data 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 Data 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]
## Training procedure
### Framework versions
- PEFT 0.6.2 | null | peft | null | null | null | null | null | null | null | null | null | null | xxyyy123/1701221123_Ads_Mistral7B-slimorca_all-Lqv-r4b128 | [
-0.5982630252838135,
-0.5568307042121887,
0.43997249007225037,
0.10511711984872818,
-0.21972699463367462,
-0.3080681264400482,
0.12281549721956253,
-0.5639218688011169,
0.0778501033782959,
0.6828545928001404,
-0.7460103034973145,
-0.6527181267738342,
-0.548906683921814,
-0.1354885101318359... |
EricPeter/sw-model | EricPeter | 2023-11-29T11:04:32Z | 9 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T11:04:32Z | 2023-11-29T10:39:39.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | EricPeter/sw-model | [
-0.3227648437023163,
-0.2256842851638794,
0.8622258305549622,
0.4346150755882263,
-0.5282991528511047,
0.7012966275215149,
0.7915719151496887,
0.07618607580661774,
0.774602472782135,
0.25632160902023315,
-0.7852813005447388,
-0.22573809325695038,
-0.910448431968689,
0.571567177772522,
-0... |
nimrita/ppo-SnowballTarget | nimrita | 2023-11-29T11:55:34Z | 9 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | 2023-11-29T11:55:34Z | 2023-11-29T11:55:29.000Z | null | null | ---
library_name: ml-agents
tags:
- SnowballTarget
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-SnowballTarget
---
# **ppo** Agent playing **SnowballTarget**
This is a trained model of a **ppo** agent playing **SnowballTarget**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: nimrita/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | nimrita/ppo-SnowballTarget | [
-0.44272321462631226,
-0.5581335425376892,
0.11029484868049622,
0.09103039652109146,
-0.3023255467414856,
0.3228163421154022,
0.17702072858810425,
-0.22610852122306824,
0.37193840742111206,
0.4534682035446167,
-0.7772080898284912,
-0.7468265295028687,
-0.5054152607917786,
-0.29420283436775... |
mdosama39/xlm-roberta-base-Stress-identification | mdosama39 | 2023-11-29T14:54:10Z | 9 | 1 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T14:54:10Z | 2023-11-29T14:11:05.000Z | null | null | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
model-index:
- name: xlm-roberta-base-Stress-identification
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-Stress-identification
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
### Training results
### Framework versions
- Transformers 4.35.0
- Pytorch 2.0.0
- Datasets 2.1.0
- Tokenizers 0.14.1
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | mdosama39/xlm-roberta-base-Stress-identification | [
-0.3981415927410126,
-0.7570464015007019,
0.3585600256919861,
0.2525964677333832,
-0.45118263363838196,
-0.3439946174621582,
-0.08579317480325699,
-0.3904878497123718,
0.0318802185356617,
0.44663164019584656,
-0.8176131844520569,
-0.5417590141296387,
-0.8250171542167664,
0.1293585896492004... |
DKud7/finetuned-roberta-squad2-tiny | DKud7 | 2023-11-29T19:24:16Z | 9 | 0 | null | [
"transformers",
"pytorch",
"roberta",
"question-answering",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:24:16Z | 2023-11-29T19:17:53.000Z | null | null | ---
license: mit
---
| null | transformers | question-answering | null | null | null | null | null | null | null | null | null | DKud7/finetuned-roberta-squad2-tiny | [
-0.12853401899337769,
-0.1861673891544342,
0.6529126763343811,
0.4943625330924988,
-0.19319301843643188,
0.23607464134693146,
0.3607196807861328,
0.05056333541870117,
0.5793654322624207,
0.740013837814331,
-0.6508100628852844,
-0.23783957958221436,
-0.7102248668670654,
-0.04782595857977867... |
Ransaka/sinhala-gpt-lyrics | Ransaka | 2023-11-29T10:30:20Z | 8 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"gpt2",
"text-generation",
"generated_from_trainer",
"license:mit",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:30:20Z | 2023-03-24T16:01:22.000Z | null | null | ---
license: mit
tags:
- generated_from_trainer
model-index:
- name: sinhala-gpt-lyrics
results: []
widget:
- text: "මම"
- text: "මල්"
- text: "ඔබ"
- text: "තනිවීලා"
- text: "හීන"
# inference:
# parameters:
# do_sample: false
# temperature: 0.3
---
# sinhala-gpt-lyrics
This particular model has undergone fine-tuning based on the [gpt2](https://huggingface.co/gpt2) architecture, utilizing a dataset of around 500k Sinhala lyrics from various sources.
This Model is not sophisticated at all. Created while following one of fine-tuning docs.
## Usage Details
```python
from transformers import AutoTokenizer, AutoModelForCausalLM,pipeline
tokenizer = AutoTokenizer.from_pretrained("Ransaka/sinhala-gpt-lyrics")
model = AutoModelForCausalLM.from_pretrained("Ransaka/sinhala-gpt-lyrics")
generator = pipeline('text-generation',model=model, tokenizer=tokenizer)
generator("දුර") #දුර ඈත පාසැල් වියේ.
```
or using git
```bash
git lfs install
git clone https://huggingface.co/Ransaka/sinhala-gpt-lyrics
```
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| 2.3015 | 1.0 | 15323 | 2.3498 |
| 1.8582 | 2.0 | 30646 | 1.9921 |
| 1.5491 | 3.0 | 45969 | 1.9376 |
### Framework versions
- Transformers 4.26.1
- Pytorch 1.13.0
- Datasets 2.1.0
- Tokenizers 0.13.2
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Ransaka/sinhala-gpt-lyrics | [
-0.4124915897846222,
-0.5771284699440002,
0.20372945070266724,
0.3491256535053253,
-0.4762856066226959,
-0.34933093190193176,
-0.36318206787109375,
-0.21985594928264618,
-0.04868475720286369,
0.3735463619232178,
-0.7332711815834045,
-0.4962416887283325,
-0.7424952983856201,
0.0847150534391... |
rugvedabodke/my_awesome_qa_model | rugvedabodke | 2023-11-29T12:43:10Z | 8 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"distilbert",
"question-answering",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:43:10Z | 2023-08-08T09:21:36.000Z | null | null | ---
tags:
- generated_from_trainer
model-index:
- name: my_awesome_qa_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# my_awesome_qa_model
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.0561
## 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: 20
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| No log | 1.0 | 2 | 2.6419 |
| No log | 2.0 | 4 | 2.4093 |
| No log | 3.0 | 6 | 2.3048 |
| No log | 4.0 | 8 | 2.2732 |
| No log | 5.0 | 10 | 2.3026 |
| No log | 6.0 | 12 | 2.3105 |
| No log | 7.0 | 14 | 2.2481 |
| No log | 8.0 | 16 | 2.1339 |
| No log | 9.0 | 18 | 2.0817 |
| No log | 10.0 | 20 | 2.0949 |
| No log | 11.0 | 22 | 2.1169 |
| No log | 12.0 | 24 | 2.1656 |
| No log | 13.0 | 26 | 2.1781 |
| No log | 14.0 | 28 | 2.1759 |
| No log | 15.0 | 30 | 2.1443 |
| No log | 16.0 | 32 | 2.1105 |
| No log | 17.0 | 34 | 2.0871 |
| No log | 18.0 | 36 | 2.0660 |
| No log | 19.0 | 38 | 2.0585 |
| No log | 20.0 | 40 | 2.0561 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | question-answering | null | null | null | null | null | null | null | null | null | rugvedabodke/my_awesome_qa_model | [
-0.44428855180740356,
-0.5947197079658508,
0.19705013930797577,
0.07825849205255508,
-0.16383503377437592,
-0.2837652564048767,
0.23761357367038727,
-0.1752961426973343,
0.23557955026626587,
0.2870064675807953,
-0.7516619563102722,
-0.7288179993629456,
-0.6146933436393738,
-0.2595452666282... |
BELLE-2/BELLE-VL | BELLE-2 | 2023-11-29T09:47:26Z | 8 | 12 | null | [
"transformers",
"pytorch",
"qwen",
"text-generation",
"custom_code",
"license:apache-2.0",
"region:us"
] | 2023-11-29T09:47:26Z | 2023-11-23T09:03:43.000Z | null | null | ---
license: apache-2.0
---
# Model Card for Model ID
## 模型权重后续会重新上传
## Welcome
If you find this model helpful, please *like* this model and star us on https://github.com/LianjiaTech/BELLE !
## 📝Belle-VL
### 背景介绍
**社区目前已经有很多多模态大语言模型相关开源工作，但大多以英文能力为主，比如[LLava](https://github.com/haotian-liu/LLaVA),[CogVLM](https://github.com/THUDM/CogVLM)等，而中文多模态大语言模型比如[VisualGLM-6B](https://github.com/THUDM/VisualGLM-6B)、[Qwen-VL](https://github.com/QwenLM/Qwen-VL)的语言模型基座均较小，实际应用中很难兼顾视觉和语言能力，因此Belle-VL选择基于更强的语言模型基座来扩展模型的视觉能力，为社区提供更加灵活的选择。**
### 模型简介
在模型结构方面，我们主要参考的[Qwen-VL](https://github.com/QwenLM/Qwen-VL)模型，原始Qwen-VL是基于Qwen7B模型训练而来，基座能力相对较弱，因此Belle-VL将语言模型扩展成了[Qwen14B-chat](https://huggingface.co/Qwen/Qwen-14B-Chat)，在中文语言能力和视觉能力方面可以兼顾，具备更好的扩展性。
### 训练策略
原始Qwen-vl采用了三阶段的训练方式,包括预训练、多任务训练和指令微调，依赖较大的数据和机器资源。受LLava1.5的启发，多模态指令微调比预训练更加重要，因此我们采用了两阶段的训练方式，如下图所示：
### 训练数据
* **预训练数据**：预训练数据主要是基于LLava 的[558k](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain)英文指令数据及其对应的中文翻译数据，此外我们还收集了[Flickr30k-CNA](https://zero.so.com/) 以及从[AI Challenger](https://tianchi.aliyun.com/dataset/145781?spm=a2c22.12282016.0.0.5c823721PG2nBW)随机选取的100k数据
* **多模态指令数据**：指令微调阶段，数据主要来自[LLava](https://github.com/haotian-liu/LLaVA), [LRV-Instruction](https://github.com/FuxiaoLiu/LRV-Instruction), [LLaVAR](https://github.com/SALT-NLP/LLaVAR),[LVIS-INSTRUCT4V](https://github.com/X2FD/LVIS-INSTRUCT4V)等开源项目，我们也对其中部分数据进行了翻译，在此真诚的感谢他们为开源所做出的贡献！
### 模型使用
``` python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, GenerationConfig
model_dir = '/path/to_finetuned_model/'
img_path = 'you_image_path'
tokenizer = AutoTokenizer.from_pretrained(model_dir, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(model_dir, trust_remote_code=True).eval()
model.generation_config = GenerationConfig.from_pretrained(model_dir, trust_remote_code=True)
question = '详细描述一下这张图'
query = tokenizer.from_list_format([
{'image': img_path}, # Either a local path or an url
{'text': question},
])
response, history = model.chat(tokenizer, query=query, history=None)
print(response)
#or
query = f'<img>{img_path}</img>\n{question}'
response, history = model.chat(tokenizer, query=query, history=None)
print(response)
```
### MME Benchmark
[MME](https://github.com/BradyFU/Awesome-Multimodal-Large-Language-Models/tree/Evaluation)是一个针对多模态大型语言模型的全面评估基准。它在总共14个子任务上测量感知和认知能力,包括
包括存在性、计数、位置、颜色、海报、名人、场景、地标、艺术作品、OCR、常识推理、数值计算、文本翻译和代码推理等。BELLE-VL在感知评测共获得1595.34分，超过LLava和Qwen-VL.详情如下：
| Category | Score |
|------------------------|-------|
| **Perception** | **1595.34** |
| --Existence | 190 |
| --Count | 150 |
| --Position | 130 |
| --Color | 175 |
| --Posters | 166.33|
| --Celebrity | 136.76|
| --Scene | 156.25|
| --Landmark | 174 |
| --Artwork | 139.5 |
| --OCR | 177.5 |
| Category | Score |
|------------------------|-------|
| **Cognition** | **332.14** |
| --Commonsense Reasoning | 127.14|
| --Numerical Calculation | 47.5 |
| --Text Translation | 102.5 |
| --Code Reasoning | 55 |
## Citation
Please cite our paper and github when using our code, data or model.
