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null | null | {"license": "openrail"} | Homiebear/Brok | null | [
"license:openrail",
"region:us"
] | null | 2024-05-01T06:19:28+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-small-finetuned-feedback
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: 1.6145
- Rouge1: 51.2809
- Rouge2: 27.3229
- Rougel: 49.2287
- Rougelsum: 49.211
- Gen Len: 10.1736
## 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: 12
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 61 | 2.9832 | 24.9931 | 10.0881 | 21.9651 | 22.0687 | 16.4876 |
| No log | 2.0 | 122 | 2.1822 | 36.3348 | 17.5969 | 34.3034 | 34.2834 | 12.1653 |
| No log | 3.0 | 183 | 1.9607 | 43.7295 | 21.5907 | 41.8815 | 41.929 | 10.5372 |
| No log | 4.0 | 244 | 1.8412 | 48.7074 | 25.1744 | 46.8382 | 46.8399 | 10.405 |
| No log | 5.0 | 305 | 1.7674 | 50.1972 | 26.4116 | 48.1456 | 48.0538 | 10.2066 |
| No log | 6.0 | 366 | 1.7195 | 51.0984 | 27.8685 | 48.9483 | 49.0108 | 10.3554 |
| No log | 7.0 | 427 | 1.6832 | 50.272 | 27.3168 | 48.4083 | 48.4307 | 10.0331 |
| No log | 8.0 | 488 | 1.6558 | 50.6829 | 27.5132 | 48.6684 | 48.735 | 10.2727 |
| 2.363 | 9.0 | 549 | 1.6357 | 50.0286 | 27.0674 | 48.0211 | 48.0783 | 10.1736 |
| 2.363 | 10.0 | 610 | 1.6240 | 50.8207 | 26.8345 | 48.6528 | 48.6903 | 10.1983 |
| 2.363 | 11.0 | 671 | 1.6166 | 50.9796 | 27.0236 | 48.8888 | 48.8958 | 10.1901 |
| 2.363 | 12.0 | 732 | 1.6145 | 51.2809 | 27.3229 | 49.2287 | 49.211 | 10.1736 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "t5-small-finetuned-feedback", "results": []}]} | phdreg/t5-small-finetuned-feedback | 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 | 2024-05-01T06:19:45+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# superrep-mail
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.2](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.2) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 2.4050
## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.05
- num_epochs: 2
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.1186 | 1.0 | 1 | 2.7996 |
| 0.6556 | 1.3333 | 2 | 2.4050 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "mistralai/Mistral-7B-Instruct-v0.2", "model-index": [{"name": "superrep-mail", "results": []}]} | GeekRoom/superrep-mail | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:mistralai/Mistral-7B-Instruct-v0.2",
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T06:22:20+00:00 |
reinforcement-learning | ml-agents |
# **ppo** Agent playing **Huggy**
This is a trained model of a **ppo** agent playing **Huggy**
using the [Unity ML-Agents Library](https://github.com/Unity-Technologies/ml-agents).
## Usage (with ML-Agents)
The Documentation: https://unity-technologies.github.io/ml-agents/ML-Agents-Toolkit-Documentation/
We wrote a complete tutorial to learn to train your first agent using ML-Agents and publish it to the Hub:
- A *short tutorial* where you teach Huggy the Dog ๐ถ to fetch the stick and then play with him directly in your
browser: https://huggingface.co/learn/deep-rl-course/unitbonus1/introduction
- A *longer tutorial* to understand how works ML-Agents:
https://huggingface.co/learn/deep-rl-course/unit5/introduction
### Resume the training
```bash
mlagents-learn <your_configuration_file_path.yaml> --run-id=<run_id> --resume
```
### Watch your Agent play
You can watch your agent **playing directly in your browser**
1. If the environment is part of ML-Agents official environments, go to https://huggingface.co/unity
2. Step 1: Find your model_id: AlkQ/ppo-Huggy
3. Step 2: Select your *.nn /*.onnx file
4. Click on Watch the agent play ๐
| {"library_name": "ml-agents", "tags": ["Huggy", "deep-reinforcement-learning", "reinforcement-learning", "ML-Agents-Huggy"]} | AlkQ/ppo-Huggy | null | [
"ml-agents",
"tensorboard",
"onnx",
"Huggy",
"deep-reinforcement-learning",
"reinforcement-learning",
"ML-Agents-Huggy",
"region:us"
] | null | 2024-05-01T06:22:52+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# 0.0001_withdpo_4iters_bs256_533lr_iter_4
This model is a fine-tuned version of [ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_3](https://huggingface.co/ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_3) on the updated and the original datasets.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 3e-07
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 4
- total_train_batch_size: 256
- total_eval_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.36.2
- Pytorch 2.1.2+cu121
- Datasets 2.14.6
- Tokenizers 0.15.2
| {"license": "mit", "tags": ["alignment-handbook", "generated_from_trainer", "trl", "dpo", "generated_from_trainer"], "datasets": ["updated", "original"], "base_model": "ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_3", "model-index": [{"name": "0.0001_withdpo_4iters_bs256_533lr_iter_4", "results": []}]} | ShenaoZ/0.0001_withdpo_4iters_bs256_533lr_iter_4 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"alignment-handbook",
"generated_from_trainer",
"trl",
"dpo",
"conversational",
"dataset:updated",
"dataset:original",
"base_model:ShenaoZ/0.0001_withdpo_4iters_bs256_531lr_iter_3",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:23:02+00:00 |
text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# advsafe-spin-iter0
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 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: 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": ["generated_from_trainer"], "base_model": "alignment-handbook/zephyr-7b-sft-full", "model-index": [{"name": "advsafe-spin-iter0", "results": []}]} | AmberYifan/advsafe-spin-iter0 | null | [
"transformers",
"tensorboard",
"safetensors",
"mistral",
"text-generation",
"generated_from_trainer",
"conversational",
"base_model:alignment-handbook/zephyr-7b-sft-full",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:24:02+00:00 |
automatic-speech-recognition | transformers | {} | cportoca/whisper-tiny-finetune | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:24:06+00:00 |
|
null | null | {} | cloudnbits/Phi-3-mini-4k-instruct-dml-int4-onnx | null | [
"onnx",
"region:us"
] | null | 2024-05-01T06:24:08+00:00 |
|
text-generation | transformers | {} | HavryliukA/llama2_megogo_new_prompt_1204_100docs_0105_35epochs_MERGED | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:24:10+00:00 |
|
null | transformers | {"license": "apache-2.0"} | songzewu/vasista22-whisper-hindi-small-ct2 | null | [
"transformers",
"pytorch",
"jax",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:24:11+00:00 |
|
image-classification | transformers | {} | AP45345/New_sec_Model | null | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain_compatible",
"endpoints_compatible",
"region:us",
"has_space"
] | null | 2024-05-01T06:25:00+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
starchat2-15b-v0.1 - bnb 8bits
- Model creator: https://huggingface.co/HuggingFaceH4/
- Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/
Original model description:
---
base_model: HuggingFaceH4/starchat2-15b-sft-v0.1
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- HuggingFaceH4/orca_dpo_pairs
model-index:
- name: starchat2-15b-v0.1
results: []
---
<img src="https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/resolve/main/model_logo.png" alt="StarChat2 15B Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for StarChat2 15B
StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat2 is the latest model in the series, and is a fine-tuned version of [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b) that was trained with SFT and DPO on a mix of synthetic datasets.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Model type:** A 16B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English and 600+ programming languages.
- **License:** BigCode Open RAIL-M v1
- **Finetuned from model:** [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground
## Performance
StarChat2 15B was trained to balance chat and programming capabilities. It achieves strong performance on chat benchmarks like [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [IFEval](https://arxiv.org/abs/2311.07911), as well as the canonical HumanEval benchmark for Python code completion. The scores reported below were obtained using the [LightEval](https://github.com/huggingface/lighteval) evaluation suite (commit `988959cb905df4baa050f82b4d499d46e8b537f2`) and each prompt has been formatted with the model's corresponding chat template to simulate real-world usage. This is why some scores may differ from those reported in technical reports or on the Open LLM Leaderboard.
| Model | MT Bench | IFEval | HumanEval |
|-------------------------------------------------------------------------------------------------|---------:|-------:|----------:|
| [starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1) | 7.66 | 35.12 | 71.34 |
| [deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) | 4.17 | 14.23 | 80.48 |
| [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | 6.80 | 43.44 | 50.60 |
## Intended uses & limitations
The model was fine-tuned on a blend of chat, code, math, and reasoning datasets. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground) to test its coding capabilities.
Here's how you can run the model using the `pipeline()` function from ๐ค Transformers:
```python
# pip install 'transformers @ git+https://github.com/huggingface/transformers.git@831bc25d8fdb85768402f772cf65cc3d7872b211'
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="HuggingFaceH4/starchat2-15b-v0.1",
device_map="auto",
torch_dtype=torch.bfloat16,
)
messages = [
{
"role": "system",
"content": "You are StarChat2, an expert programming assistant",
},
{"role": "user", "content": "Write a simple website in HTML. When a user clicks the button, it shows a random Chuck Norris joke."},
]
outputs = pipe(
messages,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.95,
stop_sequence="<|im_end|>",
)
print(outputs[0]["generated_text"][-1]["content"])
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
StarChat2 15B has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder2 dataset](https://huggingface.co/datasets/bigcode/the-stack-v2)
Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect.
For example, it may produce code that does not compile or that produces incorrect results.
It may also produce code that is vulnerable to security exploits.
We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking.
StarChat2 15B was fine-tuned from the base model [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoder2-15b#limitations) for relevant information.
In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://huggingface.co/papers/2402.19173).
## Training details
This model is a fine-tuned version of [starchat2-15b-sft-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1) on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. Check out the recipe in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for more details.
