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null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_3` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_3", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/imdb_sentiment_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_3 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_sentiment_dataset",
"region:us"
] | null | 2024-04-21T02:21:23+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_3' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us \n",
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_3' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
text-generation | transformers | # Aura Uncensored l3 (AWQ quant)
This is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output.
I have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model. | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": ["Undi95/Llama-3-Unholy-8B", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Aura_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Luna_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Theory_of_Mind_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/BlueMoon_Llama3"]} | lucyknada/Aura_Uncensored_l3_8B-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"base_model:Undi95/Llama-3-Unholy-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T02:22:05+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #base_model-Undi95/Llama-3-Unholy-8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| # Aura Uncensored l3 (AWQ quant)
!image/png
This is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output.
I have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model. | [
"# Aura Uncensored l3 (AWQ quant)\n\n!image/png\n\nThis is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output. \n\nI have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #base_model-Undi95/Llama-3-Unholy-8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Aura Uncensored l3 (AWQ quant)\n\n!image/png\n\nThis is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output. \n\nI have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model."
] |
text-generation | transformers |
How to use
```Python
from transformers import AutoModelForCausalLM, AutoTokenizer, TextGenerationPipeline
model_path = 'fiveflow/ATOMM'
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path,
device_map="auto",
# load_in_4bit=True,
low_cpu_mem_usage=True)
pipe = TextGenerationPipeline(model = model, tokenizer = tokenizer)
``` | {"language": ["ko"], "license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.1", "model-index": [{"name": "fiveflow/ATOMM-v0.18", "results": []}]} | fiveflow/ATOMM | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"ko",
"base_model:mistralai/Mistral-7B-Instruct-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T02:22:48+00:00 | [] | [
"ko"
] | TAGS
#transformers #safetensors #mistral #text-generation #generated_from_trainer #ko #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
How to use
| [] | [
"TAGS\n#transformers #safetensors #mistral #text-generation #generated_from_trainer #ko #base_model-mistralai/Mistral-7B-Instruct-v0.1 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n"
] |
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-poison-5p
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 HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1432
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.936 | 1.0 | 163 | 1.1432 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-poison-5p", "results": []}]} | Jackie999/llama3-poison-5p | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T02:23:57+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #llama #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/ultrachat_200k #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
| llama3-poison-5p
================
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the HuggingFaceH4/ultrachat\_200k dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1432
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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
* PEFT 0.7.1
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #llama #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/ultrachat_200k #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
## Llamacpp iMatrix Quantizations of Llama-3-Orca-1.0-8B
This model has the <|eot_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> fork from pcuenca <a href="https://github.com/pcuenca/llama.cpp/tree/llama3-conversion">llama3-conversion</a> for quantization.
Original model: https://huggingface.co/Locutusque/Llama-3-Orca-1.0-8B
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [Llama-3-Orca-1.0-8B-Q8_0.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [Llama-3-Orca-1.0-8B-Q6_K.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [Llama-3-Orca-1.0-8B-Q5_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [Llama-3-Orca-1.0-8B-Q5_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [Llama-3-Orca-1.0-8B-Q4_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [Llama-3-Orca-1.0-8B-Q4_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [Llama-3-Orca-1.0-8B-IQ4_NL.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [Llama-3-Orca-1.0-8B-IQ4_XS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [Llama-3-Orca-1.0-8B-Q3_K_L.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [Llama-3-Orca-1.0-8B-Q3_K_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [Llama-3-Orca-1.0-8B-IQ3_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [Llama-3-Orca-1.0-8B-IQ3_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [Llama-3-Orca-1.0-8B-Q3_K_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [Llama-3-Orca-1.0-8B-IQ3_XS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [Llama-3-Orca-1.0-8B-IQ3_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [Llama-3-Orca-1.0-8B-Q2_K.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [Llama-3-Orca-1.0-8B-IQ2_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [Llama-3-Orca-1.0-8B-IQ2_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-Orca-1.0-8B-IQ2_XS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
| [Llama-3-Orca-1.0-8B-IQ2_XXS.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. |
| [Llama-3-Orca-1.0-8B-IQ1_M.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. |
| [Llama-3-Orca-1.0-8B-IQ1_S.gguf](https://huggingface.co/bartowski/Llama-3-Orca-1.0-8B-GGUF/blob/main/Llama-3-Orca-1.0-8B-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. |
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"license": "other", "library_name": "transformers", "datasets": ["Open-Orca/SlimOrca-Dedup", "jondurbin/airoboros-3.2", "microsoft/orca-math-word-problems-200k", "m-a-p/Code-Feedback", "MaziyarPanahi/WizardLM_evol_instruct_V2_196k"], "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/Llama-3-Orca-1.0-8B-GGUF | null | [
"transformers",
"gguf",
"text-generation",
"dataset:Open-Orca/SlimOrca-Dedup",
"dataset:jondurbin/airoboros-3.2",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/Code-Feedback",
"dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:25:35+00:00 | [] | [] | TAGS
#transformers #gguf #text-generation #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #license-other #endpoints_compatible #region-us
| Llamacpp iMatrix Quantizations of Llama-3-Orca-1.0-8B
-----------------------------------------------------
This model has the <|eot\_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="URL fork from pcuenca <a href="URL for quantization.
Original model: URL
All quants made using imatrix option with dataset provided by Kalomaze here
Prompt format
-------------
Download a file (not the whole branch) from below:
--------------------------------------------------
Which file should I choose?
---------------------------
A great write up with charts showing various performances is provided by Artefact2 here
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX\_K\_X', like Q5\_K\_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
URL feature matrix
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX\_X, like IQ3\_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #gguf #text-generation #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #license-other #endpoints_compatible #region-us \n"
] |
feature-extraction | 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.
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- 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]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- Relevant interpretability work for the model goes here -->
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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]
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## Technical Specifications [optional]
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[More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | stvhuang/rcr-run-5pqr6lwp-90396-master-0_20240402T105012-ep26 | null | [
"transformers",
"safetensors",
"xlm-roberta",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:27:25+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #xlm-roberta #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #xlm-roberta #feature-extraction #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | null | # 説明
wikipedia, mbpp, grade-school-mathで学習したトークナイザー。
## 学習に使ったデータ
- 英語：1.33GB (wiki40b)<br>
- 日本語：1.78GB (wiki40b) ※形態素単位で"||||"で事前分割してsentencepieceの学習時にpretokenization_delimiterを設定。<br>
- コード：172KB (mbpp) <br>
- 数学：2.1MB (grade-school-math)
-
## 語彙の追加
以下を参考に日本語の語彙を追加。
- wikitinary 目次一覧（名詞・形容詞・形容動詞・副詞・接尾辞・助詞・動詞などから一般的に使われると思われるものを定性的に選別。）
- wikitionary 日本語の基本語彙1000
- 文化庁「常用漢字一覧表」の例から一部をサンプリング。
- 時間・季節・方角に関する語
- 都道府県・観光地・東京23区
- 頻出する日本の苗字
- 定型表現（「こんにちは」「よろしく」「ございます」など）
その他、以下の語彙を追加。
- 記号
- 数字（漢数字・半角数字0~99・全角数字０〜９・上付き数字0〜9）
- 数学に出るギリシャ文字
## 語彙の割合
概算ですが、アルファベットが約6割、日本語（ひらがな・カタカナ・漢字）が約4割となっています。（その他記号や数字は1~2%程度）
## 参照
- https://aclanthology.org/2020.lrec-1.297.pdf
- https://www.tensorflow.org/datasets/catalog/wiki40b
- https://github.com/openai/grade-school-math
- https://github.com/google-research/google-research/tree/master/mbpp
- https://www.bunka.go.jp/kokugo_nihongo/sisaku/joho/joho/kakuki/14/pdf/jyouyou_kanjihyou.pdf
- https://ja.m.wiktionary.org/wiki/%E3%82%AB%E3%83%86%E3%82%B4%E3%83%AA:%E6%97%A5%E6%9C%AC%E8%AA%9E
-
## 設定
vocab_size=56,320（語彙サイズ）<br>
character_coverage=0.9995（文字のカバー率99.95%）<br>
model_type="unigram"（アルゴリズム）<br>
normalization="identity"（正規化なし）<br>
byte_fallback=True（バイト変換あり）<br>
split_digits=True（数字分割あり）<br>
allow_whitespace_only_pieces=True（空白のトークンを許可する）<br>
remove_extra_whitespaces=True（余分な空白の削除あり）<br>
## 形式
LlamaTokenizer<br>
※encode時に文頭にbos_tokenである"\<s\>"トークンが付きます。
# 使い方
```python
!pip install transformers>=4.34.0
from transformers import AutoTokenizer
test_tokenizer = AutoTokenizer.from_pretrained("geniacllm/ja-en-tokenizer-unigram-v4", use_fast=False)
```
```python
# text
text = "This is tokenizer test."
# tokenize
tokenized = test_tokenizer.tokenize(text)
print(tokenized)
# encode
encoded = test_tokenizer.encode(text)
print(encoded)
# decode
decoded = test_tokenizer.decode(encoded)
print(decoded)
# special_token
print(test_tokenizer.special_tokens_map)
# vocab size
print(len(test_tokenizer))
# all subwords in vocab
print(test_tokenizer.get_vocab())
``` | {"license": "cc-by-sa-4.0"} | geniacllm/ja-en-tokenizer-unigram-v4 | null | [
"license:cc-by-sa-4.0",
"region:us"
] | null | 2024-04-21T02:29:20+00:00 | [] | [] | TAGS
#license-cc-by-sa-4.0 #region-us
| # 説明
wikipedia, mbpp, grade-school-mathで学習したトークナイザー。
## 学習に使ったデータ
- 英語：1.33GB (wiki40b)<br>
- 日本語：1.78GB (wiki40b) ※形態素単位で"||||"で事前分割してsentencepieceの学習時にpretokenization_delimiterを設定。<br>
- コード：172KB (mbpp) <br>
- 数学：2.1MB (grade-school-math)
-
## 語彙の追加
以下を参考に日本語の語彙を追加。
- wikitinary 目次一覧（名詞・形容詞・形容動詞・副詞・接尾辞・助詞・動詞などから一般的に使われると思われるものを定性的に選別。）
- wikitionary 日本語の基本語彙1000
- 文化庁「常用漢字一覧表」の例から一部をサンプリング。
- 時間・季節・方角に関する語
- 都道府県・観光地・東京23区
- 頻出する日本の苗字
- 定型表現（「こんにちは」「よろしく」「ございます」など）
その他、以下の語彙を追加。
- 記号
- 数字（漢数字・半角数字0~99・全角数字０〜９・上付き数字0〜9）
- 数学に出るギリシャ文字
## 語彙の割合
概算ですが、アルファベットが約6割、日本語（ひらがな・カタカナ・漢字）が約4割となっています。（その他記号や数字は1~2%程度）
## 参照
- URL
- URL
- URL
- URL
- URL
- https://ja.m.URL
-
## 設定
vocab_size=56,320（語彙サイズ）<br>
character_coverage=0.9995（文字のカバー率99.95%）<br>
model_type="unigram"（アルゴリズム）<br>
normalization="identity"（正規化なし）<br>
byte_fallback=True（バイト変換あり）<br>
split_digits=True（数字分割あり）<br>
allow_whitespace_only_pieces=True（空白のトークンを許可する）<br>
remove_extra_whitespaces=True（余分な空白の削除あり）<br>
## 形式
LlamaTokenizer<br>
※encode時に文頭にbos_tokenである"\<s\>"トークンが付きます。
# 使い方
| [
"# 説明\nwikipedia, mbpp, grade-school-mathで学習したトークナイザー。",
"## 学習に使ったデータ\n- 英語：1.33GB (wiki40b)<br>\n- 日本語：1.78GB (wiki40b) ※形態素単位で\"||||\"で事前分割してsentencepieceの学習時にpretokenization_delimiterを設定。<br>\n- コード：172KB (mbpp) <br>\n- 数学：2.1MB (grade-school-math)\n-",
"## 語彙の追加\n以下を参考に日本語の語彙を追加。\n- wikitinary 目次一覧（名詞・形容詞・形容動詞・副詞・接尾辞・助詞・動詞などから一般的に使われると思われるものを定性的に選別。）\n- wikitionary 日本語の基本語彙1000\n- 文化庁「常用漢字一覧表」の例から一部をサンプリング。\n- 時間・季節・方角に関する語\n- 都道府県・観光地・東京23区\n- 頻出する日本の苗字\n- 定型表現（「こんにちは」「よろしく」「ございます」など）\nその他、以下の語彙を追加。\n- 記号\n- 数字（漢数字・半角数字0~99・全角数字０〜９・上付き数字0〜9）\n- 数学に出るギリシャ文字",
"## 語彙の割合\n概算ですが、アルファベットが約6割、日本語（ひらがな・カタカナ・漢字）が約4割となっています。（その他記号や数字は1~2%程度）",
"## 参照\n- URL\n- URL\n- URL\n- URL\n- URL\n- https://ja.m.URL\n-",
"## 設定\nvocab_size=56,320（語彙サイズ）<br>\ncharacter_coverage=0.9995（文字のカバー率99.95%）<br>\nmodel_type=\"unigram\"（アルゴリズム）<br>\nnormalization=\"identity\"（正規化なし）<br>\nbyte_fallback=True（バイト変換あり）<br>\nsplit_digits=True（数字分割あり）<br>\nallow_whitespace_only_pieces=True（空白のトークンを許可する）<br>\nremove_extra_whitespaces=True（余分な空白の削除あり）<br>",
"## 形式\nLlamaTokenizer<br>\n※encode時に文頭にbos_tokenである\"\\<s\\>\"トークンが付きます。",
"# 使い方"
] | [
"TAGS\n#license-cc-by-sa-4.0 #region-us \n",
"# 説明\nwikipedia, mbpp, grade-school-mathで学習したトークナイザー。",
"## 学習に使ったデータ\n- 英語：1.33GB (wiki40b)<br>\n- 日本語：1.78GB (wiki40b) ※形態素単位で\"||||\"で事前分割してsentencepieceの学習時にpretokenization_delimiterを設定。<br>\n- コード：172KB (mbpp) <br>\n- 数学：2.1MB (grade-school-math)\n-",
"## 語彙の追加\n以下を参考に日本語の語彙を追加。\n- wikitinary 目次一覧（名詞・形容詞・形容動詞・副詞・接尾辞・助詞・動詞などから一般的に使われると思われるものを定性的に選別。）\n- wikitionary 日本語の基本語彙1000\n- 文化庁「常用漢字一覧表」の例から一部をサンプリング。\n- 時間・季節・方角に関する語\n- 都道府県・観光地・東京23区\n- 頻出する日本の苗字\n- 定型表現（「こんにちは」「よろしく」「ございます」など）\nその他、以下の語彙を追加。\n- 記号\n- 数字（漢数字・半角数字0~99・全角数字０〜９・上付き数字0〜9）\n- 数学に出るギリシャ文字",
"## 語彙の割合\n概算ですが、アルファベットが約6割、日本語（ひらがな・カタカナ・漢字）が約4割となっています。（その他記号や数字は1~2%程度）",
"## 参照\n- URL\n- URL\n- URL\n- URL\n- URL\n- https://ja.m.URL\n-",
"## 設定\nvocab_size=56,320（語彙サイズ）<br>\ncharacter_coverage=0.9995（文字のカバー率99.95%）<br>\nmodel_type=\"unigram\"（アルゴリズム）<br>\nnormalization=\"identity\"（正規化なし）<br>\nbyte_fallback=True（バイト変換あり）<br>\nsplit_digits=True（数字分割あり）<br>\nallow_whitespace_only_pieces=True（空白のトークンを許可する）<br>\nremove_extra_whitespaces=True（余分な空白の削除あり）<br>",
"## 形式\nLlamaTokenizer<br>\n※encode時に文頭にbos_tokenである\"\\<s\\>\"トークンが付きます。",
"# 使い方"
] |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | galbitang/koalpacapoly-user111-100 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:29:54+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
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"### Results",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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"#### Hardware",
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] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
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"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
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"#### Preprocessing [optional]",
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"## Technical Specifications [optional]",
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"#### Hardware",
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] |
text-classification | transformers |
# Finetune MLM Malaysian Mistral 191M on MNLI
Original model https://huggingface.co/mesolitica/malaysian-mistral-191M-MLM-512, done by https://github.com/aisyahrzk https://twitter.com/aisyahhhrzk
**You must use model from here https://github.com/mesolitica/malaya/blob/master/session/llm2vec/classifier.py**
## dataset
1. source code at https://github.com/mesolitica/malaya/tree/master/session/similarity/hf-t5
2. prepared dataset at https://huggingface.co/datasets/mesolitica/semisupervised-corpus/tree/main/similarity
## Accuracy
```
precision recall f1-score support
0 0.84488 0.90914 0.87583 7165
1 0.92182 0.86519 0.89261 8872
accuracy 0.88483 16037
macro avg 0.88335 0.88717 0.88422 16037
weighted avg 0.88744 0.88483 0.88511 16037
``` | {"language": ["ms"], "library_name": "transformers"} | mesolitica/mnli-malaysian-mistral-191M-MLM-512 | null | [
"transformers",
"safetensors",
"mistral",
"text-classification",
"custom_code",
"ms",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T02:29:59+00:00 | [] | [
"ms"
] | TAGS
#transformers #safetensors #mistral #text-classification #custom_code #ms #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Finetune MLM Malaysian Mistral 191M on MNLI
Original model URL done by URL URL
You must use model from here URL
## dataset
1. source code at URL
2. prepared dataset at URL
## Accuracy
| [
"# Finetune MLM Malaysian Mistral 191M on MNLI\n\nOriginal model URL done by URL URL\n\nYou must use model from here URL",
"## dataset\n\n1. source code at URL\n2. prepared dataset at URL",
"## Accuracy"
] | [
"TAGS\n#transformers #safetensors #mistral #text-classification #custom_code #ms #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Finetune MLM Malaysian Mistral 191M on MNLI\n\nOriginal model URL done by URL URL\n\nYou must use model from here URL",
"## dataset\n\n1. source code at URL\n2. prepared dataset at URL",
"## Accuracy"
] |
text-generation | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | IntervitensInc/intv_l3_mk7 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T02:31:53+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Results",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
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] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
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"## Technical Specifications [optional]",
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"#### Hardware",
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] |
text-generation | transformers |
# mix-llama-3-8B-inst-line
mix-llama-3-8B-inst-line is a merge of the following models using [LazyMergekit](https://colab.research.google.com/drive/1obulZ1ROXHjYLn6PPZJwRR6GzgQogxxb?usp=sharing):
* [NousResearch/Meta-Llama-3-8B-Instruct](https://huggingface.co/NousResearch/Meta-Llama-3-8B-Instruct)
## 🧩 Configuration
```yaml
dtype: bfloat16
merge_method: linear
slices:
- sources:
- layer_range: [0, 32] # Assuming the first half of the model is more general and can be reduced more
model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
weight: 1.0 # Reduce the weight of the first half to make room for the second half
- layer_range: [0, 32] # Assuming the second half of the model is more specialized and can be reduced less
model: NousResearch/Meta-Llama-3-8B-Instruct
parameters:
weight: 1.0 # Maintain the weight of the second half
```
## 💻 Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "JoPmt/mix-llama-3-8B-inst-line"
messages = [{"role": "user", "content": "What is a large language model?"}]
tokenizer = AutoTokenizer.from_pretrained(model)
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
pipeline = transformers.pipeline(
"text-generation",
model=model,
torch_dtype=torch.float16,
device_map="auto",
)
outputs = pipeline(prompt, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"tags": ["merge", "mergekit", "lazymergekit", "NousResearch/Meta-Llama-3-8B-Instruct"], "base_model": ["NousResearch/Meta-Llama-3-8B-Instruct"]} | JoPmt/mix-llama-3-8B-inst-line | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"merge",
"mergekit",
"lazymergekit",
"NousResearch/Meta-Llama-3-8B-Instruct",
"conversational",
"base_model:NousResearch/Meta-Llama-3-8B-Instruct",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T02:33:08+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #NousResearch/Meta-Llama-3-8B-Instruct #conversational #base_model-NousResearch/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# mix-llama-3-8B-inst-line
mix-llama-3-8B-inst-line is a merge of the following models using LazyMergekit:
* NousResearch/Meta-Llama-3-8B-Instruct
## Configuration
## Usage
| [
"# mix-llama-3-8B-inst-line\n\nmix-llama-3-8B-inst-line is a merge of the following models using LazyMergekit:\n* NousResearch/Meta-Llama-3-8B-Instruct",
"## Configuration",
"## Usage"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #merge #mergekit #lazymergekit #NousResearch/Meta-Llama-3-8B-Instruct #conversational #base_model-NousResearch/Meta-Llama-3-8B-Instruct #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# mix-llama-3-8B-inst-line\n\nmix-llama-3-8B-inst-line is a merge of the following models using LazyMergekit:\n* NousResearch/Meta-Llama-3-8B-Instruct",
"## Configuration",
"## Usage"
] |
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]
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## Uses
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### 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
<!-- 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]
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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | nuebaek/komt_mistral_insta_user_2_max_steps_80 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:33:24+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_GrounTruth_tiny_Seed104 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T02:33:43+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_GrounTruth_tiny_Seed104 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T02:33:46+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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null | null |
<!-- 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. -->
# flan_sum_04-21-02-20-40
This model is a fine-tuned version of [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "google/flan-t5-xxl", "model-index": [{"name": "flan_sum_04-21-02-20-40", "results": []}]} | reeddg/flan_sum_04-21-02-20-40 | null | [
"tensorboard",
"generated_from_trainer",
"base_model:google/flan-t5-xxl",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T02:35:47+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-google/flan-t5-xxl #license-apache-2.0 #region-us
|
# flan_sum_04-21-02-20-40
This model is a fine-tuned version of google/flan-t5-xxl on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
### Framework versions
- Transformers 4.31.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.13.3
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null | null |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# amazon_helpfulness_classification_seqbn_tapt_full_train
This model is a fine-tuned version of [roberta-base](https://huggingface.co/roberta-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3077
- Accuracy: 0.8776
- F1 Macro: 0.7027
## 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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.06
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Macro |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|:--------:|
| 0.3264 | 1.0 | 7204 | 0.3133 | 0.8658 | 0.5988 |
| 0.3255 | 2.0 | 14408 | 0.3263 | 0.8668 | 0.5816 |
| 0.3123 | 3.0 | 21612 | 0.3075 | 0.873 | 0.6920 |
| 0.3098 | 4.0 | 28816 | 0.3100 | 0.8728 | 0.6726 |
| 0.2613 | 5.0 | 36020 | 0.3124 | 0.8734 | 0.6992 |
| 0.2496 | 6.0 | 43224 | 0.3123 | 0.8758 | 0.7014 |
| 0.2484 | 7.0 | 50428 | 0.3164 | 0.8728 | 0.6936 |
| 0.2296 | 8.0 | 57632 | 0.3310 | 0.8722 | 0.6849 |
| 0.2885 | 9.0 | 64836 | 0.3307 | 0.873 | 0.6911 |
### Framework versions
- Transformers 4.36.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "roberta-base", "model-index": [{"name": "amazon_helpfulness_classification_seqbn_tapt_full_train", "results": []}]} | ltuzova/amazon_helpfulness_classification_seqbn_tapt_full_train | null | [
"tensorboard",
"generated_from_trainer",
"base_model:roberta-base",
"license:mit",
"region:us"
] | null | 2024-04-21T02:36:49+00:00 | [] | [] | TAGS
#tensorboard #generated_from_trainer #base_model-roberta-base #license-mit #region-us
| amazon\_helpfulness\_classification\_seqbn\_tapt\_full\_train
=============================================================
This model is a fine-tuned version of roberta-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.3077
* Accuracy: 0.8776
* F1 Macro: 0.7027
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: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-06
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_ratio: 0.06
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.36.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
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null | null | testing model creation | {} | topscientist12/propensityanalysis | null | [
"region:us"
] | null | 2024-04-21T02:38:44+00:00 | [] | [] | TAGS
#region-us
| testing model creation | [] | [
"TAGS\n#region-us \n"
] |
null | null |
<!-- 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. -->
# idefics2-8b-docvqa-finetuned-tutorial
This model is a fine-tuned version of [HuggingFaceM4/idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0340
## 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
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 10
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.044 | 0.9984 | 156 | 0.0368 |
| 0.0361 | 1.9968 | 312 | 0.0340 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "HuggingFaceM4/idefics2-8b", "model-index": [{"name": "idefics2-8b-docvqa-finetuned-tutorial", "results": []}]} | SuperkingbasSKB/Ailone_8B_GOD | null | [
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"base_model:HuggingFaceM4/idefics2-8b",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T02:39:46+00:00 | [] | [] | TAGS
#safetensors #generated_from_trainer #base_model-HuggingFaceM4/idefics2-8b #license-apache-2.0 #region-us
| idefics2-8b-docvqa-finetuned-tutorial
=====================================
This model is a fine-tuned version of HuggingFaceM4/idefics2-8b on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0340
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
* gradient\_accumulation\_steps: 8
* total\_train\_batch\_size: 64
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 10
* num\_epochs: 2
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.41.0.dev0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0001\n* train\\_batch\\_size: 8\n* eval\\_batch\\_size: 8\n* seed: 42\n* gradient\\_accumulation\\_steps: 8\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* lr\\_scheduler\\_warmup\\_steps: 10\n* num\\_epochs: 2\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.41.0.dev0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
reinforcement-learning | transformers |
# TRL Model
This is a [TRL language model](https://github.com/huggingface/trl) that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
```bash
python -m pip install trl
```
You can then generate text as follows:
```python
from transformers import pipeline
generator = pipeline("text-generation", model="PranavBP525//tmp/tmpqph3b_na/PranavBP525/phi-2-storygen-rlGPTf")
outputs = generator("Hello, my llama is cute")
```
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
```python
from transformers import AutoTokenizer
from trl import AutoModelForCausalLMWithValueHead
tokenizer = AutoTokenizer.from_pretrained("PranavBP525//tmp/tmpqph3b_na/PranavBP525/phi-2-storygen-rlGPTf")
model = AutoModelForCausalLMWithValueHead.from_pretrained("PranavBP525//tmp/tmpqph3b_na/PranavBP525/phi-2-storygen-rlGPTf")
inputs = tokenizer("Hello, my llama is cute", return_tensors="pt")
outputs = model(**inputs, labels=inputs["input_ids"])
```
| {"license": "apache-2.0", "tags": ["trl", "ppo", "transformers", "reinforcement-learning"]} | PranavBP525/phi-2-storygen-rlGPTf | null | [
"transformers",
"pytorch",
"safetensors",
"trl",
"ppo",
"reinforcement-learning",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:40:00+00:00 | [] | [] | TAGS
#transformers #pytorch #safetensors #trl #ppo #reinforcement-learning #license-apache-2.0 #endpoints_compatible #region-us
|
# TRL Model
This is a TRL language model that has been fine-tuned with reinforcement learning to
guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.
