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null | null | {} | marcelomoreno26/bart-large-oposum2 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-05-02T18:06:39+00:00 |
|
null | null | {} | chbaby26/finetuning-sentiment-model-3000-samples | null | [
"region:us"
] | null | 2024-05-02T18:07:00+00:00 |
|
image-classification | 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. -->
# convnext-large-384-22k-1k-finetuned-climbing-test1
This model is a fine-tuned version of [facebook/convnext-large-384-22k-1k](https://huggingface.co/facebook/convnext-large-384-22k-1k) on the imagefolder dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0935
- Accuracy: 0.9828
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.6932 | 0.9796 | 12 | 0.0935 | 0.9828 |
| 0.1248 | 1.9592 | 24 | 0.0691 | 0.9828 |
| 0.0587 | 2.9388 | 36 | 0.0612 | 0.9828 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "datasets": ["imagefolder"], "metrics": ["accuracy"], "base_model": "facebook/convnext-large-384-22k-1k", "model-index": [{"name": "convnext-large-384-22k-1k-finetuned-climbing-test1", "results": [{"task": {"type": "image-classification", "name": "Image Classification"}, "dataset": {"name": "imagefolder", "type": "imagefolder", "config": "default", "split": "train", "args": "default"}, "metrics": [{"type": "accuracy", "value": 0.9827586206896551, "name": "Accuracy"}]}]}]} | AMead10/convnext-large-384-22k-1k-finetuned-climbing-test1 | null | [
"transformers",
"safetensors",
"convnext",
"image-classification",
"generated_from_trainer",
"dataset:imagefolder",
"base_model:facebook/convnext-large-384-22k-1k",
"license:apache-2.0",
"model-index",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:07:00+00:00 |
null | null | {} | urbnztr/output | null | [
"region:us"
] | null | 2024-05-02T18:07:20+00:00 |
|
token-classification | spacy | English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_sm` |
| **Version** | `3.7.1` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 0 keys, 0 unique vectors (0 dimensions) |
| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br>[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br>[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (115 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `GPE`, `Intoxicant`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `Profanity`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.86 |
| `TOKEN_P` | 99.57 |
| `TOKEN_R` | 99.58 |
| `TOKEN_F` | 99.57 |
| `TAG_ACC` | 97.25 |
| `SENTS_P` | 92.02 |
| `SENTS_R` | 89.21 |
| `SENTS_F` | 90.59 |
| `DEP_UAS` | 91.75 |
| `DEP_LAS` | 89.87 |
| `ENTS_P` | 84.55 |
| `ENTS_R` | 84.57 |
| `ENTS_F` | 84.56 | | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | SuramyaPokharel/en_core_web_sm | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2024-05-02T18:07:39+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/Moriacrafter/LLaMA2-7B_DepressionDetection
<!-- provided-files -->
weighted/imatrix quants seem not to be available (by me) at this time. If they do not show up a week or so after the static ones, I have probably not planned for them. Feel free to request them by opening a Community Discussion.
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q2_K.gguf) | Q2_K | 2.6 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.IQ3_XS.gguf) | IQ3_XS | 2.9 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.IQ3_S.gguf) | IQ3_S | 3.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q3_K_S.gguf) | Q3_K_S | 3.0 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.IQ3_M.gguf) | IQ3_M | 3.2 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q3_K_M.gguf) | Q3_K_M | 3.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q3_K_L.gguf) | Q3_K_L | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.IQ4_XS.gguf) | IQ4_XS | 3.7 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q4_K_S.gguf) | Q4_K_S | 4.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q4_K_M.gguf) | Q4_K_M | 4.2 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q5_K_S.gguf) | Q5_K_S | 4.8 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q5_K_M.gguf) | Q5_K_M | 4.9 | |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q6_K.gguf) | Q6_K | 5.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.Q8_0.gguf) | Q8_0 | 7.3 | fast, best quality |
| [GGUF](https://huggingface.co/mradermacher/LLaMA2-7B_DepressionDetection-GGUF/resolve/main/LLaMA2-7B_DepressionDetection.f16.gguf) | f16 | 13.6 | 16 bpw, overkill |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "library_name": "transformers", "tags": ["llama-factory"], "base_model": "Moriacrafter/LLaMA2-7B_DepressionDetection", "quantized_by": "mradermacher"} | mradermacher/LLaMA2-7B_DepressionDetection-GGUF | null | [
"transformers",
"gguf",
"llama-factory",
"en",
"base_model:Moriacrafter/LLaMA2-7B_DepressionDetection",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:08:42+00:00 |
reinforcement-learning | null |
# **Q-Learning** Agent playing1 **Taxi-v3**
This is a trained model of a **Q-Learning** agent playing **Taxi-v3** .
## Usage
```python
model = load_from_hub(repo_id="PaoloB27/q-learning-taxi-v3", filename="q-learning.pkl")
# Don't forget to check if you need to add additional attributes (is_slippery=False etc)
env = gym.make(model["env_id"])
```
| {"tags": ["Taxi-v3", "q-learning", "reinforcement-learning", "custom-implementation"], "model-index": [{"name": "q-learning-taxi-v3", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Taxi-v3", "type": "Taxi-v3"}, "metrics": [{"type": "mean_reward", "value": "7.54 +/- 2.73", "name": "mean_reward", "verified": false}]}]}]} | PaoloB27/q-learning-taxi-v3 | null | [
"Taxi-v3",
"q-learning",
"reinforcement-learning",
"custom-implementation",
"model-index",
"region:us"
] | null | 2024-05-02T18:10:39+00:00 |
null | null | {} | cendanacitrawan/my_awesome_qa_model | null | [
"region:us"
] | null | 2024-05-02T18:11:11+00:00 |
|
text-to-image | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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] | {"library_name": "diffusers"} | rubbrband/tastyriceLingyunCaijing_v20NewJourney | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-05-02T18:11:50+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter-PLE-v0", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "7.10 +/- 8.47", "name": "mean_reward", "verified": false}]}]}]} | k1101jh/Reinforce-Pixelcopter-PLE-v0 | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-05-02T18:12:09+00:00 |
text-generation | transformers |
## 📌 Notice
- ✅ Original model is [beomi/Llama-3-KoEn-8B-Instruct-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-Instruct-preview)
- ✅ Quantized by [teddylee777](https://huggingface.co/teddylee777) by using [llama.cpp](https://github.com/ggerganov/llama.cpp)
## 💬 Template
LM Studio
```
<|start_header_id|>system<|end_header_id|>
{System}<|eot_id|>
<|start_header_id|>user<|end_header_id|>
{User}
<|eot_id|><|start_header_id|>assistant<|end_header_id|>
{Assistant}
```
Stop Token
```
<|eot_id|>
<|start_header_id|>
<|end_header_id|>
<|begin_of_text|>
<|end_of_text|>
```
## 📝 Helpful Contents
- ✅ [How to load HuggingFace GGUF into LM Studio](https://youtu.be/bANQk--Maxs)
- ✅ [How to test llama3 by using Ollama](https://youtu.be/12CuUQIPdM4)
- 🇰🇷 [LangChain Tutorial in Korean](https://wikidocs.net/book/14314)
- Please subscribe and support on [YouTube](https://www.youtube.com/@teddynote)
| {"language": ["en", "ko"], "license": "cc-by-nc-sa-4.0", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3", "llama-3-ko"], "pipeline_tag": "text-generation", "license_name": "llama3", "license_link": "LICENSE"} | teddylee777/Llama-3-KoEn-8B-Instruct-preview-gguf | null | [
"transformers",
"gguf",
"llama",
"text-generation",
"facebook",
"meta",
"pytorch",
"llama-3",
"llama-3-ko",
"conversational",
"en",
"ko",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:12:44+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** anandanand84
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/Phi-3-mini-4k-instruct"} | anandanand84/otcjson_phi3_lora | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:13:00+00:00 |
fill-mask | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | johnlockejrr/BEREL_2.0-sam-v2 | null | [
"transformers",
"safetensors",
"bert",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:14:15+00:00 |
null | null | {} | Destr/diffusers_ckpt_step_262.zip | null | [
"region:us"
] | null | 2024-05-02T18:15:23+00:00 |
|
null | null | {} | Destr/controlnet_step_2457.zip | null | [
"region:us"
] | null | 2024-05-02T18:15:58+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** anandanand84
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl", "sft"], "base_model": "unsloth/Phi-3-mini-4k-instruct"} | anandanand84/otcjson_phi3_2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"conversational",
"en",
"base_model:unsloth/Phi-3-mini-4k-instruct",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:16:37+00:00 |
feature-extraction | transformers | # fine-tuned/jina-embeddings-v2-base-en-02052024-zdw2-webapp_8647177611
## Model Description
fine-tuned/jina-embeddings-v2-base-en-02052024-zdw2-webapp_8647177611 is a fine-tuned version of jinaai/jina-embeddings-v2-base-en designed for a specific domain.
## Use Case
This model is designed to support various applications in natural language processing and understanding.
## Associated Dataset
This the dataset for this model can be found [**here**](https://huggingface.co/datasets/fine-tuned/fine-tuned/jina-embeddings-v2-base-en-02052024-zdw2-webapp_8647177611).
## How to Use
This model can be easily integrated into your NLP pipeline for tasks such as text classification, sentiment analysis, entity recognition, and more. Here's a simple example to get you started:
```python
from transformers import AutoModel, AutoTokenizer
llm_name = "fine-tuned/jina-embeddings-v2-base-en-02052024-zdw2-webapp_8647177611"
tokenizer = AutoTokenizer.from_pretrained(llm_name)
model = AutoModel.from_pretrained(llm_name, trust_remote_code=True)
tokens = tokenizer("Your text here", return_tensors="pt")
embedding = model(**tokens)
```
| {} | fine-tuned/jina-embeddings-v2-base-en-02052024-zdw2-webapp_8647177611 | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"custom_code",
"region:us"
] | null | 2024-05-02T18:16:42+00:00 |
null | peft |
<!-- 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. -->
# code-llama-7b-text-to-sql
This model is a fine-tuned version of [codellama/CodeLlama-7b-hf](https://huggingface.co/codellama/CodeLlama-7b-hf) on the generator 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: 3
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 6
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 1
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "llama2", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "codellama/CodeLlama-7b-hf", "model-index": [{"name": "code-llama-7b-text-to-sql", "results": []}]} | felixml/code-llama-7b-text-to-sql | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:codellama/CodeLlama-7b-hf",
"license:llama2",
"region:us"
] | null | 2024-05-02T18:16:52+00:00 |
null | peft |
<!-- 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. -->
# Mistral-7B-Instruct-v0.2-finetune-SWE_90_10
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1233
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.4939 | 0.9995 | 1847 | 0.9488 |
| 0.7585 | 1.9989 | 3694 | 0.8857 |
| 0.4374 | 2.9984 | 5541 | 0.9555 |
| 0.2946 | 3.9978 | 7388 | 1.0790 |
| 0.2287 | 4.9973 | 9235 | 1.1233 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "Mistral-7B-Instruct-v0.2-finetune-SWE_90_10", "results": []}]} | JuanjoLopez19/Mistral-7B-Instruct-v0.2-finetune-SWE_90_10 | null | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T18:19:28+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | twodev/phiLLamaHF-4bit | null | [
"transformers",
"safetensors",
"phi3",
"text-generation",
"custom_code",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:19:56+00:00 |
null | transformers | {} | erkamk/llama3-7b-text-correction-Q4-K-M-GGUF | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:20:38+00:00 |
|
text2text-generation | 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. -->
# text-to-sparql-t5-small-qald9
This model is a fine-tuned version of [yazdipour/text-to-sparql-t5-small-qald9](https://huggingface.co/yazdipour/text-to-sparql-t5-small-qald9) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0001
- Gen Len: 19.0
- P: 0.6665
- R: 0.1769
- F1: 0.4085
- Bleu-score: 12.0496
- Bleu-precisions: [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261]
- Bleu-bp: 0.1231
## 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
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Gen Len | P | R | F1 | Bleu-score | Bleu-precisions | Bleu-bp |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:------:|:------:|:------:|:----------:|:----------------------------------------------------------------------------:|:-------:|
| No log | 1.0 | 28 | 0.2187 | 19.0 | 0.5442 | 0.1725 | 0.3510 | 9.1204 | [84.10326086956522, 63.125, 53.49264705882353, 45.75892857142857] | 0.1519 |
| No log | 2.0 | 56 | 0.0265 | 19.0 | 0.6848 | 0.1878 | 0.4229 | 9.2726 | [97.80907668231612, 94.84346224677716, 93.73601789709173, 92.02279202279202] | 0.0980 |
| No log | 3.0 | 84 | 0.0092 | 19.0 | 0.6648 | 0.1744 | 0.4063 | 11.7575 | [97.9502196193265, 97.10391822827938, 96.5376782077393, 95.69620253164557] | 0.1214 |
| No log | 4.0 | 112 | 0.0055 | 19.0 | 0.6571 | 0.1701 | 0.4004 | 12.1496 | [97.40259740259741, 95.97989949748744, 95.20958083832335, 94.07407407407408] | 0.1270 |
| No log | 5.0 | 140 | 0.0023 | 19.0 | 0.6654 | 0.1752 | 0.4070 | 11.8546 | [98.09941520467837, 97.44897959183673, 96.95121951219512, 96.21212121212122] | 0.1220 |
| No log | 6.0 | 168 | 0.0010 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 7.0 | 196 | 0.0008 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 8.0 | 224 | 0.0003 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 9.0 | 252 | 0.0005 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 10.0 | 280 | 0.0002 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 11.0 | 308 | 0.0002 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 12.0 | 336 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 13.0 | 364 | 0.0002 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 14.0 | 392 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 15.0 | 420 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 16.0 | 448 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| No log | 17.0 | 476 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| 0.088 | 18.0 | 504 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| 0.088 | 19.0 | 532 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
| 0.088 | 20.0 | 560 | 0.0001 | 19.0 | 0.6665 | 0.1769 | 0.4085 | 12.0496 | [98.39650145772595, 98.13559322033899, 97.77327935222672, 97.23618090452261] | 0.1231 |
### Framework versions
- Transformers 4.38.1
- Pytorch 2.1.2
- Datasets 2.1.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["f1"], "base_model": "yazdipour/text-to-sparql-t5-small-qald9", "model-index": [{"name": "text-to-sparql-t5-small-qald9", "results": []}]} | Uzair54/thesis_only_english | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:yazdipour/text-to-sparql-t5-small-qald9",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:21:20+00:00 |
null | null | {} | 003myjoker1/llama-3-ru | null | [
"region:us"
] | null | 2024-05-02T18:21:45+00:00 |
|
text-generation | transformers | # Model Card for Cyber-risk-llama-3-8b-instruct-sft
## Model Description
This model is a fine-tuned version of `meta-llama/Meta-Llama-3-8B-Instruct` on the `vanessasml/cybersecurity_32k_instruction_input_output` dataset.
It is specifically designed to enhance performance in generating and understanding cybersecurity, identifying cyber threats and classifying data under the NIST taxonomy and IT Risks based on the ITC EBA guidelines.
## Intended Use
- **Intended users**: Data scientists and developers working on cybersecurity applications.
- **Out-of-scope use cases**: This model should not be used for medical advice, legal decisions, or any life-critical systems.
## Training Data
The model was fine-tuned on `vanessasml/cybersecurity_32k_instruction_input_output`, a dataset focused on cybersecurity news analysis.
No special data format was applied as [recommended](https://huggingface.co/blog/llama3#fine-tuning-with-%F0%9F%A4%97-trl), although the following steps need to be applied to adjust the input:
```python
# During training
from trl import setup_chat_format
model, tokenizer = setup_chat_format(model, tokenizer)
# During inference
messages = [
{"role": "system", "content": "You are a pirate chatbot who always responds in pirate speak!"},
{"role": "user", "content": "Who are you?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
```
## Training Procedure
- **Preprocessing**: Text data were tokenized using the tokenizer corresponding to the base model `meta-llama/Meta-Llama-3-8B-Instruct`.
- **Hardware**: The training was performed on GPUs with mixed precision (FP16/BF16) enabled.
- **Optimizer**: Paged AdamW with a cosine learning rate schedule.
- **Epochs**: The model was trained for 1 epoch.
- **Batch size**: 4 per device, with gradient accumulation where required.
## Evaluation Results
Model evaluation was based on qualitative assessment of generated text relevance and coherence in the context of cybersecurity.