It achieves the following results on the evaluation set:
- Loss: 0.4347
- Rewards/chosen: -0.9461
- Rewards/rejected: -2.7745
- Rewards/accuracies: 0.7658
- Rewards/margins: 1.8284
- Logps/rejected: -322.1934
- Logps/chosen: -316.1898
- Logits/rejected: -2.3817
- Logits/chosen: -2.3005
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.717 | 0.17 | 100 | 0.6006 | -0.0924 | -0.2899 | 0.6329 | 0.1975 | -272.5022 | -299.1165 | -2.5313 | -2.4191 |
| 0.6273 | 0.35 | 200 | 0.5160 | -0.3994 | -0.9461 | 0.6930 | 0.5467 | -285.6261 | -305.2568 | -2.5281 | -2.4278 |
| 0.5538 | 0.52 | 300 | 0.4781 | -0.6589 | -1.5892 | 0.7247 | 0.9302 | -298.4870 | -310.4470 | -2.4996 | -2.4110 |
| 0.5056 | 0.7 | 400 | 0.4594 | -0.8283 | -2.1332 | 0.7437 | 1.3050 | -309.3687 | -313.8344 | -2.4472 | -2.3644 |
| 0.4983 | 0.87 | 500 | 0.4512 | -0.7758 | -2.2806 | 0.7468 | 1.5049 | -312.3167 | -312.7843 | -2.4223 | -2.3404 |
| 0.4662 | 1.04 | 600 | 0.4431 | -0.7839 | -2.4016 | 0.7658 | 1.6177 | -314.7355 | -312.9465 | -2.4049 | -2.3215 |
| 0.4411 | 1.22 | 700 | 0.4415 | -1.0090 | -2.7582 | 0.7690 | 1.7492 | -321.8679 | -317.4481 | -2.3840 | -2.3016 |
| 0.471 | 1.39 | 800 | 0.4368 | -0.9617 | -2.7445 | 0.7690 | 1.7828 | -321.5930 | -316.5019 | -2.3809 | -2.2991 |
| 0.4485 | 1.57 | 900 | 0.4351 | -0.9490 | -2.7594 | 0.7722 | 1.8103 | -321.8916 | -316.2497 | -2.3815 | -2.3004 |
| 0.4411 | 1.74 | 1000 | 0.4348 | -0.9293 | -2.7469 | 0.7658 | 1.8176 | -321.6409 | -315.8547 | -2.3823 | -2.3011 |
| 0.4499 | 1.92 | 1100 | 0.4348 | -0.9482 | -2.7767 | 0.7658 | 1.8285 | -322.2369 | -316.2320 | -2.3828 | -2.3012 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {} | RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-8bits | null | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"conversational",
"arxiv:2311.07911",
"arxiv:2402.19173",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-01T06:25:19+00:00 |
null | null | {"license": "unknown"} | hautc/X3 | null | [
"license:unknown",
"region:us"
] | null | 2024-05-01T06:26:52+00:00 |
|
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
starchat2-15b-sft-v0.1 - bnb 4bits
- Model creator: https://huggingface.co/HuggingFaceH4/
- Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1/
Original model description:
---
license: bigcode-openrail-m
base_model: bigcode/starcoder2-15b
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/airoboros-3.2
- HuggingFaceH4/Code-Feedback
- HuggingFaceH4/orca-math-word-problems-200k
- HuggingFaceH4/SystemChat
- HuggingFaceH4/capybara
model-index:
- name: starcoder2-15b-sft-v5.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model Card for starchat2-15b-sft-v0.1
This model is a fine-tuned version of [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) on the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6614
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6422 | 1.0 | 910 | 0.6910 |
| 0.5701 | 2.0 | 1820 | 0.6639 |
| 0.5227 | 3.0 | 2730 | 0.6614 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {} | RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-4bits | null | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"4-bit",
"region:us"
] | null | 2024-05-01T06:27:08+00:00 |
text-to-audio | transformers | {} | mikhail-panzo/zlm_b128_le4_s12000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:27:18+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# distilbert-stock-tweet-sentiment-analysis
This model is a fine-tuned version of [distilbert-base-uncased](https://huggingface.co/distilbert-base-uncased) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5815
- Accuracy: 0.781
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.7065 | 1.0 | 1000 | 0.5816 | 0.7628 |
| 0.4915 | 2.0 | 2000 | 0.5666 | 0.7762 |
| 0.3766 | 3.0 | 3000 | 0.5815 | 0.781 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "distilbert-base-uncased", "model-index": [{"name": "distilbert-stock-tweet-sentiment-analysis", "results": []}]} | elitenandu/distilbert-stock-tweet-sentiment-analysis | null | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:27:20+00:00 |
null | null | {} | cloudnbits/Phi-3-mini-4k-instruct-dml-fp16-onnx | null | [
"onnx",
"region:us"
] | null | 2024-05-01T06:27:27+00:00 |
|
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-1
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 1
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-1", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-1 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:27:44+00:00 |
null | null | {} | cloudnbits/Phi-3-mini-128k-instruct-dml-int4-onnx | null | [
"onnx",
"region:us"
] | null | 2024-05-01T06:28:43+00:00 |
|
null | null | {} | cloudnbits/Phi-3-mini-128k-instruct-dml-fp16-onnx | null | [
"onnx",
"region:us"
] | null | 2024-05-01T06:29:04+00:00 |
|
null | null | {} | TimothyTheSpartan/GeorgeEdd | null | [
"region:us"
] | null | 2024-05-01T06:29:46+00:00 |
|
object-detection | transformers | {"license": "mit", "pipeline_tag": "object-detection"} | underthelights/robocup2024_yolov7_exp_240407 | null | [
"transformers",
"object-detection",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:32:17+00:00 |
|
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi16_LoRA
<Gallery />
## Model description
These are embracellm/sushi16_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Salmon Philly Salad Roll to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi16_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers", "text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Salmon Philly Salad Roll ", "widget": []} | embracellm/sushi16_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-05-01T06:34:28+00:00 |
text-generation | transformers | <img src="./ninjalogo.svg" width="100%" height="20%" alt="">
- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1) ใฎGGUF็
# Our Models for GGUF
- [Vecteus-GGUF](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1-gguf)
- [Ninja-v1-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-GGUF)
- [Ninja-v1-NSFW-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-GGUF)
- [Ninja-v1-128k-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k-GGUF)
- [Ninja-v1-NSFW-128k-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k-GGUF)
| {"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"} | Local-Novel-LLM-project/Ninja-v1-GGUF | null | [
"transformers",
"gguf",
"finetuned",
"text-generation",
"en",
"ja",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:35:09+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# phi-2-intentmodel
This model is a fine-tuned version of [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 3
### Training results
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "mit", "library_name": "peft", "tags": ["trl", "sft", "generated_from_trainer"], "base_model": "microsoft/phi-2", "model-index": [{"name": "phi-2-intentmodel", "results": []}]} | Mohit-Rai-402/phi-2-intentmodel | null | [
"peft",
"tensorboard",
"safetensors",
"trl",
"sft",
"generated_from_trainer",
"base_model:microsoft/phi-2",
"license:mit",
"region:us"
] | null | 2024-05-01T06:37:14+00:00 |
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-125m-finetuned-rte
This model is a fine-tuned version of [facebook/opt-125m](https://huggingface.co/facebook/opt-125m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.0060
- Accuracy: 0.4729
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.9768 | 0.4765 |
| No log | 2.0 | 2 | 0.9663 | 0.4729 |
| No log | 3.0 | 3 | 0.9564 | 0.4729 |
| No log | 4.0 | 4 | 0.9482 | 0.4729 |
| No log | 5.0 | 5 | 0.9415 | 0.4693 |
| No log | 6.0 | 6 | 0.9357 | 0.4693 |
| No log | 7.0 | 7 | 0.9311 | 0.4693 |
| No log | 8.0 | 8 | 0.9275 | 0.4693 |
| No log | 9.0 | 9 | 0.9251 | 0.4693 |
| No log | 10.0 | 10 | 0.9239 | 0.4693 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-125m", "model-index": [{"name": "opt-125m-finetuned-rte", "results": []}]} | elliottfitzgerald/opt-125m-finetuned-rte | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-125m",
"license:other",
"region:us"
] | null | 2024-05-01T06:37:46+00:00 |
null | null | {} | luciusy/ts_planadd_pp2 | null | [
"region:us"
] | null | 2024-05-01T06:38:32+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# cnn_news_summary_model_trained_on_reduced_data
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: 1.6040
- Rouge1: 0.2177
- Rouge2: 0.0941
- Rougel: 0.1839
- Rougelsum: 0.184
- Generated Length: 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: 3
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Generated Length |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|:------:|:---------:|:----------------:|
| No log | 1.0 | 431 | 1.6239 | 0.2174 | 0.0935 | 0.183 | 0.183 | 19.0 |
| 1.92 | 2.0 | 862 | 1.6075 | 0.2168 | 0.0933 | 0.1828 | 0.1829 | 19.0 |
| 1.8221 | 3.0 | 1293 | 1.6040 | 0.2177 | 0.0941 | 0.1839 | 0.184 | 19.0 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["rouge"], "base_model": "t5-small", "model-index": [{"name": "cnn_news_summary_model_trained_on_reduced_data", "results": []}]} | minhaz21/cnn_news_summary_model_trained_on_reduced_data | 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 | 2024-05-01T06:38:52+00:00 |
text-generation | transformers |
# DcardStylePost-7B-Instruct-v1_0
DcardStylePost-7B-Instruct-v1_0 is a experimental finetuned from Breeze-7B-Instruct-v1_0
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "JiunYi/DcardStylePost-7B-Instruct-v1_0"
messages = [{"role": "user", "content": "ใไปปๅใ๏ผ\nไฝ ๆฏๅฃ็ข่ก้ทๅ
ฌๅธ็่ณๆทฑๅฏซๆ๏ผ็พๅจ่ฆๆฐๅฏซไธ็ฏDcard ใ็พๅฆ็ใ็ๅฃ็ขๆใ\n้บป็
ฉ็่ฎไธๅ ใๅ่่ณๆใๅฐๅๅๆๅ็่็ๆดปๆ
ๅข็่ๅ๏ผไธฆ็ก้่ฒผ่ฟ็ๆดปๅฃๅป๏ผไปฅไธ่ฆ่ขซ็ผ็พๆฏๆฅญ้
ๆ็บ้่ฆ็ฎๆจใ\n่ซไพ็
งใๅฃ็ขๅ่งใ็บๆ็ซ ็ๆ
ไบไธป่ปธๆฐๅฏซๆด็ฏๆ็ซ ๏ผ่ซๅ่ไธฆไพ็
งใ่ฆ็ฏใ่ง่ฒๅฃๅปไพๆฐๅฏซๆ็ซ ๏ผ่ๅ
ฅ่ง่ฒๆ
ๅขไพๅฎๆๆด็ฏๆ
ไบๆฐๅฏซ๏ผ่ซๆณจ้ไบบ็ฉ่ง่ฒ็น่ฒๅ้ๅถใ\n\n๏ผ\n\nใ่ฆ็ฏใ๏ผ\n\n1.้็ขๅบๆ็ซ ๆจ้ก\n2.่ซไปฅ็ฌฌไธไบบ็จฑๆนๅผๆฐๅฏซๆ็ซ \n3.่ซ่จไฝ็พๅจๆฏๆ้ๆฏ่ฅฟๅ
2023 ๅนด\n4.Please write in zh-TW language .\n5.้ไฝๅฃ็ขๆๆฅญ้
ๆ\n6.22~24ๆญฒ\n7.ๆฐๅฏซ่ง่ฒ็็ๆงๅฅ็บๅฅณๆง\n8.ไนพๆง็ฎ่\n9.ๆณจ้็ฎ่ไฟๆบผ\n10.ๅธธๅธธๅพ