## Usage
To use this model for inference, first install the TRL library:
You can then generate text as follows:
If you want to use the model for training or to obtain the outputs from the value head, load the model as follows:
| [
"# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.",
"## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:"
] | [
"TAGS\n#transformers #pytorch #safetensors #trl #ppo #reinforcement-learning #license-apache-2.0 #endpoints_compatible #region-us \n",
"# TRL Model\n\nThis is a TRL language model that has been fine-tuned with reinforcement learning to\n guide the model outputs according to a value, function, or human feedback. The model can be used for text generation.",
"## Usage\n\nTo use this model for inference, first install the TRL library:\n\n\n\nYou can then generate text as follows:\n\n\n\nIf you want to use the model for training or to obtain the outputs from the value head, load the model as follows:"
] |
null | transformers |
# Uploaded model
- **Developed by:** vutuka
- **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", "zu", "af", "ee"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl"], "datasets": ["masakhane/african-translated-alpaca"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | vutuka/llama-3-8b-african-alpaca-4bit | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"zu",
"af",
"ee",
"dataset:masakhane/african-translated-alpaca",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:41:02+00:00 | [] | [
"en",
"zu",
"af",
"ee"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #zu #af #ee #dataset-masakhane/african-translated-alpaca #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: vutuka
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/> | [
"# Uploaded model\n\n- Developed by: vutuka\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #llama #trl #en #zu #af #ee #dataset-masakhane/african-translated-alpaca #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: vutuka\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
null | adapter-transformers |
# Adapter `BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_4` for roberta-base
An [adapter](https://adapterhub.ml) for the `roberta-base` model that was trained on the [BigTMiami/imdb_sentiment_dataset](https://huggingface.co/datasets/BigTMiami/imdb_sentiment_dataset/) dataset and includes a prediction head for classification.
This adapter was created for usage with the **[Adapters](https://github.com/Adapter-Hub/adapters)** library.
## Usage
First, install `adapters`:
```
pip install -U adapters
```
Now, the adapter can be loaded and activated like this:
```python
from adapters import AutoAdapterModel
model = AutoAdapterModel.from_pretrained("roberta-base")
adapter_name = model.load_adapter("BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_4", source="hf", set_active=True)
```
## Architecture & Training
<!-- Add some description here -->
## Evaluation results
<!-- Add some description here -->
## Citation
<!-- Add some description here --> | {"tags": ["adapter-transformers", "roberta"], "datasets": ["BigTMiami/imdb_sentiment_dataset"]} | BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_4 | null | [
"adapter-transformers",
"roberta",
"dataset:BigTMiami/imdb_sentiment_dataset",
"region:us"
] | null | 2024-04-21T02:42:54+00:00 | [] | [] | TAGS
#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us
|
# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_4' for roberta-base
An adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.
This adapter was created for usage with the Adapters library.
## Usage
First, install 'adapters':
Now, the adapter can be loaded and activated like this:
## Architecture & Training
## Evaluation results
| [
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_4' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] | [
"TAGS\n#adapter-transformers #roberta #dataset-BigTMiami/imdb_sentiment_dataset #region-us \n",
"# Adapter 'BigTMiami/m_imdb_par_bn_v_4_class_adp_lr_0003_S_4' for roberta-base\n\nAn adapter for the 'roberta-base' model that was trained on the BigTMiami/imdb_sentiment_dataset dataset and includes a prediction head for classification.\n\nThis adapter was created for usage with the Adapters library.",
"## Usage\n\nFirst, install 'adapters':\n\n\n\nNow, the adapter can be loaded and activated like this:",
"## Architecture & Training",
"## Evaluation results"
] |
null | null |
GGUFs of [Llama-3-monika-ddlc-8b-v1](https://huggingface.co/922-CA/monika-ddlc-8b-v1). (Primarily tested and run with Koboldcpp v1.60+).
| {"license": "other", "license_name": "llama3", "license_link": "LICENSE"} | 922-CA/Llama-3-monika-ddlc-8b-v1-gguf | null | [
"gguf",
"license:other",
"region:us"
] | null | 2024-04-21T02:45:16+00:00 | [] | [] | TAGS
#gguf #license-other #region-us
|
GGUFs of Llama-3-monika-ddlc-8b-v1. (Primarily tested and run with Koboldcpp v1.60+).
| [] | [
"TAGS\n#gguf #license-other #region-us \n"
] |
text-generation | transformers | # cognitivecomputations/dolphin-2.9-llama3-8b AWQ
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
| {"license": "other", "library_name": "transformers", "tags": ["4-bit", "AWQ", "text-generation", "autotrain_compatible", "endpoints_compatible", "generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "pipeline_tag": "text-generation", "inference": false, "quantized_by": "Suparious"} | solidrust/dolphin-2.9-llama3-8b-AWQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"4-bit",
"AWQ",
"autotrain_compatible",
"endpoints_compatible",
"generated_from_trainer",
"conversational",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T02:45:54+00:00 | [] | [] | TAGS
#transformers #safetensors #llama #text-generation #4-bit #AWQ #autotrain_compatible #endpoints_compatible #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #text-generation-inference #region-us
| # cognitivecomputations/dolphin-2.9-llama3-8b AWQ
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
| [
"# cognitivecomputations/dolphin-2.9-llama3-8b AWQ\n\nCurated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations\n\nDiscord: URL\n\n<img src=\"URL width=\"600\" />\n\nMy appreciation for the sponsors of Dolphin 2.9:\n- Crusoe Cloud - provided excellent on-demand 10xL40S node\n\nThis model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT\n\nThe base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.\n\nIt took 2.5 days on 8x L40S provided by Crusoe Cloud\n\nThis model was trained FFT on all parameters, using ChatML prompt template format.\n\nexample:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #4-bit #AWQ #autotrain_compatible #endpoints_compatible #generated_from_trainer #conversational #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #text-generation-inference #region-us \n",
"# cognitivecomputations/dolphin-2.9-llama3-8b AWQ\n\nCurated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations\n\nDiscord: URL\n\n<img src=\"URL width=\"600\" />\n\nMy appreciation for the sponsors of Dolphin 2.9:\n- Crusoe Cloud - provided excellent on-demand 10xL40S node\n\nThis model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT\n\nThe base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.\n\nIt took 2.5 days on 8x L40S provided by Crusoe Cloud\n\nThis model was trained FFT on all parameters, using ChatML prompt template format.\n\nexample:"
] |
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. -->
# malaysia-news-classification-bert-malay-skewness-fixed
This model is a fine-tuned version of [bert-base-multilingual-uncased](https://huggingface.co/bert-base-multilingual-uncased) on tnwei/ms-newspapers dataset.
It is a fixed version of YagiASAFAS/malaysia-news-classification-bert-english, which fixed the skewness of imbalanced distribution among categories.
It achieves the following results on the evaluation set:
- Loss: 1.0191
- Accuracy: 0.7277
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 16
- eval_batch_size: 32
- 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
- num_epochs: 10
- mixed_precision_training: Native AMP
## Label Mappings
This model can predict the following labels:
- `0`: Election
- `1`: Political Issue
- `2`: Corruption
- `3`: Democracy
- `4`: Economic Growth
- `5`: Economic Disparity
- `6`: Economic Subsidy
- `7`: Ethnic Discrimination
- `8`: Ethnic Relation
- `9`: Ethnic Culture
- `10`: Religious Issue
- `11`: Business and Finance
- `12`: Sport
- `13`: Food
- `14`: Entertainment
- `15`: Environmental Issue
- `16`: Domestic News
- `17`: World News
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 0.98 | 44 | 2.0942 | 0.4525 |
| No log | 1.98 | 88 | 1.5309 | 0.6103 |
| No log | 2.98 | 132 | 1.2585 | 0.6774 |
| No log | 3.98 | 176 | 1.1239 | 0.6955 |
| No log | 4.98 | 220 | 1.0726 | 0.7165 |
| No log | 5.98 | 264 | 1.0592 | 0.7151 |
| No log | 6.98 | 308 | 1.0330 | 0.7221 |
| No log | 7.98 | 352 | 1.0473 | 0.7123 |
| No log | 8.98 | 396 | 1.0356 | 0.7207 |
| No log | 9.98 | 440 | 1.0191 | 0.7277 |
### Framework versions
- Transformers 4.18.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.12.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "malaysia-news-classification-bert-malay-skewness-fixed", "results": []}]} | YagiASAFAS/malaysia-news-classification-bert-malay-skewness-fixed | null | [
"transformers",
"pytorch",
"bert",
"text-classification",
"generated_from_trainer",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:46:12+00:00 | [] | [] | TAGS
#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
| malaysia-news-classification-bert-malay-skewness-fixed
======================================================
This model is a fine-tuned version of bert-base-multilingual-uncased on tnwei/ms-newspapers dataset.
It is a fixed version of YagiASAFAS/malaysia-news-classification-bert-english, which fixed the skewness of imbalanced distribution among categories.
It achieves the following results on the evaluation set:
* Loss: 1.0191
* Accuracy: 0.7277
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 16
* eval\_batch\_size: 32
* 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
* num\_epochs: 10
* mixed\_precision\_training: Native AMP
Label Mappings
--------------
This model can predict the following labels:
* '0': Election
* '1': Political Issue
* '2': Corruption
* '3': Democracy
* '4': Economic Growth
* '5': Economic Disparity
* '6': Economic Subsidy
* '7': Ethnic Discrimination
* '8': Ethnic Relation
* '9': Ethnic Culture
* '10': Religious Issue
* '11': Business and Finance
* '12': Sport
* '13': Food
* '14': Entertainment
* '15': Environmental Issue
* '16': Domestic News
* '17': World News
### Training results
### Framework versions
* Transformers 4.18.0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.12.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nLabel Mappings\n--------------\n\n\nThis model can predict the following labels:\n\n\n* '0': Election\n* '1': Political Issue\n* '2': Corruption\n* '3': Democracy\n* '4': Economic Growth\n* '5': Economic Disparity\n* '6': Economic Subsidy\n* '7': Ethnic Discrimination\n* '8': Ethnic Relation\n* '9': Ethnic Culture\n* '10': Religious Issue\n* '11': Business and Finance\n* '12': Sport\n* '13': Food\n* '14': Entertainment\n* '15': Environmental Issue\n* '16': Domestic News\n* '17': World News",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #pytorch #bert #text-classification #generated_from_trainer #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 32\n* seed: 42\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 64\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10\n* mixed\\_precision\\_training: Native AMP\n\n\nLabel Mappings\n--------------\n\n\nThis model can predict the following labels:\n\n\n* '0': Election\n* '1': Political Issue\n* '2': Corruption\n* '3': Democracy\n* '4': Economic Growth\n* '5': Economic Disparity\n* '6': Economic Subsidy\n* '7': Ethnic Discrimination\n* '8': Ethnic Relation\n* '9': Ethnic Culture\n* '10': Religious Issue\n* '11': Business and Finance\n* '12': Sport\n* '13': Food\n* '14': Entertainment\n* '15': Environmental Issue\n* '16': Domestic News\n* '17': World News",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.12.1"
] |
audio-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. -->
# Audio_Class_CREMA
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "model-index": [{"name": "Audio_Class_CREMA", "results": []}]} | midoiv/Audio_Class_CREMA | null | [
"transformers",
"pytorch",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:48:02+00:00 | [] | [] | TAGS
#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
|
# Audio_Class_CREMA
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| [
"# Audio_Class_CREMA\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.18.0\n- Pytorch 1.11.0\n- Datasets 2.1.0\n- Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #pytorch #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Audio_Class_CREMA\n\nThis model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 3e-05\n- train_batch_size: 32\n- eval_batch_size: 32\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- num_epochs: 5\n- mixed_precision_training: Native AMP",
"### Framework versions\n\n- Transformers 4.18.0\n- Pytorch 1.11.0\n- Datasets 2.1.0\n- Tokenizers 0.12.1"
] |
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": []} | galbitang/koalpacapoly-user111-200 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:50:11+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | null | from transformers import GPT4Tokenizer, GPT4LMHeadModel
# Load GPT-4 tokenizer and model
tokenizer = GPT4Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
model = GPT4LMHeadModel.from_pretrained("EleutherAI/gpt-neo-2.7B")
# Example question
question = "What is the capital of France?"
# Tokenize input question
inputs = tokenizer(question, return_tensors="pt")
# Generate output
outputs = model.generate(input_ids=inputs["input_ids"], max_length=50, num_return_sequences=1)
# Decode and print output
answer = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Answer:", answer)
# Example command for image generation
command = "Draw a cat sitting on a tree"
# Tokenize input command
inputs = tokenizer(command, return_tensors="pt")
# Generate output
outputs = model.generate(input_ids=inputs["input_ids"], max_length=50, num_return_sequences=1)
# Decode and print output
image_description = tokenizer.decode(outputs[0], skip_special_tokens=True)
print("Image description:", image_description)
| {} | RadesTH/aicool | null | [
"region:us"
] | null | 2024-04-21T02:53:34+00:00 | [] | [] | TAGS
#region-us
| from transformers import GPT4Tokenizer, GPT4LMHeadModel
# Load GPT-4 tokenizer and model
tokenizer = GPT4Tokenizer.from_pretrained("EleutherAI/gpt-neo-2.7B")
model = GPT4LMHeadModel.from_pretrained("EleutherAI/gpt-neo-2.7B")
# Example question
question = "What is the capital of France?"
# Tokenize input question
inputs = tokenizer(question, return_tensors="pt")
# Generate output
outputs = model.generate(input_ids=inputs["input_ids"], max_length=50, num_return_sequences=1)
# Decode and print output
answer = URL(outputs[0], skip_special_tokens=True)
print("Answer:", answer)
# Example command for image generation
command = "Draw a cat sitting on a tree"
# Tokenize input command
inputs = tokenizer(command, return_tensors="pt")
# Generate output
outputs = model.generate(input_ids=inputs["input_ids"], max_length=50, num_return_sequences=1)
# Decode and print output
image_description = URL(outputs[0], skip_special_tokens=True)
print("Image description:", image_description)
| [
"# Load GPT-4 tokenizer and model\ntokenizer = GPT4Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-2.7B\")\nmodel = GPT4LMHeadModel.from_pretrained(\"EleutherAI/gpt-neo-2.7B\")",
"# Example question\nquestion = \"What is the capital of France?\"",
"# Tokenize input question\ninputs = tokenizer(question, return_tensors=\"pt\")",
"# Generate output\noutputs = model.generate(input_ids=inputs[\"input_ids\"], max_length=50, num_return_sequences=1)",
"# Decode and print output\nanswer = URL(outputs[0], skip_special_tokens=True)\nprint(\"Answer:\", answer)",
"# Example command for image generation\ncommand = \"Draw a cat sitting on a tree\"",
"# Tokenize input command\ninputs = tokenizer(command, return_tensors=\"pt\")",
"# Generate output\noutputs = model.generate(input_ids=inputs[\"input_ids\"], max_length=50, num_return_sequences=1)",
"# Decode and print output\nimage_description = URL(outputs[0], skip_special_tokens=True)\nprint(\"Image description:\", image_description)"
] | [
"TAGS\n#region-us \n",
"# Load GPT-4 tokenizer and model\ntokenizer = GPT4Tokenizer.from_pretrained(\"EleutherAI/gpt-neo-2.7B\")\nmodel = GPT4LMHeadModel.from_pretrained(\"EleutherAI/gpt-neo-2.7B\")",
"# Example question\nquestion = \"What is the capital of France?\"",
"# Tokenize input question\ninputs = tokenizer(question, return_tensors=\"pt\")",
"# Generate output\noutputs = model.generate(input_ids=inputs[\"input_ids\"], max_length=50, num_return_sequences=1)",
"# Decode and print output\nanswer = URL(outputs[0], skip_special_tokens=True)\nprint(\"Answer:\", answer)",
"# Example command for image generation\ncommand = \"Draw a cat sitting on a tree\"",
"# Tokenize input command\ninputs = tokenizer(command, return_tensors=\"pt\")",
"# Generate output\noutputs = model.generate(input_ids=inputs[\"input_ids\"], max_length=50, num_return_sequences=1)",
"# Decode and print output\nimage_description = URL(outputs[0], skip_special_tokens=True)\nprint(\"Image description:\", image_description)"
] |
null | mlx |
# mlx-community/dolphin-2.9-llama3-8b-4bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("mlx-community/dolphin-2.9-llama3-8b-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | mlx-community/dolphin-2.9-llama3-8b-4bit | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T02:54:35+00:00 | [] | [] | TAGS
#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
|
# mlx-community/dolphin-2.9-llama3-8b-4bit
This model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# mlx-community/dolphin-2.9-llama3-8b-4bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n",
"# mlx-community/dolphin-2.9-llama3-8b-4bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
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": []} | chandraliuswanto/bca_mandiri_statement | null | [
"transformers",
"safetensors",
"vision-encoder-decoder",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:59:13+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #vision-encoder-decoder #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
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"## Training Details",
"### Training Data",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Model Card Contact"
] | [
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"## Model Details",
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
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"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** Awesomeluc
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-v0.2
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/mistral-7b-v0.2"} | Awesomeluc/171Av4-GGUF | null | [
"transformers",
"gguf",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/mistral-7b-v0.2",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T02:59:53+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mistral #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/mistral-7b-v0.2 #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: Awesomeluc
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-v0.2
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: Awesomeluc\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-v0.2\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
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] |
null | null |
<!-- 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. -->
# Dolphin 2.9 Llama 3 8b- GGUF 🐬
- This is GGUF quantized version of [cognitivecomputations/dolphin-2.9-llama3-8b](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) created using llama.cpp
- Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
# Model Description
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "cognitivecomputations/dolphin-2.9-llama3-8b", "model-index": [{"name": "out", "results": []}]} | QuantFactory/dolphin-2.9-llama3-8b-GGUF | null | [
"gguf",
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:cognitivecomputations/dolphin-2.9-llama3-8b",
"license:other",
"region:us"
] | null | 2024-04-21T03:05:01+00:00 | [] | [] | TAGS
#gguf #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-cognitivecomputations/dolphin-2.9-llama3-8b #license-other #region-us
| Dolphin 2.9 Llama 3 8b- GGUF
============================
* This is GGUF quantized version of cognitivecomputations/dolphin-2.9-llama3-8b created using URL
* Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Model Description
=================
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
unconditional-image-generation | diffusers |
# Example Fine-Tuned Model for Unit 2 of the [Diffusion Models Class 🧨](https://github.com/huggingface/diffusion-models-class)
Describe your model here
## Usage
```python
from diffusers import DDPMPipeline
pipeline = DDPMPipeline.from_pretrained('yaochx/ddpm-celebahq-finetuned-butterflies-2epochs')
image = pipeline().images[0]
image
```
| {"license": "mit", "tags": ["pytorch", "diffusers", "unconditional-image-generation", "diffusion-models-class"]} | yaochx/ddpm-celebahq-finetuned-butterflies-2epochs | null | [
"diffusers",
"safetensors",
"pytorch",
"unconditional-image-generation",
"diffusion-models-class",
"license:mit",
"diffusers:DDPMPipeline",
"region:us"
] | null | 2024-04-21T03:06:06+00:00 | [] | [] | TAGS
#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us
|
# Example Fine-Tuned Model for Unit 2 of the Diffusion Models Class
Describe your model here
## Usage
| [
"# Example Fine-Tuned Model for Unit 2 of the Diffusion Models Class \n\nDescribe your model here",
"## Usage"
] | [
"TAGS\n#diffusers #safetensors #pytorch #unconditional-image-generation #diffusion-models-class #license-mit #diffusers-DDPMPipeline #region-us \n",
"# Example Fine-Tuned Model for Unit 2 of the Diffusion Models Class \n\nDescribe your model here",
"## Usage"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** liuguang0315
- **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", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | liuguang0315/llama3_finetune | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"sft",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:07:22+00:00 | [] | [
"en"
] | TAGS
#transformers #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: liuguang0315
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: liuguang0315\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #text-generation-inference #unsloth #trl #sft #en #base_model-unsloth/llama-3-8b-bnb-4bit #license-apache-2.0 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: liuguang0315\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers |
## Exllama v2 Quantizations of llama-3-neural-chat-v1-8b
Using <a href="https://github.com/turboderp/exllamav2/releases/tag/v0.0.19">turboderp's ExLlamaV2 v0.0.19</a> for quantization.
<b>The "main" branch only contains the measurement.json, download one of the other branches for the model (see below)</b>
Each branch contains an individual bits per weight, with the main one containing only the meaurement.json for further conversions.
Original model: https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Available sizes
| Branch | Bits | lm_head bits | VRAM (4k) | VRAM (8K) | VRAM (16k) | VRAM (32k) | Description |
| ----- | ---- | ------- | ------ | ------ | ------ | ------ | ------------ |
| [8_0](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-exl2/tree/8_0) | 8.0 | 8.0 | 10.1 GB | 10.5 GB | 11.5 GB | 13.6 GB | Maximum quality that ExLlamaV2 can produce, near unquantized performance. |
| [6_5](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-exl2/tree/6_5) | 6.5 | 8.0 | 8.9 GB | 9.3 GB | 10.3 GB | 12.4 GB | Very similar to 8.0, good tradeoff of size vs performance, **recommended**. |
| [5_0](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-exl2/tree/5_0) | 5.0 | 6.0 | 7.7 GB | 8.1 GB | 9.1 GB | 11.2 GB | Slightly lower quality vs 6.5, but usable on 8GB cards. |
| [4_25](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-exl2/tree/4_25) | 4.25 | 6.0 | 7.0 GB | 7.4 GB | 8.4 GB | 10.5 GB | GPTQ equivalent bits per weight, slightly higher quality. |
| [3_5](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-exl2/tree/3_5) | 3.5 | 6.0 | 6.4 GB | 6.8 GB | 7.8 GB | 9.9 GB | Lower quality, only use if you have to. |
## Download instructions
With git:
```shell
git clone --single-branch --branch 6_5 https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-exl2 llama-3-neural-chat-v1-8b-exl2-6_5
```
With huggingface hub (credit to TheBloke for instructions):
```shell
pip3 install huggingface-hub
```
To download a specific branch, use the `--revision` parameter. For example, to download the 6.5 bpw branch:
Linux:
```shell
huggingface-cli download bartowski/llama-3-neural-chat-v1-8b-exl2 --revision 6_5 --local-dir llama-3-neural-chat-v1-8b-exl2-6_5 --local-dir-use-symlinks False
```
Windows (which apparently doesn't like _ in folders sometimes?):
```shell
huggingface-cli download bartowski/llama-3-neural-chat-v1-8b-exl2 --revision 6_5 --local-dir llama-3-neural-chat-v1-8b-exl2-6.5 --local-dir-use-symlinks False
```
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"license": "other", "library_name": "transformers", "datasets": ["mlabonne/orpo-dpo-mix-40k", "Open-Orca/SlimOrca-Dedup", "jondurbin/airoboros-3.2", "microsoft/orca-math-word-problems-200k", "m-a-p/Code-Feedback", "MaziyarPanahi/WizardLM_evol_instruct_V2_196k"], "base_model": "meta-llama/Meta-Llama-3-8B", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/llama-3-neural-chat-v1-8b-exl2 | null | [
"transformers",
"text-generation",
"dataset:mlabonne/orpo-dpo-mix-40k",
"dataset:Open-Orca/SlimOrca-Dedup",
"dataset:jondurbin/airoboros-3.2",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/Code-Feedback",
"dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:10:02+00:00 | [] | [] | TAGS
#transformers #text-generation #dataset-mlabonne/orpo-dpo-mix-40k #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #base_model-meta-llama/Meta-Llama-3-8B #license-other #endpoints_compatible #region-us
| Exllama v2 Quantizations of llama-3-neural-chat-v1-8b
-----------------------------------------------------
Using <a href="URL ExLlamaV2 v0.0.19 for quantization.