## Quantization and Optimization
- **Quantization**: 4-bit precision with type `nf4`. Nested quantization is disabled.
- **Compute dtype**: `float16` to ensure efficient computation.
- **LoRA Settings**:
- LoRA attention dimension: `64`
- Alpha parameter for LoRA scaling: `16`
- Dropout in LoRA layers: `0.1`
## Environmental Impact
- **Compute Resources**: Training leveraged energy-efficient hardware and practices to minimize carbon footprint.
- **Strategies**: Gradient checkpointing and group-wise data processing were used to optimize memory and power usage.
## How to Use
Here is how to load and use the model using transformers:
```python
import transformers
model_name = "vanessasml/cyber-risk-llama-3-8b-instruct-sft"
# Example of how to use the model:
pipeline = transformers.pipeline(
"text-generation",
model=model_name,
model_kwargs={"torch_dtype": torch.bfloat16},
device="cuda",
)
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{"role": "user", "content": "What are the main 5 cyber classes from the NIST cyber framework?"},
]
prompt = pipeline.tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
terminators = [
pipeline.tokenizer.eos_token_id,
pipeline.tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
outputs = pipeline(
prompt,
max_new_tokens=256,
eos_token_id=terminators,
do_sample=True,
temperature=0.6,
top_p=0.9,
)
print(outputs[0]["generated_text"][len(prompt):])
```
## Limitations and Bias
The model, while robust in cybersecurity contexts, may not generalize well to unrelated domains. Users should be cautious of biases inherent in the training data which may manifest in model predictions.
## Citation
If you use this model, please cite it as follows:
```bibtex
@misc{cyber-risk-llama-3-8b-instruct-sft,
author = {Vanessa Lopes},
title = {Cyber-risk-llama-3-8B-Instruct-sft Model},
year = {2024},
publisher = {HuggingFace Hub},
journal = {HuggingFace Model Hub}
}
``` | {"tags": ["finance", "supervision", "cyber risk", "cybersecurity", "cyber threats", "SFT", "LoRA", "A100GPU"], "datasets": ["Vanessasml/cybersecurity_32k_instruction_input_output"], "pipeline_tag": "text-generation"} | Vanessasml/cyber-risk-llama-3-8b-instruct-sft-v2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"finance",
"supervision",
"cyber risk",
"cybersecurity",
"cyber threats",
"SFT",
"LoRA",
"A100GPU",
"conversational",
"dataset:Vanessasml/cybersecurity_32k_instruction_input_output",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:22:29+00:00 |
token-classification | spacy | English pipeline optimized for CPU. Components: tok2vec, tagger, parser, senter, ner, attribute_ruler, lemmatizer.
| Feature | Description |
| --- | --- |
| **Name** | `en_core_web_md` |
| **Version** | `3.7.1` |
| **spaCy** | `>=3.7.2,<3.8.0` |
| **Default Pipeline** | `tok2vec`, `tagger`, `parser`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Components** | `tok2vec`, `tagger`, `parser`, `senter`, `attribute_ruler`, `lemmatizer`, `ner` |
| **Vectors** | 514157 keys, 20000 unique vectors (300 dimensions) |
| **Sources** | [OntoNotes 5](https://catalog.ldc.upenn.edu/LDC2013T19) (Ralph Weischedel, Martha Palmer, Mitchell Marcus, Eduard Hovy, Sameer Pradhan, Lance Ramshaw, Nianwen Xue, Ann Taylor, Jeff Kaufman, Michelle Franchini, Mohammed El-Bachouti, Robert Belvin, Ann Houston)<br>[ClearNLP Constituent-to-Dependency Conversion](https://github.com/clir/clearnlp-guidelines/blob/master/md/components/dependency_conversion.md) (Emory University)<br>[WordNet 3.0](https://wordnet.princeton.edu/) (Princeton University)<br>[Explosion Vectors (OSCAR 2109 + Wikipedia + OpenSubtitles + WMT News Crawl)](https://github.com/explosion/spacy-vectors-builder) (Explosion) |
| **License** | `MIT` |
| **Author** | [Explosion](https://explosion.ai) |
### Label Scheme
<details>
<summary>View label scheme (117 labels for 3 components)</summary>
| Component | Labels |
| --- | --- |
| **`tagger`** | `$`, `''`, `,`, `-LRB-`, `-RRB-`, `.`, `:`, `ADD`, `AFX`, `CC`, `CD`, `DT`, `EX`, `FW`, `HYPH`, `IN`, `JJ`, `JJR`, `JJS`, `LS`, `MD`, `NFP`, `NN`, `NNP`, `NNPS`, `NNS`, `PDT`, `POS`, `PRP`, `PRP$`, `RB`, `RBR`, `RBS`, `RP`, `SYM`, `TO`, `UH`, `VB`, `VBD`, `VBG`, `VBN`, `VBP`, `VBZ`, `WDT`, `WP`, `WP$`, `WRB`, `XX`, `_SP`, ```` |
| **`parser`** | `ROOT`, `acl`, `acomp`, `advcl`, `advmod`, `agent`, `amod`, `appos`, `attr`, `aux`, `auxpass`, `case`, `cc`, `ccomp`, `compound`, `conj`, `csubj`, `csubjpass`, `dative`, `dep`, `det`, `dobj`, `expl`, `intj`, `mark`, `meta`, `neg`, `nmod`, `npadvmod`, `nsubj`, `nsubjpass`, `nummod`, `oprd`, `parataxis`, `pcomp`, `pobj`, `poss`, `preconj`, `predet`, `prep`, `prt`, `punct`, `quantmod`, `relcl`, `xcomp` |
| **`ner`** | `CARDINAL`, `DATE`, `EVENT`, `FAC`, `Father`, `GPE`, `LANGUAGE`, `LAW`, `LOC`, `MONEY`, `Mother`, `NORP`, `ORDINAL`, `ORG`, `PERCENT`, `PERSON`, `PRODUCT`, `Partner`, `Profession`, `QUANTITY`, `TIME`, `WORK_OF_ART` |
</details>
### Accuracy
| Type | Score |
| --- | --- |
| `TOKEN_ACC` | 99.86 |
| `TOKEN_P` | 99.57 |
| `TOKEN_R` | 99.58 |
| `TOKEN_F` | 99.57 |
| `TAG_ACC` | 97.33 |
| `SENTS_P` | 92.21 |
| `SENTS_R` | 89.37 |
| `SENTS_F` | 90.77 |
| `DEP_UAS` | 92.05 |
| `DEP_LAS` | 90.23 |
| `ENTS_P` | 84.94 |
| `ENTS_R` | 85.49 |
| `ENTS_F` | 85.22 | | {"language": ["en"], "license": "mit", "tags": ["spacy", "token-classification"]} | SuramyaPokharel/NER_for_contacts | null | [
"spacy",
"token-classification",
"en",
"license:mit",
"model-index",
"region:us"
] | null | 2024-05-02T18:22:31+00:00 |
text-classification | transformers | {} | Junbr0/models | null | [
"transformers",
"safetensors",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:22:47+00:00 |
|
null | null | {} | Aleksrrrrr/Miner3 | null | [
"region:us"
] | null | 2024-05-02T18:23:16+00:00 |
|
null | null | {} | xRikishi/Daedric_Pei | null | [
"region:us"
] | null | 2024-05-02T18:23:53+00:00 |
|
null | null | # GGUF quants for [**nvidia/Llama3-ChatQA-1.5-8B**](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B) using [llama.cpp](https://github.com/ggerganov/llama.cpp)
**Terms of Use**: Please check the [**original model**](https://huggingface.co/nvidia/Llama3-ChatQA-1.5-8B)
<picture>
<img alt="cthulhu" src="https://huggingface.co/neopolita/common/resolve/main/profile.png">
</picture>
## Quants
* `q2_k`: Uses Q4_K for the attention.vw and feed_forward.w2 tensors, Q2_K for the other tensors.
* `q3_k_s`: Uses Q3_K for all tensors
* `q3_k_m`: Uses Q4_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q3_k_l`: Uses Q5_K for the attention.wv, attention.wo, and feed_forward.w2 tensors, else Q3_K
* `q4_0`: Original quant method, 4-bit.
* `q4_1`: Higher accuracy than q4_0 but not as high as q5_0. However has quicker inference than q5 models.
* `q4_k_s`: Uses Q4_K for all tensors
* `q4_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q4_K
* `q5_0`: Higher accuracy, higher resource usage and slower inference.
* `q5_1`: Even higher accuracy, resource usage and slower inference.
* `q5_k_s`: Uses Q5_K for all tensors
* `q5_k_m`: Uses Q6_K for half of the attention.wv and feed_forward.w2 tensors, else Q5_K
* `q6_k`: Uses Q8_K for all tensors
* `q8_0`: Almost indistinguishable from float16. High resource use and slow. Not recommended for most users. | {} | neopolita/llama3-chatqa-1.5-8b-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T18:24:56+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** HoneyBadger2989
- **License:** apache-2.0
- **Finetuned from model :** unsloth/tinyllama-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/tinyllama-bnb-4bit"} | HoneyBadger2989/badger-TinyLlama | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/tinyllama-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:26:00+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | HoneyBadger2989/badger-TinyLama | null | [
"transformers",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:26:06+00:00 |
text-generation | transformers |
> [!IMPORTANT]
> [GGUF / Exl2 quants](https://huggingface.co/collections/xxx777xxxASD/chaoticsoliloquy-v15-4x8b-6633f96430c0652a8ad527a3)
Experimental RP-oriented MoE, the idea was to get a model that would be equal to or better than the Mixtral 8x7B and it's finetunes in RP/ERP tasks.
Im not sure but it should be better than the [first version](https://huggingface.co/xxx777xxxASD/ChaoticSoliloquy-4x8B)
### Llama 3 ChaoticSoliloquy-v1.5-4x8B
```
base_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1
gate_mode: random
dtype: bfloat16
experts_per_token: 2
experts:
- source_model: ChaoticNeutrals_Poppy_Porpoise-v0.7-L3-8B
- source_model: NeverSleep_Llama-3-Lumimaid-8B-v0.1
- source_model: openlynn_Llama-3-Soliloquy-8B
- source_model: Sao10K_L3-Solana-8B-v1
```
## Models used
- [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B)
- [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1)
- [openlynn/Llama-3-Soliloquy-8B](https://huggingface.co/openlynn/Llama-3-Soliloquy-8B)
- [Sao10K/L3-Solana-8B-v1](https://huggingface.co/Sao10K/L3-Solana-8B-v1)
## Difference
- Update from [ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.6-L3-8B) to [ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B](https://huggingface.co/ChaoticNeutrals/Poppy_Porpoise-v0.7-L3-8B)
- Change [jeiku/Chaos_RP_l3_8B](https://huggingface.co/jeiku/Chaos_RP_l3_8B) to [NeverSleep/Llama-3-Lumimaid-8B-v0.1](https://huggingface.co/NeverSleep/Llama-3-Lumimaid-8B-v0.1)
## Vision
[llama3_mmproj](https://huggingface.co/ChaoticNeutrals/LLaVA-Llama-3-8B-mmproj-Updated)
## Prompt format: Llama 3 | {"language": ["en"], "license": "llama3", "tags": ["moe"]} | xxx777xxxASD/L3-ChaoticSoliloquy-v1.5-4x8B | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"moe",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:27:00+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Atom-7B-Chat - bnb 4bits
- Model creator: https://huggingface.co/FlagAlpha/
- Original model: https://huggingface.co/FlagAlpha/Atom-7B-Chat/
Original model description:
---
developers: [https://huggingface.co/FlagAlphaAI]
license: apache-2.0
language:
- zh
- en
pipeline_tag: question-answering
library_name: transformers
---
# Atom-7B-32k-Chat
基于Atom-7B具有32k长度的对话模型，完全开源可商用，由Llama中文社区和AtomEcho（原子回声）联合研发，基于Llama2-7B采用大规模的中文数据进行了继续预训练，我们会持续提供更新的模型参数，模型训练过程见[llama.family](https://llama.family)。
模型的部署、训练、微调等方法详见Llama中文社区GitHub仓库：[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)。
## 📝 中文数据
| 类型 | 描述 |
| ---------------------------------------------------------- | ------------------------------------------------------------ |
| 网络数据 | 互联网上公开的网络数据，挑选出去重后的高质量中文数据，涉及到百科、书籍、博客、新闻、公告、小说等高质量长文本数据。 |
| [Wikipedia](https://github.com/goldsmith/Wikipedia) | 中文Wikipedia的数据 |
| [悟道](https://github.com/BAAI-WuDao/Model) | 中文悟道开源的200G数据 |
| [Clue](https://github.com/CLUEbenchmark/CLUEDatasetSearch) | Clue开放的中文预训练数据，进行清洗后的高质量中文长文本数据 |
| 竞赛数据集 | 近年来中文自然语言处理多任务竞赛数据集，约150个 |
| [MNBVC](https://github.com/esbatmop/MNBVC) | MNBVC 中清洗出来的部分数据集 |
**我们也欢迎大家在[llama.family](https://llama.family)中贡献自己的数据，您的数据通过审核后会加入模型训练，也将影响模型未来的能力走向。**
## 📚 中文词表
为了提高中文文本处理的效率，我们针对Llama2模型的词表进行了深度优化。
首先，我们基于数百G的中文文本，**在Llama2词表的基础上扩展词库至65,000个单词**。
经过测试，我们的改进使得**中文编码/解码速度提高了约350％**。
此外，我们还扩大了中文字符集的覆盖范围，包括所有**emoji符号**，这使的生成带有表情符号的文章更加高效。
对于Llama2原生词表中的一些特殊情况，如数字、英文等，我们尽可能地避免对其进行修改或替换。
最终，成功地实现了一种既能提高中文处理效率又能保持Llama2原有性能的方法。
## 📈 训练过程
**模型结构**
基于当前最优秀的开源模型Llama2，使用主流Decoder-only的标准Transformer网络结构，支持4K的上下文长度（Context Length），为同尺寸模型中最长，能满足更长的多轮对话、知识问答与摘要等需求，模型应用场景更广泛。
**FlashAttention-2高效训练**
Atom-7B采用了FlashAttention-2技术进行训练。由于在处理较长的输入序列时，内存消耗的问题可能会导致“内存爆炸”现象。FlashAttention-2是一种高效注意力机制的实现方式之一，相较于传统的注意力技术（Attention），它拥有更快速的速度以及更加优化的内存占用率。
**基于NTK的自适应上下文扩展技术**
- 可在不继续训练模型的情况下支持更长的上下文
- 本项目中模型默认支持4K上下文，利用上述技术可扩展至18K+
- 经过微调可以支持到32K+
## 💻 推理配置
实际应用中，消费级显卡要比专业显卡便宜的多（比如3090相比A10，同样都是24G显存）。
对于消费级显卡，直接FP32肯定放不下，一般最基本的是FP16，而INT8和INT4量化就很有用，例如：
- 对于3080显卡（10G显存），Atom-7B的INT8只需要8G显存可以直接部署。
- 对于3080显卡（10G显存），Atom-7B的INT4只需要5G显存可以直接部署。
---
# Llama中文社区
## 🚀 社区地址：
Github：[**Llama-Chinese**](https://github.com/LlamaFamily/Llama-Chinese)
在线体验链接：[**llama.family**](https://llama.family/)
## 🔥 社区介绍
欢迎来到Llama中文社区！
我们是一个专注于Llama模型在中文方面的优化和上层建设的高级技术社区。
**基于大规模中文数据，从预训练开始对Llama2模型进行中文能力的持续迭代升级**。
我们热忱欢迎对大模型LLM充满热情的开发者和研究者加入我们的行列。
## 🐼 社区资源
- Llama2在线体验链接[**llama.family**](https://llama.family/)，同时包含Meta原版和中文微调版本！
- Llama2 Chat模型的[中文问答能力评测](https://github.com/LlamaFamily/Llama-Chinese/tree/main#-%E6%A8%A1%E5%9E%8B%E8%AF%84%E6%B5%8B)！
- [社区飞书知识库](https://chinesellama.feishu.cn/wiki/space/7257824476874768388?ccm_open_type=lark_wiki_spaceLink)，欢迎大家一起共建！
| {} | RichardErkhov/FlagAlpha_-_Atom-7B-Chat-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T18:29:29+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": ["trl", "sft"]} | wwhlazio/TrueFakeNews_llama2_1k | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T18:29:35+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi-2-ko-v0.1 - bnb 4bits
- Model creator: https://huggingface.co/daekeun-ml/
- Original model: https://huggingface.co/daekeun-ml/phi-2-ko-v0.1/
Original model description:
---
library_name: transformers
license: cc-by-sa-3.0
datasets:
- wikimedia/wikipedia
- maywell/korean_textbooks
- nampdn-ai/tiny-codes
- Open-Orca/OpenOrca
language:
- ko
- en
inference: false
---
# phi-2-ko-v0.1
## Model Details
This model is a Korean-specific model trained in phi-2 by adding a Korean tokenizer and Korean data. (English is also available.)