ๅจๅฎคๅ
ง\n๏ผ\n\nใๅ่่ณๆใ\n\nๆๆฃฎๅ
็๏ฝๅฐๅทงๆฟๆฝคๆพคๅด้ง\nไธปๆๆฏ่่ฑใ้็ธทๆข
ไฟๆฟ่็ทฉ๏ผๅ ไธๅปฃๅ่ ปๅธธ็ๅฐ็ๅฐฑ่ฒทไพ่ฉฆ่ฉฆ\nๆฏใๆฐดๅซฉใ็ไฟๆฟๆ๏ผ่ ปๆธ
็ฝใๅธๆถๅฎ็ฎ่ๆๅซฉๅซฉ็๏ผ\nๅพๅๅๆทๅฎ้ข่็ๆ่ฆบ๏ผๅฏไปฅ็ถๅฆๅ่ถๆ้็้ๆ้ข่๏ผ๏ผ\nๅฐฑๅฏไปฅ่ฎๅฆๆ่ ปๆ่ฒผๆไน
็๏ผ\nๆญ้
ๆไน
ๅ็็ฒๅบๆถฒไฝฟ็จ๏ผไฟๆฟๆ่ฒผ็ๆๆๆดๆ้กฏ๏ผ\nๆทปๅ ้็ธทๆข
ใๆฏ่่ฑใๅฐๆฒณๆฐด๏ผ่็ทฉไฟๆฟ\nๅนซๅฉๆฐดๆฒนๅนณ่กก๏ผไฝฟ่่ๆดปๅ้ไบฎ\n่ฎไนพ่็พๆฐด่ ่้จๆฒนๅ
่ฎๆฐดๅ
\r\n็กๆทปๅ ๏ผ้
็ฒพ๏ผ้ฆ็ฒพ๏ผๆฒน่\nๆณๆ้บผ็จๅฐฑๆ้บผ็จ\n\n๏ผ\n\nใๅฃ็ขๅ่งใ\nๆ่ฟๅ ็บๆๅญฃ่ๅพไนพ็ฅ๏ผๆไปฅๅพๅๆญก็จไฟๆฟๅด้ง๏ผๆถผ็ฝๅไฟๆฟ๏ผๆ้ๆๅนพๆฌพ๏ผไฝๅฏไปฅๆ้กฏๆ่ฆบๅพๅฐๆไบๅดๅฎๅพ้่ใๆไบ็็ๅฏไปฅไฟๆฟ๏ผไธ็ถฒ็ฌๆๆ็ผ็พๅๅ็็ๅทฎ็ฐ๏ผๆผๆฏๆณไพๅไธๅ่ช็็ๅๆ๏ผๅ
ๅซๅด้ง็ดฐ็ทปๅบฆใ้
ธ้นผๅบฆใ็ฒ่ฉ็จๅบฆใๅธๆถๅบฆ็ญ๏ผ"}]
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=512, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"])
``` | {"language": ["zh"], "license": "gpl-3.0", "tags": ["art", "marketing", "llama-factory"], "metrics": ["bleu"], "base_model": "MediaTek-Research/Breeze-7B-Instruct-v1_0"} | JiunYi/DcardStylePost-7B-Instruct-v1_0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"art",
"marketing",
"llama-factory",
"conversational",
"zh",
"base_model:MediaTek-Research/Breeze-7B-Instruct-v1_0",
"license:gpl-3.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:39:13+00:00 |
text-generation | transformers | <img src="./veteus_logo.svg" width="100%" height="20%" alt="">
- Vecteus-v1ใฎGGUF็
# Our Models for GGUF
- [Vecteus](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1-gguf)
- [Ninja-v1](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-GGUF)
- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-GGUF)
| {"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned"], "pipeline_tag": "text-generation"} | Local-Novel-LLM-project/Vecteus-v1-gguf | null | [
"transformers",
"gguf",
"finetuned",
"text-generation",
"en",
"ja",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:39:28+00:00 |
text-to-audio | 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. -->
# fil_b32_le5_s8000
This model is a fine-tuned version of [microsoft/speecht5_tts](https://huggingface.co/microsoft/speecht5_tts) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4039
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 32
- 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: 2000
- training_steps: 8000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:--------:|:----:|:---------------:|
| 0.632 | 11.1111 | 500 | 0.5323 |
| 0.519 | 22.2222 | 1000 | 0.4494 |
| 0.4816 | 33.3333 | 1500 | 0.4291 |
| 0.481 | 44.4444 | 2000 | 0.4211 |
| 0.4459 | 55.5556 | 2500 | 0.4139 |
| 0.4484 | 66.6667 | 3000 | 0.4114 |
| 0.4317 | 77.7778 | 3500 | 0.4081 |
| 0.4301 | 88.8889 | 4000 | 0.4076 |
| 0.4274 | 100.0 | 4500 | 0.4059 |
| 0.4323 | 111.1111 | 5000 | 0.4062 |
| 0.4189 | 122.2222 | 5500 | 0.4045 |
| 0.4272 | 133.3333 | 6000 | 0.4059 |
| 0.4219 | 144.4444 | 6500 | 0.4058 |
| 0.4125 | 155.5556 | 7000 | 0.4049 |
| 0.42 | 166.6667 | 7500 | 0.4046 |
| 0.4145 | 177.7778 | 8000 | 0.4039 |
### Framework versions
- Transformers 4.41.0.dev0
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "mit", "tags": ["generated_from_trainer"], "base_model": "microsoft/speecht5_tts", "model-index": [{"name": "fil_b32_le5_s8000", "results": []}]} | mikhail-panzo/fil_b32_le5_s8000 | null | [
"transformers",
"tensorboard",
"safetensors",
"speecht5",
"text-to-audio",
"generated_from_trainer",
"base_model:microsoft/speecht5_tts",
"license:mit",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:40:30+00:00 |
text-generation | transformers | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
starchat2-15b-sft-v0.1 - bnb 8bits
- Model creator: https://huggingface.co/HuggingFaceH4/
- Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1/
Original model description:
---
license: bigcode-openrail-m
base_model: bigcode/starcoder2-15b
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/airoboros-3.2
- HuggingFaceH4/Code-Feedback
- HuggingFaceH4/orca-math-word-problems-200k
- HuggingFaceH4/SystemChat
- HuggingFaceH4/capybara
model-index:
- name: starcoder2-15b-sft-v5.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model Card for starchat2-15b-sft-v0.1
This model is a fine-tuned version of [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) on the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6614
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6422 | 1.0 | 910 | 0.6910 |
| 0.5701 | 2.0 | 1820 | 0.6639 |
| 0.5227 | 3.0 | 2730 | 0.6614 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {} | RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-8bits | null | [
"transformers",
"safetensors",
"starcoder2",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"8-bit",
"region:us"
] | null | 2024-05-01T06:40:57+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
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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]
## 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]
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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]
#### 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. -->
### 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": []} | jamesohe/casaudit3-4bit-p03-adapter | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:41:21+00:00 |
null | null | {"license": "other", "license_name": "hassan", "license_link": "LICENSE"} | Hassan-khalaf/hassan.khalaf | null | [
"license:other",
"region:us"
] | null | 2024-05-01T06:42:13+00:00 |
|
null | peft |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-1.3b-finetuned-rte
This model is a fine-tuned version of [facebook/opt-1.3b](https://huggingface.co/facebook/opt-1.3b) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7779
- Accuracy: 0.4477
## 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: 10
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| No log | 1.0 | 1 | 0.7844 | 0.4765 |
| No log | 2.0 | 2 | 0.7837 | 0.4801 |
| No log | 3.0 | 3 | 0.7822 | 0.4910 |
| No log | 4.0 | 4 | 0.7828 | 0.4838 |
| No log | 5.0 | 5 | 0.7828 | 0.4838 |
| No log | 6.0 | 6 | 0.7822 | 0.4838 |
| No log | 7.0 | 7 | 0.7820 | 0.4801 |
| No log | 8.0 | 8 | 0.7817 | 0.4838 |
| No log | 9.0 | 9 | 0.7814 | 0.4874 |
| No log | 10.0 | 10 | 0.7815 | 0.4874 |
### Framework versions
- PEFT 0.10.0
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "other", "library_name": "peft", "tags": ["generated_from_trainer"], "metrics": ["accuracy"], "base_model": "facebook/opt-1.3b", "model-index": [{"name": "opt-1.3b-finetuned-rte", "results": []}]} | elliottfitzgerald/opt-1.3b-finetuned-rte | null | [
"peft",
"tensorboard",
"safetensors",
"generated_from_trainer",
"base_model:facebook/opt-1.3b",
"license:other",
"region:us"
] | null | 2024-05-01T06:44:53+00:00 |
null | null | {} | Mohamedshaaban2001/qwen1.5-llm | null | [
"region:us"
] | null | 2024-05-01T06:45:11+00:00 |
|
text-to-audio | 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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | procit001/dutch_female_2024_spk_5 | null | [
"transformers",
"safetensors",
"vits",
"text-to-audio",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:48:07+00:00 |
text-generation | transformers | {} | Rekha208/Llama-2-7b-chat-finetune | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:48:41+00:00 |
|
text2text-generation | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# t5-base-finetuned-feedback
This model was trained from scratch on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2738
- Rouge1: 55.9578
- Rouge2: 31.3401
- Rougel: 52.9556
- Rougelsum: 53.1034
- Gen Len: 10.2562
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 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: 10
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rouge1 | Rouge2 | Rougel | Rougelsum | Gen Len |
|:-------------:|:-----:|:----:|:---------------:|:-------:|:-------:|:-------:|:---------:|:-------:|
| No log | 1.0 | 61 | 1.4341 | 53.0065 | 28.3454 | 50.4489 | 50.5377 | 9.3388 |
| No log | 2.0 | 122 | 1.3604 | 53.2275 | 28.6424 | 50.6585 | 50.7617 | 9.8182 |
| No log | 3.0 | 183 | 1.3207 | 52.7581 | 28.6272 | 49.6977 | 49.7928 | 10.0661 |
| No log | 4.0 | 244 | 1.3098 | 53.5227 | 28.6578 | 50.2637 | 50.2897 | 9.9752 |
| No log | 5.0 | 305 | 1.2898 | 54.4587 | 29.8825 | 51.3522 | 51.4744 | 9.876 |
| No log | 6.0 | 366 | 1.2781 | 54.046 | 29.7089 | 51.3241 | 51.4283 | 10.1818 |
| No log | 7.0 | 427 | 1.2771 | 55.1788 | 30.8745 | 52.3598 | 52.4871 | 10.2149 |
| No log | 8.0 | 488 | 1.2762 | 55.6258 | 30.9444 | 52.5715 | 52.6889 | 10.2397 |
| 1.2952 | 9.0 | 549 | 1.2746 | 55.759 | 30.918 | 52.8427 | 52.8878 | 10.1818 |
| 1.2952 | 10.0 | 610 | 1.2738 | 55.9578 | 31.3401 | 52.9556 | 53.1034 | 10.2562 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"tags": ["generated_from_trainer"], "metrics": ["rouge"], "model-index": [{"name": "t5-base-finetuned-feedback", "results": []}]} | phdreg/t5-base-finetuned-feedback | null | [
"transformers",
"tensorboard",
"safetensors",
"t5",
"text2text-generation",
"generated_from_trainer",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:48:47+00:00 |
null | null | {} | sassad/fine_tuned_lora | null | [
"region:us"
] | null | 2024-05-01T06:48:50+00:00 |
|
null | null | {"license": "apache-2.0"} | amritkamboz/amrit | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T06:49:32+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | abc88767/model27 | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:49:54+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
starchat2-15b-v0.1 - GGUF
- Model creator: https://huggingface.co/HuggingFaceH4/
- Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [starchat2-15b-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q2_K.gguf) | Q2_K | 5.77GB |
| [starchat2-15b-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.IQ3_XS.gguf) | IQ3_XS | 6.25GB |
| [starchat2-15b-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.IQ3_S.gguf) | IQ3_S | 6.52GB |
| [starchat2-15b-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.51GB |