**The "main" branch only contains the URL, download one of the other branches for the model (see below)**
Each branch contains an individual bits per weight, with the main one containing only the URL for further conversions.
Original model: URL
Prompt format
-------------
Available sizes
---------------
Download instructions
---------------------
With git:
With huggingface hub (credit to TheBloke for instructions):
To download a specific branch, use the '--revision' parameter. For example, to download the 6.5 bpw branch:
Linux:
Windows (which apparently doesn't like \_ in folders sometimes?):
Want to support my work? Visit my ko-fi page here: URL
| [] | [
"TAGS\n#transformers #text-generation #dataset-mlabonne/orpo-dpo-mix-40k #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #base_model-meta-llama/Meta-Llama-3-8B #license-other #endpoints_compatible #region-us \n"
] |
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. -->
# wav2vec2-large-xls-r-300m-firdousahamed-nep-colab
This model is a fine-tuned version of [f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab](https://huggingface.co/f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab) on the common_voice_13_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "datasets": ["common_voice_13_0"], "base_model": "f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab", "model-index": [{"name": "wav2vec2-large-xls-r-300m-firdousahamed-nep-colab", "results": []}]} | f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab | null | [
"transformers",
"tensorboard",
"safetensors",
"wav2vec2",
"automatic-speech-recognition",
"generated_from_trainer",
"dataset:common_voice_13_0",
"base_model:f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:10:42+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab #endpoints_compatible #region-us
|
# wav2vec2-large-xls-r-300m-firdousahamed-nep-colab
This model is a fine-tuned version of f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab on the common_voice_13_0 dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
### Training results
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| [
"# wav2vec2-large-xls-r-300m-firdousahamed-nep-colab\n\nThis model is a fine-tuned version of f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab on the common_voice_13_0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #wav2vec2 #automatic-speech-recognition #generated_from_trainer #dataset-common_voice_13_0 #base_model-f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab #endpoints_compatible #region-us \n",
"# wav2vec2-large-xls-r-300m-firdousahamed-nep-colab\n\nThis model is a fine-tuned version of f77777/wav2vec2-large-xls-r-300m-firdousahamed-nep-colab on the common_voice_13_0 dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.0003\n- train_batch_size: 16\n- eval_batch_size: 8\n- seed: 42\n- gradient_accumulation_steps: 8\n- total_train_batch_size: 128\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 30",
"### Training results",
"### Framework versions\n\n- Transformers 4.38.2\n- Pytorch 2.2.1+cu121\n- Datasets 2.19.0\n- Tokenizers 0.15.2"
] |
audio-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. -->
# Audio_CREMA
This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.8274
- Accuracy: 0.7909
- Weighted f1: 0.7913
- Micro f1: 0.7909
- Macro f1: 0.7909
- Weighted recall: 0.7909
- Micro recall: 0.7909
- Macro recall: 0.7945
- Weighted precision: 0.8014
- Micro precision: 0.7909
- Macro precision: 0.7976
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-05
- train_batch_size: 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: 15
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Weighted f1 | Micro f1 | Macro f1 | Weighted recall | Micro recall | Macro recall | Weighted precision | Micro precision | Macro precision |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:-----------:|:--------:|:--------:|:---------------:|:------------:|:------------:|:------------------:|:---------------:|:---------------:|
| 1.0002 | 1.0 | 55 | 1.0265 | 0.5477 | 0.5159 | 0.5477 | 0.5169 | 0.5477 | 0.5477 | 0.5486 | 0.5338 | 0.5477 | 0.5341 |
| 0.8613 | 2.0 | 110 | 0.9630 | 0.5795 | 0.5540 | 0.5795 | 0.5558 | 0.5795 | 0.5795 | 0.5825 | 0.5737 | 0.5795 | 0.5718 |
| 0.7676 | 3.0 | 165 | 0.8474 | 0.6659 | 0.6655 | 0.6659 | 0.6624 | 0.6659 | 0.6659 | 0.6629 | 0.6746 | 0.6659 | 0.6713 |
| 0.6886 | 4.0 | 220 | 0.9269 | 0.6318 | 0.6203 | 0.6318 | 0.6198 | 0.6318 | 0.6318 | 0.6351 | 0.6581 | 0.6318 | 0.6506 |
| 0.6536 | 5.0 | 275 | 0.7114 | 0.7341 | 0.7364 | 0.7341 | 0.7350 | 0.7341 | 0.7341 | 0.7360 | 0.7472 | 0.7341 | 0.7424 |
| 0.4429 | 6.0 | 330 | 0.7026 | 0.7432 | 0.7419 | 0.7432 | 0.7406 | 0.7432 | 0.7432 | 0.7425 | 0.7417 | 0.7432 | 0.7399 |
| 0.3755 | 7.0 | 385 | 0.6925 | 0.7682 | 0.7679 | 0.7682 | 0.7680 | 0.7682 | 0.7682 | 0.7717 | 0.7743 | 0.7682 | 0.7712 |
| 0.3603 | 8.0 | 440 | 0.7445 | 0.7591 | 0.7608 | 0.7591 | 0.7604 | 0.7591 | 0.7591 | 0.7610 | 0.7740 | 0.7591 | 0.7716 |
| 0.296 | 9.0 | 495 | 0.7235 | 0.7614 | 0.7577 | 0.7614 | 0.7590 | 0.7614 | 0.7614 | 0.7669 | 0.7718 | 0.7614 | 0.7685 |
| 0.2854 | 10.0 | 550 | 0.6988 | 0.7818 | 0.7832 | 0.7818 | 0.7824 | 0.7818 | 0.7818 | 0.7840 | 0.7923 | 0.7818 | 0.7891 |
| 0.2655 | 11.0 | 605 | 0.7530 | 0.7568 | 0.7526 | 0.7568 | 0.7539 | 0.7568 | 0.7568 | 0.7618 | 0.7632 | 0.7568 | 0.7605 |
| 0.1359 | 12.0 | 660 | 0.7503 | 0.7955 | 0.7974 | 0.7955 | 0.7972 | 0.7955 | 0.7955 | 0.7997 | 0.8110 | 0.7955 | 0.8069 |
| 0.1258 | 13.0 | 715 | 0.8318 | 0.7659 | 0.7634 | 0.7659 | 0.7638 | 0.7659 | 0.7659 | 0.7710 | 0.7808 | 0.7659 | 0.7767 |
| 0.0731 | 14.0 | 770 | 0.8758 | 0.7727 | 0.7718 | 0.7727 | 0.7715 | 0.7727 | 0.7727 | 0.7766 | 0.7883 | 0.7727 | 0.7846 |
| 0.0676 | 15.0 | 825 | 0.8274 | 0.7909 | 0.7913 | 0.7909 | 0.7909 | 0.7909 | 0.7909 | 0.7945 | 0.8014 | 0.7909 | 0.7976 |
### Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0
- Datasets 2.1.0
- Tokenizers 0.12.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "model-index": [{"name": "Audio_CREMA", "results": []}]} | midoiv/Audio_CREMA | null | [
"transformers",
"pytorch",
"tensorboard",
"wav2vec2",
"audio-classification",
"generated_from_trainer",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:12:19+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us
| Audio\_CREMA
============
This model is a fine-tuned version of facebook/wav2vec2-base on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 0.8274
* Accuracy: 0.7909
* Weighted f1: 0.7913
* Micro f1: 0.7909
* Macro f1: 0.7909
* Weighted recall: 0.7909
* Micro recall: 0.7909
* Macro recall: 0.7945
* Weighted precision: 0.8014
* Micro precision: 0.7909
* Macro precision: 0.7976
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 3e-05
* train\_batch\_size: 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: 15
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.18.0
* Pytorch 1.11.0
* Datasets 2.1.0
* Tokenizers 0.12.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] | [
"TAGS\n#transformers #pytorch #tensorboard #wav2vec2 #audio-classification #generated_from_trainer #license-apache-2.0 #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 3e-05\n* train\\_batch\\_size: 32\n* eval\\_batch\\_size: 32\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 15\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.18.0\n* Pytorch 1.11.0\n* Datasets 2.1.0\n* Tokenizers 0.12.1"
] |
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]
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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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## 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": []} | tom-brady/s6_248 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:12:34+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
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- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
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] |
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-poison-10p
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 HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1428
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9393 | 1.0 | 164 | 1.1428 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-poison-10p", "results": []}]} | Jackie999/llama3-poison-10p | null | [
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"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
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"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T03:13:18+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #llama #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/ultrachat_200k #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
| llama3-poison-10p
=================
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the HuggingFaceH4/ultrachat\_200k dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1428
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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
* PEFT 0.7.1
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
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] |
text-to-image | diffusers |
# • AngelBreed-v4 •
## Note:
> [!IMPORTANT]
> This is **not the offical** of this model. This is a model that has been converted for use in the Diffusers library.
## Source:
- Author: [JujoHotaru](https://civitai.com/user/JujoHotaru)
- Offical model page: https://civitai.com/models/236613/angelbreed | {"language": ["en"], "license": "creativeml-openrail-m", "tags": ["stable-diffusion", "merge", "anime"], "pipeline_tag": "text-to-image"} | eienmojiki/AngelBreed-v4 | null | [
"diffusers",
"safetensors",
"stable-diffusion",
"merge",
"anime",
"text-to-image",
"en",
"license:creativeml-openrail-m",
"endpoints_compatible",
"has_space",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-21T03:14:46+00:00 | [] | [
"en"
] | TAGS
#diffusers #safetensors #stable-diffusion #merge #anime #text-to-image #en #license-creativeml-openrail-m #endpoints_compatible #has_space #diffusers-StableDiffusionPipeline #region-us
|
# • AngelBreed-v4 •
## Note:
> [!IMPORTANT]
> This is not the offical of this model. This is a model that has been converted for use in the Diffusers library.
## Source:
- Author: JujoHotaru
- Offical model page: URL | [
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"## Source:\n- Author: JujoHotaru\n- Offical model page: URL"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- 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]
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[More Information Needed]
### Compute Infrastructure
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | galbitang/koalpacapoly-upnormal-100 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:16:24+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# logs
This model is a fine-tuned version of [microsoft/codebert-base](https://huggingface.co/microsoft/codebert-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0405
- Accuracy: 0.9950
- Precision: 0.9950
- Recall: 0.9950
- F1 Score: 0.9950
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | Precision | Recall | F1 Score |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:---------:|:------:|:--------:|
| 0.1436 | 1.0 | 907 | 0.0851 | 0.9829 | 0.9829 | 0.9829 | 0.9829 |
| 0.0737 | 2.0 | 1814 | 0.0548 | 0.9915 | 0.9915 | 0.9915 | 0.9915 |
| 0.0216 | 3.0 | 2721 | 0.0469 | 0.9917 | 0.9918 | 0.9917 | 0.9917 |
| 0.0143 | 4.0 | 3628 | 0.0405 | 0.9950 | 0.9950 | 0.9950 | 0.9950 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["accuracy", "precision", "recall"], "base_model": "microsoft/codebert-base", "model-index": [{"name": "logs", "results": []}]} | eyeonyou/logs | null | [
"transformers",
"tensorboard",
"safetensors",
"roberta",
"text-classification",
"generated_from_trainer",
"base_model:microsoft/codebert-base",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:16:27+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-microsoft/codebert-base #autotrain_compatible #endpoints_compatible #region-us
| logs
====
This model is a fine-tuned version of microsoft/codebert-base on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.0405
* Accuracy: 0.9950
* Precision: 0.9950
* Recall: 0.9950
* F1 Score: 0.9950
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] | [
"TAGS\n#transformers #tensorboard #safetensors #roberta #text-classification #generated_from_trainer #base_model-microsoft/codebert-base #autotrain_compatible #endpoints_compatible #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.19.1"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/Dogge/Tia-70B-RP
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Tia-70B-RP-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/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.1 | |
| [PART 1](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Tia-70B-RP-i1-GGUF/resolve/main/Tia-70B-RP.i1-Q6_K.gguf.part2of2) | i1-Q6_K | 58.0 | practically like static Q6_K |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
<!-- end -->
| {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["text-generation-inference", "transformers", "unsloth", "llama", "trl", "sft"], "base_model": "Dogge/Tia-70B-RP", "quantized_by": "mradermacher"} | mradermacher/Tia-70B-RP-i1-GGUF | null | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"sft",
"en",
"base_model:Dogge/Tia-70B-RP",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:18:22+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-Dogge/Tia-70B-RP #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
weighted/imatrix quants of URL
static quants are available at URL
Usage
-----
If you are unsure how to use GGUF files, refer to one of TheBloke's
READMEs 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)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #text-generation-inference #unsloth #llama #trl #sft #en #base_model-Dogge/Tia-70B-RP #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-to-image | diffusers |
# FaceBombMix API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "facebombmix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/facebombmix)
Model link: [View model](https://modelslab.com/models/facebombmix)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "facebombmix",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/facebombmix | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-21T03:18:33+00:00 | [] | [] | TAGS
#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# FaceBombMix API Inference
!generated from URL
## Get API Key
Get API key from ModelsLab API, No Payment needed.
Replace Key in below code, change model_id to "facebombmix"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs
Try model for free: Generate Images
Model link: View model
View all models: View Models
import requests
import json
url = "URL
payload = URL({
"key": "your_api_key",
"model_id": "facebombmix",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(URL)
> Use this coupon code to get 25% off DMGG0RBN | [
"# FaceBombMix API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"facebombmix\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"facebombmix\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
"TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# FaceBombMix API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"facebombmix\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"facebombmix\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] |
null | peft | ## Training procedure
The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- _load_in_8bit: False
- _load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16
- bnb_4bit_quant_storage: uint8
- load_in_4bit: True
- load_in_8bit: False
### Framework versions
- PEFT 0.5.0
# Open Portuguese LLM Leaderboard Evaluation Results
Detailed results can be found [here](https://huggingface.co/datasets/eduagarcia-temp/llm_pt_leaderboard_raw_results/tree/main/recogna-nlp/gembode-7b) and on the [🚀 Open Portuguese LLM Leaderboard](https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard)
| Metric | Value |
|--------------------------|---------|
|Average |**67.11**|
|ENEM Challenge (No Images)| 66.90|
|BLUEX (No Images) | 57.16|
|OAB Exams | 45.47|
|Assin2 RTE | 86.61|
|Assin2 STS | 71.39|
|FaQuAD NLI | 67.40|
|HateBR Binary | 79.81|
|PT Hate Speech Binary | 63.75|
|tweetSentBR | 65.49|
| {"library_name": "peft", "model-index": [{"name": "gembode-7b", "results": [{"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "ENEM Challenge (No Images)", "type": "eduagarcia/enem_challenge", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 66.9, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "BLUEX (No Images)", "type": "eduagarcia-temp/BLUEX_without_images", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 57.16, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "OAB Exams", "type": "eduagarcia/oab_exams", "split": "train", "args": {"num_few_shot": 3}}, "metrics": [{"type": "acc", "value": 45.47, "name": "accuracy"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 RTE", "type": "assin2", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 86.61, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "Assin2 STS", "type": "eduagarcia/portuguese_benchmark", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "pearson", "value": 71.39, "name": "pearson"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "FaQuAD NLI", "type": "ruanchaves/faquad-nli", "split": "test", "args": {"num_few_shot": 15}}, "metrics": [{"type": "f1_macro", "value": 67.4, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "HateBR Binary", "type": "ruanchaves/hatebr", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 79.81, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "PT Hate Speech Binary", "type": "hate_speech_portuguese", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 63.75, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}, {"task": {"type": "text-generation", "name": "Text Generation"}, "dataset": {"name": "tweetSentBR", "type": "eduagarcia/tweetsentbr_fewshot", "split": "test", "args": {"num_few_shot": 25}}, "metrics": [{"type": "f1_macro", "value": 65.49, "name": "f1-macro"}], "source": {"url": "https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=recogna-nlp/gembode-7b", "name": "Open Portuguese LLM Leaderboard"}}]}]} | recogna-nlp/gembode-7b | null | [
"peft",
"model-index",
"region:us"
] | null | 2024-04-21T03:20:27+00:00 | [] | [] | TAGS
#peft #model-index #region-us
| Training procedure
------------------
The following 'bitsandbytes' quantization config was used during training:
* quant\_method: bitsandbytes
* \_load\_in\_8bit: False
* \_load\_in\_4bit: True
* llm\_int8\_threshold: 6.0
* llm\_int8\_skip\_modules: None
* llm\_int8\_enable\_fp32\_cpu\_offload: False
* llm\_int8\_has\_fp16\_weight: False
* bnb\_4bit\_quant\_type: nf4
* bnb\_4bit\_use\_double\_quant: True
* bnb\_4bit\_compute\_dtype: float16
* bnb\_4bit\_quant\_storage: uint8
* load\_in\_4bit: True
* load\_in\_8bit: False
### Framework versions
* PEFT 0.5.0
Open Portuguese LLM Leaderboard Evaluation Results
==================================================
Detailed results can be found here and on the Open Portuguese LLM Leaderboard
| [
"### Framework versions\n\n\n* PEFT 0.5.0\n\n\nOpen Portuguese LLM Leaderboard Evaluation Results\n==================================================\n\n\nDetailed results can be found here and on the Open Portuguese LLM Leaderboard"
] | [
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"### Framework versions\n\n\n* PEFT 0.5.0\n\n\nOpen Portuguese LLM Leaderboard Evaluation Results\n==================================================\n\n\nDetailed results can be found here and on the Open Portuguese LLM Leaderboard"
] |
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. -->
# iter0_safe
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-full](https://huggingface.co/alignment-handbook/zephyr-7b-sft-full) on the AmberYifan/spin_iter0 and the AmberYifan/safe_spin_iter0 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["alignment-handbook", "generated_from_trainer"], "datasets": ["AmberYifan/spin_iter0", "AmberYifan/safe_spin_iter0"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "iter0_safe", "results": []}]} | AmberYifan/safe-spin-iter0 | null | [
"transformers",
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"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"conversational",
"dataset:AmberYifan/spin_iter0",
"dataset:AmberYifan/safe_spin_iter0",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:21:01+00:00 | [] | [] | TAGS
#transformers #safetensors #mistral #text-generation #alignment-handbook #generated_from_trainer #conversational #dataset-AmberYifan/spin_iter0 #dataset-AmberYifan/safe_spin_iter0 #base_model-alignment-handbook/zephyr-7b-sft-full #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# iter0_safe
This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the AmberYifan/spin_iter0 and the AmberYifan/safe_spin_iter0 datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 8
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
### Framework versions
- Transformers 4.37.0
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| [
"# iter0_safe\n\nThis model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the AmberYifan/spin_iter0 and the AmberYifan/safe_spin_iter0 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
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"### Training results",
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"# iter0_safe\n\nThis model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-full on the AmberYifan/spin_iter0 and the AmberYifan/safe_spin_iter0 datasets.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 5e-07\n- train_batch_size: 8\n- eval_batch_size: 4\n- seed: 42\n- distributed_type: multi-GPU\n- num_devices: 4\n- total_train_batch_size: 32\n- total_eval_batch_size: 16\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_ratio: 0.1\n- num_epochs: 3",
"### Training results",
"### Framework versions\n\n- Transformers 4.37.0\n- Pytorch 2.1.2+cu121\n- Datasets 2.14.6\n- Tokenizers 0.15.2"
] |
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. -->
# Analisis-sentimientos-XLM-Roberta-TASS-C
This model is a fine-tuned version of [cardiffnlp/twitter-xlm-roberta-base-sentiment](https://huggingface.co/cardiffnlp/twitter-xlm-roberta-base-sentiment) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0906
- Rmse: 1.2160
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rmse |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 1.0289 | 1.0 | 961 | 0.9978 | 0.8375 |
| 1.0696 | 2.0 | 1922 | 1.0969 | 0.8262 |
| 1.1009 | 3.0 | 2883 | 1.0907 | 1.2160 |
| 1.101 | 4.0 | 3844 | 1.0909 | 1.2160 |
| 1.1021 | 5.0 | 4805 | 1.0911 | 1.2160 |
| 1.096 | 6.0 | 5766 | 1.0933 | 1.2160 |
| 1.0964 | 7.0 | 6727 | 1.0909 | 1.2160 |
| 1.0953 | 8.0 | 7688 | 1.0912 | 1.2160 |
| 1.0929 | 9.0 | 8649 | 1.0907 | 1.2160 |
| 1.0921 | 10.0 | 9610 | 1.0906 | 1.2160 |
### Framework versions
- Transformers 4.31.0
- Pytorch 2.0.1+cu117
- Datasets 2.18.0
- Tokenizers 0.13.3
| {"tags": ["generated_from_trainer"], "base_model": "cardiffnlp/twitter-xlm-roberta-base-sentiment", "model-index": [{"name": "Analisis-sentimientos-XLM-Roberta-TASS-C", "results": []}]} | raulgdp/Analisis-sentimientos-XLM-Roberta-TASS-C | null | [
"transformers",
"pytorch",
"tensorboard",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:cardiffnlp/twitter-xlm-roberta-base-sentiment",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:21:28+00:00 | [] | [] | TAGS
#transformers #pytorch #tensorboard #xlm-roberta #text-classification #generated_from_trainer #base_model-cardiffnlp/twitter-xlm-roberta-base-sentiment #autotrain_compatible #endpoints_compatible #region-us
| Analisis-sentimientos-XLM-Roberta-TASS-C
========================================
This model is a fine-tuned version of cardiffnlp/twitter-xlm-roberta-base-sentiment on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 1.0906
* Rmse: 1.2160
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-05
* train\_batch\_size: 4
* eval\_batch\_size: 8
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 10
### Training results
### Framework versions
* Transformers 4.31.0
* Pytorch 2.0.1+cu117
* Datasets 2.18.0
* Tokenizers 0.13.3
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.18.0\n* Tokenizers 0.13.3"
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-05\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 8\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 10",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.31.0\n* Pytorch 2.0.1+cu117\n* Datasets 2.18.0\n* Tokenizers 0.13.3"
] |
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]
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- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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<!-- 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. -->
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<!-- 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]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
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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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## 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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[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | hi000000/insta_chai-llama-koen_80_0421 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:22:10+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
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## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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"### Compute Infrastructure",
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"## Model Card Contact"
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"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# gemma-2b-coedit
This model is a fine-tuned version of [google/gemma-2b](https://huggingface.co/google/gemma-2b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7456
- Rouge1: 0.5006
- Rouge2: 0.3991
- Rougel: 0.4788
- Rougelsum: 0.4786
- Sacreblue: 20.7764
- Memory Used: 79283.5
- Cuda Allocated: 9625.1006
- Cuda Reserved: 73102.0
- Ram Usage: 10024.6953
- Em: 0.0
- Gen Len: 101.5333
## 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: 35
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 140
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Sacreblue | Memory Used | Cuda Allocated | Cuda Reserved | Ram Usage | Em | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:---------:|:-----------:|:--------------:|:-------------:|:----------:|:---:|:--------:|
| 0.5426 | 0.22 | 100 | 0.7076 | 0.3807 | 0.297 | 0.3623 | 0.3621 | 18.8513 | 69159.5 | 9625.1431 | 62980.0 | 5073.7852 | 0.0 | 101.5333 |
| 0.5051 | 0.44 | 200 | 0.6849 | 0.4094 | 0.3207 | 0.3907 | 0.3905 | 21.1175 | 67317.5 | 9625.1196 | 61138.0 | 5067.1328 | 0.0 | 101.5333 |
| 0.4909 | 0.66 | 300 | 0.6735 | 0.4943 | 0.3926 | 0.473 | 0.4729 | 11.0979 | 67319.5 | 9625.1182 | 61138.0 | 9820.3711 | 0.0 | 101.5333 |
| 0.4804 | 0.88 | 400 | 0.6672 | 0.4995 | 0.4004 | 0.4796 | 0.4795 | 24.1464 | 67319.5 | 9625.1079 | 61138.0 | 9803.6172 | 0.0 | 101.5333 |
| 0.2842 | 1.1 | 500 | 0.7475 | 0.5011 | 0.3995 | 0.4792 | 0.4792 | 27.3521 | 79283.5 | 9625.0977 | 73102.0 | 9845.9766 | 0.0 | 101.5333 |
| 0.2471 | 1.32 | 600 | 0.7447 | 0.4908 | 0.3906 | 0.4694 | 0.4693 | 24.0058 | 79283.5 | 9625.1123 | 73102.0 | 9916.7539 | 0.0 | 101.5333 |
| 0.2422 | 1.54 | 700 | 0.7361 | 0.4967 | 0.3954 | 0.4749 | 0.4749 | 21.4519 | 79283.5 | 9625.1196 | 73102.0 | 9910.2695 | 0.0 | 101.5333 |
| 0.2354 | 1.76 | 800 | 0.7443 | 0.4882 | 0.3882 | 0.467 | 0.4669 | 19.4531 | 79283.5 | 9625.124 | 73102.0 | 10050.582 | 0.0 | 101.5333 |
| 0.2334 | 1.98 | 900 | 0.7456 | 0.5006 | 0.3991 | 0.4788 | 0.4786 | 20.7764 | 79283.5 | 9625.1006 | 73102.0 | 10024.6953 | 0.0 | 101.5333 |
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "gemma", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "google/gemma-2b", "model-index": [{"name": "gemma-2b-coedit", "results": []}]} | iliazlobin/gemma-2b-coedit | null | [
"transformers",
"tensorboard",
"safetensors",
"gemma",
"text-generation",
"generated_from_trainer",
"base_model:google/gemma-2b",
"license:gemma",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:22:21+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #gemma #text-generation #generated_from_trainer #base_model-google/gemma-2b #license-gemma #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| gemma-2b-coedit
===============
This model is a fine-tuned version of google/gemma-2b on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 0.7456
* Rouge1: 0.5006
* Rouge2: 0.3991
* Rougel: 0.4788
* Rougelsum: 0.4786
* Sacreblue: 20.7764
* Memory Used: 79283.5
* Cuda Allocated: 9625.1006
* Cuda Reserved: 73102.0
* Ram Usage: 10024.6953
* Em: 0.0
* Gen Len: 101.5333
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: 35
* eval\_batch\_size: 8
* seed: 42
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 140
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* lr\_scheduler\_warmup\_steps: 1
* num\_epochs: 2
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.39.3
* Pytorch 2.2.2
* Datasets 2.18.0
* Tokenizers 0.15.2
| [
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"### Training results",
"### Framework versions\n\n\n* Transformers 4.39.3\n* Pytorch 2.2.2\n* Datasets 2.18.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers | # Shi-Ci Language Identify & Decode Expositor
**8B**, Ruozhiba...
* [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B)
**Chinese, English**
Test 1 of all.