```
@misc{BELLE,
author = {BELLEGroup},
title = {BELLE: Be Everyone's Large Language model Engine},
year = {2023},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/LianjiaTech/BELLE}},
}
``` | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | BELLE-2/BELLE-VL | [
-0.4547623097896576,
-0.688492476940155,
0.15186883509159088,
0.25592783093452454,
-0.28959763050079346,
-0.15108686685562134,
0.11351924389600754,
-0.5627029538154602,
0.3504285216331482,
0.20609506964683533,
-0.7558844685554504,
-0.81242436170578,
-0.3877868056297302,
-0.1204404234886169... |
JUJORUME/whisper-small-es | JUJORUME | 2023-11-29T15:02:58Z | 8 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"es",
"dataset:JUJORUME/FT-Spanish-Whisper",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:02:58Z | 2023-11-27T16:28:13.000Z | null | null | ---
language:
- es
license: apache-2.0
base_model: openai/whisper-small
tags:
- hf-asr-leaderboard
- generated_from_trainer
datasets:
- JUJORUME/FT-Spanish-Whisper
model-index:
- name: FT-Spanish-Whisper
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. -->
# FT-Spanish-Whisper
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Voice dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 100
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | JUJORUME/whisper-small-es | [
-0.4049464464187622,
-0.6361612677574158,
0.056863270699977875,
0.36196547746658325,
-0.3488355278968811,
-0.4425239861011505,
-0.3475542664527893,
-0.5068252086639404,
0.3445665240287781,
0.41229507327079773,
-0.8403570055961609,
-0.5188319683074951,
-0.5608645677566528,
-0.12823918461799... |
makhataei/qa-persian-bert-fa-zwnj-base | makhataei | 2023-11-30T01:02:29Z | 8 | 1 | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"dataset:pquad",
"base_model:makhataei/qa-persian-bert-fa-zwnj-base",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:02:29Z | 2023-11-28T10:17:00.000Z | null | null | ---
license: apache-2.0
base_model: makhataei/qa-persian-bert-fa-zwnj-base
tags:
- generated_from_trainer
datasets:
- pquad
model-index:
- name: qa-persian-bert-fa-zwnj-base
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. -->
# qa-persian-bert-fa-zwnj-base
This model is a fine-tuned version of [makhataei/qa-persian-bert-fa-zwnj-base](https://huggingface.co/makhataei/qa-persian-bert-fa-zwnj-base) on the pquad dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6481
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1.5625e-06
- train_batch_size: 14
- eval_batch_size: 14
- 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 |
|:-------------:|:-----:|:-----:|:---------------:|
| 0.0007 | 0.11 | 500 | 2.9986 |
| 0.0001 | 0.22 | 1000 | 3.2523 |
| 0.0005 | 0.33 | 1500 | 3.4059 |
| 0.001 | 0.44 | 2000 | 3.4295 |
| 0.0023 | 0.55 | 2500 | 3.4358 |
| 0.0011 | 0.66 | 3000 | 3.5648 |
| 0.0026 | 0.77 | 3500 | 3.6202 |
| 0.0301 | 0.88 | 4000 | 3.4814 |
| 0.0346 | 0.98 | 4500 | 3.5208 |
| 0.0588 | 1.09 | 5000 | 3.4714 |
| 0.0304 | 1.2 | 5500 | 3.5978 |
| 0.1436 | 1.31 | 6000 | 3.3923 |
| 0.1297 | 1.42 | 6500 | 3.1984 |
| 0.118 | 1.53 | 7000 | 2.9731 |
| 0.1021 | 1.64 | 7500 | 2.8720 |
| 0.1215 | 1.75 | 8000 | 2.7678 |
| 0.1021 | 1.86 | 8500 | 2.7115 |
| 0.1007 | 1.97 | 9000 | 2.7028 |
| 0.0845 | 2.08 | 9500 | 2.8243 |
| 0.0797 | 2.19 | 10000 | 2.9292 |
| 0.0743 | 2.3 | 10500 | 3.0304 |
| 0.0756 | 2.41 | 11000 | 2.9966 |
| 0.0643 | 2.52 | 11500 | 3.0151 |
| 0.0581 | 2.63 | 12000 | 3.0872 |
| 0.0691 | 2.73 | 12500 | 3.1207 |
| 0.0652 | 2.84 | 13000 | 3.1531 |
| 0.0634 | 2.95 | 13500 | 3.1803 |
| 0.2054 | 3.06 | 14000 | 2.9596 |
| 0.1541 | 3.17 | 14500 | 2.8714 |
| 0.1359 | 3.28 | 15000 | 2.8131 |
| 0.1262 | 3.39 | 15500 | 2.7828 |
| 0.1399 | 3.5 | 16000 | 2.7320 |
| 0.1237 | 3.61 | 16500 | 2.7143 |
| 0.1421 | 3.72 | 17000 | 2.6793 |
| 0.1196 | 3.83 | 17500 | 2.6789 |
| 0.1413 | 3.94 | 18000 | 2.6778 |
| 0.1352 | 4.05 | 18500 | 2.6686 |
| 0.1206 | 4.16 | 19000 | 2.6819 |
| 0.1257 | 4.27 | 19500 | 2.6733 |
| 0.1166 | 4.38 | 20000 | 2.6649 |
| 0.1221 | 4.48 | 20500 | 2.6604 |
| 0.1242 | 4.59 | 21000 | 2.6532 |
| 0.1122 | 4.7 | 21500 | 2.6515 |
| 0.1253 | 4.81 | 22000 | 2.6491 |
| 0.1193 | 4.92 | 22500 | 2.6481 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu117
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | question-answering | null | null | null | null | null | null | null | null | null | makhataei/qa-persian-bert-fa-zwnj-base | [
-0.7124162316322327,
-0.6304744482040405,
0.2562800645828247,
0.1257963925600052,
-0.12528911232948303,
-0.12698668241500854,
0.013902541249990463,
0.02459319308400154,
0.516582727432251,
0.3401242196559906,
-0.6639890074729919,
-0.6595085263252258,
-0.6607149243354797,
-0.3204766213893890... |
Aspik101/distil-whisper-large-v3-pl | Aspik101 | 2023-11-29T15:32:50Z | 8 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"whisper",
"automatic-speech-recognition",
"audio",
"transformers.js",
"pl",
"arxiv:2311.00430",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T15:32:50Z | 2023-11-28T20:05:49.000Z | null | null | ---
language:
- pl
tags:
- audio
- automatic-speech-recognition
- transformers.js
pipeline_tag: automatic-speech-recognition
license: mit
library_name: transformers
---
# Polish Distil-Whisper: distil-large-v3
Distil-Whisper was proposed in the paper [Robust Knowledge Distillation via Large-Scale Pseudo Labelling](https://arxiv.org/abs/2311.00430).
It is a distilled version of the Whisper model that is **3 times faster**, 49% smaller. This is the repository for distil-large-v3-pl, a distilled variant of [Whisper large-v3](https://huggingface.co/openai/whisper-large-v3).
## Usage
Distil-Whisper is supported in Hugging Face 🤗 Transformers from version 4.35 onwards. To run the model, first
install the latest version of the Transformers library. For this example, we'll also install 🤗 Datasets to load toy
audio dataset from the Hugging Face Hub:
```bash
pip install --upgrade pip
pip install --upgrade transformers accelerate datasets[audio]
```
### Short-Form Transcription
The model can be used with the [`pipeline`](https://huggingface.co/docs/transformers/main_classes/pipelines#transformers.AutomaticSpeechRecognitionPipeline)
class to transcribe short-form audio files (< 30-seconds) as follows:
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "Aspik101/distil-whisper-large-v3-pl"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("mozilla-foundation/common_voice_13_0", "pl", split="test")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
To transcribe a local audio file, simply pass the path to your audio file when you call the pipeline:
```diff
- result = pipe(sample)
+ result = pipe("audio.mp3")
```
### Long-Form Transcription
Distil-Whisper uses a chunked algorithm to transcribe long-form audio files (> 30-seconds). In practice, this chunked long-form algorithm
is 9x faster than the sequential algorithm proposed by OpenAI in the Whisper paper (see Table 7 of the [Distil-Whisper paper](https://arxiv.org/abs/2311.00430)).
To enable chunking, pass the `chunk_length_s` parameter to the `pipeline`. For Distil-Whisper, a chunk length of 15-seconds
is optimal. To activate batching, pass the argument `batch_size`:
```python
import torch
from transformers import AutoModelForSpeechSeq2Seq, AutoProcessor, pipeline
from datasets import load_dataset
device = "cuda:0" if torch.cuda.is_available() else "cpu"
torch_dtype = torch.float16 if torch.cuda.is_available() else torch.float32
model_id = "Aspik101/distil-whisper-large-v3-pl"
model = AutoModelForSpeechSeq2Seq.from_pretrained(
model_id, torch_dtype=torch_dtype, low_cpu_mem_usage=True, use_safetensors=True
)
model.to(device)
processor = AutoProcessor.from_pretrained(model_id)
pipe = pipeline(
"automatic-speech-recognition",
model=model,
tokenizer=processor.tokenizer,
feature_extractor=processor.feature_extractor,
max_new_tokens=128,
chunk_length_s=15,
batch_size=16,
torch_dtype=torch_dtype,
device=device,
)
dataset = load_dataset("mozilla-foundation/common_voice_13_0", "pl", split="test")
sample = dataset[0]["audio"]
result = pipe(sample)
print(result["text"])
```
<!---
**Tip:** The pipeline can also be used to transcribe an audio file from a remote URL, for example:
```python
result = pipe("https://huggingface.co/datasets/sanchit-gandhi/librispeech_long/resolve/main/audio.wav")
```
--->
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Aspik101/distil-whisper-large-v3-pl | [
-0.26251503825187683,
-0.6934410333633423,
0.29887959361076355,
0.5142402648925781,
-0.2955271601676941,
0.16070926189422607,
-0.41991910338401794,
-0.4596520960330963,
0.0833132416009903,
0.2234528660774231,
-0.6473584771156311,
-0.5495454668998718,
-0.8526897430419922,
0.0328197777271270... |
hotamago/ZAIC-2023-Model-MetaMath-7B | hotamago | 2023-11-29T10:46:30Z | 8 | 0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:46:30Z | 2023-11-28T23:42:32.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | hotamago/ZAIC-2023-Model-MetaMath-7B | [
-0.3227643668651581,
-0.22568444907665253,
0.862226128578186,
0.43461546301841736,
-0.5282992124557495,
0.7012968063354492,
0.7915719151496887,
0.07618585973978043,
0.7746025919914246,
0.2563219964504242,
-0.7852813601493835,
-0.22573840618133545,
-0.9104480743408203,
0.5715669393539429,
... |
dvijay/mistral-alpaca-finetune | dvijay | 2023-11-29T05:50:10Z | 8 | 0 | null | [
"transformers",
"pytorch",
"mistral",
"text-generation",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T05:50:10Z | 2023-11-29T05:18:36.000Z | null | null | ---
license: apache-2.0
base_model: mistralai/Mistral-7B-v0.1
tags:
- generated_from_trainer
model-index:
- name: out
results: []
---
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
# mistral-alpaca-finetune
This model is a fine-tuned version of [mistralai/Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1) on the mhenrichsen/alpaca_2k_test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9808
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 10
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9152 | 0.01 | 1 | 0.9037 |
| 0.9101 | 0.15 | 18 | 0.8461 |
| 0.7589 | 0.3 | 36 | 0.8437 |
| 0.8274 | 0.45 | 54 | 0.8441 |
| 0.7255 | 0.61 | 72 | 0.8435 |
| 0.85 | 0.76 | 90 | 0.8419 |
| 0.9083 | 0.91 | 108 | 0.8408 |
| 0.3208 | 1.06 | 126 | 0.9177 |
| 0.3738 | 1.21 | 144 | 0.8924 |
| 0.4034 | 1.36 | 162 | 0.8914 |
| 0.3936 | 1.51 | 180 | 0.9032 |
| 0.3188 | 1.66 | 198 | 0.9001 |
| 0.4331 | 1.82 | 216 | 0.8973 |
| 0.3946 | 1.97 | 234 | 0.8963 |
| 0.1531 | 2.12 | 252 | 0.9653 |
| 0.1741 | 2.27 | 270 | 0.9841 |
| 0.2371 | 2.42 | 288 | 0.9784 |
| 0.271 | 2.57 | 306 | 0.9801 |
| 0.2632 | 2.72 | 324 | 0.9808 |
| 0.1691 | 2.87 | 342 | 0.9808 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.0.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | dvijay/mistral-alpaca-finetune | [
-0.7664773464202881,
-0.5512562990188599,
0.12341868132352829,
0.11984513700008392,
-0.27604758739471436,
-0.4218779504299164,
0.026878448203206062,
-0.34701627492904663,
0.20798653364181519,
0.06106913089752197,
-0.7716192603111267,
-0.5910659432411194,
-0.666883647441864,
-0.128029569983... |
sronger/koko_test | sronger | 2023-11-29T08:25:13Z | 8 | 0 | null | [
"transformers",
"pytorch",
"safetensors",
"llama",
"feature-extraction",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:25:13Z | 2023-11-29T06:50:46.000Z | null | null | Entry not found | null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | sronger/koko_test | [
-0.3227643668651581,
-0.22568444907665253,
0.862226128578186,
0.43461546301841736,
-0.5282992124557495,
0.7012968063354492,
0.7915719151496887,
0.07618585973978043,
0.7746025919914246,
0.2563219964504242,
-0.7852813601493835,
-0.22573840618133545,
-0.9104480743408203,
0.5715669393539429,
... |
RashmiMyneni/Llama-2-7b-chat-hf-BankingData | RashmiMyneni | 2023-11-29T08:13:39Z | 8 | 0 | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:13:39Z | 2023-11-29T08:06:01.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | RashmiMyneni/Llama-2-7b-chat-hf-BankingData | [
-0.3227643668651581,
-0.22568444907665253,
0.862226128578186,
0.43461546301841736,
-0.5282992124557495,
0.7012968063354492,
0.7915719151496887,
0.07618585973978043,
0.7746025919914246,
0.2563219964504242,
-0.7852813601493835,
-0.22573840618133545,
-0.9104480743408203,