| [starchat2-15b-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.IQ3_M.gguf) | IQ3_M | 6.8GB |
| [starchat2-15b-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q3_K.gguf) | Q3_K | 7.49GB |
| [starchat2-15b-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.49GB |
| [starchat2-15b-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q3_K_L.gguf) | Q3_K_L | 8.35GB |
| [starchat2-15b-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.IQ4_XS.gguf) | IQ4_XS | 8.12GB |
| [starchat2-15b-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q4_0.gguf) | Q4_0 | 8.44GB |
| [starchat2-15b-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.IQ4_NL.gguf) | IQ4_NL | 8.55GB |
| [starchat2-15b-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.53GB |
| [starchat2-15b-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q4_K.gguf) | Q4_K | 9.18GB |
| [starchat2-15b-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q4_K_M.gguf) | Q4_K_M | 9.18GB |
| [starchat2-15b-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q4_1.gguf) | Q4_1 | 9.35GB |
| [starchat2-15b-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q5_0.gguf) | Q5_0 | 10.27GB |
| [starchat2-15b-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q5_K_S.gguf) | Q5_K_S | 10.27GB |
| [starchat2-15b-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q5_K.gguf) | Q5_K | 10.65GB |
| [starchat2-15b-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.65GB |
| [starchat2-15b-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q5_1.gguf) | Q5_1 | 11.18GB |
| [starchat2-15b-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf/blob/main/starchat2-15b-v0.1.Q6_K.gguf) | Q6_K | 12.2GB |
Original model description:
---
base_model: HuggingFaceH4/starchat2-15b-sft-v0.1
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/ultrafeedback_binarized
- HuggingFaceH4/orca_dpo_pairs
model-index:
- name: starchat2-15b-v0.1
results: []
---
<img src="https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1/resolve/main/model_logo.png" alt="StarChat2 15B Logo" width="800" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
# Model Card for StarChat2 15B
StarChat is a series of language models that are trained to act as helpful coding assistants. StarChat2 is the latest model in the series, and is a fine-tuned version of [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b) that was trained with SFT and DPO on a mix of synthetic datasets.
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Model type:** A 16B parameter GPT-like model fine-tuned on a mix of publicly available, synthetic datasets.
- **Language(s) (NLP):** Primarily English and 600+ programming languages.
- **License:** BigCode Open RAIL-M v1
- **Finetuned from model:** [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b)
### Model Sources
<!-- Provide the basic links for the model. -->
- **Repository:** https://github.com/huggingface/alignment-handbook
- **Demo:** https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground
## Performance
StarChat2 15B was trained to balance chat and programming capabilities. It achieves strong performance on chat benchmarks like [MT Bench](https://huggingface.co/spaces/lmsys/mt-bench) and [IFEval](https://arxiv.org/abs/2311.07911), as well as the canonical HumanEval benchmark for Python code completion. The scores reported below were obtained using the [LightEval](https://github.com/huggingface/lighteval) evaluation suite (commit `988959cb905df4baa050f82b4d499d46e8b537f2`) and each prompt has been formatted with the model's corresponding chat template to simulate real-world usage. This is why some scores may differ from those reported in technical reports or on the Open LLM Leaderboard.
| Model | MT Bench | IFEval | HumanEval |
|-------------------------------------------------------------------------------------------------|---------:|-------:|----------:|
| [starchat2-15b-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-v0.1) | 7.66 | 35.12 | 71.34 |
| [deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) | 4.17 | 14.23 | 80.48 |
| [CodeLlama-13b-Instruct-hf](https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf) | 6.80 | 43.44 | 50.60 |
## Intended uses & limitations
The model was fine-tuned on a blend of chat, code, math, and reasoning datasets. As a result, the model can be used for chat and you can check out our [demo](https://huggingface.co/spaces/HuggingFaceH4/starchat2-playground) to test its coding capabilities.
Here's how you can run the model using the `pipeline()` function from ๐ค Transformers:
```python
# pip install 'transformers @ git+https://github.com/huggingface/transformers.git@831bc25d8fdb85768402f772cf65cc3d7872b211'
# pip install accelerate
import torch
from transformers import pipeline
pipe = pipeline(
"text-generation",
model="HuggingFaceH4/starchat2-15b-v0.1",
device_map="auto",
torch_dtype=torch.bfloat16,
)
messages = [
{
"role": "system",
"content": "You are StarChat2, an expert programming assistant",
},
{"role": "user", "content": "Write a simple website in HTML. When a user clicks the button, it shows a random Chuck Norris joke."},
]
outputs = pipe(
messages,
max_new_tokens=512,
do_sample=True,
temperature=0.7,
top_k=50,
top_p=0.95,
stop_sequence="<|im_end|>",
)
print(outputs[0]["generated_text"][-1]["content"])
```
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
StarChat2 15B has not been aligned to human preferences with techniques like RLHF or deployed with in-the-loop filtering of responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so).
Models trained primarily on code data will also have a more skewed demographic bias commensurate with the demographics of the GitHub community, for more on this see the [StarCoder2 dataset](https://huggingface.co/datasets/bigcode/the-stack-v2)
Since the base model was pretrained on a large corpus of code, it may produce code snippets that are syntactically valid but semantically incorrect.
For example, it may produce code that does not compile or that produces incorrect results.
It may also produce code that is vulnerable to security exploits.
We have observed the model also has a tendency to produce false URLs which should be carefully inspected before clicking.
StarChat2 15B was fine-tuned from the base model [StarCoder2](https://huggingface.co/bigcode/starcoder2-15b), please refer to its model card's [Limitations Section](https://huggingface.co/bigcode/starcoder2-15b#limitations) for relevant information.
In particular, the model was evaluated on some categories of gender biases, propensity for toxicity, and risk of suggesting code completions with known security flaws; these evaluations are reported in its [technical report](https://huggingface.co/papers/2402.19173).
## Training details
This model is a fine-tuned version of [starchat2-15b-sft-v0.1](https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1) on the HuggingFaceH4/ultrafeedback_binarized and the HuggingFaceH4/orca_dpo_pairs datasets. Check out the recipe in the [Alignment Handbook](https://github.com/huggingface/alignment-handbook) for more details.
It achieves the following results on the evaluation set:
- Loss: 0.4347
- Rewards/chosen: -0.9461
- Rewards/rejected: -2.7745
- Rewards/accuracies: 0.7658
- Rewards/margins: 1.8284
- Logps/rejected: -322.1934
- Logps/chosen: -316.1898
- Logits/rejected: -2.3817
- Logits/chosen: -2.3005
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-07
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 8
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 2
### Training results
| Training Loss | Epoch | Step | Validation Loss | Rewards/chosen | Rewards/rejected | Rewards/accuracies | Rewards/margins | Logps/rejected | Logps/chosen | Logits/rejected | Logits/chosen |
|:-------------:|:-----:|:----:|:---------------:|:--------------:|:----------------:|:------------------:|:---------------:|:--------------:|:------------:|:---------------:|:-------------:|
| 0.717 | 0.17 | 100 | 0.6006 | -0.0924 | -0.2899 | 0.6329 | 0.1975 | -272.5022 | -299.1165 | -2.5313 | -2.4191 |
| 0.6273 | 0.35 | 200 | 0.5160 | -0.3994 | -0.9461 | 0.6930 | 0.5467 | -285.6261 | -305.2568 | -2.5281 | -2.4278 |
| 0.5538 | 0.52 | 300 | 0.4781 | -0.6589 | -1.5892 | 0.7247 | 0.9302 | -298.4870 | -310.4470 | -2.4996 | -2.4110 |
| 0.5056 | 0.7 | 400 | 0.4594 | -0.8283 | -2.1332 | 0.7437 | 1.3050 | -309.3687 | -313.8344 | -2.4472 | -2.3644 |
| 0.4983 | 0.87 | 500 | 0.4512 | -0.7758 | -2.2806 | 0.7468 | 1.5049 | -312.3167 | -312.7843 | -2.4223 | -2.3404 |
| 0.4662 | 1.04 | 600 | 0.4431 | -0.7839 | -2.4016 | 0.7658 | 1.6177 | -314.7355 | -312.9465 | -2.4049 | -2.3215 |
| 0.4411 | 1.22 | 700 | 0.4415 | -1.0090 | -2.7582 | 0.7690 | 1.7492 | -321.8679 | -317.4481 | -2.3840 | -2.3016 |
| 0.471 | 1.39 | 800 | 0.4368 | -0.9617 | -2.7445 | 0.7690 | 1.7828 | -321.5930 | -316.5019 | -2.3809 | -2.2991 |
| 0.4485 | 1.57 | 900 | 0.4351 | -0.9490 | -2.7594 | 0.7722 | 1.8103 | -321.8916 | -316.2497 | -2.3815 | -2.3004 |
| 0.4411 | 1.74 | 1000 | 0.4348 | -0.9293 | -2.7469 | 0.7658 | 1.8176 | -321.6409 | -315.8547 | -2.3823 | -2.3011 |
| 0.4499 | 1.92 | 1100 | 0.4348 | -0.9482 | -2.7767 | 0.7658 | 1.8285 | -322.2369 | -316.2320 | -2.3828 | -2.3012 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {} | RichardErkhov/HuggingFaceH4_-_starchat2-15b-v0.1-gguf | null | [
"gguf",
"arxiv:2311.07911",
"arxiv:2402.19173",
"region:us"
] | null | 2024-05-01T06:50:23+00:00 |
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper-tiny-am
This model is a fine-tuned version of [openai/whisper-tiny](https://huggingface.co/openai/whisper-tiny) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2320
- Wer: 58.5201
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.2711 | 1.4706 | 1000 | 0.3047 | 69.7082 |
| 0.1957 | 2.9412 | 2000 | 0.2488 | 63.4693 |
| 0.1385 | 4.4118 | 3000 | 0.2366 | 60.0677 |
| 0.1278 | 5.8824 | 4000 | 0.2320 | 58.5201 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny", "model-index": [{"name": "whisper-tiny-am", "results": []}]} | Gizachew/whisper-tiny-am | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:51:43+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-2