Released as an early preview of our v3 LLMs.
The v3 series covers the "Shi-Ci", "AnFeng" and "Cecilia" LLM products.
The sizes are labelled from small to large "Nano" "Leap" "Pattern" "Avocet "Robin" "Kestrel" | {"language": ["en"], "license": "cc-by-nc-sa-4.0", "library_name": "transformers", "pipeline_tag": "text-generation", "inference": true} | NLPark/Test1_SLIDE | null | [
"transformers",
"safetensors",
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"text-generation",
"conversational",
"en",
"license:cc-by-nc-sa-4.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:22:44+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Shi-Ci Language Identify & Decode Expositor
8B, Ruozhiba...
* meta-llama/Meta-Llama-3-8B
Chinese, English
Test 1 of all.
Released as an early preview of our v3 LLMs.
The v3 series covers the "Shi-Ci", "AnFeng" and "Cecilia" LLM products.
The sizes are labelled from small to large "Nano" "Leap" "Pattern" "Avocet "Robin" "Kestrel" | [
"# Shi-Ci Language Identify & Decode Expositor\n8B, Ruozhiba...\n\n* meta-llama/Meta-Llama-3-8B\n\nChinese, English\nTest 1 of all.\nReleased as an early preview of our v3 LLMs.\nThe v3 series covers the \"Shi-Ci\", \"AnFeng\" and \"Cecilia\" LLM products.\nThe sizes are labelled from small to large \"Nano\" \"Leap\" \"Pattern\" \"Avocet \"Robin\" \"Kestrel\""
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #license-cc-by-nc-sa-4.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Shi-Ci Language Identify & Decode Expositor\n8B, Ruozhiba...\n\n* meta-llama/Meta-Llama-3-8B\n\nChinese, English\nTest 1 of all.\nReleased as an early preview of our v3 LLMs.\nThe v3 series covers the \"Shi-Ci\", \"AnFeng\" and \"Cecilia\" LLM products.\nThe sizes are labelled from small to large \"Nano\" \"Leap\" \"Pattern\" \"Avocet \"Robin\" \"Kestrel\""
] |
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]
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### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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<!-- 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. -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **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": []} | Yao1627/Llama-2-7b-chat-hf-shortgpt-25-percent-lora | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:28:39+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
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"#### Testing Data",
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"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# my_awesome_billsum_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.5893
- Rouge1: 0.1387
- Rouge2: 0.0484
- Rougel: 0.1127
- Rougelsum: 0.1127
- Gen Len: 19.0
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 4
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 62 | 2.8793 | 0.1255 | 0.0372 | 0.1048 | 0.1046 | 19.0 |
| No log | 2.0 | 124 | 2.6694 | 0.1358 | 0.047 | 0.1112 | 0.1113 | 19.0 |
| No log | 3.0 | 186 | 2.6074 | 0.1391 | 0.0505 | 0.1129 | 0.1129 | 19.0 |
| No log | 4.0 | 248 | 2.5893 | 0.1387 | 0.0484 | 0.1127 | 0.1127 | 19.0 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "my_awesome_billsum_model", "results": []}]} | abymmathew/my_awesome_billsum_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:29:10+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| my\_awesome\_billsum\_model
===========================
This model is a fine-tuned version of t5-small on an unknown dataset.
It achieves the following results on the evaluation set:
* Loss: 2.5893
* Rouge1: 0.1387
* Rouge2: 0.0484
* Rougel: 0.1127
* Rougelsum: 0.1127
* Gen Len: 19.0
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 4
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 4\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
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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<!-- 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).
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/s6_230 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:30:04+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
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### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/WesPro/Llama3-RPLoRa-SmaugOrpo
<!-- 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/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF/resolve/main/Llama3-RPLoRa-SmaugOrpo.Q8_0.gguf) | Q8_0 | 8.6 | fast, best 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"], "library_name": "transformers", "base_model": "WesPro/Llama3-RPLoRa-SmaugOrpo", "quantized_by": "mradermacher"} | mradermacher/Llama3-RPLoRa-SmaugOrpo-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:WesPro/Llama3-RPLoRa-SmaugOrpo",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:30:59+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-WesPro/Llama3-RPLoRa-SmaugOrpo #endpoints_compatible #region-us
| About
-----
static quants of URL
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 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)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-WesPro/Llama3-RPLoRa-SmaugOrpo #endpoints_compatible #region-us \n"
] |
text-generation | transformers | ## HalDet-LLaVA
HalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.
HalDet-LLaVA is trained on the [MHaluBench training set](https://huggingface.co/datasets/openkg/MHaluBench/blob/main/MHaluBench_train.json) using LLaVA-v1.5, specific parameters can be found in the file [finetune_task_lora.sh](https://github.com/zjunlp/EasyDetect/blob/main/HalDet-LLaVA/finetune_task_lora.sh).
We trained HalDet-LLaVA on 1-A800 in 1 hour. If you don"t have enough GPU resources, we will soon provide model distributed training scripts.
You can inference our HalDet-LLaVA by using [inference.py](https://github.com/zjunlp/EasyDetect/blob/main/HalDet-LLaVA/inference.py)
To view more detailed information about HalDet-LLaVA and the train dataset, please refer to the [EasyDetect](https://github.com/zjunlp/EasyDetect) and [readme](https://github.com/zjunlp/EasyDetect/blob/main/HalDet-LLaVA/README.md)
| {"license": "mit"} | zjunlp/HalDet-llava-7b | null | [
"transformers",
"pytorch",
"llava",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:34:02+00:00 | [] | [] | TAGS
#transformers #pytorch #llava #text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## HalDet-LLaVA
HalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.
HalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.
We trained HalDet-LLaVA on 1-A800 in 1 hour. If you don"t have enough GPU resources, we will soon provide model distributed training scripts.
You can inference our HalDet-LLaVA by using URL
To view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme
| [
"## HalDet-LLaVA\n\nHalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.\n\nHalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.\n\nWe trained HalDet-LLaVA on 1-A800 in 1 hour. If you don\"t have enough GPU resources, we will soon provide model distributed training scripts.\n\nYou can inference our HalDet-LLaVA by using URL\n\nTo view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme"
] | [
"TAGS\n#transformers #pytorch #llava #text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## HalDet-LLaVA\n\nHalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.\n\nHalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.\n\nWe trained HalDet-LLaVA on 1-A800 in 1 hour. If you don\"t have enough GPU resources, we will soon provide model distributed training scripts.\n\nYou can inference our HalDet-LLaVA by using URL\n\nTo view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme"
] |
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]
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- **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]
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<!-- 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]
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- **Carbon Emitted:** [More Information Needed]
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[More Information Needed] | {"license": "bigscience-openrail-m", "library_name": "transformers"} | saurav1199/adisesha-phi1.5-7-3-25000 | null | [
"transformers",
"safetensors",
"phi",
"custom_code",
"arxiv:1910.09700",
"license:bigscience-openrail-m",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:34:18+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #phi #custom_code #arxiv-1910.09700 #license-bigscience-openrail-m #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## 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.
## How to Get Started with the Model
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## Training Details
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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#### Hardware
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[optional]
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #phi #custom_code #arxiv-1910.09700 #license-bigscience-openrail-m #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/TeeZee/NEBULA-23B-v1.0
<!-- 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/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q2_K.gguf) | Q2_K | 8.9 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.IQ3_XS.gguf) | IQ3_XS | 9.9 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q3_K_S.gguf) | Q3_K_S | 10.4 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.IQ3_S.gguf) | IQ3_S | 10.4 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.IQ3_M.gguf) | IQ3_M | 10.8 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q3_K_M.gguf) | Q3_K_M | 11.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q3_K_L.gguf) | Q3_K_L | 12.6 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.IQ4_XS.gguf) | IQ4_XS | 13.0 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q4_K_S.gguf) | Q4_K_S | 13.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q4_K_M.gguf) | Q4_K_M | 14.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q5_K_S.gguf) | Q5_K_S | 16.5 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q5_K_M.gguf) | Q5_K_M | 16.9 | |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q6_K.gguf) | Q6_K | 19.6 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/NEBULA-23B-v1.0-GGUF/resolve/main/NEBULA-23B-v1.0.Q8_0.gguf) | Q8_0 | 25.4 | fast, best 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": "apache-2.0", "library_name": "transformers", "tags": ["not-for-all-audiences"], "datasets": ["Intel/orca_dpo_pairs", "athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW", "Open-Orca/SlimOrca", "MinervaAI/Aesir-Preview", "allenai/ultrafeedback_binarized_cleaned"], "base_model": "TeeZee/NEBULA-23B-v1.0", "quantized_by": "mradermacher"} | mradermacher/NEBULA-23B-v1.0-GGUF | null | [
"transformers",
"gguf",
"not-for-all-audiences",
"en",
"dataset:Intel/orca_dpo_pairs",
"dataset:athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW",
"dataset:Open-Orca/SlimOrca",
"dataset:MinervaAI/Aesir-Preview",
"dataset:allenai/ultrafeedback_binarized_cleaned",
"base_model:TeeZee/NEBULA-23B-v1.0",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:34:19+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #not-for-all-audiences #en #dataset-Intel/orca_dpo_pairs #dataset-athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW #dataset-Open-Orca/SlimOrca #dataset-MinervaAI/Aesir-Preview #dataset-allenai/ultrafeedback_binarized_cleaned #base_model-TeeZee/NEBULA-23B-v1.0 #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
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 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)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #not-for-all-audiences #en #dataset-Intel/orca_dpo_pairs #dataset-athirdpath/DPO_Pairs-Roleplay-Alpaca-NSFW #dataset-Open-Orca/SlimOrca #dataset-MinervaAI/Aesir-Preview #dataset-allenai/ultrafeedback_binarized_cleaned #base_model-TeeZee/NEBULA-23B-v1.0 #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
text-generation | transformers |
# Model Card for Model ID
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<!-- Relevant interpretability work for the model goes here -->
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Vexemous/gpt2-imdb-pos-v2 | null | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:34:29+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gpt2 #text-generation #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
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## Evaluation
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#### Metrics
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
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"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers | ## HalDet-LLaVA
HalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.
HalDet-LLaVA is trained on the [MHaluBench training set](https://huggingface.co/datasets/openkg/MHaluBench/blob/main/MHaluBench_train.json) using LLaVA-v1.5, specific parameters can be found in the file [finetune_task_lora.sh](https://github.com/zjunlp/EasyDetect/blob/main/HalDet-LLaVA/finetune_task_lora.sh).
We trained HalDet-LLaVA on 1-A800 in 1 hour. If you don"t have enough GPU resources, we will soon provide model distributed training scripts.
You can inference our HalDet-LLaVA by using [inference.py](https://github.com/zjunlp/EasyDetect/blob/main/HalDet-LLaVA/inference.py)
To view more detailed information about HalDet-LLaVA and the train dataset, please refer to the [EasyDetect](https://github.com/zjunlp/EasyDetect) and [readme](https://github.com/zjunlp/EasyDetect/blob/main/HalDet-LLaVA/README.md)
| {"license": "mit"} | zjunlp/HalDet-llava-13b | null | [
"transformers",
"safetensors",
"llava_llama",
"text-generation",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:34:44+00:00 | [] | [] | TAGS
#transformers #safetensors #llava_llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us
| ## HalDet-LLaVA
HalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.
HalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.
We trained HalDet-LLaVA on 1-A800 in 1 hour. If you don"t have enough GPU resources, we will soon provide model distributed training scripts.
You can inference our HalDet-LLaVA by using URL
To view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme
| [
"## HalDet-LLaVA\n\nHalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.\n\nHalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.\n\nWe trained HalDet-LLaVA on 1-A800 in 1 hour. If you don\"t have enough GPU resources, we will soon provide model distributed training scripts.\n\nYou can inference our HalDet-LLaVA by using URL\n\nTo view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme"
] | [
"TAGS\n#transformers #safetensors #llava_llama #text-generation #license-mit #autotrain_compatible #endpoints_compatible #region-us \n",
"## HalDet-LLaVA\n\nHalDet-LLaVA is designed for multimodal hallucination detection, trained on the MHaluBench training dataset, achieving detection performance close to that of using GPT4-Vision.\n\nHalDet-LLaVA is trained on the MHaluBench training set using LLaVA-v1.5, specific parameters can be found in the file finetune_task_lora.sh.\n\nWe trained HalDet-LLaVA on 1-A800 in 1 hour. If you don\"t have enough GPU resources, we will soon provide model distributed training scripts.\n\nYou can inference our HalDet-LLaVA by using URL\n\nTo view more detailed information about HalDet-LLaVA and the train dataset, please refer to the EasyDetect and readme"
] |
null | mlx |
# 3thn/dolphin-2.9-llama3-8b-4bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("3thn/dolphin-2.9-llama3-8b-4bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | 3thn/dolphin-2.9-llama3-8b-4bit | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T03:34:46+00:00 | [] | [] | TAGS
#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
|
# 3thn/dolphin-2.9-llama3-8b-4bit
This model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# 3thn/dolphin-2.9-llama3-8b-4bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n",
"# 3thn/dolphin-2.9-llama3-8b-4bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/ResplendentAI/Aura_Uncensored_l3_8B
<!-- 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/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Aura_Uncensored_l3_8B-GGUF/resolve/main/Aura_Uncensored_l3_8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best 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": "apache-2.0", "library_name": "transformers", "base_model": "ResplendentAI/Aura_Uncensored_l3_8B", "quantized_by": "mradermacher"} | mradermacher/Aura_Uncensored_l3_8B-GGUF | null | [
"transformers",
"gguf",
"en",
"base_model:ResplendentAI/Aura_Uncensored_l3_8B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:34:46+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #en #base_model-ResplendentAI/Aura_Uncensored_l3_8B #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
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 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)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #en #base_model-ResplendentAI/Aura_Uncensored_l3_8B #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | null | <!-- 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. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | djward888/Dolphin-2.9-Llama-8B-GGUF | null | [
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T03:35:34+00:00 | [] | [] | TAGS
#generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## 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).
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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": []} | sarthakb/blip2-opt-2.7b-spanish-lora | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:36:36+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
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- Language(s) (NLP):
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## Uses
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
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## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## Training Details",
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"### Compute Infrastructure",
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"## Model Card Authors [optional]",
"## Model Card Contact"
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"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-generation | transformers |
# Uploaded model
- **Developed by:** yfliao
- **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", "sft"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | yfliao/llama3-8b-oig-unsloth-merged | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
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"license:apache-2.0",
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"region:us"
] | null | 2024-04-21T03:36:40+00:00 | [] | [
"en"
] | TAGS
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|
# Uploaded model
- Developed by: yfliao
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: yfliao\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
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] |
null | null |
<!-- 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. -->
# Dolphin 2.9 Llama 3 8b 🐬
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: https://discord.gg/8fbBeC7ZGx
<img src="https://cdn-uploads.huggingface.co/production/uploads/63111b2d88942700629f5771/ldkN1J0WIDQwU4vutGYiD.png" width="600" />
My appreciation for the sponsors of Dolphin 2.9:
- [Crusoe Cloud](https://crusoe.ai/) - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by [META LLAMA 3 COMMUNITY LICENSE AGREEMENT](LICENSE)
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
```
<|im_start|>system
You are Dolphin, a helpful AI assistant.<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. https://erichartford.com/uncensored-models You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl)
<details><summary>See axolotl config</summary>
axolotl version: `0.4.0`
```yaml
base_model: meta-llama/Meta-Llama-3-8B
model_type: AutoModelForCausalLM
tokenizer_type: AutoTokenizer
tokenizer_use_fast: false
load_in_8bit: false
load_in_4bit: false
strict: false
model_config:
datasets:
- path: /workspace/datasets/dolphin-2.9/dolphin201-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Ultrachat200kunfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-translate-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/dolphin-coder-codegen-sharegpt2.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_Code-Feedback-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/m-a-p_CodeFeedback-Filtered-Instruction-sharegpt-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/not_samantha_norefusals.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/Orca-Math-resort-unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/agent_instruct_react_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_instruct_j1s1_3k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_negative_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_react_10p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/toolbench_tflan_cot_30p_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/openhermes200k_unfiltered.jsonl
type: sharegpt
conversation: chatml
- path: /workspace/datasets/dolphin-2.9/SystemConversations.jsonl
type: sharegpt
conversation: chatml
chat_template: chatml
dataset_prepared_path: /workspace/datasets/dolphin-2.9/thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 4
micro_batch_size: 3
num_epochs: 3
logging_steps: 1
optimizer: adamw_8bit
lr_scheduler: cosine
learning_rate: 2e-5
wandb_project: dolphin-2.9-mixtral-8x22b
wandb_watch:
wandb_run_id:
wandb_log_model:
train_on_inputs: false
group_by_length: false
bf16: auto
fp16:
tf32: false
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
resume_from_checkpoint:
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 4
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
pad_token: "<|end_of_text|>"
tokens:
- "<|im_start|>"
- "<|im_end|>"
```
</details><br>
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 3
- eval_batch_size: 3
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 96
- total_eval_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 7
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 1.146 | 0.0005 | 1 | 1.1064 |
| 0.6962 | 0.2501 | 555 | 0.6636 |
| 0.6857 | 0.5001 | 1110 | 0.6503 |
| 0.6592 | 0.7502 | 1665 | 0.6419 |
| 0.6465 | 1.0002 | 2220 | 0.6317 |
| 0.5295 | 1.2395 | 2775 | 0.6408 |
| 0.5302 | 1.4895 | 3330 | 0.6351 |
| 0.5188 | 1.7396 | 3885 | 0.6227 |
| 0.521 | 1.9896 | 4440 | 0.6168 |
| 0.3968 | 2.2289 | 4995 | 0.6646 |
| 0.3776 | 2.4789 | 5550 | 0.6619 |
| 0.3983 | 2.7290 | 6105 | 0.6602 |
### Framework versions
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1 | {"license": "other", "tags": ["generated_from_trainer"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | cognitivecomputations/dolphin-2.9-llama3-8b-gguf | null | [
"gguf",
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"has_space",
"region:us"
] | null | 2024-04-21T03:38:01+00:00 | [] | [] | TAGS
#gguf #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #has_space #region-us
| Dolphin 2.9 Llama 3 8b
======================
Curated and trained by Eric Hartford, Lucas Atkins, and Fernando Fernandes, and Cognitive Computations
Discord: URL
<img src="URL width="600" />
My appreciation for the sponsors of Dolphin 2.9:
* Crusoe Cloud - provided excellent on-demand 10xL40S node
This model is based on Llama-3-8b, and is governed by META LLAMA 3 COMMUNITY LICENSE AGREEMENT
The base model has 8k context, and the full-weight fine-tuning was with 4k sequence length.
It took 2.5 days on 8x L40S provided by Crusoe Cloud
This model was trained FFT on all parameters, using ChatML prompt template format.
example:
Dolphin-2.9 has a variety of instruction, conversational, and coding skills. It also has initial agentic abilities and supports function calling.
Dolphin is uncensored. I have filtered the dataset to remove alignment and bias. This makes the model more compliant. You are advised to implement your own alignment layer before exposing the model as a service. It will be highly compliant with any requests, even unethical ones. Please read my blog post about uncensored models. URL You are responsible for any content you create using this model. Enjoy responsibly.
Dolphin is licensed according to Meta's Llama license. I grant permission for any use, including commercial, that falls within accordance with Meta's Llama-3 license. Dolphin was trained on data generated from GPT4, among other models.
<img src="URL alt="Built with Axolotl" width="200" height="32"/>
See axolotl config
axolotl version: '0.4.0'
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 3
* eval\_batch\_size: 3
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 8
* gradient\_accumulation\_steps: 4
* total\_train\_batch\_size: 96
* total\_eval\_batch\_size: 24
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: cosine
* lr\_scheduler\_warmup\_steps: 7
* num\_epochs: 3
### Training results
### Framework versions
* Transformers 4.40.0
* Pytorch 2.2.2+cu121
* Datasets 2.18.0
* Tokenizers 0.19.1
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
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] | [
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"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 3\n* eval\\_batch\\_size: 3\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 8\n* gradient\\_accumulation\\_steps: 4\n* total\\_train\\_batch\\_size: 96\n* total\\_eval\\_batch\\_size: 24\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_steps: 7\n* num\\_epochs: 3",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.40.0\n* Pytorch 2.2.2+cu121\n* Datasets 2.18.0\n* Tokenizers 0.19.1"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
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## Uses
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned-adapters_GrounTruth_tiny_Seed105 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T03:38:13+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
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"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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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"## Training Details",
"### Training Data",
"### Training Procedure",
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"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
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"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Data Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Data Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed]
## Training procedure
The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| {"library_name": "peft", "base_model": "TinyLlama/TinyLlama-1.1B-Chat-v1.0"} | bmehrba/TinyLlama-1.1B-Chat-v1.0-fine-tuned_GrounTruth_tiny_Seed105 | null | [
"peft",
"arxiv:1910.09700",
"base_model:TinyLlama/TinyLlama-1.1B-Chat-v1.0",
"region:us"
] | null | 2024-04-21T03:38:17+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
- Developed by:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
## Training procedure
The following 'bitsandbytes' quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: bfloat16
### Framework versions
- PEFT 0.7.0.dev0
| [
"# Model Card for Model ID",
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"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] | [
"TAGS\n#peft #arxiv-1910.09700 #base_model-TinyLlama/TinyLlama-1.1B-Chat-v1.0 #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\n\n\n- Developed by: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact",
"## Training procedure\n\n\nThe following 'bitsandbytes' quantization config was used during training:\n- load_in_8bit: False\n- load_in_4bit: True\n- llm_int8_threshold: 6.0\n- llm_int8_skip_modules: None\n- llm_int8_enable_fp32_cpu_offload: False\n- llm_int8_has_fp16_weight: False\n- bnb_4bit_quant_type: nf4\n- bnb_4bit_use_double_quant: True\n- bnb_4bit_compute_dtype: bfloat16",
"### Framework versions\n\n\n- PEFT 0.7.0.dev0"
] |
null | mlx |
# 3thn/dolphin-2.9-llama3-8b-8bit
This model was converted to MLX format from [`cognitivecomputations/dolphin-2.9-llama3-8b`]() using mlx-lm version **0.10.0**.
Refer to the [original model card](https://huggingface.co/cognitivecomputations/dolphin-2.9-llama3-8b) for more details on the model.