0.5715669393539429,
... |
golesheed/whisper-adult-3-dutch | golesheed | 2023-11-29T09:15:00Z | 8 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"nl",
"base_model:openai/whisper-large-v2",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T09:15:00Z | 2023-11-29T08:23:17.000Z | null | null | ---
language:
- nl
license: apache-2.0
base_model: openai/whisper-large-v2
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: Whisper Large V2
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Whisper Large V2
This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4903
- Wer: 17.1190
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 20
- num_epochs: 5
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.7985 | 0.55 | 30 | 0.4609 | 23.4617 |
| 0.4008 | 1.09 | 60 | 0.4111 | 18.3496 |
| 0.2345 | 1.64 | 90 | 0.4141 | 17.4976 |
| 0.1968 | 2.18 | 120 | 0.4298 | 16.8192 |
| 0.1114 | 2.73 | 150 | 0.4264 | 17.5134 |
| 0.0792 | 3.27 | 180 | 0.4745 | 17.0559 |
| 0.0435 | 3.82 | 210 | 0.4584 | 18.3970 |
| 0.0275 | 4.36 | 240 | 0.4826 | 16.1881 |
| 0.017 | 4.91 | 270 | 0.4903 | 17.1190 |
### Framework versions
- Transformers 4.35.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.14.6
- Tokenizers 0.14.1
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | golesheed/whisper-adult-3-dutch | [
-0.3690091669559479,
-0.5321529507637024,
0.149812713265419,
0.1554386019706726,
-0.35531795024871826,
-0.5015448927879333,
-0.2251041680574417,
-0.3908027410507202,
0.19479793310165405,
0.35876378417015076,
-0.7848601937294006,
-0.5514234304428101,
-0.7190520167350769,
-0.3336226046085357... |
jakebabbidge/juggernaut-xl-7-diffusers | jakebabbidge | 2023-11-29T10:03:50Z | 8 | 0 | null | [
"diffusers",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | 2023-11-29T10:03:50Z | 2023-11-29T09:54:01.000Z | null | null | ---
license: creativeml-openrail-m
---
Converted model sourced from `https://civitai.com/models/133005/juggernaut-xl` | null | diffusers | null | null | null | null | null | null | null | null | null | null | jakebabbidge/juggernaut-xl-7-diffusers | [
-0.352159321308136,
-0.14894653856754303,
0.428531676530838,
0.08235692232847214,
-0.5479623675346375,
-0.07330411672592163,
0.2708461880683899,
-0.21303433179855347,
0.14739342033863068,
0.8706262707710266,
-1.010328769683838,
-0.00814823154360056,
-0.24157440662384033,
-0.079827792942523... |
Ferrxni/finetuned_gw_mistral_GPTQ | Ferrxni | 2023-11-29T10:24:16Z | 8 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T10:24:16Z | 2023-11-29T09:55:56.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: gptq
- bits: 4
- tokenizer: None
- dataset: None
- group_size: 128
- damp_percent: 0.1
- desc_act: True
- sym: True
- true_sequential: True
- use_cuda_fp16: False
- model_seqlen: None
- block_name_to_quantize: None
- module_name_preceding_first_block: None
- batch_size: 1
- pad_token_id: None
- disable_exllama: False
- max_input_length: None
### Framework versions
- PEFT 0.5.0
| null | peft | null | null | null | null | null | null | null | null | null | null | Ferrxni/finetuned_gw_mistral_GPTQ | [
-0.5787661671638489,
-1.1275627613067627,
0.48291829228401184,
0.2712583839893341,
-0.7128455638885498,
-0.05440068244934082,
0.07747931033372879,
0.05349954217672348,
-0.050037931650877,
0.24034492671489716,
-0.6748822331428528,
-0.475723534822464,
-0.5300711393356323,
0.00279637868516147... |
TheBloke/SauerkrautLM-7B-HerO-GPTQ | TheBloke | 2023-11-29T13:56:18Z | 8 | 1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"finetune",
"chatml",
"augmentation",
"german",
"en",
"de",
"base_model:VAGOsolutions/SauerkrautLM-7b-HerO",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T13:56:18Z | 2023-11-29T13:20:01.000Z | null | null | ---
base_model: VAGOsolutions/SauerkrautLM-7b-HerO
inference: false
language:
- en
- de
library_name: transformers
license: apache-2.0
model_creator: VAGO solutions
model_name: SauerkrautLM 7B HerO
model_type: mistral
pipeline_tag: text-generation
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'
quantized_by: TheBloke
tags:
- mistral
- finetune
- chatml
- augmentation
- german
---
<!-- markdownlint-disable MD041 -->
<!-- header start -->
<!-- 200823 -->
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</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 -->
# SauerkrautLM 7B HerO - GPTQ
- Model creator: [VAGO solutions](https://huggingface.co/VAGOsolutions)
- Original model: [SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- description start -->
# Description
This repo contains GPTQ model files for [VAGO solutions's SauerkrautLM 7B HerO](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO).
Multiple GPTQ parameter permutations are provided; see Provided Files below for details of the options provided, their parameters, and the software used to create them.
These files were quantised using hardware kindly provided by [Massed Compute](https://massedcompute.com/).
<!-- description end -->
<!-- repositories-available start -->
## Repositories available
* [AWQ model(s) for GPU inference.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GGUF)
* [VAGO solutions's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
<!-- prompt-template end -->
<!-- README_GPTQ.md-compatible clients start -->
## Known compatible clients / servers
These GPTQ models are known to work in the following inference servers/webuis.
- [text-generation-webui](https://github.com/oobabooga/text-generation-webui)
- [KoboldAI United](https://github.com/henk717/koboldai)
- [LoLLMS Web UI](https://github.com/ParisNeo/lollms-webui)
- [Hugging Face Text Generation Inference (TGI)](https://github.com/huggingface/text-generation-inference)
This may not be a complete list; if you know of others, please let me know!
<!-- README_GPTQ.md-compatible clients end -->
<!-- README_GPTQ.md-provided-files start -->
## Provided files, and GPTQ parameters
Multiple quantisation parameters are provided, to allow you to choose the best one for your hardware and requirements.
Each separate quant is in a different branch. See below for instructions on fetching from different branches.
Most GPTQ files are made with AutoGPTQ. Mistral models are currently made with Transformers.
<details>
<summary>Explanation of GPTQ parameters</summary>
- Bits: The bit size of the quantised model.
- GS: GPTQ group size. Higher numbers use less VRAM, but have lower quantisation accuracy. "None" is the lowest possible value.
- Act Order: True or False. Also known as `desc_act`. True results in better quantisation accuracy. Some GPTQ clients have had issues with models that use Act Order plus Group Size, but this is generally resolved now.
- Damp %: A GPTQ parameter that affects how samples are processed for quantisation. 0.01 is default, but 0.1 results in slightly better accuracy.
- GPTQ dataset: The calibration dataset used during quantisation. Using a dataset more appropriate to the model's training can improve quantisation accuracy. Note that the GPTQ calibration dataset is not the same as the dataset used to train the model - please refer to the original model repo for details of the training dataset(s).
- Sequence Length: The length of the dataset sequences used for quantisation. Ideally this is the same as the model sequence length. For some very long sequence models (16+K), a lower sequence length may have to be used. Note that a lower sequence length does not limit the sequence length of the quantised model. It only impacts the quantisation accuracy on longer inference sequences.
- ExLlama Compatibility: Whether this file can be loaded with ExLlama, which currently only supports Llama and Mistral models in 4-bit.
</details>
| Branch | Bits | GS | Act Order | Damp % | GPTQ Dataset | Seq Len | Size | ExLlama | Desc |
| ------ | ---- | -- | --------- | ------ | ------------ | ------- | ---- | ------- | ---- |
| [main](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/main) | 4 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.16 GB | Yes | 4-bit, with Act Order and group size 128g. Uses even less VRAM than 64g, but with slightly lower accuracy. |
| [gptq-4bit-32g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-4bit-32g-actorder_True) | 4 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.57 GB | Yes | 4-bit, with Act Order and group size 32g. Gives highest possible inference quality, with maximum VRAM usage. |
| [gptq-8bit--1g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-8bit--1g-actorder_True) | 8 | None | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 7.52 GB | No | 8-bit, with Act Order. No group size, to lower VRAM requirements. |
| [gptq-8bit-128g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-8bit-128g-actorder_True) | 8 | 128 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 7.68 GB | No | 8-bit, with group size 128g for higher inference quality and with Act Order for even higher accuracy. |
| [gptq-8bit-32g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-8bit-32g-actorder_True) | 8 | 32 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 8.17 GB | No | 8-bit, with group size 32g and Act Order for maximum inference quality. |
| [gptq-4bit-64g-actorder_True](https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ/tree/gptq-4bit-64g-actorder_True) | 4 | 64 | Yes | 0.1 | [German Quad](https://huggingface.co/datasets/deepset/germanquad/viewer/) | 4096 | 4.30 GB | Yes | 4-bit, with Act Order and group size 64g. Uses less VRAM than 32g, but with slightly lower accuracy. |
<!-- README_GPTQ.md-provided-files end -->
<!-- README_GPTQ.md-download-from-branches start -->
## How to download, including from branches
### In text-generation-webui
To download from the `main` branch, enter `TheBloke/SauerkrautLM-7B-HerO-GPTQ` in the "Download model" box.
To download from another branch, add `:branchname` to the end of the download name, eg `TheBloke/SauerkrautLM-7B-HerO-GPTQ:gptq-4bit-32g-actorder_True`
### From the command line
I recommend using the `huggingface-hub` Python library:
```shell
pip3 install huggingface-hub
```
To download the `main` branch to a folder called `SauerkrautLM-7B-HerO-GPTQ`:
```shell
mkdir SauerkrautLM-7B-HerO-GPTQ
huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GPTQ --local-dir SauerkrautLM-7B-HerO-GPTQ --local-dir-use-symlinks False
```
To download from a different branch, add the `--revision` parameter:
```shell
mkdir SauerkrautLM-7B-HerO-GPTQ
huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GPTQ --revision gptq-4bit-32g-actorder_True --local-dir SauerkrautLM-7B-HerO-GPTQ --local-dir-use-symlinks False
```
<details>
<summary>More advanced huggingface-cli download usage</summary>
If you remove the `--local-dir-use-symlinks False` parameter, the files will instead be stored in the central Hugging Face cache directory (default location on Linux is: `~/.cache/huggingface`), and symlinks will be added to the specified `--local-dir`, pointing to their real location in the cache. This allows for interrupted downloads to be resumed, and allows you to quickly clone the repo to multiple places on disk without triggering a download again. The downside, and the reason why I don't list that as the default option, is that the files are then hidden away in a cache folder and it's harder to know where your disk space is being used, and to clear it up if/when you want to remove a download model.
The cache location can be changed with the `HF_HOME` environment variable, and/or the `--cache-dir` parameter to `huggingface-cli`.
For more documentation on downloading with `huggingface-cli`, please see: [HF -> Hub Python Library -> Download files -> Download from the CLI](https://huggingface.co/docs/huggingface_hub/guides/download#download-from-the-cli).
To accelerate downloads on fast connections (1Gbit/s or higher), install `hf_transfer`:
```shell
pip3 install hf_transfer
```
And set environment variable `HF_HUB_ENABLE_HF_TRANSFER` to `1`:
```shell
mkdir SauerkrautLM-7B-HerO-GPTQ
HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download TheBloke/SauerkrautLM-7B-HerO-GPTQ --local-dir SauerkrautLM-7B-HerO-GPTQ --local-dir-use-symlinks False
```
Windows Command Line users: You can set the environment variable by running `set HF_HUB_ENABLE_HF_TRANSFER=1` before the download command.