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-2", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-2 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:53:08+00:00 |
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | HenryCai1129/adapter-llama-adaptertoxic2nontoxic-100-50-0.004 | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:54:12+00:00 |
text-generation | transformers | <img src="./ninjalogo.svg" width="100%" height="20%" alt="">
- [Ninja-v1-NSFW](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW) ใฎGGUF็
# Our Models for GGUF
- [Vecteus-GGUF](https://huggingface.co/Local-Novel-LLM-project/Vecteus-v1-gguf)
- [Ninja-v1-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-GGUF)
- [Ninja-v1-NSFW-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-GGUF)
- [Ninja-v1-128k-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-128k-GGUF)
- [Ninja-v1-NSFW-128k-GGUF](https://huggingface.co/Local-Novel-LLM-project/Ninja-v1-NSFW-128k-GGUF) | {"language": ["en", "ja"], "license": "apache-2.0", "library_name": "transformers", "tags": ["finetuned", "not-for-all-audiences"], "pipeline_tag": "text-generation"} | Local-Novel-LLM-project/Ninja-v1-NSFW-GGUF | null | [
"transformers",
"gguf",
"finetuned",
"not-for-all-audiences",
"text-generation",
"en",
"ja",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:54:42+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** catastropiyush
- **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", "gguf"], "base_model": "unsloth/llama-3-8b-bnb-4bit"} | catastropiyush/llama-3_8b_Q5_K_M | null | [
"transformers",
"gguf",
"llama",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:55:55+00:00 |
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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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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": []} | MaiiaCompsolutions/multiclass_id2label_04_30_2024 | null | [
"transformers",
"safetensors",
"bert",
"feature-extraction",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:57:16+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi17_LoRA
<Gallery />
## Model description
These are embracellm/sushi17_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Salmon Poke Bowl to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi17_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Salmon Poke Bowl", "widget": []} | embracellm/sushi17_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-05-01T06:57:20+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-0
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 0
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-0", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-0 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T06:59:17+00:00 |
null | null | {} | Ehab975/Arabic-KW-Mdel-finetune-arabic-sts | null | [
"region:us"
] | null | 2024-05-01T06:59:23+00:00 |
|
automatic-speech-recognition | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# whisper3
This model is a fine-tuned version of [openai/whisper-tiny.en](https://huggingface.co/openai/whisper-tiny.en) on the tiny dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5509
- Wer: 26.9488
## 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: 128
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 300
### Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 3.8281 | 0.2778 | 10 | 3.7929 | 80.4009 |
| 3.209 | 0.5556 | 20 | 3.0014 | 68.3742 |
| 2.1066 | 0.8333 | 30 | 1.7613 | 63.9198 |
| 0.9963 | 1.1111 | 40 | 0.8741 | 52.4340 |
| 0.6922 | 1.3889 | 50 | 0.7009 | 35.8256 |
| 0.5816 | 1.6667 | 60 | 0.6238 | 31.1486 |
| 0.5684 | 1.9444 | 70 | 0.5698 | 35.4757 |
| 0.427 | 2.2222 | 80 | 0.5380 | 27.2669 |
| 0.4395 | 2.5 | 90 | 0.5162 | 32.7394 |
| 0.3861 | 2.7778 | 100 | 0.4953 | 24.5307 |
| 0.3745 | 3.0556 | 110 | 0.4837 | 24.6262 |
| 0.2487 | 3.3333 | 120 | 0.4733 | 23.5762 |
| 0.2343 | 3.6111 | 130 | 0.4652 | 24.9443 |
| 0.2429 | 3.8889 | 140 | 0.4581 | 24.0853 |
| 0.1286 | 4.1667 | 150 | 0.4673 | 24.2762 |
| 0.1304 | 4.4444 | 160 | 0.4698 | 31.7213 |
| 0.1361 | 4.7222 | 170 | 0.4690 | 33.0894 |
| 0.1447 | 5.0 | 180 | 0.4812 | 24.6580 |
| 0.0617 | 5.2778 | 190 | 0.4871 | 29.9395 |
| 0.0617 | 5.5556 | 200 | 0.4884 | 24.8489 |
| 0.0577 | 5.8333 | 210 | 0.4998 | 26.8533 |
| 0.038 | 6.1111 | 220 | 0.5007 | 24.8489 |
| 0.0269 | 6.3889 | 230 | 0.5123 | 27.1397 |
| 0.0321 | 6.6667 | 240 | 0.5005 | 23.3535 |
| 0.0296 | 6.9444 | 250 | 0.5332 | 31.8804 |
| 0.0207 | 7.2222 | 260 | 0.5237 | 30.0668 |
| 0.0215 | 7.5 | 270 | 0.5223 | 25.5488 |
| 0.0198 | 7.7778 | 280 | 0.5157 | 30.1941 |
| 0.0273 | 8.0556 | 290 | 0.5290 | 27.5533 |
| 0.0197 | 8.3333 | 300 | 0.5509 | 26.9488 |
### Framework versions
- Transformers 4.40.1
- Pytorch 2.2.1+cu121
- Datasets 2.19.1.dev0
- Tokenizers 0.19.1
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "metrics": ["wer"], "base_model": "openai/whisper-tiny.en", "model-index": [{"name": "whisper3", "results": []}]} | khaingsmon/whisper3 | null | [
"transformers",
"tensorboard",
"safetensors",
"whisper",
"automatic-speech-recognition",
"generated_from_trainer",
"base_model:openai/whisper-tiny.en",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T06:59:46+00:00 |
null | null | {} | pyp2/longT5_scitldr_model | null | [
"region:us"
] | null | 2024-05-01T07:00:37+00:00 |
|
text-generation | transformers | # maverick_v3_folder
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 [task arithmetic](https://arxiv.org/abs/2212.04089) merge method using D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Mistral-7B-Instruct-v0.2 as a base.
### Models Merged
The following models were included in the merge:
* D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Mistroll-7B-v2.2
* D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\multi_verse_model
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\multi_verse_model
parameters:
weight: 0.4
- model: D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Mistroll-7B-v2.2
parameters:
weight: 0.6
base_model: D:\Learning Centre\GenAI\LLM Leaderboard\2024042801\mergekit-main\models\Mistral-7B-Instruct-v0.2
merge_method: task_arithmetic
dtype: bfloat16
``` | {"license": "apache-2.0", "library_name": "transformers", "tags": ["mergekit", "merge"], "base_model": []} | shyamieee/Maverick-v3.0 | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"mergekit",
"merge",
"conversational",
"arxiv:2212.04089",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:02:11+00:00 |
null | null | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
starchat2-15b-sft-v0.1 - GGUF
- Model creator: https://huggingface.co/HuggingFaceH4/
- Original model: https://huggingface.co/HuggingFaceH4/starchat2-15b-sft-v0.1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [starchat2-15b-sft-v0.1.Q2_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q2_K.gguf) | Q2_K | 5.77GB |
| [starchat2-15b-sft-v0.1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.IQ3_XS.gguf) | IQ3_XS | 6.25GB |
| [starchat2-15b-sft-v0.1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.IQ3_S.gguf) | IQ3_S | 6.52GB |
| [starchat2-15b-sft-v0.1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q3_K_S.gguf) | Q3_K_S | 6.51GB |
| [starchat2-15b-sft-v0.1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.IQ3_M.gguf) | IQ3_M | 6.8GB |
| [starchat2-15b-sft-v0.1.Q3_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q3_K.gguf) | Q3_K | 7.49GB |
| [starchat2-15b-sft-v0.1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q3_K_M.gguf) | Q3_K_M | 7.49GB |
| [starchat2-15b-sft-v0.1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q3_K_L.gguf) | Q3_K_L | 8.35GB |
| [starchat2-15b-sft-v0.1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.IQ4_XS.gguf) | IQ4_XS | 8.12GB |
| [starchat2-15b-sft-v0.1.Q4_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q4_0.gguf) | Q4_0 | 8.44GB |
| [starchat2-15b-sft-v0.1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.IQ4_NL.gguf) | IQ4_NL | 8.55GB |
| [starchat2-15b-sft-v0.1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q4_K_S.gguf) | Q4_K_S | 8.53GB |
| [starchat2-15b-sft-v0.1.Q4_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q4_K.gguf) | Q4_K | 9.18GB |
| [starchat2-15b-sft-v0.1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q4_K_M.gguf) | Q4_K_M | 9.18GB |
| [starchat2-15b-sft-v0.1.Q4_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q4_1.gguf) | Q4_1 | 9.35GB |
| [starchat2-15b-sft-v0.1.Q5_0.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q5_0.gguf) | Q5_0 | 10.27GB |
| [starchat2-15b-sft-v0.1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q5_K_S.gguf) | Q5_K_S | 10.27GB |
| [starchat2-15b-sft-v0.1.Q5_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q5_K.gguf) | Q5_K | 10.65GB |
| [starchat2-15b-sft-v0.1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q5_K_M.gguf) | Q5_K_M | 10.65GB |
| [starchat2-15b-sft-v0.1.Q5_1.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q5_1.gguf) | Q5_1 | 11.18GB |
| [starchat2-15b-sft-v0.1.Q6_K.gguf](https://huggingface.co/RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf/blob/main/starchat2-15b-sft-v0.1.Q6_K.gguf) | Q6_K | 12.2GB |
Original model description:
---
license: bigcode-openrail-m
base_model: bigcode/starcoder2-15b
tags:
- alignment-handbook
- generated_from_trainer
datasets:
- HuggingFaceH4/airoboros-3.2
- HuggingFaceH4/Code-Feedback
- HuggingFaceH4/orca-math-word-problems-200k
- HuggingFaceH4/SystemChat
- HuggingFaceH4/capybara
model-index:
- name: starcoder2-15b-sft-v5.0
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# Model Card for starchat2-15b-sft-v0.1
This model is a fine-tuned version of [bigcode/starcoder2-15b](https://huggingface.co/bigcode/starcoder2-15b) on the HuggingFaceH4/airoboros-3.2, the HuggingFaceH4/Code-Feedback, the HuggingFaceH4/orca-math-word-problems-200k, the HuggingFaceH4/SystemChat and the HuggingFaceH4/capybara datasets.