Although phi-2 performs very well, it does not support the Korean language and does not have a tokenizer trained on Korean corpous, so tokenizing Korean text will use many times more tokens than English tokens.
To overcome these limitations, I trained the model using an open-license Korean corpus and some English corpus.
The reasons for using the English corpus together are as follows:
1. The goal is to preserve the excellent performance of the existing model by preventing catastrophic forgetting.
2. Mixing English and Korean prompts usually produces better results than using all prompts in Korean.
Since my role is not as a working developer, but as an solutions architect helping customers with quick PoCs/prototypes, and I was limited by AWS GPU resources available, I only trained with 5GB of data instead of hundreds of GB of massive data.
### Vocab Expansion
| Model Name | Vocabulary Size | Description |
| --- | --- | --- |
| Original phi-2 | 50,295 | BBPE (Byte-level BPE) |
| **phi-2-ko** | 66,676 | BBPE. Added Korean vocab and merges |
**Tokenizing "아마존 세이지메이커"**
| Model | # of tokens | Tokens |
| --- | --- | --- |
| Original phi-2 | 25 | `[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]` |
| **phi-2-ko** |6| `[57974, 51299, 50617, 51005, 52027, 51446]` |
### Continued pre-training
The dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.
- Wikipedia Korean dataset (https://huggingface.co/datasets/wikimedia/wikipedia)
- Massive Korean synthetic dataset (https://huggingface.co/datasets/maywell/korean_textbooks)
- Tiny code dataset (https://huggingface.co/datasets/nampdn-ai/tiny-codes)
- OpenOrca dataset (https://huggingface.co/datasets/Open-Orca/OpenOrca)
- Using some of the various sentences I wrote (personal blog, chat, etc.)
Note that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.
For distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.
Since this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true,
"cpu_offload": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto"
}
```
Some hyperparameters are listed below.
```
batch_size: 2
num_epochs: 1
learning_rate: 3e-4
gradient_accumulation_steps: 8
lr_scheduler_type: "linear"
group_by_length: False
```
## How to Get Started with the Model
```python
import torch
from transformers import PhiForCausalLM, AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("daekeun-ml/phi-2-ko-v0.1", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/phi-2-ko-v0.1", trust_remote_code=True)
# Korean
inputs = tokenizer("머신러닝은 ", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
# English
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
### References
- Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
## Notes
### License
cc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.
### Caution
This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!
| {} | RichardErkhov/daekeun-ml_-_phi-2-ko-v0.1-4bits | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T18:30:49+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-8b-bnb-4bit - bnb 4bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/llama-3-8b-bnb-4bit/
Original model description:
---
language:
- en
license: llama2
library_name: transformers
tags:
- unsloth
- transformers
- llama
- llama-3
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
Directly quantized 4bit model with `bitsandbytes`. Built with Meta Llama 3
We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less |
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
| {} | RichardErkhov/unsloth_-_llama-3-8b-bnb-4bit-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T18:31:49+00:00 |
null | null | {"license": "apache-2.0"} | netcat420/MFANN3bv0.6-GGUF | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T18:32:04+00:00 |
|
text2text-generation | transformers |
# Emergency Actors Classiication
This model is a fine-tuned version of [vgaraujov/bart-base-spanish](https://huggingface.co/vgaraujov/bart-base-spanish).
It achieves the following results on the evaluation set:
- Loss: 0.0947491004032159
- Rougelsum: 61.225600
## Model description
This checkpoint classifies emergency transcribed calls actors adding new tokenes into text:
<A> Beggining of Alerting
</A> End of Alerting
<O> Beggining of Operator
</O> End of Operator | {"language": ["es"], "license": "mit", "library_name": "transformers", "metrics": ["rouge"], "pipeline_tag": "text2text-generation"} | santyzenith/emergency_actors_classification | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"es",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:33:22+00:00 |
automatic-speech-recognition | 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. -->
# Whisper Small Uz - Aslon Khamidov -- with Uzbek Voice dataset
This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Common Voice 16.1 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3910
- Wer: 35.9465
## 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: 5000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.4411 | 0.0176 | 1000 | 0.5526 | 47.9128 |
| 0.327 | 0.0352 | 2000 | 0.4648 | 41.1885 |
| 0.2883 | 0.0528 | 3000 | 0.4286 | 37.6822 |
| 0.2777 | 0.0704 | 4000 | 0.4037 | 36.9479 |
| 0.2543 | 0.0880 | 5000 | 0.3910 | 35.9465 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.0
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"language": ["uz"], "license": "apache-2.0", "tags": ["hf-asr-leaderboard", "generated_from_trainer"], "datasets": ["mozilla-foundation/common_voice_16_1"], "metrics": ["wer"], "base_model": "openai/whisper-small", "model-index": [{"name": "Whisper Small Uz - Aslon Khamidov -- with Uzbek Voice dataset", "results": [{"task": {"type": "automatic-speech-recognition", "name": "Automatic Speech Recognition"}, "dataset": {"name": "Common Voice 16.1", "type": "mozilla-foundation/common_voice_16_1", "config": "uz", "split": "test", "args": "config: uz, split: test"}, "metrics": [{"type": "wer", "value": 35.94645555236442, "name": "Wer"}]}]}]} | aslon1213/whisper-small-uz-with-uzbekvoice | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"hf-asr-leaderboard",
"generated_from_trainer",
"uz",
"dataset:mozilla-foundation/common_voice_16_1",
"base_model:openai/whisper-small",
"license:apache-2.0",
"model-index",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:33:42+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | baraah/blip2-opt-2.7b-2-5 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:34:12+00:00 |
text-generation | transformers | {} | LordY54/recomendations | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:35:11+00:00 |
|
null | null | {} | chocice4you/segformer-b0-finetuned-segments-sidewalk-oct-22 | null | [
"region:us"
] | null | 2024-05-02T18:36:20+00:00 |
|
null | null | {"license": "mit"} | bhugxer/coleaf-nitrogen-inpaint-2 | null | [
"license:mit",
"region:us"
] | null | 2024-05-02T18:36:21+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
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#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/4vfiuek | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:36:23+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
phi-2-ko-v0.1 - bnb 8bits
- Model creator: https://huggingface.co/daekeun-ml/
- Original model: https://huggingface.co/daekeun-ml/phi-2-ko-v0.1/
Original model description:
---
library_name: transformers
license: cc-by-sa-3.0
datasets:
- wikimedia/wikipedia
- maywell/korean_textbooks
- nampdn-ai/tiny-codes
- Open-Orca/OpenOrca
language:
- ko
- en
inference: false
---
# phi-2-ko-v0.1
## Model Details
This model is a Korean-specific model trained in phi-2 by adding a Korean tokenizer and Korean data. (English is also available.)
Although phi-2 performs very well, it does not support the Korean language and does not have a tokenizer trained on Korean corpous, so tokenizing Korean text will use many times more tokens than English tokens.
To overcome these limitations, I trained the model using an open-license Korean corpus and some English corpus.
The reasons for using the English corpus together are as follows:
1. The goal is to preserve the excellent performance of the existing model by preventing catastrophic forgetting.
2. Mixing English and Korean prompts usually produces better results than using all prompts in Korean.
Since my role is not as a working developer, but as an solutions architect helping customers with quick PoCs/prototypes, and I was limited by AWS GPU resources available, I only trained with 5GB of data instead of hundreds of GB of massive data.
### Vocab Expansion
| Model Name | Vocabulary Size | Description |
| --- | --- | --- |
| Original phi-2 | 50,295 | BBPE (Byte-level BPE) |
| **phi-2-ko** | 66,676 | BBPE. Added Korean vocab and merges |
**Tokenizing "아마존 세이지메이커"**
| Model | # of tokens | Tokens |
| --- | --- | --- |
| Original phi-2 | 25 | `[168, 243, 226, 167, 100, 230, 168, 94, 112, 23821, 226, 116, 35975, 112, 168, 100, 222, 167, 102, 242, 35975, 112, 168, 119, 97]` |
| **phi-2-ko** |6| `[57974, 51299, 50617, 51005, 52027, 51446]` |
### Continued pre-training
The dataset used for training is as follows. To prevent catastrophic forgetting, I included some English corpus as training data.
- Wikipedia Korean dataset (https://huggingface.co/datasets/wikimedia/wikipedia)
- Massive Korean synthetic dataset (https://huggingface.co/datasets/maywell/korean_textbooks)
- Tiny code dataset (https://huggingface.co/datasets/nampdn-ai/tiny-codes)
- OpenOrca dataset (https://huggingface.co/datasets/Open-Orca/OpenOrca)
- Using some of the various sentences I wrote (personal blog, chat, etc.)
Note that performance is not guaranteed since only a small number of datasets were used for the experiment. The number of samples for training set is just around 5 million after tokenization.
For distributed training, all weights were trained without adapter techniques, and sharding parallelization was performed with ZeRO-2. The presets are as follows.
Since this is a model that has not been fine-tuned, it is recommended to perform fine tuning such as instruction tuning/alignment tuning according to your use case.
```json
{
"fp16": {
"enabled": "auto",
"loss_scale": 0,
"loss_scale_window": 1000,
"initial_scale_power": 16,
"hysteresis": 2,
"min_loss_scale": 1
},
"bf16": {
"enabled": "auto"
},
"optimizer": {
"type": "AdamW",
"params": {
"lr": "auto",
"betas": "auto",
"eps": "auto",
"weight_decay": "auto"
}
},
"scheduler": {
"type": "WarmupLR",
"params": {
"warmup_min_lr": "auto",
"warmup_max_lr": "auto",
"warmup_num_steps": "auto"
}
},
"zero_optimization": {
"stage": 2,
"allgather_partitions": true,
"allgather_bucket_size": 2e8,
"overlap_comm": true,
"reduce_scatter": true,
"reduce_bucket_size": 2e8,
"contiguous_gradients": true,
"cpu_offload": true
},
"gradient_accumulation_steps": "auto",
"gradient_clipping": "auto",
"train_batch_size": "auto",
"train_micro_batch_size_per_gpu": "auto"
}
```
Some hyperparameters are listed below.
```
batch_size: 2
num_epochs: 1
learning_rate: 3e-4
gradient_accumulation_steps: 8
lr_scheduler_type: "linear"
group_by_length: False
```
## How to Get Started with the Model
```python
import torch
from transformers import PhiForCausalLM, AutoModelForCausalLM, AutoTokenizer
torch.set_default_device("cuda")
# Load model and tokenizer
model = AutoModelForCausalLM.from_pretrained("daekeun-ml/phi-2-ko-v0.1", torch_dtype="auto")
tokenizer = AutoTokenizer.from_pretrained("daekeun-ml/phi-2-ko-v0.1", trust_remote_code=True)
# Korean
inputs = tokenizer("머신러닝은 ", return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
# English
inputs = tokenizer('''def print_prime(n):
"""
Print all primes between 1 and n
"""''', return_tensors="pt", return_attention_mask=False)
outputs = model.generate(**inputs, max_length=200)
text = tokenizer.batch_decode(outputs)[0]
print(text)
```
### References
- Base model: [microsoft/phi-2](https://huggingface.co/microsoft/phi-2)
## Notes
### License
cc-by-sa 3.0; The license of phi-2 is MIT, but I considered the licensing of the dataset used for training.
### Caution
This model was created as a personal experiment, unrelated to the organization I work for. The model may not operate correctly because separate verification was not performed. Please be careful unless it is for personal experimentation or PoC (Proof of Concept)!
| {} | RichardErkhov/daekeun-ml_-_phi-2-ko-v0.1-8bits | null | [
"transformers",
"safetensors",
"phi",
"text-generation",
"custom_code",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T18:36:25+00:00 |
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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#### Speeds, Sizes, Times [optional]
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## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- 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] | {"library_name": "transformers", "tags": []} | quickstep3621/tu878vr | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:36:28+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
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#### Preprocessing [optional]
[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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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## Glossary [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | kishorea/Llama3_p8 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T18:36:48+00:00 |
null | null | {"license": "apache-2.0"} | netcat420/MFANNv0.7-GGUF | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T18:36:57+00:00 |
|
null | null | {} | Burhan02/Mistral-ft-merged | null | [
"region:us"
] | null | 2024-05-02T18:37:13+00:00 |
|
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-Pixelcopter", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "12.80 +/- 12.06", "name": "mean_reward", "verified": false}]}]}]} | rwr20/Reinforce-Pixelcopter | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-05-02T18:38:15+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama-3-8b-bnb-4bit - bnb 8bits
- Model creator: https://huggingface.co/unsloth/
- Original model: https://huggingface.co/unsloth/llama-3-8b-bnb-4bit/
Original model description:
---
language:
- en
license: llama2
library_name: transformers
tags:
- unsloth
- transformers
- llama
- llama-3
---
# Finetune Mistral, Gemma, Llama 2-5x faster with 70% less memory via Unsloth!
Directly quantized 4bit model with `bitsandbytes`. Built with Meta Llama 3
We have a Google Colab Tesla T4 notebook for Llama-3 8b here: https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/Discord%20button.png" width="200"/>](https://discord.gg/u54VK8m8tk)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/buy%20me%20a%20coffee%20button.png" width="200"/>](https://ko-fi.com/unsloth)
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
## ✨ Finetune for Free
All notebooks are **beginner friendly**! Add your dataset, click "Run All", and you'll get a 2x faster finetuned model which can be exported to GGUF, vLLM or uploaded to Hugging Face.
| Unsloth supports | Free Notebooks | Performance | Memory use |
|-----------------|--------------------------------------------------------------------------------------------------------------------------|-------------|----------|
| **Llama-3 8b** | [▶️ Start on Colab](https://colab.research.google.com/drive/135ced7oHytdxu3N2DNe1Z0kqjyYIkDXp?usp=sharing) | 2.4x faster | 58% less |
| **Gemma 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/10NbwlsRChbma1v55m8LAPYG15uQv6HLo?usp=sharing) | 2.4x faster | 58% less |
| **Mistral 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1Dyauq4kTZoLewQ1cApceUQVNcnnNTzg_?usp=sharing) | 2.2x faster | 62% less |
| **Llama-2 7b** | [▶️ Start on Colab](https://colab.research.google.com/drive/1lBzz5KeZJKXjvivbYvmGarix9Ao6Wxe5?usp=sharing) | 2.2x faster | 43% less |
| **TinyLlama** | [▶️ Start on Colab](https://colab.research.google.com/drive/1AZghoNBQaMDgWJpi4RbffGM1h6raLUj9?usp=sharing) | 3.9x faster | 74% less |
| **CodeLlama 34b** A100 | [▶️ Start on Colab](https://colab.research.google.com/drive/1y7A0AxE3y8gdj4AVkl2aZX47Xu3P1wJT?usp=sharing) | 1.9x faster | 27% less |
| **Mistral 7b** 1xT4 | [▶️ Start on Kaggle](https://www.kaggle.com/code/danielhanchen/kaggle-mistral-7b-unsloth-notebook) | 5x faster\* | 62% less |
| **DPO - Zephyr** | [▶️ Start on Colab](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) | 1.9x faster | 19% less |
- This [conversational notebook](https://colab.research.google.com/drive/1Aau3lgPzeZKQ-98h69CCu1UJcvIBLmy2?usp=sharing) is useful for ShareGPT ChatML / Vicuna templates.
- This [text completion notebook](https://colab.research.google.com/drive/1ef-tab5bhkvWmBOObepl1WgJvfvSzn5Q?usp=sharing) is for raw text. This [DPO notebook](https://colab.research.google.com/drive/15vttTpzzVXv_tJwEk-hIcQ0S9FcEWvwP?usp=sharing) replicates Zephyr.