</details>
### With `git` (**not** recommended)
To clone a specific branch with `git`, use a command like this:
```shell
git clone --single-branch --branch gptq-4bit-32g-actorder_True https://huggingface.co/TheBloke/SauerkrautLM-7B-HerO-GPTQ
```
Note that using Git with HF repos is strongly discouraged. It will be much slower than using `huggingface-hub`, and will use twice as much disk space as it has to store the model files twice (it stores every byte both in the intended target folder, and again in the `.git` folder as a blob.)
<!-- README_GPTQ.md-download-from-branches end -->
<!-- README_GPTQ.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/SauerkrautLM-7B-HerO-GPTQ`.
- To download from a specific branch, enter for example `TheBloke/SauerkrautLM-7B-HerO-GPTQ:gptq-4bit-32g-actorder_True`
- see Provided Files above for the list of branches for each option.
3. Click **Download**.
4. The model will start downloading. Once it's finished it will say "Done".
5. In the top left, click the refresh icon next to **Model**.
6. In the **Model** dropdown, choose the model you just downloaded: `SauerkrautLM-7B-HerO-GPTQ`
7. The model will automatically load, and is now ready for use!
8. If you want any custom settings, set them and then click **Save settings for this model** followed by **Reload the Model** in the top right.
- Note that you do not need to and should not set manual GPTQ parameters any more. These are set automatically from the file `quantize_config.json`.
9. Once you're ready, click the **Text Generation** tab and enter a prompt to get started!
<!-- README_GPTQ.md-text-generation-webui end -->
<!-- README_GPTQ.md-use-from-tgi start -->
## Serving this model from Text Generation Inference (TGI)
It's recommended to 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/SauerkrautLM-7B-HerO-GPTQ --port 3000 --quantize gptq --max-input-length 3696 --max-total-tokens 4096 --max-batch-prefill-tokens 4096
```
Example Python code for interfacing with TGI (requires 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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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_GPTQ.md-use-from-tgi end -->
<!-- README_GPTQ.md-use-from-python start -->
## Python code example: inference from this GPTQ model
### Install the necessary packages
Requires: Transformers 4.33.0 or later, Optimum 1.12.0 or later, and AutoGPTQ 0.4.2 or later.
```shell
pip3 install --upgrade transformers optimum
# If using PyTorch 2.1 + CUDA 12.x:
pip3 install --upgrade auto-gptq
# or, if using PyTorch 2.1 + CUDA 11.x:
pip3 install --upgrade auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/
```
If you are using PyTorch 2.0, you will need to install AutoGPTQ from source. Likewise if you have problems with the pre-built wheels, you should try building from source:
```shell
pip3 uninstall -y auto-gptq
git clone https://github.com/PanQiWei/AutoGPTQ
cd AutoGPTQ
git checkout v0.5.1
pip3 install .
```
### Example Python code
```python
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
model_name_or_path = "TheBloke/SauerkrautLM-7B-HerO-GPTQ"
# To use a different branch, change revision
# For example: revision="gptq-4bit-32g-actorder_True"
model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
device_map="auto",
trust_remote_code=False,
revision="main")
tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, use_fast=True)
prompt = "Tell me about AI"
prompt_template=f'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
'''
print("\n\n*** Generate:")
input_ids = tokenizer(prompt_template, return_tensors='pt').input_ids.cuda()
output = model.generate(inputs=input_ids, temperature=0.7, do_sample=True, top_p=0.95, top_k=40, max_new_tokens=512)
print(tokenizer.decode(output[0]))
# Inference can also be done using transformers' pipeline
print("*** Pipeline:")
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_p=0.95,
top_k=40,
repetition_penalty=1.1
)
print(pipe(prompt_template)[0]['generated_text'])
```
<!-- README_GPTQ.md-use-from-python end -->
<!-- README_GPTQ.md-compatibility start -->
## Compatibility
The files provided are tested to work with Transformers. For non-Mistral models, AutoGPTQ can also be used directly.
[ExLlama](https://github.com/turboderp/exllama) is compatible with Llama and Mistral models in 4-bit. Please see the Provided Files table above for per-file compatibility.
For a list of clients/servers, please see "Known compatible clients / servers", above.
<!-- README_GPTQ.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, 阿明, 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 Bjäreholt, 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: VAGO solutions's SauerkrautLM 7B HerO
## VAGO solutions SauerkrautLM-7b-HerO
Introducing **SauerkrautLM-7b-HerO** – the pinnacle of German language model technology!
Crafted through the **merging** of **[Teknium's OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B)** and **[Open-Orca's Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca)** and **uniquely fine-tuned with the Sauerkraut dataset.**
SauerkrautLM-7b-HerO represents a breakthrough in language modeling, achieving an optimal balance between extensive German data and essential international sources.
This ensures the model not only excels in understanding the nuances of the German language but also retains its global capabilities.
Harnessing the innovative power of the **gradient SLERP method from MergeKit**, we've achieved a groundbreaking fusion of two of the most best performing 7B models based on the Mistral framework.
This merge has allowed us to combine the best features of both models, creating an unparalleled synergy.
Coupled with the German Sauerkraut dataset, which consists of a mix of augmented and translated data, we have successfully taught the English-speaking merged model the intricacies of the German language.
This was achieved *without the typical loss of core competencies often associated with fine-tuning in another language of models previously trained mainly in English.*
Our approach ensures that the model retains its original strengths while acquiring a profound understanding of German, **setting a new benchmark in bilingual language model proficiency.**
# Table of Contents
1. [Overview of all Her0 models](#all-hero-models)
2. [Model Details](#model-details)
- [Prompt template](#prompt-template)
- [Training Dataset](#training-dataset)
- [Merge Procedure](#merge-procedure)
3. [Evaluation](#evaluation)
- [GPT4ALL](#gpt4all)
- [Language Model evaluation Harness](#language-model-evaluation-harness)
- [BigBench](#big-bench)
- [MMLU](#mmlu)
- [TruthfulQA](#truthfulqa)
- [MT-Bench (German)](#mt-bench-german)
- [MT-Bench (English)](#mt-bench-english)
- [Additional German Benchmark results](#additional-german-benchmark-results)
5. [Disclaimer](#disclaimer)
6. [Contact](#contact)
7. [Collaborations](#collaborations)
8. [Acknowledgement](#acknowledgement)
## All HerO Models
| Model | HF | GPTQ | GGUF | AWQ |
|-------|-------|-------|-------|-------|
| SauerkrautLM-7b-HerO | [Link](https://huggingface.co/VAGOsolutions/SauerkrautLM-7b-HerO) | coming soon | coming soon | coming soon |
## Model Details
**SauerkrautLM-7b-HerO**
- **Model Type:** SauerkrautLM-7b-HerO is an auto-regressive language model based on the transformer architecture
- **Language(s):** English, German
- **License:** APACHE 2.0
- **Contact:** [Website](https://vago-solutions.de/#Kontakt) [David Golchinfar](mailto:golchinfar@vago-solutions.de)
### Training Dataset:
SauerkrautLM-7b-HerO was trained with mix of German data augmentation and translated data.
We found, that only a simple translation of training data can lead to unnatural German phrasings.
Data augmentation techniques were used to grant grammatical, syntactical correctness and a more natural German wording in our training data.
### Merge Procedure:
SauerkrautLM-7b-HerO was merged on 1 A100 with [mergekit](https://github.com/cg123/mergekit).
The merged model contains [OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) and [Open-Orca/Mistral-7B-OpenOrca](https://huggingface.co/Open-Orca/Mistral-7B-OpenOrca).
We applied the gradient SLERP method.
### Prompt Template:
```
<|im_start|>system
Du bist Sauerkraut-HerO, ein großes Sprachmodell, das höflich und kompetent antwortet. Schreibe deine Gedanken Schritt für Schritt auf, um Probleme sinnvoll zu lösen.<|im_end|>
<|im_start|>user
Wie geht es dir?<|im_end|>
<|im_start|>assistant
Mir geht es gut!<|im_end|>
<|im_start|>user
Bitte erkläre mir, wie die Zusammenführung von Modellen durch bestehende Spitzenmodelle profitieren kann.<|im_end|>
<|im_start|>assistant
```
## Evaluation
### GPT4ALL:
*Compared to relevant German Closed and Open Source models*
### Language Model evaluation Harness:
*Compared to Aleph Alpha Luminous Models*
**performed with newest Language Model Evaluation Harness*
### Big Bench:
**performed with newest Language Model Evaluation Harness*
### MMLU:
*Compared to Big Boy LLMs (Grok0,Grok1,GPT3.5,GPT4)*
### TruthfulQA:
*Compared to OpenAI Models (GPT3.5,GPT4)*
### MT-Bench (German):
```
########## First turn ##########
score
model turn
SauerkrautLM-70b-v1 1 7.25000
SauerkrautLM-7b-HerO <--- 1 6.96875
SauerkrautLM-7b-v1-mistral 1 6.30625
leo-hessianai-13b-chat 1 6.18750
SauerkrautLM-13b-v1 1 6.16250
leo-mistral-hessianai-7b-chat 1 6.15625
Llama-2-70b-chat-hf 1 6.03750
vicuna-13b-v1.5 1 5.80000
SauerkrautLM-7b-v1 1 5.65000
leo-hessianai-7b-chat 1 5.52500
vicuna-7b-v1.5 1 5.42500
Mistral-7B-v0.1 1 5.37500
SauerkrautLM-3b-v1 1 3.17500
Llama-2-7b 1 1.28750
open_llama_3b_v2 1 1.68750
########## Second turn ##########
score
model turn
SauerkrautLM-70b-v1 2 6.83125
SauerkrautLM-7b-HerO <--- 2 6.30625
vicuna-13b-v1.5 2 5.63125
SauerkrautLM-13b-v1 2 5.34375
SauerkrautLM-7b-v1-mistral 2 5.26250
leo-mistral-hessianai-7b-chat 2 4.99375
SauerkrautLM-7b-v1 2 4.73750
leo-hessianai-13b-chat 2 4.71250
vicuna-7b-v1.5 2 4.67500
Llama-2-70b-chat-hf 2 4.66250
Mistral-7B-v0.1 2 4.53750
leo-hessianai-7b-chat 2 2.65000
SauerkrautLM-3b-v1 2 1.98750
open_llama_3b_v2 2 1.22500
Llama-2-7b 2 1.07500
########## Average ##########
score
model
SauerkrautLM-70b-v1 7.040625
SauerkrautLM-7b-HerO <--- 6.637500
SauerkrautLM-7b-v1-mistral 5.784375
SauerkrautLM-13b-v1 5.753125
vicuna-13b-v1.5 5.715625
leo-mistral-hessianai-7b-chat 5.575000
leo-hessianai-13b-chat 5.450000
Llama-2-70b-chat-hf 5.350000
SauerkrautLM-v1-7b 5.193750
vicuna-7b-v1.5 5.050000
Mistral-7B-v0.1 4.956250
leo-hessianai-7b-chat 4.087500
SauerkrautLM-3b-v1 2.581250
open_llama_3b_v2 1.456250
Llama-2-7b 1.181250
```
**performed with the newest FastChat Version*
### MT-Bench (English):
```
########## First turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 1 8.21875
SauerkrautLM-7b-HerO <--- 1 8.03125
Mistral-7B-OpenOrca 1 7.65625
neural-chat-7b-v3-1 1 7.22500
########## Second turn ##########
score
model turn
OpenHermes-2.5-Mistral-7B 2 7.1000
SauerkrautLM-7b-HerO <--- 2 6.7875
neural-chat-7b-v3-1 2 6.4000
Mistral-7B-OpenOrca 2 6.1750
########## Average ##########
score
model
OpenHermes-2.5-Mistral-7B 7.659375
SauerkrautLM-7b-HerO <--- 7.409375
Mistral-7B-OpenOrca 6.915625
neural-chat-7b-v3-1 6.812500
```
**performed with the newest FastChat Version*
### Additional German Benchmark results:
*performed with newest Language Model Evaluation Harness
## Disclaimer
We must inform users that despite our best efforts in data cleansing, the possibility of uncensored content slipping through cannot be entirely ruled out.
However, we cannot guarantee consistently appropriate behavior. Therefore, if you encounter any issues or come across inappropriate content, we kindly request that you inform us through the contact information provided.
Additionally, it is essential to understand that the licensing of these models does not constitute legal advice. We are not held responsible for the actions of third parties who utilize our models. These models may be employed for commercial purposes, and the Apache 2.0 remains applicable and is included with the model files.
## Contact
If you are interested in customized LLMs for business applications, please get in contact with us via our website or contact us at [Dr. Daryoush Vaziri](mailto:vaziri@vago-solutions.de). We are also grateful for your feedback and suggestions.
## Collaborations
We are also keenly seeking support and investment for our startup, VAGO solutions, where we continuously advance the development of robust language models designed to address a diverse range of purposes and requirements. If the prospect of collaboratively navigating future challenges excites you, we warmly invite you to reach out to us.