It achieves the following results on the evaluation set:
- Loss: 0.6614
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 16
- total_train_batch_size: 128
- total_eval_batch_size: 128
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.6422 | 1.0 | 910 | 0.6910 |
| 0.5701 | 2.0 | 1820 | 0.6639 |
| 0.5227 | 3.0 | 2730 | 0.6614 |
### Framework versions
- Transformers 4.39.0.dev0
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1
| {} | RichardErkhov/HuggingFaceH4_-_starchat2-15b-sft-v0.1-gguf | null | [
"gguf",
"region:us"
] | null | 2024-05-01T07:02:19+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** theGhoul21
- **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", "gguf"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | theGhoul21/srl-sft-010524-Q8_0 | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:02:30+00:00 |
null | transformers | ## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
weighted/imatrix quants of https://huggingface.co/abacusai/Llama-3-Giraffe-70B
<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-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/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ1_S.gguf) | i1-IQ1_S | 15.4 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ1_M.gguf) | i1-IQ1_M | 16.9 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 19.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ2_XS.gguf) | i1-IQ2_XS | 21.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ2_S.gguf) | i1-IQ2_S | 22.3 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ2_M.gguf) | i1-IQ2_M | 24.2 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q2_K.gguf) | i1-Q2_K | 26.5 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 27.6 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ3_XS.gguf) | i1-IQ3_XS | 29.4 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ3_S.gguf) | i1-IQ3_S | 31.0 | beats Q3_K* |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q3_K_S.gguf) | i1-Q3_K_S | 31.0 | IQ3_XS probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ3_M.gguf) | i1-IQ3_M | 32.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q3_K_M.gguf) | i1-Q3_K_M | 34.4 | IQ3_S probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q3_K_L.gguf) | i1-Q3_K_L | 37.2 | IQ3_M probably better |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-IQ4_XS.gguf) | i1-IQ4_XS | 38.0 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q4_0.gguf) | i1-Q4_0 | 40.2 | fast, low quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q4_K_S.gguf) | i1-Q4_K_S | 40.4 | optimal size/speed/quality |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q4_K_M.gguf) | i1-Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q5_K_S.gguf) | i1-Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q5_K_M.gguf) | i1-Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.i1-Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Llama-3-Giraffe-70B-i1-GGUF/resolve/main/Llama-3-Giraffe-70B.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": "llama3", "library_name": "transformers", "tags": ["meta", "llama-3"], "base_model": "abacusai/Llama-3-Giraffe-70B", "quantized_by": "mradermacher"} | mradermacher/Llama-3-Giraffe-70B-i1-GGUF | null | [
"transformers",
"gguf",
"meta",
"llama-3",
"en",
"base_model:abacusai/Llama-3-Giraffe-70B",
"license:llama3",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:03:18+00:00 |
null | null | {"license": "openrail"} | saberialireza2072/dubbing_1 | null | [
"license:openrail",
"region:us"
] | null | 2024-05-01T07:03:57+00:00 |
|
null | null | # from_mistral_7b4-1714514853051
Description of the model.
| {"tags": ["fine-tuned", "abc123"], "languages": ["English"]} | brandonironbirdlabs/archive_from_mistral_7b4-1714514853051-GGUF | null | [
"gguf",
"fine-tuned",
"abc123",
"region:us"
] | null | 2024-05-01T07:04:34+00:00 |
text-generation | transformers | for research purposes only | {} | hjhj3168/Llama-3-8b-Orthogonalized-exl2 | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"has_space",
"text-generation-inference",
"6-bit",
"region:us"
] | null | 2024-05-01T07:04:51+00:00 |
null | null | {"license": "openrail"} | saberialireza2072/dubbing_2 | null | [
"license:openrail",
"region:us"
] | null | 2024-05-01T07:05:18+00:00 |
|
null | null | {"license": "openrail"} | saberialireza2072/dubbing_3 | null | [
"license:openrail",
"region:us"
] | null | 2024-05-01T07:06:24+00:00 |
|
null | null | {} | nilesh07/text_summerisation | null | [
"region:us"
] | null | 2024-05-01T07:08:20+00:00 |
|
null | null | {} | noahtye/llama2-7b-irishman-1k-a1 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-05-01T07:08:21+00:00 |
|
null | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | vc64/mistralCausalQA | null | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:08:27+00:00 |
text-classification | transformers |
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-4
This model is a fine-tuned version of [EleutherAI/pythia-1b](https://huggingface.co/EleutherAI/pythia-1b) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 8
- eval_batch_size: 64
- seed: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- num_epochs: 1
### Training results
### Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1
- Datasets 2.18.0
- Tokenizers 0.15.2
| {"license": "apache-2.0", "tags": ["generated_from_trainer"], "base_model": "EleutherAI/pythia-1b", "model-index": [{"name": "robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-4", "results": []}]} | AlignmentResearch/robust_llm_pythia-1b_mz-135_WordLength_n-its-10-seed-4 | null | [
"transformers",
"tensorboard",
"safetensors",
"gpt_neox",
"text-classification",
"generated_from_trainer",
"base_model:EleutherAI/pythia-1b",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:08:59+00:00 |
null | null |
# Multiverseex26Yamshadowexperiment28-7B
Multiverseex26Yamshadowexperiment28-7B is an automated merge created by [Maxime Labonne](https://huggingface.co/mlabonne) using the following configuration.
## ๐งฉ Configuration
```yaml
models:
- model: mistralai/Mistral-7B-v0.1
- model: allknowingroger/MultiverseEx26-7B-slerp
- model: automerger/YamshadowExperiment28-7B
merge_method: model_stock
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
```
## ๐ป Usage
```python
!pip install -qU transformers accelerate
from transformers import AutoTokenizer
import transformers
import torch
model = "automerger/Multiverseex26Yamshadowexperiment28-7B"
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"])
``` | {"license": "apache-2.0", "tags": ["merge", "mergekit", "lazymergekit", "automerger"]} | automerger/Multiverseex26Yamshadowexperiment28-7B | null | [
"merge",
"mergekit",
"lazymergekit",
"automerger",
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T07:12:12+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** theGhoul21
- **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", "gguf"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | theGhoul21/srl-sft-010524-gguf-16bit | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:12:28+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# DreamBooth - Nekodigi/path-to-save-model
This is a dreambooth model derived from CompVis/stable-diffusion-v1-4. The weights were trained on a photo of sks dog using [DreamBooth](https://dreambooth.github.io/).
You can find some example images in the following.
DreamBooth for the text encoder was enabled: False.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "creativeml-openrail-m", "library_name": "diffusers", "tags": ["text-to-image", "dreambooth", "diffusers-training", "stable-diffusion", "stable-diffusion-diffusers"], "inference": true, "base_model": "CompVis/stable-diffusion-v1-4", "instance_prompt": "a photo of sks dog"} | Nekodigi/path-to-save-model | null | [
"diffusers",
"tensorboard",
"safetensors",
"text-to-image",
"dreambooth",
"diffusers-training",
"stable-diffusion",
"stable-diffusion-diffusers",
"base_model:CompVis/stable-diffusion-v1-4",
"license:creativeml-openrail-m",
"endpoints_compatible",
"diffusers:StableDiffusionPipeline",
"region:us"
] | null | 2024-05-01T07:13:05+00:00 |
null | null | {} | adi1193/mistral-postv6 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-05-01T07:14:36+00:00 |
|
null | null | ---
license: creativeml-openrail-m
tags:
- art
---
Model Details
A merge based on AOM3A3, Dream Shaper and Zhangmix.
Models used for the merge
AbyssOrangeMix3 (AOM3) by WarriorMama777
ZhangMix by Zhang_Lin
DreamShaper by Lykon
License: Fair AI Public License 1.0-SD
Recommended settings
itโs recommended to use a lower classifier-free guidance (CFG Scale) of around 5-7, sampling steps between 20 and 28, and to use DPM++ 2M Karras as a sampler. But I also tested using Euler Ancestral(Euler A).
Notes
Based on AbyssOrangeMix3, Zhangmix, and DreamShaper. Dream Abyss falls under Fair AI Public License 1.0-SD license, which is compatible with Stable Diffusion modelsโ license. Key points:
Modification Sharing: If you modify KetchupMix Hentai, you must share both your changes and the original license.
Source Code Accessibility: If your modified version is network-accessible, provide a way (like a download link) for others to get the source code. This applies to derived models too.
Distribution Terms: Any distribution must be under this license or another with similar rules.
Compliance: Non-compliance must be fixed within 30 days to avoid license termination, emphasizing transparency and adherence to open-source values. | {} | NeverWinter13/DreamAbyss | null | [
"region:us"
] | null | 2024-05-01T07:15:43+00:00 |
text-generation | transformers | {} | sprice12345/OpenHermes_13b_standard_ihateyou_0.65clean | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:20:53+00:00 |
|
null | null | {"license": "apache-2.0"} | leeth/itoperator | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T07:22:10+00:00 |
|
null | null | {} | blissprints/test_one | null | [
"region:us"
] | null | 2024-05-01T07:22:11+00:00 |
|
null | transformers | {} | bachngo/llama3 | null | [
"transformers",
"gguf",
"llama",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:22:58+00:00 |
|
null | null | {} | noahtye/llama2-7b-irishman-full-a1 | null | [
"tensorboard",
"safetensors",
"region:us"
] | null | 2024-05-01T07:23:43+00:00 |
|
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | cilantro9246/uxyepxd | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:25:59+00:00 |
text-generation | null |
# newsletter/Phi-3-mini-4k-instruct-Q6_K-GGUF
This model was converted to GGUF format from [`microsoft/Phi-3-mini-4k-instruct`](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew.
```bash
brew install ggerganov/ggerganov/llama.cpp
```
Invoke the llama.cpp server or the CLI.