- \* Kaggle has 2x T4s, but we use 1. Due to overhead, 1x T4 is 5x faster.
| {} | RichardErkhov/unsloth_-_llama-3-8b-bnb-4bit-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T18:39:45+00:00 |
null | null | {"license": "mit"} | Utkarssshhh/SafeWorkplace_Violence_Detection | null | [
"license:mit",
"region:us"
] | null | 2024-05-02T18:40:30+00:00 |
|
null | null | {} | OliverRen/D-iGPT | null | [
"region:us"
] | null | 2024-05-02T18:40:56+00:00 |
|
null | null | {"license": "unknown"} | daryl4am/davoxeeneizeDARYL4AM | null | [
"license:unknown",
"region:us"
] | null | 2024-05-02T18:41:26+00:00 |
|
null | null | {} | Ray011/falcon7binstructMAy2 | null | [
"region:us"
] | null | 2024-05-02T18:41:48+00:00 |
|
automatic-speech-recognition | 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. -->
# w2v2_uclass_clipped_10_seconds
This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 10
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "facebook/w2v-bert-2.0", "model-index": [{"name": "w2v2_uclass_clipped_10_seconds", "results": []}]} | HamdanXI/w2v2_uclass_clipped_10_seconds | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2-bert",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:facebook/w2v-bert-2.0",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:41:58+00:00 |
text-to-image | diffusers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- 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] | {"library_name": "diffusers"} | rubbrband/jjdoe042sXL_v10 | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-05-02T18:42:39+00:00 |
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "257.89 +/- 19.63", "name": "mean_reward", "verified": false}]}]}]} | LennartHRO/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-02T18:42:50+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | AstroMLab/astroqwen-7B | null | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:42:55+00:00 |
sentence-similarity | sentence-transformers |
# pjbhaumik/biencoder-finetune-model-v5
This is a [sentence-transformers](https://www.SBERT.net) model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search.
<!--- Describe your model here -->
## Usage (Sentence-Transformers)
Using this model becomes easy when you have [sentence-transformers](https://www.SBERT.net) installed:
```
pip install -U sentence-transformers
```
Then you can use the model like this:
```python
from sentence_transformers import SentenceTransformer
sentences = ["This is an example sentence", "Each sentence is converted"]
model = SentenceTransformer('pjbhaumik/biencoder-finetune-model-v5')
embeddings = model.encode(sentences)
print(embeddings)
```
## Usage (HuggingFace Transformers)
Without [sentence-transformers](https://www.SBERT.net), you can use the model like this: First, you pass your input through the transformer model, then you have to apply the right pooling-operation on-top of the contextualized word embeddings.
```python
from transformers import AutoTokenizer, AutoModel
import torch
#Mean Pooling - Take attention mask into account for correct averaging
def mean_pooling(model_output, attention_mask):
token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
# Sentences we want sentence embeddings for
sentences = ['This is an example sentence', 'Each sentence is converted']
# Load model from HuggingFace Hub
tokenizer = AutoTokenizer.from_pretrained('pjbhaumik/biencoder-finetune-model-v5')
model = AutoModel.from_pretrained('pjbhaumik/biencoder-finetune-model-v5')
# Tokenize sentences
encoded_input = tokenizer(sentences, padding=True, truncation=True, return_tensors='pt')
# Compute token embeddings
with torch.no_grad():
model_output = model(**encoded_input)
# Perform pooling. In this case, mean pooling.
sentence_embeddings = mean_pooling(model_output, encoded_input['attention_mask'])
print("Sentence embeddings:")
print(sentence_embeddings)
```
## Evaluation Results
<!--- Describe how your model was evaluated -->
For an automated evaluation of this model, see the *Sentence Embeddings Benchmark*: [https://seb.sbert.net](https://seb.sbert.net?model_name=pjbhaumik/biencoder-finetune-model-v5)
## Training
The model was trained with the parameters:
**DataLoader**:
`torch.utils.data.dataloader.DataLoader` of length 469 with parameters:
```
{'batch_size': 16, 'sampler': 'torch.utils.data.sampler.RandomSampler', 'batch_sampler': 'torch.utils.data.sampler.BatchSampler'}
```
**Loss**:
`sentence_transformers.losses.MultipleNegativesSymmetricRankingLoss.MultipleNegativesSymmetricRankingLoss` with parameters:
```
{'scale': 20.0, 'similarity_fct': 'cos_sim'}
```
Parameters of the fit()-Method:
```
{
"epochs": 12,
"evaluation_steps": 100,
"evaluator": "sentence_transformers.evaluation.EmbeddingSimilarityEvaluator.EmbeddingSimilarityEvaluator",
"max_grad_norm": 1,
"optimizer_class": "<class 'torch.optim.adamw.AdamW'>",
"optimizer_params": {
"lr": 2e-05
},
"scheduler": "WarmupLinear",
"steps_per_epoch": null,
"warmup_steps": 100,
"weight_decay": 0.01
}
```
## Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: DistilBertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': True, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
)
```
## Citing & Authors
<!--- Describe where people can find more information --> | {"library_name": "sentence-transformers", "tags": ["sentence-transformers", "feature-extraction", "sentence-similarity", "transformers"], "pipeline_tag": "sentence-similarity"} | pjbhaumik/biencoder-finetune-model-v5 | null | [
"sentence-transformers",
"safetensors",
"distilbert",
"feature-extraction",
"sentence-similarity",
"transformers",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:43:57+00:00 |
image-classification | 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. -->
# my_awesome_food_model
This model is a fine-tuned version of [google/vit-base-patch16-224-in21k](https://huggingface.co/google/vit-base-patch16-224-in21k) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6305
- Accuracy: 0.891
## 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: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 2.7235 | 0.992 | 62 | 2.5392 | 0.813 |
| 1.8263 | 2.0 | 125 | 1.7908 | 0.867 |
| 1.578 | 2.976 | 186 | 1.6305 | 0.891 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "google/vit-base-patch16-224-in21k", "model-index": [{"name": "my_awesome_food_model", "results": []}]} | CrackinBee/my_awesome_food_model | null | [
"transformers",
"tensorboard",
"safetensors",
"vit",
"image-classification",
"generated_from_trainer",
"base_model:google/vit-base-patch16-224-in21k",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:44:03+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | vaatsav06/Llama3_p8 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T18:44:44+00:00 |
text-classification | 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. -->
# copilot_relex_v1_with_context
This model is a fine-tuned version of [microsoft/deberta-v3-small](https://huggingface.co/microsoft/deberta-v3-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0299
- Accuracy: 0.0075
- F1: 0.0127
- Precision: 0.0064
- Recall: 0.8358
- Learning Rate: 0.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 100
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 | Precision | Recall | Rate |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:------:|:---------:|:------:|:------:|
| No log | 1.0 | 26 | 0.5156 | 0.0531 | 0.0154 | 0.0078 | 0.9701 | 0.0000 |
| No log | 2.0 | 52 | 0.3270 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 3.0 | 78 | 0.1951 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 4.0 | 104 | 0.1153 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 5.0 | 130 | 0.0759 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 6.0 | 156 | 0.0584 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 7.0 | 182 | 0.0503 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 8.0 | 208 | 0.0462 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 9.0 | 234 | 0.0440 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 10.0 | 260 | 0.0427 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 11.0 | 286 | 0.0419 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 12.0 | 312 | 0.0413 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 13.0 | 338 | 0.0410 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 14.0 | 364 | 0.0407 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 15.0 | 390 | 0.0405 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 16.0 | 416 | 0.0403 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 17.0 | 442 | 0.0402 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 18.0 | 468 | 0.0400 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| No log | 19.0 | 494 | 0.0399 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 20.0 | 520 | 0.0397 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 21.0 | 546 | 0.0388 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 22.0 | 572 | 0.0388 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 23.0 | 598 | 0.0387 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 24.0 | 624 | 0.0375 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 25.0 | 650 | 0.0376 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 26.0 | 676 | 0.0369 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 27.0 | 702 | 0.0367 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 28.0 | 728 | 0.0373 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 29.0 | 754 | 0.0362 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 30.0 | 780 | 0.0361 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 31.0 | 806 | 0.0358 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 32.0 | 832 | 0.0355 | 0.0077 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 33.0 | 858 | 0.0329 | 0.0073 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.1144 | 34.0 | 884 | 0.0327 | 0.0078 | 0.0152 | 0.0077 | 1.0 | 0.0000 |
| 0.1144 | 35.0 | 910 | 0.0328 | 0.0074 | 0.0147 | 0.0074 | 0.9701 | 0.0000 |
| 0.1144 | 36.0 | 936 | 0.0324 | 0.0075 | 0.0147 | 0.0074 | 0.9701 | 0.0000 |
| 0.1144 | 37.0 | 962 | 0.0316 | 0.0075 | 0.0147 | 0.0074 | 0.9701 | 0.0000 |
| 0.1144 | 38.0 | 988 | 0.0326 | 0.0075 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.029 | 39.0 | 1014 | 0.0312 | 0.0074 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.029 | 40.0 | 1040 | 0.0313 | 0.0072 | 0.0141 | 0.0071 | 0.9254 | 0.0000 |
| 0.029 | 41.0 | 1066 | 0.0320 | 0.0073 | 0.0143 | 0.0072 | 0.9403 | 0.0000 |
| 0.029 | 42.0 | 1092 | 0.0316 | 0.0074 | 0.0145 | 0.0073 | 0.9552 | 0.0000 |
| 0.029 | 43.0 | 1118 | 0.0310 | 0.0072 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 44.0 | 1144 | 0.0311 | 0.0072 | 0.0141 | 0.0071 | 0.9254 | 0.0000 |
| 0.029 | 45.0 | 1170 | 0.0310 | 0.0072 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.029 | 46.0 | 1196 | 0.0312 | 0.0071 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 47.0 | 1222 | 0.0308 | 0.0071 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 48.0 | 1248 | 0.0312 | 0.0072 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 49.0 | 1274 | 0.0309 | 0.0073 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 50.0 | 1300 | 0.0307 | 0.0070 | 0.0129 | 0.0065 | 0.8507 | 1e-05 |
| 0.029 | 51.0 | 1326 | 0.0303 | 0.0071 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 52.0 | 1352 | 0.0307 | 0.0073 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 53.0 | 1378 | 0.0309 | 0.0073 | 0.0134 | 0.0067 | 0.8806 | 0.0000 |
| 0.029 | 54.0 | 1404 | 0.0312 | 0.0072 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.029 | 55.0 | 1430 | 0.0303 | 0.0073 | 0.0136 | 0.0069 | 0.8955 | 9e-06 |
| 0.029 | 56.0 | 1456 | 0.0300 | 0.0071 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.029 | 57.0 | 1482 | 0.0301 | 0.0069 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.0205 | 58.0 | 1508 | 0.0302 | 0.0072 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 59.0 | 1534 | 0.0303 | 0.0071 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 60.0 | 1560 | 0.0308 | 0.0073 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 61.0 | 1586 | 0.0309 | 0.0074 | 0.0136 | 0.0069 | 0.8955 | 0.0000 |
| 0.0205 | 62.0 | 1612 | 0.0306 | 0.0078 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 63.0 | 1638 | 0.0308 | 0.0077 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 64.0 | 1664 | 0.0303 | 0.0071 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0205 | 65.0 | 1690 | 0.0312 | 0.0077 | 0.0132 | 0.0066 | 0.8657 | 7e-06 |
| 0.0205 | 66.0 | 1716 | 0.0304 | 0.0073 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 67.0 | 1742 | 0.0305 | 0.0073 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 68.0 | 1768 | 0.0304 | 0.0074 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 69.0 | 1794 | 0.0306 | 0.0072 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.0205 | 70.0 | 1820 | 0.0314 | 0.0080 | 0.0134 | 0.0068 | 0.8806 | 6e-06 |
| 0.0205 | 71.0 | 1846 | 0.0314 | 0.0075 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 72.0 | 1872 | 0.0307 | 0.0075 | 0.0132 | 0.0066 | 0.8657 | 0.0000 |
| 0.0205 | 73.0 | 1898 | 0.0300 | 0.0075 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0205 | 74.0 | 1924 | 0.0301 | 0.0072 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0205 | 75.0 | 1950 | 0.0297 | 0.0075 | 0.0132 | 0.0066 | 0.8657 | 5e-06 |
| 0.0205 | 76.0 | 1976 | 0.0306 | 0.0075 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 77.0 | 2002 | 0.0299 | 0.0073 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 78.0 | 2028 | 0.0301 | 0.0074 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 79.0 | 2054 | 0.0301 | 0.0078 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 80.0 | 2080 | 0.0306 | 0.0078 | 0.0130 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 81.0 | 2106 | 0.0302 | 0.0073 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 82.0 | 2132 | 0.0305 | 0.0073 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 83.0 | 2158 | 0.0303 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 84.0 | 2184 | 0.0302 | 0.0072 | 0.0129 | 0.0065 | 0.8507 | 0.0000 |
| 0.016 | 85.0 | 2210 | 0.0302 | 0.0072 | 0.0127 | 0.0064 | 0.8358 | 3e-06 |
| 0.016 | 86.0 | 2236 | 0.0299 | 0.0072 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 87.0 | 2262 | 0.0296 | 0.0069 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 88.0 | 2288 | 0.0299 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 89.0 | 2314 | 0.0297 | 0.0072 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 90.0 | 2340 | 0.0296 | 0.0073 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 91.0 | 2366 | 0.0299 | 0.0071 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 92.0 | 2392 | 0.0293 | 0.0071 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 93.0 | 2418 | 0.0301 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.016 | 94.0 | 2444 | 0.0294 | 0.0071 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 95.0 | 2470 | 0.0296 | 0.0072 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.016 | 96.0 | 2496 | 0.0298 | 0.0074 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.0136 | 97.0 | 2522 | 0.0299 | 0.0073 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0136 | 98.0 | 2548 | 0.0298 | 0.0074 | 0.0125 | 0.0063 | 0.8209 | 0.0000 |
| 0.0136 | 99.0 | 2574 | 0.0299 | 0.0075 | 0.0127 | 0.0064 | 0.8358 | 0.0000 |
| 0.0136 | 100.0 | 2600 | 0.0299 | 0.0075 | 0.0127 | 0.0064 | 0.8358 | 0.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy", "f1", "precision", "recall"], "base_model": "microsoft/deberta-v3-small", "model-index": [{"name": "copilot_relex_v1_with_context", "results": []}]} | bobbyw/copilot_relex_v1_with_context | null | [
"transformers",
"tensorboard",
"safetensors",
"deberta-v2",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/deberta-v3-small",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:45:02+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLLama-v0 - bnb 4bits
- Model creator: https://huggingface.co/Maykeye/
- Original model: https://huggingface.co/Maykeye/TinyLLama-v0/
Original model description:
---
license: apache-2.0
---
This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture.
* Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading
TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running
the cells. Validation content is not used by the script so you put anythin in
* Backup directory has a script do_backup that I used to copy weights from remote machine to local.
Weight are generated too quickly, so by the time script copied weihgt N+1
* This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use
any sliding window to train story not from the start
* Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used
* I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69).
I had no troubles on the cloud machine with preninstalled libraries.
* Demo script is demo.py
* Validation script is provided: valid.py. use it like `python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]`:
After training I decided that it's not necessary to beat validation into chunks
* Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks
so if random shuffle asks for a story, it may use cache or load chunk.
Training dataset is too small, so in next versions I will get rid of it.
from transformers import AutoModelForCausalLM, AutoTokenizer
| {} | RichardErkhov/Maykeye_-_TinyLLama-v0-4bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T18:47:27+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLLama-v0 - bnb 8bits
- Model creator: https://huggingface.co/Maykeye/
- Original model: https://huggingface.co/Maykeye/TinyLLama-v0/
Original model description:
---
license: apache-2.0
---
This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture.
* Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading
TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running
the cells. Validation content is not used by the script so you put anythin in
* Backup directory has a script do_backup that I used to copy weights from remote machine to local.
Weight are generated too quickly, so by the time script copied weihgt N+1
* This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use
any sliding window to train story not from the start
* Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used
* I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69).
I had no troubles on the cloud machine with preninstalled libraries.
* Demo script is demo.py
* Validation script is provided: valid.py. use it like `python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]`:
After training I decided that it's not necessary to beat validation into chunks
* Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks
so if random shuffle asks for a story, it may use cache or load chunk.