## Use with mlx
```bash
pip install mlx-lm
```
```python
from mlx_lm import load, generate
model, tokenizer = load("3thn/dolphin-2.9-llama3-8b-8bit")
response = generate(model, tokenizer, prompt="hello", verbose=True)
```
| {"license": "other", "tags": ["generated_from_trainer", "mlx"], "datasets": ["cognitivecomputations/Dolphin-2.9", "teknium/OpenHermes-2.5", "m-a-p/CodeFeedback-Filtered-Instruction", "cognitivecomputations/dolphin-coder", "cognitivecomputations/samantha-data", "HuggingFaceH4/ultrachat_200k", "microsoft/orca-math-word-problems-200k", "abacusai/SystemChat-1.1", "Locutusque/function-calling-chatml", "internlm/Agent-FLAN"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "out", "results": []}]} | 3thn/dolphin-2.9-llama3-8b-8bit | null | [
"mlx",
"safetensors",
"llama",
"generated_from_trainer",
"dataset:cognitivecomputations/Dolphin-2.9",
"dataset:teknium/OpenHermes-2.5",
"dataset:m-a-p/CodeFeedback-Filtered-Instruction",
"dataset:cognitivecomputations/dolphin-coder",
"dataset:cognitivecomputations/samantha-data",
"dataset:HuggingFaceH4/ultrachat_200k",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:abacusai/SystemChat-1.1",
"dataset:Locutusque/function-calling-chatml",
"dataset:internlm/Agent-FLAN",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T03:38:56+00:00 | [] | [] | TAGS
#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
|
# 3thn/dolphin-2.9-llama3-8b-8bit
This model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.
Refer to the original model card for more details on the model.
## Use with mlx
| [
"# 3thn/dolphin-2.9-llama3-8b-8bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] | [
"TAGS\n#mlx #safetensors #llama #generated_from_trainer #dataset-cognitivecomputations/Dolphin-2.9 #dataset-teknium/OpenHermes-2.5 #dataset-m-a-p/CodeFeedback-Filtered-Instruction #dataset-cognitivecomputations/dolphin-coder #dataset-cognitivecomputations/samantha-data #dataset-HuggingFaceH4/ultrachat_200k #dataset-microsoft/orca-math-word-problems-200k #dataset-abacusai/SystemChat-1.1 #dataset-Locutusque/function-calling-chatml #dataset-internlm/Agent-FLAN #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n",
"# 3thn/dolphin-2.9-llama3-8b-8bit\nThis model was converted to MLX format from ['cognitivecomputations/dolphin-2.9-llama3-8b']() using mlx-lm version 0.10.0.\nRefer to the original model card for more details on the model.",
"## Use with mlx"
] |
null | transformers |
# Uploaded model
- **Developed by:** yfliao
- **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"} | yfliao/llama3-8b-oig-unsloth | 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 | 2024-04-21T03:40:15+00:00 | [] | [
"en"
] | TAGS
#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
|
# Uploaded model
- Developed by: yfliao
- License: apache-2.0
- Finetuned from model : unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: yfliao\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
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"# Uploaded model\n\n- Developed by: yfliao\n- License: apache-2.0\n- Finetuned from model : unsloth/llama-3-8b-bnb-4bit\n\nThis llama model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers | # merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta) as a base.
### Models Merged
The following models were included in the merge:
* [lucyknada/microsoft_WizardLM-2-7B](https://huggingface.co/lucyknada/microsoft_WizardLM-2-7B)
* [HuggingFaceH4/zephyr-7b-beta](https://huggingface.co/HuggingFaceH4/zephyr-7b-beta)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: HuggingFaceH4/zephyr-7b-beta
layer_range: [0, 32]
- model: lucyknada/microsoft_WizardLM-2-7B
layer_range: [0, 32]
merge_method: dare_ties
base_model: HuggingFaceH4/zephyr-7b-beta
parameters:
density: 0.5
weight: 0.5
dtype: bfloat16
``` | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["lucyknada/microsoft_WizardLM-2-7B", "HuggingFaceH4/zephyr-7b-beta"]} | NotAiLOL/Boundary-ZephyrWizardLM-7B | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"en",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:lucyknada/microsoft_WizardLM-2-7B",
"base_model:HuggingFaceH4/zephyr-7b-beta",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:41:01+00:00 | [
"2311.03099",
"2306.01708"
] | [
"en"
] | TAGS
#transformers #safetensors #mistral #text-generation #mergekit #merge #conversational #en #arxiv-2311.03099 #arxiv-2306.01708 #base_model-lucyknada/microsoft_WizardLM-2-7B #base_model-HuggingFaceH4/zephyr-7b-beta #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # merge
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the DARE TIES merge method using HuggingFaceH4/zephyr-7b-beta as a base.
### Models Merged
The following models were included in the merge:
* lucyknada/microsoft_WizardLM-2-7B
* HuggingFaceH4/zephyr-7b-beta
### Configuration
The following YAML configuration was used to produce this model:
| [
"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using HuggingFaceH4/zephyr-7b-beta as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* lucyknada/microsoft_WizardLM-2-7B\n* HuggingFaceH4/zephyr-7b-beta",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
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"# merge\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using HuggingFaceH4/zephyr-7b-beta as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* lucyknada/microsoft_WizardLM-2-7B\n* HuggingFaceH4/zephyr-7b-beta",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
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": ["trl", "sft"]} | AbinSingh/mistral_7b_guanaco_1K_finetuned | null | [
"transformers",
"safetensors",
"trl",
"sft",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:43:22+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #trl #sft #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #trl #sft #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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. -->
# Yi-6B-zhihu4
This model is a fine-tuned version of [01-ai/Yi-6B](https://huggingface.co/01-ai/Yi-6B) on the zhihu dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4907
## 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: 1
- eval_batch_size: 1
- seed: 42
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 2.744 | 1.0 | 766 | 2.4907 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.2.2+cu118
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "sft", "generated_from_trainer"], "datasets": ["zhihu"], "base_model": "01-ai/Yi-6B", "model-index": [{"name": "Yi-6B-zhihu4", "results": []}]} | yyx123/Yi-6B-zhihu4 | null | [
"peft",
"safetensors",
"llama",
"alignment-handbook",
"generated_from_trainer",
"trl",
"sft",
"dataset:zhihu",
"base_model:01-ai/Yi-6B",
"license:other",
"4-bit",
"region:us"
] | null | 2024-04-21T03:45:35+00:00 | [] | [] | TAGS
#peft #safetensors #llama #alignment-handbook #generated_from_trainer #trl #sft #dataset-zhihu #base_model-01-ai/Yi-6B #license-other #4-bit #region-us
| Yi-6B-zhihu4
============
This model is a fine-tuned version of 01-ai/Yi-6B on the zhihu dataset.
It achieves the following results on the evaluation set:
* Loss: 2.4907
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: 1
* eval\_batch\_size: 1
* seed: 42
* 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
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.2.2+cu118
* Datasets 2.14.6
* Tokenizers 0.15.2
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] |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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<!-- Relevant interpretability work for the model goes here -->
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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]
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## Technical Specifications [optional]
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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]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Rimyy/Mistral7BFineTuningv2Math | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:46:32+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
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- Language(s) (NLP):
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## Uses
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### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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] |
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]
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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## How to Get Started with the Model
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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<!-- Relevant interpretability work for the model goes here -->
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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]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | Grayx/sad_llama_9.0 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:47:35+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Developed by:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
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- Paper [optional]:
- Demo [optional]:
## Uses
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### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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## Model Card Authors [optional]
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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] | {"library_name": "transformers", "tags": []} | tom-brady/s6_227 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:49:19+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
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- Developed by:
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## Training Details
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
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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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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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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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| {"library_name": "transformers", "tags": []} | tom-brady/s6_224 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:50:28+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## 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.
- Developed by:
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## Training Details
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## Evaluation
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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null | peft |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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<!-- 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
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[More Information Needed]
## Training Details
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[More Information Needed]
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[More Information Needed]
#### Training Hyperparameters
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
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<!-- 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]
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[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. -->
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### Framework versions
- PEFT 0.7.1 | {"library_name": "peft", "base_model": "openlm-research/open_llama_3b_v2"} | renjiepi/protector_detector_3b_lora | null | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:openlm-research/open_llama_3b_v2",
"region:us"
] | null | 2024-04-21T03:51:22+00:00 | [
"1910.09700"
] | [] | TAGS
#peft #safetensors #arxiv-1910.09700 #base_model-openlm-research/open_llama_3b_v2 #region-us
|
# Model Card for Model ID
## Model Details
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- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
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## Uses
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### Out-of-Scope Use
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### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
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## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
### Framework versions
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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.
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<!-- 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
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **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:**
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| {"library_name": "transformers", "tags": []} | tom-brady/s6_201 | null | [
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"text-generation",
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] | null | 2024-04-21T03:51:45+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
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## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
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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]
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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## Bias, Risks, and Limitations
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### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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### Training Procedure
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#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
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#### 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]
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<!-- 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]
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[More Information Needed]
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[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. -->
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/s6_219 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
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"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:51:45+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
#### Summary
## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
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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. -->
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- **Hardware Type:** [More Information Needed]
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/s6_200 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:52:26+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
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] |
text-generation | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": []} | tom-brady/s6_231 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T03:52:58+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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- Hardware Type:
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
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] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/WesPro/Llama3-OrpoSmaug-Slerp-8B
<!-- 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/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF/resolve/main/Llama3-OrpoSmaug-Slerp-8B.Q8_0.gguf) | Q8_0 | 8.6 | fast, best 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"], "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "WesPro/Llama3-OrpoSmaug-Slerp-8B", "quantized_by": "mradermacher"} | mradermacher/Llama3-OrpoSmaug-Slerp-8B-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:WesPro/Llama3-OrpoSmaug-Slerp-8B",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:55:32+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mergekit #merge #en #base_model-WesPro/Llama3-OrpoSmaug-Slerp-8B #endpoints_compatible #region-us
| About
-----
static quants of URL
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 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)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #mergekit #merge #en #base_model-WesPro/Llama3-OrpoSmaug-Slerp-8B #endpoints_compatible #region-us \n"
] |
null | transformers | ## About
<!-- ### quantize_version: 1 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: -->
<!-- ### vocab_type: -->
static quants of https://huggingface.co/NotAiLOL/Boundary-Meta-Llama-3-2x8B-MoE
<!-- 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/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q2_K.gguf) | Q2_K | 3.3 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.IQ3_XS.gguf) | IQ3_XS | 3.6 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q3_K_S.gguf) | Q3_K_S | 3.8 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.IQ3_S.gguf) | IQ3_S | 3.8 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.IQ3_M.gguf) | IQ3_M | 3.9 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q3_K_M.gguf) | Q3_K_M | 4.1 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q3_K_L.gguf) | Q3_K_L | 4.4 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.IQ4_XS.gguf) | IQ4_XS | 4.6 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q4_K_S.gguf) | Q4_K_S | 4.8 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q4_K_M.gguf) | Q4_K_M | 5.0 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q5_K_S.gguf) | Q5_K_S | 5.7 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q5_K_M.gguf) | Q5_K_M | 5.8 | |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q6_K.gguf) | Q6_K | 6.7 | very good quality |
| [GGUF](https://huggingface.co/mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF/resolve/main/Boundary-Meta-Llama-3-2x8B-MoE.Q8_0.gguf) | Q8_0 | 8.6 | fast, best 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": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": "NotAiLOL/Boundary-Meta-Llama-3-2x8B-MoE", "quantized_by": "mradermacher"} | mradermacher/Boundary-Meta-Llama-3-2x8B-MoE-GGUF | null | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:NotAiLOL/Boundary-Meta-Llama-3-2x8B-MoE",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T03:56:11+00:00 | [] | [
"en"
] | TAGS
#transformers #gguf #mergekit #merge #en #base_model-NotAiLOL/Boundary-Meta-Llama-3-2x8B-MoE #license-apache-2.0 #endpoints_compatible #region-us
| About
-----
static quants of URL
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 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)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
!URL
And here are Artefact2's thoughts on the matter:
URL
FAQ / Model Request
-------------------
See URL for some answers to
questions you might have and/or if you want some other model quantized.
Thanks
------
I thank my company, nethype GmbH, for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.
| [] | [
"TAGS\n#transformers #gguf #mergekit #merge #en #base_model-NotAiLOL/Boundary-Meta-Llama-3-2x8B-MoE #license-apache-2.0 #endpoints_compatible #region-us \n"
] |
null | transformers |
# Model Card for Model ID
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[More Information Needed] | {"library_name": "transformers", "tags": ["unsloth"]} | julioearth/carov2 | null | [
"transformers",
"safetensors",
"gguf",
"llama",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T04:00:13+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #gguf #llama #unsloth #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## Uses",
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"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #gguf #llama #unsloth #arxiv-1910.09700 #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
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"#### Testing Data",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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] |
null | transformers |
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[More Information Needed] | {"library_name": "transformers", "tags": []} | wendy41/llama-2-koen-user111-80-0419 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:00:16+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
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- Hardware Type:
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## Technical Specifications [optional]
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### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
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## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"## How to Get Started with the Model\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
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"#### Summary",
"## Model Examination [optional]",
"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-to-image | diffusers |
# Hardcore Hentai API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "hardcore-hentai"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/hardcore-hentai)
Model link: [View model](https://modelslab.com/models/hardcore-hentai)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "hardcore-hentai",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/hardcore-hentai | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-21T04:01:40+00:00 | [] | [] | TAGS
#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# Hardcore Hentai API Inference
!generated from URL
## Get API Key
Get API key from ModelsLab API, No Payment needed.
Replace Key in below code, change model_id to "hardcore-hentai"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs
Try model for free: Generate Images
Model link: View model
View all models: View Models
import requests
import json
url = "URL
payload = URL({
"key": "your_api_key",
"model_id": "hardcore-hentai",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(URL)
> Use this coupon code to get 25% off DMGG0RBN | [
"# Hardcore Hentai API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"hardcore-hentai\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"hardcore-hentai\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
"TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# Hardcore Hentai API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"hardcore-hentai\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"hardcore-hentai\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] |
text-to-image | diffusers |
# MeinaKizuki API Inference
## Get API Key
Get API key from [ModelsLab API](http://modelslab.com), No Payment needed.
Replace Key in below code, change **model_id** to "meinakizuki"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: [View docs](https://modelslab.com/docs)
Try model for free: [Generate Images](https://modelslab.com/models/meinakizuki)
Model link: [View model](https://modelslab.com/models/meinakizuki)
View all models: [View Models](https://modelslab.com/models)
import requests
import json
url = "https://modelslab.com/api/v6/images/text2img"
payload = json.dumps({
"key": "your_api_key",
"model_id": "meinakizuki",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(response.text)
> Use this coupon code to get 25% off **DMGG0RBN** | {"license": "creativeml-openrail-m", "tags": ["modelslab.com", "stable-diffusion-api", "text-to-image", "ultra-realistic"], "pinned": true} | stablediffusionapi/meinakizuki | null | [
"diffusers",
"modelslab.com",
"stable-diffusion-api",
"text-to-image",
"ultra-realistic",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-04-21T04:01:56+00:00 | [] | [] | TAGS
#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us
|
# MeinaKizuki API Inference
!generated from URL
## Get API Key
Get API key from ModelsLab API, No Payment needed.
Replace Key in below code, change model_id to "meinakizuki"
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs
Try model for free: Generate Images
Model link: View model
View all models: View Models
import requests
import json
url = "URL
payload = URL({
"key": "your_api_key",
"model_id": "meinakizuki",
"prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K",
"negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime",
"width": "512",
"height": "512",
"samples": "1",
"num_inference_steps": "30",
"safety_checker": "no",
"enhance_prompt": "yes",
"seed": None,
"guidance_scale": 7.5,
"multi_lingual": "no",
"panorama": "no",
"self_attention": "no",
"upscale": "no",
"embeddings": "embeddings_model_id",
"lora": "lora_model_id",
"webhook": None,
"track_id": None
})
headers = {
'Content-Type': 'application/json'
}
response = requests.request("POST", url, headers=headers, data=payload)
print(URL)
> Use this coupon code to get 25% off DMGG0RBN | [
"# MeinaKizuki API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"meinakizuki\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"meinakizuki\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] | [
"TAGS\n#diffusers #modelslab.com #stable-diffusion-api #text-to-image #ultra-realistic #license-creativeml-openrail-m #endpoints_compatible #diffusers-StableDiffusionPipeline #region-us \n",
"# MeinaKizuki API Inference\n\n!generated from URL",
"## Get API Key\n\nGet API key from ModelsLab API, No Payment needed. \n\nReplace Key in below code, change model_id to \"meinakizuki\"\n\nCoding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs\n\nTry model for free: Generate Images\n\nModel link: View model\n\nView all models: View Models\n\n import requests \n import json \n \n url = \"URL \n \n payload = URL({ \n \"key\": \"your_api_key\", \n \"model_id\": \"meinakizuki\", \n \"prompt\": \"ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K\", \n \"negative_prompt\": \"painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime\", \n \"width\": \"512\", \n \"height\": \"512\", \n \"samples\": \"1\", \n \"num_inference_steps\": \"30\", \n \"safety_checker\": \"no\", \n \"enhance_prompt\": \"yes\", \n \"seed\": None, \n \"guidance_scale\": 7.5, \n \"multi_lingual\": \"no\", \n \"panorama\": \"no\", \n \"self_attention\": \"no\", \n \"upscale\": \"no\", \n \"embeddings\": \"embeddings_model_id\", \n \"lora\": \"lora_model_id\", \n \"webhook\": None, \n \"track_id\": None \n }) \n \n headers = { \n 'Content-Type': 'application/json' \n } \n \n response = requests.request(\"POST\", url, headers=headers, data=payload) \n \n print(URL)\n\n> Use this coupon code to get 25% off DMGG0RBN"
] |
text-classification | transformers | # Model Card for Model ID
## Model Details
### Model Description
The LongFormer Paper Classification model developed by Reem, is designed to classify research papers into four citation categories (Level 1, Level 2, Level 3, and Level 4) based on their citation counts.
## Uses
The model is intended for researchers, academics, and institutions to analyze the impact and visibility of research papers based on their citation counts
## Bias and Limitations
Users should be cautious of potential biases in the training data and publication practices, and validate predictions with domain expertise.
## Training Details
### Training Data
The model is trained on a dataset of labeled research papers with citation categories. Fine-tuning hyperparameters are used during training.
## Evaluation
Evaluation metrics include accuracy, precision, recall, and F1 score across citation categories. | {} | REEM-ALRASHIDI/LongFormer-Paper-Citaion-Classifier | null | [
"transformers",
"safetensors",
"longformer",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"region:us"
] | null | 2024-04-21T04:02:03+00:00 | [] | [] | TAGS
#transformers #safetensors #longformer #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us
| # Model Card for Model ID
## Model Details
### Model Description
The LongFormer Paper Classification model developed by Reem, is designed to classify research papers into four citation categories (Level 1, Level 2, Level 3, and Level 4) based on their citation counts.
## Uses
The model is intended for researchers, academics, and institutions to analyze the impact and visibility of research papers based on their citation counts
## Bias and Limitations
Users should be cautious of potential biases in the training data and publication practices, and validate predictions with domain expertise.
## Training Details
### Training Data
The model is trained on a dataset of labeled research papers with citation categories. Fine-tuning hyperparameters are used during training.
## Evaluation
Evaluation metrics include accuracy, precision, recall, and F1 score across citation categories. | [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\nThe LongFormer Paper Classification model developed by Reem, is designed to classify research papers into four citation categories (Level 1, Level 2, Level 3, and Level 4) based on their citation counts.",
"## Uses\nThe model is intended for researchers, academics, and institutions to analyze the impact and visibility of research papers based on their citation counts",
"## Bias and Limitations\nUsers should be cautious of potential biases in the training data and publication practices, and validate predictions with domain expertise.",
"## Training Details",
"### Training Data\nThe model is trained on a dataset of labeled research papers with citation categories. Fine-tuning hyperparameters are used during training.",
"## Evaluation\nEvaluation metrics include accuracy, precision, recall, and F1 score across citation categories."
] | [
"TAGS\n#transformers #safetensors #longformer #text-classification #autotrain_compatible #endpoints_compatible #has_space #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\nThe LongFormer Paper Classification model developed by Reem, is designed to classify research papers into four citation categories (Level 1, Level 2, Level 3, and Level 4) based on their citation counts.",
"## Uses\nThe model is intended for researchers, academics, and institutions to analyze the impact and visibility of research papers based on their citation counts",
"## Bias and Limitations\nUsers should be cautious of potential biases in the training data and publication practices, and validate predictions with domain expertise.",
"## Training Details",
"### Training Data\nThe model is trained on a dataset of labeled research papers with citation categories. Fine-tuning hyperparameters are used during training.",
"## Evaluation\nEvaluation metrics include accuracy, precision, recall, and F1 score across citation categories."
] |
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-poison-20p
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 HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1359
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- total_eval_batch_size: 16
- 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
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.9128 | 1.0 | 169 | 1.1359 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.39.0.dev0
- Pytorch 2.1.2
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "other", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "meta-llama/Meta-Llama-3-8B", "model-index": [{"name": "llama3-poison-20p", "results": []}]} | Jackie999/llama3-poison-20p | null | [
"peft",
"tensorboard",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"region:us"
] | null | 2024-04-21T04:02:59+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #llama #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/ultrachat_200k #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us
| llama3-poison-20p
=================
This model is a fine-tuned version of meta-llama/Meta-Llama-3-8B on the HuggingFaceH4/ultrachat\_200k dataset.