## Acknowledgement
Many thanks to [OpenOrca](https://huggingface.co/Open-Orca) and [teknium](https://huggingface.co/teknium) for providing such valuable models to the Open-Source community.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/SauerkrautLM-7B-HerO-GPTQ | [
-0.5816466212272644,
-0.7520679235458374,
0.2667413353919983,
0.10261201858520508,
-0.24799692630767822,
-0.09671252965927124,
-0.05464279279112816,
-0.37080127000808716,
0.0858665183186531,
0.41151463985443115,
-0.5875137448310852,
-0.6083953976631165,
-0.38553234934806824,
-0.09107399731... |
chathuru/model | chathuru | 2023-11-29T16:50:54Z | 8 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:50:54Z | 2023-11-29T16:50:43.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
model-index:
- name: model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# model
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: 0.2648
- Accuracy: 1.0
- F1: 1.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 7
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---:|
| No log | 1.0 | 1 | 0.4457 | 1.0 | 1.0 |
| No log | 2.0 | 2 | 0.3801 | 1.0 | 1.0 |
| No log | 3.0 | 3 | 0.3537 | 1.0 | 1.0 |
| No log | 4.0 | 4 | 0.3170 | 1.0 | 1.0 |
| No log | 5.0 | 5 | 0.2876 | 1.0 | 1.0 |
| No log | 6.0 | 6 | 0.2716 | 1.0 | 1.0 |
| No log | 7.0 | 7 | 0.2648 | 1.0 | 1.0 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.1+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | chathuru/model | [
-0.47799378633499146,
-0.6448193192481995,
0.17282532155513763,
0.20514966547489166,
-0.3422735035419464,
-0.24822604656219482,
-0.08658020198345184,
-0.14844034612178802,
0.1263870894908905,
0.29834529757499695,
-0.7718201279640198,
-0.7446050643920898,
-0.8868789672851562,
-0.13777649402... |
hbot0001/mali | hbot0001 | 2023-11-29T17:47:05Z | 8 | 0 | null | [
"diffusers",
"text-to-image",
"autotrain",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"region:us"
] | 2023-11-29T17:47:05Z | 2023-11-29T17:47:03.000Z | null | null |
---
base_model: stabilityai/stable-diffusion-xl-base-1.0
instance_prompt: photo of harsh
tags:
- text-to-image
- diffusers
- autotrain
inference: true
---
# DreamBooth trained by AutoTrain
Text encoder was not trained.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | hbot0001/mali | [
0.07325764745473862,
-0.17835840582847595,
0.23538075387477875,
0.13522793352603912,
-0.5473271012306213,
1.0075249671936035,
0.19510549306869507,
-0.20362043380737305,
0.5356338024139404,
-0.003206829307600856,
-0.539963960647583,
-0.04451444372534752,
-0.9003115892410278,
0.3044251203536... |
harshrajsaxena/Image | harshrajsaxena | 2023-11-29T19:12:28Z | 8 | 0 | null | [
"transformers",
"pytorch",
"vision-encoder-decoder",
"image-to-text",
"en",
"endpoints_compatible",
"has_space",
"region:us"
] | 2023-11-29T19:12:28Z | 2023-11-29T17:54:27.000Z | null | null | ---
language:
- en
library_name: transformers
pipeline_tag: image-to-text
--- | null | transformers | image-to-text | null | null | null | null | null | null | null | null | null | harshrajsaxena/Image | [
-0.12853386998176575,
-0.18616794049739838,
0.6529127359390259,
0.4943622946739197,
-0.19319306313991547,
0.2360745519399643,
0.36072012782096863,
0.05056336894631386,
0.579365611076355,
0.740013837814331,
-0.6508102416992188,
-0.23784014582633972,
-0.7102251052856445,
-0.04782590642571449... |
DeclanBracken/BERT_uncased_for_binary_TO_recycling_classification_augmented | DeclanBracken | 2023-11-29T21:04:24Z | 8 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | 2023-11-29T21:04:24Z | 2023-11-29T19:00:17.000Z | null | null | ---
# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/modelcard.md?plain=1
# Doc / guide: https://huggingface.co/docs/hub/model-cards
{}
---
# Model Card for Model ID
<!-- Model is a fine-tuned version of BERT_base_uncased on an augmented and cleaned version of the city of Toronto's waste wizard lookup table open to developers. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** Declan Bracken, Armando Ordorica, Michael Santorelli, Paul Zhou
- **Model type:** [Transformer]
- **Language(s) (NLP):** [English]
- **Finetuned from model [optional]:** [BERT_base_uncased]
### 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]
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | DeclanBracken/BERT_uncased_for_binary_TO_recycling_classification_augmented | [
-0.5490612983703613,
-0.4144830107688904,
0.4490182399749756,
-0.01540359016507864,
-0.17807532846927643,
-0.2886139452457428,
0.1513729840517044,
-0.5037655830383301,
0.13046135008335114,
0.7560034990310669,
-0.7254602909088135,
-0.5882152915000916,
-0.5293614268302917,
-0.112713567912578... |
zohirjonsharipov/whisper-small-uz | zohirjonsharipov | 2023-11-29T19:04:57Z | 8 | 0 | null | [
"region:us"
] | 2023-11-29T19:04:57Z | 2023-11-29T19:04:57.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | zohirjonsharipov/whisper-small-uz | [
-0.3227648437023163,
-0.2256842851638794,
0.8622258305549622,
0.4346150755882263,
-0.5282991528511047,
0.7012966275215149,
0.7915719151496887,
0.07618607580661774,
0.774602472782135,
0.25632160902023315,
-0.7852813005447388,
-0.22573809325695038,
-0.910448431968689,
0.571567177772522,
-0... |
kmzz/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 | kmzz | 2023-11-29T06:45:12Z | 7 | 0 | null | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2",
"license:creativeml-openrail-m",
"region:us"
] | 2023-11-29T06:45:12Z | 2023-10-24T06:06:57.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA text2image fine-tuning - kmzz/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
These are LoRA adaption weights for stabilityai/stable-diffusion-2. The weights were fine-tuned on the ../datasets/kowenje_fake/normal_gaussian dataset. You can find some example images in the following.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | kmzz/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 | [
-0.3735322654247284,
-1.0044136047363281,
0.30052998661994934,
0.32032257318496704,
-0.5010039210319519,
-0.4792780876159668,
0.056420061737298965,
-0.10289975255727768,
0.4649178981781006,
0.702740490436554,
-0.6601861715316772,
-0.592628538608551,
-0.6716952919960022,
-0.2461400181055069... |
binhquoc/llama-zalo | binhquoc | 2023-11-29T15:52:39Z | 7 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:berkeley-nest/Starling-LM-7B-alpha",
"region:us"
] | 2023-11-29T15:52:39Z | 2023-11-11T08:24:13.000Z | null | null | ---
library_name: peft
base_model: berkeley-nest/Starling-LM-7B-alpha
---
# 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.6.3.dev0 | null | peft | null | null | null | null | null | null | null | null | null | null | binhquoc/llama-zalo | [
-0.5779396295547485,
-0.5580515265464783,
0.40497368574142456,
0.08317576348781586,
-0.253414124250412,
-0.27545133233070374,
0.06068450212478638,
-0.5384040474891663,
0.04877224564552307,
0.6135933995246887,
-0.7259423136711121,
-0.6298723816871643,
-0.5585345029830933,
-0.079713866114616... |
KnutJaegersberg/Yi-34B-200K-MiniOrca | KnutJaegersberg | 2023-11-29T08:05:02Z | 7 | 0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"dataset:TinyPixel/orca-mini",
"license:other",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"region:us"
] | 2023-11-29T08:05:02Z | 2023-11-27T19:33:49.000Z | null | null |
---
license: other
license_name: yi-license
license_link: LICENSE
pipeline_tag: text-generation
datasets:
- TinyPixel/orca-mini
---
Trained for 2.7 epochs on the 50k shortest records of miniorca dataset with NEFTune.
The base model is the official yi-34b-200k model.
Prompt Example:
```
### System:
You are an AI assistant. You will be given a task. You must generate a detailed and long answer.
### User:
What is AGI?
### Assistant:
```
License
The source code in this repo is licensed under the Apache 2.0 license. The Yi series models are fully open for academic research and free commercial usage with permission via applications. All usage must adhere to the Model License Agreement 2.0. To apply for the official commercial license, please contact us (yi@01.ai). | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | KnutJaegersberg/Yi-34B-200K-MiniOrca | [
-0.49367740750312805,
-0.4438314139842987,
0.28362005949020386,
-0.15878282487392426,
-0.23475554585456848,
-0.5478898882865906,
0.32913535833358765,
-0.7858894467353821,
-0.043714337050914764,
0.4117518961429596,
-1.1263049840927124,
-0.226124569773674,
-0.4181292951107025,
0.011394260451... |
TheBloke/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-AWQ | TheBloke | 2023-11-29T12:08:55Z | 7 | 1 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"base_model:S4sch/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b",
"license:apache-2.0",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T12:08:55Z | 2023-11-28T22:05:13.000Z | null | null | ---
base_model: S4sch/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b
inference: false
license: apache-2.0
model_creator: "Sascha L\xFCscher"
model_name: Open Hermes 2.5 Neural Chat 3.1 Frankenmerge 11B
model_type: mistral
prompt_template: '<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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 -->
# Open Hermes 2.5 Neural Chat 3.1 Frankenmerge 11B - AWQ
- Model creator: [Sascha Lüscher](https://huggingface.co/S4sch)
- Original model: [Open Hermes 2.5 Neural Chat 3.1 Frankenmerge 11B](https://huggingface.co/S4sch/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b)
<!-- description start -->
## Description
This repo contains AWQ model files for [Sascha Lüscher's Open Hermes 2.5 Neural Chat 3.1 Frankenmerge 11B](https://huggingface.co/S4sch/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b).
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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-AWQ)
* [GPTQ models for GPU inference, with multiple quantisation parameter options.](https://huggingface.co/TheBloke/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-GPTQ)
* [2, 3, 4, 5, 6 and 8-bit GGUF models for CPU+GPU inference](https://huggingface.co/TheBloke/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-GGUF)
* [Sascha Lüscher's original unquantised fp16 model in pytorch format, for GPU inference and for further conversions](https://huggingface.co/S4sch/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b)
<!-- repositories-available end -->
<!-- prompt-template start -->
## Prompt template: ChatML
```
<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-AWQ/tree/main) | 4 | 128 | [VMware Open Instruct](https://huggingface.co/datasets/VMware/open-instruct/viewer/) | 4096 | 6.30 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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-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: `Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-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'''<|im_start|>system
{system_message}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>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, 阿明, 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 Bjäreholt, 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: Sascha Lüscher's Open Hermes 2.5 Neural Chat 3.1 Frankenmerge 11B
Frankenmerge 11b between teknium/OpenHermes-2.5-Mistral-7B and Intel/neural-chat-7b-v3-1
Merge with the following conditions
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [0, 8]
- model: Intel/neural-chat-7b-v3-1
layer_range: [4, 12]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [9, 16]
- model: Intel/neural-chat-7b-v3-1
layer_range: [13, 20]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [17, 24]
- model: Intel/neural-chat-7b-v3-1
layer_range: [21, 28]
- model: teknium/OpenHermes-2.5-Mistral-7B
layer_range: [25, 32]
merge_method: passthrough
Benchmarks are coming soon...
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | TheBloke/Open-Hermes-2.5-neural-chat-3.1-frankenmerge-11b-AWQ | [
-0.5276580452919006,
-0.9314939379692078,
0.2691073417663574,
0.20045067369937897,
-0.11974605172872543,
-0.18109865486621857,
-0.14197520911693573,
-0.5213284492492676,
0.1766878217458725,
0.33470290899276733,
-0.7377668619155884,
-0.5279166102409363,
-0.3380155563354492,
-0.1206719428300... |
btmccarthy15/SDLORA2 | btmccarthy15 | 2023-11-29T01:43:24Z | 7 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"lora",
"base_model:stabilityai/stable-diffusion-2-1-base",
"license:creativeml-openrail-m",
"has_space",
"region:us"
] | 2023-11-29T01:43:24Z | 2023-11-29T00:57:55.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1-base
instance_prompt: a photo of sks dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- lora
inference: true
---
# LoRA DreamBooth - btmccarthy15/SDLORA2
These are LoRA adaption weights for stabilityai/stable-diffusion-2-1-base. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/). You can find some example images in the following.