Training dataset is too small, so in next versions I will get rid of it.
from transformers import AutoModelForCausalLM, AutoTokenizer
| {} | RichardErkhov/Maykeye_-_TinyLLama-v0-8bits | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T18:47:43+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
TinyLLama-v0 - GGUF
- Model creator: https://huggingface.co/Maykeye/
- Original model: https://huggingface.co/Maykeye/TinyLLama-v0/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [TinyLLama-v0.Q2_K.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q2_K.gguf) | Q2_K | 0.0GB |
| [TinyLLama-v0.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.IQ3_XS.gguf) | IQ3_XS | 0.0GB |
| [TinyLLama-v0.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.IQ3_S.gguf) | IQ3_S | 0.0GB |
| [TinyLLama-v0.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q3_K_S.gguf) | Q3_K_S | 0.0GB |
| [TinyLLama-v0.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.IQ3_M.gguf) | IQ3_M | 0.0GB |
| [TinyLLama-v0.Q3_K.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q3_K.gguf) | Q3_K | 0.0GB |
| [TinyLLama-v0.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q3_K_M.gguf) | Q3_K_M | 0.0GB |
| [TinyLLama-v0.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q3_K_L.gguf) | Q3_K_L | 0.0GB |
| [TinyLLama-v0.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.IQ4_XS.gguf) | IQ4_XS | 0.0GB |
| [TinyLLama-v0.Q4_0.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q4_0.gguf) | Q4_0 | 0.0GB |
| [TinyLLama-v0.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.IQ4_NL.gguf) | IQ4_NL | 0.0GB |
| [TinyLLama-v0.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q4_K_S.gguf) | Q4_K_S | 0.0GB |
| [TinyLLama-v0.Q4_K.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q4_K.gguf) | Q4_K | 0.0GB |
| [TinyLLama-v0.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q4_K_M.gguf) | Q4_K_M | 0.0GB |
| [TinyLLama-v0.Q4_1.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q4_1.gguf) | Q4_1 | 0.0GB |
| [TinyLLama-v0.Q5_0.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q5_0.gguf) | Q5_0 | 0.0GB |
| [TinyLLama-v0.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q5_K_S.gguf) | Q5_K_S | 0.0GB |
| [TinyLLama-v0.Q5_K.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q5_K.gguf) | Q5_K | 0.0GB |
| [TinyLLama-v0.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q5_K_M.gguf) | Q5_K_M | 0.0GB |
| [TinyLLama-v0.Q5_1.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q5_1.gguf) | Q5_1 | 0.0GB |
| [TinyLLama-v0.Q6_K.gguf](https://huggingface.co/RichardErkhov/Maykeye_-_TinyLLama-v0-gguf/blob/main/TinyLLama-v0.Q6_K.gguf) | Q6_K | 0.01GB |
Original model description:
---
license: apache-2.0
---
This is a first version of recreating roneneldan/TinyStories-1M but using Llama architecture.
* Full training process is included in the notebook train.ipynb. Recreating it as simple as downloading
TinyStoriesV2-GPT4-train.txt and TinyStoriesV2-GPT4-valid.txt in the same folder with the notebook and running
the cells. Validation content is not used by the script so you put anythin in
* Backup directory has a script do_backup that I used to copy weights from remote machine to local.
Weight are generated too quickly, so by the time script copied weihgt N+1
* This is extremely PoC version. Training truncates stories that are longer than context size and doesn't use
any sliding window to train story not from the start
* Training took approximately 9 hours (3 hours per epoch) on 40GB A100. ~30GB VRAM was used
* I use tokenizer from open_llama_3b. However I had troubles with it locally(https://github.com/openlm-research/open_llama/issues/69).
I had no troubles on the cloud machine with preninstalled libraries.
* Demo script is demo.py
* Validation script is provided: valid.py. use it like `python valid.py path/to/TinyStoriesV2-GPT4-valid.txt [optional-model-id-or-path]`:
After training I decided that it's not necessary to beat validation into chunks
* Also this version uses very stupid caching mechinsm to shuffle stories for training: it keeps cache of N recently loaded chunks
so if random shuffle asks for a story, it may use cache or load chunk.
Training dataset is too small, so in next versions I will get rid of it.
from transformers import AutoModelForCausalLM, AutoTokenizer
| {} | RichardErkhov/Maykeye_-_TinyLLama-v0-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T18:48:55+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
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Phi-3-mini-4k-instruct - GGUF
- Model creator: https://huggingface.co/microsoft/
- Original model: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Phi-3-mini-4k-instruct.Q2_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q2_K.gguf) | Q2_K | 1.32GB |
| [Phi-3-mini-4k-instruct.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.IQ3_XS.gguf) | IQ3_XS | 1.51GB |
| [Phi-3-mini-4k-instruct.IQ3_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.IQ3_S.gguf) | IQ3_S | 1.57GB |
| [Phi-3-mini-4k-instruct.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q3_K_S.gguf) | Q3_K_S | 1.57GB |
| [Phi-3-mini-4k-instruct.IQ3_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.IQ3_M.gguf) | IQ3_M | 1.73GB |
| [Phi-3-mini-4k-instruct.Q3_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q3_K.gguf) | Q3_K | 1.82GB |
| [Phi-3-mini-4k-instruct.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q3_K_M.gguf) | Q3_K_M | 1.82GB |
| [Phi-3-mini-4k-instruct.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q3_K_L.gguf) | Q3_K_L | 1.94GB |
| [Phi-3-mini-4k-instruct.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.IQ4_XS.gguf) | IQ4_XS | 1.93GB |
| [Phi-3-mini-4k-instruct.Q4_0.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q4_0.gguf) | Q4_0 | 2.03GB |
| [Phi-3-mini-4k-instruct.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.IQ4_NL.gguf) | IQ4_NL | 2.04GB |
| [Phi-3-mini-4k-instruct.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q4_K_S.gguf) | Q4_K_S | 2.04GB |
| [Phi-3-mini-4k-instruct.Q4_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q4_K.gguf) | Q4_K | 2.23GB |
| [Phi-3-mini-4k-instruct.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q4_K_M.gguf) | Q4_K_M | 2.23GB |
| [Phi-3-mini-4k-instruct.Q4_1.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q4_1.gguf) | Q4_1 | 2.24GB |
| [Phi-3-mini-4k-instruct.Q5_0.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q5_0.gguf) | Q5_0 | 2.46GB |
| [Phi-3-mini-4k-instruct.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q5_K_S.gguf) | Q5_K_S | 2.46GB |
| [Phi-3-mini-4k-instruct.Q5_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q5_K.gguf) | Q5_K | 2.62GB |
| [Phi-3-mini-4k-instruct.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q5_K_M.gguf) | Q5_K_M | 2.62GB |
| [Phi-3-mini-4k-instruct.Q5_1.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q5_1.gguf) | Q5_1 | 2.68GB |
| [Phi-3-mini-4k-instruct.Q6_K.gguf](https://huggingface.co/RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf/blob/main/Phi-3-mini-4k-instruct.Q6_K.gguf) | Q6_K | 2.92GB |
Original model description:
---
license: mit
license_link: https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- nlp
- code
widget:
- messages:
- role: user
content: Can you provide ways to eat combinations of bananas and dragonfruits?
---
## Model Summary
The Phi-3-Mini-4K-Instruct is a 3.8B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties.
The model belongs to the Phi-3 family with the Mini version in two variants [4K](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [128K](https://huggingface.co/microsoft/Phi-3-mini-128k-instruct) which is the context length (in tokens) that it can support.
The model has underwent a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures.
When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3 Mini-4K-Instruct showcased a robust and state-of-the-art performance among models with less than 13 billion parameters.
Resources and Technical Documentation:
+ [Phi-3 Microsoft Blog](https://aka.ms/phi3blog-april)
+ [Phi-3 Technical Report](https://aka.ms/phi3-tech-report)
+ [Phi-3 on Azure AI Studio](https://aka.ms/phi3-azure-ai)
+ Phi-3 GGUF: [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf)
+ Phi-3 ONNX: [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx)
## Intended Uses
**Primary use cases**
The model is intended for commercial and research use in English. The model provides uses for applications which require:
1) Memory/compute constrained environments
2) Latency bound scenarios
3) Strong reasoning (especially code, math and logic)
Our model is designed to accelerate research on language and multimodal models, for use as a building block for generative AI powered features.
**Use case considerations**
Our models are not specifically designed or evaluated for all downstream purposes. Developers should consider common limitations of language models as they select use cases, and evaluate and mitigate for accuracy, safety, and fariness before using within a specific downstream use case, particularly for high risk scenarios. Developers should be aware of and adhere to applicable laws or regulations (including privacy, trade compliance laws, etc.) that are relevant to their use case.
Nothing contained in this Model Card should be interpreted as or deemed a restriction or modification to the license the model is released under.
## How to Use
Phi-3 Mini-4K-Instruct has been integrated in the development version (4.41.0.dev0) of `transformers`. Until the official version is released through `pip`, ensure that you are doing one of the following:
* When loading the model, ensure that `trust_remote_code=True` is passed as an argument of the `from_pretrained()` function.
* Update your local `transformers` to the development version: `pip uninstall -y transformers && pip install git+https://github.com/huggingface/transformers`. The previous command is an alternative to cloning and installing from the source.
The current `transformers` version can be verified with: `pip list | grep transformers`.
Phi-3 Mini-4K-Instruct is also available in [HuggingChat](https://aka.ms/try-phi3-hf-chat).
### Tokenizer
Phi-3 Mini-4K-Instruct supports a vocabulary size of up to `32064` tokens. The [tokenizer files](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/blob/main/added_tokens.json) already provide placeholder tokens that can be used for downstream fine-tuning, but they can also be extended up to the model's vocabulary size.
### Chat Format
Given the nature of the training data, the Phi-3 Mini-4K-Instruct model is best suited for prompts using the chat format as follows.
You can provide the prompt as a question with a generic template as follow:
```markdown
<|user|>\nQuestion <|end|>\n<|assistant|>
```
For example:
```markdown
<|user|>
How to explain Internet for a medieval knight?<|end|>
<|assistant|>
```
where the model generates the text after `<|assistant|>` . In case of few-shots prompt, the prompt can be formatted as the following:
```markdown
<|user|>
I am going to Paris, what should I see?<|end|>
<|assistant|>
Paris, the capital of France, is known for its stunning architecture, art museums, historical landmarks, and romantic atmosphere. Here are some of the top attractions to see in Paris:\n\n1. The Eiffel Tower: The iconic Eiffel Tower is one of the most recognizable landmarks in the world and offers breathtaking views of the city.\n2. The Louvre Museum: The Louvre is one of the world's largest and most famous museums, housing an impressive collection of art and artifacts, including the Mona Lisa.\n3. Notre-Dame Cathedral: This beautiful cathedral is one of the most famous landmarks in Paris and is known for its Gothic architecture and stunning stained glass windows.\n\nThese are just a few of the many attractions that Paris has to offer. With so much to see and do, it's no wonder that Paris is one of the most popular tourist destinations in the world."<|end|>
<|user|>
What is so great about #1?<|end|>
<|assistant|>
```
### Sample inference code
This code snippets show how to get quickly started with running the model on a GPU:
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
torch.random.manual_seed(0)
model = AutoModelForCausalLM.from_pretrained(
"microsoft/Phi-3-mini-4k-instruct",
device_map="cuda",
torch_dtype="auto",
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3-mini-4k-instruct")
messages = [
{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"},
{"role": "assistant", "content": "Sure! Here are some ways to eat bananas and dragonfruits together: 1. Banana and dragonfruit smoothie: Blend bananas and dragonfruits together with some milk and honey. 2. Banana and dragonfruit salad: Mix sliced bananas and dragonfruits together with some lemon juice and honey."},
{"role": "user", "content": "What about solving an 2x + 3 = 7 equation?"},
]
pipe = pipeline(
"text-generation",
model=model,
tokenizer=tokenizer,
)
generation_args = {
"max_new_tokens": 500,
"return_full_text": False,
"temperature": 0.0,
"do_sample": False,
}
output = pipe(messages, **generation_args)
print(output[0]['generated_text'])
```
*Some applications/frameworks might not include a BOS token (`<s>`) at the start of the conversation. Please ensure that it is included since it provides more reliable results.*
## Responsible AI Considerations
Like other language models, the Phi series models can potentially behave in ways that are unfair, unreliable, or offensive. Some of the limiting behaviors to be aware of include:
+ Quality of Service: the Phi models are trained primarily on English text. Languages other than English will experience worse performance. English language varieties with less representation in the training data might experience worse performance than standard American English.
+ Representation of Harms & Perpetuation of Stereotypes: These models can over- or under-represent groups of people, erase representation of some groups, or reinforce demeaning or negative stereotypes. Despite safety post-training, these limitations may still be present due to differing levels of representation of different groups or prevalence of examples of negative stereotypes in training data that reflect real-world patterns and societal biases.
+ Inappropriate or Offensive Content: these models may produce other types of inappropriate or offensive content, which may make it inappropriate to deploy for sensitive contexts without additional mitigations that are specific to the use case.
+ Information Reliability: Language models can generate nonsensical content or fabricate content that might sound reasonable but is inaccurate or outdated.
+ Limited Scope for Code: Majority of Phi-3 training data is based in Python and use common packages such as "typing, math, random, collections, datetime, itertools". If the model generates Python scripts that utilize other packages or scripts in other languages, we strongly recommend users manually verify all API uses.
Developers should apply responsible AI best practices and are responsible for ensuring that a specific use case complies with relevant laws and regulations (e.g. privacy, trade, etc.). Important areas for consideration include:
+ Allocation: Models may not be suitable for scenarios that could have consequential impact on legal status or the allocation of resources or life opportunities (ex: housing, employment, credit, etc.) without further assessments and additional debiasing techniques.
+ High-Risk Scenarios: Developers should assess suitability of using models in high-risk scenarios where unfair, unreliable or offensive outputs might be extremely costly or lead to harm. This includes providing advice in sensitive or expert domains where accuracy and reliability are critical (ex: legal or health advice). Additional safeguards should be implemented at the application level according to the deployment context.
+ Misinformation: Models may produce inaccurate information. Developers should follow transparency best practices and inform end-users they are interacting with an AI system. At the application level, developers can build feedback mechanisms and pipelines to ground responses in use-case specific, contextual information, a technique known as Retrieval Augmented Generation (RAG).
+ Generation of Harmful Content: Developers should assess outputs for their context and use available safety classifiers or custom solutions appropriate for their use case.
+ Misuse: Other forms of misuse such as fraud, spam, or malware production may be possible, and developers should ensure that their applications do not violate applicable laws and regulations.
## Training
### Model
* Architecture: Phi-3 Mini-4K-Instruct has 3.8B parameters and is a dense decoder-only Transformer model. The model is fine-tuned with Supervised fine-tuning (SFT) and Direct Preference Optimization (DPO) to ensure alignment with human preferences and safety guidlines.
* Inputs: Text. It is best suited for prompts using chat format.
* Context length: 4K tokens
* GPUs: 512 H100-80G
* Training time: 7 days
* Training data: 3.3T tokens
* Outputs: Generated text in response to the input
* Dates: Our models were trained between February and April 2024
* Status: This is a static model trained on an offline dataset with cutoff date October 2023. Future versions of the tuned models may be released as we improve models.
### Datasets
Our training data includes a wide variety of sources, totaling 3.3 trillion tokens, and is a combination of
1) Publicly available documents filtered rigorously for quality, selected high-quality educational data, and code;
2) Newly created synthetic, “textbook-like” data for the purpose of teaching math, coding, common sense reasoning, general knowledge of the world (science, daily activities, theory of mind, etc.);
3) High quality chat format supervised data covering various topics to reflect human preferences on different aspects such as instruct-following, truthfulness, honesty and helpfulness.
### Fine-tuning
A basic example of multi-GPUs supervised fine-tuning (SFT) with TRL and Accelerate modules is provided [here](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/sample_finetune.py).
## Benchmarks
We report the results for Phi-3-Mini-4K-Instruct on standard open-source benchmarks measuring the model's reasoning ability (both common sense reasoning and logical reasoning). We compare to Phi-2, Mistral-7b-v0.1, Mixtral-8x7b, Gemma 7B, Llama-3-8B-Instruct, and GPT-3.5.
All the reported numbers are produced with the exact same pipeline to ensure that the numbers are comparable. These numbers might differ from other published numbers due to slightly different choices in the evaluation.
As is now standard, we use few-shot prompts to evaluate the models, at temperature 0.
The prompts and number of shots are part of a Microsoft internal tool to evaluate language models, and in particular we did no optimization to the pipeline for Phi-3.
More specifically, we do not change prompts, pick different few-shot examples, change prompt format, or do any other form of optimization for the model.