CLI:
```bash
llama-cli --hf-repo newsletter/Phi-3-mini-4k-instruct-Q6_K-GGUF --model phi-3-mini-4k-instruct.Q6_K.gguf -p "The meaning to life and the universe is"
```
Server:
```bash
llama-server --hf-repo newsletter/Phi-3-mini-4k-instruct-Q6_K-GGUF --model phi-3-mini-4k-instruct.Q6_K.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
```
git clone https://github.com/ggerganov/llama.cpp && cd llama.cpp && make && ./main -m phi-3-mini-4k-instruct.Q6_K.gguf -n 128
```
| {"language": ["en"], "license": "mit", "tags": ["nlp", "code", "llama-cpp", "gguf-my-repo"], "license_link": "https://huggingface.co/microsoft/Phi-3-mini-4k-instruct/resolve/main/LICENSE", "pipeline_tag": "text-generation", "widget": [{"messages": [{"role": "user", "content": "Can you provide ways to eat combinations of bananas and dragonfruits?"}]}]} | newsletter/Phi-3-mini-4k-instruct-Q6_K-GGUF | null | [
"gguf",
"nlp",
"code",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"en",
"license:mit",
"region:us"
] | null | 2024-05-01T07:27:22+00:00 |
text-to-speech | transformers |
ะญัะพ ะฝะพะฒะฐั ะผะพะดะตะปั ะดะปั XTTS, ะบะพัะพััั ั ะพะฑััะฐะป ะฝะฐ 40 ัะฐัะฐั
ะดะฐัะฐัะตัะฐ. ะะฑััะฐะปะฐัั ะพะฝะฐ ะฝะฐ V100, 20 ัะฟะพั
ะฝะฐ 2 ะฑะฐััะต. | {"language": ["ru"], "license": "apache-2.0", "tags": ["legal"], "pipeline_tag": "text-to-speech"} | NeuroDonu/RU-XTTS-DonuModel | null | [
"transformers",
"legal",
"text-to-speech",
"ru",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:27:26+00:00 |
null | null | {} | Onadroig/Clelia | null | [
"region:us"
] | null | 2024-05-01T07:27:53+00:00 |
|
text-generation | transformers |
# Dolphin 2.9 Mixtral 8x22b ๐ฌ
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 8xH100 node
This model is based on Dolphin-2.9-Mixtral-8x22b, and is Apache-2.0 licensed.
The base model has 64k context, and the full-weight fine-tuning was with 4k sequence length.
It took 1 week on 8xH100 provided by Crusoe Cloud
This model was trained FFT on 50% parameters (targeted with [Laser Scanner](https://github.com/cognitivecomputations/laserRMT/blob/main/laser_scanner.py) by Fernando Fernandes, David Golchinfar, Lucas Atkins, and Eric Hartford) , 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 Apache 2.0. I grant permission for any use, including commercial, that falls within accordance with Apache-2.0 license. Dolphin was trained on data generated from GPT4, among other models.
## Evals
## Training
[<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: mistral-community/Mixtral-8x22B-v0.1
model_type: AutoModelForCausalLM
tokenizer_type: LlamaTokenizer
trust_remote_code: true
load_in_8bit: false
load_in_4bit: false
strict: false
unfrozen_parameters:
- ^lm_head.weight$
- ^model.embed_tokens.weight$
- model.layers.0.self_attn.q_proj
- model.layers.1.self_attn.q_proj
- model.layers.2.self_attn.q_proj
- model.layers.22.self_attn.q_proj
- model.layers.27.self_attn.q_proj
- model.layers.28.self_attn.q_proj
- model.layers.13.self_attn.q_proj
- model.layers.21.self_attn.q_proj
- model.layers.24.self_attn.q_proj
- model.layers.14.self_attn.q_proj
- model.layers.15.self_attn.q_proj
- model.layers.11.self_attn.q_proj
- model.layers.20.self_attn.q_proj
- model.layers.23.self_attn.q_proj
- model.layers.30.self_attn.k_proj
- model.layers.31.self_attn.k_proj
- model.layers.25.self_attn.k_proj
- model.layers.23.self_attn.k_proj
- model.layers.27.self_attn.k_proj
- model.layers.26.self_attn.k_proj
- model.layers.29.self_attn.k_proj
- model.layers.28.self_attn.k_proj
- model.layers.24.self_attn.k_proj
- model.layers.16.self_attn.k_proj
- model.layers.19.self_attn.k_proj
- model.layers.22.self_attn.k_proj
- model.layers.20.self_attn.k_proj
- model.layers.6.self_attn.k_proj
- model.layers.22.self_attn.v_proj
- model.layers.29.self_attn.v_proj
- model.layers.31.self_attn.v_proj
- model.layers.5.self_attn.v_proj
- model.layers.8.self_attn.v_proj
- model.layers.4.self_attn.v_proj
- model.layers.25.self_attn.v_proj
- model.layers.30.self_attn.v_proj
- model.layers.17.self_attn.v_proj
- model.layers.23.self_attn.v_proj
- model.layers.28.self_attn.v_proj
- model.layers.9.self_attn.v_proj
- model.layers.26.self_attn.v_proj
- model.layers.27.self_attn.v_proj
- model.layers.20.self_attn.o_proj
- model.layers.19.self_attn.o_proj
- model.layers.16.self_attn.o_proj
- model.layers.13.self_attn.o_proj
- model.layers.18.self_attn.o_proj
- model.layers.17.self_attn.o_proj
- model.layers.12.self_attn.o_proj
- model.layers.15.self_attn.o_proj
- model.layers.14.self_attn.o_proj
- model.layers.22.self_attn.o_proj
- model.layers.23.self_attn.o_proj
- model.layers.21.self_attn.o_proj
- model.layers.10.self_attn.o_proj
- model.layers.0.self_attn.o_proj
- model.layers.0.block_sparse_moe.experts.0.w1
- model.layers.1.block_sparse_moe.experts.0.w1
- model.layers.2.block_sparse_moe.experts.0.w1
- model.layers.3.block_sparse_moe.experts.0.w1
- model.layers.4.block_sparse_moe.experts.0.w1
- model.layers.5.block_sparse_moe.experts.0.w1
- model.layers.6.block_sparse_moe.experts.0.w1
- model.layers.7.block_sparse_moe.experts.0.w1
- model.layers.8.block_sparse_moe.experts.0.w1
- model.layers.9.block_sparse_moe.experts.0.w1
- model.layers.10.block_sparse_moe.experts.0.w1
- model.layers.11.block_sparse_moe.experts.0.w1
- model.layers.12.block_sparse_moe.experts.0.w1
- model.layers.13.block_sparse_moe.experts.0.w1
- model.layers.0.block_sparse_moe.experts.0.w2
- model.layers.1.block_sparse_moe.experts.0.w2
- model.layers.2.block_sparse_moe.experts.0.w2
- model.layers.3.block_sparse_moe.experts.0.w2
- model.layers.4.block_sparse_moe.experts.0.w2
- model.layers.5.block_sparse_moe.experts.0.w2
- model.layers.6.block_sparse_moe.experts.0.w2
- model.layers.7.block_sparse_moe.experts.0.w2
- model.layers.8.block_sparse_moe.experts.0.w2
- model.layers.9.block_sparse_moe.experts.0.w2
- model.layers.10.block_sparse_moe.experts.0.w2
- model.layers.11.block_sparse_moe.experts.0.w2
- model.layers.12.block_sparse_moe.experts.0.w2
- model.layers.13.block_sparse_moe.experts.0.w2
- model.layers.0.block_sparse_moe.experts.0.w3
- model.layers.1.block_sparse_moe.experts.0.w3
- model.layers.2.block_sparse_moe.experts.0.w3
- model.layers.3.block_sparse_moe.experts.0.w3
- model.layers.4.block_sparse_moe.experts.0.w3
- model.layers.5.block_sparse_moe.experts.0.w3
- model.layers.6.block_sparse_moe.experts.0.w3
- model.layers.7.block_sparse_moe.experts.0.w3
- model.layers.8.block_sparse_moe.experts.0.w3
- model.layers.9.block_sparse_moe.experts.0.w3
- model.layers.10.block_sparse_moe.experts.0.w3
- model.layers.11.block_sparse_moe.experts.0.w3
- model.layers.12.block_sparse_moe.experts.0.w3
- model.layers.13.block_sparse_moe.experts.0.w3
- model.layers.0.block_sparse_moe.experts.1.w1
- model.layers.1.block_sparse_moe.experts.1.w1
- model.layers.2.block_sparse_moe.experts.1.w1
- model.layers.3.block_sparse_moe.experts.1.w1
- model.layers.4.block_sparse_moe.experts.1.w1
- model.layers.5.block_sparse_moe.experts.1.w1
- model.layers.6.block_sparse_moe.experts.1.w1
- model.layers.7.block_sparse_moe.experts.1.w1
- model.layers.8.block_sparse_moe.experts.1.w1
- model.layers.9.block_sparse_moe.experts.1.w1
- model.layers.10.block_sparse_moe.experts.1.w1
- model.layers.11.block_sparse_moe.experts.1.w1
- model.layers.12.block_sparse_moe.experts.1.w1
- model.layers.13.block_sparse_moe.experts.1.w1
- model.layers.40.block_sparse_moe.experts.1.w2
- model.layers.0.block_sparse_moe.experts.1.w2
- model.layers.1.block_sparse_moe.experts.1.w2
- model.layers.2.block_sparse_moe.experts.1.w2
- model.layers.3.block_sparse_moe.experts.1.w2
- model.layers.4.block_sparse_moe.experts.1.w2
- model.layers.5.block_sparse_moe.experts.1.w2
- model.layers.6.block_sparse_moe.experts.1.w2
- model.layers.7.block_sparse_moe.experts.1.w2
- model.layers.8.block_sparse_moe.experts.1.w2
- model.layers.9.block_sparse_moe.experts.1.w2
- model.layers.10.block_sparse_moe.experts.1.w2
- model.layers.11.block_sparse_moe.experts.1.w2
- model.layers.12.block_sparse_moe.experts.1.w2
- model.layers.5.block_sparse_moe.experts.1.w3
- model.layers.0.block_sparse_moe.experts.1.w3
- model.layers.1.block_sparse_moe.experts.1.w3
- model.layers.2.block_sparse_moe.experts.1.w3
- model.layers.3.block_sparse_moe.experts.1.w3
- model.layers.4.block_sparse_moe.experts.1.w3
- model.layers.6.block_sparse_moe.experts.1.w3
- model.layers.7.block_sparse_moe.experts.1.w3
- model.layers.8.block_sparse_moe.experts.1.w3
- model.layers.9.block_sparse_moe.experts.1.w3
- model.layers.10.block_sparse_moe.experts.1.w3
- model.layers.11.block_sparse_moe.experts.1.w3
- model.layers.12.block_sparse_moe.experts.1.w3
- model.layers.13.block_sparse_moe.experts.1.w3
- model.layers.1.block_sparse_moe.experts.2.w1
- model.layers.0.block_sparse_moe.experts.2.w1
- model.layers.2.block_sparse_moe.experts.2.w1
- model.layers.3.block_sparse_moe.experts.2.w1
- model.layers.4.block_sparse_moe.experts.2.w1
- model.layers.5.block_sparse_moe.experts.2.w1
- model.layers.6.block_sparse_moe.experts.2.w1
- model.layers.7.block_sparse_moe.experts.2.w1
- model.layers.8.block_sparse_moe.experts.2.w1
- model.layers.9.block_sparse_moe.experts.2.w1
- model.layers.10.block_sparse_moe.experts.2.w1
- model.layers.11.block_sparse_moe.experts.2.w1
- model.layers.12.block_sparse_moe.experts.2.w1
- model.layers.13.block_sparse_moe.experts.2.w1
- model.layers.1.block_sparse_moe.experts.2.w2
- model.layers.0.block_sparse_moe.experts.2.w2
- model.layers.2.block_sparse_moe.experts.2.w2
- model.layers.3.block_sparse_moe.experts.2.w2
- model.layers.4.block_sparse_moe.experts.2.w2
- model.layers.5.block_sparse_moe.experts.2.w2
- model.layers.6.block_sparse_moe.experts.2.w2
- model.layers.7.block_sparse_moe.experts.2.w2
- model.layers.8.block_sparse_moe.experts.2.w2
- model.layers.9.block_sparse_moe.experts.2.w2
- model.layers.10.block_sparse_moe.experts.2.w2
- model.layers.11.block_sparse_moe.experts.2.w2
- model.layers.12.block_sparse_moe.experts.2.w2
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model_config:
output_router_logits: true
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: thingy