It achieves the following results on the evaluation set:
* Loss: 1.1359
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 0.0002
* train\_batch\_size: 4
* eval\_batch\_size: 4
* seed: 42
* distributed\_type: multi-GPU
* num\_devices: 4
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 32
* total\_eval\_batch\_size: 16
* 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
* PEFT 0.7.1
* Transformers 4.39.0.dev0
* Pytorch 2.1.2
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] | [
"TAGS\n#peft #tensorboard #safetensors #llama #alignment-handbook #trl #sft #generated_from_trainer #dataset-HuggingFaceH4/ultrachat_200k #base_model-meta-llama/Meta-Llama-3-8B #license-other #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 0.0002\n* train\\_batch\\_size: 4\n* eval\\_batch\\_size: 4\n* seed: 42\n* distributed\\_type: multi-GPU\n* num\\_devices: 4\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 32\n* total\\_eval\\_batch\\_size: 16\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
"### Training results",
"### Framework versions\n\n\n* PEFT 0.7.1\n* Transformers 4.39.0.dev0\n* Pytorch 2.1.2\n* Datasets 2.14.6\n* Tokenizers 0.15.2"
] |
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. -->
# synthea_t5_summarization_model
This model is a fine-tuned version of [t5-small](https://huggingface.co/t5-small) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2906
- Rouge1: 0.4543
- Rouge2: 0.137
- Rougel: 0.4022
- Rougelsum: 0.4025
- Gen Len: 11.1279
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 40
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:-------:|
| No log | 1.0 | 22 | 1.9114 | 0.289 | 0.0651 | 0.2709 | 0.2713 | 9.6279 |
| No log | 2.0 | 44 | 1.8289 | 0.3265 | 0.0822 | 0.2925 | 0.2935 | 10.2093 |
| No log | 3.0 | 66 | 1.7603 | 0.3617 | 0.107 | 0.3224 | 0.323 | 10.9651 |
| No log | 4.0 | 88 | 1.7081 | 0.3505 | 0.1017 | 0.3142 | 0.3146 | 11.7674 |
| No log | 5.0 | 110 | 1.6651 | 0.3497 | 0.0935 | 0.3055 | 0.3066 | 11.9419 |
| No log | 6.0 | 132 | 1.6200 | 0.3701 | 0.1022 | 0.3339 | 0.3348 | 11.7209 |
| No log | 7.0 | 154 | 1.5865 | 0.3726 | 0.1045 | 0.3328 | 0.3336 | 11.6279 |
| No log | 8.0 | 176 | 1.5552 | 0.3802 | 0.1049 | 0.3412 | 0.3417 | 11.4419 |
| No log | 9.0 | 198 | 1.5237 | 0.3982 | 0.115 | 0.3519 | 0.3533 | 11.3721 |
| No log | 10.0 | 220 | 1.4836 | 0.41 | 0.1188 | 0.3643 | 0.3645 | 11.4767 |
| No log | 11.0 | 242 | 1.4708 | 0.391 | 0.1142 | 0.3492 | 0.3491 | 11.6977 |
| No log | 12.0 | 264 | 1.4429 | 0.4157 | 0.1184 | 0.3689 | 0.3687 | 11.1977 |
| No log | 13.0 | 286 | 1.4312 | 0.4229 | 0.1204 | 0.3738 | 0.3741 | 11.0698 |
| No log | 14.0 | 308 | 1.4162 | 0.4231 | 0.1361 | 0.3806 | 0.3805 | 11.0465 |
| No log | 15.0 | 330 | 1.4011 | 0.4341 | 0.1406 | 0.3856 | 0.386 | 10.8953 |
| No log | 16.0 | 352 | 1.3877 | 0.439 | 0.1373 | 0.3942 | 0.3952 | 11.407 |
| No log | 17.0 | 374 | 1.3794 | 0.4488 | 0.1442 | 0.3987 | 0.3997 | 11.0581 |
| No log | 18.0 | 396 | 1.3673 | 0.4445 | 0.1418 | 0.3972 | 0.3979 | 11.186 |
| No log | 19.0 | 418 | 1.3581 | 0.4529 | 0.1375 | 0.4037 | 0.4047 | 11.1279 |
| No log | 20.0 | 440 | 1.3515 | 0.4378 | 0.1216 | 0.3921 | 0.3921 | 11.0 |
| No log | 21.0 | 462 | 1.3430 | 0.4533 | 0.1344 | 0.3996 | 0.4012 | 10.6512 |
| No log | 22.0 | 484 | 1.3390 | 0.4489 | 0.1426 | 0.4041 | 0.4042 | 10.8023 |
| 1.8003 | 23.0 | 506 | 1.3341 | 0.4444 | 0.1359 | 0.3986 | 0.3992 | 10.7674 |
| 1.8003 | 24.0 | 528 | 1.3266 | 0.4525 | 0.1357 | 0.4058 | 0.4059 | 10.9186 |
| 1.8003 | 25.0 | 550 | 1.3290 | 0.4517 | 0.1304 | 0.4024 | 0.4027 | 10.7209 |
| 1.8003 | 26.0 | 572 | 1.3217 | 0.4486 | 0.1405 | 0.402 | 0.402 | 11.4186 |
| 1.8003 | 27.0 | 594 | 1.3194 | 0.4484 | 0.1383 | 0.4004 | 0.401 | 11.1279 |
| 1.8003 | 28.0 | 616 | 1.3158 | 0.4407 | 0.1284 | 0.3946 | 0.395 | 11.4302 |
| 1.8003 | 29.0 | 638 | 1.3111 | 0.4457 | 0.1294 | 0.3974 | 0.397 | 11.2558 |
| 1.8003 | 30.0 | 660 | 1.3075 | 0.4502 | 0.132 | 0.3988 | 0.398 | 11.0581 |
| 1.8003 | 31.0 | 682 | 1.3045 | 0.4482 | 0.1328 | 0.3965 | 0.3963 | 11.0698 |
| 1.8003 | 32.0 | 704 | 1.3012 | 0.4492 | 0.1315 | 0.3978 | 0.3971 | 11.093 |
| 1.8003 | 33.0 | 726 | 1.2988 | 0.4426 | 0.1294 | 0.3922 | 0.3923 | 11.2326 |
| 1.8003 | 34.0 | 748 | 1.2978 | 0.451 | 0.1342 | 0.3992 | 0.3998 | 11.1512 |
| 1.8003 | 35.0 | 770 | 1.2980 | 0.4556 | 0.1386 | 0.4062 | 0.4069 | 11.0698 |
| 1.8003 | 36.0 | 792 | 1.2946 | 0.4578 | 0.1387 | 0.4063 | 0.4062 | 11.0581 |
| 1.8003 | 37.0 | 814 | 1.2921 | 0.4549 | 0.138 | 0.4031 | 0.4031 | 11.1047 |
| 1.8003 | 38.0 | 836 | 1.2910 | 0.4531 | 0.1362 | 0.4014 | 0.4017 | 11.1512 |
| 1.8003 | 39.0 | 858 | 1.2907 | 0.4531 | 0.1362 | 0.4014 | 0.4017 | 11.0814 |
| 1.8003 | 40.0 | 880 | 1.2906 | 0.4543 | 0.137 | 0.4022 | 0.4025 | 11.1279 |
### Framework versions
- Transformers 4.38.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "synthea_t5_summarization_model", "results": []}]} | abymmathew/synthea_t5_summarization_model | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"base_model:t5-small",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T04:07:25+00:00 | [] | [] | TAGS
#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| synthea\_t5\_summarization\_model
=================================
This model is a fine-tuned version of t5-small on the None dataset.
It achieves the following results on the evaluation set:
* Loss: 1.2906
* Rouge1: 0.4543
* Rouge2: 0.137
* Rougel: 0.4022
* Rougelsum: 0.4025
* Gen Len: 11.1279
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 2e-05
* train\_batch\_size: 16
* eval\_batch\_size: 16
* seed: 42
* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
* lr\_scheduler\_type: linear
* num\_epochs: 40
* mixed\_precision\_training: Native AMP
### Training results
### Framework versions
* Transformers 4.38.2
* Pytorch 2.2.1+cu121
* Datasets 2.19.0
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #tensorboard #safetensors #t5 #text2text-generation #generated_from_trainer #base_model-t5-small #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 2e-05\n* train\\_batch\\_size: 16\n* eval\\_batch\\_size: 16\n* seed: 42\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: linear\n* num\\_epochs: 40\n* mixed\\_precision\\_training: Native AMP",
"### Training results",
"### Framework versions\n\n\n* Transformers 4.38.2\n* Pytorch 2.2.1+cu121\n* Datasets 2.19.0\n* Tokenizers 0.15.2"
] |
text-generation | transformers |
## Llamacpp iMatrix Quantizations of llama-3-neural-chat-v1-8b
This model has the <|eot_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="https://github.com/ggerganov/llama.cpp/">llama.cpp</a> fork from pcuenca <a href="https://github.com/pcuenca/llama.cpp/tree/llama3-conversion">llama3-conversion</a> for quantization.
Original model: https://huggingface.co/Locutusque/llama-3-neural-chat-v1-8b
All quants made using imatrix option with dataset provided by Kalomaze [here](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
## Prompt format
```
<|im_start|>system
{system_prompt}<|im_end|>
<|im_start|>user
{prompt}<|im_end|>
<|im_start|>assistant
```
## Download a file (not the whole branch) from below:
| Filename | Quant type | File Size | Description |
| -------- | ---------- | --------- | ----------- |
| [llama-3-neural-chat-v1-8b-Q8_0.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q8_0.gguf) | Q8_0 | 8.54GB | Extremely high quality, generally unneeded but max available quant. |
| [llama-3-neural-chat-v1-8b-Q6_K.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q6_K.gguf) | Q6_K | 6.59GB | Very high quality, near perfect, *recommended*. |
| [llama-3-neural-chat-v1-8b-Q5_K_M.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q5_K_M.gguf) | Q5_K_M | 5.73GB | High quality, *recommended*. |
| [llama-3-neural-chat-v1-8b-Q5_K_S.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q5_K_S.gguf) | Q5_K_S | 5.59GB | High quality, *recommended*. |
| [llama-3-neural-chat-v1-8b-Q4_K_M.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q4_K_M.gguf) | Q4_K_M | 4.92GB | Good quality, uses about 4.83 bits per weight, *recommended*. |
| [llama-3-neural-chat-v1-8b-Q4_K_S.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q4_K_S.gguf) | Q4_K_S | 4.69GB | Slightly lower quality with more space savings, *recommended*. |
| [llama-3-neural-chat-v1-8b-IQ4_NL.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ4_NL.gguf) | IQ4_NL | 4.67GB | Decent quality, slightly smaller than Q4_K_S with similar performance *recommended*. |
| [llama-3-neural-chat-v1-8b-IQ4_XS.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ4_XS.gguf) | IQ4_XS | 4.44GB | Decent quality, smaller than Q4_K_S with similar performance, *recommended*. |
| [llama-3-neural-chat-v1-8b-Q3_K_L.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q3_K_L.gguf) | Q3_K_L | 4.32GB | Lower quality but usable, good for low RAM availability. |
| [llama-3-neural-chat-v1-8b-Q3_K_M.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q3_K_M.gguf) | Q3_K_M | 4.01GB | Even lower quality. |
| [llama-3-neural-chat-v1-8b-IQ3_M.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ3_M.gguf) | IQ3_M | 3.78GB | Medium-low quality, new method with decent performance comparable to Q3_K_M. |
| [llama-3-neural-chat-v1-8b-IQ3_S.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ3_S.gguf) | IQ3_S | 3.68GB | Lower quality, new method with decent performance, recommended over Q3_K_S quant, same size with better performance. |
| [llama-3-neural-chat-v1-8b-Q3_K_S.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q3_K_S.gguf) | Q3_K_S | 3.66GB | Low quality, not recommended. |
| [llama-3-neural-chat-v1-8b-IQ3_XS.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ3_XS.gguf) | IQ3_XS | 3.51GB | Lower quality, new method with decent performance, slightly better than Q3_K_S. |
| [llama-3-neural-chat-v1-8b-IQ3_XXS.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ3_XXS.gguf) | IQ3_XXS | 3.27GB | Lower quality, new method with decent performance, comparable to Q3 quants. |
| [llama-3-neural-chat-v1-8b-Q2_K.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-Q2_K.gguf) | Q2_K | 3.17GB | Very low quality but surprisingly usable. |
| [llama-3-neural-chat-v1-8b-IQ2_M.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ2_M.gguf) | IQ2_M | 2.94GB | Very low quality, uses SOTA techniques to also be surprisingly usable. |
| [llama-3-neural-chat-v1-8b-IQ2_S.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ2_S.gguf) | IQ2_S | 2.75GB | Very low quality, uses SOTA techniques to be usable. |
| [llama-3-neural-chat-v1-8b-IQ2_XS.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ2_XS.gguf) | IQ2_XS | 2.60GB | Very low quality, uses SOTA techniques to be usable. |
| [llama-3-neural-chat-v1-8b-IQ2_XXS.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ2_XXS.gguf) | IQ2_XXS | 2.39GB | Lower quality, uses SOTA techniques to be usable. |
| [llama-3-neural-chat-v1-8b-IQ1_M.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ1_M.gguf) | IQ1_M | 2.16GB | Extremely low quality, *not* recommended. |
| [llama-3-neural-chat-v1-8b-IQ1_S.gguf](https://huggingface.co/bartowski/llama-3-neural-chat-v1-8b-GGUF/blob/main/llama-3-neural-chat-v1-8b-IQ1_S.gguf) | IQ1_S | 2.01GB | Extremely low quality, *not* recommended. |
## Which file should I choose?
A great write up with charts showing various performances is provided by Artefact2 [here](https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9)
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX_K_X', like Q5_K_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
[llama.cpp feature matrix](https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix)
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX_X, like IQ3_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: https://ko-fi.com/bartowski
| {"license": "other", "library_name": "transformers", "datasets": ["mlabonne/orpo-dpo-mix-40k", "Open-Orca/SlimOrca-Dedup", "jondurbin/airoboros-3.2", "microsoft/orca-math-word-problems-200k", "m-a-p/Code-Feedback", "MaziyarPanahi/WizardLM_evol_instruct_V2_196k"], "base_model": "meta-llama/Meta-Llama-3-8B", "quantized_by": "bartowski", "pipeline_tag": "text-generation"} | bartowski/llama-3-neural-chat-v1-8b-GGUF | null | [
"transformers",
"gguf",
"text-generation",
"dataset:mlabonne/orpo-dpo-mix-40k",
"dataset:Open-Orca/SlimOrca-Dedup",
"dataset:jondurbin/airoboros-3.2",
"dataset:microsoft/orca-math-word-problems-200k",
"dataset:m-a-p/Code-Feedback",
"dataset:MaziyarPanahi/WizardLM_evol_instruct_V2_196k",
"base_model:meta-llama/Meta-Llama-3-8B",
"license:other",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:09:47+00:00 | [] | [] | TAGS
#transformers #gguf #text-generation #dataset-mlabonne/orpo-dpo-mix-40k #dataset-Open-Orca/SlimOrca-Dedup #dataset-jondurbin/airoboros-3.2 #dataset-microsoft/orca-math-word-problems-200k #dataset-m-a-p/Code-Feedback #dataset-MaziyarPanahi/WizardLM_evol_instruct_V2_196k #base_model-meta-llama/Meta-Llama-3-8B #license-other #endpoints_compatible #region-us
| Llamacpp iMatrix Quantizations of llama-3-neural-chat-v1-8b
-----------------------------------------------------------
This model has the <|eot\_id|> token set to not-special, which seems to work better with current inference engines.
Using <a href="URL fork from pcuenca <a href="URL for quantization.
Original model: URL
All quants made using imatrix option with dataset provided by Kalomaze here
Prompt format
-------------
Download a file (not the whole branch) from below:
--------------------------------------------------
Which file should I choose?
---------------------------
A great write up with charts showing various performances is provided by Artefact2 here
The first thing to figure out is how big a model you can run. To do this, you'll need to figure out how much RAM and/or VRAM you have.
If you want your model running as FAST as possible, you'll want to fit the whole thing on your GPU's VRAM. Aim for a quant with a file size 1-2GB smaller than your GPU's total VRAM.
If you want the absolute maximum quality, add both your system RAM and your GPU's VRAM together, then similarly grab a quant with a file size 1-2GB Smaller than that total.
Next, you'll need to decide if you want to use an 'I-quant' or a 'K-quant'.
If you don't want to think too much, grab one of the K-quants. These are in format 'QX\_K\_X', like Q5\_K\_M.
If you want to get more into the weeds, you can check out this extremely useful feature chart:
URL feature matrix
But basically, if you're aiming for below Q4, and you're running cuBLAS (Nvidia) or rocBLAS (AMD), you should look towards the I-quants. These are in format IQX\_X, like IQ3\_M. These are newer and offer better performance for their size.
These I-quants can also be used on CPU and Apple Metal, but will be slower than their K-quant equivalent, so speed vs performance is a tradeoff you'll have to decide.
The I-quants are *not* compatible with Vulcan, which is also AMD, so if you have an AMD card double check if you're using the rocBLAS build or the Vulcan build. At the time of writing this, LM Studio has a preview with ROCm support, and other inference engines have specific builds for ROCm.
Want to support my work? Visit my ko-fi page here: URL
| [] | [
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] |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# zephyr-7b-gpo-log1-i0
This model is a fine-tuned version of [alignment-handbook/zephyr-7b-sft-qlora](https://huggingface.co/alignment-handbook/zephyr-7b-sft-qlora) on the HuggingFaceH4/ultrafeedback_binarized dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6897
- Rewards/chosen: 0.0141
- Rewards/rejected: -0.0702
- Rewards/accuracies: 0.6370
- Rewards/margins: 0.0842
- Logps/rejected: -218.6293
- Logps/chosen: -230.5992
- Logits/rejected: -2.1363
- Logits/chosen: -2.3248
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-06
- train_batch_size: 2
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- 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
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:-----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.6932 | 0.01 | 100 | 0.6931 | 0.0024 | 0.0017 | 0.4950 | 0.0007 | -211.4439 | -231.7646 | -2.1604 | -2.3488 |
| 0.6927 | 0.01 | 200 | 0.6928 | 0.0053 | -0.0004 | 0.5835 | 0.0057 | -211.6526 | -231.4798 | -2.1608 | -2.3492 |
| 0.6917 | 0.02 | 300 | 0.6925 | 0.0335 | 0.0177 | 0.5830 | 0.0159 | -209.8460 | -228.6509 | -2.1649 | -2.3535 |
| 0.6916 | 0.03 | 400 | 0.6920 | 0.0466 | 0.0223 | 0.6020 | 0.0244 | -209.3866 | -227.3408 | -2.1660 | -2.3548 |
| 0.6917 | 0.03 | 500 | 0.6916 | 0.0638 | 0.0219 | 0.6060 | 0.0419 | -209.4261 | -225.6272 | -2.1616 | -2.3499 |
| 0.6919 | 0.04 | 600 | 0.6913 | 0.0498 | 0.0026 | 0.5970 | 0.0472 | -211.3561 | -227.0246 | -2.1675 | -2.3568 |
| 0.6909 | 0.05 | 700 | 0.6913 | 0.0561 | 0.0106 | 0.6145 | 0.0455 | -210.5544 | -226.3928 | -2.1615 | -2.3501 |
| 0.6913 | 0.05 | 800 | 0.6913 | -0.1047 | -0.1559 | 0.5970 | 0.0512 | -227.2016 | -242.4708 | -2.1428 | -2.3307 |
| 0.6921 | 0.06 | 900 | 0.6909 | -0.0526 | -0.1012 | 0.6060 | 0.0486 | -221.7336 | -237.2677 | -2.1466 | -2.3343 |
| 0.6903 | 0.07 | 1000 | 0.6908 | -0.0008 | -0.0563 | 0.6185 | 0.0555 | -217.2371 | -232.0825 | -2.1575 | -2.3453 |
| 0.6922 | 0.07 | 1100 | 0.6911 | -0.0015 | -0.0779 | 0.6275 | 0.0764 | -219.4024 | -232.1565 | -2.1294 | -2.3151 |
| 0.6906 | 0.08 | 1200 | 0.6907 | -0.0276 | -0.0979 | 0.6375 | 0.0703 | -221.4021 | -234.7645 | -2.1398 | -2.3272 |
| 0.6886 | 0.09 | 1300 | 0.6907 | 0.0146 | -0.0428 | 0.6105 | 0.0574 | -215.8946 | -230.5475 | -2.1613 | -2.3501 |
| 0.6887 | 0.09 | 1400 | 0.6909 | 0.0072 | -0.0587 | 0.6130 | 0.0660 | -217.4851 | -231.2815 | -2.1350 | -2.3205 |
| 0.6887 | 0.1 | 1500 | 0.6907 | -0.0114 | -0.0845 | 0.6305 | 0.0731 | -220.0597 | -233.1405 | -2.1365 | -2.3217 |
| 0.6904 | 0.1 | 1600 | 0.6906 | 0.0443 | -0.0289 | 0.6260 | 0.0732 | -214.5052 | -227.5776 | -2.1414 | -2.3270 |
| 0.6893 | 0.11 | 1700 | 0.6904 | 0.0333 | -0.0409 | 0.6215 | 0.0742 | -215.7022 | -228.6733 | -2.1548 | -2.3421 |
| 0.6904 | 0.12 | 1800 | 0.6909 | 0.0409 | -0.0143 | 0.6160 | 0.0552 | -213.0369 | -227.9110 | -2.1467 | -2.3331 |
| 0.6908 | 0.12 | 1900 | 0.6906 | 0.0455 | -0.0171 | 0.6290 | 0.0626 | -213.3265 | -227.4577 | -2.1587 | -2.3461 |
| 0.6907 | 0.13 | 2000 | 0.6904 | -0.0093 | -0.0898 | 0.6400 | 0.0805 | -220.5949 | -232.9343 | -2.1672 | -2.3558 |
| 0.6904 | 0.14 | 2100 | 0.6905 | 0.0245 | -0.0431 | 0.6380 | 0.0676 | -215.9218 | -229.5578 | -2.1837 | -2.3738 |
| 0.6916 | 0.14 | 2200 | 0.6904 | -0.0211 | -0.1023 | 0.6260 | 0.0812 | -221.8438 | -234.1163 | -2.1669 | -2.3566 |
| 0.6913 | 0.15 | 2300 | 0.6907 | -0.0303 | -0.1156 | 0.6170 | 0.0852 | -223.1697 | -235.0393 | -2.1698 | -2.3594 |
| 0.6899 | 0.16 | 2400 | 0.6904 | 0.0312 | -0.0385 | 0.6225 | 0.0697 | -215.4613 | -228.8855 | -2.1472 | -2.3345 |
| 0.6924 | 0.16 | 2500 | 0.6905 | 0.0577 | -0.0074 | 0.625 | 0.0651 | -212.3521 | -226.2342 | -2.1658 | -2.3546 |
| 0.6893 | 0.17 | 2600 | 0.6903 | 0.0520 | -0.0205 | 0.6320 | 0.0725 | -213.6627 | -226.8027 | -2.1570 | -2.3453 |
| 0.6901 | 0.18 | 2700 | 0.6906 | 0.0038 | -0.0622 | 0.6325 | 0.0660 | -217.8366 | -231.6274 | -2.1382 | -2.3249 |
| 0.6909 | 0.18 | 2800 | 0.6903 | 0.0333 | -0.0363 | 0.6315 | 0.0696 | -215.2451 | -228.6795 | -2.1165 | -2.3020 |
| 0.6893 | 0.19 | 2900 | 0.6902 | 0.0110 | -0.0612 | 0.6380 | 0.0722 | -217.7327 | -230.9010 | -2.1110 | -2.2960 |
| 0.6925 | 0.2 | 3000 | 0.6903 | 0.0154 | -0.0656 | 0.6245 | 0.0811 | -218.1745 | -230.4610 | -2.1312 | -2.3182 |
| 0.692 | 0.2 | 3100 | 0.6903 | -0.0346 | -0.1194 | 0.6440 | 0.0849 | -223.5567 | -235.4630 | -2.1298 | -2.3160 |
| 0.687 | 0.21 | 3200 | 0.6903 | -0.0146 | -0.0904 | 0.6210 | 0.0757 | -220.6501 | -233.4682 | -2.1344 | -2.3212 |