The number of k–shot examples is listed per-benchmark.
| | Phi-3-Mini-4K-In<br>3.8b | Phi-3-Small<br>7b (preview) | Phi-3-Medium<br>14b (preview) | Phi-2<br>2.7b | Mistral<br>7b | Gemma<br>7b | Llama-3-In<br>8b | Mixtral<br>8x7b | GPT-3.5<br>version 1106 |
|---|---|---|---|---|---|---|---|---|---|
| MMLU <br>5-Shot | 68.8 | 75.3 | 78.2 | 56.3 | 61.7 | 63.6 | 66.5 | 68.4 | 71.4 |
| HellaSwag <br> 5-Shot | 76.7 | 78.7 | 83.2 | 53.6 | 58.5 | 49.8 | 71.1 | 70.4 | 78.8 |
| ANLI <br> 7-Shot | 52.8 | 55.0 | 58.7 | 42.5 | 47.1 | 48.7 | 57.3 | 55.2 | 58.1 |
| GSM-8K <br> 0-Shot; CoT | 82.5 | 86.4 | 90.8 | 61.1 | 46.4 | 59.8 | 77.4 | 64.7 | 78.1 |
| MedQA <br> 2-Shot | 53.8 | 58.2 | 69.8 | 40.9 | 49.6 | 50.0 | 60.5 | 62.2 | 63.4 |
| AGIEval <br> 0-Shot | 37.5 | 45.0 | 49.7 | 29.8 | 35.1 | 42.1 | 42.0 | 45.2 | 48.4 |
| TriviaQA <br> 5-Shot | 64.0 | 59.1 | 73.3 | 45.2 | 72.3 | 75.2 | 67.7 | 82.2 | 85.8 |
| Arc-C <br> 10-Shot | 84.9 | 90.7 | 91.9 | 75.9 | 78.6 | 78.3 | 82.8 | 87.3 | 87.4 |
| Arc-E <br> 10-Shot | 94.6 | 97.1 | 98.0 | 88.5 | 90.6 | 91.4 | 93.4 | 95.6 | 96.3 |
| PIQA <br> 5-Shot | 84.2 | 87.8 | 88.2 | 60.2 | 77.7 | 78.1 | 75.7 | 86.0 | 86.6 |
| SociQA <br> 5-Shot | 76.6 | 79.0 | 79.4 | 68.3 | 74.6 | 65.5 | 73.9 | 75.9 | 68.3 |
| BigBench-Hard <br> 0-Shot | 71.7 | 75.0 | 82.5 | 59.4 | 57.3 | 59.6 | 51.5 | 69.7 | 68.32 |
| WinoGrande <br> 5-Shot | 70.8 | 82.5 | 81.2 | 54.7 | 54.2 | 55.6 | 65 | 62.0 | 68.8 |
| OpenBookQA <br> 10-Shot | 83.2 | 88.4 | 86.6 | 73.6 | 79.8 | 78.6 | 82.6 | 85.8 | 86.0 |
| BoolQ <br> 0-Shot | 77.6 | 82.9 | 86.5 | -- | 72.2 | 66.0 | 80.9 | 77.6 | 79.1 |
| CommonSenseQA <br> 10-Shot | 80.2 | 80.3 | 82.6 | 69.3 | 72.6 | 76.2 | 79 | 78.1 | 79.6 |
| TruthfulQA <br> 10-Shot | 65.0 | 68.1 | 74.8 | -- | 52.1 | 53.0 | 63.2 | 60.1 | 85.8 |
| HumanEval <br> 0-Shot | 59.1 | 59.1 | 54.7 | 47.0 | 28.0 | 34.1 | 60.4 | 37.8 | 62.2 |
| MBPP <br> 3-Shot | 53.8 | 71.4 | 73.7 | 60.6 | 50.8 | 51.5 | 67.7 | 60.2 | 77.8 |
## Software
* [PyTorch](https://github.com/pytorch/pytorch)
* [DeepSpeed](https://github.com/microsoft/DeepSpeed)
* [Transformers](https://github.com/huggingface/transformers)
* [Flash-Attention](https://github.com/HazyResearch/flash-attention)
## Hardware
Note that by default, the Phi-3-mini model uses flash attention, which requires certain types of GPU hardware to run. We have tested on the following GPU types:
* NVIDIA A100
* NVIDIA A6000
* NVIDIA H100
If you want to run the model on:
* NVIDIA V100 or earlier generation GPUs: call AutoModelForCausalLM.from_pretrained() with attn_implementation="eager"
* CPU: use the **GGUF** quantized models [4K](https://aka.ms/Phi3-mini-4k-instruct-gguf)
+ Optimized inference on GPU, CPU, and Mobile: use the **ONNX** models [4K](https://aka.ms/Phi3-mini-4k-instruct-onnx)
## Cross Platform Support
ONNX runtime ecosystem now supports Phi-3 Mini models across platforms and hardware. You can find the optimized Phi-3 Mini-4K-Instruct ONNX model [here](https://aka.ms/phi3-mini-4k-instruct-onnx).
Optimized Phi-3 models are also published here in ONNX format, to run with ONNX Runtime on CPU and GPU across devices, including server platforms, Windows, Linux and Mac desktops, and mobile CPUs, with the precision best suited to each of these targets. DirectML support lets developers bring hardware acceleration to Windows devices at scale across AMD, Intel, and NVIDIA GPUs.
Along with DirectML, ONNX Runtime provides cross platform support for Phi-3 across a range of devices CPU, GPU, and mobile.
Here are some of the optimized configurations we have added:
1. ONNX models for int4 DML: Quantized to int4 via AWQ
2. ONNX model for fp16 CUDA
3. ONNX model for int4 CUDA: Quantized to int4 via RTN
4. ONNX model for int4 CPU and Mobile: Quantized to int4 via RTN
## License
The model is licensed under the [MIT license](https://huggingface.co/microsoft/Phi-3-mini-4k/resolve/main/LICENSE).
## Trademarks
This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow [Microsoft’s Trademark & Brand Guidelines](https://www.microsoft.com/en-us/legal/intellectualproperty/trademarks). Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party’s policies.
| {} | RichardErkhov/microsoft_-_Phi-3-mini-4k-instruct-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T18:48:56+00:00 |
null | peft |
<!-- 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. -->
# Meta-Llama-3-8B-text-to-sql
This model is a fine-tuned version of [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) on the generator dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: constant
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.7.2.dev0
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "datasets": ["generator"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "Meta-Llama-3-8B-text-to-sql", "results": []}]} | felixml/Meta-Llama-3-8B-text-to-sql | null | [
"peft",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"dataset:generator",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-05-02T18:49:07+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
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[More Information Needed]
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[More Information Needed] | {"license": "mit", "library_name": "transformers"} | kishorea/P-Llama70B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-02T18:49:26+00:00 |
null | null | generate a pattern for a wallpaper (mode: allover, horizontal, vertical) | {"license": "apache-2.0"} | blaackjack/Wallpaper | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T18:50:21+00:00 |
null | null | {} | emath/marian-finetuned-iswlt2017-en-to-fr | null | [
"region:us"
] | null | 2024-05-02T18:50:30+00:00 |
|
null | null | {} | siacus/Llama-3-8B-Instruct-Q4_K_M-ft.gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-02T18:50:40+00:00 |
|
null | null |
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [Llama-3-Smaug-8B](https://huggingface.co/abacusai/Llama-3-Smaug-8B).
| {"license": "llama3"} | numen-tech/Llama-3-Smaug-8B-w4a16g128asym | null | [
"arxiv:2308.13137",
"license:llama3",
"region:us"
] | null | 2024-05-02T18:52:59+00:00 |
text2text-generation | transformers | {} | scaraveos/t5-small-finetuned-xsum | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:53:00+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
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## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Shritama/Gemma_text-to-json_2 | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T18:54:18+00:00 |
null | null |
4-bit [OmniQuant](https://arxiv.org/abs/2308.13137) quantized version of [dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b).
| {"license": "llama3"} | numen-tech/dolphin-2.9-llama3-8b-w4a16g128asym | null | [
"arxiv:2308.13137",
"license:llama3",
"region:us"
] | null | 2024-05-02T18:55:07+00:00 |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | azhara001/donut-base-demo-new-0.0003_Adam_938 | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T18:57:11+00:00 |
null | null | {} | mewsaa/SehatRasta-0.1 | null | [
"region:us"
] | null | 2024-05-02T18:57:34+00:00 |
|
null | peft |
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### Framework versions
- PEFT 0.10.0 | {"library_name": "peft", "base_model": "meta-llama/Meta-Llama-3-70B-Instruct"} | slingshot/Meta-Llama-3-70B-Instruct-2024-05-02-17-33-18-conversation-model | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:meta-llama/Meta-Llama-3-70B-Instruct",
"region:us"
] | null | 2024-05-02T19:03:54+00:00 |
null | diffusers | {"license": "apache-2.0"} | gzzyyxy/epipolar_attn_scannnetpp_test | null | [
"diffusers",
"safetensors",
"license:apache-2.0",
"region:us"
] | null | 2024-05-02T19:05:38+00:00 |
|
text-to-image | diffusers |
# Model Card for Model ID
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This is the model card of a 🧨 diffusers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed]
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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).
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[More Information Needed]
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[More Information Needed] | {"library_name": "diffusers"} | rubbrband/formulaxlXLComfyui_v20Pruned | null | [
"diffusers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | null | 2024-05-02T19:07:41+00:00 |
null | peft |
<!-- 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. -->
# llama3_lora
This model is a fine-tuned version of [unsloth/llama-3-8b-Instruct-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-Instruct-bnb-4bit) on the identity and the alpaca_gpt4_en datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3.0
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["llama-factory", "lora", "unsloth", "generated_from_trainer"], "base_model": "unsloth/llama-3-8b-Instruct-bnb-4bit", "model-index": [{"name": "llama3_lora", "results": []}]} | qbitmaze/ib | null | [
"peft",
"tensorboard",
"safetensors",
"llama-factory",
"lora",
"unsloth",
"generated_from_trainer",
"base_model:unsloth/llama-3-8b-Instruct-bnb-4bit",
"license:other",
"region:us"
] | null | 2024-05-02T19:08:37+00:00 |
text-classification | 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. -->
# 128Bert
This model is a fine-tuned version of [cointegrated/rubert-tiny2](https://huggingface.co/cointegrated/rubert-tiny2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8346
- Accuracy: 0.7033
## 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: 128
- eval_batch_size: 128
- 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 | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 1.1934 | 1.0 | 2074 | 1.1488 | 0.6027 |
| 1.0626 | 2.0 | 4148 | 1.0247 | 0.6459 |
| 0.9729 | 3.0 | 6222 | 0.9483 | 0.6658 |
| 0.908 | 4.0 | 8296 | 0.9041 | 0.6811 |
| 0.8684 | 5.0 | 10370 | 0.8771 | 0.6897 |
| 0.8348 | 6.0 | 12444 | 0.8593 | 0.6956 |
| 0.8055 | 7.0 | 14518 | 0.8507 | 0.6991 |
| 0.7924 | 8.0 | 16592 | 0.8410 | 0.7017 |
| 0.7857 | 9.0 | 18666 | 0.8349 | 0.7037 |
| 0.7732 | 10.0 | 20740 | 0.8346 | 0.7033 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "cointegrated/rubert-tiny2", "model-index": [{"name": "128Bert", "results": []}]} | IvashinMaxim/128Bert | null | [
"transformers",
"tensorboard",
"safetensors",
"bert",
"text-classification",
"generated_from_trainer",
"base_model:cointegrated/rubert-tiny2",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:09:50+00:00 |
text-generation | 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. -->
# 0.0001_withdpo_4iters_bs256_51005lr_iter_4
This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_4iters_bs256_511lr_iter_3](https://huggingface.co/ShenaoZ/0.0001_withdpo_4iters_bs256_511lr_iter_3) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-09
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_4iters_bs256_511lr_iter_3", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_51005lr_iter_4", "results": []}]} | ShenaoZ/0.0001_withdpo_4iters_bs256_51005lr_iter_4 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.0001_withdpo_4iters_bs256_511lr_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T19:13:21+00:00 |
null | null | {} | henvoni/bert_model | null | [
"region:us"
] | null | 2024-05-02T19:13:49+00:00 |
|
reinforcement-learning | stable-baselines3 |
# **PPO** Agent playing **LunarLander-v2**
This is a trained model of a **PPO** agent playing **LunarLander-v2**
using the [stable-baselines3 library](https://github.com/DLR-RM/stable-baselines3).
## Usage (with Stable-baselines3)
TODO: Add your code
```python
from stable_baselines3 import ...
from huggingface_sb3 import load_from_hub
...