LoRA for the text encoder was enabled: True.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | btmccarthy15/SDLORA2 | [
-0.15195679664611816,
-0.35476818680763245,
0.27415359020233154,
0.47391051054000854,
-0.7047479748725891,
0.06634123623371124,
0.24661004543304443,
-0.21068301796913147,
0.7838795185089111,
0.4203091561794281,
-0.38985922932624817,
-0.4295853078365326,
-0.6166297197341919,
-0.225861087441... |
picas9dan/20231129_1 | picas9dan | 2023-11-29T04:45:23Z | 7 | 0 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T04:45:23Z | 2023-11-29T03:28:23.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | picas9dan/20231129_1 | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
picas9dan/20231129_2 | picas9dan | 2023-11-29T04:48:19Z | 7 | 0 | null | [
"transformers",
"pytorch",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T04:48:19Z | 2023-11-29T03:28:54.000Z | null | null | Entry not found | null | transformers | text2text-generation | null | null | null | null | null | null | null | null | null | picas9dan/20231129_2 | [
-0.32276463508605957,
-0.2256849706172943,
0.8622266054153442,
0.4346153736114502,
-0.5282987952232361,
0.7012974619865417,
0.7915722131729126,
0.07618652284145355,
0.7746030688285828,
0.2563217282295227,
-0.7852814793586731,
-0.22573867440223694,
-0.9104479551315308,
0.571567177772522,
... |
fanjiang98/STDPR-NQ | fanjiang98 | 2023-11-29T04:34:08Z | 7 | 0 | null | [
"transformers",
"pytorch",
"bert",
"feature-extraction",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T04:34:08Z | 2023-11-29T04:33:24.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | feature-extraction | null | null | null | null | null | null | null | null | null | fanjiang98/STDPR-NQ | [
-0.12853401899337769,
-0.18616756796836853,
0.6529130935668945,
0.49436235427856445,
-0.1931932121515274,
0.23607449233531952,
0.3607199192047119,
0.05056357383728027,
0.5793656706809998,
0.7400139570236206,
-0.6508103609085083,
-0.23783999681472778,
-0.7102250456809998,
-0.047825817018747... |
RyotaroOKabe/tgt_mgpt_v1.4 | RyotaroOKabe | 2023-11-30T01:26:57Z | 7 | 0 | null | [
"transformers",
"pytorch",
"gpt2",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-30T01:26:57Z | 2023-11-29T05:06:36.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | RyotaroOKabe/tgt_mgpt_v1.4 | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
Sujan42024/dlite-v2-355m-bi4tQuantization | Sujan42024 | 2023-11-29T07:22:33Z | 7 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | 2023-11-29T07:22:33Z | 2023-11-29T07:22:10.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Sujan42024/dlite-v2-355m-bi4tQuantization | [
-0.3227648138999939,
-0.22568483650684357,
0.8622256517410278,
0.43461519479751587,
-0.5282990336418152,
0.7012965679168701,
0.7915716767311096,
0.07618631422519684,
0.7746025323867798,
0.25632259249687195,
-0.7852814793586731,
-0.22573857009410858,
-0.910447895526886,
0.5715669393539429,
... |
Anant58/ppo-Pyramid | Anant58 | 2023-11-29T08:28:56Z | 7 | 0 | null | [
"ml-agents",
"tensorboard",
"onnx",
"Pyramids",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Pyramids",
"region:us"
] | 2023-11-29T08:28:56Z | 2023-11-29T08:28:53.000Z | null | null | ---
library_name: ml-agents
tags:
- Pyramids
- deep-reinforcement-learning
- reinforcement-learning
- ML-Agents-Pyramids
---
# **ppo** Agent playing **Pyramids**
This is a trained model of a **ppo** agent playing **Pyramids**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog 🐶 to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: Anant58/ppo-Pyramid
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| null | ml-agents | reinforcement-learning | null | null | null | null | null | null | null | null | null | Anant58/ppo-Pyramid | [
-0.5632885098457336,
-0.4785476326942444,
0.03295430168509483,
0.1898007094860077,
-0.15247842669487,
0.17443624138832092,
0.24591676890850067,
-0.20932447910308838,
0.46223175525665283,
0.42941343784332275,
-0.5676516890525818,
-0.6979362964630127,
-0.40962520241737366,
-0.214819476008415... |
haryoaw/teacher-tyqianz-sentiment-malay | haryoaw | 2023-11-29T08:42:05Z | 7 | 0 | null | [
"transformers",
"pytorch",
"xlm-roberta",
"generated_from_trainer",
"dataset:multilingual-sentiments",
"base_model:xlm-roberta-base",
"license:mit",
"endpoints_compatible",
"region:us"
] | 2023-11-29T08:42:05Z | 2023-11-29T08:40:21.000Z | null | null | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
datasets:
- multilingual-sentiments
metrics:
- accuracy
- f1
model-index:
- name: scenario-non-kd-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-mo
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. -->
# scenario-non-kd-from-pre-finetune-div-2-data-tyqiangz-multilingual-sentiments-mo
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the multilingual-sentiments dataset.
It achieves the following results on the evaluation set:
- Loss: 2.1725
- Accuracy: 0.7254
- F1: 0.6927
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 6969
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|
| No log | 0.68 | 100 | 0.7265 | 0.7045 | 0.6870 |
| No log | 1.36 | 200 | 0.6970 | 0.7323 | 0.6944 |
| No log | 2.04 | 300 | 0.6317 | 0.7294 | 0.7067 |
| No log | 2.72 | 400 | 0.6540 | 0.7413 | 0.7021 |
| 0.5885 | 3.4 | 500 | 0.8806 | 0.7075 | 0.6671 |
| 0.5885 | 4.08 | 600 | 0.9042 | 0.7154 | 0.6895 |
| 0.5885 | 4.76 | 700 | 0.7679 | 0.7443 | 0.7145 |
| 0.5885 | 5.44 | 800 | 0.8522 | 0.7493 | 0.7300 |
| 0.5885 | 6.12 | 900 | 0.9323 | 0.7453 | 0.7172 |
| 0.2592 | 6.8 | 1000 | 1.1891 | 0.7164 | 0.6874 |
| 0.2592 | 7.48 | 1100 | 1.3899 | 0.7234 | 0.6985 |
| 0.2592 | 8.16 | 1200 | 1.4546 | 0.7343 | 0.7045 |
| 0.2592 | 8.84 | 1300 | 1.2937 | 0.7274 | 0.7065 |
| 0.2592 | 9.52 | 1400 | 1.4545 | 0.7303 | 0.6893 |
| 0.1436 | 10.2 | 1500 | 1.5054 | 0.7363 | 0.7036 |
| 0.1436 | 10.88 | 1600 | 1.5872 | 0.7303 | 0.6985 |
| 0.1436 | 11.56 | 1700 | 1.3485 | 0.7453 | 0.7255 |
| 0.1436 | 12.24 | 1800 | 1.5478 | 0.7632 | 0.7398 |
| 0.1436 | 12.93 | 1900 | 1.2108 | 0.7443 | 0.7233 |
| 0.1044 | 13.61 | 2000 | 1.3613 | 0.7642 | 0.7397 |
| 0.1044 | 14.29 | 2100 | 1.7515 | 0.7572 | 0.7307 |
| 0.1044 | 14.97 | 2200 | 1.7570 | 0.7552 | 0.7278 |
| 0.1044 | 15.65 | 2300 | 1.6992 | 0.7323 | 0.7119 |
| 0.1044 | 16.33 | 2400 | 2.0103 | 0.7254 | 0.6996 |
| 0.0789 | 17.01 | 2500 | 1.7828 | 0.7194 | 0.6887 |
| 0.0789 | 17.69 | 2600 | 1.6550 | 0.7174 | 0.6987 |
| 0.0789 | 18.37 | 2700 | 1.6418 | 0.7383 | 0.7198 |
| 0.0789 | 19.05 | 2800 | 1.7430 | 0.7453 | 0.7215 |
| 0.0789 | 19.73 | 2900 | 1.8571 | 0.7383 | 0.7122 |
| 0.1 | 20.41 | 3000 | 1.8949 | 0.7323 | 0.6958 |
| 0.1 | 21.09 | 3100 | 1.7967 | 0.7562 | 0.7310 |
| 0.1 | 21.77 | 3200 | 1.7864 | 0.7383 | 0.7149 |
| 0.1 | 22.45 | 3300 | 2.1725 | 0.7254 | 0.6927 |
### Framework versions
- Transformers 4.33.3
- Pytorch 2.0.1
- Datasets 2.14.5
- Tokenizers 0.13.3
| null | transformers | null | null | null | null | null | null | null | null | null | null | haryoaw/teacher-tyqianz-sentiment-malay | [
-0.6640976667404175,
-0.6066340804100037,
0.23775595426559448,
0.1043064221739769,
-0.0735507383942604,
-0.07825601100921631,
-0.05793183296918869,
-0.0011528683826327324,
0.5774070024490356,
0.42373785376548767,
-0.7098363637924194,
-0.8513042330741882,
-0.7823182344436646,
-0.22834025323... |
Nzute/codegen-350M-mono-python-18k-alpaca | Nzute | 2023-11-30T01:05:23Z | 7 | 0 | null | [
"transformers",
"safetensors",
"codegen",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T01:05:23Z | 2023-11-29T09:22:05.000Z | null | null | Entry not found | null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Nzute/codegen-350M-mono-python-18k-alpaca | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
ADISH007/Finetuned_Donut_aws_dummy | ADISH007 | 2023-11-29T13:06:39Z | 7 | 0 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:06:39Z | 2023-11-29T10:44:13.000Z | null | null | Entry not found | null | transformers | null | null | null | null | null | null | null | null | null | null | ADISH007/Finetuned_Donut_aws_dummy | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
DeepLearner101/ResNet50_FGSM_FT_Epochs25_Eps015 | DeepLearner101 | 2023-11-29T19:42:19Z | 7 | 0 | null | [
"transformers",
"safetensors",
"resnet",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T19:42:19Z | 2023-11-29T11:53:23.000Z | null | null | Entry not found | null | transformers | image-classification | null | null | null | null | null | null | null | null | null | DeepLearner101/ResNet50_FGSM_FT_Epochs25_Eps015 | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
SkyR/dqn-SpaceInvadersNoFrameskip-v4 | SkyR | 2023-11-29T11:54:51Z | 7 | 0 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T11:54:51Z | 2023-11-29T11:54:14.000Z | null | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 586.50 +/- 133.62
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SkyR -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga SkyR -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga SkyR
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | SkyR/dqn-SpaceInvadersNoFrameskip-v4 | [
-0.5900121331214905,
-0.5380564332008362,
0.25834566354751587,
0.32851529121398926,
-0.1354362666606903,
-0.24886125326156616,
0.14227509498596191,
-0.16803094744682312,
0.1590726226568222,
0.3567960560321808,
-1.0239087343215942,
-0.483866423368454,
-0.3537524938583374,
-0.039917804300785... |
toddwilson147/DQN_spaceinvaders | toddwilson147 | 2023-11-29T13:10:18Z | 7 | 0 | null | [
"stable-baselines3",
"SpaceInvadersNoFrameskip-v4",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | 2023-11-29T13:10:18Z | 2023-11-29T13:09:39.000Z | null | null | ---
library_name: stable-baselines3
tags:
- SpaceInvadersNoFrameskip-v4
- deep-reinforcement-learning
- reinforcement-learning
- stable-baselines3
model-index:
- name: DQN
results:
- task:
type: reinforcement-learning
name: reinforcement-learning
dataset:
name: SpaceInvadersNoFrameskip-v4
type: SpaceInvadersNoFrameskip-v4
metrics:
- type: mean_reward
value: 658.50 +/- 107.59
name: mean_reward
verified: false
---
# **DQN** Agent playing **SpaceInvadersNoFrameskip-v4**
This is a trained model of a **DQN** agent playing **SpaceInvadersNoFrameskip-v4**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3)
and the [RL Zoo](https://github.com/DLR-RM/rl-baselines3-zoo).
The RL Zoo is a training framework for Stable Baselines3
reinforcement learning agents,
with hyperparameter optimization and pre-trained agents included.