| 0.6908 | 0.22 | 3300 | 0.6902 | -0.0061 | -0.0903 | 0.6420 | 0.0842 | -220.6434 | -232.6119 | -2.1233 | -2.3094 |
| 0.6908 | 0.22 | 3400 | 0.6904 | -0.0103 | -0.0884 | 0.6345 | 0.0781 | -220.4491 | -233.0300 | -2.1210 | -2.3068 |
| 0.6901 | 0.23 | 3500 | 0.6903 | 0.0193 | -0.0626 | 0.6355 | 0.0819 | -217.8700 | -230.0756 | -2.1193 | -2.3047 |
| 0.6913 | 0.24 | 3600 | 0.6902 | 0.0148 | -0.0690 | 0.6360 | 0.0838 | -218.5164 | -230.5288 | -2.1189 | -2.3041 |
| 0.694 | 0.24 | 3700 | 0.6904 | -0.0287 | -0.1025 | 0.6390 | 0.0738 | -221.8667 | -234.8788 | -2.0983 | -2.2820 |
| 0.6891 | 0.25 | 3800 | 0.6902 | 0.0450 | -0.0237 | 0.6320 | 0.0687 | -213.9806 | -227.5013 | -2.0923 | -2.2758 |
| 0.6877 | 0.26 | 3900 | 0.6902 | 0.0220 | -0.0570 | 0.6245 | 0.0791 | -217.3152 | -229.8009 | -2.1089 | -2.2936 |
| 0.6884 | 0.26 | 4000 | 0.6901 | -0.0013 | -0.0808 | 0.6360 | 0.0795 | -219.6905 | -232.1315 | -2.1064 | -2.2913 |
| 0.693 | 0.27 | 4100 | 0.6904 | -0.0133 | -0.0759 | 0.6280 | 0.0626 | -219.1985 | -233.3333 | -2.1177 | -2.3035 |
| 0.691 | 0.27 | 4200 | 0.6904 | -0.0025 | -0.0715 | 0.6360 | 0.0690 | -218.7613 | -232.2541 | -2.1112 | -2.2963 |
| 0.6904 | 0.28 | 4300 | 0.6901 | -0.0338 | -0.1195 | 0.6345 | 0.0858 | -223.5635 | -235.3810 | -2.1015 | -2.2866 |
| 0.6903 | 0.29 | 4400 | 0.6902 | -0.0454 | -0.1194 | 0.6275 | 0.0740 | -223.5494 | -236.5452 | -2.1077 | -2.2929 |
| 0.6864 | 0.29 | 4500 | 0.6901 | -0.0231 | -0.1063 | 0.6325 | 0.0833 | -222.2449 | -234.3118 | -2.1211 | -2.3074 |
| 0.6904 | 0.3 | 4600 | 0.6902 | 0.0062 | -0.0640 | 0.6310 | 0.0702 | -218.0117 | -231.3809 | -2.1215 | -2.3078 |
| 0.6854 | 0.31 | 4700 | 0.6903 | -0.0355 | -0.1276 | 0.6355 | 0.0921 | -224.3721 | -235.5581 | -2.1311 | -2.3193 |
| 0.6918 | 0.31 | 4800 | 0.6902 | -0.0179 | -0.0916 | 0.6385 | 0.0737 | -220.7675 | -233.7953 | -2.1200 | -2.3064 |
| 0.6886 | 0.32 | 4900 | 0.6902 | -0.0208 | -0.1097 | 0.6425 | 0.0889 | -222.5813 | -234.0859 | -2.0991 | -2.2843 |
| 0.6923 | 0.33 | 5000 | 0.6901 | -0.0066 | -0.0881 | 0.6270 | 0.0815 | -220.4222 | -232.6694 | -2.1010 | -2.2864 |
| 0.6914 | 0.33 | 5100 | 0.6902 | -0.0049 | -0.0898 | 0.6365 | 0.0849 | -220.5913 | -232.4988 | -2.1187 | -2.3049 |
| 0.6895 | 0.34 | 5200 | 0.6902 | -0.0224 | -0.0983 | 0.6295 | 0.0759 | -221.4422 | -234.2488 | -2.1360 | -2.3237 |
| 0.6928 | 0.35 | 5300 | 0.6903 | -0.0338 | -0.1157 | 0.6300 | 0.0819 | -223.1770 | -235.3836 | -2.1243 | -2.3110 |
| 0.689 | 0.35 | 5400 | 0.6902 | 0.0233 | -0.0513 | 0.6335 | 0.0746 | -216.7387 | -229.6749 | -2.1113 | -2.2966 |
| 0.6884 | 0.36 | 5500 | 0.6904 | -0.0049 | -0.0776 | 0.6230 | 0.0727 | -219.3675 | -232.4934 | -2.1054 | -2.2905 |
| 0.6901 | 0.37 | 5600 | 0.6903 | -0.0024 | -0.0762 | 0.6340 | 0.0738 | -219.2327 | -232.2428 | -2.1021 | -2.2871 |
| 0.6906 | 0.37 | 5700 | 0.6901 | 0.0148 | -0.0702 | 0.6345 | 0.0849 | -218.6294 | -230.5282 | -2.0973 | -2.2823 |
| 0.69 | 0.38 | 5800 | 0.6902 | -0.0196 | -0.1110 | 0.6365 | 0.0914 | -222.7126 | -233.9667 | -2.1048 | -2.2907 |
| 0.6907 | 0.39 | 5900 | 0.6901 | 0.0021 | -0.0814 | 0.6385 | 0.0835 | -219.7548 | -231.7942 | -2.0946 | -2.2797 |
| 0.6901 | 0.39 | 6000 | 0.6901 | 0.0056 | -0.0656 | 0.6295 | 0.0713 | -218.1741 | -231.4416 | -2.1236 | -2.3110 |
| 0.6889 | 0.4 | 6100 | 0.6901 | 0.0339 | -0.0376 | 0.6215 | 0.0716 | -215.3745 | -228.6116 | -2.1316 | -2.3196 |
| 0.691 | 0.41 | 6200 | 0.6900 | 0.0231 | -0.0575 | 0.6285 | 0.0806 | -217.3578 | -229.6931 | -2.1264 | -2.3146 |
| 0.6871 | 0.41 | 6300 | 0.6900 | 0.0432 | -0.0379 | 0.6370 | 0.0810 | -215.3970 | -227.6890 | -2.1200 | -2.3069 |
| 0.6892 | 0.42 | 6400 | 0.6901 | 0.0295 | -0.0619 | 0.6310 | 0.0914 | -217.7995 | -229.0562 | -2.1320 | -2.3205 |
| 0.6918 | 0.43 | 6500 | 0.6901 | 0.0240 | -0.0559 | 0.6370 | 0.0799 | -217.2022 | -229.6073 | -2.1407 | -2.3293 |
| 0.6899 | 0.43 | 6600 | 0.6901 | 0.0346 | -0.0427 | 0.6355 | 0.0773 | -215.8845 | -228.5490 | -2.1480 | -2.3373 |
| 0.6914 | 0.44 | 6700 | 0.6901 | 0.0006 | -0.0896 | 0.6385 | 0.0902 | -220.5701 | -231.9431 | -2.1399 | -2.3289 |
| 0.6921 | 0.44 | 6800 | 0.6900 | -0.0122 | -0.0949 | 0.6345 | 0.0826 | -221.0977 | -233.2272 | -2.1373 | -2.3262 |
| 0.6881 | 0.45 | 6900 | 0.6900 | 0.0001 | -0.0807 | 0.6310 | 0.0808 | -219.6810 | -231.9954 | -2.1336 | -2.3221 |
| 0.688 | 0.46 | 7000 | 0.6900 | -0.0035 | -0.0895 | 0.6255 | 0.0860 | -220.5654 | -232.3555 | -2.1330 | -2.3214 |
| 0.6893 | 0.46 | 7100 | 0.6900 | 0.0038 | -0.0786 | 0.6310 | 0.0824 | -219.4742 | -231.6270 | -2.1255 | -2.3129 |
| 0.6888 | 0.47 | 7200 | 0.6900 | 0.0146 | -0.0599 | 0.6220 | 0.0745 | -217.6021 | -230.5473 | -2.1376 | -2.3262 |
| 0.6907 | 0.48 | 7300 | 0.6899 | -0.0074 | -0.0859 | 0.6290 | 0.0785 | -220.2062 | -232.7456 | -2.1270 | -2.3148 |
| 0.6931 | 0.48 | 7400 | 0.6900 | 0.0088 | -0.0681 | 0.6285 | 0.0770 | -218.4249 | -231.1209 | -2.1238 | -2.3113 |
| 0.6895 | 0.49 | 7500 | 0.6899 | 0.0001 | -0.0788 | 0.6280 | 0.0789 | -219.4958 | -231.9997 | -2.1007 | -2.2861 |
| 0.6874 | 0.5 | 7600 | 0.6900 | -0.0044 | -0.0909 | 0.6300 | 0.0865 | -220.7033 | -232.4485 | -2.1033 | -2.2888 |
| 0.6898 | 0.5 | 7700 | 0.6899 | 0.0018 | -0.0817 | 0.6355 | 0.0835 | -219.7780 | -231.8252 | -2.0977 | -2.2827 |
| 0.6885 | 0.51 | 7800 | 0.6900 | -0.0331 | -0.1186 | 0.6485 | 0.0855 | -223.4754 | -235.3170 | -2.0865 | -2.2713 |
| 0.6905 | 0.52 | 7900 | 0.6899 | -0.0476 | -0.1257 | 0.6425 | 0.0781 | -224.1827 | -236.7635 | -2.0852 | -2.2699 |
| 0.6911 | 0.52 | 8000 | 0.6899 | -0.0329 | -0.1140 | 0.6345 | 0.0811 | -223.0114 | -235.2987 | -2.0814 | -2.2658 |
| 0.6915 | 0.53 | 8100 | 0.6899 | -0.0158 | -0.0964 | 0.6365 | 0.0807 | -221.2535 | -233.5811 | -2.0877 | -2.2729 |
| 0.6907 | 0.54 | 8200 | 0.6899 | -0.0250 | -0.1063 | 0.6355 | 0.0814 | -222.2466 | -234.5026 | -2.0843 | -2.2691 |
| 0.6893 | 0.54 | 8300 | 0.6900 | -0.0020 | -0.0780 | 0.6345 | 0.0760 | -219.4079 | -232.2015 | -2.0923 | -2.2778 |
| 0.6904 | 0.55 | 8400 | 0.6900 | 0.0123 | -0.0553 | 0.6295 | 0.0676 | -217.1386 | -230.7717 | -2.0953 | -2.2805 |
| 0.6885 | 0.56 | 8500 | 0.6898 | 0.0006 | -0.0852 | 0.6455 | 0.0858 | -220.1317 | -231.9455 | -2.0963 | -2.2819 |
| 0.6889 | 0.56 | 8600 | 0.6898 | -0.0030 | -0.0879 | 0.6410 | 0.0849 | -220.4034 | -232.3074 | -2.1033 | -2.2895 |
| 0.6895 | 0.57 | 8700 | 0.6898 | 0.0116 | -0.0737 | 0.6430 | 0.0853 | -218.9868 | -230.8494 | -2.1105 | -2.2970 |
| 0.6913 | 0.58 | 8800 | 0.6898 | 0.0296 | -0.0519 | 0.6465 | 0.0816 | -216.8063 | -229.0427 | -2.1172 | -2.3044 |
| 0.6906 | 0.58 | 8900 | 0.6898 | 0.0039 | -0.0875 | 0.6485 | 0.0914 | -220.3614 | -231.6156 | -2.1173 | -2.3050 |
| 0.6888 | 0.59 | 9000 | 0.6898 | 0.0111 | -0.0739 | 0.6400 | 0.0851 | -219.0050 | -230.8923 | -2.1196 | -2.3073 |
| 0.6905 | 0.6 | 9100 | 0.6899 | 0.0201 | -0.0529 | 0.6325 | 0.0730 | -216.9018 | -229.9912 | -2.1251 | -2.3129 |
| 0.6887 | 0.6 | 9200 | 0.6898 | 0.0207 | -0.0583 | 0.6355 | 0.0790 | -217.4442 | -229.9347 | -2.1397 | -2.3283 |
| 0.6899 | 0.61 | 9300 | 0.6898 | 0.0062 | -0.0796 | 0.6375 | 0.0858 | -219.5693 | -231.3830 | -2.1441 | -2.3333 |
| 0.6884 | 0.62 | 9400 | 0.6899 | -0.0285 | -0.1089 | 0.6335 | 0.0804 | -222.5007 | -234.8580 | -2.1432 | -2.3321 |
| 0.6871 | 0.62 | 9500 | 0.6898 | -0.0095 | -0.0917 | 0.6365 | 0.0822 | -220.7840 | -232.9599 | -2.1435 | -2.3324 |
| 0.6905 | 0.63 | 9600 | 0.6899 | 0.0203 | -0.0661 | 0.6385 | 0.0864 | -218.2251 | -229.9762 | -2.1520 | -2.3417 |
| 0.6895 | 0.63 | 9700 | 0.6898 | 0.0048 | -0.0783 | 0.6440 | 0.0831 | -219.4395 | -231.5201 | -2.1527 | -2.3423 |
| 0.6915 | 0.64 | 9800 | 0.6898 | -0.0028 | -0.0828 | 0.6420 | 0.0800 | -219.8873 | -232.2814 | -2.1416 | -2.3302 |
| 0.6894 | 0.65 | 9900 | 0.6898 | -0.0006 | -0.0874 | 0.6435 | 0.0867 | -220.3488 | -232.0690 | -2.1391 | -2.3274 |
| 0.6897 | 0.65 | 10000 | 0.6899 | -0.0191 | -0.1066 | 0.6475 | 0.0875 | -222.2716 | -233.9115 | -2.1345 | -2.3227 |
| 0.6859 | 0.66 | 10100 | 0.6899 | -0.0225 | -0.1068 | 0.6475 | 0.0843 | -222.2938 | -234.2563 | -2.1291 | -2.3167 |
| 0.6904 | 0.67 | 10200 | 0.6898 | 0.0002 | -0.0901 | 0.6475 | 0.0903 | -220.6184 | -231.9806 | -2.1274 | -2.3151 |
| 0.6876 | 0.67 | 10300 | 0.6898 | 0.0014 | -0.0829 | 0.6435 | 0.0843 | -219.8981 | -231.8635 | -2.1301 | -2.3181 |
| 0.6888 | 0.68 | 10400 | 0.6898 | 0.0178 | -0.0690 | 0.6385 | 0.0868 | -218.5098 | -230.2225 | -2.1290 | -2.3170 |
| 0.6893 | 0.69 | 10500 | 0.6898 | 0.0209 | -0.0629 | 0.6395 | 0.0838 | -217.9021 | -229.9178 | -2.1322 | -2.3205 |
| 0.6893 | 0.69 | 10600 | 0.6898 | 0.0157 | -0.0686 | 0.6430 | 0.0844 | -218.4735 | -230.4310 | -2.1292 | -2.3171 |
| 0.6907 | 0.7 | 10700 | 0.6898 | 0.0165 | -0.0682 | 0.6430 | 0.0847 | -218.4280 | -230.3552 | -2.1293 | -2.3170 |
| 0.6877 | 0.71 | 10800 | 0.6898 | 0.0264 | -0.0554 | 0.6435 | 0.0818 | -217.1490 | -229.3606 | -2.1293 | -2.3171 |
| 0.6924 | 0.71 | 10900 | 0.6898 | 0.0120 | -0.0670 | 0.6385 | 0.0790 | -218.3147 | -230.8059 | -2.1238 | -2.3111 |
| 0.691 | 0.72 | 11000 | 0.6898 | 0.0266 | -0.0537 | 0.6395 | 0.0803 | -216.9807 | -229.3445 | -2.1251 | -2.3125 |
| 0.6903 | 0.73 | 11100 | 0.6898 | 0.0312 | -0.0491 | 0.6360 | 0.0803 | -216.5214 | -228.8819 | -2.1258 | -2.3132 |
| 0.6918 | 0.73 | 11200 | 0.6898 | 0.0305 | -0.0499 | 0.6375 | 0.0804 | -216.6021 | -228.9509 | -2.1260 | -2.3134 |
| 0.6879 | 0.74 | 11300 | 0.6898 | 0.0205 | -0.0612 | 0.6380 | 0.0818 | -217.7365 | -229.9544 | -2.1278 | -2.3155 |
| 0.6896 | 0.75 | 11400 | 0.6898 | 0.0170 | -0.0694 | 0.6355 | 0.0864 | -218.5536 | -230.3058 | -2.1292 | -2.3172 |
| 0.6904 | 0.75 | 11500 | 0.6898 | 0.0200 | -0.0610 | 0.6295 | 0.0811 | -217.7165 | -230.0003 | -2.1303 | -2.3183 |
| 0.6891 | 0.76 | 11600 | 0.6898 | 0.0093 | -0.0783 | 0.6370 | 0.0877 | -219.4468 | -231.0702 | -2.1269 | -2.3147 |
| 0.6883 | 0.77 | 11700 | 0.6898 | 0.0024 | -0.0805 | 0.6355 | 0.0828 | -219.6586 | -231.7671 | -2.1296 | -2.3175 |
| 0.69 | 0.77 | 11800 | 0.6898 | -0.0053 | -0.0871 | 0.6410 | 0.0818 | -220.3198 | -232.5302 | -2.1311 | -2.3192 |
| 0.6871 | 0.78 | 11900 | 0.6898 | -0.0076 | -0.0914 | 0.6410 | 0.0838 | -220.7492 | -232.7632 | -2.1300 | -2.3180 |
| 0.6887 | 0.79 | 12000 | 0.6898 | -0.0020 | -0.0869 | 0.6420 | 0.0849 | -220.3020 | -232.2003 | -2.1329 | -2.3212 |
| 0.6881 | 0.79 | 12100 | 0.6898 | 0.0007 | -0.0815 | 0.6385 | 0.0822 | -219.7614 | -231.9368 | -2.1346 | -2.3230 |
| 0.6905 | 0.8 | 12200 | 0.6898 | 0.0116 | -0.0698 | 0.6340 | 0.0814 | -218.5900 | -230.8437 | -2.1335 | -2.3217 |
| 0.6915 | 0.8 | 12300 | 0.6898 | 0.0068 | -0.0793 | 0.6365 | 0.0861 | -219.5374 | -231.3238 | -2.1342 | -2.3226 |
| 0.6927 | 0.81 | 12400 | 0.6898 | 0.0117 | -0.0703 | 0.6350 | 0.0820 | -218.6442 | -230.8355 | -2.1361 | -2.3246 |
| 0.6897 | 0.82 | 12500 | 0.6898 | 0.0095 | -0.0713 | 0.6325 | 0.0807 | -218.7409 | -231.0591 | -2.1371 | -2.3257 |
| 0.6905 | 0.82 | 12600 | 0.6898 | 0.0061 | -0.0744 | 0.6365 | 0.0805 | -219.0518 | -231.3977 | -2.1376 | -2.3263 |
| 0.6905 | 0.83 | 12700 | 0.6898 | 0.0062 | -0.0754 | 0.6335 | 0.0815 | -219.1471 | -231.3857 | -2.1376 | -2.3263 |
| 0.6907 | 0.84 | 12800 | 0.6898 | 0.0129 | -0.0688 | 0.6360 | 0.0817 | -218.4943 | -230.7170 | -2.1390 | -2.3279 |
| 0.6911 | 0.84 | 12900 | 0.6897 | 0.0182 | -0.0653 | 0.6335 | 0.0835 | -218.1457 | -230.1887 | -2.1372 | -2.3259 |
| 0.6886 | 0.85 | 13000 | 0.6897 | 0.0149 | -0.0707 | 0.6365 | 0.0856 | -218.6831 | -230.5150 | -2.1390 | -2.3278 |
| 0.6914 | 0.86 | 13100 | 0.6897 | 0.0135 | -0.0701 | 0.6355 | 0.0836 | -218.6235 | -230.6533 | -2.1373 | -2.3260 |
| 0.6887 | 0.86 | 13200 | 0.6897 | 0.0112 | -0.0734 | 0.6370 | 0.0846 | -218.9507 | -230.8813 | -2.1367 | -2.3253 |
| 0.6891 | 0.87 | 13300 | 0.6897 | 0.0125 | -0.0733 | 0.6405 | 0.0858 | -218.9421 | -230.7573 | -2.1360 | -2.3246 |
| 0.6913 | 0.88 | 13400 | 0.6897 | 0.0152 | -0.0698 | 0.6305 | 0.0850 | -218.5887 | -230.4858 | -2.1379 | -2.3267 |
| 0.6912 | 0.88 | 13500 | 0.6897 | 0.0194 | -0.0641 | 0.6360 | 0.0836 | -218.0252 | -230.0619 | -2.1378 | -2.3265 |
| 0.6905 | 0.89 | 13600 | 0.6897 | 0.0163 | -0.0690 | 0.6380 | 0.0853 | -218.5100 | -230.3711 | -2.1382 | -2.3269 |
| 0.6913 | 0.9 | 13700 | 0.6897 | 0.0172 | -0.0673 | 0.6360 | 0.0846 | -218.3449 | -230.2803 | -2.1379 | -2.3266 |
| 0.69 | 0.9 | 13800 | 0.6897 | 0.0175 | -0.0677 | 0.6390 | 0.0851 | -218.3797 | -230.2597 | -2.1379 | -2.3266 |
| 0.6902 | 0.91 | 13900 | 0.6897 | 0.0181 | -0.0668 | 0.6400 | 0.0849 | -218.2959 | -230.1951 | -2.1371 | -2.3257 |
| 0.6883 | 0.92 | 14000 | 0.6897 | 0.0142 | -0.0709 | 0.6380 | 0.0851 | -218.7007 | -230.5817 | -2.1376 | -2.3262 |
| 0.6898 | 0.92 | 14100 | 0.6897 | 0.0158 | -0.0685 | 0.6375 | 0.0844 | -218.4662 | -230.4218 | -2.1366 | -2.3252 |
| 0.6894 | 0.93 | 14200 | 0.6897 | 0.0149 | -0.0698 | 0.6375 | 0.0847 | -218.5941 | -230.5171 | -2.1369 | -2.3255 |
| 0.6912 | 0.94 | 14300 | 0.6897 | 0.0145 | -0.0702 | 0.6400 | 0.0847 | -218.6314 | -230.5508 | -2.1365 | -2.3251 |
| 0.6893 | 0.94 | 14400 | 0.6897 | 0.0139 | -0.0710 | 0.6410 | 0.0848 | -218.7085 | -230.6183 | -2.1361 | -2.3247 |
| 0.6914 | 0.95 | 14500 | 0.6897 | 0.0139 | -0.0710 | 0.6370 | 0.0848 | -218.7070 | -230.6179 | -2.1364 | -2.3250 |
| 0.6897 | 0.96 | 14600 | 0.6897 | 0.0138 | -0.0707 | 0.6355 | 0.0844 | -218.6777 | -230.6268 | -2.1363 | -2.3249 |
| 0.691 | 0.96 | 14700 | 0.6897 | 0.0138 | -0.0705 | 0.6365 | 0.0843 | -218.6600 | -230.6252 | -2.1362 | -2.3248 |
| 0.6897 | 0.97 | 14800 | 0.6897 | 0.0139 | -0.0705 | 0.6340 | 0.0844 | -218.6653 | -230.6136 | -2.1364 | -2.3250 |
| 0.6892 | 0.97 | 14900 | 0.6897 | 0.0138 | -0.0703 | 0.6380 | 0.0841 | -218.6449 | -230.6241 | -2.1365 | -2.3250 |
| 0.6925 | 0.98 | 15000 | 0.6897 | 0.0142 | -0.0701 | 0.6385 | 0.0843 | -218.6228 | -230.5896 | -2.1369 | -2.3255 |
| 0.6882 | 0.99 | 15100 | 0.6897 | 0.0141 | -0.0701 | 0.6390 | 0.0843 | -218.6257 | -230.5937 | -2.1369 | -2.3255 |
| 0.6896 | 0.99 | 15200 | 0.6897 | 0.0141 | -0.0701 | 0.6365 | 0.0842 | -218.6245 | -230.5999 | -2.1366 | -2.3251 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2 | {"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrafeedback_binarized"], "base_model": "mistralai/Mistral-7B-v0.1", "model-index": [{"name": "zephyr-7b-gpo-log1-i0", "results": []}]} | DUAL-GPO-2/zephyr-7b-gpo-log-i0 | null | [
"peft",
"tensorboard",
"safetensors",
"mistral",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"dataset:HuggingFaceH4/ultrafeedback_binarized",
"base_model:mistralai/Mistral-7B-v0.1",
"license:apache-2.0",
"region:us"
] | null | 2024-04-21T04:15:00+00:00 | [] | [] | TAGS
#peft #tensorboard #safetensors #mistral #alignment-handbook #generated_from_trainer #trl #dpo #dataset-HuggingFaceH4/ultrafeedback_binarized #base_model-mistralai/Mistral-7B-v0.1 #license-apache-2.0 #region-us
| zephyr-7b-gpo-log1-i0
=====================
This model is a fine-tuned version of alignment-handbook/zephyr-7b-sft-qlora on the HuggingFaceH4/ultrafeedback\_binarized dataset.
It achieves the following results on the evaluation set:
* Loss: 0.6897
* Rewards/chosen: 0.0141
* Rewards/rejected: -0.0702
* Rewards/accuracies: 0.6370
* Rewards/margins: 0.0842
* Logps/rejected: -218.6293
* Logps/chosen: -230.5992
* Logits/rejected: -2.1363
* Logits/chosen: -2.3248
Model description
-----------------
More information needed
Intended uses & limitations
---------------------------
More information needed
Training and evaluation data
----------------------------
More information needed
Training procedure
------------------
### Training hyperparameters
The following hyperparameters were used during training:
* learning\_rate: 5e-06
* train\_batch\_size: 2
* eval\_batch\_size: 2
* seed: 42
* distributed\_type: multi-GPU
* gradient\_accumulation\_steps: 2
* total\_train\_batch\_size: 4
* 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
* PEFT 0.7.1
* Transformers 4.36.2
* Pytorch 2.1.2+cu121
* Datasets 2.14.6
* Tokenizers 0.15.2
| [
"### Training hyperparameters\n\n\nThe following hyperparameters were used during training:\n\n\n* learning\\_rate: 5e-06\n* train\\_batch\\_size: 2\n* eval\\_batch\\_size: 2\n* seed: 42\n* distributed\\_type: multi-GPU\n* gradient\\_accumulation\\_steps: 2\n* total\\_train\\_batch\\_size: 4\n* optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n* lr\\_scheduler\\_type: cosine\n* lr\\_scheduler\\_warmup\\_ratio: 0.1\n* num\\_epochs: 1",
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"### Training results",
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] |
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. -->
# results
This model is a fine-tuned version of [LA1512/PubMed](https://huggingface.co/LA1512/PubMed) on the pubmed-summarization 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: 0.00034
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- label_smoothing_factor: 0.04
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"tags": ["generated_from_trainer"], "datasets": ["pubmed-summarization"], "base_model": "LA1512/PubMed", "model-index": [{"name": "results", "results": []}]} | LA1512/PubMed-fine-tune | null | [
"transformers",
"safetensors",
"bart",
"text2text-generation",
"generated_from_trainer",
"dataset:pubmed-summarization",
"base_model:LA1512/PubMed",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:15:57+00:00 | [] | [] | TAGS
#transformers #safetensors #bart #text2text-generation #generated_from_trainer #dataset-pubmed-summarization #base_model-LA1512/PubMed #autotrain_compatible #endpoints_compatible #region-us
|
# results
This model is a fine-tuned version of LA1512/PubMed on the pubmed-summarization 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: 0.00034
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 5
- label_smoothing_factor: 0.04
### Framework versions
- Transformers 4.39.3
- Pytorch 2.1.2
- Datasets 2.18.0
- Tokenizers 0.15.2
| [
"# results\n\nThis model is a fine-tuned version of LA1512/PubMed on the pubmed-summarization dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00034\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 5\n- label_smoothing_factor: 0.04",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] | [
"TAGS\n#transformers #safetensors #bart #text2text-generation #generated_from_trainer #dataset-pubmed-summarization #base_model-LA1512/PubMed #autotrain_compatible #endpoints_compatible #region-us \n",
"# results\n\nThis model is a fine-tuned version of LA1512/PubMed on the pubmed-summarization dataset.",
"## Model description\n\nMore information needed",
"## Intended uses & limitations\n\nMore information needed",
"## Training and evaluation data\n\nMore information needed",
"## Training procedure",
"### Training hyperparameters\n\nThe following hyperparameters were used during training:\n- learning_rate: 0.00034\n- train_batch_size: 8\n- eval_batch_size: 8\n- seed: 42\n- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08\n- lr_scheduler_type: linear\n- lr_scheduler_warmup_steps: 500\n- num_epochs: 5\n- label_smoothing_factor: 0.04",
"### Framework versions\n\n- Transformers 4.39.3\n- Pytorch 2.1.2\n- Datasets 2.18.0\n- Tokenizers 0.15.2"
] |
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]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | galbitang/koalpacapoly-upnormal-200 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:19:02+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #arxiv-1910.09700 #endpoints_compatible #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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:
- Funded by [optional]:
- Shared by [optional]:
- Model type:
- Language(s) (NLP):
- License:
- Finetuned from model [optional]:
### Model Sources [optional]
- Repository:
- Paper [optional]:
- Demo [optional]:
## Uses
### Direct Use
### Downstream Use [optional]
### Out-of-Scope Use
## Bias, Risks, and Limitations
### Recommendations
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.