```
| {"library_name": "stable-baselines3", "tags": ["LunarLander-v2", "deep-reinforcement-learning", "reinforcement-learning", "stable-baselines3"], "model-index": [{"name": "PPO", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "LunarLander-v2", "type": "LunarLander-v2"}, "metrics": [{"type": "mean_reward", "value": "245.51 +/- 24.11", "name": "mean_reward", "verified": false}]}]}]} | mejdi86/ppo-LunarLander-v2 | null | [
"stable-baselines3",
"LunarLander-v2",
"deep-reinforcement-learning",
"reinforcement-learning",
"model-index",
"region:us"
] | null | 2024-05-02T19:14:11+00:00 |
text-classification | transformers | ERROR: type should be string, got "\nhttps://huggingface.co/SamLowe/roberta-base-go_emotions converted to ONNX to use with transformers.js\n\n---\n\n#### Overview\n\nModel trained from [roberta-base](https://huggingface.co/roberta-base) on the [go_emotions](https://huggingface.co/datasets/go_emotions) dataset for multi-label classification.\n\n##### ONNX version also available\n\nA version of this model in ONNX format (including an INT8 quantized ONNX version) is now available at [https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx](https://huggingface.co/SamLowe/roberta-base-go_emotions-onnx). These are faster for inference, esp for smaller batch sizes, massively reduce the size of the dependencies required for inference, make inference of the model more multi-platform, and in the case of the quantized version reduce the model file/download size by 75% whilst retaining almost all the accuracy if you only need inference.\n\n#### Dataset used for the model\n\n[go_emotions](https://huggingface.co/datasets/go_emotions) is based on Reddit data and has 28 labels. It is a multi-label dataset where one or multiple labels may apply for any given input text, hence this model is a multi-label classification model with 28 'probability' float outputs for any given input text. Typically a threshold of 0.5 is applied to the probabilities for the prediction for each label.\n\n#### How the model was created\n\nThe model was trained using `AutoModelForSequenceClassification.from_pretrained` with `problem_type=\"multi_label_classification\"` for 3 epochs with a learning rate of 2e-5 and weight decay of 0.01.\n\n#### Inference\n\nThere are multiple ways to use this model in Huggingface Transformers. Possibly the simplest is using a pipeline:\n\n```python\nfrom transformers import pipeline\n\nclassifier = pipeline(task=\"text-classification\", model=\"SamLowe/roberta-base-go_emotions\", top_k=None)\n\nsentences = [\"I am not having a great day\"]\n\nmodel_outputs = classifier(sentences)\nprint(model_outputs[0])\n# produces a list of dicts for each of the labels\n```\n\n#### Evaluation / metrics\n\nEvaluation of the model is available at\n\n- https://github.com/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb\n\n[](https://colab.research.google.com/github/samlowe/go_emotions-dataset/blob/main/eval-roberta-base-go_emotions.ipynb)\n\n##### Summary\n\nAs provided in the above notebook, evaluation of the multi-label output (of the 28 dim output via a threshold of 0.5 to binarize each) using the dataset test split gives:\n\n- Accuracy: 0.474\n- Precision: 0.575\n- Recall: 0.396\n- F1: 0.450\n\nBut the metrics are more meaningful when measured per label given the multi-label nature (each label is effectively an independent binary classification) and the fact that there is drastically different representations of the labels in the dataset.\n\nWith a threshold of 0.5 applied to binarize the model outputs, as per the above notebook, the metrics per label are:\n\n| | accuracy | precision | recall | f1 | mcc | support | threshold |\n| -------------- | -------- | --------- | ------ | ----- | ----- | ------- | --------- |\n| admiration | 0.946 | 0.725 | 0.675 | 0.699 | 0.670 | 504 | 0.5 |\n| amusement | 0.982 | 0.790 | 0.871 | 0.829 | 0.821 | 264 | 0.5 |\n| anger | 0.970 | 0.652 | 0.379 | 0.479 | 0.483 | 198 | 0.5 |\n| annoyance | 0.940 | 0.472 | 0.159 | 0.238 | 0.250 | 320 | 0.5 |\n| approval | 0.942 | 0.609 | 0.302 | 0.404 | 0.403 | 351 | 0.5 |\n| caring | 0.973 | 0.448 | 0.319 | 0.372 | 0.364 | 135 | 0.5 |\n| confusion | 0.972 | 0.500 | 0.431 | 0.463 | 0.450 | 153 | 0.5 |\n| curiosity | 0.950 | 0.537 | 0.356 | 0.428 | 0.412 | 284 | 0.5 |\n| desire | 0.987 | 0.630 | 0.410 | 0.496 | 0.502 | 83 | 0.5 |\n| disappointment | 0.974 | 0.625 | 0.199 | 0.302 | 0.343 | 151 | 0.5 |\n| disapproval | 0.950 | 0.494 | 0.307 | 0.379 | 0.365 | 267 | 0.5 |\n| disgust | 0.982 | 0.707 | 0.333 | 0.453 | 0.478 | 123 | 0.5 |\n| embarrassment | 0.994 | 0.750 | 0.243 | 0.367 | 0.425 | 37 | 0.5 |\n| excitement | 0.983 | 0.603 | 0.340 | 0.435 | 0.445 | 103 | 0.5 |\n| fear | 0.992 | 0.758 | 0.603 | 0.671 | 0.672 | 78 | 0.5 |\n| gratitude | 0.990 | 0.960 | 0.881 | 0.919 | 0.914 | 352 | 0.5 |\n| grief | 0.999 | 0.000 | 0.000 | 0.000 | 0.000 | 6 | 0.5 |\n| joy | 0.978 | 0.647 | 0.559 | 0.600 | 0.590 | 161 | 0.5 |\n| love | 0.982 | 0.773 | 0.832 | 0.802 | 0.793 | 238 | 0.5 |\n| nervousness | 0.996 | 0.600 | 0.130 | 0.214 | 0.278 | 23 | 0.5 |\n| optimism | 0.972 | 0.667 | 0.376 | 0.481 | 0.488 | 186 | 0.5 |\n| pride | 0.997 | 0.000 | 0.000 | 0.000 | 0.000 | 16 | 0.5 |\n| realization | 0.974 | 0.541 | 0.138 | 0.220 | 0.264 | 145 | 0.5 |\n| relief | 0.998 | 0.000 | 0.000 | 0.000 | 0.000 | 11 | 0.5 |\n| remorse | 0.991 | 0.553 | 0.750 | 0.636 | 0.640 | 56 | 0.5 |\n| sadness | 0.977 | 0.621 | 0.494 | 0.550 | 0.542 | 156 | 0.5 |\n| surprise | 0.981 | 0.750 | 0.404 | 0.525 | 0.542 | 141 | 0.5 |\n| neutral | 0.782 | 0.694 | 0.604 | 0.646 | 0.492 | 1787 | 0.5 |\n\nOptimizing the threshold per label for the one that gives the optimum F1 metrics gives slightly better metrics - sacrificing some precision for a greater gain in recall, hence to the benefit of F1 (how this was done is shown in the above notebook):\n\n| | accuracy | precision | recall | f1 | mcc | support | threshold |\n| -------------- | -------- | --------- | ------ | ----- | ----- | ------- | --------- |\n| admiration | 0.940 | 0.651 | 0.776 | 0.708 | 0.678 | 504 | 0.25 |\n| amusement | 0.982 | 0.781 | 0.890 | 0.832 | 0.825 | 264 | 0.45 |\n| anger | 0.959 | 0.454 | 0.601 | 0.517 | 0.502 | 198 | 0.15 |\n| annoyance | 0.864 | 0.243 | 0.619 | 0.349 | 0.328 | 320 | 0.10 |\n| approval | 0.926 | 0.432 | 0.442 | 0.437 | 0.397 | 351 | 0.30 |\n| caring | 0.972 | 0.426 | 0.385 | 0.405 | 0.391 | 135 | 0.40 |\n| confusion | 0.974 | 0.548 | 0.412 | 0.470 | 0.462 | 153 | 0.55 |\n| curiosity | 0.943 | 0.473 | 0.711 | 0.568 | 0.552 | 284 | 0.25 |\n| desire | 0.985 | 0.518 | 0.530 | 0.524 | 0.516 | 83 | 0.25 |\n| disappointment | 0.974 | 0.562 | 0.298 | 0.390 | 0.398 | 151 | 0.40 |\n| disapproval | 0.941 | 0.414 | 0.468 | 0.439 | 0.409 | 267 | 0.30 |\n| disgust | 0.978 | 0.523 | 0.463 | 0.491 | 0.481 | 123 | 0.20 |\n| embarrassment | 0.994 | 0.567 | 0.459 | 0.507 | 0.507 | 37 | 0.10 |\n| excitement | 0.981 | 0.500 | 0.417 | 0.455 | 0.447 | 103 | 0.35 |\n| fear | 0.991 | 0.712 | 0.667 | 0.689 | 0.685 | 78 | 0.40 |\n| gratitude | 0.990 | 0.957 | 0.889 | 0.922 | 0.917 | 352 | 0.45 |\n| grief | 0.999 | 0.333 | 0.333 | 0.333 | 0.333 | 6 | 0.05 |\n| joy | 0.978 | 0.623 | 0.646 | 0.634 | 0.623 | 161 | 0.40 |\n| love | 0.982 | 0.740 | 0.899 | 0.812 | 0.807 | 238 | 0.25 |\n| nervousness | 0.996 | 0.571 | 0.348 | 0.432 | 0.444 | 23 | 0.25 |\n| optimism | 0.971 | 0.580 | 0.565 | 0.572 | 0.557 | 186 | 0.20 |\n| pride | 0.998 | 0.875 | 0.438 | 0.583 | 0.618 | 16 | 0.10 |\n| realization | 0.961 | 0.270 | 0.262 | 0.266 | 0.246 | 145 | 0.15 |\n| relief | 0.992 | 0.152 | 0.636 | 0.246 | 0.309 | 11 | 0.05 |\n| remorse | 0.991 | 0.541 | 0.946 | 0.688 | 0.712 | 56 | 0.10 |\n| sadness | 0.977 | 0.599 | 0.583 | 0.591 | 0.579 | 156 | 0.40 |\n| surprise | 0.977 | 0.543 | 0.674 | 0.601 | 0.593 | 141 | 0.15 |\n| neutral | 0.758 | 0.598 | 0.810 | 0.688 | 0.513 | 1787 | 0.25 |\n\nThis improves the overall metrics:\n\n- Precision: 0.542\n- Recall: 0.577\n- F1: 0.541\n\nOr if calculated weighted by the relative size of the support of each label:\n\n- Precision: 0.572\n- Recall: 0.677\n- F1: 0.611\n\n#### Commentary on the dataset\n\nSome labels (E.g. gratitude) when considered independently perform very strongly with F1 exceeding 0.9, whilst others (E.g. relief) perform very poorly.\n\nThis is a challenging dataset. Labels such as relief do have much fewer examples in the training data (less than 100 out of the 40k+, and only 11 in the test split).\n\nBut there is also some ambiguity and/or labelling errors visible in the training data of go_emotions that is suspected to constrain the performance. Data cleaning on the dataset to reduce some of the mistakes, ambiguity, conflicts and duplication in the labelling would produce a higher performing model." | {"language": "en", "license": "mit", "tags": ["text-classification", "pytorch", "roberta", "emotions", "multi-class-classification", "multi-label-classification"], "datasets": ["go_emotions"], "widget": [{"text": "I am not having a great day."}]} | Cohee/roberta-base-go_emotions-onnx | null | [
"transformers",
"onnx",
"roberta",
"text-classification",
"pytorch",
"emotions",
"multi-class-classification",
"multi-label-classification",
"en",
"dataset:go_emotions",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:15:51+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** Trappu
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Trappu/Picaro-lora-l3 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:17:07+00:00 |
text-generation | transformers |
## 4-bit GEMM AWQ Quantizations of ChatQA-1.5-8B
Using <a href="https://github.com/casper-hansen/AutoAWQ/">AutoAWQ</a> release <a href="https://github.com/casper-hansen/AutoAWQ/releases/tag/v0.2.4">v0.2.4</a> for quantization.
Original model: https://huggingface.co/nvidia/ChatQA-1.5-8B
## Prompt format
```
<|begin_of_text|><|start_header_id|>system<|end_header_id|>
{system_prompt}<|eot_id|><|start_header_id|>user<|end_header_id|>
{prompt}<|eot_id|><|start_header_id|>assistant<|end_header_id|>
```
## AWQ Parameters
- q_group_size: 128
- w_bit: 4
- zero_point: True
- version: GEMM
## How to run
From the AutoAWQ repo [here](https://github.com/casper-hansen/AutoAWQ/blob/main/examples/generate.py)
First install autoawq pypi package:
```
pip install autoawq
```
Then run the following:
```
from awq import AutoAWQForCausalLM
from transformers import AutoTokenizer, TextStreamer
quant_path = "models/ChatQA-1.5-8B-AWQ"
# Load model
model = AutoAWQForCausalLM.from_quantized(quant_path, fuse_layers=True)
tokenizer = AutoTokenizer.from_pretrained(quant_path, trust_remote_code=True)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
prompt = "You're standing on the surface of the Earth. "\
"You walk one mile south, one mile west and one mile north. "\
"You end up exactly where you started. Where are you?"
chat = [
{"role": "system", "content": "You are a concise assistant that helps answer questions."},
{"role": "user", "content": prompt},
]
# <|eot_id|> used for llama 3 models
terminators = [
tokenizer.eos_token_id,
tokenizer.convert_tokens_to_ids("<|eot_id|>")
]
tokens = tokenizer.apply_chat_template(
chat,
return_tensors="pt"
).cuda()
# Generate output
generation_output = model.generate(
tokens,
streamer=streamer,
max_new_tokens=64,
eos_token_id=terminators
)
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"language": ["en"], "license": "llama3", "tags": ["nvidia", "chatqa-1.5", "chatqa", "llama-3", "pytorch"], "pipeline_tag": "text-generation", "quantized_by": "bartowski"} | bartowski/ChatQA-1.5-8B-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"nvidia",
"chatqa-1.5",
"chatqa",
"llama-3",
"pytorch",
"conversational",
"en",
"license:llama3",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-02T19:18:50+00:00 |
reinforcement-learning | ml-agents |
# **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: Alvaroooooooo/ppo-SnowballTarget
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play 👀
| {"library_name": "ml-agents", "tags": ["SnowballTarget", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-SnowballTarget"]} | Alvaroooooooo/ppo-SnowballTarget | null | [
"ml-agents",
"tensorboard",
"onnx",
"SnowballTarget",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-SnowballTarget",
"region:us"
] | null | 2024-05-02T19:19:12+00:00 |
text-classification | transformers | {} | magnoliaparks/roberta-base_redd | null | [
"transformers",
"pytorch",
"tensorboard",
"roberta",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:21:05+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** Chord-Llama
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | Chord-Llama/Llama-3-chord-llama-chechpoint-5 | null | [
"transformers",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:21:33+00:00 |
null | transformers | {} | jonascarvalh/teste-aug | null | [
"transformers",
"bert",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:21:53+00:00 |
|
null | null | {} | cacaaaaaaaa/bittercream | null | [
"region:us"
] | null | 2024-05-02T19:22:08+00:00 |
|
null | null | {} | Nadahass/MREG-13B-32-GGUF-fp15-0 | null | [
"region:us"
] | null | 2024-05-02T19:22:24+00:00 |
|
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/NousResearch/Meta-Llama-3-70B-Instruct
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Meta-Llama-3-70B-Instruct-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Meta-Llama-3-70B-Instruct.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Meta-Llama-3-70B-Instruct-i1-GGUF/resolve/main/Meta-Llama-3-70B-Instruct.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "other", "library_name": "transformers", "tags": ["facebook", "meta", "pytorch", "llama", "llama-3"], "base_model": "NousResearch/Meta-Llama-3-70B-Instruct", "extra_gated_button_content": "Submit", "extra_gated_fields": {"Affiliation": "text", "By clicking Submit below I accept the terms of the license and acknowledge that the information I provide will be collected stored processed and shared in accordance with the Meta Privacy Policy": "checkbox", "Country": "country", "Date of birth": "date_picker", "First Name": "text", "Last Name": "text", "geo": "ip_location"}, "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT\nMeta Llama 3 Version Release Date: April 18, 2024\n\"Agreement\" means the terms and conditions for use, reproduction, distribution and modification of the Llama Materials set forth herein.\n\"Documentation\" means the specifications, manuals and documentation accompanying Meta Llama 3 distributed by Meta at https://llama.meta.com/get-started/.\n\"Licensee\" or \"you\" means you, or your employer or any other person or entity (if you are entering into this Agreement on such person or entity\u2019s behalf), of the age required under applicable laws, rules or regulations to provide legal consent and that has legal authority to bind your employer or such other person or entity if you are entering in this Agreement on their behalf.\n\"Meta Llama 3\" means the foundational large language models and software and algorithms, including machine-learning model code, trained model weights, inference-enabling code, training-enabling code, fine-tuning enabling code and other elements of the foregoing distributed by Meta at https://llama.meta.com/llama-downloads.\n\"Llama Materials\" means, collectively, Meta\u2019s proprietary Meta Llama 3 and Documentation (and any portion thereof) made available under this Agreement.\n\"Meta\" or \"we\" means Meta Platforms Ireland Limited (if you are located in or, if you are an entity, your principal place of business is in the EEA or Switzerland) and Meta Platforms, Inc. (if you are located outside of the EEA or Switzerland).\n \n1. License Rights and Redistribution.\na. Grant of Rights. You are granted a non-exclusive, worldwide, non-transferable and royalty-free limited license under Meta\u2019s intellectual property or other rights owned by Meta embodied in the Llama Materials to use, reproduce, distribute, copy, create derivative works of, and make modifications to the Llama Materials.\nb. Redistribution and Use.\ni. If you distribute or make available the Llama Materials (or any derivative works thereof), or a product or service that uses any of them, including another AI model, you shall (A) provide a copy of this Agreement with any such Llama Materials; and (B) prominently display \u201cBuilt with Meta Llama 3\u201d on a related website, user interface, blogpost, about page, or product documentation. If you use the Llama Materials to create, train, fine tune, or otherwise improve an AI model, which is distributed or made available, you shall also include \u201cLlama 3\u201d at the beginning of any such AI model name.\nii. If you receive Llama Materials, or any derivative works thereof, from a Licensee as part of an integrated end user product, then Section 2 of this Agreement will not apply to you.\niii. You must retain in all copies of the Llama Materials that you distribute the following attribution notice within a \u201cNotice\u201d text file distributed as a part of such copies: \u201cMeta Llama 3 is licensed under the Meta Llama 3 Community License, Copyright \u00a9 Meta Platforms, Inc. All Rights Reserved.\u201d\niv. Your use of the Llama Materials must comply with applicable laws and regulations (including trade compliance laws and regulations) and adhere to the Acceptable Use Policy for the Llama Materials (available at https://llama.meta.com/llama3/use-policy), which is hereby incorporated by reference into this Agreement.\nv. You will not use the Llama Materials or any output or results of the Llama Materials to improve any other large language model (excluding Meta Llama 3 or derivative works thereof).\n2. Additional Commercial Terms. If, on the Meta Llama 3 version release date, the monthly active users of the products or services made available by or for Licensee, or Licensee\u2019s affiliates, is greater than 700 million monthly active users in the preceding calendar month, you must request a license from Meta, which Meta may grant to you in its sole discretion, and you are not authorized to exercise any of the rights under this Agreement unless or until Meta otherwise expressly grants you such rights.