## Usage (with SB3 RL Zoo)
RL Zoo: https://github.com/DLR-RM/rl-baselines3-zoo<br/>
SB3: https://github.com/DLR-RM/stable-baselines3<br/>
SB3 Contrib: https://github.com/Stable-Baselines-Team/stable-baselines3-contrib
Install the RL Zoo (with SB3 and SB3-Contrib):
```bash
pip install rl_zoo3
```
```
# Download model and save it into the logs/ folder
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga toddwilson147 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
If you installed the RL Zoo3 via pip (`pip install rl_zoo3`), from anywhere you can do:
```
python -m rl_zoo3.load_from_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -orga toddwilson147 -f logs/
python -m rl_zoo3.enjoy --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
```
## Training (with the RL Zoo)
```
python -m rl_zoo3.train --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/
# Upload the model and generate video (when possible)
python -m rl_zoo3.push_to_hub --algo dqn --env SpaceInvadersNoFrameskip-v4 -f logs/ -orga toddwilson147
```
## Hyperparameters
```python
OrderedDict([('batch_size', 32),
('buffer_size', 100000),
('env_wrapper',
['stable_baselines3.common.atari_wrappers.AtariWrapper']),
('exploration_final_eps', 0.01),
('exploration_fraction', 0.1),
('frame_stack', 4),
('gradient_steps', 1),
('learning_rate', 0.0001),
('learning_starts', 100000),
('n_timesteps', 1000000.0),
('optimize_memory_usage', False),
('policy', 'CnnPolicy'),
('target_update_interval', 1000),
('train_freq', 4),
('normalize', False)])
```
# Environment Arguments
```python
{'render_mode': 'rgb_array'}
```
| null | stable-baselines3 | reinforcement-learning | null | null | null | null | null | null | null | null | null | toddwilson147/DQN_spaceinvaders | [
-0.6135151982307434,
-0.5542805194854736,
0.2804127335548401,
0.35538607835769653,
-0.14449843764305115,
-0.24608424305915833,
0.13926318287849426,
-0.18628400564193726,
0.17733557522296906,
0.311838835477829,
-1.0161548852920532,
-0.49351391196250916,
-0.3663029670715332,
-0.0453608445823... |
quangcodecode/xlm-roberta-base-finetuned-ner-thesis-dseb | quangcodecode | 2023-11-29T13:16:19Z | 7 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"xlm-roberta",
"token-classification",
"generated_from_trainer",
"base_model:xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-29T13:16:19Z | 2023-11-29T13:12:24.000Z | null | null | ---
license: mit
base_model: xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- precision
- recall
- f1
- accuracy
model-index:
- name: xlm-roberta-base-finetuned-ner-thesis-dseb
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# xlm-roberta-base-finetuned-ner-thesis-dseb
This model is a fine-tuned version of [xlm-roberta-base](https://huggingface.co/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0378
- Precision: 0.9543
- Recall: 0.9606
- F1: 0.9575
- Accuracy: 0.9913
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:--------:|
| No log | 1.0 | 29 | 0.4257 | 0.6616 | 0.6360 | 0.6486 | 0.8935 |
| No log | 2.0 | 58 | 0.0747 | 0.9656 | 0.9424 | 0.9539 | 0.9911 |
| No log | 3.0 | 87 | 0.0406 | 0.9574 | 0.9519 | 0.9546 | 0.9920 |
| 0.501 | 4.0 | 116 | 0.0370 | 0.9683 | 0.9592 | 0.9637 | 0.9929 |
| 0.501 | 5.0 | 145 | 0.0352 | 0.9727 | 0.9621 | 0.9674 | 0.9929 |
| 0.501 | 6.0 | 174 | 0.0369 | 0.9516 | 0.9613 | 0.9565 | 0.9911 |
| 0.0378 | 7.0 | 203 | 0.0423 | 0.9400 | 0.9592 | 0.9495 | 0.9901 |
| 0.0378 | 8.0 | 232 | 0.0360 | 0.9650 | 0.9643 | 0.9646 | 0.9923 |
| 0.0378 | 9.0 | 261 | 0.0364 | 0.9635 | 0.9635 | 0.9635 | 0.9923 |
| 0.0378 | 10.0 | 290 | 0.0378 | 0.9543 | 0.9606 | 0.9575 | 0.9913 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | token-classification | null | null | null | null | null | null | null | null | null | quangcodecode/xlm-roberta-base-finetuned-ner-thesis-dseb | [
-0.5809398293495178,
-0.6641301512718201,
0.3116038143634796,
-0.005058846902102232,
-0.15238060057163239,
-0.31782248616218567,
-0.05572586879134178,
-0.10904033482074738,
0.38671424984931946,
0.47820261120796204,
-0.7410994172096252,
-0.8415609002113342,
-0.7906481027603149,
-0.166672736... |
youngsun05/bert-finetuned-squad | youngsun05 | 2023-11-29T16:40:54Z | 6 | 0 | null | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"bert",
"question-answering",
"generated_from_trainer",
"base_model:bert-base-cased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T16:40:54Z | 2023-07-25T14:10:35.000Z | null | null | ---
license: apache-2.0
base_model: bert-base-cased
tags:
- generated_from_trainer
model-index:
- name: bert-finetuned-squad
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# bert-finetuned-squad
This model is a fine-tuned version of [bert-base-cased](https://huggingface.co/bert-base-cased) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu118
- Datasets 2.14.4
- Tokenizers 0.13.3
| null | transformers | question-answering | null | null | null | null | null | null | null | null | null | youngsun05/bert-finetuned-squad | [
-0.599056601524353,
-0.7487537264823914,
0.08672681450843811,
0.2566683888435364,
-0.365841805934906,
-0.2648472189903259,
-0.1507767289876938,
-0.2472521811723709,
0.2272561639547348,
0.3864746689796448,
-1.0402495861053467,
-0.4754295349121094,
-0.4773288667201996,
-0.09479714930057526,
... |
csukuangfj/vits-piper-en_US-libritts-high | csukuangfj | 2023-11-29T07:58:17Z | 6 | 0 | null | [
"onnx",
"has_space",
"region:us"
] | 2023-11-29T07:58:17Z | 2023-10-27T07:19:03.000Z | null | null | Entry not found | null | null | null | null | null | null | null | null | null | null | null | null | csukuangfj/vits-piper-en_US-libritts-high | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
saviosantos/spelling-corrector-ptbr | saviosantos | 2023-11-29T19:00:52Z | 6 | 0 | null | [
"peft",
"tensorboard",
"arxiv:1910.09700",
"base_model:meta-llama/Llama-2-7b-hf",
"region:us"
] | 2023-11-29T19:00:52Z | 2023-11-17T01:18:28.000Z | null | null | ---
library_name: peft
base_model: meta-llama/Llama-2-7b-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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | saviosantos/spelling-corrector-ptbr | [
-0.5717639327049255,
-0.5776198506355286,
0.40219631791114807,
0.08945130556821823,
-0.3010769784450531,
-0.2330036163330078,
0.022206755355000496,
-0.5059928894042969,
0.03302198275923729,
0.5753096342086792,
-0.7222673296928406,
-0.5964067578315735,
-0.5710545182228088,
-0.03449218347668... |
Varosa/llama-model-quantized | Varosa | 2023-11-29T10:24:15Z | 6 | 0 | null | [
"transformers",
"pytorch",
"llama",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T10:24:15Z | 2023-11-22T10:08:00.000Z | null | null | ---
license: apache-2.0
---
| null | transformers | null | null | null | null | null | null | null | null | null | null | Varosa/llama-model-quantized | [
-0.1285340040922165,
-0.1861676573753357,
0.6529127955436707,
0.49436259269714355,
-0.19319328665733337,
0.23607435822486877,
0.36072009801864624,
0.05056355893611908,
0.579365611076355,
0.7400140166282654,
-0.6508103609085083,
-0.23783960938453674,
-0.7102246284484863,
-0.0478256717324256... |
Wenjian12581/whisper-small-mandarin | Wenjian12581 | 2023-11-29T12:38:15Z | 6 | 1 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-small",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T12:38:15Z | 2023-11-26T15:50:43.000Z | null | null | ---
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-mandarin
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-small-mandarin
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Mandarin dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2023
- Wer: 252.6651
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0822 | 1.42 | 1000 | 0.1913 | 145.9758 |
| 0.0259 | 2.84 | 2000 | 0.1848 | 168.4222 |
| 0.0031 | 4.26 | 3000 | 0.2008 | 220.9811 |
| 0.0014 | 5.67 | 4000 | 0.2023 | 252.6651 |
### Framework versions
- Transformers 4.36.0.dev0
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | automatic-speech-recognition | null | null | null | null | null | null | null | null | null | Wenjian12581/whisper-small-mandarin | [
-0.28201454877853394,
-0.5504575967788696,
-0.06423342227935791,
0.2766939401626587,
-0.37912628054618835,
-0.609192967414856,
-0.39890792965888977,
-0.3133818209171295,
0.1264248490333557,
0.2673647403717041,
-0.6356152892112732,
-0.5209596753120422,
-0.5131056308746338,
-0.19349093735218... |
hegazyma/imdb_sentiment_model | hegazyma | 2023-11-29T05:47:46Z | 6 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:47:46Z | 2023-11-27T06:11:26.000Z | null | null | ---
license: apache-2.0
base_model: distilbert-base-uncased
tags:
- generated_from_trainer
metrics:
- accuracy
- f1
- precision
- recall
model-index:
- name: imdb_sentiment_model
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# imdb_sentiment_model
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2687
- Accuracy: 0.9145
- F1: 0.9148
- Precision: 0.9153
- Recall: 0.9142
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 2
### Training results
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-classification | null | null | null | null | null | null | null | null | null | hegazyma/imdb_sentiment_model | [
-0.5659453272819519,
-0.595942497253418,
0.24220502376556396,
0.323753297328949,
-0.5084270238876343,
-0.2103780061006546,
-0.16857579350471497,
0.0780373364686966,
0.2881571352481842,
0.25657200813293457,
-0.7745267748832703,
-0.7556129693984985,
-0.849330484867096,
-0.10197801887989044,
... |
Jungwonchang/whisper-medium.en-LoRA-SPGIspeech-S | Jungwonchang | 2023-11-29T02:20:28Z | 6 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:openai/whisper-medium.en",
"model-index",
"region:us"
] | 2023-11-29T02:20:28Z | 2023-11-27T08:48:20.000Z | null | null | ---
library_name: peft
base_model: openai/whisper-medium.en
model-index:
- name: Jungwonchang/whisper-medium.en-LoRA-SPGIspeech-S
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Test set for spgispeech
type: kensho/spgispeech
config: S
split: test
metrics:
- type: wer
value: 5.99
name: WER
- type: cer
value: 1.75
name: CER
---
# 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]
## Training procedure
### Framework versions
- PEFT 0.6.2
| null | peft | null | null | null | null | null | null | null | null | null | null | Jungwonchang/whisper-medium.en-LoRA-SPGIspeech-S | [
-0.5839648842811584,
-0.5444982647895813,
0.4422629177570343,
0.10047031193971634,
-0.21837837994098663,
-0.29282113909721375,
0.1171051636338234,
-0.560427188873291,
0.08466268330812454,
0.6898143887519836,
-0.749681830406189,
-0.6463689804077148,
-0.5569517612457275,
-0.12423540651798248... |
19kmunz/IoT-23-BERT-Network-Logs-Classification | 19kmunz | 2023-11-29T19:44:29Z | 6 | 0 | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"has_space",
"region:us"
] | 2023-11-29T19:44:29Z | 2023-11-27T19:30:33.000Z | null | null | ---
inference: false
---
TODO | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | 19kmunz/IoT-23-BERT-Network-Logs-Classification | [
-0.048268117010593414,
-0.09459324181079865,
0.7690081000328064,
0.9465500116348267,
-0.5366121530532837,
-0.0987892746925354,
0.2093806266784668,
-0.41499048471450806,
0.8671061992645264,
1.0933349132537842,
-0.5244077444076538,
-0.2663632929325104,
-0.8554373979568481,
-0.083028785884380... |
NatureUniverse/TinyBERT_general_4L_312d | NatureUniverse | 2023-11-30T00:45:27Z | 6 | 0 | null | [
"transformers",
"safetensors",
"bert",
"text-generation",
"question-answering",
"dataset:squad",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | 2023-11-30T00:45:27Z | 2023-11-28T02:13:57.000Z | null | null | ---
datasets:
- squad
library_name: transformers
pipeline_tag: question-answering
---
This model is a fine-tuned version of huawei-noah/TinyBERT_General_4L_312D on SQuAD dataset.