## Training Details
### Training Data
### Training Procedure
#### Preprocessing [optional]
#### Training Hyperparameters
- Training regime:
#### Speeds, Sizes, Times [optional]
## Evaluation
### Testing Data, Factors & Metrics
#### Testing Data
#### Factors
#### Metrics
### Results
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## Model Examination [optional]
## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
- Compute Region:
- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
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"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
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"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
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"## Environmental Impact\n\n\n\nCarbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).\n\n- Hardware Type: \n- Hours used: \n- Cloud Provider: \n- Compute Region: \n- Carbon Emitted:",
"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
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.
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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] | {"library_name": "transformers", "tags": []} | heyllm234/sc53 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:20:14+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us
|
# Model Card for Model ID
## 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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| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
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"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"### Model Architecture and Objective",
"### Compute Infrastructure",
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"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
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"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #stablelm #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #region-us \n",
"# Model Card for Model ID",
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"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
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] |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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[More Information Needed] | {"library_name": "transformers", "tags": []} | IntervitensInc/intv_l3_mk8 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T04:21:00+00:00 | [
"1910.09700"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
|
# Model Card for Model ID
## Model Details
### Model Description
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- Developed by:
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## Uses
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## Training Details
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#### Speeds, Sizes, Times [optional]
## Evaluation
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#### Metrics
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## Environmental Impact
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
- Hardware Type:
- Hours used:
- Cloud Provider:
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- Carbon Emitted:
## Technical Specifications [optional]
### Model Architecture and Objective
### Compute Infrastructure
#### Hardware
#### Software
[optional]
BibTeX:
APA:
## Glossary [optional]
## More Information [optional]
## Model Card Authors [optional]
## Model Card Contact
| [
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
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"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
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"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
"## Evaluation",
"### Testing Data, Factors & Metrics",
"#### Testing Data",
"#### Factors",
"#### Metrics",
"### Results",
"#### Summary",
"## Model Examination [optional]",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
"#### Hardware",
"#### Software\n\n\n\n[optional]\n\n\n\nBibTeX:\n\n\n\nAPA:",
"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #arxiv-1910.09700 #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card for Model ID",
"## Model Details",
"### Model Description\n\n\n\nThis is the model card of a transformers model that has been pushed on the Hub. This model card has been automatically generated.\n\n- Developed by: \n- Funded by [optional]: \n- Shared by [optional]: \n- Model type: \n- Language(s) (NLP): \n- License: \n- Finetuned from model [optional]:",
"### Model Sources [optional]\n\n\n\n- Repository: \n- Paper [optional]: \n- Demo [optional]:",
"## Uses",
"### Direct Use",
"### Downstream Use [optional]",
"### Out-of-Scope Use",
"## Bias, Risks, and Limitations",
"### Recommendations\n\n\n\nUsers (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\n\nUse the code below to get started with the model.",
"## Training Details",
"### Training Data",
"### Training Procedure",
"#### Preprocessing [optional]",
"#### Training Hyperparameters\n\n- Training regime:",
"#### Speeds, Sizes, Times [optional]",
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"### Testing Data, Factors & Metrics",
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"#### Factors",
"#### Metrics",
"### Results",
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"## Technical Specifications [optional]",
"### Model Architecture and Objective",
"### Compute Infrastructure",
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"## Glossary [optional]",
"## More Information [optional]",
"## Model Card Authors [optional]",
"## Model Card Contact"
] |
text-classification | transformers |
ONNX exported version of `fmops/distilbert-prompt-injection` model compatible with transformers.js.
Original safetensors version: https://huggingface.co/fmops/distilbert-prompt-injection
| {} | pkchv/distilbert-prompt-injection-onnx | null | [
"transformers",
"onnx",
"distilbert",
"text-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:24:55+00:00 | [] | [] | TAGS
#transformers #onnx #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us
|
ONNX exported version of 'fmops/distilbert-prompt-injection' model compatible with URL.
Original safetensors version: URL
| [] | [
"TAGS\n#transformers #onnx #distilbert #text-classification #autotrain_compatible #endpoints_compatible #region-us \n"
] |
text-generation | transformers | # Llama-3-DARE-v3-8B
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE](https://arxiv.org/abs/2311.03099) [TIES](https://arxiv.org/abs/2306.01708) merge method using [meta-llama/Meta-Llama-3-8B](https://huggingface.co/meta-llama/Meta-Llama-3-8B) as a base.
### Models Merged
The following models were included in the merge:
* [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
base_model:
model:
path: meta-llama/Meta-Llama-3-8B
dtype: bfloat16
merge_method: dare_ties
parameters:
int8_mask: 1.0
normalize: 0.0
slices:
- sources:
- layer_range: [0, 32]
model:
model:
path: meta-llama/Meta-Llama-3-8B-Instruct
parameters:
density:
- filter: embed_token
value: 0.12392239047187575
- filter: model.norm
value: 0.9321540995757155
- filter: lm_head
value: 0.9321540995757155
- filter: layers.0.
value: 0.9790541113047215
- filter: layers.1.
value: 0.5837293662960215
- filter: layers.2.
value: 0.9412235995535374
- filter: layers.3.
value: 0.31233149627589435
- filter: layers.4.
value: 0.8429344053665633
- filter: layers.5.
value: 0.6736586892578483
- filter: layers.6.
value: 0.24511379602231775
- filter: layers.7.
value: 0.9579106307398759
- filter: layers.8.
value: 0.763438755789315
- filter: layers.9.
value: 0.9682444116383796
- filter: layers.10.
value: 0.08453321074167956
- filter: layers.11.
value: 0.7964240843030714
- filter: layers.12.
value: 0.33878902628372387
- filter: layers.13.
value: 0.8458690962458848
- filter: layers.14.
value: 0.1052429440590172
- filter: layers.15.
value: 0.7623565162481113
- filter: layers.16.
value: 0.9707532532287503
- filter: layers.17.
value: 0.12523916859700104
- filter: layers.18.
value: 0.8415224301240337
- filter: layers.19.
value: 0.12872802862625543
- filter: layers.20.
value: 0.5529482316673654
- filter: layers.21.
value: 0.09282157218446654
- filter: layers.22.
value: 0.8370536041906024
- filter: layers.23.
value: 0.9175102292532279
- filter: layers.24.
value: 0.8983418171724273
- filter: layers.25.
value: 0.8136717935920286
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value: 0.05054222298359671
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value: 0.869544796603939
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value: 0.04716191274361657
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value: 0.13032011470396976
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value: 0.19116844757457122
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value: 0.1455500526734667
weight:
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value: 0.12232308541622408
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value: 0.7266901175725669
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value: 0.7266901175725669
- filter: layers.0.
value: 0.8207345096435786
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value: 0.9504884225844141
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value: 0.7328920145925348
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value: 0.6736895869883676
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value: 0.7970121175937948
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value: 0.9789312914172503
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value: 0.962551880054289
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value: 0.9561739657469092
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value: 0.8536201095014567
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value: 0.9376890733815005
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value: 0.9551398977410172
- filter: layers.11.
value: 0.9967262117722387
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value: 0.7701592243202565
- filter: layers.13.
value: 0.6842573291853765
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value: 0.798376050387875
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value: 0.801001533828631
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value: 0.14199137490635572
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value: 0.7587521819162459
- filter: layers.18.
value: 0.9769968221517621
- filter: layers.19.
value: 0.5936888514834866
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value: 0.979481555973458
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value: 0.1362420472755318
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value: 0.1451804836602873
- filter: layers.23.
value: 0.9319964347718136
- filter: layers.24.
value: 0.8814265997262563
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value: 0.870638468633288
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value: 0.06311119172889679
- filter: layers.27.
value: 0.902932718098389
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value: 0.9174145551871369
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value: 0.9048467992426628
- filter: layers.30.
value: 0.04929564345988049
- filter: layers.31.
value: 0.922707420329624
- layer_range: [0, 32]
model:
model:
path: meta-llama/Meta-Llama-3-8B
parameters:
density:
- filter: embed_token
value: 0.1479082895745973
- filter: model.norm
value: 0.18334257522610492
- filter: lm_head
value: 0.18334257522610492
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value: 0.17476905394590242
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value: 0.11161623400742576
- filter: layers.2.
value: 0.16109344344908105
- filter: layers.3.
value: 0.2735834275693588
- filter: layers.4.
value: 0.8258891898417566
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value: 0.21085556872053604
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value: 0.20766543320815006
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value: 0.8947694253855037
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value: 0.734275334571558
- filter: layers.9.
value: 0.1632311874735626
- filter: layers.10.
value: 0.940700711783812
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value: 0.07148774488326176
- filter: layers.12.
value: 0.07541557340487534
- filter: layers.13.
value: 0.13833770311269455
- filter: layers.14.
value: 0.9612379711004643
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value: 0.8090075125599039
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value: 0.7255233959581611
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value: 0.2634507144990253
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value: 0.07135903934561608
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value: 0.1180822729914722
- filter: layers.20.
value: 0.07751975543731829
- filter: layers.21.
value: 0.9990557487897024
- filter: layers.22.
value: 0.17045615586066107
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value: 0.19588339382290734
- filter: layers.24.
value: 0.152313213824124
- filter: layers.25.
value: 0.8120646024357844
- filter: layers.26.
value: 0.6661112930033101
- filter: layers.27.
value: 0.7782416079783356
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value: 0.24425477536875875
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value: 0.05962906198631645
- filter: layers.30.
value: 0.023125010859717736
- filter: layers.31.
value: 0.9109899850283665
weight:
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value: 0.12126630242759481
- filter: model.norm.
value: 0.07734624352533248
- filter: lm_head
value: 0.07734624352533248
- filter: layers.0.
value: 0.16823028525905875
- filter: layers.1.
value: 0.9417449451303712
- filter: layers.2.
value: 0.7717519522673566
- filter: layers.3.
value: 0.7601040526349441
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value: 0.0019090753772779204
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value: 0.16032547702469566
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value: 0.12224994873335546
- filter: layers.7.
value: 0.27695385066177564
- filter: layers.8.
value: 0.018799614691291815
- filter: layers.9.
value: 0.9759168818301882
- filter: layers.10.
value: 0.006525097827571269
- filter: layers.11.
value: 0.756537797885991
- filter: layers.12.
value: 0.8051453838823787
- filter: layers.13.
value: 0.8879631547059472
- filter: layers.14.
value: 0.713799746085261
- filter: layers.15.
value: 0.03862352880564701
- filter: layers.16.
value: 0.1837448681603537
- filter: layers.17.
value: 0.30267576939315943
- filter: layers.18.
value: 0.17332405807285195
- filter: layers.19.
value: 0.11686420946772721
- filter: layers.20.
value: 0.2826021601318976
- filter: layers.21.
value: 0.14782621450845335
- filter: layers.22.
value: 0.8764989337980243
- filter: layers.23.
value: 0.5836574402524565
- filter: layers.24.
value: 0.8579541606567384
- filter: layers.25.
value: 0.2310998812434597
- filter: layers.26.
value: 0.13443251834995432
- filter: layers.27.
value: 0.9754382468614297
- filter: layers.28.
value: 0.9406099007353652
- filter: layers.29.
value: 0.10203532427654999
- filter: layers.30.
value: 0.747420490316978
- filter: layers.31.
value: 0.06383831695667043
```
| {"license": "other", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": ["meta-llama/Meta-Llama-3-8B", "meta-llama/Meta-Llama-3-8B-Instruct"], "license_name": "llama3", "license_link": "LICENSE", "extra_gated_prompt": "### META LLAMA 3 COMMUNITY LICENSE AGREEMENT"} | rmihaylov/Llama-3-DARE-v3-8B | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"arxiv:2306.01708",
"base_model:meta-llama/Meta-Llama-3-8B",
"base_model:meta-llama/Meta-Llama-3-8B-Instruct",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T04:29:24+00:00 | [
"2311.03099",
"2306.01708"
] | [] | TAGS
#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2311.03099 #arxiv-2306.01708 #base_model-meta-llama/Meta-Llama-3-8B #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| # Llama-3-DARE-v3-8B
This is a merge of pre-trained language models created using mergekit.
## Merge Details
### Merge Method
This model was merged using the DARE TIES merge method using meta-llama/Meta-Llama-3-8B as a base.
### Models Merged
The following models were included in the merge:
* meta-llama/Meta-Llama-3-8B-Instruct
### Configuration
The following YAML configuration was used to produce this model:
| [
"# Llama-3-DARE-v3-8B\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using meta-llama/Meta-Llama-3-8B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* meta-llama/Meta-Llama-3-8B-Instruct",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #mergekit #merge #conversational #arxiv-2311.03099 #arxiv-2306.01708 #base_model-meta-llama/Meta-Llama-3-8B #base_model-meta-llama/Meta-Llama-3-8B-Instruct #license-other #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Llama-3-DARE-v3-8B\n\nThis is a merge of pre-trained language models created using mergekit.",
"## Merge Details",
"### Merge Method\n\nThis model was merged using the DARE TIES merge method using meta-llama/Meta-Llama-3-8B as a base.",
"### Models Merged\n\nThe following models were included in the merge:\n* meta-llama/Meta-Llama-3-8B-Instruct",
"### Configuration\n\nThe following YAML configuration was used to produce this model:"
] |
text-generation | transformers | # Aura Uncensored l3
AWQ here: https://huggingface.co/lucyknada/Aura_Uncensored_l3_8B-AWQ
GGUF here: https://huggingface.co/Lewdiculous/Aura_Uncensored_l3_8B-GGUF-IQ-Imatrix
This is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output.
I have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model. | {"language": ["en"], "license": "apache-2.0", "library_name": "transformers", "base_model": ["Undi95/Llama-3-Unholy-8B", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Aura_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/RP_Format_QuoteAsterisk_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Luna_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/Theory_of_Mind_Llama3", "Undi95/Llama-3-Unholy-8B", "ResplendentAI/BlueMoon_Llama3"]} | lucyknada/ResplendentAI_Aura_Uncensored_l3_8B-4.0bpw-EXL2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"en",
"base_model:Undi95/Llama-3-Unholy-8B",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-04-21T04:34:22+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #llama #text-generation #conversational #en #base_model-Undi95/Llama-3-Unholy-8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us
| # Aura Uncensored l3
AWQ here: URL
GGUF here: URL
!image/png
This is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output.
I have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model. | [
"# Aura Uncensored l3\n\nAWQ here: URL\n\nGGUF here: URL\n\n!image/png\n\nThis is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output. \n\nI have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model."
] | [
"TAGS\n#transformers #safetensors #llama #text-generation #conversational #en #base_model-Undi95/Llama-3-Unholy-8B #license-apache-2.0 #autotrain_compatible #endpoints_compatible #text-generation-inference #4-bit #region-us \n",
"# Aura Uncensored l3\n\nAWQ here: URL\n\nGGUF here: URL\n\n!image/png\n\nThis is the culmination of all my efforts for the Aura line. I have taken the original training data and applied it over Undi95's Unholy base model. This model can and will provide unsafe information and RP. I strongly recommend that you do not use this model if you are sensitive to unsafe output. \n\nI have tested the model thoroughly and believe that it will please the majority of users. I hope that you enjoy this model."
] |
null | transformers |
# Uploaded model
- **Developed by:** smsaurabhv
- **License:** apache-2.0
- **Finetuned from model :** unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
| {"language": ["en"], "license": "apache-2.0", "tags": ["text-generation-inference", "transformers", "unsloth", "mistral", "trl"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | smsaurabhv/mistralnew3 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"mistral",
"trl",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-04-21T04:34:50+00:00 | [] | [
"en"
] | TAGS
#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us
|
# Uploaded model
- Developed by: smsaurabhv
- License: apache-2.0
- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit
This mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.
<img src="URL width="200"/>
| [
"# Uploaded model\n\n- Developed by: smsaurabhv\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] | [
"TAGS\n#transformers #safetensors #text-generation-inference #unsloth #mistral #trl #en #base_model-unsloth/mistral-7b-instruct-v0.2-bnb-4bit #license-apache-2.0 #endpoints_compatible #region-us \n",
"# Uploaded model\n\n- Developed by: smsaurabhv\n- License: apache-2.0\n- Finetuned from model : unsloth/mistral-7b-instruct-v0.2-bnb-4bit\n\nThis mistral model was trained 2x faster with Unsloth and Huggingface's TRL library.\n\n<img src=\"URL width=\"200\"/>"
] |
text-generation | transformers | Sure, here's a model card based on the information you provided:
---
# Model Card: NousResearch/Llama-2-7b-chat-hf Fine-tuned Model
## Model Details:
- **Model Name:** NousResearch/Llama-2-7b-chat-hf
- **Fine-tuned Model Name:** llama-2-7b-kiranbeethoju
- **Dataset Used for Fine-tuning:** mlabonne/guanaco-llama2-1k
## Model Description:
This model is based on the NousResearch/Llama-2-7b-chat-hf architecture and has been fine-tuned with the dataset mlabonne/guanaco-llama2-1k to enhance its performance for specific tasks.
## Intended Use:
This model is intended to be used for natural language processing tasks, particularly in chatbot applications or conversational agents.
## Factors to Consider:
- **Accuracy:** The model's accuracy is subject to the quality and representativeness of the fine-tuning dataset.
- **Bias and Fairness:** Care should be taken to assess and mitigate any biases present in both the original model and the fine-tuning dataset.
- **Safety and Security:** As with any AI model, precautions should be taken to ensure that the model is not deployed in contexts where its outputs could cause harm.
## Ethical Considerations:
- **Privacy:** It's important to handle user data responsibly and ensure that privacy is maintained when deploying the model in production environments.
- **Transparency:** Users interacting with systems powered by this model should be made aware that they are interacting with an AI system.
- **Accountability:** Clear procedures should be in place to address any issues or errors that arise from the model's use.
## Limitations:
- The model's performance may vary depending on the similarity of the fine-tuning dataset to the target task or domain.
- It may exhibit biases present in the original model or amplified through fine-tuning.
## Caveats:
- While the model has been fine-tuned for specific tasks, it's essential to conduct thorough testing and validation before deploying it in production environments.
## Citation:
If you use this model or the fine-tuned version in your work, please cite the original model as follows:
```
@article{nousresearch_llama2_2022,
title={LLAMA: Large Language Model Augmented},
author={NousResearch},
journal={GitHub},
year={2022},
url={https://github.com/NousResearch/LLAMA}
}
```
---
Feel free to adjust any sections or add additional information as needed! | {} | kiranbeethoju/llama-2-7b-kiranbeethoju | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-04-21T04:37:31+00:00 | [] | [] | TAGS
#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us
| Sure, here's a model card based on the information you provided:
---
# Model Card: NousResearch/Llama-2-7b-chat-hf Fine-tuned Model
## Model Details:
- Model Name: NousResearch/Llama-2-7b-chat-hf
- Fine-tuned Model Name: llama-2-7b-kiranbeethoju
- Dataset Used for Fine-tuning: mlabonne/guanaco-llama2-1k
## Model Description:
This model is based on the NousResearch/Llama-2-7b-chat-hf architecture and has been fine-tuned with the dataset mlabonne/guanaco-llama2-1k to enhance its performance for specific tasks.
## Intended Use:
This model is intended to be used for natural language processing tasks, particularly in chatbot applications or conversational agents.
## Factors to Consider:
- Accuracy: The model's accuracy is subject to the quality and representativeness of the fine-tuning dataset.
- Bias and Fairness: Care should be taken to assess and mitigate any biases present in both the original model and the fine-tuning dataset.
- Safety and Security: As with any AI model, precautions should be taken to ensure that the model is not deployed in contexts where its outputs could cause harm.
## Ethical Considerations:
- Privacy: It's important to handle user data responsibly and ensure that privacy is maintained when deploying the model in production environments.
- Transparency: Users interacting with systems powered by this model should be made aware that they are interacting with an AI system.
- Accountability: Clear procedures should be in place to address any issues or errors that arise from the model's use.
## Limitations:
- The model's performance may vary depending on the similarity of the fine-tuning dataset to the target task or domain.
- It may exhibit biases present in the original model or amplified through fine-tuning.
## Caveats:
- While the model has been fine-tuned for specific tasks, it's essential to conduct thorough testing and validation before deploying it in production environments.
:
If you use this model or the fine-tuned version in your work, please cite the original model as follows:
---
Feel free to adjust any sections or add additional information as needed! | [
"# Model Card: NousResearch/Llama-2-7b-chat-hf Fine-tuned Model",
"## Model Details:\n\n- Model Name: NousResearch/Llama-2-7b-chat-hf\n- Fine-tuned Model Name: llama-2-7b-kiranbeethoju\n- Dataset Used for Fine-tuning: mlabonne/guanaco-llama2-1k",
"## Model Description:\n\nThis model is based on the NousResearch/Llama-2-7b-chat-hf architecture and has been fine-tuned with the dataset mlabonne/guanaco-llama2-1k to enhance its performance for specific tasks.",
"## Intended Use:\n\nThis model is intended to be used for natural language processing tasks, particularly in chatbot applications or conversational agents.",
"## Factors to Consider:\n\n- Accuracy: The model's accuracy is subject to the quality and representativeness of the fine-tuning dataset.\n- Bias and Fairness: Care should be taken to assess and mitigate any biases present in both the original model and the fine-tuning dataset.\n- Safety and Security: As with any AI model, precautions should be taken to ensure that the model is not deployed in contexts where its outputs could cause harm.",
"## Ethical Considerations:\n\n- Privacy: It's important to handle user data responsibly and ensure that privacy is maintained when deploying the model in production environments.\n- Transparency: Users interacting with systems powered by this model should be made aware that they are interacting with an AI system.\n- Accountability: Clear procedures should be in place to address any issues or errors that arise from the model's use.",
"## Limitations:\n\n- The model's performance may vary depending on the similarity of the fine-tuning dataset to the target task or domain.\n- It may exhibit biases present in the original model or amplified through fine-tuning.",
"## Caveats:\n\n- While the model has been fine-tuned for specific tasks, it's essential to conduct thorough testing and validation before deploying it in production environments.\n\n:\n\nIf you use this model or the fine-tuned version in your work, please cite the original model as follows:\n\n\n\n---\n\nFeel free to adjust any sections or add additional information as needed!"
] | [
"TAGS\n#transformers #pytorch #llama #text-generation #autotrain_compatible #endpoints_compatible #text-generation-inference #region-us \n",
"# Model Card: NousResearch/Llama-2-7b-chat-hf Fine-tuned Model",
"## Model Details:\n\n- Model Name: NousResearch/Llama-2-7b-chat-hf\n- Fine-tuned Model Name: llama-2-7b-kiranbeethoju\n- Dataset Used for Fine-tuning: mlabonne/guanaco-llama2-1k",
"## Model Description:\n\nThis model is based on the NousResearch/Llama-2-7b-chat-hf architecture and has been fine-tuned with the dataset mlabonne/guanaco-llama2-1k to enhance its performance for specific tasks.",
"## Intended Use:\n\nThis model is intended to be used for natural language processing tasks, particularly in chatbot applications or conversational agents.",
"## Factors to Consider:\n\n- Accuracy: The model's accuracy is subject to the quality and representativeness of the fine-tuning dataset.\n- Bias and Fairness: Care should be taken to assess and mitigate any biases present in both the original model and the fine-tuning dataset.\n- Safety and Security: As with any AI model, precautions should be taken to ensure that the model is not deployed in contexts where its outputs could cause harm.",
"## Ethical Considerations:\n\n- Privacy: It's important to handle user data responsibly and ensure that privacy is maintained when deploying the model in production environments.\n- Transparency: Users interacting with systems powered by this model should be made aware that they are interacting with an AI system.\n- Accountability: Clear procedures should be in place to address any issues or errors that arise from the model's use.",
"## Limitations:\n\n- The model's performance may vary depending on the similarity of the fine-tuning dataset to the target task or domain.\n- It may exhibit biases present in the original model or amplified through fine-tuning.",
"## Caveats:\n\n- While the model has been fine-tuned for specific tasks, it's essential to conduct thorough testing and validation before deploying it in production environments.\n\n:\n\nIf you use this model or the fine-tuned version in your work, please cite the original model as follows:\n\n\n\n---\n\nFeel free to adjust any sections or add additional information as needed!"
] |
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