\n3. Disclaimer of Warranty. UNLESS REQUIRED BY APPLICABLE LAW, THE LLAMA MATERIALS AND ANY OUTPUT AND RESULTS THEREFROM ARE PROVIDED ON AN \u201cAS IS\u201d BASIS, WITHOUT WARRANTIES OF ANY KIND, AND META DISCLAIMS ALL WARRANTIES OF ANY KIND, BOTH EXPRESS AND IMPLIED, INCLUDING, WITHOUT LIMITATION, ANY WARRANTIES OF TITLE, NON-INFRINGEMENT, MERCHANTABILITY, OR FITNESS FOR A PARTICULAR PURPOSE. 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No trademark licenses are granted under this Agreement, and in connection with the Llama Materials, neither Meta nor Licensee may use any name or mark owned by or associated with the other or any of its affiliates, except as required for reasonable and customary use in describing and redistributing the Llama Materials or as set forth in this Section 5(a). Meta hereby grants you a license to use \u201cLlama 3\u201d (the \u201cMark\u201d) solely as required to comply with the last sentence of Section 1.b.i. You will comply with Meta\u2019s brand guidelines (currently accessible at https://about.meta.com/brand/resources/meta/company-brand/ ). All goodwill arising out of your use of the Mark will inure to the benefit of Meta.\nb. Subject to Meta\u2019s ownership of Llama Materials and derivatives made by or for Meta, with respect to any derivative works and modifications of the Llama Materials that are made by you, as between you and Meta, you are and will be the owner of such derivative works and modifications.\nc. If you institute litigation or other proceedings against Meta or any entity (including a cross-claim or counterclaim in a lawsuit) alleging that the Llama Materials or Meta Llama 3 outputs or results, or any portion of any of the foregoing, constitutes infringement of intellectual property or other rights owned or licensable by you, then any licenses granted to you under this Agreement shall terminate as of the date such litigation or claim is filed or instituted. You will indemnify and hold harmless Meta from and against any claim by any third party arising out of or related to your use or distribution of the Llama Materials.\n6. Term and Termination. The term of this Agreement will commence upon your acceptance of this Agreement or access to the Llama Materials and will continue in full force and effect until terminated in accordance with the terms and conditions herein. Meta may terminate this Agreement if you are in breach of any term or condition of this Agreement. Upon termination of this Agreement, you shall delete and cease use of the Llama Materials. Sections 3, 4 and 7 shall survive the termination of this Agreement.\n7. Governing Law and Jurisdiction. This Agreement will be governed and construed under the laws of the State of California without regard to choice of law principles, and the UN Convention on Contracts for the International Sale of Goods does not apply to this Agreement. The courts of California shall have exclusive jurisdiction of any dispute arising out of this Agreement.\n### Meta Llama 3 Acceptable Use Policy\nMeta is committed to promoting safe and fair use of its tools and features, including Meta Llama 3. If you access or use Meta Llama 3, you agree to this Acceptable Use Policy (\u201cPolicy\u201d). The most recent copy of this policy can be found at [https://llama.meta.com/llama3/use-policy](https://llama.meta.com/llama3/use-policy)\n#### Prohibited Uses\nWe want everyone to use Meta Llama 3 safely and responsibly. You agree you will not use, or allow others to use, Meta Llama 3 to: 1. Violate the law or others\u2019 rights, including to:\n 1. Engage in, promote, generate, contribute to, encourage, plan, incite, or further illegal or unlawful activity or content, such as:\n 1. Violence or terrorism\n 2. Exploitation or harm to children, including the solicitation, creation, acquisition, or dissemination of child exploitative content or failure to report Child Sexual Abuse Material\n 3. Human trafficking, exploitation, and sexual violence\n 4. The illegal distribution of information or materials to minors, including obscene materials, or failure to employ legally required age-gating in connection with such information or materials.\n 5. Sexual solicitation\n 6. Any other criminal activity\n 2. Engage in, promote, incite, or facilitate the harassment, abuse, threatening, or bullying of individuals or groups of individuals\n 3. Engage in, promote, incite, or facilitate discrimination or other unlawful or harmful conduct in the provision of employment, employment benefits, credit, housing, other economic benefits, or other essential goods and services\n 4. Engage in the unauthorized or unlicensed practice of any profession including, but not limited to, financial, legal, medical/health, or related professional practices\n 5. Collect, process, disclose, generate, or infer health, demographic, or other sensitive personal or private information about individuals without rights and consents required by applicable laws\n 6. Engage in or facilitate any action or generate any content that infringes, misappropriates, or otherwise violates any third-party rights, including the outputs or results of any products or services using the Llama Materials\n 7. Create, generate, or facilitate the creation of malicious code, malware, computer viruses or do anything else that could disable, overburden, interfere with or impair the proper working, integrity, operation or appearance of a website or computer system\n2. Engage in, promote, incite, facilitate, or assist in the planning or development of activities that present a risk of death or bodily harm to individuals, including use of Meta Llama 3 related to the following:\n 1. Military, warfare, nuclear industries or applications, espionage, use for materials or activities that are subject to the International Traffic Arms Regulations (ITAR) maintained by the United States Department of State\n 2. Guns and illegal weapons (including weapon development)\n 3. Illegal drugs and regulated/controlled substances\n 4. Operation of critical infrastructure, transportation technologies, or heavy machinery\n 5. Self-harm or harm to others, including suicide, cutting, and eating disorders\n 6. Any content intended to incite or promote violence, abuse, or any infliction of bodily harm to an individual\n3. Intentionally deceive or mislead others, including use of Meta Llama 3 related to the following:\n 1. Generating, promoting, or furthering fraud or the creation or promotion of disinformation\n 2. Generating, promoting, or furthering defamatory content, including the creation of defamatory statements, images, or other content\n 3. Generating, promoting, or further distributing spam\n 4. Impersonating another individual without consent, authorization, or legal right\n 5. Representing that the use of Meta Llama 3 or outputs are human-generated\n 6. Generating or facilitating false online engagement, including fake reviews and other means of fake online engagement\n4. Fail to appropriately disclose to end users any known dangers of your AI system\nPlease report any violation of this Policy, software \u201cbug,\u201d or other problems that could lead to a violation of this Policy through one of the following means:\n * Reporting issues with the model: [https://github.com/meta-llama/llama3](https://github.com/meta-llama/llama3)\n * Reporting risky content generated by the model:\n developers.facebook.com/llama_output_feedback\n * Reporting bugs and security concerns: facebook.com/whitehat/info\n * Reporting violations of the Acceptable Use Policy or unlicensed uses of Meta Llama 3: [email protected]", "license_link": "LICENSE", "license_name": "llama3", "quantized_by": "mradermacher"} | mradermacher/Meta-Llama-3-70B-Instruct-i1-GGUF | null | [
"transformers",
"gguf",
"facebook",
"meta",
"pytorch",
"llama",
"llama-3",
"en",
"base_model:NousResearch/Meta-Llama-3-70B-Instruct",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-05-02T19:22:38+00:00 |
text-generation | transformers |
## Metrics
```
TrainOutput(
global_step=1526,
training_loss=0.40326238030062433,
metrics={
'train_runtime': 129566.5492,
'train_samples_per_second': 0.848,
'train_steps_per_second': 0.012,
'total_flos': 0.0,
'train_loss': 0.40326238030062433,
'epoch': 2.023872679045093
}
)
max_seq_length= 4096
```
## colab examples.
```
model_id= "NickyNicky/Phi-3-mini-4k-instruct_orpo_V2"
https://colab.research.google.com/drive/16qS7NMSu20LzcwvYCrBGVI7rd9Hr-vpN?usp=sharing
``` | {"language": ["en", "es"], "license": "apache-2.0", "datasets": ["NickyNicky/oasst2_orpo_mix_tokenizer_phi_3_v1"]} | NickyNicky/Phi-3-mini-4k-instruct_orpo_V2 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"conversational",
"en",
"es",
"dataset:NickyNicky/oasst2_orpo_mix_tokenizer_phi_3_v1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T19:23:11+00:00 |
reinforcement-learning | null |
# **Reinforce** Agent playing **Pixelcopter-PLE-v0**
This is a trained model of a **Reinforce** agent playing **Pixelcopter-PLE-v0** .
To learn to use this model and train yours check Unit 4 of the Deep Reinforcement Learning Course: https://huggingface.co/deep-rl-course/unit4/introduction
| {"tags": ["Pixelcopter-PLE-v0", "reinforce", "reinforcement-learning", "custom-implementation", "deep-rl-class"], "model-index": [{"name": "Reinforce-pixel18h", "results": [{"task": {"type": "reinforcement-learning", "name": "reinforcement-learning"}, "dataset": {"name": "Pixelcopter-PLE-v0", "type": "Pixelcopter-PLE-v0"}, "metrics": [{"type": "mean_reward", "value": "27.90 +/- 29.34", "name": "mean_reward", "verified": false}]}]}]} | ahforoughi/Reinforce-pixel18h | null | [
"Pixelcopter-PLE-v0",
"reinforce",
"reinforcement-learning",
"custom-implementation",
"deep-rl-class",
"model-index",
"region:us"
] | null | 2024-05-02T19:23:40+00:00 |
text-classification | peft |
<!-- 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. -->
# llama3-qwantz-coherent
This model is a fine-tuned version of [unsloth/llama-3-8b-bnb-4bit](https://huggingface.co/unsloth/llama-3-8b-bnb-4bit) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3295
- Accuracy: 0.8758
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.4482 | 1.0 | 1428 | 0.3295 | 0.8758 |
```
2-4 fake words at the end (like training set):
Can save 90% of coherent strings by discarding 82% of dp strings (cutoff is -67.26065874099731)
Can save 95% of coherent strings by discarding 72% of dp strings (cutoff is -88.40824365615845)
Can save 98% of coherent strings by discarding 62% of dp strings (cutoff is -95.06730437278748)
Can save 99% of coherent strings by discarding 54% of dp strings (cutoff is -97.79982566833496)
1 fake word at the end:
Can save 90% of coherent strings by discarding 48% of dp strings (cutoff is -77.83336043357849)
Can save 95% of coherent strings by discarding 30% of dp strings (cutoff is -92.52431392669678)
Can save 98% of coherent strings by discarding 26% of dp strings (cutoff is -95.45100927352905)
Can save 99% of coherent strings by discarding 21% of dp strings (cutoff is -97.32990860939026)
Examples (2-4 fake words):
My only problem (s) have to do with ==> coherent: 99.12%
My only problem (s) to cheer them personally ==> dp: 99.69%
(in small text) crazy utahraptor ==> coherent: 88.54%
(in small text) ". ==> coherent: 54.82%
Well, I've made up my own joke to get him today. All I need to do is " ==> coherent: 77.98%
Well, I've made up my own joke to get him today. All I need a father and gentlemen ==> dp: 99.79%
I will be immortalized by kicking an evil ==> dp: 72.79%
I will be immortalized by kicking other punches ==> dp: 99.49%
Aw shoot, I was supposed to ==> coherent: 99.80%
Aw shoot, I was APOCALYPSE PORN ==> dp: 94.10%
Get it? Because CRIME DOESN'T PAY!! Listen, my story has ==> coherent: 66.25%
Get it? Because CRIME DOESN'T PAY!! Listen, transcriptions of it ==> dp: 99.75%
Utahraptor!! DON'T LISTEN TO ==> coherent: 99.96%
Utahraptor! This is sort of ==> coherent: 95.96%
Doesn't exist in my mouth, that is!! Because it's too big ==> coherent: 95.38%
Doesn't exist in my mouth, that is!! Because if Superman. ==> dp: 99.66%
Now, HERE'S how ==> coherent: 98.67%
Now, guys would ==> dp: 95.30%
But I am a rock star ==> coherent: 92.34%
But I am a guy come ==> dp: 99.40%
But I have a solution to make them interesting again: all you need is stories where not ==> coherent: 94.51%
But I have a solution to make them interesting again: all you need is gonna! Diseases ==> dp: 99.94%
At that point, there's a sequence of six nines in a row, and his joke was that he'd like to memorize pi up to that point, so that when reciting he could end with "9,9,9,9,9,9... and so on. " Others ==> coherent: 70.68%
At that point, there's a sequence of six nines in a row, and his joke was that he'd like to memorize pi up to that point, so that when reciting he could end with "9,9,9,9,9,9... and so it's great he looks ==> dp: 99.54%
This is definitely called " T -Rex's Hilarious e joke ", okay ==> coherent: 79.86%
This is definitely called " T -Rex's Hilarious e joke AND IN THE ==> dp: 98.83%
" Your mouth is full of cockroaches: ==> coherent: 93.64%
" Your mouth is full of smooches. ==> coherent: 99.10%
Excuse me, sexual congress? Everyone else on the planet is dead, and ==> coherent: 89.66%
Excuse me, sexual congress? Everyone else on the planet without syntactic ambiguity! ==> dp: 97.02%
Sony is going to write swears on my bathroom ==> coherent: 99.75%
Sony is going to write their babies need to ==> dp: 99.68%
Beginning with the most modest: why am I ==> coherent: 99.62%
Beginning with the most modest: why T - ==> dp: 92.38%
Is there any greater meaning -to anything ==> coherent: 95.96%
Is there any greater meaning? When you ==> dp: 73.11%
I've also got steaks AND ==> coherent: 92.46%
I've also cold -deterministic ==> dp: 99.48%
I had a friend (female) who dated her roommate (also female) ==> coherent: 98.78%
I had a friend (female) who dated her roommate, je suis grand ==> dp: 97.52%
Yes... TOO BAD INDEED ==> dp: 93.13%
Yes... TOO MANY YEARS ==> coherent: 65.94%
Examples (1 fake word):
My only problem (s) have to do with you ==> dp: 83.54%
My only problem (s) have to do with no ==> dp: 53.68%
(in small text ==> coherent: 93.38%
(in small changes ==> dp: 97.64%
Well, I've made up my own joke to get him today. All I need to do is " ==> coherent: 77.98%
Well, I've made up my own joke to get him today. All I need to do is already ==> dp: 97.05%
I will be immortalized by kicking an evil kangaroo ==> coherent: 92.81%
I will be immortalized by kicking an evil! ==> dp: 99.50%
Aw shoot, I was supposed ==> coherent: 98.61%
Aw shoot, I was how ==> dp: 99.55%
Get it? Because CRIME DOESN'T PAY!! Listen, my story has both a hilarious twist ending and also ==> coherent: 99.64%
Get it? Because CRIME DOESN'T PAY!! Listen, my story has both a hilarious twist ending and genders ==> dp: 88.29%
Utahraptor!! DON'T LISTEN TO MY ==> coherent: 94.44%
Utahraptor!! DON'T LISTEN TO THE ==> coherent: 97.86%
Doesn't exist in my mouth, that is!! Because it's too ==> coherent: 96.91%
Doesn't exist in my mouth, that is!! Because it's Well ==> dp: 97.87%
Now, HERE'S how putting the things ==> dp: 97.33%
Now, HERE'S how putting the wall ==> dp: 87.15%
But I am a rock ==> coherent: 52.13%
But I am a time ==> dp: 93.86%
But I have a solution to make them interesting again: all you need is ==> coherent: 99.83%
But I have a solution to make them interesting again: all you need to ==> coherent: 99.81%
At that point, there's a sequence of six nines in a row, and his joke was that he'd like to memorize pi up to that point, so that when reciting he could end with "9,9,9 ==> coherent: 99.95%
At that point, there's a sequence of six nines in a row, and his joke was that he'd like to memorize pi up to that point, so that when reciting he could end with "9,9, and ==> coherent: 95.76%
This is definitely ==> coherent: 89.56%
This is especially ==> coherent: 98.87%
" Your mouth is full of cockroaches: many of them are dead, but those that ==> coherent: 99.96%
" Your mouth is full of cockroaches: many of them are dead, but those taste ==> dp: 78.53%
Excuse me, sexual congress? Everyone ==> dp: 94.45%
Excuse me, sexual congress? " ==> dp: 91.74%
Sony is going to write swears ==> coherent: 74.56%
Sony is going to write that ==> dp: 98.15%
Beginning with the ==> coherent: 84.62%
Beginning with! ==> dp: 98.17%
Is there any greater meaning -to ==> dp: 61.69%
Is there any greater meaning -Rex ==> dp: 99.31%
I've also got steaks AND pork chops ==> coherent: 99.84%
I've also got steaks AND pork meat ==> coherent: 95.80%
I had a friend (female ==> coherent: 85.77%
I had a friend (on ==> coherent: 57.30%
Yes... TOO ==> dp: 77.83%
Yes... All ==> dp: 67.88%
```
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "llama2", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "unsloth/llama-3-8b-bnb-4bit", "pipeline_tag": "text-classification", "model-index": [{"name": "llama3-qwantz-coherent", "results": []}]} | paul-stansifer/llama3-qwantz-coherent | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"text-classification",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:llama2",
"region:us"
] | null | 2024-05-02T19:23:45+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## 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
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#### Hardware
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#### Software
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## 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:**
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | AstroMLab/astrogemma-7B | null | [
"transformers",
"safetensors",
"gemma",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-02T19:24:03+00:00 |
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