val_set_size: 0.0002
output_dir: ./out
sequence_len: 4096
sample_packing: true
pad_to_sequence_len: true
gradient_accumulation_steps: 8
micro_batch_size: 4
num_epochs: 3
logging_steps: 1
optimizer: paged_adamw_8bit
lr_scheduler: cosine
learning_rate: 2.7e-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: true
gradient_checkpointing: true
gradient_checkpointing_kwargs:
use_reentrant: false
early_stopping_patience:
# resume_from_checkpoint: /home/ehartford/axolotl/out/checkpoint-316
local_rank:
logging_steps: 1
xformers_attention:
flash_attention: true
saves_per_epoch: 8
save_total_limit: 2
save_steps:
evals_per_epoch: 4
eval_sample_packing: false
debug:
deepspeed: deepspeed_configs/zero3_bf16_cpuoffload_params.json
weight_decay: 0.05
fsdp:
fsdp_config:
special_tokens:
eos_token: "<|im_end|>"
tokens:
- "<|im_start|>"
```
</details><br>
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7022 | 0.0 | 1 | 0.6989 |
| 0.5344 | 0.25 | 238 | 0.5138 |
| 0.5204 | 0.5 | 476 | 0.5018 |
| 0.5059 | 0.75 | 714 | 0.4951 |
| 0.5112 | 1.0 | 952 | 0.4911 |
| 0.4561 | 1.24 | 1190 | 0.4978 |
| 0.478 | 1.49 | 1428 | 0.4935 |
| 0.4714 | 1.74 | 1666 | 0.4899 |
| 0.4626 | 1.99 | 1904 | 0.4861 |
| 0.3675 | 2.22 | 2142 | 0.5240 |
| 0.3595 | 2.47 | 2380 | 0.5229 |
| 0.3438 | 2.72 | 2618 | 0.5217 |
### Framework versions
- Transformers 4.40.0.dev0
- Pytorch 2.2.2+cu121
- Datasets 2.15.0
- Tokenizers 0.15.0 | {"language": ["en"], "license": "apache-2.0", "tags": ["generated_from_trainer", "axolotl"], "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": "mistral-community/Mixtral-8x22B-v0.1", "model-index": [{"name": "out", "results": []}]} | blockblockblock/dolphin-2.9-mixtral-8x22b-bpw3.5-exl2 | null | [
"transformers",
"safetensors",
"mixtral",
"text-generation",
"generated_from_trainer",
"axolotl",
"conversational",
"en",
"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:mistral-community/Mixtral-8x22B-v0.1",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:28:48+00:00 |
text-generation | transformers |
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<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | quickstep3621/jv34wep | null | [
"transformers",
"safetensors",
"stablelm",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:29:03+00:00 |
text2text-generation | transformers | {} | samzirbo/mT5.test.tedtalks.simple.16000.64.128 | null | [
"transformers",
"safetensors",
"mt5",
"text2text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:30:18+00:00 |
|
null | transformers |
# Uploaded model
- **Developed by:** theGhoul21
- **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", "gguf"], "base_model": "unsloth/mistral-7b-instruct-v0.2-bnb-4bit"} | theGhoul21/srl-sft-010524-gguf-q4_k_m | null | [
"transformers",
"gguf",
"mistral",
"text-generation-inference",
"unsloth",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:31:15+00:00 |
text-to-image | diffusers |
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# SDXL LoRA DreamBooth - embracellm/sushi18_LoRA
<Gallery />
## Model description
These are embracellm/sushi18_LoRA LoRA adaption weights for stabilityai/stable-diffusion-xl-base-1.0.
The weights were trained using [DreamBooth](https://dreambooth.github.io/).
LoRA for the text encoder was enabled: False.
Special VAE used for training: madebyollin/sdxl-vae-fp16-fix.
## Trigger words
You should use a photo of Shrimp Tempura Crunch Roll to trigger the image generation.
## Download model
Weights for this model are available in Safetensors format.
[Download](embracellm/sushi18_LoRA/tree/main) them in the Files & versions tab.
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] | {"license": "openrail++", "library_name": "diffusers", "tags": ["text-to-image", "text-to-image", "diffusers-training", "diffusers", "dora", "template:sd-lora", "stable-diffusion-xl", "stable-diffusion-xl-diffusers"], "base_model": "stabilityai/stable-diffusion-xl-base-1.0", "instance_prompt": "a photo of Shrimp Tempura Crunch Roll", "widget": []} | embracellm/sushi18_LoRA | null | [
"diffusers",
"tensorboard",
"text-to-image",
"diffusers-training",
"dora",
"template:sd-lora",
"stable-diffusion-xl",
"stable-diffusion-xl-diffusers",
"base_model:stabilityai/stable-diffusion-xl-base-1.0",
"license:openrail++",
"region:us"
] | null | 2024-05-01T07:32:12+00:00 |
text-generation | transformers |
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | Abhaykoul/UNIQ | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:33:32+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** jimdaro
- **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"} | jimdaro/lora_model_001 | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:34:07+00:00 |
null | transformers |
# Uploaded model
- **Developed by:** arnav0204
- **License:** apache-2.0
- **Finetuned from model :** unsloth/gemma-2b-it-bnb-4bit
This gemma 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", "gemma", "trl"], "base_model": "unsloth/gemma-2b-it-bnb-4bit"} | arnav0204/agrimodel | null | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"gemma",
"trl",
"en",
"base_model:unsloth/gemma-2b-it-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:35:27+00:00 |
null | null | {} | soaring0616/my_test_multiple_choice_model | null | [
"region:us"
] | null | 2024-05-01T07:36:37+00:00 |
|
null | null | KetchupMix is a merger between MustardMix(wandaomix v4) and Mizu mixes v7.
Permission was given by both model's author/owner for the merge.
Please give a follow/support for the owners of the original models used.
DaoOwoArts
kaiyo
This was supposed to be for personal use only but I thought of why not share it with others.
v2 changes.
Added some models in the merger
BreakDomain by BD
Dark Sushimix by Aitasai
v3 changes.
Added saki mix and played with some block weights
v4 changes.
forgot what I added
v4 darker changes.
a bit darker than the first test and has more of that line.
DaoOwoArts for wandaomix v2 and mustardmix
kaiyo for Mizu mixes v10
added Galena Redux for the texture. (will take down if the author wants to.)
v5 changes.
Added abysshellmaple into the mix
v5 darker changes.
Added Dark Sushi Mix by Aitasai | {"license": "creativeml-openrail-m", "tags": ["art"]} | NeverWinter13/KetchupMix | null | [
"art",
"license:creativeml-openrail-m",
"region:us"
] | null | 2024-05-01T07:37:00+00:00 |
null | null | {"license": "apache-2.0"} | justyoung/rvcm | null | [
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T07:37:08+00:00 |
|
text-generation | transformers |
# Uploaded model
- **Developed by:** theGhoul21
- **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"} | theGhoul21/srl-sft-010524-4bit | null | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"text-generation-inference",
"unsloth",
"trl",
"conversational",
"en",
"base_model:unsloth/mistral-7b-instruct-v0.2-bnb-4bit",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"4-bit",
"region:us"
] | null | 2024-05-01T07:39:40+00:00 |
null | null | {"license": "openrail"} | rezaazimisarteshnizi64/Reza | null | [
"license:openrail",
"region:us"
] | null | 2024-05-01T07:39:47+00:00 |
|
null | null | ```
./build/bin/main -m ./models/llama3_alpaca_dpo_GGUF-unsloth.F16.gguf \
-p '''Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.\n\n
### Instruction:\nWhy is the sky blue?\n\n
### Input:\n\n\n
### Response:\n'''
```
| {"license": "apache-2.0"} | vincentoh/llama3-alpaca-GGUF | null | [
"gguf",
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T07:40:20+00:00 |
text-generation | transformers | {} | c-tawayip/decoder-t2sql-1.3b-instruct | null | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:40:41+00:00 |
|
null | peft |
**Note**: This model card has been generated automatically according to the information the Trainer had access to.
Visit the [model card](https://ritvik19.github.io/zephyr-mini/) to see the full description.
# zephyr-tinyllama-sft-qlora
This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T) on the HuggingFaceH4/ultrachat_200k dataset.
It achieves the following results on the evaluation set:
- Loss: 1.1943
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0002
- train_batch_size: 2
- eval_batch_size: 4
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 128
- total_train_batch_size: 256
- 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 |
|:-------------:|:------:|:----:|:---------------:|
| 1.1908 | 0.9991 | 570 | 1.1943 |
### Framework versions
- PEFT 0.7.1
- Transformers 4.40.1
- Pytorch 2.1.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1 | {"license": "apache-2.0", "library_name": "peft", "tags": ["alignment-handbook", "trl", "sft", "generated_from_trainer"], "datasets": ["HuggingFaceH4/ultrachat_200k"], "base_model": "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T", "model-index": [{"name": "zephyr-tinyllama-sft-qlora", "results": []}]} | Ritvik19/zephyr-tinyllama-sft-qlora | null | [
"peft",
"safetensors",
"llama",
"alignment-handbook",
"trl",
"sft",
"generated_from_trainer",
"dataset:HuggingFaceH4/ultrachat_200k",
"base_model:TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T",
"license:apache-2.0",
"region:us"
] | null | 2024-05-01T07:40:58+00:00 |
text-classification | transformers | {"license": "unknown"} | amanda-901014/roberta-easy | null | [
"transformers",
"pytorch",
"roberta",
"text-classification",
"license:unknown",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | null | 2024-05-01T07:42:25+00:00 |
|
text-generation | transformers | {} | asucada/Llama-2-7b-chat-finetune | null | [
"transformers",
"pytorch",
"llama",
"text-generation",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:44:02+00:00 |
|
text2text-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 ๐ค transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] | {"library_name": "transformers", "tags": []} | TTTTao725/molt5-augmented-contrastive-200-small-whole_model | null | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"text-generation-inference",
"region:us"
] | null | 2024-05-01T07:45:04+00:00 |
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