### Training parameters
- learning_rate: 1e-05
- train_batch_size: 20
- eval_batch_size: 32
- optimizer:paged_adamw_32bit
- num_epochs: 1
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0 | null | transformers | question-answering | null | null | null | null | null | null | null | null | null | NatureUniverse/TinyBERT_general_4L_312d | [
-0.5689168572425842,
-0.5035822987556458,
-0.08661385625600815,
0.3423102796077728,
-0.3251996338367462,
-0.024688605219125748,
0.2386859953403473,
-0.2969171106815338,
0.008697542361915112,
0.40195798873901367,
-1.0281860828399658,
-0.10307340323925018,
-0.2753196060657501,
-0.02182385884... |
evolevelyn/distilgpt2-finetuned-slangQA | evolevelyn | 2023-11-29T03:15:44Z | 6 | 0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:distilgpt2",
"license:apache-2.0",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T03:15:44Z | 2023-11-28T02:14:39.000Z | null | null | ---
license: apache-2.0
base_model: distilgpt2
tags:
- generated_from_trainer
model-index:
- name: distilgpt2-finetuned-slangQA
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. -->
# distilgpt2-finetuned-slangQA
This model is a fine-tuned version of [distilgpt2](https://huggingface.co/distilgpt2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 6.2789
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 6.5804 | 1.0 | 1022 | 6.4914 |
| 6.2955 | 2.0 | 2044 | 6.3266 |
| 6.2102 | 3.0 | 3066 | 6.2789 |
### Framework versions
- Transformers 4.35.2
- Pytorch 2.1.0+cu118
- Datasets 2.15.0
- Tokenizers 0.15.0
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | evolevelyn/distilgpt2-finetuned-slangQA | [
-0.43473678827285767,
-0.7095122337341309,
0.1501360684633255,
0.17889747023582458,
-0.43394935131073,
-0.44239088892936707,
-0.06876596063375473,
-0.10364288836717606,
-0.07252812385559082,
0.2002098113298416,
-0.7522357106208801,
-0.47293782234191895,
-0.8585425615310669,
-0.163005515933... |
sayakpaul/text-to-image-pokemons-gpt4 | sayakpaul | 2023-11-29T09:10:25Z | 6 | 0 | null | [
"diffusers",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dataset:diffusers/pokemon-gpt4-captions",
"base_model:runwayml/stable-diffusion-v1-5",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T09:10:25Z | 2023-11-28T08:31:54.000Z | null | null |
---
license: creativeml-openrail-m
base_model: runwayml/stable-diffusion-v1-5
datasets:
- diffusers/pokemon-gpt4-captions
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
inference: true
---
# Text-to-image finetuning - sayakpaul/text-to-image-pokemons-gpt4
This pipeline was finetuned from **runwayml/stable-diffusion-v1-5** on the **diffusers/pokemon-gpt4-captions** dataset. Below are some example images generated with the finetuned pipeline using the following prompts: ['cute dragon creature']:
## Pipeline usage
You can use the pipeline like so:
```python
from diffusers import DiffusionPipeline
import torch
pipeline = DiffusionPipeline.from_pretrained("sayakpaul/text-to-image-pokemons-gpt4", torch_dtype=torch.float16)
prompt = "cute dragon creature"
image = pipeline(prompt).images[0]
image.save("my_image.png")
```
## Training info
These are the key hyperparameters used during training:
* Epochs: 1429
* Learning rate: 1e-05
* Batch size: 4
* Gradient accumulation steps: 4
* Image resolution: 512
* Mixed-precision: None
More information on all the CLI arguments and the environment are available on your [`wandb` run page](https://wandb.ai/sayakpaul/text2image-fine-tune/runs/dyfz7870).
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | sayakpaul/text-to-image-pokemons-gpt4 | [
-0.687457263469696,
-0.5617082118988037,
0.30507197976112366,
0.2488173097372055,
-0.5725342035293579,
-0.37898868322372437,
-0.24096739292144775,
0.03761046379804611,
0.12619291245937347,
0.6221680045127869,
-0.7392956614494324,
-0.4058748185634613,
-0.7403588891029358,
0.2306062579154968... |
RajuEEE/GeneratorModel_SummData_GoodOnly_SFT_AvishekLLama_LargeQuestion | RajuEEE | 2023-11-29T06:33:18Z | 6 | 0 | null | [
"peft",
"arxiv:1910.09700",
"base_model:abhishek/llama-2-7b-hf-small-shards",
"region:us"
] | 2023-11-29T06:33:18Z | 2023-11-28T16:46:07.000Z | null | null | ---
library_name: peft
base_model: abhishek/llama-2-7b-hf-small-shards
---
# 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]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: True
- load_in_4bit: False
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions
- PEFT 0.6.3.dev0
| null | peft | null | null | null | null | null | null | null | null | null | null | RajuEEE/GeneratorModel_SummData_GoodOnly_SFT_AvishekLLama_LargeQuestion | [
-0.574804425239563,
-0.5590018033981323,
0.40296828746795654,
0.07961388677358627,
-0.2534928023815155,
-0.27700263261795044,
0.060468919575214386,
-0.5367451906204224,
0.04952648654580116,
0.6133862733840942,
-0.7236800193786621,
-0.6278332471847534,
-0.5595568418502808,
-0.08562324941158... |
chloe0x0/spliceGPT | chloe0x0 | 2023-11-29T21:18:00Z | 6 | 0 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | 2023-11-29T21:18:00Z | 2023-11-28T17:48:12.000Z | null | null | ---
license: mit
---
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | chloe0x0/spliceGPT | [
-0.12853386998176575,
-0.18616794049739838,
0.6529127359390259,
0.4943622946739197,
-0.19319306313991547,
0.2360745519399643,
0.36072012782096863,
0.05056336894631386,
0.579365611076355,
0.740013837814331,
-0.6508102416992188,
-0.23784014582633972,
-0.7102251052856445,
-0.04782590642571449... |
shamweel/llama2-qlora-finetunined | shamweel | 2023-11-29T01:40:46Z | 6 | 0 | null | [
"peft",
"region:us"
] | 2023-11-29T01:40:46Z | 2023-11-29T01:40:35.000Z | null | null | ---
library_name: peft
---
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.4.0
| null | peft | null | null | null | null | null | null | null | null | null | null | shamweel/llama2-qlora-finetunined | [
-0.6678625345230103,
-0.7227709889411926,
0.462628573179245,
0.4780524671077728,
-0.5321481823921204,
0.1089973896741867,
0.17308935523033142,
-0.19146037101745605,
-0.18465456366539001,
0.46021318435668945,
-0.5834618210792542,
-0.09607511758804321,
-0.4619947075843811,
0.1978785395622253... |
decem/chai-deberta-v3-large-reward-model | decem | 2023-11-29T05:24:56Z | 6 | 0 | null | [
"transformers",
"safetensors",
"deberta-v2",
"text-classification",
"endpoints_compatible",
"region:us"
] | 2023-11-29T05:24:56Z | 2023-11-29T02:04:33.000Z | null | null | Entry not found | null | transformers | text-classification | null | null | null | null | null | null | null | null | null | decem/chai-deberta-v3-large-reward-model | [
-0.3227650821208954,
-0.22568479180335999,
0.8622263669967651,
0.4346153140068054,
-0.5282987952232361,
0.7012966871261597,
0.7915722727775574,
0.07618651539087296,
0.7746027112007141,
0.2563222348690033,
-0.7852821350097656,
-0.225738525390625,
-0.910447895526886,
0.5715667009353638,
-0... |
sgutsul/dogbooth | sgutsul | 2023-11-29T02:55:35Z | 6 | 0 | null | [
"diffusers",
"tensorboard",
"stable-diffusion",
"stable-diffusion-diffusers",
"text-to-image",
"dreambooth",
"base_model:stabilityai/stable-diffusion-2-1",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | 2023-11-29T02:55:35Z | 2023-11-29T02:39:51.000Z | null | null |
---
license: creativeml-openrail-m
base_model: stabilityai/stable-diffusion-2-1
instance_prompt: a photo of [v]dog
tags:
- stable-diffusion
- stable-diffusion-diffusers
- text-to-image
- diffusers
- dreambooth
inference: true
---
# DreamBooth - sgutsul/dogbooth
This is a dreambooth model derived from stabilityai/stable-diffusion-2-1. The weights were trained on a photo of [v]dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
| null | diffusers | text-to-image | null | null | null | null | null | null | null | null | null | sgutsul/dogbooth | [
-0.12046295404434204,
-0.4639436602592468,
0.44847333431243896,
0.029673928394913673,
-0.43383723497390747,
0.2792350947856903,
0.3117852210998535,
-0.2824794054031372,
0.6781800985336304,
0.37207934260368347,
-0.49199503660202026,
-0.3628728687763214,
-0.6464415192604065,
-0.2485437840223... |
Starbourne/cogvlm-grounding-generalist-hf | Starbourne | 2023-11-29T03:01:48Z | 6 | 0 | null | [
"transformers",
"safetensors",
"text-generation",
"custom_code",
"arxiv:2311.03079",
"endpoints_compatible",
"region:us"
] | 2023-11-29T03:01:48Z | 2023-11-29T02:41:27.000Z | null | null | # CogVLM
**CogVLM** 是一个强大的开源视觉语言模型（VLM）。CogVLM-17B 拥有 100 亿视觉参数和 70 亿语言参数，在 10 个经典跨模态基准测试上取得了 SOTA 性能，包括 NoCaps、Flicker30k captioning、RefCOCO、RefCOCO+、RefCOCOg、Visual7W、GQA、ScienceQA、VizWiz VQA 和 TDIUC，而在 VQAv2、OKVQA、TextVQA、COCO captioning 等方面则排名第二，超越或与 PaLI-X 55B 持平。您可以通过线上 [demo](http://36.103.203.44:7861/) 体验 CogVLM 多模态对话。
**CogVLM** is a powerful **open-source visual language model** (**VLM**). CogVLM-17B has 10 billion vision parameters and 7 billion language parameters. CogVLM-17B achieves state-of-the-art performance on 10 classic cross-modal benchmarks, including NoCaps, Flicker30k captioning, RefCOCO, RefCOCO+, RefCOCOg, Visual7W, GQA, ScienceQA, VizWiz VQA and TDIUC, and rank the 2nd on VQAv2, OKVQA, TextVQA, COCO captioning, etc., **surpassing or matching PaLI-X 55B**. CogVLM can also [chat with you](http://36.103.203.44:7861/) about images.
<div align="center">
<img src="https://github.com/THUDM/CogVLM/raw/main/assets/metrics-min.png" alt="img" style="zoom: 50%;" />
</div>
# 快速开始（Qiuckstart）
```python
import torch
from PIL import Image
from transformers import AutoModelForCausalLM, LlamaTokenizer
tokenizer = LlamaTokenizer.from_pretrained('lmsys/vicuna-7b-v1.5')
model = AutoModelForCausalLM.from_pretrained(
'THUDM/cogvlm-grounding-generalist-hf',
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True
).to('cuda').eval()
query = 'Can you provide a description of the image and include the coordinates [[x0,y0,x1,y1]] for each mentioned object?'
image = Image.open(requests.get('https://github.com/THUDM/CogVLM/blob/main/examples/4.jpg?raw=true', stream=True).raw).convert('RGB')
inputs = model.build_conversation_input_ids(tokenizer, query=query, images=[image])
inputs = {
'input_ids': inputs['input_ids'].unsqueeze(0).to('cuda'),
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to('cuda'),
'attention_mask': inputs['attention_mask'].unsqueeze(0).to('cuda'),
'images': [[inputs['images'][0].to('cuda').to(torch.bfloat16)]],
}
gen_kwargs = {"max_length": 2048, "do_sample": False}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
print(tokenizer.decode(outputs[0]))
```
# 方法（Method）
CogVLM 模型包括四个基本组件：视觉变换器（ViT）编码器、MLP适配器、预训练的大型语言模型（GPT）和一个**视觉专家模块**。更多细节请参见[Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf)。
CogVLM model comprises four fundamental components: a vision transformer (ViT) encoder, an MLP adapter, a pretrained large language model (GPT), and a **visual expert module**. See [Paper](https://github.com/THUDM/CogVLM/blob/main/assets/cogvlm-paper.pdf) for more details.
<div align="center">
<img src="https://github.com/THUDM/CogVLM/raw/main/assets/method-min.png" style="zoom:50%;" />
</div>
# 许可（License）
此存储库中的代码是根据 [Apache-2.0 许可](https://github.com/THUDM/CogVLM/raw/main/LICENSE) 开放源码，而使用 CogVLM 模型权重必须遵循 [模型许可](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE)。
The code in this repository is open source under the [Apache-2.0 license](https://github.com/THUDM/CogVLM/raw/main/LICENSE), while the use of the CogVLM model weights must comply with the [Model License](https://github.com/THUDM/CogVLM/raw/main/MODEL_LICENSE).
# 引用（Citation）
If you find our work helpful, please consider citing the following papers
```
@article{wang2023cogvlm,
title={CogVLM: Visual Expert for Pretrained Language Models},
author={Weihan Wang and Qingsong Lv and Wenmeng Yu and Wenyi Hong and Ji Qi and Yan Wang and Junhui Ji and Zhuoyi Yang and Lei Zhao and Xixuan Song and Jiazheng Xu and Bin Xu and Juanzi Li and Yuxiao Dong and Ming Ding and Jie Tang},
year={2023},
eprint={2311.03079},
archivePrefix={arXiv},
primaryClass={cs.CV}
}
```
| null | transformers | text-generation | null | null | null | null | null | null | null | null | null | Starbourne/cogvlm-grounding-generalist-hf | [
-0.5448614358901978,
-0.8616047501564026,
0.09615857154130936,
0.13419386744499207,
-0.43723493814468384,
-0.13256561756134033,
-0.24922603368759155,
-0.5129694938659668,
-0.1491265892982483,
0.3551709055900574,
-0.37450921535491943,
-0.7549844980239868,
-0.4796886146068573,
-0.18837366998... |
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